Moving forward

Last night I watched the documentary Zeitgeist: Moving forward. It reminds us of how ill this world is. You most likely know this. The documentary has a central theme - instead of focussing on a human value based system the world has adopted a money value based system. This is easy to observe when viewing business models, economic models and trying to understand how the monetary system works. It ascribes many of the world's problems, such as ecological disasters, poverty, inequality and war, to the failure of the market economy to address true human needs. The documentary highlights how the market economy ignores many aspects of being human - caring, cooperation and love. People only have the right to eat in as much as they can pay for food, or own land or are able overcome hunger by little welfare benefits from the state. The market economy is pretty harsh on individuals who are born in poverty. The chances for the poor to attain a good education are small. Thus the probability of escaping poverty is also small.

The documentary offers some solutions. Referring to the work of Jacques Fresco, we ought to embrace technological progress, resist the lures of money and status, build cities that maximise ecological sustainability and enhances human welfare, and build a system of environmental inventory to monitor the global use of natural resources. This means that we take only what we really need and enjoy life while artificial intelligence does the work for us. In theory this sounds plausible and even wonderful. I just don't know whether humans have the capacity for such a change.

I agree with the documentary in many areas. I think the world monetary system is one that enslaves. I think freedom, as promoted by this system, is an illusion. At the heart of this system lie interest rates. We charge interest for many reasons. The time value or opportunity cost of money in one person's hand is compensated by interest paid by another person who borrowed money. These interest rates are not equal between borrower and lender. Financial intermediaries (banks) put premiums on loans in order to cover their operating costs and maximise their business profits. This is why there is such a massive distinction between deposit, lending and interbank rates. Furthermore, the way interest rates are charged is perverted. The current model charges higher interest rates for people deemed riskier (i.e. people with a high probability of loan default). The problem is that higher interest rates increase an already high probability of default. The perversion is further exacerbated by forcing poor people to take out loans in the first place, otherwise they do not have a roof over their heads or food or a warm bed (some of the most basic things humans need). The idea that charging interest rates is a bad idea is nothing new (the Bible and other religious texts prohibit usury).

Fundamental to all of this is pride. The bad world we see today goes much deeper than flawed monetary systems. It goes to the very heart of human desire. Our desire is never filled and is an ever growing spiral into nothingness. We consume, we cheat, we lie, we steal, we murder, we are myopic, we are unsympathetic and we are self-absorbed; All because we put desire above everything else. No one is as important as the "I". 

There is really no peace to be found when desire consumes us with flames of empty promises. I am not as optimistic as the creators of Zeitgeist. To me it seems inevitable that man self-destructs. Of course I hope that this will never happen. Despite our feelings about the world we might as well try to make it a better place. Put aside the gloomy picture of the future and focus on things that are good. Truly care for those in need and care for the environment in every possible way. Let go of the desires of being wealthy, powerful and popular; these desires make the soul very sick and is never satiable. Focus on spiritual growth. We do not do good and do not feel well because we have neglected a fundamental part of being human - spirit. We pay too much attention on mental and physical well-being and think very little of caring for the spirit. I have followed the suggestion of a friend and started reading the The ascent of mount Carmel by St. John of the cross. His work has definitely put many things in perspective.

But perhaps easiest of all to remember, and definitely the most important, is to follow God's commandments. Love God with everything and love your neighbour as yourself. There is no place for pride when we do this.

The SARB hike might just be justified

The recent interest rate decision by the South African Reserve Bank (SARB) was not all that surprising. The SARB, with its forward guidance policy, has signalled a bout of rate hikes when the repo rate increased by 50 basis points in January 2014. That hike was a bit surprising given low growth expectations among high rates of unemployment in various sectors. But, it was also surprising because some might argue that the rate hike happened too late - if the SARB forecasts inflation above 6% in period t+6 (quarters) then it ought to increase interest rates in period t. This is if we are to believe that it takes about six quarters for monetary policy to influence aggregate consumer prices.

The rate hikes would thus seem justified - the SARB is only following its guiding law of low and stable prices. Unfortunately this leaves us with a few unanswered questions: The SARB tells us that SA has a negative output gap (GDP < production capacity), should we not then expect minimum pressure on prices from a demand side? We know, however, that South Africa has been bombarded by a weaker currency and by persistent high oil prices. Thus there have definitely been some supply-side shocks. No doubt that the SARB will be worried about second round effects of inflation (perhaps that is why the interest rate hike took so long).

