Macroeconomics Essay

 

 

 

Abstract

This research paper will comprehensively discuss the Theory of Purchasing Power Parity and the comparison of this between Germany and United Kingdom. This will give specific analysis of the different areas about Purchasing Power Parity. Given by data researched from different economic documents, the study of the structures of Purchasing power parity are tackled and explored in this paper.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

 

I. Introduction

Purchasing Power Parity (PPP) is the method of using the long-run equilibrium exchange rate of two currencies to equalize the currencies’ purchasing power. This theory was formulated and developed by Gustav Cassel in the year 1920.

The Swedish economist Gustav Cassel was a founding member of the Swedish School of economics, together with Knut Wicksell and David Davidson. He earned a degree in mathematics at the University of Uppsala and studied economics in Germany before returning to the University of Stockholm, where he worked from 1903 to 1936. Two of his most prominent students were later Nobel laureates, Gunnar Myrdal and

Bertil Ohlin. Some of his most important work is on the theory of interest, which he conceived of as a regular price, namely the price of the input ‘waiting’. He also popularized the notion of purchasing power parity, as discussed in this chapter. Supposedly, his dying words were: ‘A world currency!’

 

It is based on the Law of One Price which stated that in an efficient market, identical goods must have only one price. Purchasing power parity is also called absolute purchasing power parity to distinguish it from a related idea called relative purchasing power parity, which predicts the relationship between the two countries relative inflation rates and the change in the exchange rate of their currencies.

A purchasing power parity exchange rate equalizes the purchasing power of different currencies in their home countries for a given basket of goods. These special exchange rates are often used to compare the standards of living of two or more countries. The adjustments are meant to give a better picture than comparing gross domestic products (GDP) using market exchange rates. This type of adjustment to an exchange rate is controversial because of the difficulties of finding comparable baskets of goods to compare purchasing power across countries. Market exchange rates fluctuate widely, but many believe that PPP exchange rates reflect the long run equilibrium value. The distortions caused by using market rates are accentuated because prices of non-traded goods and services are usually lower in poorer economies.

The differences between PPP and market exchange rates can be significant. For example, the World Bank’s World Development Indicators 2005 estimates that one United States dollar is equivalent to approximately 1.8 Chinese Yuan by purchasing power parity in 2003. [1]. However, based on nominal exchange rates, one U.S. dollar is currently equal to 7.6 Yuan. This discrepancy has large implications; for instance, GDP per capita in the People’s Republic of China is about US$1,800 while on a PPP basis it is about US$7,204. This is frequently used to assert that China is the world’s second largest economy, but such a calculation would only be valid under the PPP theory. At the other extreme, Japan’s nominal GDP per capita is around US$37,600, but its PPP figure is only US$30,615

From the econometrics literature, it is clear that nonstationarity and nonlinearity are closely related. It has been well known for many years that it is difficult to distinguish statistically between difference stationary series and nonlinear but stationary series. Increasingly, the analysis uses the fractional integration framework rather than the ‘knife-edge’I (1)/I(0) approach to consider the interaction between nonlinearity and nonstationarity. For example, Diebold and Inoue (2001) and Perron and Qu (2004) investigate the effects of nonlinearity on the estimation of the fractional integration parameter, while Hsu (2001) and Kr¨ammer and Sibbertsen (2002) examine the impact of long memory on estimates and tests of structural change. Other recent work by Dolado, et al. (2005), Gil-Alana (2004) and Mayoral (2005) has devised new test procedures for fractionality and/or nonlinearity. However, in most cases the form of the nonlinearity needs to be known (Nessen, 2006).

 

II. Explanation for Purchasing Power Parity

 

