Data Inspired Insights

Tag: Government (Page 2 of 2)

Corruption in Kosovo: A Comparative Analysis

Cross posted from OpenDataKosovo.org:

Previously in Part I of this series, we looked at corruption in Kosovo from the perspective of Kosovo civil servants, as documented in a United Nations Development Programme (UNDP) report entitled Gender Equality Related Corruption Risks and Vulnerabilities in Civil Service in Kosovo[1].

In Part II we are now going to look at global corruption perception statistics compiled by Transparency International to consider how Kosovo compares internationally.

An International Comparison of Corruption

Transparency International is an organization that works to reduce corruption[2] through increasing the transparency of Governments around the world. Arguably Transparency International’s most well known contribution is the Corruption Perceptions Index (CPI), an index measuring “the perceived levels of public sector corruption worldwide”. In 2014[3] the CPI was calculated by aggregating 12 indices and data sources collected from 11 different independent institutions specializing in governance and business climate analysis over the past 24 months. The 2014 CPI covered 175 countries, including Kosovo.

In addition to the CPI, Transparency International does its own survey and data collection in the form of the Global Corruption Barometer (GCB survey). The GCB survey focuses on the public’s opinion of corruption within their own country, and in 2013 (the latest edition of the GCB available at the time of writing) collected the opinions of over 114,000 people across 107 countries – including Kosovo.

So what did these two reports show?

Results

In the CPI, Kosovo performs poorly, placing 110th out of 175 countries with a score of 33 out of 100 (unchanged from 2013). To give some perspective, Kosovo finished equal 110th with 4 other countries – Albania, Ecuador, Ethiopia, and Malawi. This placed it behind Argentina (107th), Mexico (103rd), China (100th), India (85th) and Greece (69th), countries that are often associated with high levels of corruption. Finally, this was the lowest ranking for any country in the Balkans region (tied with Albania).

Chart 1 – GCB Survey Q6 – Perceptions of Corruption by Institution for 6 Countries

WAC_2_1

The GCB survey, however, shows that the people in Kosovo have a different perception of corruption in several areas to that reported in the CPI. Based on the responses to question 6[4] (see Chart 1) and question 7[5] (see Chart 2) of the GCB survey, people in Kosovo are somewhat more optimistic about the levels of corruption in their country than the low rating on the CPI might indicate. Kosovo scores well in several areas:

  • Only 16% of people reported having paid a bribe in the last 12 months. This placed Kosovo 35th out of the 95 countries that provided a response to question 7.
  • 46% of Kosovars generally believe their public institutions to be corrupt or extremely corrupt. This sounds high but actually puts Kosovo ahead of the US (47%) and only slightly behind Germany (40%). The results for certain institutions were even better:
    • The Military is believed to be corrupt or extremely corrupt by only 8% of those interviewed – only four countries had a lower percentage than Kosovo on this part of question 6.
    • NGOs and Religious bodies were also seen as uncorrupt by large majorities.
    • 44% of people believed public officials and civil servants were corrupt, placing Kosovo ahead of Germany, France and the US, among others.

Chart 2 – GCB Survey Q7 – Reports of Bribes Paid by Institution for 6 Countries

WAC_2_2

But not all the results were positive. Questions 1[6], 4[7] and 5[8] in the GCB survey in particular highlight a more pessimistic outlook:

  • In response to question 1, 66% of Kosovars stated that they believed corruption had increased over the past 2 years, while only 8% believed it had decreased.
  • In response to question 4, 74% of Kosovars stated they believed their Government is run by large entities largely or entirely for their own benefit.
  • In response to question 5, only 11% of Kosovars surveyed believed the actions of their Government in the fight against corruption are effective.

What does all this mean? Why does Kosovo perform so poorly on the CPI, and on some GCB survey questions, but on other questions the perceived level of corruption of people in Kosovo is comparable to some developed nations?

Perceptions vs. Reality

One of the issues when looking at the results of the GCB survey is that the responses to most of these questions are subjective. What constitutes corruption or extreme corruption varies by country and culture based on what people are used to living with. What someone in South Asia or sub-Saharan Africa considers standard practice and harmless may be considered unbelievably corrupt by people in other parts of the world.

These different standards are really highlighted when we compare the percentage of people believing an institution is corrupt with the number of people reporting to have paid a bribe to that institution, using questions 6 and 7 of the GCB survey. There are four institutions that appear as options for both questions, allowing us to make a direct comparison:

  1. Education
  2. Judiciary
  3. Medical and Health, and
  4. Police

In the comparison (see Chart 3), we find numerous examples where the percentage of people that reported paying bribes was higher than the percentage of people who believed the institution was corrupt. The implication of this finding is that significant numbers of people in these countries believe that paying a bribe is not a sign of corruption.

Chart 3 – Comparison of Perceived Corruption with Bribes Paid

WAC_2_3

Kosovo and most developed nations were examples of the opposite case – they generally reported relatively high numbers of people who believed the four comparable institutions were corrupt, and relatively low percentages of people reporting bribes being paid. Bribery, of course, is not the only form of corruption, and this result could simply be an indicator that different forms of corruption are more prevalent in these countries. But it could also be an indicator that people in some countries are particularly cynical about the fidelity of their institutions.

