Sunday, November 2, 2014

Big Data and Economic Policy

A useful book to read on Big Data is entitled “Big Data,” by Viktor Mayer-Schonbeger and Kenneth Cukier.

No rigorous definition of Big Data is available. According to the book, essentially, Big Data means “things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, and relationship between citizens and governments, and more.” The question is how this new phenomenon can affect economic policy, such as monetary policy.

The book discusses a few important issues surrounding Big Data, which are worth mentioning here, and may require pondering. First, the sheer number of observations, or N=all, implies that the principle of randomness, which we use in statistics, is no longer applicable. Second, Big Data maybe messy, hard to structure and tabulate, but the authors argue that “more trumps better”. Indeed, MIT economists Alberto Cavallo and Roberto Rigobon collected half a million prices of products sold in the US every day, and used them to measure inflation. While it is a messy data set, they claim that they have immediately detected the deflationary episodes in prices after the Lehman Brothers collapse in September 2008, while the official CPI data showed that in November 2008. Third, Big Data could tell us more about correlation between X and Y, for example, but nothing about causality. Fourth, correlation is used in predictions. Fifth, Big Data handles nonlinearity better than small samples.

I do not have much quarrel with these assertions.

Let us talk about policy. Take for example monetary policy in the US, the EU, New Zealand, and in a number of the advanced countries, where the primary objective is price stability. Regardless of the operational details of the policy, which vary from one central bank to another, the policy is, essentially, demand management. Demand management is based on economic theory: as data arrive in time, central banks try to discern the nature and the permanency of the shocks. When the shocks are thought to alter the future paths of output and prices, central banks intervene by moving the current short-term interest or change the current money supply, its growth rate,  or whatever the policy instrument is. This forwardness is the essence of monetary policy because we know that monetary policy affects the real economy with a lag, and that these lags are long and variable. For example, for the central bank to change the future path of output, it moves the interest rate a year or a year and a half earlier. A popular economic theory among central banks predicts that when central banks project output to be above its potential, i.e., a positive output gap, aggregate demand increases, and inflation would increase above its expected level.

The problem is that the most important variables within the demand management framework are unobservable to the policymaker. The central bank needs to estimate, propose, or calibrate these variables. Neither potential output nor expected inflation is observable. Similarly, the Wicksellian Natural Rate of Interest, which is also of interest to some policymakers, is also unobservable.

Given that most of the policy-relevant variables are unobservable, the question is how could Big Data benefit policymakers? I suspect that “more data” can actually help policymakers measure the values of the important unobservable variables needed for policy-making. However, Big Data may provide an estimate of future aggregate demand for goods and services that the policymaker can use to infer future inflation. Alternatively, Big Data could tell us whether we would have higher future inflation directly without inferring it from excess demand, i.e., as in Cavallo and Rigobon. That would raise a number of questions. For example, would that mean the end of economic theory in the conduct of monetary policy? If super computers can tell the policymakers that prices of goods and services are going up with such accuracy, could the policy be tightened, i.e., increase the short-term interest rate? If so, there would be no more fuss about models, estimations, predictions, etc. Could this be the future of monetary policy? The models used by central banks are based on a set of assumptions, which reflect a certain economic paradigm or belief. Therefore, it is hard to change how central banks think and work. Milton Friedman advocated a different way to do monetary policy, i.e., the x% money growth rule, but it was highly resisted by central banks because implementing such policy regime would have left very little for them to do. 

Even if Big Data could tell us something about inflation a couple of months ahead, such as in the Cavallo and Rigobon’s study, it would not be sufficient. They say that they knew in September 2008 that prices were falling, while the CPI data showed that decline in November 2008. That might be true for the public, but it cannot be the case for economists working in the central banks because they actually would know the prices of more than 70 percent of the goods and services in the CPI basket before November. Indeed, central bank forecasts for the next quarter’s CPI is accurate most of the time. Instead, the one-year-ahead inflation rate is most relevant for policy, which is very difficult to forecast. Could Big Data tell us what would inflation be a year ahead?

One could imagine scenarios where Big Data could shed more light on aggregate demand. For example, one could find out whether millions of people, for example, are shopping online for a new car, new homes, or durable goods in general. That might be a useful signal about future aggregate demand. Would policymakers alter policy because of such information? Similar information could be obtained online about imports and exports, which affects aggregate demand. This idea is not very different from measuring vacancies by counting job ads on the Internet, which has been used to fit the Beveridge curve (the empirical relationship between vacancy and unemployment).

Government’s security agencies seem to be benefiting from the Internet Big Data phenomenon, but other departments have not invested yet in it. Also see an article by Kalev Leetaru published in Foreign Policy in May 29, 2014. He uses Global Database of Events, Language, and Tone Online Library for 2.4 million protests to analyze the Arab Spring. I think that the time will come. In the past, central banks used large-scale models, which failed to increase the forecasting accuracy and to make a better policy. Then central banks used factor VAR’s, where a large number of variables are used. The forecasting accuracy did not increase either. I do not think Big Data would improve forecasting accuracy in economics, but that does not mean that central banks will not explore this avenue. I have a feeling that various governmental departments, and the central banks, are likely to be investing in Big Data this decade. 

