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Studying economics will provide you with quantitative skills, critical thinking skills, and allow you to use threshold concepts. These skills are increasingly in demand from government and the private sector, including the finance and pharmaceutical industry.

The wikipedia definition suggests that “An economist is an expert in the social science of economics. The individual may also study, develop, and apply theories and concepts from economics and write about economic policy.” But essentially economists analyze how people create goods and services by allocating limited resources like raw materials, land, technology, and labor. They do this by researching and monitoring things like exchange rates, business trends, taxation, employment rates, inflation, and costs of materials. They then try to find trends and develop predictions based on the data.

Many corporations require the skills of microeconomists, like projecting customer demand or product sales, both of the client firm and their competitors to maximize profit. These economists also review new legislation, like minimum wage requirements or tariffs, and how it will affect their client. Large business with many smaller branches might also have economists assess the economy of countries where branches exists or where they are looking to open new branches. Smaller businesses often hire economists as consultants or those who work in research firms. Consulting firms in the United States provide a large proportion of the macroeconomic study and prediction by gathering many different statistics, compiling large databases, finding trends. These firms often publish their findings.

Governments also employ a large percentage of economists, across many of their department. Both the US Federal Government and the United Kingdom’s Government Economic Service are major recruiters of economists. Often their work is to forecast the consequences of new legislation or policies, or use cost benefit and welfare analysis to provide evidence for the usefulness of a policy proposal.

Different types of economists

• Microeconomists: These people study individual companies or people. They look at supply and demand to find out how to maximize production, for example, or to project how high the demand for a particular product would be.
• Macroeconomists: They look at the economy as a whole to find long-term, overarching trends throughout history. They can then make generalizations and draw conclusions about investment productivity, inflation, unemployment, etc.
• International Economists: They look at markets internationally, studying currency exchange and the effects of tariffs and trade procedures and laws.
• Organizational or Industrial Economists: They examine the markets of individual industries, studying competitors and making predictions based on the decisions of competitors. They may also be involved in protecting the industry against trusts and monopolies
• Labor Economists: They look at trends in salary, such as how it’s determined, and the need for labor. They are especially interested in causes of unemployment and the results of changes in demographic, such as a baby boom, on labor.
• Public Finance Economists: They look at the government’s involvement in the economy, such as taxation, deficits or surpluses in budget, or policies concerning welfare.
• Econometricians: They use mathematics and statistics to display empirical evidence using methods like calculus and regression analysis. These models explain economic happenings and help to project future economic occurrences and trends.

Economics Career Potential and Job Outlook

The number of jobs for economists is expected to grow in the future. 

A lot of growth potential is located in the private sector, most notably in scientific research and consulting firms. The higher demand for the services of economists will be caused by the increasing intricacy of the world economy, higher competition, and the fact that more and more business predictions are based on quantitative analysis. As the skills of economists become more valuable in many different industries, there will be more and more jobs. However, as the popularity of consulting firms increase the need of companies for in-house economists decreases.

Economics Posts economics careers, economics jobs, economist career, economist jobs

From the International Herald Tribune

Economics as the dismal science? Not in some quarters.

In the midst of the deepest recession in the experience of most Americans, many professional forecasters are optimistically heading into the new year declaring that the worst may soon be over.

For this rosy scenario to play out, they are counting on the Obama administration and Congress to come through with a substantial public stimulus package, up to $1 trillion over two years. They say that will get the economy moving again in the face of persistently weak spending by consumers and businesses, not to mention banks that are reluctant to extend credit.

If the dominoes fall the right way, the economy should bottom out and start growing again in small steps by July, according to the December survey of 50 professional forecasters by Blue Chip Economic Indicators. Investors seemed to be in a similarly optimistic mood on Friday, bidding up stocks by about 3 percent.

But in the absence of that government stimulus, the grim economic headlines of 2008 will probably continue for some time, these forecasters acknowledge.

“Without this federal largess, the consensus forecast for 2009 is for the recession to continue through most of the year,” said Randell Moore, executive editor of Blue Chip Economic Indicators, which conducts the monthly survey of forecasters.

Many economists are more pessimistic, of course. Nouriel Roubini at New York University, who called the 2008 market disaster correctly, wrote in a recent commentary on Bloomberg News that he foresees “a deep and protracted contraction lasting at least through the end of 2009.”