To get a better sense of how concerned people really are about corruption, lets now take a look at some of the responses to other questions in the survey.

Is a Person’s Willingness to Take Action a Better Indicator?

One of the questions asked on the survey that could potentially reveal some further information was question 10 – “Are you willing to get involved in the fight against corruption?” Respondents were then provided with a range of activities, both active and passive, and were requested to indicate whether they would be willing to participate.

At a high level, the responses to this question appear to show an inverse correlation between the value of the CPI for a country and how willing people in that country were to do something active to fight corruption. In other words, the higher the percentage of people willing to do something active to fight corruption, the lower the CPI index for that country (i.e. a higher level of corruption).

Using a statistical model (such as regression), we can check whether this relationship is real and how strong it is. However to do this, we need to consider countries with regimes that punish dissent and crack down on protests and/or organizations that might try to combat corruption. In these countries, you would expect to have a low percentage of people willing to take action against corruption despite corruption being high.

To account for this, we need to have some sort of indicator of how worried people are about speaking out in their country. The best piece of information that we have from the GCB survey that can serve this purpose was the question asking if the respondent would be willing to report corruption.

Using these two pieces of information, we can try to test the following hypotheses:

  1. A high percentage of people willing to take action against corruption in a given country is indicative of a high level of corruption.
  2. A low percentage of people willing to take action against corruption in a given country, but a high level willing to report corruption is indicative of a low level of corruption.
  3. A low percentage of people willing to take action against corruption in a given country, and a low level willing to report corruption is indicative of a high level of corruption in a repressive regime.

Based on these hypotheses, we also expect that there would be no (or very few) cases where there is high percentage of people willing to take action against corruption and a low level of people willing to report corruption.

Building a Model

Using our two pieces of information described above, and with the assumption that the CPI is the most accurate indicator of the true level of corruption within a country[9], we can build a model to predict CPI for each country and test our hypotheses. The formula for this model will be as follows:

Where:

Yi = the actual value of CPI for country i

β0 = a constant

Xi1 = the percentage of people willing to do something active to fight corruption[10] in country i

β1 = a constant applied to Xi1

Xi2 = the percentage of people willing report an incidence of corruption in country i

β2 = a constant applied to Xi2

εi = the residual or error

Using ordinary least squares (OLS) and the data for the 101 countries for which the CPI and the two variables (X1 and X2) described above are provided, the results of the model is as follows:

β0 β1 β2
Coefficient 27.9735 -0.8417 0.8798
Standard Error 4.8427 0.0705 0.0738
R2 66.7%

The first thing to note is that the coefficients support the three hypotheses we mentioned above:

  1. A strongly negative coefficient β1 indicates that the larger the percentage of people willing to do something active to fight corruption, the lower the predicted CPI.
  2. A strongly positive coefficient β2 indicates that the larger the percentage of people willing to report corruption, the higher the predicted CPI.

General Insights

Aside from providing support for our hypotheses, the other thing this model reveals is the countries that are not very well explained by this model. Chart 4 shows the CPI predicted by the model as compared to the actual CPI value for 2014.

Chart 4 – Predicted CPI vs. Actual CPI by Country

WAC_2_4

At a high level, we can split the chart into two parts:

  1. Points below and to the right of the line reflect countries where the actual level of the CPI was lower than the predicted level
  2. Points above and to the left of the line reflect countries where the actual level of the CPI was higher than the predicted level.

Starting with the first group – countries that were more corrupt than the model predicted – these cases appear to fall into two categories:

  • Conflict Affected Countries – In these cases, of which Sudan is the most extreme example, there was typically a low percentage of people willing to do something active to fight corruption, and therefore the CPI was predicted to be significantly higher than it is in reality. This is likely to be due the citizenry of these countries facing more immediate problems. This pattern was seen across Sudan, Afghanistan, Iraq, Libya and South Sudan.
  • Other – In these cases, of which Russia was the best example, there was generally a high percentage of people willing to report corruption (86% for Russia) and a relatively low percentage of people willing to do something active to fight corruption (47% in Russia). As a result the model predicted a relatively high CPI. The explanation for this is not as clear as above, but the evidence would seem to suggest that the people in these countries are either not aware of the high level of corruption present in their country, or that they have a significantly different opinion as to what constitutes corruption.

Contrasting with the above cases, we can also see there are countries above and to the left of the line in Chart 4. This represents countries that were less corrupt than the model predicted. In these cases the responses to the two questions were indicative of a country with a higher level of corruption than actually existed. The following were two interesting cases:

  • Finland – the model was thrown off by a surprisingly low percentage of people willing to report corruption. Of the respondents from Finland, only 65% of people surveyed reported they would be willing to report corruption – a surprisingly low percentage for a country with a CPI value of 89. In fact, Finland and Japan were the only countries with a CPI above 60 that reported a percentage below 80% for this question.
  • The United States – neither of the data points used for the US in the model were hugely abnormal for countries in the same CPI range. 80% of people said they would be willing to report corruption (a little lower than you would expect) and 50% said they be willing to do something active to fight corruption (a little higher than you would expect). Both of these potentially show a slightly higher level of mistrust in government than other developed nations, something that does tie in with the politics of large parts of the US.