A simple statement of the purchasing power parity hypothesis is that national price levels should be equal when expressed in a common currency. More formally, if st is the logarithm of the nominal exchange rate (expressed as units of foreign currency per unit of domestic currency), pt and p.t are the logarithms of the domestic and foreign price levels, respectively, and qt is the logarithm of the real exchange rate in period t = 1, 2, …, T, then for all t, qt = st + pt – p.t . (1) It follows that qt must be stationary for long run ppp to hold. If the mean of qt, E(qt), is zero, we have absolute ppp, whereas if E(qt) _= 0, we have relative ppp. Most of the empirical studies of ppp have either been concerned with testing whether qt has a mean reversion tendency over time or whether st, pt and p.t move together over time. This latter work has generally been concerned with models whose simplest form is st = á0 + á1pt + á2p.t + _t, (2) where _t is white noise. Early studies were concerned with whether the estimated values of the parameters of various versions of Equation (2) were as predicted. As awareness of time series dynamics increased, the issue changed to one of whether Equation (2) is a cointegrating regression. In some cases, ppp could not be accepted, whereas in others it could not be rejected. Nonrejection seemed most common when either prices were split into their component parts or other variables were included in the model. For instance, Kenny and McGettigan (1999) distinguished between prices in the traded and nontraded sectors; Wright (1994) considered interest rate deferential, along with the variables in Equation (2).

In recent years, the emphasis has generally shifted from considering models

of the form of Equation (2), to considering directly the behavior of

{qt}T t=1, the sequence of real exchange rate values. Within the I(1)/I(0) framework, most of the initial studies failed to reject the hypothesis of real exchange rates being I(1) for recent periods of flexible exchange rates.2 This failure to reject the possibility of unit roots in real exchange rate series implies a lack of mean reversion, which undermines the ppp hypothesis. The explanation often given for this nonrejection is the recognized low power of traditional unit root tests, such as the standard Dickey-Fuller test. To overcome this problem, two general approaches have been adopted. The rest has been the construction and use of long series of exchange rate data and more powerful asymptotic tests. The second, using panel data, attempts to estimate the half life of the mean reversion of the real exchange rate. Another explanation has been that the real exchange rate is time varying and requires the use of other factors in its modelling, who identify relative output levels, terms of trade and the net foreign assets in their linear model for the Irish real exchange rate. There is, though, a third possibility that is receiving increasing attention, and this is described in some detail in the following section (Jacobson, 2004).

III. Relative Purchasing Power Parity

 

Relative PPP relates the inflation rate (the change of price levels) in each country to the change in the exchange market rate where St is the spot rate in Foreign Currency/Domestic Currency and Pt is the price level in period t (foreign values are marked by an asterisk). This relation is necessary but not sufficient for absolute purchasing power parity

According to this theory, the change in the exchange rate is determined by price level changes in both countries. For example, if prices in the United States rise by 3% and prices in the European Union rise by 1% the purchasing power of the USD should depreciate by 2% compared to the purchasing power of the EUR (equivalently the EUR will appreciate by about 2% (Taylor,2003).

IV. Nonlinearity and Nonstationarity

 

The alternative explanation that has been gaining ground in the literature suggests the possibility that real exchange rate generating processes are in fact nonlinear. It is argued that nonlinearities arise because of transactions costs in international arbitrage.

The standard way to model the nonlinearities has been to use smooth transition autoregressive (star) models. Assuming that the real exchange rate is a stationary process, the star representation can be written as qt = ._zt + è_ztG(ã, c, ôt) + _t, (3) where _t ¡« iid(0, ó2), zt = [1qt-1 . . . qt-p]_, and . And è are (p + 1)-vectors of parameters. The transition function G(ã, c, ôt) determines the degree of mean reversion and is itself a function of ã, the slope coefficient, c the location parameter and ôt the transition variable. Normally ôt is set to be a lagged value of qt. There has been little discussion about the choice of specification of the transition function G. It is generally accepted, following Taylor, et al. (2002), that its form is exponential: G(ã, c, ôt) = 1 – exp _-ã(ôt – c)2_, (4) and the resultant model is known as the exponential smooth transition autoregressive model. The reason for this choice is that it is felt that the movement of the real exchange rate is symmetrical. However others argue that the asymmetric logistic function should also be considered, i.e., G(ã, c, ôt) = [1+exp[-ã(ôt – c)]]-1 , (5) on the grounds that there is little empirical evidence to support the use of estar models. A more general alternative to the estar model is the lstar2 model:

G(ã, c, ôt) = _1 + exp_-ã 2 _k=1 (ôt – ck)__-1 . (6)

The use of the lstar2 model overcomes the problem that, as 㠁¨ ‡,

Equation (4) becomes linear. However, there is a very different alternative method available (Caginlap, 1982).

