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Section III.A

Results

A summary of the results is presented below in Table III.1, more detailed results are available in Appendix III.A. Observing the table it is clear to see that the values of the coefficients are reasonably different between the two time periods. The fourth column states whether the 1980-1992 period coefficient was more negative, which implies a stronger SRPC relationship. Of the 18 measured, 13 had a stronger (more negative) relationship in the 1980’s than the 1990’s. Whilst this provides evidence for a weakening of the Phillip’s curve relationship in recent years, it is by no means a universal result. Of the five results that are against the trend, three were Scandinavian (Denmark, Finland, and Sweden), the other two were Belgium and Greece.

Country	1990 coefficient	1980 coefficient	More or less negative	R2 1980	R2 1990
Australia	-0.2921132	-0.3144941	More	0.0495	0.229
Austria	N/A	N/A	N/A
Belgium	-0.2803118	0.2192757	Less	0.0288	0.5177
Canada	-0.2758749	-0.6219785	More	0.1692	0.2872
Denmark	-0.0587913	0.4952924	Less	0.0642	0.2266
Finland	-0.1889717	0.5505874	Less	0.0543	0.5296
France	-0.3261998	-2.546941	More	0.8191	0.5048
Germany	-0.7446806	-0.898316	More	0.8421	0.774
Greece	-3.500502	-0.9726864	Less	0.4785	0.797
Ireland	-0.1665611	-1.620649	More	0.7881	0.5699
Italy	-0.4672068	-2.715058	More	0.9079	0.165
Japan	-0.7274278	-3.248461	More	0.8174	0.7437
Netherlands	-0.19729	-0.828843	More	0.5425	0.275
NZ	-0.0801462	N/A	N/A
Norway	-0.0809503	-1.932664	More	0.6805	0.0249
Portugal	-0.955628	N/A	N/A
Spain	0.0041549	-0.8059819	More	0.5719	0.0002
Sweden	-0.65429	-0.5096597	Less	0.0358	0.4727
Switzerland	-0.6544623	-4.325381	More	0.7361	0.4433
UK	0.3375825	-0.745788	More	0.4628	0.1685
USA	0.1854605	-0.0590364	More	0.0032	0.1187
Average	-0.45621	-1.16004		0.447327778	0.380433
Average (without Greece)	-0.29598463	-1.17106448		0.445494118	0.355929

Table III.1

On the whole, approximately two-thirds of the countries experienced a weakening of the unemployment-inflation trade-off. This is further represented by the smaller average coefficient for the 1993-2004 period, falling from -1.16 to -0.46 (or -0.30 without Greece), which is a considerable decrease. Before it took on average a 0.86% movement of unemployment to induce a 1% change of inflation, now it takes over double, specifically a 2.17% (or 3.3% excluding Greece) change of unemployment to induce a 1% change of inflation.

It was of interest to test whether these coefficients were significantly different from each other in order to solidify the change. This was done using a Chow test, the results of which are displayed in Table III.2 (for more detailed data see Appendix III.A)

	Chow Test	Critical value 1%	Critical Value 5%
		5.78	3.47
Australia	37.9937802	Significant	Significant
Belgium	2.60119227	Not Significant	Not Significant
Canada	16.6855827	Significant	Significant
Denmark	10.229843	Significant	Significant
Finland	9.80924029	Significant	Significant
France	27.1391792	Significant	Significant
Germany	5.43887172	Not Significant	Significant
Greece	13.4510827	Significant	Significant
Ireland	53.7109918	Significant	Significant
Italy	9.72249844	Significant	Significant
Japan	12.966096	Significant	Significant
Netherlands	6.58404975	Significant	Significant
Norway	25.6649534	Significant	Significant
Spain	37.0365333	Significant	Significant
Sweden	6.65975593	Significant	Significant
Switz	4.84969637	Not Significant	Significant
UK	15.3281276	Significant	Significant
US	5.35254499	Not Significant	Significant

Table III.2

As the table shows, all but Belgium were significantly different at 5%, and all but Belgium, Germany, Switzerland[1], and US were significantly different at 1%. Consequently it can be stated with greater confidence that there has been a change of the unemployment-inflation trade-off between the two periods, and that the implications need to be explored.

Further, by observing the R² values of the two periods, it can be seen that on average unemployment movements explain inflation movements less in the 1993-2004 period. Although the value is smaller (0.38 in 1993-2004, 0.45 in 1980-2004), it is still clear that inflation changes can be largely explained by unemployment movements; thus it is not that the Phillips’ curve relation has disappeared, instead it has become flatter.

Section III.B

Results

The first result of this section is the aggregate cross section regression of all the countries for the period 1993-2004 with quarterly data. The results are represented by Table III.3 and Figure III.1 below.