Unlike the above examples, Kosovo appeared fairly typical for the model. Let’s now take a deeper look into the results of the model for Kosovo.

Insights for Kosovo

For Kosovo, the model was able to fairly accurately predict the CPI using the two variables described. Kosovo has both a high percentage of people willing to do something active to fight corruption (80%) and a high percentage of people willing to report corruption (84%). As a result, the model predicted a high level of corruption in Kosovo, a CPI of 35, which was just below the actual CPI value of 33.

However, aside from proving the accuracy of the model in this case, these high values reveal important information about the people of Kosovo. It reveals Kosovars do believe corruption is an issue, and that they are willing to do something about it.

Summary

Overall, there are positives and negatives for Kosovo that can be taken from the Transparency International data. On the negative side, the CPI highlights that corruption is a significant issue in Kosovo. Even in a region with consistently low CPI scores (the best performer is Slovenia with a score of 58) Kosovo is a significant underperformer. The most disappointing aspect of this underperformance is that Kosovo has had the significant advantage of 15 years of assistance from various international agencies in setting up infrastructure for good governance.

That said, there is a big positive that comes from the GCB survey data, and it is also potentially an important clue as to the best way forward for Kosovo and the international organizations involved in the region. That positive is that the people of Kosovo appear to be aware of the issues of corruption in their country, and more importantly, they are very willing to take an active role to fight it. Compared to Albania, a country with the same CPI as Kosovo, almost twice the percentage of survey respondents stated they were willing to do something active to fight corruption in Kosovo (80% vs. 44%), and significantly more people said they were willing to report corruption (84% vs. 51%).

What this suggests is that, if harnessed effectively, anti-corruption efforts in Kosovo could be very popular, and therefore powerful. But the right strategies have to be implemented and publicized to garner public support.

Somewhat unsurprisingly, we believe a key strategy has to be raising awareness of how data can be used to reduce corruption and bring about change. This can apply equally to data that is currently collected by government agencies but isn’t publically released, or new datasets that the public can assist in collecting. With the right data and right analysis, these datasets can help to improve governance in numerous ways including:

  • exposing systematic corruption
  • identifying gaps in anti‑corruption controls, and
  • better targeting of anti-corruption efforts.

Using this open data approach also helps reduce reliance on the bravery of individual whistleblowers. Although whistleblowers are often vital in helping to identify incidents and even patterns of corruption, the fact is that, even in developed nations, they will always risk retaliation and other subtler forms of retribution (reduced career prospects, being ostracized by their peers and generally being perceived as untrustworthy).

Overall, what the results of the Transparency International data shows us is that, with better coordination and targeting of anti-corruption efforts, there is the potential to actively involve large numbers of Kosovars. If that can be achieved and funneled into meaningful strategies, the future of Kosovo could be very bright indeed.

Have any suggestions for ways data could be used to fight corruption? Disagree completely? Feel free to leave your thoughts in the comments!

 

[1] Gender Equality Related Corruption Risks and Vulnerabilities in Civil Service Kosovo, United Nations Development Programme. November 2014. Gender Corruption final Eng.pdf

[2] Defined by Transparency International ‘… as “the abuse of entrusted power for private gain”. Corruption can be classified as grand, petty and political, depending on the amounts of money lost and the sector where it occurs.’

[3] The methodology for compiling the CPI is reviewed on a yearly basis with data sources added and removed as needed.

[4] “To what extent do you see the following categories in this country affected by corruption?” – responses of “corrupt” or “extremely corrupt” recorded as a positive response.

[5] “In your contact or contacts with the institutions have you or anyone living in your household paid a bribe in any form in the past 12 months?“

[6] “Over the past 2 years, how has the level of corruption in this country changed?”

[7] “To what extent is this country’s government run by a few big entities acting in their own best interests?”

[8] “How effective do you think your government’s actions are in the fight against corruption?”

[9] By their own admission, Transparency International’s CPI is not a perfect measure of corruption. Corruption by its nature is hidden and so there is no objective measure of the true level of corruption. However, the CPI is currently the most respected measure of corruption available and so we make the assumption that it is also the most accurate for the purposes of constructing this model.

[10] Taken as the average of the percentage of people who said they would take part in a peaceful protest and the percentage of people who said they would join an organization that works to reduce corruption as an active member

Greece Says ‘OXI’!

And how! With over a third of the vote counted, it looks like ‘Oxi’ (‘No’ to accepting the conditions of the creditors latest offer) will win in somewhat of a landslide. Current numbers are showing over 60% of Greeks voted ‘No’. No matter whether you think this is the right choice or not, you have to admire the bravery of the Greek people choosing what is almost certainly the high risk option. So what happens next?