Wednesday, September 17, 2014

Comments on Economic Policies for 2014 New Zealand's Election

My former colleagues at the Reserve Bank of New Zealand, Sean Collins, Francisco Nadal De Simone and David Hargreaves wrote a very informative Bulletin article in 1998 about the current account imbalances in New Zealand.[1] Economists at the RBNZ were also concerned about our relatively high real interest rate. In 2005, my colleagues at the New Zealand Treasury, Julia Hall and Grant Scobie wrote about the problem of shallow capital in New Zealand.[2] These issues are just as important today as they were then, and they are highly related to the current election's debates. 

The law of diminishing returns implies that the marginal product of capital, which is equal to the rate of return on capital investment in the long run, is relatively higher in the less productive country. Given that capital is freely mobile across borders, the neoclassical model of growth and trade predicts that capital investment flows from the relatively more productive country to the relatively less productive country until the capital-labour ratios, wages and returns (real interest rates) are equalized.

New Zealand is relatively less productive than Australia and G7 countries. One reason that GDP per worker is relatively low is that we have relatively less capital to work with. Thus, the marginal product of capital is higher in New Zealand than in Australia and in the G7 countries. Thus, the rate of return on capital investment in New Zealand is relatively higher. Relatively capital rich countries must find investments in New Zealand attractive. This is consistent with the data. We have relatively lower output per worker (thus relatively lower labour productivity), a lower saving rate, a smaller capital stock, a higher real interest rate, and a persistent current account deficit.

Suppose that both Australia’s and New Zealand’s output per worker can be represented by a simple Cobb-Douglas production function, where output per worker is a function of capital per worker raised to a power, which is the share of capital in output, y = Ak^b, where y is output per worker, A represents exogenous technological progress, k is the stock of capital per worker, the hat symbol means that the variable is raised to the power, and b is the share of capital. The marginal product of capital is then equal to the return on capital r= b A k^ (b-1). Re-writing this in terms of output per worker, we would have r= b A^(1/b) y ^ (b-1/b). Australia’s real GDP per capita in 2010 was 1.45 times larger than ours, and thus the marginal product of capital in New Zealand relative to Australia’s is approximately (1.45)^(b-1/b). If we assume that the share of capital is 0.4, our marginal product of capital is 1.8 times more than Australia’s. It follows that the Australians invest more than New Zealanders.

The same is true for the G7 countries. I computed the same ratio for all the G7 countries relative to New Zealand. The Penn Table publishes the chain GDP per capita PPP-adjusted figures for 2010. I used these figures to compute the relative rates of return on capital. The estimates for New the Zealand’s rates of returns on capital relative to France, Germany, Italy, Japan, the U.K., and the U.S. respectively are 1.52, 1.51, 1.46, 1.51, 1.26, 1.47, and 2.0. These relative rates of return on capital imply that investment flowing into New Zealand from these countries must exceed New Zealand’s investment in them, with the U.S. and Australia being the largest investors in New Zealand.

Further, the level (and the growth rate) of human capital in New Zealand are similar to that of Australia’s and the G7 countries. Human capital level as estimated by Barro and Lee is the average years of schooling, which is approximately the same in New Zealand, Australia, and every G7 country. Average years of schooling in 2010 were 12.1, 11.37, 10.53, 11.82, 9.50, 11.52, 9.59, 12.2, and 12.69 for Australia, Canada, France, Germany, Italy, Japan, U.K., the U.S., and New Zealand, respectively. These numbers indicate that differences in technological progress or human capital are not large enough to affect the model’s prediction that investment will continue to flow from Australia and the G7 countries to New Zealand, until at some point the capital/labour ratio, wages and the rates of returns equalize. This can take decades.  

Foreign investment in New Zealand will diminish as the relative rate of return on investment approaches one. This means that foreign investors are indifferent between investing in New Zealand or in his or her own country. This simple model predicts that for this to happen, output per worker in New Zealand must increase relative to the other countries. Productivity is the key to resolving all these issues.

The 2014 elections in New Zealand produced many ideas, which aim at resolving New Zealand’s productivity problem. Proposed policies, which I believe could alleviate the imbalances mentioned above in the long run include:

  1. Compulsory savings may increase capital and productivity. They also reduce the marginal productivity of capital and the rate of return on capital in the long run. They may also resolve the current account imbalance and change the international investment position of the country over time.
  2. Direct large investments in infrastructure would also work as a direct increase in capital;
  3. Investment in knowledge such as via increasing the quality of human capital and R&D may increase growth because they boost technical progress as well as increase and speed-up the diffusion of new knowledge;
  4. Encouraging manufacturing, especially environmentally-friendly manufacturing may also help as it has been the driver of all successful growth experiences around the world. Example may include encouraging the manufacturing of high value added exportable goods rather than exporting row materials.
Policies, which aim at reducing immigration are seriously misguided and they would adversely affect productivity growth. Expanding the labour force via immigration, especially if immigrants are well educated, increases productivity by raising the probability of finding new ideas, which are essential for growth. Similarly, policies that aim at restricting international trade would be unhelpful. Monetary policy has nothing to do with long run growth so changing the Reserve Bank Act would be a useless policy and may endanger the preconditions of higher growth, which unavoidably include price stability. 

Thursday, September 11, 2014

The Minimum Wage and the 2014 New Zealand’s Election

Yesterday we listened to the debate between the Prime Minister and David Cunliffe on TV 3. The leaders spoke at length about the minimum wage. The Prime Minister’s story that the increase of the minimum wage would increase unemployment and that would actually make us worse off.  He said that the correlation between the minimum wage and the unemployment rate is positive. Put simply, a rise in the minimum wage of $2 would increase the cost of production for small businesses. In turn, they either reduce employment or pass the cost’s increase to consumers by raising prices. This is the typical textbook argument. 

