Demographics - Toolkit to Anticipate Potential Booms

Demographics is one of the key predictive indicators of a country’s economy, current and future. It can single-handedly explain a country’s circumstances better than almost any other macro variable. A solid understanding of demographics can readily help one clarify and evaluate different countries for product distribution, expansion, or acquisition. The primary purpose of this post is to demonstrate which countries currently have or will have the most potential for economic growth. However, secondary impacts of demographics on preferences of risk, consumption, and entrepreneurship should also influence the strategic decisions you take in these markets.

To better understand how demographics relate to a country, I break down a country’s demographic transition into three phases:
Stage 1 – High birth rate/low median age. Characteristics of this stage include poor GDP per capita, high infant mortality, low female formal workforce participation, and low-quality institutions and infrastructure.
Stage 2 – Low birth rate/medium median age. Characteristics of this stage include rapid growth, improving institutions and infrastructure, and a high degree of entrepreneurship.
Stage 3 – Low birth rate/high median age. Characteristics of this stage include slow growth of GDP per capita, less dynamism, and difficulties in fulfilling government spending on transfer payments.

Stage 2 is referred to as a “demographic dividend.” The demographic dividend and the transition between stages can be understood in terms of an economy of 100 people on an island. The economy begins with 60 people picking coconuts, and most of the remaining 40 are children. Families assume voluntary responsibility for the raising and support of their own children. However, as the birth rate declines, a large proportion of the population is of working age. An unprecedented boom develops, and 80 people are picking coconuts, children and the elderly are divided among the 20 non-working portion of the population. The children and elderly can easily be provided for during this period of prosperity, and the economy becomes more dynamic as barista-staffed coconut juice bars begin to dot the island. As the population ages, the economy becomes less dynamic and the percentage of the population working declines until it arrives at 60 again. However, there are some key differences between this stage and the first stage. Most of the non-workers are elderly, and individuals do not generally voluntarily provide for them, so government transfers coconuts from the younger population to the elderly, creating more disincentives and deadweight social loss than previously. The economy is less dynamic with a diminished risk-appetite. Although much richer in the final stage than the first stage, growth prospects are not as strong as previously. Some of the key factors that change during this stage include percentage of the population working, voluntary versus involuntary transfers, and appetite for risk.

This relationship becomes apparent in a straightforward demographic analysis of countries worldwide.¹ The demographic dividend occurs when the birth rate falls, but the median age has not yet significantly shifted.²

“Demographic Dividend” – Breaking Down the Winners and Not-So-Winners
Certain countries have birth rates that drop rapidly. When this occurs, the opportunities from the demographic shift are greater than usual. Identifying countries that are experiencing and will soon experience this optimal period of growth can assist one in making the correct expansionary decisions. By placing a value for the optimal set of conditions (population, median age, and birth rates), I have created a narrow list of countries experiencing a phase of demographic shift.

I. The Current Crop

 

The first category is the current demographic dividend countries that experienced an especially sharp change in birth rate and median age. This represents the current crop of highest potential economies based solely on its demographic profile.
1) Primetime (red): These names appear in publications either as the “who’s who” of developing nations or as autocratic wrecks
2) The exception (purple): Brazil. Although Brazil should be pushing to the next category, “moving on,” it experienced a sharper change in median age and births than any other country in the group. Accordingly, the boom will last longer, but the eventual landing will be harder.
3) Moving on (greenish gray): The drop in birth rate is catching up to this group, and they will soon join countries like Chile and Uruguay, which are enjoying a more natural and level stage of the demographic dividend. Unfortunately, none of the three countries experienced growth during its sharp demographic dividend compared to the top performers in Primetime.
The interesting element of these 19 countries is that almost without exception they have either been growing quite quickly or have been managed by extremely poor institutions (and even then sometimes still experience solid growth).

II. Generation X

 

The next wave of countries have smaller populations and are just beginning to hit a sharp period of demographic transition. This beginning phase represents an opportunity for each one of these countries to break away from its historical performance.

III. Next-Generation Hopefuls

 

The last wave of countries projected to begin a sharp period of demographic transition (2020-2035) is perhaps the most interesting bunch. Most of the countries have obvious drawbacks (civil war, highest homicide rates in the world, minimal infrastructure, high corruption, lack of or extremely poor institutions, etc.). However, because the demographic shift is still far enough in the future, these countries could change sufficiently before the favorable demographic change arrives. Could Syria follow a path similar to Peru? Could Bangladesh be the next Mexico? Many forget that a number of countries that are currently rising stars were in deplorable conditions just a few decades before.