Even in 2010, he added, the recovery may be so weak “that it will feel terrible even if the recession is technically over.”

But Roubini is not among the economists surveyed by Blue Chip Economic Indicators. These professional forecasters are typically employed by investment banks, trade associations and big corporations.

Read more here

Economics Posts credit crunch, economists, financial crisis

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Section IV

Whilst many theories have been put forward to explain the persistence of high unemployment in Europe - including Labour market institutions and cultural differences - none to my knowledge have considered that unemployment persistence may be being maintained as a result of a co-ordination failure between agents in the economy. Considering the salience of common information when making decisions, this model proposes that Pareto inefficient equilibrium may be occurring as a result of asymmetries of information between firms, wage bargainers and a central bank pursuing strict inflation targets, such as the ECB. Whilst the ECB strictly does not classify itself as an inflation targeter, its mandate for price stability is considered to be an equivalent.

First, consider an economy with two sectors (Sector 1 and Sector 2), all firms are price setters, and all agents are rational. In addition, the economy has an explicit forward looking inflation target that publishes the expected future inflation path. In this situation workers will be knowledgeable about what nominal wage increases they should be experiencing in the present and near future. In addition, rational wage bargainers are acquainted with the SRPC that is believed to characterise the economy and will respond to falls of unemployment with increasing wage demands. This system can act as a substitute for high unionisation because workers will be conscious of whether they are receiving a relatively fair wage. As a result, the theory of the fairness wage - which hinges on the fact that workers exhibit a high degree of loss aversion – puts pressure on firms to increase wages when other firms do, even in the absence of labour market institutions.

Sector 1 firms face an improvement of productivity, and thus a downward shift of their supply curves. If the information regarding the improvement of productivity does not permeate to the economy and Central Bank then inflation expectations will remain unchanged. In a scenario with perfect information, inflation expectations would be lower given the improved supply schedule of Sector 1.

If inflation expectations remain unchanged then wage growth demands will also remain the same for all sectors. Thus firms in both sectors are faced with higher wage growth demands than if the economy was aware of Sector 1’s productivity improvement. Consequently, output and hiring of new workers will be constrained by the exuberant wage bill, and firms will need to raise prices above what they would have done in a perfect information economy. In effect, the credibility of the inflation expectations is making the inflation self-fulfilling, rather than being determined by the fundamentals of the economy. Thus productivity increases may not be exploited, which provides a mechanism for the NAIRU to be downward-sticky to positive supply shocks.

Conversely, the NAIRU in this situation will be upward-flexible. Consider that Sector 1 faces a slowing of productivity, and the information is not conveyed to the economy. Although inflation expectations and wage growth demands remain the same, Sector 1 firms will be forced to increase prices above inflation expectations in the face of the increasing marginal costs. Thus prices rise in this sector regardless of current expectations, and future expectations will adjust in the wake of this experience. To prevent this from continuing into a wage-price spiral, firms in Sector 1 will have to reduce output and thus raise unemployment in the economy. As a result the NAIRU has increased.

Therefore, efficiency losses may be plausible as a result of the information of productivity improvements not being communicated to the economy. In effect, this is a co-ordination failure between firms, workers and the Central Bank. For this to be feasible there needs to be an incentive for the individual firm to not disclose its marginal cost functions, otherwise the firm would happily communicate its favourable movements in productivity. This is believable because transparency of supply curves would lead to superfluous profits being eroded by wage demands and competitors who find weaknesses in the firm’s business model. The difficult experience of finding true marginal cost curves for internalising externalities underscores this argument, as polluting firms have been inclined to hide their true supply schedule to reduce pollution taxes.

