Saturday, May 18, 2013

International Transmission Mechanism and Tourism

I've started a new job!  I'm now a Principal Analyst within the Sector Performance team within the Ministry of Business, Innovation and Employment (MBIE).  What a rush!  This has been my third week and I feel like I'm starting to get my feet under the table and hang of the role. 

The Sector Performance team, as its name suggests, has responsibility for analyzing the economic performance of different economic sectors within the economy.  Its focus is sectors - not necessarily industries - for example, we look at the Tourism sector, and the Science and Innovation sector.  Those sector's economic activity span different industry definitions.  We look across industries and at the performance of an economic activity as a whole.

Tourism is one of our foci.  This week, MBIE released this:

International Trade Flows
Take a look at the picture on that webpage and think about what is happening. 

One of the lessons from Open Economy economics is the role trade plays in transmitting economic shocks around the globe.  Back in the days of the Gold Standard, gold underpinned the monetary base of countries.  Changes in the volume of gold held by a country expanded or contracted their banking and monetary base, leading to expansions or contractions in aggregate demand.  The economic prospects of countries were intimately entwined.  Prosperity in one country lead to an increase in import demand from that country.  Gold would move from the prosperous country out to other countries, in the process, reducing the monetary base of the prosperous country, and expanding the monetary base of foreign countries.  As gold flowed according to trade patterns, changes in gold base between different countries "transmitted" economic fortunes between countries.

Moving on neigh on 100 years to now (World War 1 saw countries depart form the Gold Standard - it stuttered and stammered back into life after the war until World War 2 when it was dropped again) international trade flows continue to be one of the main mechanisms for changing economic fortunes between countries.  Upturns and downturns in domestic conditions between different countries continue to play out as increases and decreases in imports demanded from other countries.  One countries imports is another's exports, and another's economic production and source of incomes.  Changes in imports impact and influence economic conditions in other countries.

So ... with that little story under your belt, have another look at the graph on that web page and read it again.  Here it is below:

One of the most notable trends is the long term decline in tourist expenditure from USA and UK tourists.  Since 2001, USA tourist spending in New Zealand has generally declined.  Since 2006, UK tourist spending in New Zealand has approximately halved.   Japanese tourists have decreased their spending.  On the flipside, China's tourists spend, since 2006, has more that doubled.

And the domestic fortunes of the USA, UK, Japan and China?  From here and here:

Until recently Japan's economic growth has been significantly lower than the OECD average.  The UK economic growth bobbed along at about 3 percent between 1999 and 2007.  But since 2007, its economic fortunes have drastically declined and its economic growth rate has been significantly below the OECD average.  And the USA has been on a long run slow economic growth decline since 1999.

China, however, according to the OECD, has mostly experienced double digit economic growth, without growth only slightly falling over the Global Financial Crisis period.

These tourism spend measures turn out to be quite cute thermometer and example of the international economic transmission mechanism.

Tuesday, April 23, 2013

And on a sadder note...

Bit unhappy today World.  One of my all time favourite rockers died on the 21 April 2013.  As a 16 year old, I and some mates jumped the fence at the 1988 Brisbane Ekka Show to see her and the Divinyls play live.

Rock on Chrissy Amphlett 

Monday, March 25, 2013

Thinking about Regions: Updated with R Code

Regional Industry Employment

The blogs have been slow this month - sorry people.  Things have been quite busy in my neck of the woods.  One of the things I am looking at at the moment though are three employment related questions based on Statistics New Zealand's Business Demographics data:
  1. What does the regional patten of industry employment and business size "look like" between the different geographic regions?
  2. Is there a story of "regional comparative advantage" in the data?
  3. How has the regional patter of employment and business size varied over time, especially in response to the Global Financial Crisis period of downturn?
Here's what I've got at the moment - its a work in progress thing :)  I'll keep updating this blog until I'm happy with it.  Also, I'd like New Zealand Economists to use more R in their lives.  As a result, I'm posting the R code and the source data at the bottom of this post.