My colleagues Dean Hyslop and Steven Stillman, both professors of labour economics at leading New Zealand universities, studied the youth minimum wage in New Zealand. It might be worth restating their findings . They say, "we find no robust evidence of adverse effects on youth employment or hours worked. In fact, we find strong evidence of positive employment responses to the changes for both groups of teenagers, and that 16-17 year-olds increased their hours worked by 10-15 percent following the minimum wage changes. Given the absence of any adverse employment effects, we find significant increases in labour earnings and total income of teenagers relative to young adults. However, we find some evidence of a decline in educational enrolment, and in unemployment inactivity, although these results depend on the specification adopted."

In another paper on the issue, which they examined the 2008 youth minimum wage reform, they say," The study found that the introduction of the New Entrant (NE) minimum wage was largely ignored by businesses and that most 16 an 17 years old workers were moved on the adult minimum wage, which resulted in an increase in the minimum wage of 28 percent of this group. This research found that the minimum wag increase accounted for approximately 20-40 percent of the fall in the proportion of 16 and 17 years olds in employment (4,500 - 9,000 jobs) by 2010. The introduction of the NE minimum wage did not have a significant impact on unemployment, because most of the 16 and 17 years old impacted were students who were combining study with part-time employment." 

I have no doubt that both the Prime Minister and Mr. Cunliffe care about jobs, want to put people to work, and provide them with decent wages. They have the same objective (same preferences), but different policies (different budget constraints) to achieve it. However, the fact is that there is an empirically significant relationship between wages, productivity and unemployment; i.e., the Wage Curve, which has been ignored in the debate. I have shown in a previous blog that there is a significant correlation between the real wage rate-labour productivity wedge and unemployment.

However, I want to provide an alternative, appealing, interventionist or activist, policy proposition, which might encompass both of the PM and Mr. Cunliffe’s views. It is appealing because it accounts for productivity, wages and unemployment. The Economics Nobel Laureate Edmund Phelps argues that the government can subsidize low-wage employment, by paying employers for every full-time low-wage worker they hire, and calibrate the subsidy to the employee’s wage cost to the firm. The higher the wage cost, the lower the subsidy, until it has tapered off to zero. With such wage subsidies, competitive forces would cause employers to hire more workers, and the resulting fall in unemployment would cause most of the subsidy to be paid-out as direct or indirect labor compensation. People could benefit from the subsidy only by engaging in productive work – that is, a job that employers deem worth paying something for.

Monday, August 11, 2014

The Rock Star Economy

More than ten years ago, I was flying from Wellington to Auckland and the man sitting next to me began a friendly chat. He was a farmer in his seventies. He asked me what I do and I said that I am an economist, which encouraged him to ask questions about the New Zealand’s economy. He said so what is the main problem with our economy? I said, well, the numbers suggest that we, as a nation, are not very productive compared to others. He wanted details.

I told him that one day I was flying from Honolulu to Auckland and an old fellow sitting next to me was a rich native Hawaiian American who was coming to New Zealand to play golf. The Hawaiian asked me, very bluntly, if we are lazy. I was taken by a surprise by his question. He noticed, and replied that Hawaiians are lazy because Hawaii is a beautiful place. People like to enjoy the sun, the warmth so they forget about work. He said, New Zealand must be just as beautiful as Hawaii and that he assumes that we are just as lazy. I said if you mean we are not as productive as our neighbors the answer is yes. The Kiwi farmer acknowledged. So what do we do, he said. I said it is hard to explain all that in a layman term. But let’s think about your dairy farm. Suppose that you produce milk and sell it to the rest of the world at a dollar a bottle. If you produce two bottles, your income would be two dollars. Suppose that the Chinese, the Europeans etc like you milk very much and they ask for more. Without you producing more, the price of your milk goes up, say to two dollars a bottle. Your income is four dollars now. He nodded in agreement. Income doubled while you still produce two bottle of milk. You production has not doubled. That increase in income is a result of higher prices and has nothing to do with productivity. Economists call it the term of trade effect. He fully agreed. Then where is the problem? The problem is that when the rest of the world reduces its demand for our milk for whatever reason, your income will plummet. He asked so what do we do? I said in my wild imaginations I reckon that you have to do something to increase the real value of your milk. How? He added. I said maybe you want to produce milk that when you and I drink it we get younger!

Useable Knowledge can do that as Simone Kuznets discovered. Research that can transform milk into a magical product increases its value. Products change everyday. Newer products are more valuable. The smart phones we use are made of some cheep plastics and metals that are not worth much, but we pay a lot to buy them. We do so because we pay for the knowledge, which is used to make them. The Kiwi farmer agreed. He said he would like to drink that milk that makes him twenty years younger.

A highly educated CEO of a major government department told me that many OECD countries, which are more productive than New Zealand, are richer than New Zealand so for us to increase productivity we ought to be rich. Stunning, isn’t it? While the Kiwi farmer understood the difference between a term of trade effect and productivity effect, the CEO did not, unfortunately.