 

The other approach to modelling nonlinearity is provided by random field regression. Dahl (2002) showed that the random field approach has relatively better small sample fitting abilities than a wide range of parametric and nonparametric alternatives, including lstar and estar models. The idea of using random field models to estimate and test for nonlinear economic relationships was introduced by Hamilton (2001) and is as follows. If yt is a stationary process, _t ¡« nid(0, ó2), and xt is a k-vector, that may include lagged dependent variables, then the basic model is yt = µ(xt) + _t, (7)where the form of the conditional expectation functional, µ(xt), is unknown  and assumed to be determined by the outcome of a random field. Hamilton suggests representing µ(xt) as consisting of two components. The first is the usual linear component, while the second, a nonlinear component, is treated as stochastic and hence unobservable. Both the linear and nonlinear components contain unknown parameters that need to be estimated. Following Hamilton, the conditional mean function is written as µ(xt) = á0 + ?_1xt + ëm(¯xt), (8) where ¯xt = g_xt, g is a k-vector of parameters and _ denotes the Hadamard (element-by-element) product of matrices. The function m(¯xt) is referred to as the random .eld. If the random .eld is Gaussian, it is denied fully by its first two moments. If Hk is the covariance matrix of the random field, with a typical element Hk(x, z) = E[m(x)_m(z)], Equation (7) can be rewritten as yt = á0 + ?_1xt + ut, (9) where ut = ëm(¯xt) + _t, (10) (Taylor, 2003).

 

V. Purchasing Power Parity adjustments to Gross Domestic Product

 

Using market exchange rates to compare countries’ standard of living or per capita Gross Domestic Product can give a very misleading picture. The exchange rate only reflects traded goods in contrast to non-traded ones. Also, currencies are traded for purposes other than trade in goods and services, e.g., to buy capital assets whose prices vary more than those of physical goods. Also, different interest rates, speculation, hedging or interventions by central banks can influence the market. The PPP method is used as an alternative. For example, if the value of the Mexican peso falls by half compared to the U.S. dollar, the Mexican Gross Domestic Product measured in dollars will also halve. However, this exchange rate results from international trade and financial markets. It does not necessarily mean that Mexicans are half poorer; if incomes and prices measured in pesos stay the same, they will be no worse off assuming that imported goods are not essential to the quality of life of individuals. Measuring income in different countries using PPP exchange rates helps to avoid this problem PPP exchange rates are especially useful when official exchange rates are artificially manipulated by governments. Countries with strong government control of the economy sometimes enforce official exchange rates that make their own currency artificially strong. By contrast, the currency’s black market exchange rate is artificially weak. In such cases a PPP exchange rate is likely the most realistic basis for economic comparison (Taylor, 2003).

VI. Purchasing Power Parity comparison in Welfare Economic

 

While using PPP exchange rates for income comparison is an improvement over using market exchange rates, it is still imperfect, and comparisons using the PPP method can still be misleading. Comparing standards of living using the PPP method implicitly assumes that the real value placed on goods is the same in different countries. In reality, what is considered a luxury in one culture could be considered a necessity in another? The PPP method does not account for this. (This is not primarily a flaw in the exchange rate methodology, as cultural and interpersonal differences in utility functions are a more fundamental microeconomic problem). A PPP exchange rate varies depending on the choice of goods used for the index (CPI). Hence, it is possible to deliberately or accidentally bias a PPP exchange rate by the choice of a bundle. Indeed, it may be hard to construct equivalent representative bundles for the consumption habits of very different societies. PPP could also have difficulty accounting for differences in quality between goods in one country and equivalent goods in another.

Differing levels of government involvement in social spheres further complicate development of good CPI baskets (and, consequently, PPP measurements). For example, in 1986, nominal GDP of the United States was almost 4 times larger than the nominal GDP of the Soviet Union (on a per capita basis). Direct comparison failed to capture, however, that the Soviet Union provided free secondary and higher education and free healthcare to all its citizens, whereas Americans had to pay for education and healthcare themselves. To properly account for differences in quality of life in this situation, the CPI basket would have to include these expenditures explicitly. More importantly, government subsidies can potentially have large effect on consumption levels (free higher education will result in more college graduates), making it difficult to choose weights for individual components of CPI.

