Fitch Ratings Report Suggests Independent Economic Growth

Fitch Ratings released a report entitle Mapping a Subpar Economic Recovery: What Can History Tell Us? that details the differences in historical economic recovery and growth.  They analyzed various recoveries in countries like Germany, U.S., U.K, and Japan and found similar trends that define these recoveries. Many of the eras of recovery saw the wealth effect, lower inflation and interest rates, higher government spending, new technology, changing consumer preferences, global competitiveness, trading relationships and currency effects. Today’s recovery has some unique differences that challenge basic economic assumptions. 

Interest rates are at historic lows and have been for some time. It is not believed that continued low interest rates will add much more to the economy. The same can be said of the stimulus policy. Each of these naturally has an influence on growth but can become increasingly burdensome after their initial shock impact.  

Exports often rise during a recovery and some small signs of growth are spurting upwards but are not yet conclusive enough. There have been some indications of growing health in the global economy but many developed nations do not yet appear to be sucking in American products. The trade deficit is still in the negative but has improved slightly indicating a potential better market.

The stock market has outperformed expectations and is doing better than during many other recoveries. They are at triple the level of recession bottom and there is some indication that growth will continue. Stocks are a sign of investment and wealth generation that help show that company positions are growing. 

Manufacturing within the U.S. is growing as more companies return to home soil. The increase in productivity, technology, and energy costs is helping to drive growth in this sector. The authors cannot state conclusively whether or not growth will continue but do highlight that services seems to be a bright spot and lower energy production costs seems to be making its way into the economy from a macro perspective. 

The report is inconclusive as the nature of economic forecasting can be a little like looking into a crystal ball or throwing darts onto a map to determine the best vacation spot. However, after reviewing the report you can see that there is a slight upswing in each of the sections such as share price, home values, manufacturing, and exports. The interest rate is slightly rising and government spending appears to be moving downward while unemployment is declining. The higher stock returns and improvement in home based manufacturing are extremely important in supporting long-term investment and growth. It is often the multiple markers of upward trends that hint to a possible re-emergence of the American economy based upon its fundamental merits.  

Changes and Opportunities in the Post-Recession Economy

The economy is adding jobs and that is great news. Unfortunately, the types of jobs have moved more into service sector and administrative positions that do not carry the same high wages as pre-recession employment. According to a report by the National Employment Project (NELP) low-wage industries have grown significantly since the end of the recession but this is leaving many Americans without significant savings. A mixed economic blessing that teeters between recovery and replacement.

Lower-wage industries have accounted for 22% of the recession loss but 44% of the employment growth over the past 4 years. It now employs 1.85 million more workers than it did in the past. Mid-range employment jobs lost were around 37% while recent increases are around 26% for a total of 958,000 lost. High wage losses include 41% and a 30% increase leaving us with a 976,000 fewer jobs.

The recession was longer than anticipated and even though the jobs have returned they have returned at a lower wage rate.  Since January 2008 to the low of 2010 the economy lost 8.8 million jobs while the study indicates that as of March 2014 a total of 8.9 million jobs have been raised. The positive news is that people are again finding various types of work in different sectors.

The study focused primarily on the private sector work recovery. However, government positions declined 627,000 jobs of which 44% were in education. Private sector work recovered in the service industry, the professional service and scientific industries, and private education and health services. The results for construction were mixed.

The changes also indicate an adjustment in the demographics of the country based upon global trends. Moving manufacturing to cheaper locations overseas impacts a major source of middle class income. There are efforts to raise the high tech sectors of manufacturing to create additional jobs and growth within the country. The use of higher skill and scientific work is one method of ensuring that processes and products are not easily copied or displaced.

The Boston Consulting Group released a report that the U.S. will soon reach parity with low cost manufacturers like China. This creates an opportunity to reverse trends in manufacturing losses and bring back a higher percentage of middle class jobs. However, this industry will need to push a larger section of the sector into the high technology manufacturing areas to develop the industry to a greater extent.