Fig1. Regional Employment by Industry and Year

Fig2.  Regional Employment by Industry and Year - Selected Regional Councils

From question 1, I'm looking at what proportion of the workforce employed within geographical regional council area are employed within the different industries. For example, looking at Fig 2 first, Public Services stands out as a large industry source of employment for Wellington - no surprises there (and that's what I want).

My goal is to be able to express each of these regionals in terms of 'similarity to' and 'difference from' other regions.  For example, Auckland and Wellington are similar to each other in Agriculture, Forestry and Fishing and Retail Trade, but different from Canterbury and Waikato.  Auckland, Christchurch and Waikato are similar to each other in Manufacturing, but different from Wellington.

Which leads to the second question: do Canterbury and Waikato have a comparative advantage in the production of Agriculture, Forestry and Fishing and Retail Trade industry commodities over the production of those same commodities in Auckland and Wellington?  If they do have a comparative advantage, which has revealed itself in different employment shares regionally within each industry, then what does this mean for their sensitivity to factors outside of their control and world market related?  For example, in Fig 1, in Mining, that top line is the West Coast, which according to Tony Ryall has its coal production currently being hit by the 'perfect storm' from international coal price decline.

Thirdly, how has each industry in each region fared over the long term, especially since the Global Financial Crisis (GFC) era of economic decline.  The start of the GFC is usually attributed to September 2008, with the fall of Leyman Brothers leading to a credit shortage which ultimately slowed economic production.  However, in New Zealand's case, the decline in economic growth started much earlier, with economic growth decline and increases in the unemployment rate evident from December 2007 on.

What I'm hoping to see in the Fig1 above is how regionally and industrially the economic decline from December 2007 on manifested within the regions.

Principal Components on Regional Industry Breakdown

First off, which regions are "similar" and which are not?  Given the different industry employment profiles, principal component analysis (PCA) might be one way to simplify the different regional industry employment compositions into measures which reflect regional similarity.

PCA is a statistical technical which decomposes the variations in multiple measures on something into its 'principal' 'components'.  It is a variable reduction technique which reduces a large number of variables down into a few key variables which, when the technique works well, describe the bulk of the variation occurring within the multiple measured variables.  For example, if there are 20 variables reflecting some separate aspect of the thing measured and which are highly correlated, then PCA might reduce the information content of those 20 variables into 2 -3 'principal components' which explain the bulk of the variations within the data.

The 'principal components' are weights given to each measured variable which  together discriminate between the measured variables according to some dimension within the data. The technique works best when the multiple measurements made on the single thing are highly correlated.  For highly correlated measurement data, large sources of variation within the multiple measure data can normally be described by the first/second principal components.  The beauty of PCA is each component is 'orthogonal' to each other: it captures some source of variation within the data which is completely and utterly separate from the sources of variation captured within other principal components.  Usually, the variation captured by each PCA can be given 'meaning' and interpretative content, although the technique never identifies what dimension it is actually capturing.  There's an element of interpretion in figuring out what each component means.

Principal Components Analysis:  Results

I've run PCA over the regional industry employment breakdowns for the 2000 year.  The thing measured is each region.  The multiple different measures are the industry employment proportions.  There are as many components as there are regions (16).