Even if a good PPP is used, GDP per capita is still a measure of the economic output of the whole economy, not a direct measure of the mean or median person’s quality of life. Other factors such as the standards of homes and schools, access to public services, the extent of pollution, and strength of consumer protection laws are hard to quantify and generally not fully reflected in the GDP. Even a PPP-adjusted measure of GDP per capita must be used with caution, for all the usual reasons that the GDP figure itself is limited (for instance, its inability to capture the surplus between subjective value and payment price)

For example, in 2002, the nominal GDP per capita in Japan was about US$40,000, while the equivalent PPP into a U.S. goods basket was estimated at $27,000. In the U.S., GDP per capita was about $36,400 (nominal and real if based on 2002 dollars). This means that the average U.S. citizen could enjoy slightly more consumption than the average Japanese (vastly more if private saving is removed from consumption income). However, it does not necessarily follow, that this implies a “higher standard of living” in the sense of “enjoying life” more; the U.S. has higher crime rates and less social cohesion than Japan, while Japan has much less physical space per person and arguably less individual freedom. Ultimately, the quality of life will depend on subjective judgment and individual preferences.

While per-capita income does not take into account inequalities in wealth distribution, neither does the PPP-scaled income. While per-capita income does not take into account inequalities in wealth distribution, neither does the PPP-scaled income (Jacobson, 2004)

VII. Comparing Economies of Countries using Purchasing Power Parity

It is useful to have a ready yardstick for comparing the economy of key countries when making international or country specific investment decisions. The following three charts present data about the top 15 countries in terms of the Purchasing Power Parity of their GDP in 2005 as estimated by the United States Central Intelligence Agency.

Purchasing Power Parity [PPP] essentially adjusts the size of economies not based on the exchange rate for currencies, but based on the costs of a spectrum of goods and services within the country. PPP is less accurate than exchange rate measures, because it uses a sampling method, but is a better indicator of economic prosperity inside each country than an exchange rate based comparison of GDPs.

These data will tell many different stories depending on who is looking at them and why they are looking at them. We won’t try to say which stories are most important, because the most important story is the one they tell to you based on the issues you are weighing. The permutations of possible observations and relationships are many.

For us there was no surprise to see that China and India lead the pack in growth rate of PPP GDP, but we were a bit surprised to learn how high they ranked in total national PPP GDP. On an exchange rate based GDP comparison, for example, we have read that China’s economy is #4 behind the U.S.A, Japan and Germany (Hastnat, 1999).

;

;

;

However, on a purchasing power basis, Germany and the UK are not only behind China but also behind India.

Russia is ahead of both Brazil and Canada in national PPP GDP and in the rate of growth of GDP, but we would probably prefer to invest in Canada for energy resources and in Brazil for emerging market exposure due to Russia’s bad behavior relative to private property in the last few years. We’d rather take the populist and inflationary risks in Brazil than risk the re-nationalization trends in Putin’s Russia.

The great gap between the per capita PPP GDP of the BRIC countries (Brazil, India Russia and China) and the per capital levels of the developed countries, taken in combination with the rapid growth rates of the GDPs of the BRIC countries, bodes well for world economic growth – short of a major global military shock which would put everything into a cocked hat.

With China as a former enemy and a possible future adversary, we are personally worried about the degree to which our strategic manufacturing may be outsourced to them, but on an investment level we see India as far less likely to be an enemy and far more likely to be an ally, making them a potentially safer bet in the event of a global military problem.

In terms of risks of asset confiscation, we see India as more based on the rule of law and respect for contracts and private property than China and particularly Russia.

India is slower in opening its economy than China and Russia, but is not confiscating investors’ assets as they are doing in Russia, and has a legal system more similar to ours for the protection of investors.

We have great personal concerns about geopolitical risks to energy security for the U.S.A. which causes us as investors to favor energy resources that are located domestically or in countries where our energy trade relationships are not fundamentally in question (Canada and Mexico on these charts, plus Australia, and maybe Brazil for ethanol).

Overall, for us, given our political risk concerns and the growth charts above, we would tend to lean more toward India and Brazil and less toward China and Russia.

;

;

The figures given on the table for the zones are OECD estimates. They are actually derived using purchasing power parity and GDP of their constituent countries.

;

(Purchasing Power Parities for OECD Countries since 1980)

Yearly Basis Analysis:

Years prior to 1995: The 1995 PPPs for all countries have been backdated using the relative rates of inflation between countries as measured by their implicit price deflators for GDP.