Next Steps

This is the part no one is sure about. Within Greece, Syriza has been campaigning for the ‘No’ vote on the basis that it will not result in a Greek exit from the Eurozone, but that it will strengthen the hand of the Greek government in negotiations with the creditors. While it certainly provides them with a strong mandate to turn down the current offer, getting a better deal depends on the creditors.

Outside Greece, popular opinion is that the creditors have too much to lose from making concessions to Greece. The fear is that if concessions are made this would encourage other countries, primarily Spain, Portugal and Ireland, to elect anti-austerity parties, similar to Syriza, and also request concessions.

This doesn’t mean further negotiations are pointless, there could be a middle ground. The bargaining positions of the two parties before the referendum were already very close, with Syriza then making further concessions after calling the referendum. It would seem conceivable that the creditors could quietly agree to the final offer from Syriza (or something close to it), lose a little bit of face, but still basically get their way. Will Merkel, Junker, Schäuble et al be able to stomach making any concessions at this point? That remains to be seen. If no deal is reached though, a Greek exit could be on the cards in the very near future.

What About Europe?

Regardless of what happens economically, the impact of this referendum appears certain to have ongoing political fallout. The level of excitement and the joyous reconnection with the democratic process that occurred in Greece, in addition to the result, is sure to resonate with people across Europe. In countries that have also been struggling with high unemployment and poor economic performance, largely as a result of austerity policies, people are sure to be taking particular notice. Although the economies of these countries are now performing significantly better than the Greek economy, and have more manageable debt burdens, the improved conditions are yet to be felt by the majority of people. In Spain, a country that has itself undergone high levels of very unpopular austerity, the economy has been growing strongly over the past year, but unemployment still sits above 20%. 

For this reason, the governments in these countries (particularly Mariano Rajoy in Spain) were often the ones arguing the hardest for no concessions to be given to Greece. In what appears to be a purely political calculation, this stance was taken not with any thought for the suffering of people in Greece or their own countries, but to short circuit popular support for anti-austerity parties domestically. Those leaders will surely have some tough weeks (and probably years) ahead.

Australian Housing Bubble – Further Reading

Over the past 2-3 months, the mainstream media coverage of housing prices in Australia has exploded. Every commentator appears to have had a piece on this topic and was waiting for the right time to publish it. That right time is apparently now. For those interested in additional reading on this topic, here are some of the better pieces I’ve come across:

The banks and real estate: a Ponzi scheme that could ruin us? – Ian Verrender | ABC News

The housing crash we had to have: A Gen Y perspective on the bubble – Matt Ellis | Rational Radical

Another interest rate cut will fuel a housing bubble in danger of bursting – Greg Jericho | The Guardian

It’s not Hockey’s job comment that should worry us most – Michael Janda | ABC News

Blowing bubbles: the tricky task of tackling Sydney’s property market – Amy Auster | The Conversation

4 charts of the ‘largest housing bubble on record’ – Wolf Richter | Wolf Street

The Sydney housing bubble to pop – but how? – Michael Pascoe | The SMH

The mother of all housing bubbles – Chris Joye | The Australian Financial Review

Australian Housing Bubble Redux

In the recent piece about the Australian economy we touched on the issue of the bubble in Australian house prices. Over the weekend, Saul Eslake, Chief Economist at Bank of America Merrill Lynch and one of Australia’s most respected economists, added his thoughts to the debate. A lot of his concern is around the longer term affects on people who are locked out of the housing market:

“I would say [rising house prices] are causing social harm because they are widening the gap between those who have houses and those who don’t, and freezing younger generations out of home ownership,”

In a country like Australia where, much like the US, owning your house is seen as a noble goal that everyone should be able to achieve, this could signal a cultural change. Home ownership in Australia is at its lowest level since 1950 as investors increasingly snap up properties, not for the rent/income they will generate, but for the assumed capital gains. In recently released data from the Australian Taxation Office (ATO) for the 2012-13 financial year, 1,967,260 (or just over 15% of all taxpayers) claimed rental income. Of those, 64% declared a net loss (i.e. they claimed deductions for negative gearing). Think about that for a second – almost 2 out of every 3 people with an investment property in Australia are actively losing money on that investment. What do these investors do if their expectation of further capital gains changes?

“2 out of every 3 people with an investment property in Australia are actively losing money on that investment.”

With all these statistics, why is there still an argument about whether a housing bubble exists? A big part of the problem is that there is no qualitative measure of a bubble. In hindsight they tend to be blindingly obvious, but one of the reasons bubbles occur at all is that most people don’t notice them as they are inflating. Adding to the problem is the reluctancy of politicians and commentators to call out bubbles or even use the word ‘bubble’ because of the negative connotations – bubbles tend to burst. The following was the response of Australian Assistant Treasurer Josh Frydenberg when asked about the possibility of a housing bubble on the ABC Insiders program on Sunday morning:

“I don’t think there is a housing bubble… In the early 2000s housing prices increased by 20 per cent for three years in a row and then were steady for a decade. And there wasn’t a bubble that led to a major correction.”

