Change in Relative Competitiveness Post-Coronavirus: Individual, Industry and Country Level Thoughts

Change in Relative Competitiveness Post-Coronavirus: Individual, Industry and Country Level Thoughts

Part 1: Individual Level Effects

Will Coronavirus episode effect the relative competitiveness of countries, companies and individuals? If so, how can we measure this? How can we make a projection or at least some sensible prediction on the subject?

Starting from bottom up, individual positioning will be effected by the following factors:

·         Individual’s knowledge base (a)

·         Individual’s economic organization (company, organization etc) (b)

·         Individual’s country or relevant superstructure (c)

·         Global developments (d)

·         Good old random factors sometimes called luck (e)

While sometimes the most effective, let us leave out the effect of random events and/or luck, as it is hard to quantify yet effective on all other factors. The equation for an individual’s competitiveness based on these factors will be something like:

( (a x w1) x (b x w2) x (c x w3) x (d x w4) ) x e

In this equation, w figures represent the relative weights of each factor and e is a global effect.

To put this in perspective, let us take three practical examples. John is a waiter at a busy city center restaurant, Mary is a software engineer working for a videoconferencing company and Sarah is a nurse with an intensive care specialty. For this example, let us base them all in London.

For John:

·         John is undertaking a generic job, which worker replicability is high and barriers to entry are low. On a ranking of 100, he gets 20 for this job (a).

·         Restaurant sector is relatively hard hit but with restrictions easing in couple of months, a significant recovery (albeit maybe not to the previous level without some competitors going out) is to be expected. On a ranking of 100, restaurant sector gets 70 (b).

·         UK’s and London’s performance during the pandemic and following it will not have a material positive or negative impact is my assumption. Thus as one of the primary global locations, the rating will be 100. (c)

·         However, with global travel demand falling, the prime location status will be negatively affected even if for a shorter duration. The rating is 70 for this factor as well. (d)

·         For John alone, the most important factors in this specific line of business go as follows: John’s performance w1 (30%), specific business w2 (40%), and w3 and w4 getting 15% each.

For Mary:

·         Mary has a specialized job with global high demand now. On a ranking of 100, she gets 80 for this job (a).

·         Videoconferencing software is going through a boom now. On a ranking of 100, her sector gets 100 (b).

·         London or any other city is not a solo defining competitive characteristic for this industry and the ranking is a positive natural with 60 (c).

·         Global demand for videoconferencing is high and expected to rise. However, there is intensive competition reducing this rating. The rating is 80 for this factor as well. (d)

·         For Mary, the most important factors in this specific line of business go as follows: Mary’s performance w1 (30%), specific business w2 (35%), , w3 gets 5% minimum and w4 gets 30%.

For Sarah:

·         Sarah has a specialized job with global high demand now. On a ranking of 100, she gets 90 for this job (a).

·         The hospital Sarah works for is on the frontlines now and other hospitals might engage her services. On a ranking of 100, her sector gets 100 (b).

·         London as a big city is relatively more robust with job opportunities for Sarah: 90 (c).

·         Global demand will remain very high for the next 2 years and furthermore might keep up with national epidemic planning. However, there is a big personal health risk factor in this occupation: 80 (d).

·         For Sarah, the most important factors in this specific line of business go as follows: Sarah’s performance w1 (15%), specific business w2 (40%), w3 gets 15% and w4 gets 30%.

If we tabulate the inputs, this the summary output:

 

 

Based on the output, the following inferences can be made:

·         Each occupation along with the setting where it is performed and global trends requires a different weighting to analysis.

·         There are marked differences in the competitiveness of different jobs.

·         However, this is a dynamic analysis as factors favoring or disfavoring will change. For example in a high demand situation such as today, a nurse’s performance is less important than the fact that he/she is a nurse. Such a factor will normalize and emphasis on performance will take more weight in a normalizing situation. 

In summary, as we all knew different occupations had different attractiveness and competitiveness before the coronavirus episode. However, coronavirus has amplified some expertise and sectors over the others. While most of these effects will normalize over time, prevalence of certain skill sets pertaining especially to digital expertise along with adaptation to new modes work will have even greater role. Combined with the quickening of the general business cycle with emphasis on new modes of doing business (videoconference before you meet, collaborate remotely on the document etc) and change in relative growth differentials among sectors will have a profound effect on individual competitiveness in the very near future.

…

Part 2: Company / Sector Level Positioning

I have prepared a subjective assessment of a variety of industries and listed a number of factors effecting them all and grading them on -10 to +10 scale. The data table is presented below.

The validity of the assessment is subjective and with most of the work I am doing with the coronavirus episode, it is mostly intended to foster further thinking on the subject. To that end, there are various observations of note from the data:

·         There is a huge divergence in the Total Score with industries such as Airline, Luxury Items, Automotive, Tourism and Restaurants hit very hard while Online Media, Pharmaceuticals, Healthcare, Telecoms and Software benefitting from the situation.

·         This reaffirms the premise of this article as to the point that while the capitalist superstructure will be intact, relative distribution of winners post coronavirus episode will change meaningfully.

·         Individual effect of factors are interesting to watch as Digital Transformation and Government Intervention (most to provide support with different measures) will have positive impact in general.

·         No change in long-term growth potential or consumer behavior is predicted via this table.

·         However, current measures are having and will have a major toll on certain industries as to be expected.

…

Part 3: Country Level Competitiveness

I have looked at changes in GDP and relative share of global GDP per country to look at historical change in country level competitiveness. While there could be other and better factors to explain relative competitiveness changes, in a 10-year period or longer GDP performance would be a general all-inclusive metric to reflect most other factors combined.

Let us look at the data first. Note that Data is primarily from World Bank and I have updated 2019 figures with Countryeconomy.com data. 

 

 

In this part, the emphasis will be on historical context with the premise that current conditions can speed up historical factors (on the same or on the opposite trajectory – but faster). As such, this is what the data shows in general terms:

·         From 2001 to 2019, the most notable change is China’s rise from 4% of World GDP to 16.4%.

·         Similarly big relative increases are observed for India and some emerging markets to a lesser degree.

·         For this period, the relative loss is shouldered primarily by the US and Japan.

·         However once we change the scope to 2009 to 2019, then the picture is different. The US for example stays at 23.9% of World GDP for that period. China goes up by 7.9% - this time from Japan and Europe.

 

 

 

Once we look at the the even closer period from 2017 to 2019, except for China the global effects are limited and contained in certain economies with either country specific potential or problems. Yet China’s ascendance continues.

 

The table below is built on the same data but shows the ratio of a country’s share Vis a Vis different periods.

To sum everything up, there will be a major change in competitive position of both individuals, sectors and companies. The combination of these three headline variables could have profound effect on each one of us. However, it is my expectation that this major redistributive effect will still take place within the general parameters of the current global economic order. That change is yet to come later and we have enough of a change within the current system as it stands.

April 2020