That brings me to a phrase we hear a lot today, “we are a rock star economy.” Our income has increased perhaps, but it will also decrease tomorrow when the price of milk falls. The metric for a rock start economy is a secular economic growth, not an increase in income due to a temporary higher demand for milk. The road to riches requires more production of new goods and services. And, that requires usable knowledge.

I do not know what is meant by a rock star economy, but I take as it means a spectacular economic performance. The fact that the unemployment rate is 5.6 percent is good news. However, this is still a cyclical fluctuation around a “natural Rate of Unemployment”. It is far away from our estimate of the Natural Rate of Unemployment, which is between 4 and 4.5 percent.

The Natural Rate of Unemployment is unobservable. It is a hypothetical rate that is consistent with production being at its long-run level.[1] The recent fall in the unemployment rate is also consistent with the fact that productivity is higher than real wage, at the margin, and over the current business cycle. Firms hire workers as long as the marginal productivity of labor is higher than the real wage, so unemployment falls. Firms stop hiring workers when the real wage is equal to productivity and layoff workers when marginal productivity is less than real wage.

Calls to increase wages arbitrarily – without any considerations to productivity – is not a good economic policy; it sounds like it is politically and election-driven calls. Wages will increase naturally because they are below productivity now, and when the real wage is equal to productivity, hiring will cease, and the fall in unemployment comes to a stop. Presumably, that will have to happen when the unemployment rate drops to 4 or 4.5 percent.

How far are we from 4.5 percent unemployment? The time to get there depends on how fast the labor market adjusts. When the unemployment rate was 6.9 percent in December 2012 my estimated speed of adjustment implied that it would take up to 10 quarters for the unemployment rate to reach the Natural Rate. So it took a year and a half for unemployment to fall by 1.3 percentage points (from 6.9 to 5.6). Everything else remains the same, the unemployment rate is expected to drop to 4.5 (another 1.1 percent) in a year time. During this period, the average real wage will continue to rise until it is equal to labor productivity.

A further drop in milk prices will make the labor market adjustment slower. As income falls, business activity slows, and the hiring rate slows. The demand for labor will fall; but that is only one problem. If everything in the economy hinges on milk prices, we will see more problems in other markets.

Here are some statistics, which are related to my main concerns. The 2013-2014 global competitiveness index published by the World Economic Forum is based on 12 pillars: (1) the soundness of institutions, (2) infrastructure, (3) macroeconomic environment, (4) health and primary education, (5) higher education and training, (6) goods market efficiency, (7) labor market efficiency, (8) financial market development, (9) technological readiness, (10) market size, (11) business sophistication, and (12) innovations. Under each pillar, there are a number of indicators, more than 80. New Zealand ranks 18 after Switzerland, Singapore, Finland, Germany, USA, Sweden, Hong Kong, Netherlands, Japan, UK, Norway, Taiwan, Qatar, Canada, Denmark, Austria, and Belgium. Australia ranks 21. Our ranking has jumped up five places, which is great. The report says, “New Zealand emerges as an economy with a strongly articulated political commitment to environmental stewardship better than neighboring Australia.” We have done very well in many pillars and according to many indicators.

However, we still have problems. We rank 27th in infrastructures; 43rd in macroeconomic environment; 24th in technological readiness; 62nd in market size; 26th in business sophistication; and 26th in innovation.[2]        

It seems to me that the New Zealand’s market size is the main reason for New Zealand’s overall ranking. Market size along with pillars (5) to (10) above are keys for efficiency-driven economies. New Zealand domestic market size ranks 60th and its foreign market size ranks 74th. Competition fuels innovation. We rank 30th in terms of the intensity of local competition. We ought to focus on issues related to efficiency and increasing market size.

[1] W. A. Razzak, “New Zealand Labour Market Dynamics: Pre- and post-global financial crisis,” Treasury Working Paper 14/03. Many others, e.g., Brain Silverstone, have also estimated the Natural Rate to be lower than current unemployment rate.  See references in my paper.

Monday, May 5, 2014

Savings, interest rate and the exchange rate in New Zealand

The New Zealand’s opposition Labour Party announced that in order to lower average real interest rates and relief pressures on the unusually appreciated currency, they would pursue a policy of to increase mandatory savings. The details of the policy have not been worked out yet, but the gist of the argument is that higher savings lead to lower real interest rate on average, which would lower the real exchange rate (real depreciation of the currency). In general, there is no prima facia problem with advocating a mandatory savings policy. Presumably, if people perceive the policy as permanent, the stock of domestic capital would increase, consumption would fall, and diminishing marginal returns to capital would reduce the equilibrium real interest rate in the long run.[1] But there are three issues here:

First, New Zealand is an open economy, regardless of what measure we use for openness. When Kiwis want to consume more today than tomorrow they would borrow from abroad so it does not necessarily follow that consumption will fall because of a mandatory savings policy. New Zealand is a stable Western democracy and our robust institutional arrangements such as our laws guarantee that Kiwis would not default on their international obligations.

Second, we have been concerned about the relatively high real interest rate in New Zealand for a long time, but we do not have a firm knowledge why is that. New Zealand’s inflation-indexed interest rate is more than one percentage point higher than that in the U.S. The data from May 1998 to-date show that the average difference between New Zealand’s rate and the U.S. rate is 1.08 percent. To be precise, the inflation-indexed interest rate for New Zealand has been above the U.S. rate at every moment in time.[2] What explains such a persistent difference? A recent paper in the Journal of Finance provides a new theory and test.[3] In short, it says that the relative sizes of the economies matter. The U.S. economy is massive relative to New Zealand, therefore, U.S. risk-free bonds are relatively more expensive than New Zealand’s, and hence the average U.S. real interest rate is relatively lower. The author tests a large cross sectional data and shows that differences in the sizes of economies explain a large fraction of cross-section variation in currency returns. The proposed Labour Party policy would be ineffective in this case. This means that, on average, we will not be able to have a lower real interest rate than the United States, and most other sizable trading partners.