However, as the situation becomes more extreme, more and more respected commentators are starting to sound the alarm on this issue, even if they avoid calling it a bubble. Saul Eslake again:

“What I do say, without any hesitation at all, is that Australian prices of housing in most Australian cities, and particularly in Sydney, are, as [Reserve Bank governor] Glenn Stevens called them in September last year, ‘elevated’,”

So, leaving aside talk of bubbles, what are the facts?

  1. Australians have record levels of housing debt as a percentage of income
  2. Almost 2 out of 3 property investors are losing money on their properties
  3. The median house price in Sydney is now over AU$900,000
  4. Rates of home ownership are at their lowest levels in over 60 years

Whether or not you want to call it a bubble, that seems unsustainable to me.

Why the RBA doesn’t want to cut rates

The first Tuesday of the month is interest rate day in Australia, the day the Reserve Bank of Australia – the Australian equivalent of the Federal Reserve – announces any changes to the official cash rate. The decision for June was to leave interest rates on hold at 2.0%.

In a situation that will feel relatively alien to readers in the US, Australian interest rates have never really been close to 0, but have been falling since late 2011 (see Chart 1).

Chart 1 – Australian Cash Rate vs. US Federal Funds Rate

RBA_chart_1_1

What has been leading to falling rates in Australia over a period where the US has been slowly recovering and the Fed Reserve is slowly edging back to normalizing interest rate policy? As is usually the case, a mix of factors are involved.

Iron Ore and Coal Prices Return to Earth

A story that most people outside Australia have at least heard about is the large mining boom Australia has been enjoying over the past decade or so, and that it was largely driven by demand from China. What they may not know is that this mining boom has been largely driven by just two commodities (well technically three) – iron ore and coal (two types of coal – thermal and metallurgical). Chart 2 shows the prices of iron ore and thermal coal[1] in AUD/tonne since the 1995.

Chart 2 – Iron Ore and Thermal Coal Prices 1995 to Present

RBA_chart_1_2

From this chart, we can clearly see the huge increase in prices that boosted the Australian economy. This was particularly pronounced for iron ore which went from between AU$16-AU$17 a tonne for most of the 90s to over AU$180 a tonne in 2010 and 2011.

Aside from generating huge profits for anyone who happened to own a coal or iron ore mine, what this price rise also led to was a large amount of employment in areas that weren’t just digging up the commodities themselves. This included:

  • Exploration of possible new mining sites – at AU$180 a tonne everyone wanted an iron ore mine
  • Building infrastructure that facilitated the large-scale digging up and exportation of these commodities – ports needed to be built and/or expanded, mining pits dug, roads paved and so on
  • Providing services to mining companies – lawyers, accounts, caterers and so on

After peaking in 2010/11 though, things started to go into reverse. By late 2013, much of the investment in infrastructure had run its course and the people who were employed to build that infrastructure were no longer needed. Prices were falling, bringing into question the viability of a lot of higher cost mines (and the mining companies running these mines) set up during the boom period. In short, a lot of people formerly employed on mine sites or in mining services roles were finding themselves looking for a new job and the rest of the economy was (and still is) struggling to pick up the slack. This in part is because of the …

High Exchange Rate

For those that haven’t decided to brave the 20+ hours of flight time to visit Australia in the recent past, Australia has become an extraordinarily expensive place. Sydney and Melbourne have been consistent fixtures in the world’s most expensive cities to live lists over the past 10 years.

Most of this was driven by a very strong Australian dollar, which was in turn driven mostly by the mining boom. In addition to buyers of commodities needing Australian dollars to buy the products they wanted, Australia became the target of a large volume of carry trade with currency traders looking for a relatively stable economy to park money at a relatively high interest rate. As a result of this, at the height of the mining boom, the AUD was buying almost $1.10USD.

Since that peak, the Australian dollar has depreciated around 30% (see Chart 3), easing a lot of the price pressure. However, as of 2015, Sydney and Melbourne still rank 5th and 6th on the world’s most expensive city list, as provided by the Economist Intelligence Unit’s (EIU) bi-annual Worldwide Cost of Living report.

Chart 3 – AUD/USD Exchange Rate 1995 to Present

RBA_chart_1_3

The RBA has publically been stating that they believe the value of the Australian dollar is too high in an attempt to talk down the value of the Australian dollar (often called ‘jawboning’) and provide a boost to the non-mining sectors of the Australian economy. They have also progressively lowered the cash rate from 4.75% in 2011 to 2.0% today, in an attempt to stem the carry trade. As we have seen, to some degree they have been successful, but the exchange rate is still higher than they (and many other commentators) believe is optimal.

Unfortunately, some bumbling on the part of the RBA (or the execution of a plan that no one else understands) has blunted some of their efforts. At the previous monetary policy meeting at the start of May, the RBA lowered the official cash rate from 2.25% to 2.0%, but removed any talk of further cuts from the publically released meeting minutes (removing the “easing bias”). Doing this then had the opposite of the desired result and caused a spike in the Australian dollar.

