ZETA™ methodology and variation in the systemic risk of default: accounting for the effects of Type II (false negative) errors variation on lending

Olgiati Stefano - University of Bergamo, Department of Management, Economics and Quantitative Methods. Danovi Alessandro - University of Bergamo The loan manager - dealing with one single borrower at a time and being responsible for that single decision to lend - is exposed to the idiosyncratic risk of default of his customer just like the physician is exposed to the risk of a wrong diagnosis with our strep throat. At the same time – if we do not expect the strep throat diagnostic test kit to change - we would still expect that physician reading that test to become more careful – or update his prior beliefs – about his diagnoses when a flu epidemic is likely to kick in with a certain estimated probability (likelihood). However, this has not been the case with loan management - there is in fact some consensus that before 2007 a reduction in the standards of idiosyncratic risk assessment by lenders - prior to risks pooling - coupled with a worsening of the systemic risk scenario, is partly to blame for the well known 2007-2008 financial crisis, with some of the blame falling also on the incapacity of actuarial mathematical models (test kits) to update worst case scenarios or be calibrated continuously on the basis of variation in the likelihood of default of the underlying risks pool.The authors of this paper argue that, on the other hand, a standard Bayesian transformation of the ZETA bankruptcy prediction methodology introduced by Altman in 1968-1977 allows for a continuous a posterioriupdate of conditional Type I and II errors due to variation in the systemic likelihood of default. The Bayesian transformation can be used both to condition the loan manager's prior decision (generally based on Basel II-compliant Internal Rating Based system or Credit Agency's Rating) and to update such decision on the basis of any posterior hypothesis (based on actuarial frequentist assumptions of conditional hazard rates) regarding the creditworthiness and the probability of default of an underlying pool of securities.At the same time – under a Bayesian framework - the ZETA diagnostic test can be conditioned on the new evidence introduced by other tests to increase the total sensitivity of the default prediction models (IRB ratings, TTC ratings, logit, probit, neural) to update the commercial bank's lending decisions.A ground-state, static meta-analysis of Altman's et al. ZETA original article (1977) reveals that the odds of the commercial bank detecting a default after the ZETA score has been introduced (post-test) is 13.2 times more effective than the a priori prediction. Under the same assumptions, the odds of the commercial bank detecting a survival after (post-test) the ZETA score has been introduced is 12.2 times more effective than the a priori. Integration of the ZETA model with other default prediction models reaches a credibility interval of CI ≥ 95% when the updated likelihood of default is equal to 60%. As expected, the Efficiency Comparison Test ECZETA=.00243 is invariant under the Bayesian transformation

Credit risk management and cyclicality of bank lending to non-financial corporations in Italy during the financial crisis: 2008-2012. A modeling study

Credit to non-financial corporations in Italy is characterized, in the period June 2008-June 2012, by frequent and intense quarterly cyclical fluctuations (peak amplitude €39.2 billion). The amplitude of these fluctuations has been ascribed to the effects of Basel II accords during the financial crisis which, by imposing regulatory capital constraints on banks’ lending on the basis of credit risk estimates, induces an excessive credit reduction during economic recession and an excessive credit growth during economic expansion. In order to mitigate these cyclical effects, various techniques of buffering have been advocated. The authors have tested the opposite null hypothesis that the interaction between new credit given and defaults from outstanding loans tends to a steady state. It has been tested a quasi-linear distribution with a Cyclical Sensitivity Parameter (CSP) parameterized on variation of new credit supply in excess or defect of the rate of default of outstanding loans. It is found that, in the period June 2008-June 2012, frequent fluctuations of the total credit used by non-financial corporations are strongly related to the interaction between the default rate of outstanding loans and the growth rate of new credit supply. It’s concluded that credit risk management in Italy has been effective in parameterizing credit supply growth to outstanding credit reduction caused by defaulting loans within the Basel II regulatory framework. Basel III prospective point-in-time output buffers based on filtered Credit/GDP ratios and dynamic provisioning proposals should take into account this steady state pattern underlying frequent and intense credit cyclical fluctuations

Using DRG to analyze hospital production: a re-classification model based on a linear tree-network topology

Background: Hospital discharge records are widely classified through the Diagnosis Related Group (DRG) system; the version currently used in Italy counts 538 different codes, including thousands of diagnosis and procedures. These numbers reflect the considerable effort of simplification, yet the current classification system is of little use to evaluate hospital production and performance. Methods: As the case-mix of a given Hospital Unit (HU) is driven by its physicians’ specializations, a grouping of DRGs into a specialization-driven classification system has been conceived through the analysis of HUs discharging and the ICD-9-CM codes. We propose a three-folded classification, based on the analysis of 1,670,755 Hospital Discharge Cards (HDCs) produced by Lombardy Hospitals in 2010; it consists of 32 specializations (e.g. Neurosurgery), 124 sub-specialization (e.g. skull surgery) and 337 sub-sub-specialization (e.g. craniotomy). Results: We give a practical application of the three-layered approach, based on the production of a Neurosurgical HU; we observe synthetically the profile of production (1,305 hospital discharges for 79 different DRG codes of 16 different MDC are grouped in few groups of homogeneous DRG codes), a more informative production comparison (through process-specific comparisons, rather than crude or case-mix standardized comparisons) and a potentially more adequate production planning (considering the Neurosurgical HUs of the same city, those produce a limited quote of the whole neurosurgical production, because the same activity can be realized by non-Neurosugical HUs). Conclusion: Our work may help to evaluate the hospital production for a rational planning of available resources, blunting information asymmetries between physicians and managers.&nbsp