Third, in theory, relative productivity growth differentials or the covered and uncovered interest rate parities explain the exchange rate depreciation rate. But reality is different from theory when it comes to explaining the exchange rate. Those commonly used exchange rate models do not work well in practice. Parity conditions are among the six known puzzles in international economics.[4] The magnitude of the expected change in the exchange rate, nominal or real, which we observe in New Zealand cannot explain the interest rate differentials between New Zealand and the U.S. so we cannot be sure that a policy, which aims at lowering New Zealand’s equilibrium real interest rate, guarantees a depreciated currency.

In summary, the theoretical linkages between savings, real interest rate and the real exchange rate one hand, and the empirical evidence on the other, are not as clear as the Labour Party leader think they are.

Finally, let me say a few things about the real exchange rate, which might help understand the problem. One way of looking at the real exchange rate is to examine the ratio of the price of non-tradable goods to the price of the tradable goods. This ratio has been increasing because non-tradable prices have been relatively higher than tradable prices since 2006 (see the RBNZ data files online). Most of the prices of tradable goods and services are beyond our control because New Zealand is a price taker. So the real exchange rate appreciation issue, which policy might be able to influence, is related to the price of non-tradable goods. The largest components of non-tradable include health, education, housing, energy & water, and telecommunications. The demand for these goods and services keeps increasing because of the increase in population and income, etc. So prices are expected to keep increasing. Examination of the available input-output tables of the New Zealand economy (1996 and 2007) suggests that mining; energy & water and construction have experienced increasing cost per unit of output, growing output, and declining productivity – the Baumol disease.[5] Mining aside, because it is a tradable good sector, therefore, some of the increases in non-tradable prices, which cause the real exchange rate appreciation, might be related to some imbalances in the energy & water and the construction sectors. Although the shares (weights) of value added / total New Zealand's output of these two sectors are relatively smaller than those of agriculture and manufacturing, the share of construction’s value added in total output might have doubled between 1996 and 2007 input-output tables. The Policymaker may want to ask: how much of the rise of the non-tradable price is associated with the rising marginal costs in these two secorts; why productivity in these sectors have not increased; and what could be done to increase it because if the price of non-tradable goods and services continue to increase by more than the prices of tradable goods and services, the real exchange rate will continue to increase. 

[1] Wicksell’s equilibrium or natural real interest rate is the rate of returns on the economy’s capital stock.

[2] The period following the global financial crisis to-date exhibited a significant reduction of the U.S. interest rate.

[3] Tarek A. Hassan, (2013), Country Size, Currency Union, and International Asset Returns, Journal of Finance, Vol. 68, Issue 6, 2268-2308.

[4] Obstfeld, M. And K. Rogoff, (2001), ‘The Six Major Puzzles in International Macroeconomics: Is There a Common Cause?’ in NBER Macroeconomics Annual 2000, Volume 15, eds., Ben S Bernanke and Kenneth Rogoff, MIT Press.

[5] Baumol, W. J., (1968), Productivity Growth, Convergence, and Welfare: What the Long Run Data Show, American Economic Review, Vol. 76, No. 5, 1072-1085.

Monday, April 7, 2014

Why Nations Fail?

Why Nations Fail?

The president of the New Zealand Government Economic Network (GEN) Girol Karacaoglu reviewed Acemoglu and Robinson's book “Why Nations Fail: the Origin of Power, Prosperity and Poverty” on the GEN website. This is a very interesting topic and a promising field of research. The central thesis of this book, Girol says, is that “economic prosperity is associated with “inclusive” economic and political institutions, while “extractive” institutions typically lead to stagnation and poverty.” And he explained that “inclusive” (or good) economic institutions tend to be free (as distinct from unregulated) – market-supporting institutions that enable, allow, encourage and incentivise participation by the great mass of people in economic activities that male best use of their talents and skills, invest and innovate, make the choices they wish, and freely contract and exchange; that secure private property rights, and provide a level playing field, as well as an unbiased system of law; and that encourage a process of ongoing “creative destruction.” 

Here, I examine the Acemoglu-Robinson hypothesis. I provide two graphs. Acemoglu and Robinson used the case of Korea as a test of their hypothesis. The Korean peninsula was split into two different countries in 1948. North Korea, formally known as the Democratic People's Republic of Korea (DPRK) is a communist dictatorship with a centrally-planned economic system, while over time, South Korea slowly evolved into a democratic country with a market economy. The two countries share the culture, the geography, the history, and the DNA. Figure 1 plots the real GDP per capita (PPP-adjusted Maddison data). The idea of this graph is to show that differences in economic outcomes are related to differences in the political and economic systems. Note also that North Korea is almost a closed economy. They trade with China, and a few other countries.

