The sovereign credit risk of a country depends on the economic growth, human development and political stability of a country. This study aimed to investigate the factors that influence the potency (ability) of a country or firm to honour its debt obligations. The study objectively target to meet two objectives. The first objective is to examine the relationship between country cumulative probability of default (CPD) and country local factors, and the second is to determine the pre-requisite factors for sovereign creditworthiness appraisal for a foreign lending firm or country. The study met their objectives; the findings profile that GDPpercapita, GDPgrowthrate, current account balance, government budget proxy of economic growth and inequality- adjusted human development index proxy of human development, found to have a positive impact in the sovereign creditworthiness appraisal in a country. That is, it favors the country for keeping low the probability of dishonoring its debts obligations.
The model is estimated using (i) pooled OLS, (ii) fixed effects and (iii) random effects. The Pesaran (2004) test provides convincing evidence that cross sectional dependence exists in the models without the cross-section averages ( x : cavg); these preliminary results are not reported due to space limitations but are available on request. In fact, cross-sectional dependence would point to the existence of spill-over effects from one Eurozone country to another 3 . Following from this, we follow the common correlated effects (CCE) approach of Pesaran (2006) that includes the cross-section averages of the independent variables as additional regressors denoted by cavg in Tables 3 to 6. The estimated coefficients on the cross-section averages are not interpretable in a meaningful way; these are merely present to blend out the biasing impact of the unobservable common factor (see e.g. Eberhardt, 2012). Tables 3-5 report the empirical results for each one of the three main CRAs. In each model, the first two columns report all estimated coefficients and associated p-values (full model with cavg) whereas the next two columns report only the statistical significant ones (deleting one variable at the time). An improvement in GDPpercapita, GDPgrowthrate, exchange reserves and cumulated current account results in a credit rating upgrade (cumulative current account’s significance emerges only during the financial crisis). Notice also the positive impact of World Bank’s regulatory quality index; this captures perceptions of the ability of
unemployment are related to suicide (Yang and Lester, 1995) and the role of cyclical component is important in understanding the suicide (Oswald, 1997). Viren (1996) analyzes the relationship between suicide and business cycles using long Finnish time series data for the period 1878-1994 and put forward that suicide increases along with age and is related to both GDPgrowth (inversely), bankruptcies and unemployment. Using cross-sectional heteroscedastic and time-wise autoregressive technique, Chuang and Huang (1997) find that in Taiwan, apart from many socio-economic correlates, the level of percapita income have a greater impact on suicide rates at regional level than the sociological correlates. Using cross-section study for 30 countries, Jungeilges and Kirchgassner (2002) showed that increase in real income percapita and real income growth increases the likelihood of the suicide rate. However, it is sensitive to the age- group and gender. While suicide rate of middle age group increased with increase in the role of real income percapita; it is elderly segment of population where increased role of economic growth is significant. Additionally, older women hold stronger to real income growth than older men.
Arguably, the present study extends the literature on economic growth and economic freedom in a number of ways. To begin with, this study differs with most prior studies by focusing on OECD nations. In addition, it estimates a balanced four-year (2003 through 2006) panel dataset by fixed-effects. Furthermore, the present study constructs an overall average measure of economic freedom which expressly discards three of the ten Heritage Foundation (2013) economic freedoms, namely, fiscal freedom, business freedom, and financial freedom, primarily because of the multi-collinearity problems their presence creates and partly to replace them with arguably better variables to measure what the fiscal freedom, business freedom, and financial freedoms seek to measure, namely, by two separate variables: the ratio of all taxes to GDP (expressed as a percent) and a direct measure of regulatory quality, the principal
genuine savings, reflecting volatility, and t is the time index. M is the vector of control variables, which appear only in the mean equation (3). It contains age dependency (age_dep) and the urban population rate in total population (urban). X is the vector of the variables, which are expected to have impacts on both GS and its volatility. For these variables, we mainly focus on institutions (institution). Moreover, X includes other variables, such as percapitaGDPgrowth (ggdp), inflation rate (inflation), trade openness (trade), and government size (gov_size), proxied by the share of government expenditures in the GDP. These variables are chosen following Dietz et al. (2007). Z is a vector representing a set of variables that only have an impact on GS variance and contains the square of percapitaGDPgrowth (ggdp 2 ). Therefore, they are included only in equation (5). These variables affect GS indirectly through its variance.
