The economy of each nation and their links either inter-regional or international are ever-changing. These changes are increasing in terms of speed and facilitated through rapid technological advances as well as the impact of globalisation. This background provides measurement challenges through every stage of the statistical value chain starting with the business register and statistical unit through to the major aggregates produced by National Statistical Offices and/or National Central Banks like GDP, GNI, balance of payments and government debt and deficit. Furthermore, ripple effects are seen across various types of analyses and the work of researchers, economists, etc., for example, productivity and environmental impacts and opportunities for new analyses, for example, global value chains. As a result, the international guidelines like the SNA, ESA and BPM have to be updated and remain relevant. The growing role, and influence, of Supply and Use Tables (and the Input-Output framework) will play a key, central role to help assist and inform both the producer and the user of economic analyses including government policies covering taxation, environmental impacts, etc. This presentation will "touch" on these aspects and build on the 1st Edition of the Spanish School of Input-Output Analysis to illustrate why a career in this field is so fulfilling.
About 50% of the global population, that is more than 3 billion people, live on less than 3$ a day. The top 10% earn more 23$ (PPP) per day. Clearly lifestyles, consumption patterns and associated per capita carbon footprints differ enormously between rich and poor. What is the difference in terms of carbon footprint? What is the contribution to total carbon emissions of the global middle class or the global elites? Do we see a convergence of consumption patterns and carbon footprints of rich folks across countries? What are the carbon implications of moving hundreds of millions of people out of poverty as proposed in the sustainable development goals? To answer these questions we present our compilation of consumption patterns for different income categories from consumer expenditure surveys for most countries of the world, representing some 90% of the global population. These have been linked to a global multi-regional input-output model to calculate carbon footprints for different income categories, globally and specifically also for different income and lifestyle categories in the US. Our results show that when focusing on countries and averages we miss lots of interesting information. There are huge differences in carbon footprints between as well as within countries. There are interesting differences between countries. For example, in US income groups are associated with higher carbon emissions when compared to equivalent groups in European countries. We will present some reasons of why that is the case and investigate US consumption patterns and lifestyles at great spatial detail. When looking at poor countries we find huge disparities between the carbon footprint of the rich versus the poor which for some very poor countries differ by several order of magnitudes, and are generally much larger than differences between rich and poor in rich countries such as the US and Europe. A general finding is that the higher the income the higher the carbon footprint. There is no leveling off. Higher incomes generate higher carbon footprints. Thus it is not surprisingly that adding to the middle class by moving people out of poverty adds significantly to global carbon emissions and makes global targets for mitigating greenhouse gases more difficult to achieve given the slow pace of progress towards low carbon technologies and the degree of fossil fuel dependence, population growth, and emulation of resource intensive lifestyles in low income countries and transition economies.
In the US, household consumption accounts for 70% (almost 60% in Spain) of Gross Domestic Product on the expenditure side, yet most economic models persist in aggregating these expenditures into a representative household. This paper explores the impacts of household disaggregation – by income and age – in an econometric input-output model, including the effects of disaggregation on forecasts. A similar exercise explores the consequences of disaggregating labor income by age and income class. The impacts of the total exercise (disaggregation of household income and expenditures are revealed in a similar Miyazawa framework. The distributional effects can thus be assessed more effectively but will require greater integration in a dynamic framework that can capture variations in population growth by cohort, skill/occupation and migration status.