IV. Gramps

 

Seven major countries have arrived at the final stage of the demographic shift. While not all of the seven are performing horribly, I believe it is safe to say that any degree of per capita real GDP growth among these countries is considered a success.

Interesting, but this is relevant to my company because ...
The world is a big place with a lot of opportunities to work, invest, expand, etc. Demographics heavily influences growth, politics, and entrepreneurship. While every place is unique, demographics is a simple and reliable lens through which to observe and evaluate a situation, as well as anticipate potential booms. 

¹Many variables are used to understand the demographic distribution of a country (working age per dependent, percentage between 20 and 65, etc.). However, for sufficiently large countries, birth rate and median age are sufficient to being to understand demographic phenomenon as well as predict economic potential.
²I used a fairly straightforward evaluation system to determine the favorability of a country's demographic profile. Each country's graphical distance from the theoretical optimal growth point in the curve was calculated. Distances were then ranked and cleaned for population size. For simplicity and consistency, all data used in this post came from cia.gov.
Population: https://www.cia.gov/library/publications/the-world-factbook/rankorder/rawdata_2119.txt
Median Age: https://www.cia.gov/library/publications/the-world-factbook/fields/2177.html
Birth Rates: https://www.cia.gov/library/publications/the-world-factbook/rankorder/2054rank.html

Investing in Brazil - The Limiting Factor in Brazil's Economic Potential

When first approaching a developing market, many begin with the following questions: 

• "How big is the market?" 
• "Is the industry growing?" and 
• "What are the short- and long-run prospects for a given project/acquisition/initiative in this country?" 

These questions are often dismissed with an appeal to "everyone knows," a reference to the acronym "BRIC," perhaps a mentioning of natural resources or oil reserves, and no mention of the underpinnings of that economy or post-2008 growth rates. Speaking from personal experience, the experience of colleagues, and exposure to a few key data sets, I would like to focus on just one key facet of the Brazilian economy—education—and how it should be an integral part of strategic decision-making in Brazil.

An economy is inextricably linked to the underlying productivity of its labor force. One primary factor driving labor force productivity is the underlying education of the population. Education improves literacy, work ethic, communication, problem-solving, critical thinking, and innovation. These in turn increase the productivity of labor, which then drives the economy. With this in mind, consider below the three graphs depicting Brazil’s education and related wage levels for different segments of its population. The figures below are from the OECD’s Education at a Glance 2011: OECD Indicators.¹

1)      Secondary Education:

Brazil is behind the majority of its OECD peers in terms of secondary education. Although the education level has improved for younger people, the younger generation is still significantly behind the older generation of Brazil’s OECD peers.

2)      Tertiary Education

Brazil’s improvements in secondary education have not coincided with improvements in tertiary education, which is at one of the lowest levels in the OECD. Additionally, both the younger and older generations remain at about the same low level. Poor educational performance and lack of improvement are two of the greatest long-term impediments to the Brazilian economy.

3)      Education and Wages

Because of the small pool of educated labor, the premium for tertiary education and the discount for sub-secondary education are the greatest in the OECD pool. This one statistic helps illustrate the difficulty of attracting and retaining talent in Brazil. Supply is dear and the bidders are many.

The bottom line:

Short term: Top-notch skill within Brazil is both expensive and scarce. Additionally, effective and efficient local monitoring and controls may be ineffective due to the lack of experience and education of those governing the control environment. Global corporations should be aware of these limitations before committing to greenfield or acquire, and they should also seek out methods to mitigate these risks. In this type of environment, risk mitigation can be best approached through global IT systems with external monitoring, frequent auditing, and realistic strategic cost-benefit analyses.

Long term: Not only have Brazil’s rates of secondary and tertiary education completion been historically low for previous generations, but Brazil has also demonstrated only modest improvements in secondary education attendance for younger people and has not demonstrated any improvement in tertiary education attendance.

These problems in Brazil's education levels both intensify wage inequality and limit the productivity of labor, placing an upper bound on the economy. This can be overcome in the near term for resource economies (e.g., Venezuela and UAE), but high-value, easy-to-extract resources will eventually diminish, and world prices can change. Additionally, limited workforce education confines the majority of Brazil’s manufacturing sector to low-skill, low-productivity, and low-wage workers.