Source | SS df MS Number of obs = 885

————-+—————————— F( 1, 883) = 11.63

Model | 13.9439178 1 13.9439178 Prob > F = 0.0007

Residual | 1058.53122 883 1.1987896 R-squared = 0.0130

————-+—————————— Adj R-squared = 0.0119

Total | 1072.47514 884 1.21320717 Root MSE = 1.0949

INFLATION | Coef. Std. Err. T P>|t| [95% Conf. Interval]

————-+—————————————————————-

UNAIRU | -.0712213 .0208828 -3.41 0.001 -.1122071 -.0302356

Table III.3

Figure III.1

As the regression and graph show, the relationship is very weak over the period 1993-2004. With a coefficient of -0.07 the SRPC relationship is practically non-existent, although it is still significant at 1%. Indeed observing the low R² value and how the points are fairly evenly scattered around zero suggests that the unemployment gap has been a poor predictor of inflation changes.

This weak result is surprising given that the unemployment level was shown to still be a decent predictor of inflation in Section III.A. Unfortunately it is difficult to draw a firm conclusion from this result given the drawbacks of the test; these include the fact that the NAIRU values are estimated ex-post and serve little function as a predictor ex-ante; the inaccuracy of the estimates as noted by Staiger, Stock, and Watson (1996); and the fact that the values were interpolated. However, if these pitfalls are ignored it is possible to postulate that the wage and price decisions that make up inflation are near rational, since they are not adjusting to the changes of structural unemployment as the theory suggests, but are based on the unemployment level, or old NAIRU information instead.

Next, this test was performed for each individual country as summarised by Table III.4 (for more detailed regressions see Appendix III.B), which compares the coefficients with the result of Section II.A.

Country	1990 coefficient	1990 U-NAIRU Coefficient	Difference
Australia	-0.2921132	-0.3101632	0.01805
Austria	N/A	-0.0894213
Belgium	-0.2803118	-0.1405221	0.1397897
Canada	-0.2758749	N/A
Denmark	-0.0587913	-0.1155103	0.056719
Finland	-0.1889717	-0.0528902	0.1360815
France	-0.3261998	-0.3014904	0.0247094
Germany	-0.7446806	-0.2717782	0.4729024
Greece	-3.500502	-0.0601352	3.4403668
Ireland	-0.1665611	-0.9254109	0.7588498
Italy	-0.4672068	0.2175667	0.6847735
Japan	-0.7274278	-0.8203434	0.0929156
Netherlands	-0.19729	N/A
NZ	-0.0801462	-0.1410225	0.0608763
Norway	-0.0809503	-0.381808	0.3008577
Portugal	-0.955628	-0.2633415	0.6922865
Spain	0.0041549	0.0362497	0.0320948
Sweden	-0.65429	-0.0448524	0.6094376
Switzerland	-0.6544623	-0.0997716	0.5546907
UK	0.3375825	0.3649101	0.0273276
USA	0.1854605	0.0318187	0.1536418
Average	-0.4562105	-0.177258737	0.4586873
Average (without Greece)	-0.29598463	-0.1837656	0.2832943

Table III.4

As can be the seen the coefficients are generally weakly negative, only Japan and Ireland have values more negative than -0.5, and the average coefficient is -0.18. This average coefficient of -0.18 is small in comparison to the -0.46 of the Section II.A result, but not so different if Greece is considered an outlier and removed. Consequently it is difficult to reaffirm the previous statement made on near rational expectations when this result is in fact quite similar to that of Section II.A, once the individual experiences are observed. Looking at the fourth column it can be seen that the differences of coefficients are small, and on average only 0.28 (without Greece). Indeed the lack of an additional concrete finding here confirms Palley’s (1998) study which demonstrated that NAIRU values tracked unemployment values, and thus the coefficients should not be very different.

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Methodology

In order to understand the test it is necessary to briefly explain the concept of the Natural Rate and the SRPC. Originally coined by Friedman (1968) the natural rate concept states that there is a unique rate of equilibrium unemployment where inflation is stable. This is determined by structural supply-side factors in the economy, primarily the intersection of the labour supply and labour demand curves. Even though this concept is derived under a perfectly competitive general equilibrium framework, the labour market does not clear as a result of imperfections. These include job mismatching, the level of benefits, the effect of tax on earnings, the power of wage bargainers, efficiency wages, and the elasticity of product demand for firms.

Since the economy has a positive equilibrium rate of unemployment, the theory postulates that if unemployment falls below this level then inflation will accelerate as long as unemployment is below the natural rate. Conversely if unemployment is above the NAIRU then inflation will decelerate as long as it is above this level. As a result, the relation should be similar to that of Figure II.1, which is the relationship that will be tested for in Section III.A. This is represented by equation 2.1.

Figure II.1

(Equation 2.1) π = α + β(U) + ε

π = Inflation (2 year average)

U = Unemployment level

In accordance with the theory, the value of β should be negative, which will be tested using an OLS regression and the use of t-statistics.