In figure 2 I provide another, more general, test for the Acemoglu-Robinson theory. I present a scatter plot of a measure of the political institutions and the economic outcomes for 115 countries. The data are taken from Global Democracy. The political institution index is based on (1) political rights, (2) freedom of press, (3) civil liberties, which are taken from Freedom House; and (4) global gender gap report , (5) the corruption index from Transparency International; and  (6) peaceful changes of head of governments (last 13 years), and (7) peaceful changes of political party of the head of governments (last 13 years). High index value denotes "inclusive" countries - i.e., democracies. The economic outcomes index is based on (1) PPP-adjusted real GDP per capita, (2) central government debt as a percent of GDP, (3) inflation rate, (4) unemployment rate, and (5) youth unemployment rate. The data cover the period 2008-2009 (I do not plot the year 2011-2012 because they do not show any change in the data that might alter the conclusion). 

If political institutions are the only predictor of the economic outcomes we would expect a high correlation and the scatter points to be on the 45-degree line, or very close around it. However, the correlation is only 0.62, which is not very high and the scatter points are not on the 45-degree line. 

In figure 2, the European countries, particularly the Scandinavians, the U.S., Australia and New Zealand are in the top right-hand side corner. Countries above the 45-degree line are the oil-rich plutocratic political economies of Libya, Kuwait, Bahrain, and also Russia. The African countries are close to the horizontal axis. The rest of the world which includes Eastern European countries, South and Latin America, Asian countries, China, and India are in the middle of the graph and far from the 45-degree line.   

One could argue that these is a measurement problem(s); that these indices do not measure the political institutions and the economic outcomes well. Economic outcomes such as unemployment and debt for examples are highly related to policy. Even the most democratic systems and free market economies in the world suffer from boom-bust cycles related to policy errors. The conclusion is that political institutions influence the economic outcomes, but they do not map one-to-one. The case of China is a stark example. 

China is not a Western "inclusive" democracy despite the remarkable growth it experienced since the 1990s. China experienced high economic growth from the late 1990 because earlier the government embarked on a series of economic reforms and deregulation, which do not accord with communism. For example, China allowed for the market to play a major role in resource allocation. It introduced privatization and profit maximization, which are inconsistent with communism, but created a viable private sector. It also allowed for foreign investments, a degree of free capital mobility, a tax reform, and less restrictive property right laws among other local reforms. 

My conclusion, however, is that the data seem to lend some support the Acemoglu - Robinson story. 


Figure 1

Figure 2 

Tuesday, March 25, 2014

The Kiwi Dollar and Monetary Policy

The empirical exchange rate models of the seventies and the eighties, where the exchange rate depends on the relative changes in market fundamentals (e.g., output, money, interest rate, and inflation differentials between the home and the foreign country), could not outperform the random walk model in and out of sample. The fundamentals vary much less than the exchange rate; hence the correlation between them is too small. As a result, there is a general agreement among economists that the exchange rate is very difficult to predict.[1]

On March 13, 2014, the Reserve Bank of New Zealand increased the Official Cash Rate (OCR) by a quarter of a percent for the first time since 2011.  People understand that a rise in interest rate today and the near future means that the RBNZ is predicting increasing inflationary pressures. The question is to whether we can predict the exchange rate from interest rate differentials?  

The Uncovered Interest Rate Parity condition (UIP) says that risk-neutral investors would be indifferent to interest rates in the home and the foreign countries because the exchange rate between the two countries is expected to adjust  such that the dollar returns on dollar deposits is equal to the dollar return on foreign deposits, thereby eliminating the potential for an uncovered interest rate arbitrage profits. My own research at the Reserve Bank of New Zealand, more than a decade ago, showed that the UIP condition does not hold in a number of currencies Vis-a'-vis the U.S. dollar, but holds much better in the case of the New Zealand – Australian currency.

I plot the 90-day interest rate differential (i – i*) between New Zealand and the U.S., where asterisk denotes the U.S. effective federal fund rate, against the expected depreciation rate, and the same for New Zealand and Australia using monthly data from 2001. The expected depreciation rate is the spot nominal exchange rate minus the sample’s average as a proxy for the expected exchange rate.[2]

Figure 1

There correlation between the interest rate differential and the exchange rate (USD-NZD) is weak, and it breaks down completely in 2009.[3] New Zealand and the U.S. short term interest rates (policy rates) remained unchanged for a long period of time since the global financial crisis, so the change in interest rate differential remained constant while the exchange rate varied, which explains why the correlation between the interest rate differential and the exchange rate depreciation is low.[4]  

Monetary policy affects output and inflation. The increase in interest rate today reduces future inflation and output growth. Lower output growth relative to the U.S. depreciates the Kiwi dollar and a lower inflation relative to the U.S. appreciates the Kiwi dollar. The correlations between output growth differential and inflation differential between New Zealand and the U.S., and the depreciation rate are weak.[5] Based on these figures, it is unclear how the USD-NZD dollar would move in the future.[6]

Figure 2

I do not plot the Australian-U.S. UIP condition because it is just as bad as the New Zealand-U.S. case. However, contrary to the two cases which involve the U.S. dollar, the UIP for the (AUD-NZD) seems to hold. The exchange rate moved in tandem with the interest rate differential over the sample, and recent appreciation of the Kiwi against the Australian dollar is pretty much a result of monetary policy conditions summarized by the interest rate differential.[7] The correlation between the interest rate differential and the exchange rate depreciation rate is reasonably high, and significantly higher than the U.S. dollar UIP.[8] And since output growth and inflation are very close in New Zealand and Australia, they are not good predictors of the currency.[9] Thus, the short-term interest rate differential is a better predictor of the exchange rate in this case.