emigration from Africa (Clemens & Pettersson,2006). Table 2 below depicts selected HHR determinants with respect to push factors outlined in Table 1 from the WHO report. The data is cross-sectional and based on the year 2000 because HHR migration data is only available for this period. HHR dependent variables entail both physician and nurse emigrations while their determinants involve aspects of job security(GDPpercapitagrowth and health expenditure), economic considerations(savings, inflation and population growth), physical security(freedom and government effectiveness), political considerations(democracy and corruption-control), quality of life(human development index, development assistance and HIV infection rate), education(tertiary emigration rate) and globalization(trade openness and capital liberalization). Summary statistics and correlation analysis(with presentation countries) are detailed in Appendix 1 and Appendix 2 respectively.
The growthrate of real GDPpercapita is represented as a sum of two components – a monotonically decreasing economic trend and fluctuations related to the change in some specific age population. The economic trend is modeled by an inverse function of real GDPpercapita with a constant numerator. Statistical analysis data from 19 selected OECD countries for the period between 1950 and 2007 shows a very weak linear trend in the annual increment of GDPpercapita for the largest economies: the USA, Japan, France, and Italy. The UK, Australia, and Canada show a larger positive linear trend in annual increments. The fluctuations around relevant mean increments are characterized by practically normal distribution (with Levy tails). Developing countries demonstrate annual GDPpercapita increments far below those for the studied developed economies. This indicates an underperformance in spite of large relative growth rates.
For Australia, the regression slope is positive for both periods but falls from +0.024 (0.006) to +0.016 (0.006) due to the low rate of growth since 2008 as associated with the global economic crisis. Both slopes are statistically significant with p-value of 0.0003 and 0.005, respectively (see Table 3). Australia is the only example among the biggest economics with a statistically significant positive slope, which is likely related to demographic changes. Nevertheless, our prediction from 2006 that, in the long run, the regression line should be horizontal was valid and the slope has been falling since 2003. We expect the regression line approaching the zero line in the future. As we foresaw six years ago, the healthy growth of the 1990s and the early 2000s has been compensated by a significant fall in GDP. For Australia, Table 2 lists the mean value if $303.2 (1990 US$) with the standard deviation of $257.5.
Sianesi and Van Reenen (2000) had a few important findings that are worth highlighting. First, neo-classical tradition argues that a one-off permanent increase in the human capital stock will cause a one- off increase in the economy’s growthrate, until productivity per worker hour has reached its new (and permanently higher) steady state level. New Growth theories argue that the same one-off increase in human capital will cause a permanent increase in the growthrate. Dowrick (2002) also recognized that there are debates over whether changes in educational attainment ultimately affect the long-run growthrate of the economy, or only the long-run level of output. Second, there are reverse causality problems with education, which means income growth might lead to an increased demand for
Table 2 below summarizes HHR determinants with respect to push factors outlined in Table 1. The data is cross-sectional because HHR migration data is only available for the year 2000. HHR variables entail both physician and nurse emigration rates while their determinants entail aspects of job security(GDPpercapitagrowth and health expenditure), economic considerations(savings, inflation and population growth), physical security(freedom and government effectiveness), political considerations(democracy and corruption-control), quality of life(human development index, development assistance and HIV infection rate), education (tertiary emigration rate) and globalization(trade openness and capital liberalization). While the dependent variables are from Clemens & Pettersson(2006), the independent and control variables are obtained from Freedom House and African Development Indicators(ADI) of the World Bank(WB). Summary statistics and correlation analysis(with presentation of countries) are detailed in Appendix 1 and Appendix 2 respectively.