Despite the advancements of information processing and financial markets over the last few decades the consensus[1] still agree with Friedman’s (1972) evidence that because of rigidities in the economy it takes around two years for the full effects to inflation to be felt, with the peak effect taking place after one year. Consequently, in this study the inflation values will be an average of the inflation at the time, the inflation one year after, and the inflation two years after (t, t+1, t+2), in order to accommodate the lag. An average was chosen rather than using separate variables for each year because it was difficult to find significant results when each year was analysed separately.

Although this analysis can be seen as being overly simple, including other factors that contribute to inflationary pressures such as commodity prices and financial indicators (which include exchange rates, interest rate differentials and monetary aggregates) were insignificant the majority of the time, and thus not included in order to increase the already small number degrees of freedom and provide a consistent method for the test. It was surprising to find little relationship between oil prices and inflation, considering the marked correlation of the 1970’s. This can be seen in Figure II.2 which demonstrates how the relationship deteriorates from the mid 1980’s[2].

Figure II.2

For Section III.B the premise is to study in greater detail the unemployment-inflation trade-off, by taking into account the movements of the NAIRU from 1990-2003. Hence the equation regressed will be:

(2.2) π = α + β(U-U_n) + ε

By allowing for the changes in U_n in accordance with the estimate, it is hoped that the unemployment related inflation changes can be tracked with better precision and that a stronger SRPC relation can be extracted from the data. How much this improves the accuracy is difficult to estimate, especially considering the tendency of the natural rate estimates to follow the actual unemployment rate. Palley (1998) carries out a regression of OECD unemployment rates which shows that every 1% increase of the actual rate raises the NAIRU estimate by 0.915%.

This relationship will also be tested by OLS regression, and whether β is significantly negative will be analysed with t-statistics. The inflation values are taken as the average of 8 quarters inflation (t,t+1….t+8)

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Section II

Data collection

The data for inflation and unemployment values were colleted from the Economist Intelligence Unit (EIU) for each individual country. For inflation, the percentage of consumer price inflation (CPI) was taken, since this is the headline value of inflation that central banks tend to target. For unemployment, the total percentage unemployment rate was used, since it was the most commonly used and an aggregate of the unemployment statistics. Other studies[1] have suggested using capacity utilisation to monitor the output or unemployment gap, but since this study primarily concerns an investigation into the Phillip’s curve relationship, unemployment was chosen.

Due to the lack of more frequent data for the 1980’s, yearly values were used in Section III.A for comparing the experience of the 1980’s and 1990’s for each individual country. The original idea was to carry out a cross section for all the OECD countries in order to maximise the number of observations for the regression, but the wide variance of unemployment and inflation values between countries rendered the test inaccurate. Testing for an aggregate Phillips curve of highly industrialised countries is not yet possible yet, given the lack of convergence of real variables – particularly in the 1980’s. Therefore, despite the lack of observations, OLS regression tests were carried out for the individual countries, utilising each decade as a subsample. Given that data was available for the period 1980-2004 it was decided to split this period into two, so that rather than studying each decade, the subsamples chosen are in fact 1980-1992 and 1993-2004.

For Section III.B, since quarterly data from the EIU dataset was available for the 1990’s, this was used to increase the number of observations and accuracy of the regression. Data for the NAIRU estimates were collected from the OECD dataset. Since values were only available for the years 1990,1995,1999, and 2003, the values in between had to be interpolated for a smooth transition between the years with estimates. Although this reduces the validity of the test, it was pursued because the natural rate is believed to move gradually, and thus interpolation seemed a fair method of replicating this. For this section, since the unemployment gap was being measured by regressing Unemployment minus NAIRU (U-NAIRU) on Inflation, it was possible to carry out both an aggregate cross-section regression and an individual country regression. This is because using the unemployment gap accounts for differing positions of NAIRU values for each respective country.

The countries chosen for the analyses had to meet three criteria:

- They are in the OECD, and thus highly industrialised

- They had to have their NAIRU value estimated

- Countries with the majority of inflation values above 20% were ignored since this study is concerned with observing what occurs under low inflation values.

As a result, the countries analysed were: Australia, Austria, Belgium, Canada, Denmark, Finland, Germany, Greece, Italy, Ireland, Japan, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom (UK), and United States of America (US).

Data Description

Tables II.1 and II.2 represent a summary of the data used in this study, and are complemented by Figures A.1 and A.2 in the appendix which are time series line graphs showing the unemployment and inflation experiences of each country.