1995-1998: PPPs for all European countries except Czech Republic, Hungary, Poland, Slovak Republic and Turkey are annual benchmark results provided by Eurostat. PPPs for all other countries are OECD estimates based on the methods described above.

2000-2001, 2003: PPPs for all European countries are annual benchmark results provided by Eurostat. PPPs for all non-European countries are OECD estimates.

2004: PPPs for all European countries are preliminary annual benchmark results provided by Eurostat. PPPs for non-European countries are OECD estimates. Estimates and preliminary results should be interpreted with caution as they are subject to revision.

2005: PPPs for European countries are preliminary benchmark Eurostat results. Results should be interpreted with caution as they are subject to revision. PPPs for non-European countries are OECD estimates.

2006: PPPs for all countries are OECD estimates (OCED.org, 2007).

VIII. Purchasing Power Parity of United Kingdom

The United Kingdom, a leading trading power and financial centre, has an essentially capitalist economy, the fourth largest in the world in terms of market exchange rates and the sixth largest by purchasing power parity (PPP) exchange rates. Over the past three decades, the government has greatly reduced public ownership by means of privatization programmes, and has contained the growth of the Welfare State.

Agriculture is intensive, highly mechanized, and efficient by European standards, producing about 60% of food needs with only 1% of the labor force. The UK has large coal, natural gas, and oil reserves; primary energy production accounts for 10% of GDP, one of the highest shares of any industrial state.

Services, particularly banking, insurance and business services, account for by far the largest proportion of GDP. Industry continues to decline in importance, although the UK is still Europe’s largest manufacturer of armaments, petroleum products, personal computers, televisions, and mobile telephones. Tourism is also important: with over 24 million tourists a year, between China (33) and Austria (19.1), the United Kingdom is ranked as the sixth major tourist destination in the world.

The Blair government has put off the question of participation in the Euro system, citing five economic tests that would need to be met before they recommend that the UK adopts the Euro, and hold a referendum.

During the second half of the 1990s the UK economy enjoyed a sustained period of strong and stable growth. Since then the slowdown in the world economy that accompanied the collapse in equity markets has served until recently to moderate growth both domestically and abroad. But, in comparison to other major economies, the UK has so far emerged relatively unscathed. This could be the result of good fortune, well directed stabilization policies or reformed institutions. Part at least of the relatively good growth performance displayed by the UK economy comes from stronger domestic demand. It is of course difficult to disentangle strong demand growth from the impacts on income of improved supply performance. We attempt to isolate those elements that are most clearly demand side stimulants.

The impacts of developments in government spending on output in the UK have been fortunate as well as fortuitous. Changes in fiscal policy have been appropriate in the UK and have strengthened weak growth, whilst at least in France and Germany their scale and timing was not so propitious. Developments in the housing market may not be so welcome, but they have boosted demand in the UK as the world economy slowed down, whilst France and Germany did not experience the same benefit. In addition to these demand factors, the recent appreciation of the euro, especially against the dollar but also against sterling, redistributes growth away from the Euro Area and towards the US in particular, whilst giving some support to demand in the UK. Taken together, we calculate that these three factors account for the majority gap between growth in the UK and growth in France and Germany between 2002 and 2004. Between 1997 and 2000 the US was the most rapidly expanding of these (Grossman, 2006).

IX. Conclusion: Comparisons between Germany and United Kingdom

PPPs for GDP – Historical series

1980
1981
1982
1983
1984

GERMANY
1.14
1.08
1.07
1.05
1.04

UNITED KINGDOM
0.479
0.488
0.494
0.502
0.505

1985
1986
1987
1988
1989

GERMANY
1.03
1.04
1.02
1.00
0.99

UNITED KINGDOM
0.518
0.524
0.537
0.552
0.572

1990
1991
1992
1993
1994

GERMANY
0.99
0.99
1.01
1.03
1.03

UNITED KINGDOM
0.593
0.611
0.621
0.623
0.620

1995
1996
1997
1998
1999

GERMANY
1.03
1.01
1.01
1.00
1.00

UNITED KINGDOM
0.624
0.628
0.623
0.632
0.644

2000
2001
2002
2003
2004

GERMANY
0.982
0.974
0.959
0.905
0.894

UNITED KINGDOM
0.633
0.623
0.610
0.624
0.619

2005
2006

GERMANY
0.883
0.875

UNITED KINGDOM
0.619
0.618

(Consumer profile, 2004, Vol. 15 Issue 11).