Next in this model consider that a productivity improvement in Sector 1 occurs without the economy knowing, but the Central Bank makes a ‘judgement call’ to maintain the increase in demand in spite of unemployment being at the perceived NAIRU level, in the same way that the US Central Bank held interest rates 1997. Since Sector 2 does not have spare capacity, it will respond to the increase in demand with an increase in prices that will raise inflation. This will disadvantage Sector 1, which has the spare capacity to increase output, since the raised prices in Sector 2 results in an economy wide increase of wage demands which reduce the scope to increase output. Even if there are no price increases from Sector 2, if wage bargainers are near-rational and forming their wage demands on the old NAIRU, the fall of unemployment will be accompanied with a rise of wage demands in line with the SRPC. This denies the potential for a non-inflationary expansion to occur if the productivity improvement is not economy-wide, since the non-benefiting firms or the pernicious wage bargainers will push up prices in the presence of an increase of demand. If the policy is carried out with a high level of credibility, as with the much revered US Central Bank, this will serve to lighten the inflationary response and increase the likelihood of success. With the European Central Bank always reminding the population that inflation is just around the corner on any decrease of unemployment, success is less likely.

Moreover, this same co-ordination failure may occur if the NAIRU has fallen for any other reason that is not perceived by all agents in the economy. Other examples may include an increase of efficiency of job matching, a decrease of union power, or the benefits of e-commerce. Although these may benefit all sectors, if this new potential NAIRU is not perceived then wage bargainers will still raise their demands in the face of a fall of unemployment from the current NAIRU, which will rationally cause illogical inflation.

With tight inflation targeting the economy is given little time for information of productivity changes to permeate through the economy and thus allocative efficiency may not occur. Because “hollow” inflation may be created due to the sectoral differences in spare capacity or pernicious wage bargainers, the adjustment period to a lower unemployment equilibrium will be met by contractionary monetary policy, which will reverse the increase in output. If inflation comfort zones were wider in this case then the economy would be better placed to accommodate the possibility of “hollow” inflation, and minimise the possibility of not exploiting productivity improvements. Clearly the band does not need to be too wide, since both the UK and inferred US policy with a symmetrical 1%-3% target have ostensibly been sufficient for them to benefit from the productivity improvement of the 1990’s. Indeed this criticism is directed specifically at the asymmetric policy of the ECB which wishes to achieve inflation below 2%. As well as criticism regarding the nature of the target being asymmetric - which gives the psychological impression of always fighting inflation - the fact that the target is an average of its member’s rates means that large divergences of member states inflation rates may eliminate the comfort zone for inflation variation. If half the states are running 3% inflation then the other half will be forced to run below 1%, giving neither half the ability to tolerate any hollow inflation. This is made even worse for Euro member states given that they are unable to tailor monetary policy to their individual needs. If their NAIRU value has fallen it is unlikely to have much of an effect for area-wide inflation, and thus it is unlikely to receive an easing of monetary policy that would expediate the move to the new NAIRU. Indeed this easing may never occur if other member states experience a negative supply shock. Therefore, this situation not only calls for a wider target, but an increase of co-ordination between Euro states with regard the reduction of structural unemployment.

As an extension is would be useful to find a method to test this model empirically. Unfortunately there are several aspects that would be difficult to model which include: how signals are perceived by the different agents, how to account for near rationality, how to account for changes in productivity if they are not being communicated. One simple extension would be to test for the existence of “hollow” inflation using the benefit of hindsight with regard unemployment movements.

Conclusion

This study has shown that the short run Phillips’ curve relationship has reduced significantly in the period 1993-2004 vis-à-vis the period 1980-1992 for 13 of the 19 countries studied. Further, testing with the unemployment gap whilst accommodating for movements in the natural rate has been shown to be a poor predictor of inflation movements, and thus this study suggests caution when using the unemployment gap as a policy guide.

Whilst much of this reduction in the sacrifice ratio can be attributed to the changing economic climate – particularly the improvements of productivity and inflation targeting – little mention has been made in the literature of what new opportunities and restrictions have arisen for policy. Utilising a co-ordination failure model this paper proposed that in this new climate of inflation targeting the NAIRU may become downward sticky, and unable to move to a new lower equilibrium. This is the result of imperfect information between agents in the economy with differing incentives and a Central Bank that responds too quickly to inflation movements, presenting a rational sub-optimal unemployment equilibrium given the observable information in the economy.

References

Akerlof, G. (2002). “Behavioral Macroeconomics and Macroeconomic Behavior,” The American Economic Review, Vol. 92, No.3 (Jun.,2002), 411-433

Akerlof, G. and Dickens W. and Perry G. (2000). “Near-Rational Wage and Price Setting and the Long-Run Phillips Curve.” Brookings Papers on Economic Activity (1): 1–44.

