A2 Economics – UB40 and unemployment mind map

Just started unemployment with my A2 class today and below is a mind map which you might find useful. Also used the UB40 album cover as a good lesson starter – the  reverse has been made to look like the UB40 unemployment benefit attendance card from which the band took their name. Their UK top-ten hit “One In Ten” was an attack on Thatcherism and is mistakenly cited as referring to the number of unemployed in the UK at that time. It is in fact a song about government statistics in general, and how politicians use them to de-humanise problems. Useful way to introduce the subject especially if the class like reggae.

 

Chile looks to cherries for transition away from copper

As with a lot of developing countries (and developed countries for that matter) there tends to be a reliance on a particular resource which can be to the detriment of its economy. Invariably if an economy is going to become more resilient it must be able to diversify into other areas that generate growth.

Traditionally Chile has relied on copper which accounts for over 50% of its export value but if it is going to become more developed it must start to rely on other goods or services. In November 2017 a free trade agreement (FTA) between Chile and China was signed and this was the catalyst for the cherry industry to flourish. Garces Fruit, just south of the capital Santiago, has become the world’s biggest producer of cherries and the development of the industry has been due to a combination of the government and the private sector. Cherries in China are viewed as a symbol of prosperity and marketed as something closer to a luxury product rather than ordinary fruit. With the harvest in Chile around the Chinese new year they make a perfect gift. However the benefits of the primary sector began in the 1990’s, with rising exports of wine, salmon and grapes but farmers are now tearing out vines and replacing them with cherries which are more profitable. Even though the cherry industry requires a lot of labour, which Chileans are not keen on doing, between 2015 and 2017 700,000 immigrants, mainly from Haiti and Venezuela, averted a labour shortage.

Chile Cherry export destination – 2017

Cherries remain the most planted fruit in Chile along with walnuts and hazelnuts due to its high profits and increasing demand from China. However, prices in China decreased with large supplies exported to that market (demand), but China still pays higher prices than the price other country destinations offer to Chilean exporters. China is the top market for Chilean cherries. Chile exported 156,497 MT or 85 percent to that market in 2017 (see graph above), a 109 percent increase over MY2016/17. Chilean cherry export season starts in November and end in February and it focuses its market promotion and export campaigns in China. It is expected that Chilean exports to China will increase to that market since demand for Chilean fruits keeps increasing, and Chilean exporters get higher prices in China for their fruits than in other destinations.

Sources:

The Economist – January 19th 2019 – Bello Adam Smith in Chile

USDA – Chile Report Stone Fruit – 8th October 2018.

Wealth distribution in New Zealand – 2018

The Department of Statistics recently published wealth distribution figures for New Zealand. According to Stats NZ, the median household net worth in the year ended 30 June 2018 was $340,000, up from $289,000 in 2015. The increase was mainly driven by an increase in property values over the last three years.

% of net wealth held by % of Households – 2018

According to the survey, the top ten percent of households hold 53 percent of total wealth in New Zealand, which is unchanged from 2015. The top one percent of households hold 16 percent of total wealth in New Zealand, which is down slightly from 2015. New Zealand’s Gini Coefficient is approximately 0.33.

The Lorenz Curve
The Gini Coefficient is derived from the same information used to create a Lorenz Curve. The co-efficient indicates the gap between two percentages: the percentage of population, and the percentage of income received by each percentage of the population. In order to calculate this you divide the area between the Lorenz Curve and the 45° line by the total area below the 45° line eg.
Area between the Lorenz Curve and the 45° line  ÷  Total area below the 45° line.

The resulting number ranges between:
0 = perfect equality where say, 1% of the population = 1% of income, and
1 = maximum inequality where all the income of the economy is acquired by a single recipient.
* The straight line (45° line) shows absolute equality of income. That is, 10% of the households earn 10% of income, 50% of households earn 50% of income.

In 2010 China’s Gini coefficient was 0.61 which was one of the world’s most unequal countries however officially it has been falling for seven years from 0.49 in 2008 to 0.46 in 2015. Rural incomes have grown more quickly that their urban counterparts – in 2009 the average urban income was 3.3 times that of a rural worker but now it is 2.7 times. Many of those living in rural areas actually work in cities but are prevented from living there because of the strict residency system. Also companies have now been looking to the rural areas for cheap labour.
But at the top end you would get the impression that inequality of wealth is extremely high – wealth = what you own, as opposed to what you earn. China has more dollar billionaires (596) than the USA (537). Research has shown that 1% of the population control a 1/3 of China’s assets.

Human Development Report – 2018 – gaps reflect unequal opportunity

The Human Development Index (HDI) is a composite index focusing on three basic dimensions of human development:

  • the ability to lead a long and healthy life, measured by life expectancy at birth;
  • the ability to acquire knowledge, measured by mean years of schooling and expected years of schooling; and
  • the ability to achieve a decent standard of living, measured by gross national income per capita.

To measure human development more comprehensively, the Human Development Report presents four other composite indices.

  • The Inequality-adjusted HDI discounts the HDI according to the extent of inequality.
  • The Gender Development Index compares female and male HDI values.
  • The Gender Inequality Index highlights women’s empowerment.
  • And the Multidimensional Poverty Index measures non income dimensions of poverty.

The 2018 Update presents HDI values for 189 countries and territories with the most recent data for 2017. The main points are:

59 are in the very high human development group,
53 in the high,
39 in the medium
38 in the low.

In 2010, 49 countries were in the low human development group.