Between 1997 and 2000 the UK was one of the most rapidly expanding of these economies, achieving an average growth rate of over 4 per cent per annum in this period using Purchasing power parity. GDP growth was also strong in Germany. Both economies expanded at an average rate in excess of 3 per cent per annum. Figures for the German economy are less spectacular despite achieving growth of over 3 per cent per annum in 2000. The worldwide slowdown in 2001 affected all four economies. UK growth was extinguished, falling from 3.7 per cent in 2000 to 0.5 per cent in 2001, but picked up again to 2.2 per cent in 2002. German growth also retracted sharply, falling to 1.0 per cent in 2001 and further to 0.2 per cent in 2002, and it is believed that output declined in 2003. The UK economies experienced a more gradual deceleration growth in 2003.

On the demand side, the experience of the UK is similar to that of the Germany, with growth accounted for by strong domestic demand, supported by the strength of household consumption expenditure. Net trade has been weak in both economies in part as a result of this, and also because the real exchange rate in both economies was high until 2002. This is in stark contrast to the experience of the UK and German economies, where a low real exchange rate from 1999 to 2002 boosted net trade, especially in Germany, which has resulted in GDP rising ahead of domestic demand. The recent appreciation of the euro against the dollar (and sterling) has improved the net trade position of the UK and Germany.

The composition of domestic demand between 1997 and 2004 (which includes our forecast) consumption demand and government spending have been behind the major differences in the pattern of growth of domestic demand between the UK and the Germany. In the late 1990s the strength of stock markets was a factor behind these differences as strong equity markets have more impact on consumption and output in the UK and Germany than in the other two countries. More recently, strong house price growth will have been supporting consumption in both the Germany and the UK, whilst the timing of government spending increases has also boosted domestic demand growth in the same two countries (Barrel, 2003).

Domestic demand contributed an extra 5 per cent to Germany GDP in 1998 and 1999 and around 60 per cent of this increase was due to household consumption. Figures for the UK are comparable. At the same time, German household consumption made a relatively weak contribution to GDP growth. In 1999, German household consumption added 2 per cent to GDP but was a much smaller factor in the test of the period under consideration. Household consumption provided a 2 per cent boost to UK GDP in 2001, despite the collapse in the stock market. In contrast, during 2002, household consumption actually reduced GDP growth in Germany (the only country where consumption contracted). The different patterns in consumption are reflected in the average annual savings ratio. In 1998 the savings ratio in Germany exceeded 10 per cent but in the UK (excluding adjustments for equity withdrawals etc) it was 3.8 and 4.5 per cent respectively. Over the following three years UK consumers saved a much greater proportion of their income (almost 6 per cent by 2001) but consumption growth remained strong.

Savings rates, and hence consumption, are affected both by the level of current and expected income and by the level of household wealth as compared in desired levels. If wealth increases by more than anticipated because of increases in the prices of assets, such as equities or house prices, then consumption will rise. Wealth effects vary across countries. They also suggest that changes in illiquid assets (equities in their case) affect consumption more than do changes in the value of liquid assets. It is widely accepted that consumption is also affected by house prices and by housing wealth. The version of our model NiGEM that we use here has housing wealth and house price effects in the consumption equations for the UK and Germany, and we can analyze the impact of differential house price growth in these four economies using our model by applying a sequence of shocks representing the excess impact of purchasing power parity.

Purchasing power parity, and, consequently, housing wealth have increased at a remarkable pace in the UK. Between 1995 and 2000, the average house price rose by 60 per cent. In Germany, average house prices have been very stable, dipping slightly below their 1995 level for much of the late 1990s. Demand has been supported in the UK by such strong house price growth.