Brazil has always had plentiful natural resources, and the somewhat recent discovery of vast oil reserves off its coast represents a tremendous opportunity.  These resources, combined with institutions that allow private capital to grow and invest, have aided Brazil in achieving an above-average per capita GDP compared to its South American neighbors. However, long-term economic growth will ultimately depend on Brazil’s ability to improve the underlying education—and therefore productivity—of its workforce, which Brazil has not credibly demonstrated, as shown in the OECD figures. Consequently, industry growth projections should be realistic, given both the opportunities and limitations of the Brazilian economy.

¹ http://www.oecd.org/dataoecd/61/2/48631582.pdf Definitions are found on page 26. The graph concerning tertiary education is found on page 30, the graph concerning secondary education is found on page 32, and the graph concerning relative wages is found on page 138.

Granularization: A Case Study of Zipcar

Granularization is what I call the method I utilize to deconstruct and question the fundamental assumptions of a strategy. Here are the steps:
1) Break down the product's value proposition and identify how it interrelates with the company's overall strategy.
2) Determine the most basic variables that would confirm the assumptions that underpin the strategy.
3) Once this determination is made, then discover the company's justification for the strategy. Use this information to further refine your results on top of the basic assumptions that you analyze.

While granularization's use is limited and subject to confirmation bias, I have found it very useful for evaluating a strategy from a fresh perspective.

I followed Zipcar's progress since before its IPO in early 2011. Innovative, hip, a bit edgy: what's not to like? Only when I bored down into the details did it seem off.

1) Value proposition: car sharing (many "members" paying to share the same cars). Zipcar maintains a minimal physical presence to occasionally service the vehicles. Systems and call centers handle the rest. This could, in theory and when operating in large cities, hold costs to less than traditional rental car companies, but with more locations.

2) Basic Assumptions:

     A) Density. Zipcar needs a sufficient number of people within walking distance of the vehicle. Because of Zipcar's minimalist physical presence, the company isn't structured like a hub and spoke rental car company with the infrastructure to support scattered locations. The cars have to be where the people are, and the economies of scale need to be such that the cars can be serviced and cleaned on a regular basis by relatively few people. 

    B) Money. Providing a $15k-$25k piece of insured equipment isn't cheap, even with innovative technology and a creative business model. A $30 fee to transport items from a grocery or supply store and back isn't attractive for most of the population. There are far more substitutes in this segment than most people initially imagine (delivery service, taxis, friends, family, bikes, walking, rental cars, public transit, or simply going without and pursuing more local options).

The Data:

A) Density:

Cities with more than 4,000 housing units per square mile are uncommon in the US. All cities with more than 10,000 housing units per square mile are located in the greater NYC area. Accordingly, one would expect atypical results from this region. To give an idea on scale, the 200-250 range contains cities such as Las Vegas, Norfolk, and Grand Rapids; these are all moderately sized cities, but they have relatively few buildings above five floors.

B) Income:

The above graph shows all cities with a density of over 4,000 households per square mile (excluding the NYC region cities above 10,000 for truncation purposes). Each of these cities is then grouped into major metropolitan areas.

Looking at the data available, I believe there are two key points: (1) there are very few dense cities in the US in which Zipcar can optimally operate, and (2) as Zipcar expands into cities farther  from the high-density/high-income quadrant, one cannot expect similarly good results from these cities.

3) Company Reasoning (at least the publicly available information): Soon after Zipcar's IPO, I reviewed a number of explanations and projections given by management for the company's progress and future (each is paraphrased).
     A) In time Zipcar's newer cities will mature and obtain results similar to NYC, Boston, etc. Zipcar initially launched its operations in most of its ideal markets (NYC, Boston, DC). Because Zipcar's mature markets are outliers in terms of density and income, replication of NYC per-car-volume and margins in Cleveland, Denver, or Austin can't happen.
     B) Specific sub-market segments (e.g., university campuses) can be targeted and profitable. Zipcar lacks the infrastructure to execute on this very well, and this opportunity does not appear significant enough to drive the aggressive forecasts. It is a possibility though, and specific market research and pilots should bear out this further refinement of the data.
     C) Primary cities will grow at a fast pace.  This is reasonable and should be very easy to show with data. Continued fast-pace growth can be demonstrated with gross hours rented by month in that city (not just members enrolled).
     D) Opportunities outside the US will continue the growth trajectory. Certainly possible, but the execution would be quite complicated, and up until the IPO Zipcar had not demonstrated complex international expansion as a core competency.