Table 1 gives the estimation results of the relationship between the Internal Violence Index (IVI) and growth without correction of heteroskedasticity for all countries. In this table, the coe ffi cient of initial real GDPpercapita is significant and negative in all regressions. The negative coe ffi cient indicates conditional convergence with respect to real GDPpercapita. This convergence is conditional in that it concludes that the growthrate of real GDPpercapita is bigger the initial real GDPpercapita is small, only if the other regressors are kept constant. The coe ffi cient indicates that conditional convergence is very high because it is carried out at a rate of 1.40% per year 4 . All eight equations show that the Internal Violence Index is statistically significant at all conventional levels and have the expected sign. This implies that an augmentation of the Internal Violence Index diminishes the growthrate. The above-mentioned results, empirically corroborate what we have found in the theoretical part. Specifically, this means that when there is high Internal Violence, broad capital stock falls; saving and investment diminish; economic agents are uncertain about the future; etc. All these factors cause the growthrate to plunge. This feature of the estimations results is what we illustrated in our stylized facts in figure 2. Our findings illustrate that, the negative e ff ect of the Internal Violence Index on growth is robust to the introduction of di ff erent control variables. In fact, through the eight equations we have varied the introduction of the control variables but the coe ffi cient of the Internal Violence Index retains its expected sign and is always statistically significant. The magnitude of the e ff ect of the Internal Violence Index on growth is very high. Referring to regression (4), a rise in the Internal Violence Index by 100 percentage point decreases the growthrate by 3.53 percentage points. This is a very high value, suggesting that the Internal Violence Index has a huge diminishing impact on growth. This outcome suggests that reducing Internal Violence stimulates growth. We observe that the standard errors of the coe ffi cients of the Internal Violence Index are relatively small. This implies that the corresponding confidence intervals, though not reported, are tinier meaning that the coe ffi cients of the Internal Violence Index are estimated with great precision. The number of observations are stable in all eight equations, hence the phenomenon we are studying covers most of our sample. The
PID does not allow any internal part (by age or any other defining property) of population to induce any real acceleration or deceleration of the whole economic system. (Nominal changes are possible, however.) Therefore, a closed economic system can only undergo a constant speed motion. In physics, it is similar to the principle of conservation of linear momentum - particles compiling a closed ensemble and not exposed to external forces can only exchange their momentums but can not change the total momentum of the system. As a consequence, there exists a nonzero economic trend which is defined by a constant annual increment of real GDPpercapita, as observed in developed countries (Kitov, 2006c). This constant increment presumes that the growthrate is inversely proportional to the attained level of real GDPpercapita. This observation principally differs from a common assumption of economic theories that economic trend is defined by a constant or steady-state growthrate. Such a constant growthrate contradicts the whole bunch of observations in developed countries.
These findings imply that, in order to raise the overall living standard of the residents of a state, state policymakers should consider making more effort in and allocating more resources to several different areas. These should include, but are not limited to: (i) enhancing the state’s education system to offer more chances for the residents to receive higher education of greater variety so as to cultivate and expand their creativity and capability of forming innovative ideas, (ii) attracting and assisting more young people to move into (and/or to stay in) the state to work for the local existing businesses or establish, if possible, their own businesses, (iii) helping build and/or continue improve those innovative industries that can drive economic growth, and (iv) boosting employment by the high-tech firms. In addition, policymakers should also consider having some new incentives and/or enhancing those already in place to encourage more business R&D activities and venture capital investment in their state. One way to encourage R&D is to raise the tax credit businesses can receive on them. Perhaps the most effective way to bring in more (and/or retain the existing) venture capital is to lower or eliminate the existing, if any, capital gains tax rate. Capital gains tax here is in fact a tax on entrepreneurship. Furthermore, the state policymakers might consider enabling the state to commit funds in the out-of-state venture capital firms but requiring them to set up an establishment in the state. These should promote the flow of venture capital funds into the state and/or the stay of the funds within the state. At the same time, policymakers will also have to help strengthen the state’s infrastructure (for example, broadband density and transportation system). This should enable better connections between (i) businesses and their employees as well as (ii) businesses and their customers. Replacing firms in the outdated industries with firms in the new, emerging and innovative industries as well as encouraging and helping young people start their businesses (usually small for start-ups) are also something policymakers need to think about if their major objective is to improve the standard of living of the residents.