Table 1:  Principal Components Results

PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15 PC16
Trans_Warehousing -0.24 0.07 -0.43 -0.01 0.05 0.26 -0.02 0.16 -0.19 -0.07 -0.44 0.42 -0.15 0.19 0.01 -0.16
Hire_RealEstate 0.00 -0.34 0.10 0.13 0.54 -0.01 0.35 0.13 -0.01 0.03 -0.02 -0.09 0.00 0.54 -0.04 0.31
Wholesale -0.30 -0.14 -0.07 0.13 0.25 0.23 -0.38 -0.13 0.31 -0.16 -0.04 -0.05 0.37 -0.08 0.50 0.16
Other_Services -0.33 0.14 0.08 0.14 -0.07 0.20 0.08 0.06 -0.50 0.20 -0.04 -0.40 0.34 0.18 0.00 -0.22
Arts_Rec -0.12 0.32 0.19 -0.30 0.26 -0.10 -0.04 -0.41 -0.33 -0.04 -0.26 -0.27 -0.18 -0.17 0.10 0.26
Mining 0.12 0.32 -0.16 -0.32 0.07 -0.23 -0.37 0.07 0.27 0.51 -0.02 -0.10 0.07 0.44 0.03 0.02
Accom_Food 0.08 0.31 -0.22 -0.35 0.26 -0.10 0.26 -0.05 -0.15 -0.41 0.38 0.20 0.16 0.12 0.09 -0.05
Admin_Support -0.32 -0.12 0.07 -0.19 0.32 0.02 0.09 -0.20 0.38 -0.02 0.05 -0.11 -0.30 -0.11 -0.17 -0.32
Health_Social -0.07 0.33 -0.23 0.24 -0.24 0.34 0.30 -0.21 0.17 0.15 0.21 -0.05 -0.35 0.09 0.22 0.41
Retail 0.05 0.34 -0.16 0.29 0.37 -0.06 0.28 0.21 0.18 0.27 -0.08 -0.04 0.28 -0.47 -0.22 0.02
Agri_Forest_Fishing 0.30 -0.25 0.20 -0.14 -0.04 0.14 -0.01 0.07 -0.15 0.16 0.07 0.03 0.04 -0.13 0.07 0.31
Professional_Science -0.39 -0.05 0.09 0.00 0.00 0.04 -0.11 0.10 -0.01 0.15 0.15 -0.05 -0.34 0.03 -0.30 0.03
Construction 0.16 0.31 0.16 0.07 0.16 0.25 -0.32 0.56 0.03 -0.41 -0.03 -0.25 -0.25 0.00 -0.08 0.11
Financial_Insurance -0.37 -0.01 0.09 -0.20 -0.01 -0.15 -0.07 0.19 -0.10 0.12 -0.08 0.43 0.09 -0.18 -0.13 0.50
Manufacturing 0.07 -0.11 -0.41 0.34 -0.03 -0.47 -0.17 -0.21 -0.09 -0.24 -0.24 -0.22 -0.09 0.04 -0.21 0.22
Info_Telecom -0.36 0.01 -0.13 0.08 -0.13 -0.21 -0.14 0.12 -0.06 -0.15 0.56 -0.10 0.13 0.04 -0.14 0.12
Public_Services -0.23 0.08 0.19 -0.16 -0.37 -0.30 0.39 0.28 0.33 -0.22 -0.33 -0.19 0.09 0.09 0.21 0.05
Education 0.02 0.29 0.42 0.22 -0.08 0.14 -0.08 -0.36 0.19 -0.19 -0.10 0.28 0.27 0.29 -0.43 0.06
Utilities -0.05 0.18 0.34 0.44 0.15 -0.41 -0.07 0.06 -0.14 0.11 0.10 0.31 -0.26 0.05 0.43 -0.20

Importance of components

Standard deviation 2.51 2.00 1.59 1.33 1.13 0.95 0.86 0.71 0.63 0.51 0.44 0.28 0.22 0.18 0.07 0.00
Proportion of Variance 33% 21% 13% 9% 7% 5% 4% 3% 2% 1% 1% 0.4% 0.2% 0.2% 0.0% 0.0%
Cumulative Proportion33%54%67%77%83%88%92%95%97%98%99% 100% 100% 100% 100% 100%

The first principal component explains 33% of the variation in regional industry employment.  The second principal component explains 21% of the variation in regional industry employment.  Over 92% of the regional employment variation between industries is explained by the first 7 principal components.

Interpreting the Principal Components

There's a bit of art in figuring out what intepretation ought to be given to the variation captured within each component.  One approach is to evaluate both the weighing size and directions and see if, in their totality, they have some interpretation.  For example, in the PC1 Agriculture followed distantly by construction and mining hav the highest positive weighting (in bold red).  On the flipside, Professional Science / Finance_Insurance, and Information and Telecommunication industries are highly negative (in bold blue).

Conceivably, the first principal component dimension distinguishes regions of differing "market depth" and "industry complexity".