	UNEMPLOYMENT MEANS			UNEMPLOYMENT VARIANCES
	All years	1980-1992	1993-2004	All years	1980-1992	1993-2004
AUSTRALIA	7.68	7.81	7.54	2.22	2.37	2.22
AUSTRIA	4.12		4.12	0.13		0.13
BELGIUM	8.60	8.78	8.45	1.99	3.09	1.22
CANADA	9.02	9.49	8.52	2.59	2.77	2.08
DENMARK	8.36	9.96	7.70	6.35	1.06	7.20
FINLAND	8.55	5.43	11.93	17.70	4.66	9.56
FRANCE	9.93	9.21	10.71	2.09	1.57	1.57
GERMANY	8.95	7.88	10.10	2.79	2.48	0.57
GREECE	8.59	6.91	10.40	4.94	2.92	0.65
IRELAND	11.36	14.20	8.29	22.12	8.21	19.49
ITALY	9.45	8.74	10.04	1.50	0.63	1.50
JAPAN	3.22	2.41	4.10	1.24	0.08	0.99
NETHERLANDS	6.22	7.06	5.32	3.34	3.02	2.27
NEW ZEALAND	6.59	7.07	6.31	3.87	6.88	2.34
NORWAY	3.82	3.40	4.28	1.66	2.19	0.78
PORTUGAL	6.19	6.65	5.79	2.70	4.04	1.49
SPAIN	17.35	17.59	17.08	17.12	8.39	28.06
SWEDEN	4.41	2.70	6.26	5.09	1.00	2.82
SWITZERLAND	2.18	0.83	3.63	2.83	0.36	1.36
UNITED KINGDOM	7.97	9.54	6.66	5.11	2.77	3.39
UNITED STATES	6.25	7.13	5.31	2.15	1.90	0.74
Total Average	7.56	7.64	7.74	5.22	3.02	4.31

Table II.1

	INFLATION MEANS			INFLATION VARIANCES
	All years	1980-1992	1993-2004	All years	1980-1992	1993-2004
AUSTRALIA	4.83	7.12	2.54	10.81	9.16	2.00
AUSTRIA	2.76	3.56	1.97	2.37	2.83	0.76
BELGIUM	3.09	4.29	1.89	5.12	7.25	0.34
CANADA	3.71	5.57	1.85	8.30	9.28	0.55
DENMARK	3.52	5.04	1.99	7.40	10.11	0.29
FINLAND	3.46	5.68	1.43	7.20	4.62	0.79
FRANCE	3.70	5.78	1.62	11.95	15.24	0.30
GERMANY	2.27	2.77	1.77	2.62	3.91	1.03
GREECE	12.42	18.84	5.99	55.64	12.04	14.27
IRELAND	4.96	7.07	2.85	22.91	36.15	2.08
ITALY	6.10	9.20	3.00	22.88	25.59	1.30
JAPAN	1.13	2.13	0.12	2.20	1.72	0.68
NETHERLANDS	2.54	2.66	2.42	2.39	4.42	0.55
NEW ZEALAND	5.61	9.27	1.95	29.12	30.68	0.98
NORWAY	4.47	6.87	2.07	11.46	10.81	0.63
PORTUGAL	9.88	16.16	3.61	63.97	45.98	1.85
SPAIN	6.07	8.79	3.35	14.04	12.27	0.97
SWEDEN	4.43	7.31	1.55	13.42	8.09	1.84
SWITZERLAND	2.36	3.67	1.06	3.75	3.46	0.69
UNITED KINGDOM	3.63	5.57	1.69	7.07	6.21	0.35
UNITED STATES	3.53	4.56	2.51	3.44	4.61	0.30
Total Average	4.50	6.76	2.25	14.67	12.59	1.55

Table II.2

The data has been split into the subsamples representing the two periods studied in order to analyse average changes. Of notable interest is the lower inflation mean for the period 1993-2004, falling from 6.76 in 1980-1992 to 2.25. This can be largely attributed to the increasing devotion that policy makers have paid toward maintaining low and stable inflation, especially with many of these countries switching to inflation targeting and/or central bank independence in the 1990’s, starting with New Zealand in 1989. In addition to a significantly reduced mean, the mean variance of inflation has also dropped for these countries. This solidifies the proposition that lower average inflation is accompanied by lower inflation volatility.

There has been less movement in the unemployment data between the two periods, with aggregate average unemployment rising very little from 7.64 to 7.74. Whilst this aggregate suggests little movement, it is slightly misleading given that some countries experienced significant falls and others experienced significant increases of unemployment. Australia, Belgium, Portugal, New Zealand, Canada, Denmark, Ireland, Netherlands, UK and US have experienced a fall in average unemployment, with the latter six experiencing a fall of more than 1%. Even small changes of average unemployment should be considered important because these subsamples cover twelve years each, and thus the differences between the periods are unlikely to be of a cyclical nature, but rather a change of structural unemployment. The other countries suffered increases of average unemployment, particularly dramatic are the increases of at least 3% for Finland, Greece, Sweden and Switzerland. Little can be said of the changing variance of unemployment (rising from 3.02 to 4.31), except that 1993-2004 experienced a slightly larger fluctuation of unemployment rates.