Chart 4 – Consumer price index; year-ended change 2000 to 2015

RBA_chart_1_4

So why are they removing the easing bias? Why don’t they just slash rates further – after all inflation is running below the target band (see Chart 4)? The problem is they are worried about the…

Bubble in House Prices

The RBAs hesitancy to cut interest rates further is mostly due to a concern about further encouraging investment in housing and contributing to rising house prices, which look to be well into bubble territory.

For those that aren’t too familiar with Australia, particularly the modern, post ‘put-another-shrimp-on-the-Barbie’, Australia, being a property tycoon has become something of a national obsession. Home renovation shows are everywhere and are getting huge ratings. Morning news regularly holds interviews with the latest property ‘success story’.

This obsession has led to Australia becoming a world-beater when it comes to levels of household debt. The Australian Bureau of Statistics (ABS) produced a great series of charts in May 2014 showing some alarming statistics. See below for some of the highlights:

Chart 5 – Household Debt vs. Annual Income[2] in Australia 1987 to 2013

RBA_chart_1_5

Charts 6 and 7 – Household Debt vs. Annual Income – Various Countries 2001 to 2013

RBA_chart_1_6

RBA_chart_1_7

After 20 years of Australians continually buying properties off each other for ever-increasing prices, funded mostly by increasing level of mortgage debt, something changed. Perhaps it was the median house price in Sydney soaring past AU$900,000 (approximately US$700,000 at today’s exchange rate). What ever triggered it, in recent months, the talk in Australia has become all about a bubble in house prices, particularly in Sydney and parts of Melbourne. The Secretary of the Department of the Treasury, John Fraser, recently became the latest high profile public figure to weigh in:

“When you look at the housing price bubble evidence, it’s unequivocally the case in Sydney, unequivocal,”

More over, he drew a direct link between high house prices and low interest rates:

“It does worry me that the historically-low level of interest rates are encouraging people to perhaps over-invest in housing,”

And there is plenty of evidence to support the notion that the rise in housing prices is increasingly due to investors as opposed to owner-occupiers (see Chart 8).

Chart 8 – Investor Housing Credit as a Percentage of Total Housing Credit 1990 to 2014

RBA_chart_1_8

Meanwhile, belying the sparkling reputation the Australian Government has earned internationally in recent times[3], the Government has all but ruled out taking any meaningful action to reverse key policies that are currently encouraging investment in property – negative gearing and the capital gains tax concession being two of the main culprits. When asked in a recent session of question time by the leader of the Opposition Bill Shorten to respond to the comments from John Fraser, Prime Minister Tony Abbott responded as follows:

“As someone who, along with the bank, owns a house in Sydney I do hope our housing prices are increasing,”

Summing Up

All this leaves the RBA in quite a pickle. Relatively high interest rates (by the standards of developed nations internationally) continue to keep the exchange rate at higher than desired levels, which makes Australia an expensive place to do business. This in turn harms Australia’s two big non-commodity exports – higher education and tourism – just when they need to pick up the slack from a cooling mining sector. But lowering interest rates risks further fueling a bubble in house prices which the Government seems quite happy to ignore.

I don’t imagine there are too many people who would like to be in the shoes of RBA Governor Glenn Stevens right now.

Keep an eye on this space for further updates as this all unwinds.

 

[1] This is an example of that classic Australian trait – sarcasm

[2] Gross disposable household income received during the previous year.

[3] If anyone can find a good historical price series for metallurgical coal, I’d love to hear from you

Labor Statistics Part IV – The Employed

Previously in Parts II and III, we focused on two subsets of the population that are not employed – non-participants and the unemployed. In Part IV, we finally move on to looking at the population of employed people. However, in a slight change of tack, instead of focusing on the characteristics of these people, we are going to look at the changes in the employment market in general and more specifically at the changes at the industry level.

Declining Industries

Chart 1 – Industries with Declining Shares of the Employment Market 1939 to 2015

chart_4_1

Decline in Manufacturing

Looking at the data, the big story since the end of World War II (1945 for those who skipped History class) is the decline of the manufacturing industry. Manufacturing was far and away the biggest sector in the US in terms of employment at the end of the war, but has seen its share of the employment market decline to less than 9% as of 2015. The reasons for this have been the subject of a lot of discussion (see here for example), but if we look at the number of manufacturing jobs (see Chart 2), as opposed to the percentage of the non-farm employment market, we see there are two phases to this decline.

Chart 2 – US Manufacturing Jobs 1939 to 2015

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As Chart 2 makes clearer, manufacturing in the US was actually still adding jobs from 1945 through to the late 70s, it was just that the other sectors were adding more jobs, causing the manufacturing sector’s share of the employment market to decline.

From the mid 80s onwards though, the manufacturing sector started declining in both percentage and absolute terms. Increasing automation and the shift of jobs to low cost manufacturing countries such as China, India and other developing nations started what would be a long decline for the industry. There is one ray of light though, and that is that the US has actually been adding manufacturing jobs for the past 6 years. Although this looks like a positive change, it is hard to say whether this is the start of a new trend or just an aberration representing the recovery of jobs lost in the last downturn. The 90s boom saw similar gains before they were reversed very quickly in the new century.

Where is the Tech Boom?

