Inspired by reading on the works of Jane Jacobs and her attempt to look at the economy from the urban economics / networks of cities view and and seeing Manuel Castells network society become more and more dominant in my reasoning of how markets work, and challenging myself to understand the open-source economy as a dominant feature of the information age, I was intrigued to render my thinking about macroeconomics and the chase on alpha in value-oriented investments in terms of a four economy model. So while I didn’t have the opportunity to look at the statistics of the model yet, the concept appears robust when thinking about mutual exclusivity of dynamics. So this is a brief take on the conceptual world.
Let’s start with what the four economies are.
Of course, there is the traditional national economy that is being studied by economists all around the world. Its existence gave rise to a fairly solid way of recording economic activity standardized in the System of National Accounts (SNA 2008). A system actively promoted by the UN and leading to the level of standardization that eliminates at least the worst fallacies around building macroeconomic models on incompatible data sets. Nations and international treaties continue to govern the regulatory environment under which all international economic activity is taken place. A solid first start. But also looking at the academic approaches towards providing micro-foundations to our macro-economic world and policy making, it’s easy to see that the tools at hand are good enough to connect stylized micro behaviour to generate time series that resemble the dynamic interrelationships of macroeconomic dynamics. But is certainly not enough to deeply understand what is practically going on.
The network economy
The core goal in understanding the micro level of national economics is to identify partially isolated economic systems that can be studied below the macro economic level and that provide sufficient data to be studied. Industry economics is a core economic discipline, but too stylized in most cases to gain a clear understanding, although it can enhance analysis of possible market developments. The first thing that comes to mind is the separation of markets using industry classifications. This appears also a bit too standard, and the level of complexity of predicting cross-industry go-to-market and acquisition strategies is too undefined to build a strong model for it. But there is a dynamic that is both fairly stable over time and is increasingly becoming plastic in the information economy: social networks. While almost all industries connect on the policy and lobbyism level, the percolation of skills as transported by human resources among e.g. the lumber, the fashion, banking and technology industry is somewhat predictable. Mining professional networks and job rotations among industries can provide a starting point, and observing career progressions in different industries paint a clear picture that a lumber engineer will not become a tech lobbyist, a banker or consultant is more likely to fill a senior management position than a youtube star entering the defense industry, a successful serial entrepreneur is not going to sit in the back office of a bank, a star academic will not enter politics. Ignoring geography – city model – , nationalities – macro model – and individual features that can not be measured by human resource networks – willingness to learn, drive, social networking skills, etc. – there is a clear dynamic of how careers progress in social networks and how some career models create a larger cross-industry transition capability with a larger capture of rents of e.g. a highly cohesive and exclusive social network.
When looking at how industries evolve, the picture shifts from a-geographic and network driven evolution towards a clear geographic strategy. Natural resources are where they are. Logistic capacity, tax and trade systems play a role in optimizing network flow problems and overall everything culminates in geographic areas somewhere along the value chain. Whether dense urban areas provide excessive access towards consumption power – distributing fashion chains among key urban hotspots – or provide a signalling effect as value chain core hotspots – tech in Silicon Valley, banking in New York, fashion in Milan, tech manufacturing in Shenzen – or are merely hotspots in the upstream and associated services – Singapore in some sense in the global fullfillment sector, Hong Kong/Shenzen in China-based exports. Cities dominate in their own aspect and form their own hub-and-spoke architectures either consuming or distributing flows. Cities combine their local elites and key influencers who leave their social network status on a global scale to contribute to the local ecosystem of the geographically centered network architecture. Service and technology innovations mostly happen in such areas and not in isolated hubs, unless a clear systematic issue can be observed. The forces behind the dynamics is clear: integrate the tax base of the region/national state and use welfare distribution to create attraction for specific human resource pools to then create incentives for building a local culture and specialization and drive what Jane Jacobs called import replacement, or what we understand as comparative advantage in the human resource pool.The concept is somewhat clear and recently has been promoted by the „Silicon Valley export“ model. The concept of providing a regional density of core effectuators of an industry – the core driver of the value chain – in a single region and building a cohesive group dynamic among key factors that add to the success of the effectuators. Leland talked about specializations in fashion, finance, automobile, manufacturing, trade and distribution and leadership is signalling.The core issue is to understand that human resource manufacturing in the form of higher education and signal-strong target universities and its inter-relation with financing and business incubation capability and the ability to market to the total TAM all fall into the city networks economy, but that they only become fruitful if the city development is capable of providing a social network economy dominance. Something that happened in Israel, Beijing and the Valley, and did not happen in the EMEA region, the wider ASEAN, LatAm or African region. Reasons probably are manifold from lack of governance and institutions to lack of integration of regional interest via economically unfavorable isolation of national interest in local party systems.
The information economy
The final economy is the internet or free information ecomomy. It defies the dynamics and discriminating effects of both the network and city economic world and basically provides a first global economic good which also defines the required financing and support from any national interest. Anyone can add and extract value. And soon, artificial intelligence will operate on this independently.
Where is the alpha case?
It is evident that macroeconomic issues and issues of national and international regulation always play a vital role. But the capability of an industry or key players is more likely to be defined by the interplace of how these industry access social networks and populate relevant city networks. A company that dominates its industry and has access to the best local resource pool will likely fail to compete with an industry player that outperforms in occupying key city hubs and their associated networks. The question on how growth and expansion follows a successful capture the city flag model might be more relevant for determining long term value of the company than is the current optimization against regulatory regimes in the domicile country or accesses the best possible human resource and technology pool. At the same time, a company that successfully occupies key city hubs but fails to tap into the right social networks is also likely to fade in dominance over time.So from a statistical point of view, analyzing the development of the human resource pool by looking at the hiring practices and career progression of the employee base combined with analyzing successful regional expansions of the company is likely to provide a better predictor of long-term success than the current progress in valuation and the cost base of the company. The latter only becomes relevant if the company knowlingly underperforms in these dimensions and stays competing with a similarly inept peer group. And a company that completely ignores the relevancy of the open source and information economy from micro-degrees to self-trained life-long learners and individuals highly networked with their peer group using web-affiliations will likely miss to perform as well as a company that does this correctly.When looking for alpha on the macro level, the long-term value-oriented impact might be driven by the ability of a national economy to move its core economic actors within this space. Something that might be substantially more relevant than steering development using monetary and fiscal policy and looking at welfare and taxation issues.
It still is somewhat difficult for a researcher to tap into the full power of social network data to assess the long-term impact of social network effects on the economy. There is little or not any information on economic impact of information economy participation on professional performance that could be studied in a scientific manner. Economic statistics on the city level do exist however and might be something worth looking into.Opinions are my own. Comments appreciated.