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Section I

Literature Review

Given that the topic of the project has to my knowledge not been explicitly explored what follows is a brief summary of literature that have contributed ideas and formed the base. The cohesive theory is that near rational expectations result in suboptimal unemployment equilibrium.

First, consider the phenomenon of high unemployment, with special reference to the post 1979 oil shock experience of Europe vis-à-vis the United States. As Figure I.1 shows these regions suffered a reversal of fortune, with the unemployment of the United States decreasing after the shock and undercutting a previously successful Europe, where unemployment continued to rise and plateau at a high level. Labour market institutions have received the thick of accusations, but as Nickell (1997, p.65) notes “…roughly speaking, the labour market institutions were the same. So how can the labour market institutions have anything to do with unemployment?”. Clearly other explanations are required.

Figure I.1; Source: OECD Economic Outlook

Second, various economists point to the negative externalities of high unemployment. Layard, Nickell and Jackman (1991) suggest that persistent high unemployment decreases the work and job search skills of the unemployed and that the employed closely guard their wage at the expense of expanding employment. Additionally Eisner (1995) notes that the rise in wages that can accompany a fall in unemployment may cause a substitution of capital for labour which would curb inflation. As a result, Stiglitz (1997) suggests that reducing unemployment can cause a reduction of the NAIRU via a positive feedback effect and thus the economy faces a moving target, which is argued empirically by Semmler and Zhang (2004). Considering that these effects take time the economy may experience a little inflation along the way. Galbraith (1997) highlights the asymmetrical problem this presents for policymakers by suggesting that if a higher NAIRU accompanies higher unemployment in response to a negative shock to the economy, the obsession with the NAIRU as a long term target and fear of inflation prevent a major positive shock or policy move from correcting this; thus the economy may become stuck at a higher unemployment equilibria.

Third, various studies including Blanchard (2003) suggest that the NAIRU may vary over time and can be affected by the inflation rate and monetary policy. Additionally, the presence of imperfect competition, information asymmetries and price rigidity may give rise to multiple equilibria. Bomfim and Diebold (1996) demonstrate that the combination of bounded rationality and strategic complementarity can lead to strong policy effects and output persistence, whereby rational agents anticipate the behaviour of suboptimal agents and strategically imitate. Cooper and John (1988) and Pagano (1990) suggest that such coordination failures lead to multiple unemployment equilibria and that well constructed and strong policy intervention may be necessary to prevent getting stuck at high unemployment.

Fourth, the previous points give cause for policy experimentation. This is typically frowned upon because of the theorised convex and risk-averse nature of the short run Phillips curve produces unfavourable rising inflation with falls of unemployment. However, CEA empirical studies cited in Stiglitz (1997) suggest that the best fit for the US economy was concave during the 1990’s, while separate studies by Eisner (1996) and Galbraith (1997) point to the lack of correlation between low unemployment and accelerating inflation. Therefore even risk-averse policy makers should find reason to experiment, considering the economy can always reverse course without cost. This study hopes to investigate this relationship for the European countries.

Fifth, Galbraith (1997) contests that the US experience shows little evidence for a vertical long run Phillips curve, as demonstrated by Figure I.2. Akerlof, Dickens and Perry (2000) postulate that the long run Phillips curve is not vertical but bowed inward and then forward bending, suggesting that there is an optimum positive inflation rate, which greases the wheels of the economy. Consequently overly strict inflation targeting may bring efficiency losses for the economy

Figure I.2; Source Galbraith (1997)

Sixth, research in the field of behavioural economics is substantial and growing, Akerlof (2002) cites many important studies in the field; potentially relevant findings for near rational expectations are listed below. Conlisk (1996) and Kahnemann et al. (1982) have shown that the physiological limits of human cognition exhibit what has come to be known as bounded rationality, which has proven important in economics. Systematic errors are evident in decision experiments, consumer behaviour and expectations, suggesting that actors are not rational. Interestingly, studies so far have concentrated on financial issues such as asset prices and inflation expectations have not yet been considered. Geanakoplos (1992) highlights the salience of common knowledge between agents in decision making when more than one agent is involved, an example could be policy makers and the public. Brian Arthur (1994) notes that the inductive reasoning process of humans results in a built-in hysteresis; because expectational models are tested by their previous accuracy of tracking the economy, a switch to a better model will occur only once the previous model has accumulated a record of failure.