The Result: Zipcar IPO'd at $18 and popped to almost $30 based on buzz and aggressive projections. It then steadily fell over the next couple years to $7 as Zipcar continued to essentially break even without achieving its aggressive revenue targets and could not demonstrate success in the four projections shown above. Zipcar was eventually bought out by Avis with a premium that amounted to approximately 25% less than Zipcar's then 52-week high.

Granularization is especially effective at breaking apart "but this time it's different" arguments by beginning with a straightforward "blunt instrument" and then taking into account company justifications after the fact.

From Bloomberg:

Notes on Data: In order to get a consistent and complete data set with minimal judgement calls, income was pulled on a metropolitan area basis from FRED, while housing density was pulled from the census (GCT-PH1-Geography-United States: Population, Housing Units, Area, and Density:  2010). While better (public) data sets for an exercise such as this do exist, too much discretion on data classification than I am generally comfortable with would need to be used in order to generate an "apples-to-apples" comparison. Additionally, housing density by metropolitan area was not used because the results can vary considerably depending on how metropolitan area is defined. Accordingly, the more specific city-level unit of data for housing density was used.

The Right and Wrong Lessons from JC Penney - The Media is Wrong

Recap: JC Penney, under the direction of Ron Johnson from Apple, decided to transition its prices to a three-tiered system of weekly, monthly, and everyday low prices. At the same time all "sales" were eliminated from their strategy, vocabulary, etc. A year of dismal results later, Ron Johnson is out, and JC Penney will be trying out a new marketing and pricing strategy. Pundit opinion on this has either been 1) JC Penney got rid of the only reason to go to JC Penney: the sales, or 2) JC Penney is doomed, so it doesn't matter one way or the other.

I believe these opinions are wrong for a variety of reasons.  First and foremost, JC Penney's execution of the "no sales" strategy was abysmal. Horrible. Terrible. "JC Penney is getting rid of sales" is about the worst headline I can imagine. If I were to try and destroy JC Penney in the media, that's the headline I would choose. Why JC Penney didn't control the dialogue better astounds me. The correct headline should have read, "JC Penney will have sales EVERY WEEK." Second, department stores are surviving by either offering unique products that cannot be found elsewhere (e.g., internet, inexpensive suburb, etc.), or by offering a unique in-store experience. For clothing, some companies (e.g., Zara, H&M) are surviving by rotating their products very quickly, essentially providing a new and innovative experience every time the customer returns. However, with a store as big as JC Penney, it is very unlikely that someone is familiar with the entire product line. By rotating a portion of the product line through the sale every week, the store can more easily generate a feeling of excitement and newness.

A weekly rotation of the "on sale" product line also gives customers a reason to "swing by" JC Penney every time they are at a shopping mall, and the now reasonably priced non-sale items allow the customer to pick something up while they are already there just looking. This is a key point that I believe has been overlooked. If JC Penney can get 30% of all shopping mall visitors to at least look at the product line and consider purchasing, then JC Penney can be very successful with this strategy for a long time to come despite the supposedly gloomy forecasts of declining foot traffic at shopping malls (another subject for another time). Even if competitors eventually mimic the strategy, JC Penney can still benefit; that many new products and sales in so many stores could drive up shopping mall traffic in general.

Overall, weekly rotation of products together with general price decreases on previously low volume non-sale items is a strategy that can rejuvenate a brand, increase foot traffic, and increase transaction size of "non-sale" products. If a strategy is considered a failure because of poor execution, then Tiger Woods needs to change up his swing because I've been trying it for months and it's been lousy.