We need to balance weak demand with strong supply shocks regarding inflation. A hike in interest rates imply that the SARB believes that inflation will rise even further. The increase in interest rates will help (hopefully) anchor inflation expectations. In this case it will delay or even halt importers to pass the weaker rand onto final goods. This supports demand from decreasing even further. On the other hand the hike in interest rates will lead to a decline in demand through lower credit and higher debt payments that reduces overall consumption. Thus the demand benefit from increasing interest rates needs to be balanced by the demand loss from raising rates.

At this point you should have been wondering why the SARB raised rates by 25 basis points. It could be due to a numerical solution from some model, it could be due to possible fear that increasing interest rates by too much will hurt the economy, or it could be another reminder of further interest rate increases.

The problem with 25 basis points is that it does little to reduce inflation. Using a simple semi-structural model of inflation (this is just for illustrative purposes) shows that a 100 basis points hike reduces inflation at most by about 0.4 percentage points (as an example from 6% to 5.6% inflation). And this is based on an assumption that interest rates have a large weight in the New-Keynesian IS curve. Figure 1 shows what happens to the model economy when interest rates increase by 100 basis points.  Alpha is the weight on the interest rate in the IS curve. Output is the level of GDP. The shocks are deviations from baseline which is assumed to be the steady state. This means that the model does not take into account nonlinearities such as the response of output in an already depressed economy. The point about the figure is that 25 basis points hardly has any impact on inflation. Or perhaps it is exactly the right number that balances a very sensitive economy from collapsing while keeping inflation in check. This is pure speculation.

Anyhow, the interest rate is the least of South Africa's problems. Constant strikes (Toyota and Ford have shut down some operations), unproductive people (those that do nothing at one of the many district or local government municipalties), bad employment policies (yes I think the current format of BBBEE is doing harm to the economy) and corruption undermines all the good macro and micro economic policies in place. Economic policy makers can only juggle a sensitive economy for a short period of time before the fundamental problems unravel all that is good. 

A new beginning

It is difficult to move, to leave everything and to start afresh. It is painful to uproot when one becomes so attached to familiar surroundings. The fear of the unknown heightens the senses and obfuscates. Our myopic outlook makes the future a constraint to emotional happiness and quells the desire for adventure. But this is exactly why it is necessary to shake things up, especially if one has been swallowed up into a whole of complacency.

It is has been such a long time since I have been forced to re-evaluate my life. Introspection is part of everyday routine, but doing introspection thoroughly and deeply comes only upon rare occasions. The way I am dealing with leaving South Africa for the US has been interesting and challenging. The comfort of a good job, house, family and friends makes life bearable. Life finds a completely new meaning when one takes all that away. It is not so much as leaving things behind that causes anxiety, but it is the fear of the unknown. 

Unfortunately there is no certainty regarding the future, no matter what steps we take to minimize it. Forecast errors grow in proportion to the forecast horizon. Even occurrences occur at random with a given probability and there is just no taking control of it. Moving to a different country adds to the number of already uncountable factors that drive uncertainty.

There are three ways to deal with this uncertainty – you have to or else it will destroy your nervous system:
• Be oblivious about the uncertainty you are facing. This can be justified on grounds that many outcomes are probability events of which you have no control over.
• Fool yourself into thinking that you have control. List the things that causes anxiety and create a plan to address them (your plans might fail which will ultimately force you to accept the first bullet). Thinking you are in control has the psychological advantage of taking away your predicament. While it does nothing to reduce the uncertainty, it does a great deal to reduce anxiety – only because you think you are able to minimize uncertainty.
• Embrace uncertainty and see it as an adventure (I prefer this one). Since uncertainty represent chance events, it makes the future much more interesting and invokes the “anything can happen” principle. Do things that you always wanted to do (make a plan if you don’t have cash lying around to live as a vicarious spendthrift) and maximize every opportunity.

Evaluate your decisions. I am moving because I love my wife and want to share in her great adventure (she has definitely done the same for me once). But, I am also moving because my life has reached a stationary point – complacency is a slow killer.

