Although we only considered indices that included the assessment of developing countries as well, the redundancy of the results is striking. There seems to be no respect “beyondGDP” (except the ecological footprint) that may create a leadership or best practice for any developing country. The first developing country – Indonesia – appears at rank 46.
Before operationalising this framework as a means of analysing the implementation of the beyondGDP accounting and statistical agenda, we need to first unpack in greater depths the specific forms of economistic fallacy that, for Polanyi, underpinned formalist thinking. Then, we will be in a position to assess how far the implantation of this agenda reproduces or contests these premises and assumptions. In this section I outline the key features that Polanyi saw as distinctive about the place of economy in modern society and the ‘market mentality’ which developed as a theoretical response to these. In particular, I will show that there are four distinctive features of the formalist representation of the economy which emerged in response to the rise of market society from the late 18th century. Specifically, these are: 1) the idea of the economy as a self- regulating internally sufficient system of exchange co-ordinated through the price mechanism; 2) the corresponding separation of the economic from both the political and the social spheres of society; 3) the reduction of the conception of the use of money to its role in commodity exchange, thus ignoring its other social functions; 4) the identification of a distinctly ‘economic’ or ‘material’ component of human nature, namely rational utility maximisation under conditions of scarcity, and the reduction of such behaviour to the automatic response to price stimuli at the moment of exchange.
The density analysis of convergence showed that welfare differences appear to be char- acterized by different convergence clubs. In particular, the modes of the estimated stochas- tic kernel density and the related cluster analysis suggest the existence of three convergence clubs. Under this classification, a significant (absolute) beta convergence coefficient is re- covered for each club; moreover, convergence within the poorest club shows the fastest convergence rate. In terms of sigma convergence, however, only the core members of the richest club appear to be reducing their welfare differences. Overall, these results highlight a central finding in the economic growth and development literature: beta convergence is necessary but not sufficient for sigma convergence––even within convergence clubs and in a context beyondGDP.
societies should strive for and how, given the signs of climate change and increasing global inequalities, societies seem to have lost track of what we are chasing there? With reference to the different worldviews prevalent in different centuries, I will embark on a discussion of paradigms of progress, which emerged out of the particular historical context of a time, finding that our current paradigm in progress still shares many features with an outdated mechanistic worldview developed by Newton and Descartes in the 17th/18th century. Drawing then on the historical rational of the GDP, its rise and demise in this framework will be outlined concluding on the need to redefine the narrow understanding of economic welfare going beyondGDP, giving way to rethink questions about what the wealth of nations should be and what progress we want?
Past national GPI studies have indicated that in many countries, beyond a certain point, GDP growth no longer correlates with increased economic welfare. An important function of GPI is to send up a red ﬂ ag at that point. Since it is made up of many bene ﬁ t and cost components, it also allows for the identi ﬁ cation of which factors increase or decrease economic welfare. Other indicators are better guides of speci ﬁ c aspects. For example, Life Satisfaction is a better measure of overall self-reported happiness. By observing the change in individual bene ﬁ t and cost com- ponents, GPI reveals which factors cause economic welfare to rise or fall even if it does not always indicate what the driving forces are behind this. It can account for the underlying patterns of resource consumption, for example, but may not pick up the self-reinforcing evolution of markets or political power that drives change.
hen it was conceived, Gross Domestic Product (‘GDP’) was a useful signpost on the path to a better world. Increased economic activity meant jobs, income, and basic amenities to reduce worldwide social conflict and prevent a third world war. But now, economic activity has created a world very different from the one faced by global leaders at their 1944 Bretton Woods, New Hampshire, meeting to design the post-war global economic order. We live in a world overflowing with people and man-made capital, where emphasis on growing GDP, consumption and economic activity is leading the world towards increasing instability, natural resource depletion and environmental degradation, while developing nations still need to lift people from poverty.
While GDP per capita dropped strongly in 2009, the BW indicator remained relatively unchanged for another number of years. A lot of companies held on to their employees and wages kept rising, which explained why the dimensions jobs and material wellbeing only decreased slightly. Only in 2013 did the BW indicator decline strongly, mainly because unemployment increased markedly. At the same time, subjective wellbeing of households dropped, mainly because people reported lower life satisfaction. Possibly the drop in subjec- tive wellbeing is linked to the effects of the crisis and the resulting uncertainty. In addition, the dimension housing decreased faster from 2013 onwards: both tenants and home owners were less satisfied with their housing situation. For homeowner this might have to do with the decreasing housing prices, while for tenants higher rents might have played a role.