Highly developed manufacturing encourages mass manufacturing at a later date. New technologies that are cutting edge eventually make their way into mass distribution in the future. Innovation and development lead to greater opportunities that continually push manufacturing dominance. New products require a higher level of resources, science, strategy, and skilled labor that create first mover advantages that are later followed by lower cost copy cats.

The news is not all bad. The service industry is growing which means more people can find additional employment opportunities. A 2013 study confirms that employees can find greater pay increases within the service industry when compared with other low-earning lines of work. They may not start out high but they have opportunities to grow within this developing industry. The nature of the work is different than the past but the industry is budding and may someday come to full bloom.

Having employment opportunities across various sectors of society is important for people who desire to either move up within their careers, cross breed into other industries, search out various types of education, or attend training to raise their earnings potential. Diversity within the sectors also helps the U.S. maintain a competitive advantage in multiple arenas as well as maintain the potential as new opportunities rise. Ensuring and developing economic and human capital advantages in potential high growth areas keeps jobs at home.

The Report

Economies Fly High with the Kite Model

International competitiveness doesn’t exist in a vacuum. There are many components such as the internal clusters, national methodology, and the cultural grouping of countries to consider. A paper by Yu-Jen and Hsiao-Fong helps to develop higher levels of understanding of how these factors work together to raise the development properties of a nation (2012). Their work focuses on combining the Flying Geese Theory and The Diamond Model to develop a more holistic framework.

Flying Geese Theory:  The theory was developed by Japanese economist Akamatsu Kaname to categorize countries into leading nations, middle countries, and follower countries (1962). Less advanced countries will attempt to move up in the V pattern but may fall backwards based upon their abilities. The pattern is not set. Countries that adapt new technologies, higher skilled labor, better structures, and greater education will lead the flock.

The Diamond Model:  The Diamond model was developed by Michael Porter (1990) to explain how countries become more competitive. He looked at clusters and history of development. His analysis took into account Factor Conditions, Demand Conditions, Related/Supporting Industries, Firm Development, Government and Chance. The factors work together to create a type of diamond pattern. 

Both theories have their limitations and therefore do not do well in isolation describing concepts of a country in an international market. The Diamond Model can be seen as inner clustered workings of a country while the Flying Geese Theory can be seen as a country working within an international environment. Clusters help create the competitive nature of the country and like geese they jockey with related countries for position. 

The Kite Model seeks to integrate the Diamond and Flying Geese approaches into a more comprehensive framework. A country that develops must compete with others within their group for more influence and access to resources. The Kite Model has the following components:

Body: The integration of all the elements of the kite. 

Frame: Support the kite through infrastructure and human capital. 

Wings: The balance of forces that include international ties and domestic endowments. 

Tail: The fundamental aspects of an economy that allow the economy to rise. This includes government efficiency, law, regulation, political stability, capital formation, investment, ideology, policies etc. 

String: The guiding force of government that encourages movement in a particular direction. 

The largest benefit of the Kite Model is that it doesn’t leave a large gap in the analysis. Clusters, internal/external factors, and the cultural groupings of countries do have an impact on overall success of countries. These do not stand along and influence each other in varying ways. The model is not meant to be a “save all” but does help decision-makers to formalize the various economic components that lead to growth. 

Kaname, A. (1962). Historical pattern of economic growth in developing countries. The Developing Economies, 1 (1). 

Porter, E. (1990). The Competitive Advantage of Nations. Free Press: NY

Yu-Jen, C. & Hsiao-Fong, C. (2012). Kite model for national development strategy. The Journal of Contemporary Management Research, 6 (2).

Financial and Social Growth Support Each other

Asia is a hotspot of growth and researchers are trying to figure out how this growth was realized. The researchers Pradhan, et. al (2013), reviewed 15 Asian countries from 1961 to 2011 to determine the causal nexus between financial development, social development and economic growth.  They hoped to understand how these factors work together in terms of fostering growth within their target countries. 

Financial services offer help in terms of moving capital to growth markets. As countries growth they continually seek higher levels of outside resources to perpetuate this growth. Financial institutions help in transferring wealth from other locations into faster growth markets that can realize greater profit.  Financial institutions help in the process of financial transfers. 