Change is only stressful because of our attachment to things; Things that have a finite stamp and ideas that do not really matter (such as a job giving a person power and status). In fact, it makes us less human and more like robots that fulfill silly functions every day. We neglect the spirit too often by making foolish decisions and we starve the spirit of nutritious food. No bloody wonder that man is anxious about everything temporal and material – because those are the things we choose to consume and be consumed by.

Thus, while I might forgo a cushy job, a good salary, a comfortable house and leave some friends behind, I gain something that I have been yearning for. I regain a piece of myself that got lost amidst all the heaps of rubbish that I accumulated over the years. And now that I am free, free from the material, I finally breathe again. How wonderful it is to not suffocate under pretence and lies! This is a fresh start. I hope I do not forget this lesson.

The causes of South Africa's next bout of capital outflows

Background

Capital flows have far-reaching implications for monetary, fiscal and financial policy. South Africa has a relatively large current account deficit that is financed by capital inflows. A reversal of capital inflows could have serious economic consequences. While the economic effects of capital flow reversals have been studied for South Africa, less is known in terms of what prompts capital flow reversals. This is the central question addressed in this note. Our analysis shows that the probability of a capital flow reversal increases in relation to:

•     Higher debt service costs.
•     Slower economic growth.
•     Sovereign ratings downgrades.
•     Higher government debt.

The effects and causes of capital flow reversals

Empirical work shows that capital flow reversals have a negative impact on the economy (for a good summary see Smit et al. (2013)). Capital flow reversals cause:

•     Sharp currency depreciations.
•     Declining economic activity.
•     Declining asset prices.
•     Current account reversal if unaccompanied by reserve buffers.

Smit et al. (2013) shows that a capital reversal of 50% reduces economic growth by 0.3 percentage points in the first year and by 1 percentage point the following year. The 10 year government bond yield increases by 3.2 percentage points in the first year following the capital flow reversal and  by a further 1.7 percentage points the next year.

Studying the determinants of capital flow reversals is justified given its effects on the economy. It is also important in the context of economic movements recently – South Africa needs to be aware that the potential for capital flow volatility increases as the Fed tapers down its quantitative easing programme. At the same time, SA policy makers need to be cognisant of the effects of a possible EU quantitative easing (QE) programme. By no means does another QE imply an increase in capital flows – this depends on the factors that influence flows (the core research question of this note).

The literature usually cites growth differentials, interest rate differentials, foreign exchange reserve, prices, financial policies and fiscal sustainability as factors that influence capital flows. We study the impact of some of these factors on capital flow reversals. A short description of the possible effects of these variables on capital flows are summarised in Table 1:

Table 1: The influence of macroeconomic variables on capital flows

Variable	Effect
Interest rates	Higher interest rates provide higher yields for foreign investors. These yields could lead to higher capital inflows. However, these yields need to be adjusted for risk. If the risk adjusted interest rate is still low, or when a country’s public finances are perceived to be unsustainable, then changes to the interest rate could have no effect on capital flows, or even lead to a reversal if rates lead increases the probability of debt default.
GDP growth differentials	Higher GDP growth could lead to an increase in net capital inflows. This often serves, alongside the stock market, as an indication of potential future gains for investment.
Expectations	Expectations regarding the financial stability of a country are important is assessing whether foreigners will invest or not. We assume that these expectations can be measured by a country’s risk rating (caution – this is usually only a measure of risk regarding a country’s foreign denominated debt). It is expected that there will be an outflow of capital when expectations worsen, i.e. a lower rating.
Inflation	Investors are often interested in real returns to investment. Inflation erodes those returns. In inflation targeting countries, high inflation would mean higher interest rates. These higher interest rates in return would reduce economic growth.
Exchange rates	It is not the level of the exchange rate that might cause an inflow or outflow, but the view about whether the exchange rate will depreciate or appreciate. While exchange rates are endogenous to capital flows, we model exchange rate deviations from equilibrium to proxy foreign investors’ views on currency movements. As an example, an investor would want buy goods in domestic currency cheaply and sell it when the currency depreciates. Here it is assumed that investors analyse this from an equilibrium perspective – assuming that any movement away from equilibrium will move back to equilibrium.