Figures 3 and 4 plot the interest rates and output growth rates of the three countries. Clearly Australia’s interest rate is different from that of the U.S. and New Zealand. Australia’s output growth was much higher than that of the U.S. and New Zealand during the recent global financial crisis, which probably explains the different responses of monetary policy. The interest rate differential between New Zealand and Australia varies a lot more than that between New Zealand and the U.S. And that this variation explains why the UIP holds better between New Zealand and Australia compared to the New Zealand - U.S. and Australia-U.S. cases.

Figure 3

Figure 4

[1] Meese, R. and K. Rogoff, 1983a, Empirical Exchange Rate Models of the Seventies: Do They Fit Out of Sample?, Journal of International Economics, 14, 3-24.

Meese, R. and K. Rogoff, 1983b, The Out-of-Sample Failure of Empirical Exchange Rate Models: Sampling Error or Misspecification? In Jacob Frankel, ed., Exchange Rates and International Macroeconomics, University of Chicago Press. 

Flood, Robert P. and Rose, Andrew K., 1995, Fixing Exchange Rates A Virtual Quest for Fundamentals, Journal of Monetary Economics, Elsevier, vol. 36(1), pages 3-37, August. 

[2] The New Zealand data are taken from the Reserve Bank of New Zealand, the Australian data are from the Reserve Bank of Australia and the U.S. data are from the Federal Reserve Bank of St Louis.

[3] The correlation is 0.008.

[4] The standard deviation of the interest rate differential is 0.01 while the standard deviation of the exchange rate is high, 0.20.

[5] It is 37 percent, and the correlation between the inflation differential and the depreciation rate is -41 percent.

[6] Future oil price contracts (Brent, NY light, Oman crude etc.) have been falling steadily over time. This trend might be a good predictor of the future trend of the U.S. dollar. Hence, the Kiwi dollar might depreciate against the U.S. dollar in the coming year.

[7] The standard deviation of the interest rate differential is 0.01 and that of the exchange rate is 0.05, which are closer than those in the case of the U.S. dollar.

[8] The correlation coefficient between the interest rate differential and the depreciation rate is 74 percent. Further, regressing the New Zealand 90-day interest rate (i) on a constant term, trend, and the Australian 90-day interest rate plus the depreciation rate (i*+d log (s)), where (d) is the difference operator, using Fully Modified OLS method, gives a slope coefficient that is insignificantly different from unity, and statistically insignificant constant term and trend.

[9] The correlation between output growth differential and the depreciation rate is 13 percent and that between the inflation rate differential and the depreciation is zero.

Sunday, March 16, 2014

Regulations and Corruption

"That government is best which governs least." 
 Thomas Jefferson

The recent global financial crisis ignited a new wave of debate about the need for more regulations and government interventions. More regulations might very well be necessary. But it’s unclear how much regulations are needed and where. And there are always questions of over and under regulation, their efficacy, and the effects on the working of the markets. It’s quite conceivable that more regulations can lead to more corruptions and more corrupt countries can in fact have more regulations already. Here are some empirical cross-sectional correlations, which seem alarming.

Measuring corruption

Transparency International publishes a Bribe Paying Index. It involves twenty eight of the world's largest economies - they represent 80 percent of the world total outflow of goods, services and investments. It is supposed to measure the perceived likelihood of companies in these countries to pay bribes abroad. The scale is 0 to 10, where the maximum score corresponds with the view that the companies from that country never pays bribes, and zero corresponds with the view that they always do. We do not have data on bribes paid or perceived to be paid in own country. I use this index as a proxy measure of corruption. The index was published for the years 1999, 2002, 2008 and 2011. 

Measuring regulations

The World Bank publishes data on the ease (difficulty) of doing business. I chose The Number of Procedures required to establishing a business, the Number of Procedures to Deal with Constructions and the Number of Procedures to Enforce Contracts as measures of regulations. The assumption is that the longer these procedures are the more regulations the country has. There is no particular reason for me to choose these indices and I could have easily chosen other data such as getting credit, paying taxes etc.

Both indexes are for the year 2011 because the bribe index is not available for more recent years.

Putting them together

I invert the bribe paying index before I plot it, separately, against the three numbers of procedures above, which measure regulations. Figures 1, 2 and 3 show a clear positive correlation between regulations and corruption. Obviously I cannot pin down the direction of causality. But I would argue that causality is not really important. There could be bi-directional causality running from regulations to corruption or vice versa. But what matters the most is the close association between these two indices, which are constructed by two different and independent institutions.

Countries which have relatively less regulations have companies which do not anticipate paying bribes abroad. It would not be very wrong to argue that that they are also less likely to pay bribes in their own countries. Countries which are relatively highly regulated have companies, which have a propensity to paying bribes abroad, and most likely they would do so in their own countries. Further, it may well be the case that relatively more corrupt countries are also countries that probably tend to regulate more to extract more bribes.

I conjecture that a country which is characterized to be a difficult one to do business in, difficult to register a property and difficult to enforce a contract is also a country were bribes are anticipated to be paid. While countries like Canada and Australia are ones which are relatively less regulated and relatively less corrupt, countries like China, Russia, the UAE and Argentina represent the other polar side. Majority of countries are in between.