Ball, L. and Mankiw, N.G. (2002) “The NAIRU in Theory and Practice”, The Journal of Economic Perspectives, Vol. 16, No. 4. (Autumn, 2002), pp. 115-136.

Bernanke, B., and Lauback, T., and Mishkin, F., and Posen, A. (1999) “Inflation Targeting: Lessons from the International Experience,” Princeton: Princeton University Press

Bomfim, A.N. and Diebold, F.X. (1996). “Bounded Rationality and Strategic Complementarity in a Macroeconomic Model: Policy Effects, Persistence and Multipliers”, NBER Working Paper 5482, National Bureau of Economic Research, Inc

Brayton, F., and Roberts, J.M. and Williams, J.C. (1999). “What’s happened to the Phillips curve?,” Finance and Economics Discussion Series 1999-49, Board of Governors of the Federal Reserve System (U.S.)

Brian Arthur, W. (1994) “Bounded Rationality and Inductive Behavior (the El Farol Problem), American Economic Review, 84,406-411, 1994

Carlstrom, C. and Fuerst, T. (2001)”Monetary Policy and Self-fulfilling Expectations: The Danger of Using Forecasts,” (with Charles Carlstrom), 2001, Federal Reserve Bank of Cleveland Economic Review, 37(1).

Conlisk, J. (1996): “Why Bounded Rationality?”, Journal of Economic Literature 34(2), pp. 669 - 700.

Cooper, R. and John, A. (1988). “Coordinating coordination failures in Keynesian models.” Quarterly Journal of Economics, vol. 53, August, pp. 441-63

Economist, The. “The European Central Bank, Haughty indifference, or masterly inactivity?”. The Economist, Jul.14^th, 2005

Eisner, R. (1995). “Our NAIRU Limit: The Governing Myth of Economic Policy”, The American Prospect

Eisner, R. (1997). “A New View of the NAIRU,” in Paul Davidson and Jan A. Kregel, eds., Improving the Global Economy: Keynesian and the Growth in Output and Employment, Edward Elgar Publishing Cheltenham: UK and Lyme, U.S.

Friedman, M. (1968), “The role of monetary policy”, American Economic Review, 58(1), 1-17.

Galbraith, James K. (1997). “Time to Ditch the NAIRU”, The Journal of Economic Perspectives, Vol. 11, No.1 (Winter, 1997), 93-108

Geanakoplos, J. (1992) “Common Knowledge.” Journal of Economic Perspectives, 6(4), 1992 [30pp]

Hacker, M. (2002) “Are Oil Shocks Inflationary? Asymmetric and Non Linear Specifications versus Changes in Regime,” Journal of Money, Credit, and Banking, pp.540-561

Kahnemann, D., Slovic, P. & Tversky, A. (eds.) (1982): Judgement Under Uncertainty: Heuristics and Biases. Cambridge: Cambridge University Press.

Layard, R. and Nickell, S. and Jackman, R. (1991). Unemployment: Macroeconomic Performance and the Labour Market, Oxford, Oxford University Press

Nickell, S. (1997), ‘Unemployment and Labour Market Rigidities: Europe versus North America’, Journal of Economic Perspectives, 11(3), pp. 55–74.

Pagano, M. (1990). “Imperfect Competition, Underemployment Equilibria and Fiscal Policy”, The Economic Journal, Vol. 100, No. 401 (Jun.,1990), 440-463

Palley, T.I. (1999) “The Structural Unemployment Policy Trap: How NAIRU can Mislead Policymakers,” New Economy, 6 (June 1999), 79-83

Semmler, W. and Zhang W. (2004) “Monetary Policy with Nonlinear Phillips Curve and Endogenous NAIRU”

Staiger, Stock and Watson (1996). “How Precise are Estimates of the Natural Rate of Unemployment?“, NBER Working Papers 5477, National Bureau of Economic Research, Inc

Stiglitz, J. (1997). “Reflections on the Natural Rate Hypothesis”, The Journal of Economic Perspectives, Vol. 11, No. 1. (Winter, 1997), pp. 3-10.