Methodology

We are interested in variables that increase the probability of a capital flow reversal from an empirical perspective. The explanatory variables include South Africa’s GDP growth differential with G7 GDP growth, debt service costs, the interest rate differential between South Africa and USA’s federal funds rate, foreign reserves as a per cent of GDP, Fitch sovereign ratings, sovereign debt as a per cent of GDP, high interest rates (measured by squaring interest rates) and inflation. Having so many explanatory variables in a regression framework can easily bias the results making inference about the size and sign of the explanatory variables impossible. As such, we use a model[1] that explicitly takes account a large number of variables without biasing the statistical significance of the estimates. Our model is estimated over 1997 to 2013q1. Our measure of capital flow reversals is measured as a binary variable that equals 1 whenever net capital flows as a per cent of GDP is less than zero and equals zero otherwise.[2] The model is set up in a way that multiple combinations of equations are estimated. In total 2^9 (512) models are estimated (there are nine variables). Our methodology allows us to evaluate the parameter distribution - The distribution helps us to assess the significance of the coefficients (i.e. how far the mode, mean and median deviates from zero) as well as whether certain variables are more important than others (as measured by the Posterior Inclusion Probability (PIP)). The PIP varies between 0 and 100, where 100 indicate that a variable was significant in modelling capital flow reversals in all 258 model combinations.

Results

Table 1 summarises the first set of results. The mean coefficient should be interpreted with caution. The model is a probit model and the results do not have an elasticity interpretation. The mean’s sign, however, is important. We see that higher GDP growth differentials and higher levels of reserves reduce the probability of capital flow reversals. Higher GDP growth differentials imply that macroeconomic fundamentals are good relative to the rest of the world and serves as a signal for potential investors. Higher reserves imply a higher probability of being able to absorb adverse economic shocks better. Higher debt service costs, higher sovereign debt and another sovereign ratings downgrade increase the probability of capital flow reversals. The probability of capital flow reversals for South Africa decreases in the case of higher interest rate differentials. Higher interest rate differentials can attract capital due to higher returns.

Table 1: The determinants of capital flow reversals

	PIP	Mean	SD
Growth differential	10.8	-0.02	0.09
Debt service costs (DSC)	34.3	0.26	0.09
Reserves	65.5	-0.23	0.20
Interest rate	5.3	-0.01	0.04
Inflation (infl)	9.4	0.01	0.06
Ratings (Fitch)	37.5	0.41	0.62
Equilibrium fx	15.9	0.01	0.03
Debt	18.0	0.02	0.06
Very high interest	10.3	0.00	0.00

One way to interpret the results is to analyse the probability of capital outflows over different values for our explanatory variables (everything else is evaluated at their respective means). Figure 1 shows that the probability of a sudden stop varies over different shares of reserves to GDP, different GDP growth rates and different sovereign ratings. The probability of a sudden stop is then compared when debt service costs are moderately high versus when debt service costs as a per cent of GDP is zero.

Figure 1: Probability of capital flow reversals

The vertical axes show the probability of capital flow reversals (outflows). If it equals 1 then capital flow reversals are a certainty. The horizontal axis measures the actual levels of reserves, GDP growth and ratings respectively. Reserves as a per cent of GDP vary from 3 per cent to 10 per cent as an example. The ratings are assigned numerical values where the highest rating, AAA, is assigned a 1. As is expected, the probability of a capital flow reversal decreases alongside the accumulation of reserves, higher credit scores and higher growth differentials. Interestingly, higher debt service costs are associated with a higher probability of capital outflows. In the case of having positive growth differentials, debt service costs matter a lot in terms lower the event of capital outflows. The probability of capital outflows is larger when the exchange rate has deviated far from equilibrium. Currency deviations from equilibrium often imply possible currency speculation – this can greatly affect the movement in capital flows.

Conclusion

Capital flow reversals could come about due to a number of reasons. Poor performing macroeconomic indicators such as slow GDP growth and low-rated sovereign bonds could result in an outflow of capital. Our results show that a higher level of reserves serve as a signal to manage potential economic shocks, and hence reduces the probability of a sudden stop. Higher debt service costs, alongside higher government debt increases the probability of capital flow reversals substantially.