Long-term US Inflation Trends - Reevaluating Company Costs

Many decision makers in businesses are unsure of how specific macroeconomic information can apply to the situation that the business finds itself beyond, “The economy is good” or “The economy is bad.”  Unless your company makes up an economy (e.g., Samsung), generalized economic information offers little tactical information beyond general moves such as shrink/expand inventories and cut/add headcount. I would like to present just one example of how a short dive into some macroeconomic data can provide actionable information and act as a starting point for competitive intelligence and self-assessment.
CPI is the consumer price index, which is the most popular inflation index. The target set of items included in the CPI is the set of goods and services purchased for consumption purposes by urban U.S. households.¹  PPI, or the producer price index, is a measure of inflation from the businesses’ perspective. The target set of goods and services included in the PPIs is the entire marketed output of U.S. producers. The set includes both goods and services purchased by other producers as inputs to their operations or as capital investment, as well as goods and services purchased by consumers either directly from the service producer or indirectly from a retailer.² There is also the term “core” CPI (or PPI), which excludes the more volatile prices of food and energy. Sometimes in a newspaper, speaking with colleagues, or listening to the news you hear a phrase so many times, you assume it is true. You will hear in business news statements like the following quite frequently: “Inflation was 3.1% for last quarter, but the less volatile core inflation rate that the central bank pays more attention to was 2.2%.” The underlying assumption is that core CPI is a truer measure of inflation, and the total inflation randomly swings each way around the core CPI. However, it is important to know if the CPI is systematically different from  core CPI. Additionally, PPI is explained in textbooks as a leading indicator for CPI, and over the long-term they should be quite similar. However, has there been a divergence between CPI and PPI, and if there has been, what could this mean?
For simplicity, the source used for all graphs is from the bls.gov website.³

Since 1999, core CPI has been systematically lower than total CPI. While it is true that core CPI is less volatile, it is also systematically lower. The cumulative impact of this since 1999 is a price difference of 8%. However, this fact becomes more interesting when the producer’s side of inflation is added.

PPI also outpaces its core. Although CPI outpaced its core by 8%, PPI outpaced its core by 18% since 1999. Inflation in food and energy has hit consumers and producers, easily outpacing the other categories. Also, producers have faced a dramatic increase compared to consumers. How can this information be utilized?
To recap, the four key facts from above are:

• CPI has exceeded core CPI by 8% since 1998.
• PPI has exceeded core PPI by 18% since 1998.
• PPI has grown by 8% more than CPI since 1999.
• Core CPI has grown by 5% more than Core PPI since 1999.

This can be understood by incorporating the information above into one graph:

The Bottom Line – Conclusions and Tactical Considerations
As more of China and India’s 2.6 billion people see economic improvements, demand has increased for energy, food, and other natural resources. For example, China’s primary energy consumption has more than doubled since 1999.⁴ Supply for these same resources has not kept pace sufficiently to keep prices tamped down. World demand, USD depreciation against certain currencies, and other factors have contributed to an increase in US  food and energy prices. While this analysis focused on the US market, these trends also hold across most countries.
Food and energy are generally considered inelastic goods, meaning that when prices rise, consumers do not cut back on them. This is similar to negative income shock to a consumer.  In effect, the consumer must spend more on food and energy, and less on other items. This can also make consumers more cost conscious generally as they seek more utility for their remaining dollars.
Because PPI captures price movements from businesses output, the PPI is commonly understood as a leading indicator of CPI. If this relationship holds, there is a higher chance for increased CPI inflation in the future for food energy, but not for core, as the divergence between PPI core and CPI core has remained relatively constant since 2003.
Because prices have increased more for PPI than CPI, especially in food and energy, businesses will likely push back harder on price increases and be more willing to increase prices on consumers where possible. Many businesses have managed costs over the last five years by improving SG&A, labor costs, and productivity. However, as greater efficiencies in labor become harder to achieve, more attention will be focused on the cost of inputs.
The difference between the PPI and core PPI indices since 1999 is large and has exacerbated over the last three years. Because energy and food have been hit hardest by rising prices, the payoff for increased efficiency in the use of these products have increased. Companies who adapt their processes use these resources more efficiently will maintain a cost advantage. Many companies may think that because they are not in food or high consumption of energy industry, this does not affect them. However, many areas of companies are affected by these price changes, and standard processes may require updated analyses as the payoff for efficiency has increased. A few examples include: style of product packaging (volume, shape, weight, etc.), use of plastics, intermediate food processing waste, transportation utilization and route analysis, IT use and cooling, and machinery setup and batch costs.
¹ http://www.bls.gov/ppi/ppifaq.htm. CPI definition.
² Ibid. PPI definition.
³ bls.gov. The series IDs utilized to create the graphs were CUUR0000SA0 and WPUSOP3000. Where applicable, base year has been changed to 1999 for clarity.
⁴ See http://www.eia.gov/countries/cab.cfm?fips=CH