This paper is a call for better indicators of human well-being in nations around the world. We critique the inappropriate use of Gross Domestic Product (GDP) as a measure of national well-being, something for which it was never designed. We also question the idea that economic growth is always synonymous with im- proved well-being. Useful measures of progress and well-being must be measures of the degree to which society’s goals (i.e., to sustainably provide basic human needs for food, shelter, freedom, participation, etc.) are met, rather than mea- sures of the mere volume of marketed economic activity, which is only one means to that end. Various alternatives and complements to GDP are discussed in terms of their motives, objectives, and limitations. Some of these are revised measures of economic activity while others measure changes in community capital—natu- ral, social, human, and built—in an attempt to measure the extent to which development is using up the principle of community capital rather than living off its interest. We conclude that much useful work has been done; many of the alternative indicators have been used successfully in various levels of community planning. But the continued misuse of GDP as a measure of well-being neces- sitates an immediate, aggressive, and ongoing campaign to change the indica- tors that decision makers are using to guide policies and evaluate progress. We need indicators that promote truly sustainable development—development that improves the quality of human life while living within the carrying capacity of the supporting ecosystems. We end with a call for consensus on appropriate new measures of progress toward this new social goal.
The current study first investigates the nature of any systematic patterns of GDP growth across individual countries, and examines how the GDP growth of one country can interact with the others. Second, we explore GDP volatility spillovers across countries by evaluating how country-specific shocks and volatilities, as well as cross-country shocks and volatility co-movements, affect GDP volatility within one country, and the transmission of shocks among countries. Finally, we investigate the GDP volatility correlations to shed some light on how constant-conditional correlations relate to time-varying conditional variance and covariance. Specifically, we use quarterly GDP data (1961-2008) from Australia, Canada, the UK and the US for the multivariate framework of generalised autoregressive conditional heteroskedasticity (MGARCH) models.
It seems that oil prices have a huge impact on the UK macroeconomy. Increases in oil prices cause lower output, higher domestic and foreign interest rates, and higher inflation in the UK (Garratt et al. 2003). Garratt et al. also found a long run relationship between the oil price and the UK macroeconomy. Similar results are obtained by Blanchard et. al (2007) who found that oil prices increase inflation and economic activities in the OECD countries. Similarly in Russia, Ito (2008) found that the increase in oil prices causes an increase in the Russian GDP, to the extent that a 1% increase in oil prices will bring about an increase in Russian GDP by 0.25%. However, oil shocks increased Russian inflation by 0.36%. In another study, Gounder et. al (2007) found a positive relationship between New Zealand’s GDP growth and oil shocks whereas Schmidt et. al (2007) found that the impact of oil shocks on the German macro economy is insignificant. Huntington (2004) found that oil shocks helped the OECD countries to reach the full-employment level. Robalo et. al (2007) found that Portugal’s macro economy is less affected by the price of oil in the mid-1980’s.
Through the above analysis we can see, Fujian's GDP and ODI does exist a long-term dependencies. In the short term, economic growth can promote ODI, to some extent. The promotion of ODI to economic growth was not reflected from the data analysis. At present, ODI is still at the exploratory stage, therefore it is understandable the economic effect is not obvious in some ways. Fortunately, the error correction model shows that in the long time, even if the ODI and economic growth imbalances, they can be related to economic factors, and the greater the rate of recovery is to the equilibrium state.
• deWinter (2011) seems a notable exception, where the performance of private sector forecasts against statistical models in nowcasting Dutch GDP is explicitly modelled in periods of crisis. The conclusion is that augmenting a purely statistical procedure with judgement adds little value. Recently, Jansen, Jin, and deWinter (2014) argue that profes- sional forecasts, while offering some positive results tend to perform poorly when compared directly to model nowcasts. However, the re- search is provided with the caveat that real-time data sets were not utilised (so revisions to variables were not incorporated). This leads to some concern whether comparisons against the consensus view were fair, given that revisions to GDP data can be large. Further exploration of these features is provided in section 6, but note if the consensus per- formance was compared against the latest GDP vintage (rather than real-time information) the nowcast accuracy quoted in table 2 would deteriorate by nearly 50%. This suggests that statistical model com- parisons against consensus views should be conditioned on exactly the same information i.e. the data that was available in real-time to profes- sional forecasters should also be used to construct the statistical model nowcasts and is important when comparing respective predictive GDP accuracies.
Departing from the most standard approach to GDP modelling in the pre- vailing literature of the time,Campbell and Mankiw stress how detrending data would bias a priori any measure of shock persistence on output ‡uc- tuations.They argue that detrendig would force the resulting series to be trend reverting , so that today’s innovation would not have ultimate e¤ect on output at an in…nite horizon and propose instead to di¤erence the series of log real GDP. The di¤erenced series ,the growth rate of real GDP,appears stationary ,allowing them to invoke asymptotic distribution theory.