The sector that you may be surprised to see in the declining chart is the Information sector. Information Technology (“tech”) seems to be the only sector that anyone is talking about right now – glitzy product launches, podcasts (the excellent Startup) and TV shows (Silicon Valley is fantastic if you haven’t seen it). So why don’t we see it in the employment data? To explain that, it helps to break the sector down into its component industries (see Chart 3).

Chart 3 – Information Sub Industries, All Employees 1990 to 2015

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The finer level data only goes back to 1990, but this is the key period we are interested in anyway. What we see is that despite the hype, the two tech related sub industries (Data processing, hosting and related services, and Other information services) are still very small, even within the Information sector. In terms of the number of people employed, these two sectors are drowned out by the traditional publishing industry and the telecommunications sector. So even though the two tech sub industries have been adding jobs, it has simply not been enough to outweigh the job losses in the larger sub industries.

The Telecommunication Boom

The other interesting point on Chart 3 is how much the tech boom in the late 90s impacted on the telecommunications sector. Despite the popular perception that this boom was a tech boom (it is called the dot com bubble after all), the boom led to far greater increases in job numbers (and job losses after the bust) in the telecommunications sector than in the tech sectors. The boom in telecommunications was primarily driven by telecom companies rushing to upgrade networks and infrastructure in response to exploding demand for the two hot new products of the time: the internet and mobile phones. After the bubble popped, some large companies went bust, others consolidated, but the net result was a lot of job losses.

Expanding Industries

Chart 4 – Industries with Expanding Shares of the Employment Market 1939 to 2015

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Moving on from declining industries, let’s look at the industries that have grown their share of the employment market over the past half a century. The clear winners here are the Education and Health sector, and the Professional and Business Services sector.

Professional and Business Services

Professional and Business Services cover a range of services that have gone from non-existent, or the domain of niche firms, to being the domain of some of the world’s largest firms. Additionally, being employed to provide services within this sector has become very prestigious (legal services and management consulting are good examples), allowing these firms to attract some of the top talent in the market place.

Overall, the growth in the number of people employed to provide these services is largely explained by the increasing complexity of doing business. Increasing complexity creates demand in several ways, including the need for:

  • People who are experts in one or a small subset of specific business functions
  • People who are experienced in navigating an increasingly complex regulatory environment, and/or
  • Agility to quickly respond to certain business needs that preclude hiring and training staff internally

In recent times there has been talk about larger businesses attempting to ‘in-house’ some of the services that professional services firms typically provide, particularly legal services and various compliance functions. As of yet, this does not appear to be impacting the employment growth of professional services firms.

Inexorable Rise of Education and Health

The Education and Health sector has shown the strongest and most consistent growth of any industry over the last 50 years. But what explains this strong growth? Chart 5 provides a breakdown of the subsectors within this industry.

Chart 5 – Education and Health Sub Industries, All Employees 1990 to 2015

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The first thing to note is that all the sub sectors have been adding a large number of jobs over the past 25 years, but there are two standouts:

  • Social Assistance (child care workers, personal and home care aides, social and human service assistants) has gone from easily the smallest sub-sector in 1990 to employing as many people as the Education sub-sector, tripling the number of people employed.
  • Ambulatory Health Care Services (outpatient medical services like dentists, GPs, diagnostic centers and so on) has become easily the largest sub-sector over the past 25 years, adding over 4 million jobs.

Generally this provides further confirmation of what we saw in Part II of this series – that there are larger numbers of Americans retiring and as they do, the demand for certain services, particularly health care is also growing.

Childcare Catch 22

One additional point to make on this subject is regarding the growth in childcare services, a key component of the overall growth of the sector. As the model of the family has changed to one with two parents in full-time employment, there has been a corresponding growth in demand for childcare services. For a lot of families this has presented a question – is it worth paying for childcare (does the parent earning the least still earn more than the cost of childcare?).

This causes a catch 22 for the childcare industry in most countries – childcare typically struggles to attract enough suitable employees due to a combination of parents’ (understandably) high expectations and generally low pay. However, if businesses in the childcare industry were to offer higher pay to childcare workers to attract more candidates, they would need to raise the cost of the childcare to parents, leading to more parents simply dropping out of the workforce to stay home and raise their children instead. Because of a parent’s ability to provide their own childcare services, without Government intervention, it will be difficult for the wages of childcare workers to ever significantly exceed the average income for parents in the area they service.

The Financial Sector Reflects the Market

The last sector I want to spend some time on in this section is the financial sector. One of the noticeable things from Chart 4 is, as a percentage of the total non-farm employment market, the financial sector hasn’t grown since the late 80s. This would seem to contrast with the general notion of an ever-expanding financial sector that is taking over the US economy. Again, the fact that we are looking at the data in terms of the percentage of total non-farm employees can be deceptive. Chart 6 shows the total financial sector employees from 1990 to 2015.