The theories attempt to test empirically links between social protection and growth, in practice estimation has nearly used a simple model of the causes of economic growth and augmenting it with measures of social protection, and have used empirical model proposed by Solow and Swan (1956) with two factors: labour and capital others add human capital as a third variable of production as proposed by Romer and Weil (1992) pointed by Benank and Reft 44 . Bassanini and Scarpetta 45 determine the growth in GDPpercapita modelled as a function of: investment in physical capital (more investment means more capital assets percapita, so more growth); growthrate of the population (more population growth means slower growth in income percapita, given the level of physical capital); the level of human capital (more human capital means greater efficiency in using physical capital; here we have been divided into: education capital and health capital), and income.
The debate concerning the stationary properties of real GDP or percapitaGDP gained prominence following the novel study of Nelson and Posser (1982) whose finding of non- stationary behaviour in real GDP for the US economy has received overwhelming empirical support in the academic paradigm (see Cogley (1990), Kormendi and Meguire (1990), Ben- David and Papell (1995), Cheung and Chinn (1996), Rapach (2002)). Empirically, the existence of unit root behaviour in real percapitaGDP implies that aggregate demand shocks have permanent effects on output growth and that real GDP never reverts back to it’s natural rate in the face of disturbances to the economy. Theoretically, unit root behaviour in real GDP is contrary to both Neo-Keynesian and monetarist economic fundamentals which otherwise insinuates that business cycles evolve as stationary fluctuations around a deterministic trend. Thus, when real GDP is found to be a non-stationary process, Keynesian economics suggests that active monetary and fiscal stabilization policies must be implemented in order to ensure that the economy reverts to it’s state of potential GDP (Solarin and Anoruo, 2015).
The test results in Table 3 indicate that at scale 1 (2 - 4 years), there is no causality between the economic growth and the two indicators of Financial In- clusion. However, it should be noted that the Zbar statistic indicates that the economic growth causes the overall rate of growth of demographic services. In view of its contradictory results, the conclusions obtained from the Ztilde statis- tic, which is the most adapted to our study, must for reasons of robustness be taken with precautions at this scale. At scale 2 (4 - 8 years), the causality is present and even bi-directional. The overall rate of demographic penetration of financial services (supply) and the overall rate of use of financial services (de- mand) cause GDPgrowth and vice versa. The analysis of the statistic tests Ztilde at scale 2 (4 - 8 years) provides further information. Firstly, we have found out that economic growth causes more Financial Inclusion than the latter one does for economic growth. Indeed, a strong growth implies a greater income and therefore can lead to investments in financial infrastructures more ecient in or- der to sustain this growth. These investments consequently trigger an increase in the supply and quality of available financial services and thus make them more accessible to the population (geographical penetration). In addition, a rise in in- come implies a rise in the demand for financial services and therefore increases their use. Secondly, the use of financial services (demand) causes more economic growth than their demographic penetration. That can be explained by the fact that an increase in the use of financial services drives GDPpercapitagrowth. Table 3. DH panel causality at different times scales.