We can analyze the impact of higher house price growth by looking at the impact of ‘excess growth’ of prices using our model. If we assume that house prices would normally grow in line with trend nominal GDP (trend output growth and trend or target inflation) then UK house prices were adding to demand in all years in the table, and increasing it from 2000 to 2003. House prices will have been reducing growth in Germany over the whole period. Table 5 indicates the scale of the impacts of excess growth in the four economies. There is a strong positive contribution in the UK, and for most years France and the US also display positive impacts on output from house price growth. In Germany the contribution is negligible and negative (Taylor, 2003)

X. Tables

Table 1: Actual Growth per cent per anum using purchasing power parity

Euro        Germany     UK

1997         1.51           3.29
1998         1.70           3.12
1999         1.89           2.80
2000         3.09           3.78
2001         0.22           1.68
2003         0.08           2.09
2004         1.61          2.67
Table 2:

UK                                Germany

Domestic     Net Trade    Domestic     Net Trade
Demand                           Demand

1997      0.65          1.22              0.65           0.86
1998      4.07         -0.47             2.15          -0.44
1999      3.61         -0.37             2.66          -0.77
2000      4.42         -0.20             1.99          1.10

2001      1.93          0.15             -0.65         1.65

2002      1.08          0.15             -1.52         1.74

2003      1.37         -1.15             -0.07        -0.02

2004      1.54          0.35              1.03         0.59

 

 

 

Table 3: Contributions to GDP growth (per cent)

 

Germany     UK

Government

1997                -0.11     -0.26

1998                 0.40      0.37

1999                 0.29      0.61

2000                 0.13      0.44

2001                 0.15      0.48

2002                 0.25      0.62

2003                 0.13      0.68

2004                -0.02      1.07

Consumption

1997                 0.39      2.32

1998                 0.94      2.51

1999                 2.02      2.84

2000                 1.24      2.98

2001                 0.86      2.04

2002                -0.57      2.23

2003                -0.11      1.64

2004                 0.57      1.54

Investment

1997                 0.38      1.17

1998                 0.47      1.66

1999                 0.70      0.35

2000                 0.79      0.52

2001                -0.83      0.46

2002                -1.31      0.19

2003                -0.62      0.05

2004                 0.37      0.34

 

Table 4: Purchasing power parity growth in the major economies

(Annual per cent change)

 

Germany     UK

 

1997           -1.4        8.8

1998           -0.1        11.5

1999           -0.4        10.9

2000            1.7        14.9

2001            1.7        8.1

2002            1.1        16.1

2003           -0.2        14.5

2004           -2.8         3.6

 

 

 

 

 

Table 5: Impacts of excess house price growth on GDP; per cent difference in GDP growth if house prices had grown at trend

 

Germany     UK

 

1999           -0.02      0.22

2000           -0.02      0.36

2001           -0.02      0.10

2002           -0.01      0.40

2003           -0.03      0.35

2004          -0.04     -0.05

 

(Make My Day, 2004)

 

 

 

 

 

References

Caginalp, Oguz A.; Inflation Differentials and Exchange Rates: Theory and        Empirical Evidence, 1982, Journal of Economics and Business,

Vol.21, Issue 4, p.19, 13p, 1 chart

Consumer Profile- Germany; Market: Europe, Information on the Country’s Per Capita Purchasing Power parity in 2003, Vol.15 Issue 11, p6-6, 1/2p

Hasnat, Baban; Exchange Rate Misalignment and Foreign Direct Investment, 1999

June, Atlantic Economic Journal, Vol.27 Issue 2, p.235, 1p

Grossman, Axel and Soydemir, Gokce; The Impact of Productivity Adjusted      Deviations from PPP on the US Inbound FDI: Evidence from Japan, UK and    Germany, 2006, Vol.30 Issue 2, pp.140-154, 15p

GDP PPP and derived Indices for all OECD Countries

http://www.oecd.org/document/47/0,3343,en_2649_34357_36202863_1_1_1_

Jacobson, Tor and Nessen, Marianne; Examining World-Wide Purchasing Power            Parity, Empirical Economics, 2004, Vol.29 Issue 3, pp.463-476

14p, 4 charts, 4 graphs

Make My Day. Economist, 5/8/2004, Vol. 371 Issue 8374, P51-51, 1p

Nessen, Marianne; Common Trends in Prices and Exchange Rates: Test of Long-Run

Purchasing Power Parity, Empirical Economics, 1996

Vol.21 Issue 3, p.381-400. 20p, 6 charts, 3 graphs

OECD Comparative Price Levels; retrieved: October 2007,http://www.oecd.org/document/47/0,3343,en_2649_34357_36202863_1_1_1_1,00.html

Taylor, Mark P.; Purchasing Power Parity: Review of International Economics, 2003

pp. 436-452

 

 

 

 

x

Hi!
I'm Eric!

Would you like to get a custom essay? How about receiving a customized one?

Check it out