Future and potential spending on health 2015-40 : development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries

Background The amount of resources, particularly prepaid resources, available for health can affect access to health care and health outcomes. Although health spending tends to increase with economic development, tremendous variation exists among health financing systems. Estimates of future spending can be beneficial for policy makers and planners, and can identify financing gaps. In this study, we estimate future gross domestic product (GDP), all-sector government spending, and health spending disaggregated by source, and we compare expected future spending to potential future spending. Methods We extracted GDP, government spending in 184 countries from 1980-2015, and health spend data from 1995-2014. We used a series of ensemble models to estimate future GDP, all-sector government spending, development assistance for health, and government, out-of-pocket, and prepaid private health spending through 2040. We used frontier analyses to identify patterns exhibited by the countries that dedicate the most funding to health, and used these frontiers to estimate potential health spending for each low-income or middle-income country. All estimates are inflation and purchasing power adjusted. Findings We estimated that global spending on health will increase from US$9.21 trillion in 2014 to$24.24 trillion (uncertainty interval [UI] 20.47-29.72) in 2040. We expect per capita health spending to increase fastest in upper-middle-income countries, at 5.3% (UI 4.1-6.8) per year. This growth is driven by continued growth in GDP, government spending, and government health spending. Lower-middle income countries are expected to grow at 4.2% (3.8-4.9). High-income countries are expected to grow at 2.1% (UI 1.8-2.4) and low-income countries are expected to grow at 1.8% (1.0-2.8). Despite this growth, health spending per capita in low-income countries is expected to remain low, at $154 (UI 133-181) per capita in 2030 and$195 (157-258) per capita in 2040. Increases in national health spending to reach the level of the countries who spend the most on health, relative to their level of economic development, would mean $321 (157-258) per capita was available for health in 2040 in low-income countries. Interpretation Health spending is associated with economic development but past trends and relationships suggest that spending will remain variable, and low in some low-resource settings. Policy change could lead to increased health spending, although for the poorest countries external support might remain essential.Peer reviewe

Future and potential spending on health 2015-40: Development assistance for health, and government, prepaid private, and out-of-pocket health spending in 184 countries

Background: The amount of resources, particularly prepaid resources, available for health can affect access to health care and health outcomes. Although health spending tends to increase with economic development, tremendous variation exists among health financing systems. Estimates of future spending can be beneficial for policy makers and planners, and can identify financing gaps. In this study, we estimate future gross domestic product (GDP), all-sector government spending, and health spending disaggregated by source, and we compare expected future spending to potential future spending. Methods: We extracted GDP, government spending in 184 countries from 1980-2015, and health spend data from 1995-2014. We used a series of ensemble models to estimate future GDP, all-sector government spending, development assistance for health, and government, out-of-pocket, and prepaid private health spending through 2040. We used frontier analyses to identify patterns exhibited by the countries that dedicate the most funding to health, and used these frontiers to estimate potential health spending for each low-income or middle-income country. All estimates are inflation and purchasing power adjusted. Findings: We estimated that global spending on health will increase from US$9.21 trillion in 2014 to$24.24 trillion (uncertainty interval [UI] 20.47-29.72) in 2040. We expect per capita health spending to increase fastest in upper-middle-income countries, at 5.3% (UI 4.1-6.8) per year. This growth is driven by continued growth in GDP, government spending, and government health spending. Lower-middle income countries are expected to grow at 4.2% (3.8-4.9). High-income countries are expected to grow at 2.1% (UI 1.8-2.4) and low-income countries are expected to grow at 1.8% (1.0-2.8). Despite this growth, health spending per capita in low-income countries is expected to remain low, at $154 (UI 133-181) per capita in 2030 and$195 (157-258) per capita in 2040. Increases in national health spending to reach the level of the countries who spend the most on health, relative to their level of economic development, would mean $321 (157-258) per capita was available for health in 2040 in low-income countries. Interpretation: Health spending is associated with economic development but past trends and relationships suggest that spending will remain variable, and low in some low-resource settings. Policy change could lead to increased health spending, although for the poorest countries external support might remain essential

Tracking development assistance for health and for COVID-19: a review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990-2050