Seventh, Carlstrom and Fuerst (2001) have provided an interesting critique of forward-looking inflation targets. Because “policy depends on expected inflation and expected inflation, in turn, depends on policy” decision making leads to an “infinite regress” whereby the public and central bank continue to affect each other. This leaves the economy vulnerable to self-fulfilling expectations, and sunspots can occur where extraneous information can be self fulfilled by changes in behaviour by either agent. The Economist (2005) intuitively picks up on this suggesting that because the ECB constantly gives the impression that policy needs to be tight to protect from the ever-present threat of inflation, the public believe nothing can be done to improve growth, presenting a vicious circle. Mismatches between the public, firms and policymakers need not be particularly complicated to cause disruption; Ball and Mankiw (2002) cite the role of the aspiration wage and how wage setters perniciously sustain real wage increases when productivity falls.

Eighth, Staiger, Stock and Watson (1996) point to the lack of precision when calculating the NAIRU (for example the 95% confidence interval in the US was 5.1% to 7.7% for 1990). Not only does this suggest caution when utilising the NAIRU as a guide for policy, but it also serves as caution for any subsequent results this project obtains.

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Abstract

The relationship between unemployment and inflation has become rather elusive from the 1990’s onwards. The results in this paper show that the experience in the 1990’s for OECD countries has been significantly different from that in the 1980’s, specifically that during the 1990’s the trade off between unemployment and inflation has been severely dampened. Consequently the level of the Natural Rate of Unemployment for these countries has become less clear since inflation appears to be less affected by unemployment movements, particularly in an economic climate dominated by inflation targeting. The implications of this trend will be discussed, and a co-ordination failure model is proposed which suggests that countries that maintain an overly strict inflation targeting policy may experience efficiency losses as a result of near-rational expectations.

Introduction

The driving impetus behind this paper has been the unemployment experience of the United States during the 1990’s, whereby the Federal Bank pursued an expansionary policy that took unemployment below what at the time was the perceived Non Accelerating Inflation Rate of Unemployment (NAIRU hereafter) without an increase of inflation, defying conventional wisdom. This experience served to underline what appears so far to be a changing trade-off between unemployment and inflation in the industrialized countries, with many countries exhibiting a far smaller sacrifice ratio in the 1990’s than previous decades. Whilst many of the OECD countries have made a switch to formal or informal inflation targeting during the 1990’s, which has enabled them to keep inflation low and anchored, many countries, particularly European, have been left with a considerable level of unemployment, as shown by Figures 1 and 2. With inflation now largely under control the implications for monetary policy, given what seems to be an increasingly weak short run Phillips curve (SRPC hereafter) relationship, have been relatively unexplored.

Figure 1; Source: OECD

Figure 2; Source: OECD

The first test, comprising Section III.A, will be to analyze whether the experience of the OECD countries in the 1990’s and early 2000’s has been significantly different from the experience in the 1980’s. If true, what does this result mean, is the data sufficient to believe that the SRPC relationship has now changed, and what are the causality issues that may be influencing the result? All of these questions and policy implications will form the discussion in Section III.C. Whilst other studies have touched on this topic, none have covered it with such a large range of countries, or to my knowledge such recent datasets.

Second, using NAIRU data for the 1990’s period Section III.B will attempt to examine this new dynamic in closer detail by comparing inflation changes to the unemployment gap, specifically the Unemployment rate minus the NAIRU level (U-NAIRU), with the aim of disseminating the recently elusive relationship. Utilising the unemployment gap data will hopefully allow the analysis to take into account the changing dynamics of an economy, and examine the SRPC with more precision. Interestingly, since these NAIRU values are considered to be medium to long term, the mere fact that they change suggests the lack of a single long run vertical Phillips curve. Whilst this paper does agree with the theory of the long run Phillips curve – the idea that there is a floor to the level of unemployment with stable inflation is plausible given bargaining power and efficiency wage considerations – the experience of the 1990’s onwards for many countries suggests that few countries have settled on or discovered their long run levels.

This leads to the third part of the paper, making up Section IV, which proposes that some of the countries that are suffering from high unemployment may be doing so unnecessarily as a result of a co-ordination failure between agents in the economy. Without discarding any previous explanations, this theory hopes to show that in light of tight inflation targeting an economy may miss out on opportunities for expansion due to asymmetries of information and near rational expectations. This will be followed by discussion and policy implications.

The paper begins with a review of the relevant literature and ideas that contributed to this project, forming Section II. An explanation of the data and methodology form Section III.

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Whilst this result is intuitively simple the model has drawn substantial criticism particularly since Leontief’s (1953) empirical analysis and subsequent Leontief paradox, which demonstrated that capital to labour ratios did not determine the patterns of trade. In defence of the basic premise of the Heckscher-Ohlin model is the belief that the assumptions of the model are too romantic (which country exhibits perfect competition!), and that relaxing some of them can easily reverse the result of the model.