There are some interesting policy considerations that emerge from this analysis. If the objective is to avoid an altogether outflow of capital then there are a couple of policy options. Unfortunately policy options that worked for one country during a particular period might not be that effective for another country (see Magud et al. (2011) on the effectiveness of different capital controls). It should be useful to rank and quantify the effects of various policies that mitigate capital outflows. This reduces the risk of getting things seriously wrong – such as unattended consequences of a tax on speculative flows. A convincing proposal has been put forth by Korinek (2010) to impose a Pigovian tax on inflows to mitigate possible amplification effects, or externalities, caused by outflows. Korinek (2010) using a welfare theoretic foundation for risk-adjusted capital regulations, calculates the externalities caused by various types of flows for Indonesia. He shows that externalities are amplified during crises periods. The largest externality from flows comes from dollar debt, followed by inflation linked debt. The least distortionary flows come from non-financial FDI and portfolio investments.  Regrettably little is understood regarding the macroeconomic effects of different types of flows since most studies use only aggregate measures. Thus, the correct policy response should control for the type of flows too controlling for country specific effects.

References

Magud, N., Reinhart, C.M. and Rogoff, K.S. 2011. Capital controls: Myth and reality – A portfolio balance approach. National Bureau of Economic Research. NBER Working Papper 16805.

Korinek, A. 2010. Regulating capital flows to emerging markets: An externality view. University of Maryland working paper.  

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[1] We use a Bayesian Model Averaging (BMA) that estimates multiple combinations of models and averages out the coefficients. We use a flat prior indicating our lack of knowledge of the importance and size of the different variables. This implies that the likelihood function has a stronger weight than any prior chosen by the researcher. 

[2] There are alternative measures such as any deviation in capital flows of more than one standard deviation.

Can we predict the next winner of the Nobel Prize in economics?

The best way to learn new software is to experiment with data in order to answer some interesting question. My experience with R has been cool. I have also found the transition from Matlab to R (for economic applications) quite easy. There is a ton of available guides on the web to help you get into R. R’s graphics is pretty amazing too.

Now that I have R (and Rstudio as platform) I needed an interesting question. Most students in economics come across binary response variables at some point in their lives. But, a lot of students do not always ask interesting questions and also do not explore the data correctly. So here I will attempt to illustrate how one can use a simple regression to predict possible Nobel Prize winners in economics.

You can find numerous Nobel prediction cites: see here.

The point about this blog entry is to explore some of R’s commands and then to fit (imperfectly) a model that predicts possible winners. To do this I had to gather a bit of data. The following data (and their sources) were collected:

• Previous winners (winners)
• Previous JB Clark winners (winners)
• The top 2000 ranked economists according to REPEC criteria (top 2000)

Of course this data is not sufficient to build a proper model – you would need control for age (the youngest ever winner was Arrow aged 52), you will need a variable indicating gender (there was only one female winner) and possibly a variable indicating the importance of a specific paper in shaping economic thought. But the data we have is not too bad. The data gives us an idea about which universities improve your chances of winning, the field of study and the importance of that field presently in helping understand serious economic questions as well as relying on previous prizes in economics to help predict a winner.

I now have a little model that has Nobel Prize  as dependent variable (binary: 1 = won, 0 = have not won yet). Our explanatory variables include university rank, author rank, gender and JB Clark medallists). The university with the highest rank also had the most Nobel Prize winners. A university with no previous winners will receive a rank equal to zero. Author rank depends on the REPEC author ranking list. I scaled the rank so that 0 means highest REPEC rank. I must admit that my gender variable was constructed by my own understanding of gender names – it would be a very time consuming process to Google 2000 ranked authors to ascertain their gender – fortunately this variable is highly insignificant in predicting winners. The JB Clark medal variable is also a binary variable where 1 = won and 0 = have not won.

It was quite a tedious task to correctly map all 2000 candidates with the list of Nobel winners, their universities and JB Clark winners. Please send me a request if you would like this data – it is currently sorted according to REPEC ranking.