Background The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020. Methods We estimated domestic health spending and development assistance for health to generate total health-sector spending estimates for 204 countries and territories. We leveraged data from the WHO Global Health Expenditure Database to produce estimates of domestic health spending. To generate estimates for development assistance for health, we relied on project-level disbursement data from the major international development agencies' online databases and annual financial statements and reports for information on income sources. To adjust our estimates for 2020 to include disbursements related to COVID-19, we extracted project data on commitments and disbursements from a broader set of databases (because not all of the data sources used to estimate the historical series extend to 2020), including the UN Office of Humanitarian Assistance Financial Tracking Service and the International Aid Transparency Initiative. We reported all the historic and future spending estimates in inflation-adjusted 2020 US$, 2020 US$ per capita, purchasing-power parity-adjusted US$per capita, and as a proportion of gross domestic product. We used various models to generate future health spending to 2050. Findings In 2019, health spending globally reached$8. 8 trillion (95% uncertainty interval UI] 8.7-8.8) or $1132 (1119-1143) per person. Spending on health varied within and across income groups and geographical regions. Of this total,$40.4 billion (0.5%, 95% UI 0.5-0.5) was development assistance for health provided to low-income and middle-income countries, which made up 24.6% (UI 24.0-25.1) of total spending in low-income countries. We estimate that $54.8 billion in development assistance for health was disbursed in 2020. Of this,$13.7 billion was targeted toward the COVID-19 health response. $12.3 billion was newly committed and$1.4 billion was repurposed from existing health projects. $3.1 billion (22.4$2.4 billion (17.9%) was for supply chain and logistics. Only $714.4 million (7.7$1519 (1448-1591) per person in 2050, although spending across countries is expected to remain varied. Interpretation Global health spending is expected to continue to grow, but remain unequally distributed between countries. We estimate that development organisations substantially increased the amount of development assistance for health provided in 2020. Continued efforts are needed to raise sufficient resources to mitigate the pandemic for the most vulnerable, and to help curtail the pandemic for all. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd

Bank of Italy Dataset - TBD30486

<p>Dataset for Reproducible Research</p

LONG-TERM FINANCIAL SUSTAINABILITY AND INEQUALITY OF PHARMACEUTICAL EXPENDITURE IN THE EUROPEAN UNION, 2011-2060: A COMPARATIVE ANALYSIS

The European Union has achieved near-universal health coverage (Mean=99.15%; Standard Deviation=1.46%; Variation Coefficient=1%) and equity of access to healthcare services but, in the period 2000-10, such goal has been achieved at a Not Sustainable rate with total health expenditure growth (4.2%, STDDEV=2.4%) exceeding the growth rate of the GDP (1.7%,STDDEV=1.4%) by +2.5 percentage points on average and pharmaceutical expenditure (3.5%, STDDEV=3.0%) exceeding the growth rate of the GDP by +1.8 points. In the period 2011-2060 the GDP is expected to reduce and stabilize its growth rate from an average of 1.7% (STDDEV=1.4%) to an average of 1.6% with lower variation among the countries of the EU (STDDEV=0.5%). There follows that if the growth rate of health and pharmaceutical expenditure of the period 2000-10 is not reduced in the period 2011-60 only 4 nations will achieve economic and financial sustainability: Netherlands, Sweden, Denmark and Italy. Health policymakers in the European Union should account for this long-term expenditure growth pattern and reform in the efficiency and effectiveness of health and pharmaceutical care is necessary if health outcomes are to be improved and at the same time the economic and financial sustainability of the European universal welfare model is to be preserved and inequality avoided

Living Longer with Disability: Economic Implications for Healthcare Sustainability

This work focuses on the economic implications of the relationship between life expectancy, the number of years lost to disability and per-capita total health expenditure. The primary goal of the paper is to identify and plot the correlation between healthcare expenditure and the global increase in life expectancy, in order to assess if, and how, the way longer average lifespans are achieved affects healthcare sustainability. Datasets regarding the United States, the European Union and the five largest emerging healthcare systems (i.e., Brazil, the Russian Federation, India, China and South Africa) were obtained from the Institute for Health Metrics and Evaluation and the WHO Health Expenditure Statistics Repository. All analysis was performed on 2017 data. The results of the analysis showed the number of years lost to disability to be a linear function of life expectancy at birth (male R2 = 0.61; female R2 = 0.47), and per-capita total health expenditure to be an exponential function of the number of years lost to disability (male R2 = 0.60; female R2 = 0.65). This implies that improving life expectancy via social policies bears negative consequences in terms of healthcare sustainability, unless the number of years lost to disability is reduced too. Further studies should narrow the sample of countries and causes of years lost due to disability, to better inform future policy efforts

Giuseppe Felloni e l’Archivio di Stato di Genova

Il saggio vuole delineare i rapporti scientifici e istituzionali tra Giuseppe Felloni e l’Archivio di Stato di Genova. Si comincia a considerare l’attività dello studioso di storia eco- nomica dalla sua frequentazione della sala di lettura, al compimento del progetto di riordino e descrizione dell’archivio della Casa delle compere di San Giorgio. Viene infine proposto un approfondimento sulle carte personali di Felloni oggi conservate nello stesso Archivio di Stato.The essay wants to outline the scientific and institutional relationships between Giuseppe Felloni and the State Archives of Genoa. It starts considering the activity of the economic history scholar from his attendance of the reading room, to the fulfillment of the reordering and description project of the Casa delle compere di San Giorgio archives. Is finally proposed a focus on Felloni’s personal papers preserved today in the same State Archives