I would ague that one should be very wary of over-regulation as much as being wary of under-regulation. Too many regulations could be just as bad as no or fewer regulations. Striking the right amount is important, such as the marginal benefit must equal the marginal cost.

Figure 1

Figure 2

Figure 3 

Monday, February 3, 2014

Unemployment, wages, productivity and monetary policy in New Zealand

At the microeconomic level, the firm hires more labour as long as the marginal product of labour exceeds the real wage. Hiring stops when the marginal product of labour is equal to the real wage. When the wage rate is higher than marginal product of labour, the firm lays off labour, hence unemployment increases. There are some challenging issues to reconcile the microeconomic and the macroeconomics of wage dynamics. Blanchard and Katz (1991), among other papers, is an excellent discussion of this issue.

That been said, the neoclassical theory is quite intuitive and empirically verifiable at the macro level, which I will show graphically. I graph below the business cycle fluctuations of the real wage and the marginal product of labour in New Zealand.[1] The cyclical fluctuations are deviations from an HP filter’s trends. Clearly there is a wedge.

Figure 1

The wedge between the real wage and the marginal product of labour can result for a number of reasons (see Thurow, 1968). First, taxes create a wedge if the incidence of the indirect taxes is on labour. Second, monopoly power can explain differences between the marginal product of factor inputs and their prices. Third, there is a constant substitution between factor inputs along growth path. Although there is some evidence that the stock of capital is shallow in New Zealand (but it has a positive trend, nevertheless). As the stock of capital rises, labour is displaced. That might cause the returns to labour to fall below its marginal productivity. Wages fall below the marginal product of labour if the transition cost is high. When labour is not paid its marginal productivity, the optimal stock of capital is less than it would be if labour were paid its marginal productivity; the opposite can be true too. Fourth, if firms set the wage rate by the marginal product of the marginal worker rather than the marginal product of the average worker, due to heterogeneity. Maré and Hyslop, (2008) provide evidence that less skilled labour is hired at the up-turn of the New Zealand business cycle. If this were the case then wages will have to be lower than the marginal product of the average worker. Fifth, some of the wedge between the marginal products and the returns could be explained by risk premiums. Sixth, when social returns are not equal to private returns, actual returns must be corrected for taxes when possible. And, seventh, endogenous growth models assume an increasing return to scale rather (i.e., less than doubling factor inputs is needed to double output), which means that capital and labour will more than exhaust total output. Thus, the marginal product of labour will not equal the real wage. 

The second figure plots the wedge between the real wage and the marginal product of labour, and the unemployment gap (the HP filtered unemployment rate). The increase in the real wage over the marginal product of labour opens a positive wedge, and unemployment increases above its long-run trend level. The correlation coefficient is nearly 70 percent. The wedge between the wage rate and the marginal product of labour is a predictor of unemployment in New Zealand as the neoclassical model suggests.

Figure 2

The unemployment rate in New Zealand is expected to be declining as the marginal product of labour continues to improve and to rise above the real wage over the business cycle. This seems to be happening now. The unemployment rates during the last three quarters of 2013 were 3.7, 3.9 and 4.2, and have been close to the natural rate of unemployment. Razzak (2014) provides a number of estimates of the natural rate of unemployment in New Zealand, which are between 3.5 and 4.5 percent. The actual annual inflation rate since January 2012 has been below the mid-point of the target. If the wedge between the wage rate and marginal productivity of labour is expected to continue to decline, i.e., productivity is higher than wages, unemployment will be expected to decline as firms will hire more labour, and hence more expected inflationary pressures. These stylized facts must have implications for future monetary policy.

 Figure 3 is a 95-percent chi-squared confidence ellipse. It shows that the correlation between the wage-marginal product of capital wedge and the unemployment gap is significant.

Figure 3


Blanchard, O. J., and L. Katz, (1999), “Wage Dynamics: Reconciling Theory and Evidence,” American Economic Review 89 (3), 69-74.

Maré, D. C., and D. R. Hyslop, (2008), “Cyclical Earnings Variation and the Composition of Employment,” Working paper, Statistics New Zealand, Wellington, New Zealand.

Razzak, W A, (2014), “New Zealand Labour Market Dynamics – pre and post global financial crisis,” Forthcoming Working Paper, New Zealand Treasury, Wellington, New Zealand.    

Thurow, L. C., (1968), "Disequilibrium and Marginal Productivity of Capital and Labor," Review of Economics and Statistics Vol 50, No.1, 23-31.        

[1] All the data are taken from Statistics New Zealand online. The sample is March 1991to September 2013. GDP data are only available to September 2013. The marginal product of labour is a calibrated derivative of output (real GDP) with respect to labour of the Cobb-Douglas production function. I assume a simple Cobb-Douglas production function, where real GDP is a function of capital and labour with the shares fixed at 0.4 and 0.6 for capital and labour respectively. Capital stock is measured by the Perpetual Inventory equation with the assumption that the initial period capital stock equal to 3 times GDP and a depreciation rate 0.08. Labour is measured by working age population (15-64. Gross capital formation is deflated by the capital price index. Wages are average hourly total ordinary wage minus expected inflation, which I measure as a 6 quarter moving average of the inflation rate. The inflation rate is the log-difference of the CPI. Different functional forms and assumptions could be used, e.g., differentiating between skilled and unskilled labour in a CES production function. The business cycle fluctuations are deviations from trend measured by the HP filter. So unemployment fluctuations are the deviations of unemployment from trend.