As regional markets grow, they increase in complexity. Small regions are predominately primary industry and agriculture.  Telecom and Telstra-clear have few offices in downtown Ashburton.  As regions develop, manufacturing and light industry develops and manufacturing employment grows.  As regionals develop into metropolitarian areas, professional services develop, service industries mature, and hardly anyone is employed in agriculture any more.  PG Wrightsons have few offices in Wellington.  This dynamic seems to be captured in the PC1.

The second principal component strongly negative weights agriculture and real estate industry employment proportions.  On the positive side, PC2 strongly weights Retail, Health,  Arts and Recreation, Mining, Construction and Education.  The second principal component interpretation is more tricky, but it looks to distinguishes regions differing between "prodominately service" and "prodominately agriculture".

This one's more complex, and I'd welcome some comments on this, but check out what happens when you graph the regions by principal components 1 and 2.  PC2 strongly differentiates Tasman from the West Coast regions. From Table 2 below, the West Coast/Otago and Northland regions have larger proportions of service industry employment, but I can't explain why Auckland features highly negative on PC2.
Fig 5:  Principal Components Analysis

Table 2:  Regional Industry Employment - Coloured by Second Principal Component Dimensions

West Coast Otago Northland Tasman Hawke's Bay Auckland
Trans_Warehousing 5% 4% 4% 2% 4% 6%
Hire_RealEstate 1% 1% 2% 2% 1% 2%
Wholesale 2% 3% 3% 3% 4% 9%
Other_Services 3% 3% 3% 2% 2% 3%
Arts_Rec 2% 2% 2% 1% 1% 1%
Mining 4% 0% 0% 0% 0% 0%
Accom_Food 12% 10% 7% 6% 5% 6%
Admin_Support 3% 3% 3% 4% 3% 6%
Health_Social 12% 12% 12% 4% 10% 8%
Retail 12% 11% 13% 10% 10% 11%
Agri_Forest_Fishing 9% 9% 10% 36% 19% 1%
Professional_Science 2% 4% 4% 3% 3% 7%
Construction 6% 5% 5% 4% 4% 5%
Financial_Insurance 1% 2% 2% 1% 1% 4%
Manufacturing 12% 14% 13% 13% 17% 16%
Info_Telecom 1% 2% 2% 0% 1% 4%
Public_Services 4% 4% 4% 2% 4% 4%
Education 8% 11% 11% 7% 8% 7%
Utilities 0% 1% 1% 0% 0% 1%


The following data is derived from Statistics New Zealand Business Demographics data.  => Businesses =>  Business Demographic Statistics - BUD  =>  Employee count by Region 2011, ANZSIC and Size Group (ANZSIC06) (Annual-Feb)  => select all variables, all time periods, everything and re-arrange variable order like below.

Tuesday, February 5, 2013

Craft Beer Explosion in a Mature Dull Beer Market

Regular readers of New Zealand economic blogs don't have to go very far to be convinced of the attraction between Economists and beer, as the number of post on Eric Crampton and Seamus Hogan's blog Offsetting Behaviour website can attest.

Besides the obvious, what makes beer so interesting is the extraordinary proliferation of new and diverse craft beers that has occurred in recent years.  Despite the beer market being what economists would describe as "mature" and a "low volume growth" market, recent years have seen a plethora of new entrants trying their hand at commercial success making and selling beer.  In fact just down the road from me in Silverstream, Upper Hutt, is Kereru Brewing (big ups to Chris!  Like your beer mate!) who has just moved to bigger premises.

Kereru isn't alone in its journey from being a "S(sss)" to becoming more a "SM(aaa)", part of the long journey to "SME(eee)". In recent years, along that road has travelled:
And that's just off the top of my head in less than 10 seconds. In 2006, the Brewers Guild of New Zealand was founded.  Here's the members, and it reads like a who's-who of craft brewing.  

Sadly, I'm old enough to remember a New Zealand who's drinking pallets were dominated by the limited offerings of New Zealand's largest breweries: Lion Nathan and Dominion Breweries.  Together, those giants sewed up New Zealand's bars and pubs with agreements which ensured only their mediocre beers would be sold: the practice of "buying taps". 