The top five countries in the global HDI ranking are:

Norway (0.953),
Switzerland (0.944),
Australia (0.939),
Ireland (0.938) and
Germany (0.936)

New Zealand comes in at 16 with 0.917

The bottom five are:
Burundi (0.417),
Chad (0.404),
South Sudan (0.388),
the Central African Republic (0.367)
Niger (0.354).

The largest increases in HDI rank between 2012 and 2017 were for Ireland, which moved up 13 places, and for Botswana, the Dominican Republic and Turkey, which each moved up 8. The largest declines were for the Syrian Arab Republic (down 27), Libya (26) and Yemen (20) .

Why is Inequality a problem for development?
A recent Oxfam International report showed that:

“8 men own the same wealth as the 3.6 billion people who make up the poorest half of humanity”
“82 percent of all global wealth in the last year went to the top 1 percent, while the bottom half of humanity saw no increase at all”

Deep imbalances in people’s opportunities and choices stem from inequalities in:
income  – education – health  – voice – access to technology – exposure to shocks.

Human development gaps reflect unequal opportunity in access to education, health, employment, credit and natural resources due to gender, group identity, income disparities and location. Inequality is not only normatively wrong; it is also dangerous as:

  • It can fuel extremism and undermine support for inclusive and sustainable development.
  • It can lead to adverse consequences for social cohesion and the quality of institutions and policies, which in turn can slow human development progress.

The global level inequality in income contributes the most to overall inequality, followed by education and life expectancy. Countries in the very high human development group lose less from inequality than countries in lower groups

Source: Human Development Indices and Indicators 2018 – Statistical Update

Scandinavian countries most expensive but happy

Another good video here with Tom Chitty from CNBC – outlines why the cost of living is so high in Scandinavia – Norway, Sweden and Denmark. These countries on average have some of the highest tax rates (see graph) in order to fund a large welfare state. Expenditure in social welfare is one of the highest as a % of GDP and eventhough it is very expensive to live in these countries they rank as some of the happiest.

Is Artificial Intelligence like a typical economist?

Diane Coyle wrote a piece on the Project Syndicate website discussing that computers are designed to think like economists. Artificial intelligence (AI) is a faultless version of homo economicus as it is a rationally calculating, logically consistent, ends-orientated agent capable of achieving its desired outcomes with finite computational resources. They are perceived as much more effective than a human in achieving the maximum amount of utility for an individual. Coyle does go onto say that economists today cannot offer a measure of actual utility.

Jeremy Bentham’s famous formulation of utilitarianism is known as the “greatest-happiness principle”. It holds that one must always act so as to produce the greatest aggregate happiness among all sentient beings, within reason. John Stuart Mill’s method of determining the best utility is that a moral agent, when given the choice between two or more actions, ought to choose the action that contributes most to (maximises) the total happiness in the world. However this assumption can produce some unease.

  • Most of those designing algorithms are utilitarians who believe that if a ‘good’ is known, then it can be maximised. Therefore how much thought is there about possible societal impacts of algorithms as they are designed to optimise efficiency and profitability.
  • Algorithms are created using current and future data that is full of bias. The result could be the institutionalisation of biased and damaging decisions with the excuse of, to quote ‘Little Britain’, ‘the computer says no’. see video below.
  • Algorithms make it easy for consumers to decide things and it acts as a short-cut (heuristic). Therefore we become a slave to the algorithm rather than taking more ownership of our thinking /reasoning. Those who  control of the algorithm have an unfair position.

There is no doubt in certain aspects of society AI is extremely useful and can cut down bureaucracy and lead to improved efficiency in everyday life. The real issue extends beyond the use of algorithmic decision-making in corporate and political governance, and strikes at the ethical foundations of our societies. As Coyle points out we need to engage in self-reflection and decide if we really want to encode current social arrangements into the future.

Airline prices: 2014 – 2018 and dynamic pricing

Another great graphic from The Economist showing the change in the price of an economy class ticket for both short-haul and long-haul flights. Routes longer than 5,000km have generally seen price drops of 30% and 50% on some transatlantic routes.

Reasons for the drop in fares:

  • Fuel costs have come down – 2014 = US$0.81 / litre – 2016 = US$0.22 / litre
  • Increasing long-haul competition from low-cost carriers
  • More fuel efficient planes = lower costs
  • Subsidies to state owned e.g. China
  • Major airline deregulation
  • Airlines have become much better at making more efficient use of their planes – i.e. having them full

Airlines and dynamic pricing

To the average buyer, airline ticket prices appear to fluctuate without reason. But behind the process is actually the science of dynamic pricing, which has less to do with cost and more to do with artificial intelligence. See video below from Tom Chitty of CNBC

Is it time to ‘short’ the Aussie dollar

Although I wrote recently on Australia avoiding the ‘resource curse’ this video from the FT suggests otherwise and that the Aussie Dollar in 2019 is going to be volatile. The slowing down of the Chinese economy accompanied by a trade dispute with the US has meant lower demand for the Aussie Dollar. Imports of commodities, especially iron-ore, have slowed as China recorded significant reduction in exports and imports in December last year – see graph below:

A lot will depend in the US Fed and its interest rate stance and whether with weaker inflationary pressure and a slowing economy there could be a drop in rates which would help the Aussie Dollar. The cother concern is the exposure that commercial banks have in the mortgage market. Housing has long been a favoured investment option in Australia and with the housing market slowing banks could be left exposed with defaults on mortgages. So is it time to dump the Aussie Dollar?

Falling exchange rate – causes and effects.