Chart 6 – Financial Sector, All Employees 1990 to 2015

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Looking at this chart, we see the Financial sector did add a significant number of jobs between 1990 and 2015, but the number of jobs in the financial sector is still relatively small compared to the economy as a whole. Additionally, the number of jobs in the Finance sector appears to change in line with with the economy as a whole. Does that mean the Financial sector doesn’t need to be reigned in or that it isn’t sucking talent out of the US economy into relatively unproductive industry? That is a topic for a separate article, but the one thing that can be said is that in terms of the number of people being employed by the Financial sector, everything looks very much like business as usual.

Stable Industries

Chart 7 – Industries with Stable Shares of the Employment Market 1939 to 2015

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The Government Sector

Despite there being observations we could make about both the other two industries on this chart, I am going to focus on the most interesting story on this chart – the Government sector. The basic story in the chart is the build up in the percentage of non-farm employees in the Government sector from 1945 to the mid 70s, and then a slow decline through to 2015. Again looking at the percentages can be deceiving, so let’s look at the number of employees in the Government sector (Chart 8):

Chart 8 – Government Sector, All Employees 1955 to 2015

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The period from 1955 to 2009 saw a pretty consistent build up in the Government sector – close to 15 million jobs were added in this time. But since 2009, ignoring census hiring in 2010 (you can also see corresponding spikes in all years ending with ‘0’ for the same reason), the number of people employed by the Government had its biggest decline since the early 80s. To help determine what is happening, let’s look at the Government sector broken down into its three sub-sectors, Local, State and Federal (see Chart 9):

Chart 9 – Government Sub Sectors, All Employees 1955 to 2015

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At first this would seem to show a slightly confusing picture. This decline from early 2009 through to late 2014 represents almost 6 years right in the middle of Barrack Obama’s presidency, but for all the noise about political stalemate in Washington, the sequester and the Government shutdown, there appears to have been minimal impact on the number of Federal Government employees. At the same time, Local Governments have been slashing payrolls and State Governments have essentially been in a hiring freeze. The explanation for this is largely due to:

  1. The nature of Local and State Government revenue sources – the three main types of taxes that Local and State Governments collect are income tax, forms of sales tax, and property tax. All three sources took sharp downturns in the recession, with property tax continuing to decline even as income and sales tax collections were recovering.
  2. Balanced budget requirements – many State and Local Governments have balanced budget requirements, which meant in the face of sharply falling revenues, they were forced to slash expenditures. In many cases this meant cutting payrolls, which unfortunately only exacerbated the effects of the recession locally.

The combination of sharply falling revenues and the inability to use debt financing led to large job losses at the State and Local Government level. On the other hand, it is well known that Federal Government does not have a balanced budget requirement (much to the chagrin to some on Capitol Hill) and, in contrast to the State and Local Governments, significantly increased spending going into the recession (the American Recovery and Reinvestment Act of 2009). The merits and impact of Government financed stimulus may be debated, but the impact on employment within the Government sector is pretty obvious.

A Strange Observation

The other surprising observation from Chart 9 is that the Federal Government has employed more or less the same number of people since the late 1960s – all the growth in the Government sector has come from the Local and State Government sectors. The growth in Local Government makes sense, the population of the US has increased significantly in that period and providing Governance for that population requires more employees. We also see growth in State Government for the same reasons – but nothing at the Federal level.

Technology and other efficiency gains should allow fewer people to do the same amount of work over time, and the productivity gains between now the 1960s have been huge. Additionally, the impact of these efficiencies would be greatest at the Federal level where the scale of the work is typically bigger and there is less need to maintain a physical presence all over the country/state in the same way that Local or State Government has to. But the efficiencies wouldn’t apply everywhere:

  • To audit the same percentage of businesses over time, the IRS would need to continually hire additional auditors to keep up with the growing number of people and businesses
  • For Social Security to continue to service a growing population, the number of locations (and the staff to keep them running) would also need to expand significantly

Even allowing for a more efficient work force, it seems unlikely that the Federal Government has been able to maintain the same levels of service, regulatory effectiveness and Government advisory when the country has grown so much in population and complexity.

From here it would be easy to launch into a diatribe about an understaffed Federal Government leading to issues like the financial crisis, the failure to detect various huge frauds (Enron, Bernie Madoff), and the generally poor quality of Government services (the torturous immigration process comes to mind[1]). I could then also go on to talk about how using the points above to argue for further reductions in the Federal Government seems crazily wrong-headed. However, linking all these events to a shortage of Federal Government employees is far too simplistic. These events were caused by a range of factors and simply adding more Federal public servants would not have solved the problem on its own.

All that said, not increasing staffing levels for 50+ years does have an impact. The next time you are forced to suffer through some unnecessarily archaic (Federal) Government process, read about another fraud that the SEC and/or FinCEN failed to pick up, or lament that lobbyists are writing a significant amount of legislation that gets put before congress, keep in mind that collectively the Government agencies providing these functions are today operating with the same number of people as they were when Neil Armstrong took his first steps on the moon.

 

[1] Please don’t tell me that this is done intentionally to discourage applicants – there are plenty of ways to discourage applicants without wasting huge amounts of time and money.

 

Have any thoughts on what impact constant levels of Federal Government staffing since the 1960s might have had? Please leave them in the comments!

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