These financial services also create opportunities for financing, investing in the form of stocks, and the secure holding of money. A well-established financial market maintains a level of operational trust that helps in fostering growth. Services should encourage investment opportunities within the economic market by providing a safe and secure method of transfer. 

Social growth is a concept the entails ensuring that the social environment grows with the financial and economic environments. Social growth would include things like health and education. To perpetuate this growth would require basic economic assumptions that if a person is of good health and education he/she will be able to earn a high salary. Without this assumption, people within this environment do not have an incentive to improve themselves. 

The authors found that economic development relies on social development. The economic elements must find value in improving themselves within the system and there must be resources available for them to invent and develop their markets for their own self-interest. Policy makers should focus on financial development and social development. Some countries developed their financial services first while others developed their social environments first leading to support for both demand following and supply leading hypothesis. 

Comment: The authors were not able to clearly differentiate between the chicken and egg concepts of financial versus social growth. The study seems to lend support to the idea that any one of these may lead to national growth. In some cases, improvements in financial services came first and in other cases social development came first. Decision makers may be wise to consider that both financial and social growth is two sides of the same coin and should be fostered together. 

Pradham, R. et. al. (2013). Financial development, social development, and economic growth: the causal nexus in Asia. Decision, 40.

Bayesian Time Varying Models Encourage More Accurate Economic Forecasts

Artwork: Dr. Murad Abel

Understanding trends of economic growth is a primary function of economists. Economic forecasts are an attempt to reach out and snap the future between one’s fingertips. Researcher by Hoogerheide, et. al. (2010) compares and contrasts various forms of Bayesian models and come to a conclusion about which are most accurate for national forecasts. Having accurate data and the proper forecasting tools has considerable influence on the type of decisions we make today to encourage higher probabilities of beneficial outcomes.

The researchers study helped determine which Bayesian model-average approaches are likely to produce a predictive density forecasting for likely events. As all forecasts to date have weaknesses using an average help mitigate some of the uncertainty. They incorporated data sets such as U.S. stock index returns, compounded monthly return of S&P 500 index, and T-Bill rate to create 516 observational data points.

Other researchers have used GNP and other economic data sets to try and determine the likelihood of various possibilities occurring. Most research has confirmed that using multiple levels of analysis on economic forecasting is likely to be more correct than relying on a single method (Marcellino, 2004). The analysis attempted to hedge the particular strengths and weaknesses of varying Bayesian models in order come to a rounded answer based on an average.

At its core, a Bayesian prediction is based on taking a belief of a hypothesis and updating it as new information becomes available. Originally developed by the 18th century mathematician and theologian Thomas Bayes the model has been improved many times and can now be used in complex calculations such as those found in economics. Theorists have taken the model much further and are becoming sophisticated in their logical projections.

As economic data becomes available it will either lend support to a hypothesis or detract from the hypothesis. The probability of the hypothesis being true is dependent on the quality of the data being available. In time-varying models posterior data becomes a priori when it occurs and fits within the hypothesis. The model takes into account new data without being static and unchanging thereby changing the probabilities of varying events occurring. It is this incorporate of new data that helps it be more accurate.

The researchers found that model averaging is beneficial in business cycle analysis and forecasting. The choice of models must be considered with proper care to cope with estimation efficiency and structural instability. This becomes even more important when weights are determined through regression analysis. The use of time-varying models produced the highest accuracy in prediction when compared with average model mixes. Both can work well together.

The research helps economic forecasters understand that relying on a single model is easy and convenient but may not be the most accurate. Using multiple models and incorporating new information when it becomes available encourages higher levels of economic forecasting accuracy. The economic environment is not static and the incorporation of new data helps decision-makers adjust their courses and actions to encourage those factors that will adjust the overall system to the most beneficial outcomes in the future.

Hoogerheide, L. et. al. (2010). Forecast accuracy and economic gains from Bayesian model averaging using time-varying weights. Journal of Forecasting, 29

Marcellino M. 2004. Forecasting pooling for short time series of macroeconomic variables. Oxford Bulletin of Economic and Statistics, 66: 91–112.