The anti-competitive issues with those arrangements (which are coming back unfortunately) are not the subject of this blog (although they will be soon).   Instead, what I find interesting, from an economics perspective, is why, in what can only be characterised as a mature low/no growth market, there has been this prodigious growth of craft beers.  

Unlike the market for smart phones, or even social networking connectivity, beer brewing in New Zealand is as old as New Zealand itself. The rapid customer uptake of new i-phones, computer tablets, or the rush of new sign up's to Facebook, are what typifies "growth" markets that attract a rush of new markets like some modern day gold rush.  Each and every one of the recent craft beer makers has put their money where their mouth is and banked on making a dollar from a market dominated by large players in a product hardly considered new.

From the outside of the market looking in, the history of New Zealand's brewing demand doesn't looks like something which should attract these beer entrepreneurs into it.

Figure 1:  Total Beer Available for Consumption:  1984 - 2012
New Zealand's demand for beer is characterised by long run falling beer volumes. Beer volumes have been declined an average 1.1% per year over the duration of Statistics New Zealand's information. Per capita rates of beer available for consumption have approximately halved  over the same time.

However, here's something that collectively entrepreneurs may have sniffed out... Beer prices, over the long run have been steadily sneeking up.  From a low comparative price level base in 1983, consumer beer prices have been increasing fastest than general rates of consumer price inflation (Figure 2).  Beer price to general price "relativities" having been increasing in beer's favour.

Figure 2:  Consumer Price Index - All Group Consumer Prices and CPI Level 3 (Beer) Prices
So, from figure 2, beer prices have over a long period of time been increasing, relative to the price of all over household expenditure commodities.  And in response, the volume of beer consumed has been falling (figure 1).  That's the Law of Demand:  as relative price increases, demand declines.  If the large breweries set the market price for beer, then increasing it faster than the rate of general inflation has had the effect of dampening down the demand for their beer.  From figure 3, the decrease has occurred in the lower alcohol level beers.  As the price of beer in general is becoming more expensive, consumers are shifting to the comparably higher quality/alcohol beers.

But why have craft breweries entered such a market of falling quantities and increasing relative prices now? Given the 30 year history, what's been the trigger point?  What's the smoking gun?  What's the reason why dozens of entreprenuers have taken the punt?

Well - and this is me out in a limb here - but did you seen the Kereru equipment in the background here? And the Garage Project equipment in the foreground here?  Well, it looks a lot like one of these.  And from and economics point of view, THAT looks a LOT like technical progress lowering the fixed costs of micro brewing production. 

If profit is the difference between total beer revenue and total cost of production, then on the total cost of production side of the profit equation, Farra Engineer's  equipment has lowered the total cost of production through lowering the fixed set up costs of entry.  On the total beer revenue side of the profit equation, the dominant brewers have been sneeking up beer prices over time increasing the revenue of beer production.  That's provided the pricing margins for micro-brewers to test the waters with different types of beers despite the higher per unit costs of production associated with their lower volume throughput.

The trigger point probably may have been in 2009, where from figure 2, relative beer to general consumer prices took off AND sometime around then, Farra Enginneering's equipment lowered entry costs (I'm guessing about the time they released 50SBB personal brewer)?

Given most craft beers exceed 5.0% alcohol,  I'm really looking forward to see how this graph changes over the next few years:

Figure 3:  Beer Available for Consumption by Alcohol Strength

Because, despite the fact beer is an "old" market, as a consumer I want choice and variety in what is available to drink.  If lowering the cost of beer production has generated a whiff of profit in the noses of micro-brewers and created the opportunity for them to experiment with the demand for non-lagers or non-India Pale Ales, then I'm as every bit interested in giving them a go as I am in downloading the last app of Grumpy Birds for my smart phone.

And THAT untapped consumer market for non-traditional beers which retail for comparable prices as stock-standard run-of-the-mill lagers is what drives the growth in craft beer, and makes Jamesie a very happy Economist.