The prime objective of this study was to analyze the Economic, Social and Environmental factors that affected the overall standard of living (SOL) in Pakistan and Bangladesh. The study used the bound testing approach to co-integration ARDL for analyzing the relationship between the variables. Also, a straight forward relatively simple formula was used to work out the growthrate of the standard of living of Pakistan and Bangladesh. The result showed a positive relationship between gross domestic product and real percapita income in both countries, while consumer price index and population have negative and significant impact on real percapita income in Pakistan and Bangladesh. Political rights and carbon dioxide emission percapita have significant impact in Pakistan, but insignificant impact on real percapita income in Bangladesh. This study has also determined standard of living growthrate of different government’s regimes in Pakistan during 1980 to 2012 and found that Pervez Musharraf regime was the best regime as far as the growthrate of the SOL is concerned which was noted to be 2.07% (Table 3). In the light of our findings it is appropriate to suggest that the governments should formulate and implement policies to combat inflation in both countries. The governments should use suitable fiscal and monetary policies to increase GDPgrowthrate and discourage setting up of pollution intensive industries for cleaner environment. Political freedom and minorities’ rights should also be given to minorities. Furthermore, provision of leisure, safety of life and property, political freedom, freedom of speech and media, cleaner environment and the like are to be ensured because they are part of the SOL.
The data used in this research is obtained from the World Bank for the period 1960-2012. The dependent variable is GDPpercapita (as a proxy of economic development) at constant prices of 2005. The independent variables are: the stock market capitalization of the firms listed on the Stock Exchange (in U.S. dollars at current prices), the market capitalization of the listed companies as a percentage of GDP, the number of ATM per 100,000 adults, the commercial bank branches per 100,000 adults, the banking spread (lending rate minus deposit rate), and the domestic credit provided by the banking sector as a percentage of GDP. This research utilizes balanced panel data and the period is restricted to the availability of data. The panel data analysis includes the seven larger Latin American economies in the period 1960-2012. The notation for the main variables and their statistics are presented in Table 1.
However, contrary to the above-mentioned works, our approach does not aim to explain differences in level but in the growthrate of percapitaGDP. When limiting the attention to Europe, there has been a relatively large amount of empirical work on the convergence across countries; however, not much attention has been devoted to differences in economic institutions as an explanatory factor. For example, substantial empirical work has been done to assess the convergence of transition economies of Eastern European countries (Rapacki & Próchniak, 2009), based on a traditional set of macroeconomic and structural variables. Other work has focused on the identification of “convergence clubs”, i.e. country groups within the EU which have in common the level of real income percapita (Borsi & Metiu, 2013), derived from a neoclassical growth model augmented with endogenous technological progress. Borsi & Metiu (2013) found that regional linkages seem to play a significant role in determining the formation of convergence clubs and that euro-area countries belong to distinct subgroups; thus clustering is not necessarily related to EMU memberships. Already in 2008, the European Commission (2008) had pointed out that the catching-up processes have been somewhat lower in EMU than outside it, even when accounting for differences in the initial levels of GDPpercapita. Most recently, by means of a counterfactual analysis, using synthetic control methodology, Fernandez & Garcia Perea (2015) argued that the adoption of the euro did not produce the expected permanent increase in the GDPpercapitagrowthrate. While their model does not explain why this happened, the authors refer to the lack of a rise in intra trade and to the lack of policies to boost productivity as potential causes.
The following variables are included in X : (i) investment share in GDP; (ii) human capital; (iii) population growthrate; (iv) trade openness; (v) policy volatility (discussed below); (vi) terms-of-trade (ToT) volatility, which is measured as the standard deviation of the ratio of export to import prices (as a proxy for external shocks); (vii) political violence (explained in footnote 13); ix) institutional development (polity2); and x) financial development, which is proxied by the credit disbursed to the private sector by banks and other financial institutions relative to GDP. Lag (log) percapita income is also included to account for conditional convergence and the transitional dynamics to avoid a positive bias on the coefficient on BC volatility (for a discussion of this bias, see Martin and Rogers, 2000, p. 365). Acemoglu and Zilibotti (1997), Kose, Otrok, and Whiteman (2003) and Koren and Tenreyro (2005) also document that GDPgrowth is more volatile in developing than in developed countries. The variables (i)–(iii) (along with initial income level) are the most common controls in growth- volatility regressions (including Ramey and Ramey, 1995). Although investment is crucial to