Firstly by relaxing the identical technology assumption the effective capital to labour ratios will be altered by for example greater human capital or higher capital factor productivity, in essence varying the levels of total factor productivity (TFP). Re-specifying the capital to labour ratio will alter the shape of the PPF, as shown by Figure 1.4, where a capital abundant country becomes labour abundant with human capital considered (Keesing (1965) showed that the US possessed a comparative advantage in skilled labour). The result on trade is ambiguous depending on how much TFP is allowed to vary, but if the difference is sufficiently biased the direction of trade may reverse (Figure 1.4b). Considering the vast disparities in development between countries it is surprising that this was not incorporated into Leontief’s study. Empirical studies by Maskus and Webster (1999) and Trefler (1993) highlight the relevance of different technologies.

(Figure 1.4)

Similarly if the identical preferences are relaxed, it is plausible for sufficient taste bias to exist to warrant a reversal in the direction of trade – known as a demand reversal, where the capital intensive country imports the capital intensive product as a result of its preferences, as shown by Figure 1.5. Quasi-homogenous preferences could also reverse trade and explain trade of capital intensive products between developed countries.

(Figure 1.5)

Relaxing the 2x2x2 assumptions to allow for an arbitrary but equal number of commodities and factors, and potentially infinite number of countries, generates the Factor-Content theorem (Heckscher-Ohlin-Vanek theorem (1968)). This ranks each country’s relative endowments in order to paint a more comprehensive picture of trade and its subsequent direction, producing an equation such as (1.7)

(FJ¹/FW¹) > …. > (FJⁱ/FWⁱ) > s^J > …. > (FJ^m/FW^m) (1.7)

By allowing for more factors such as natural resources, land, infrastructure, and which tropical zone the country is located, the new model should prove more realistic however subsequent studies have pointed to the opposite.

While replacing constant returns to scale with increasing returns is unable to reverse the direction of trade (unless factor intensity reversal occurs) as shown by Figure 1.6, it affects other results derived from the model. Complete specialisation is more likely and factor price equalisation will not occur, instead the wage ratios will move in opposite directions.

(Figure 1.6)

Introducing barriers to trade is also unable to turnover the Heckscher-Ohlin theorem; because an import tariff solely reduces the volume of goods traded and cannot drive a country to export the commodity that intensively uses its scarce factor. This is quite a plausible result considering the effect of the factor price equalisation theorem will drive the scarce factor to lobby for protection.

(Figure 1.7)

In addition to relaxing these assumptions there are further explanations to explain the Leontief paradox and further the depth of the Heckscher-Ohlin model. These include factor intensity reversal, factor mobility, specific factors, and unbalanced trade (balance of payments) which can also overturn the H-O theorem; whilst conditions such as monopolistic competition, increasing returns, the product cycle and intra-industry trade have added further depth to the analysis and have come to represent what is now known as New Trade Theory.

With these complications in mind it is necessary to try and extract the effect of different factor endowments from all the other effects to analyse the relevance of the H-O model for policymakers. Leamer (1984) utilised regression analysis in combination with an adapted Heckscher-Ohlin-Vanek model to analyse the relationship between ten factor endowments and trade. This produced various equations showing for example an increase of capital stock by $1 million will raise net exports of capital intensive products by $16,500 and labour intensive by $1,000, confirming the H-O theorem. Whilst the equations only accounted for 50 to 60 percent of the trade patterns, a probability that “…is matched by a coin toss” (Trefler 1995, p.1029), it still shows that factor endowments are highly relevant in trade and that other factors are at work which have not as of yet been modelled. Clearly policy makers should take note of the theorem but also consider the wealth of other explanations which cloud the explanations of trade flows.

References

Keesing, D.B. (1965). “Labor Skills and International Trade: Evaluating Many Trade Flows with a Single Measuring Device.” Review of Economics and Statistics 47: pp.287-294

Leamer, E.E. (1984). “Sources of International Comparative Advantage: Theory and Evidence.” Cambridge: MIT Press.

Leontief, W.W. (1953). “Domestic Production and Foreign Trade: The American Capital Position Re-examined.” Proceedings of the American Philosophical Society 20: pp.332-349

Maskus, Keith E & Webster, Allan. (1999). “Estimating the HOV Model with Technology Differences Using Disaggregated Labor Skills for the United States and the United Kingdom,” Review of International Economics, Blackwell Publishing, vol. 7(1), pp. 8-19.

Markusen, James R. et al. (1995) “International Trade: Theory and Evidence”, McGraw Hill, p.106

Trefler, Daniel. (1993). “International Factor Price Differences: Leontief Was Right!,” Journal of Political Economy, University of Chicago Press, vol. 101(6), pages 961-87.

Trefler, Daniel. (1995). “The Case of the Missing Trade and Other Mysteries.” American Economic Review, December 1995, 85(5), pp. 1029-46.