In equation (2) remittances of Pakistan is dependent variable and GDP of Pakistan and world is the independent variable, the main purpose of this equation to evaluate the remittances behavior inflow to Pakistan. The above equation is estimated by GMM and results (Appendix 2) indicates that remittances inflow to Pakistan has countercyclical behavior (Mazhar and Junaid, 2013), so as GDP growth of Pakistan declines at the same time remittances inflow to Pakistan increases, and remittances inflow to Pakistan has positive and significant relationship with world GDP as it increases at that time remittance inflow to developing countries increases. Remittances to Pakistan have increased with high speed at the time when Pakistan facing recession or facing high economic or natural shocks. In 2010 Pakistan receive highest amount in term of remittances due to compensating natural disaster (flood 2010). Hence we can conclude that remittances have countercyclical approach in term of Pakistan.
demand and supply shocks differ see Table 11, Section 6 the outcomes in Table 8 for both shocks are strikingly similar. So, concerning GDP spillovers it does not make much difference in MULTIMOD if government consumption or private investment is shocked. The MULTIMOD results differ in some respects from the MSG2 demand shock results. MULTIMOD clearly shows more asymmetries between the US and Germany. A German demand shock yields very small, positive spillovers for the US economy whereas a US demand shock produces negative spillovers for European economies. The main difference is that the real short-term interest rate of the country generating the shock is negative the first two years after the shock. This indicates that nominal interest rate adjustments in response to price increases are slower in MULTIMOD than in MSG2. This difference is reflected in the dynamics of the money demand equations which ensure that interest rates in MULTIMOD adjust more slowly to suppress inflation than in
electricity generated domestically and imports of electricity. According to the World Development Indicators (2017), losses of electricity in Benin during the periods 1996-2000, 2006-2008 and the year 1994 (periods and year for which data is available on the World Development Indicators website at the time of analysis) have exceeded 50% of total electricity generated domestically. The proportions in 2006, 2007 and 2008 were respectively 81.81%, 56.81% and 61.13% of total electricity generated domestically (World Development Indicators, 2017). In order to improve electricity supply efficiency, the Beninese Ministry of Energy planned to reduce electricity losses by 18% from 2005 to 2010, and by 15% in 2015. However, the actual losses of electricity were far above the targets set by the Ministry for 2010 and 2015: they were respectively 18.56% and 19.35% (République du Bénin, 2008; US EIA, 2018). The cost of these losses of electricity can range between 0.5 and 1.2% of GDP in many countries of sub-Saharan Africa (Antmann, 2009). They constitute a burden for the Beninese economy. The promotion of electricity efficiency on both supply and demand sides is one of the pillars of the second objective of the national strategy for access to electricity. In alignment with such pillar, the Ministry of Energy has targeted a reduction of electricity losses by 14% in the period 2020-2025 in Benin (République du Bénin, 2008). In order to reduce technical losses of electricity, one of the goals of Benin’s electricity efficiency policy is the modernization of the distribution lines with electricity-efficient technology. In order to reduce non- technical losses of electricity, Benin targets to implement an emergency plan aiming at fighting corruption and theft of electricity, and at improving the billing system for electricity supply and consumption in the country (see République du Bénin, 2008, pp. 54 – 55).
In light of this important work with real-time data, we ask whether revisions to GDP growth contain information that can help forecast advance estimates of GDP growth. We investigate the informational content of both aggregate revisions to GDP and revisions to its components. Echoing other work in the literature, we find that revisions to GDP growth have no impact on short-run forecast accuracy. How- ever, there is strong evidence that revisions to certain GDP components, principally consumption, have important information for forecasting economic growth. Further, the improvement in forecast accuracy can be substantial over an AR(1) benchmark model. Nearly 68% of all models that contain subsets of component revisions outper- form the baseline model. The “best” component-augmented model forecasts roughly 0.2 percentage points closer to the advance estimate of GDP growth than an AR(1). There is a well-developed literature that has attempted to incorporate preliminary data into coherent forecasting frameworks. 1 Much of its focus has been on forecasting 1
This study aims to look at the impact of imposing carbon taxes as an effort to reduce the effects of greenhouse gases. By using GTAP-E, this study found that the imposition of a vehicle carbon tax of 5 percent resulted in a reduction in the GDP rate of 0.01 percent and effectively reduced the level of carbon dioxide emissions by 0.06 percent.