Now we are ready to use R. I use the ggplot2 package for nice looking figures. The data looks as follows after loading it:

Table 1: The data

	Name	Nobel	Bates	Gender	Uni	Rank
1	andreishleifer	0	1	1	0.07	0.0304
2	jamesjheckman	1	1	1	0.13	0.0345
3	robertjbarro	0	0	1	0.07	0.0487
4	josephestiglitz	1	1	1	0.07	0.0500
5	petercbphillips	0	0	1	0.04	0.0746
6	daronacemoglu	0	1	1	0.06	0.0822
7	robertelucasjr	1	0	1	0.13	0.0944

Andrei Shleifer is currently the highest ranked on REPEC. The other columns are the explanatory variables. I would first like to summarise the data before I estimate the little regression. About 63% of JB Clark medallists have gone on to winning a Nobel Prize. Some of the JB Clark medallists are still too “young” to win a Nobel Prize. There are approximately 10% of female candidates in the list of REPEC author rankings.

Next I estimate the Probit. All the slope coefficients, except for gender, are significant:

Table 2: Regression output

            Estimate Std. Error z value Pr(>|z|)   

(Intercept) -2.12882    0.39778  -5.352 8.71e-08 ***

Bates        1.21190    0.25903   4.679 2.89e-06 ***

Gender       0.41422    0.37742   1.098    0.272   

Uni          7.04045    1.59790   4.406 1.05e-05 ***

Rank        -0.06406    0.01549  -4.136 3.53e-05 ***

Remember that an increasing number in rank means that you are ranked lower on REPEC. The results make sense to me: The probability of winning a Nobel Prize increases if you were a previous JB Clark medallist, if you are a male (gender is not statistically significant so little weight should be assigned to being male or female), if you attended or are lecturing at a university that hosted previous winners and a higher rank on REPEC (in this case 0 is the highest rank).

There is no direct interpretation of the coefficients. One way to evaluate the coefficients is by means of predictive curves. The Figure below shows the probability of winning. The y-axis is the actual probability while the x-axis varies the REPEC rank. The different shades of blue control for the university rank. The figure shows that the probability of winning a Nobel Prize is over 0.6 with a very good publication record and having attended a university that hosted Nobel laureates.

Figure 1: Prediction curves

We can use this model to predict likely winners. The results will also contain past winners as well as people who have passed away (obviously they cannot win) – we can use this to evaluate the fit of the model. The table is sorted according to the probability of winning. The results, while interesting, are not that reliable. There were a number of academics who have won, but that score a fairly low probability of winning.

The results suggest that Kevin Murphy has a very high chance of winning sometime in the future:

Table 3: Likely winners

REPEC           Name Nobel Bates Gender  Uni   Rank    Probab 

2      jamesjheckman     1     1      1 0.13 0.0345 0.6592267

13       garysbecker     1     1      1 0.13 0.1917 0.6555262

142     kevinmmurphy     0     1      1 0.13 1.3531 0.6277450

162    kennethjarrow     1     1      1 0.13 1.5665 0.6225621

213     stevenlevitt     0     1      1 0.13 2.1345 0.6086614

256  daniellmcfadden     1     1      1 0.13 2.5273 0.5989653

306      davidmkreps     0     1      1 0.13 3.0071 0.5870391

449   amichaelspence     1     1      1 0.13 4.3881 0.5522986

20      paulrkrugman     1     1      1 0.08 0.2650 0.5173747

1     andreishleifer     0     1      1 0.07 0.0304 0.4952882

4    josephestiglitz     1     1      1 0.07 0.0500 0.4947874

27  lawrencehsummers     0     1      1 0.07 0.2978 0.4884562

6      daronacemoglu     0     1      1 0.06 0.0822 0.4659186

805    jonathanlevin     0     1      1 0.13 7.9372 0.4618090

67     jerryahausman     0     1      1 0.06 0.7140 0.4498638

497     robertmsolow     1     1      1 0.06 4.9049 0.3466185

667        rajchetty     0     1      1 0.07 6.4323 0.3365476

12  martinsfeldstein     0     1      1 0.00 0.1817 0.3035090

40        davidecard     0     1      1 0.00 0.3929 0.2987970

There are many interesting questions that one could ask with available data. In this case I was curious to get an idea of who the likely Nobel Prize winners will be. Unfortunately the Bates medal has the largest weight in predicting winners in this model. This is definitely not a realistic model. But it illustrates some interesting concepts – if you want to win a Nobel Prize make sure to go to a university that where many Nobel laureates made a name, make sure to have a good publication record and try to win a JB Clark medal.