Economics Posts Economics Posts, inflation, NAIRU, unemployment

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Section III.C

Causality Issues

When discussing the differences of coefficients between the periods the main concern is the effect that inflation targeting has had in reducing inflation in these countries. As Table III.4 shows, many of these countries introduced inflation targeting in the 1990’s, and were able to reduce inflation with little change of unemployment simply as a result of the increased credibility. Thus there may be many values which report a fall of inflation even without a change of unemployment which will bias the result. These fortunate results will only occur during the first few years of inflation targeting; as a result it is necessary for more years to pass before it can be confidently said that the Phillips curve relationship has been severely dampened. As an extension it would be useful to introduce some form of dummy or structural break to account for the introduction of targeting in these countries

	Year of adoption	Inflation rate at adoption (%)	Inflation target (%)	Inflation rate in 2005 (%)
New Zealand	1989	7.5	1 to 3	2.7
Canada	1991	7.5	1 to 3	2.2
UK	1992	4.7	1 to 3	2.0
Australia	1993	1.8	2 to 3	2.6
Sweden	1993	4.6	1 to 3	0.8
Israel	1997	9.0	1 to 3	1.2
Eurozone	1999	1.1	0 to 2	2.1
Brazil	1999	4.9	1.25 to 6.25	6.8
Poland	1999	7.3	1.5 to 3.5	2.2
South Korea	2000	4.1	2.5 to 3.5	2.8
Hungary	2001	9.1	2.5 to 4.5	4.0
Mexico	2001	6.4	2 to 4	4.3

Table III.4

Additionally, it may be possible that there is an element of reverse-causality, whereby inflation is affecting unemployment. Although contended, this is mentioned by Akerlof, Dickens and Perry (2000) who posit that there is an optimum level of inflation.

Finally, there is the likely omitted variable bias as a result of the other factors that contribute to inflationary pressures which were mentioned earlier.

Policy Implications

As can be seen the significant differences between the two periods suggest that there has been a flattening of the SRPC relationship. Although it is too early to consider this a lasting relationship, it is useful to postulate what this may mean for policy makers if it continues.

Firstly, as a result of the reduced sacrifice ratio it is now increasingly in the policy maker’s interest to experiment with lowering the unemployment rate, since testing the water can be now be achieved with little cost.

Second, because much of this change can be attributed to the introduction of inflation targeting and central bank independence, is there a need for a different approach to unemployment policy? It may be possible that the unemployed are being denied jobs because policy makers are clinging to old models which have not yet accumulated a record of failure, when a better model or policy may be available. With inflation under control in many of these countries, there is little need for contractionary policy except to keep expectations anchored, and thus policy makers should be able to focus primarily on reducing unemployment, either via eliminating structural factors or facilitating wage decisions and job matching. Furthermore, because this relationship is starting to hold over the medium term there may be implications for fiscal policy, which takes longer to take effect.

Third, given that the U-NAIRU test of Section III.B was outperformed by the unemployment level test, as well as the fact that the NAIRU estimate has a large standard error, it is necessary to question the use of the NAIRU as a signal for monetary policy. Successive over-estimation, as was seen in some countries in the 1990’s, may lead to expansion opportunities not being exploited if the firms in the economy believe that monetary policy will be conducted based on the over-estimated NAIRU value.

Fourth, there may be other factors at play that are keeping inflation low. For example, the increase of global competition and trade has put a ceiling on the wage demands of some low skill labourers, since their employer may outsource or move production abroad. In addition, cheap production abroad has lowered import prices, and puts a ceiling on tradeable items in the domestic economy[1]. Further, the rise of the digital economy has raised productivity, improved the facilitation of commerce and job-matching and introduced new employment opportunities, which may lower the level of structural unemployment.

Whether these favourable conditions will hold is important, a return to protectionism for example may see the Phillips curve relationship strengthening again.

Economics Posts Economics Posts, inflation, NAIRU, unemployment

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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.

Economics Posts

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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)

Economics Posts Economics Posts, inflation, NAIRU, unemployment

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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.

Economics Posts Economics Posts, inflation, jobs, unemployment

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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.

Economics Posts Economics Posts, inflation, jobs, unemployment

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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 2×2x2 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

Economics Posts Economics Posts, heckscher ohlin theorem