Vanek, J. (1968). “The Factor Proportions Theory: The n-Factor Case.” Kyklos 4: pp.749-756

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Describe the Heckscher-Ohlin model and explain the Heckscher-Ohlin Theorem. Provide a critique of the assumptions of the model. Is the Heckscher-Ohlin Theorem robust to the underlying assumptions? Explain and illustrate important points by using diagrams. Based on this critique, analyse the relevance of the model for policymakers.

Developed in the 1920’s by Swedish Economists Eli Heckscher and Bertil Ohlin, and further developed by Paul Samuelson, the Heckscher-Ohlin model attempts to provide more realistic explanations of trade than that of the previous conventional wisdom: the Ricardian Model of Comparative Advantage. Supplementing Ricardo’s model with two key assumptions, namely the introduction of two factors of production (capital and labour), and the necessity that production technology be identical in both countries, the model was able to show that trade and comparative advantage will result from the relative international difference of factor endowments instead of differing labour productivity.

By introducing two factors of production the model is often referred to as the 2x2x2 model (two countries, two commodities and two factors of production), the first three of many assumptions necessary for the model to hold, the rest are listed below as a precursor to succinctly describing the model:

· Each country possesses a fixed supply of the two factors (capital (K) and labour (L)), but their capital to labour ratios differ. Both factors are fully employed and can be substituted in production

· Perfect competition in the factor and commodity markets

· Production technology is identical in both countries, and exhibit Constant

Returns to Scale (CRS)

· One of the commodities is labour intensive and the other is capital intensive, at all input prices. There are no factor intensity reversals

· Preferences are identical and homogenous in both countries

· Perfectly mobile factors within the country, but perfectly immobile internationally

· There are no barriers to free trade, and zero transportation costs

With identical technologies the model neutralises the possibility of a Ricardian comparative advantage, and the introduction of a second factor produces the familiar neoclassical concave production function (in accordance with diminishing marginal products). This results in a more realistic scenario where both countries produce both commodities (unless endowments are radically different) rather than complete specialisation.

Further developing the framework we state that the UK has a higher capital to labour ratio (K/L) than China (Equation 1.1), resulting in an autarky

(K/L)_UK > (K/L)_CHINA (1.1)

scenario where the UK is endowed with relatively cheap capital and China relatively cheap labour in accordance with diminishing marginal products. Additionally the commodities are differentiated by factor intensities, commodity X shall be labour intensive and Y capital intensive (1.2)

(K/L)_Y > (K/L)_X (1.2)

The accumulation of these conditions generates the following Production Possibility Frontiers (PPFs)

(Figure 1.1)

noting that each country is biased towards the production of the commodity that is factor intensive for the same factor that the country is abundant in. It is important to maintain that this model generates trade via relative endowment differences and thus absolute sizes of each PPF are irrelevant in determining the direction of trade but relevant with regard to the terms of trade.

With the PPFs established it is necessary to derive the autarky equilibriums (A_UK and A_CHINA), specifically what each country produces and consumes without trade. Remembering assumptions for identical and homothetic preferences and perfect competition, production and consumption for each country occurs where

MRS = MRT (1.3)

which corresponds diagrammatically in Figure 1.2 to points A_UK and A_CHINA, where the indifference curves are tangent to the respective PPFs. The resulting tangency slope generates the autarky price ratios for each country (P_UK and P_CHINA).

(Figure 1.2)

The important result to derive here is that the differing of resource endowments was sufficient to produce differing autarky equilibrium price ratios (1.3), which is sufficient to generate incentive for international trade.

P_UK > P_CHINA (1.3)

where P = (P_X/P_Y)

With autarky equilibrium established the effects of trade are now examined. Given (1.3) UK consumers notice that commodity X is cheaper in China they will prefer to import X instead of purchase it domestically as long as it remains cheaper in China (vice-versa for China and Y). Hence trade will occur until price ratios are equalised at a level where excess supply and demand are matched, producing an international price ratio P*[1] for both countries such that:

P_CHINA < P* < P_UK (1.4)

.

In order for this to occur the change in demand from autarky to free trade must be accompanied by a change in production from both countries. China will respond to the increasing demand for X by increasing its production relative to Y, since X has become relatively more valuable (vice versa for the UK). This continues until

P*=MRT (1.5)

and thus final production occurs at their tangency (Q_UK,Q_CHINA in Figure 1.3). Similarly final consumption (C_UK, C_CHINA) will occur where

MRS = P* (1.5)

Thus resulting in free trade equilibrium

P* = MRS = MRT (1.6)

The final result is succinctly explained by Figure 1.3, noting that both countries are consuming on higher indifference curves than before, producing uneven[2] but overall welfare gains. Observing that the UK exports commodity Y and imports X the result is in accordance with the Heckscher-Ohlin theorem which states that

“Given the assumptions of the model, a country will export the commodity that intensively uses its relatively abundant factor” Markusen et al. (1995, p. 106)