One Implication for Economic Modelling

Finally, there is an interesting issue of what implications the adjustment for transfer pricing described above would entail for the results of economic models that use some components of Input-Output tables such as CGE or Input-Output models. It is reasonable to assume that the bigger the relative size of the industry in an economy the bigger its overall impact would be. However, the adjustment involves the redistribution of economic activity; hence an increase in the size of the oil sector unequivocally leads to the reduction in the size of the trade sector. The final outcome depends on the structure of the economy or more precisely, whether the loss of the second order impact of the trade sector is greater or smaller than the gain in the first order impact of the oil sector. Since Chapter 5 is concerned with estimating the economy-wide impact of the oil industry we will repeat the same scenario as in Chapter 5, but use an adjusted Input-Output table to see how exactly it would affect the results. The employed CGE model uses a SAM which embodies the 2002 official (unadjusted) Input-Output tables. The Input-Output data in this SAM will need to be replaced with the new (adjusted) tables. The trade margin in 2002 amounted to 18 percent of the total domestic output (see Table E.1. in the section E.2 of the Appendix E). Since we are mostly interested in the oil industry, we do not change trade margins for other non-energy sectors (unlike in the examples in this study) and only assume that instead of 18 percent of output that the margin in the oil and gas sector is now zero. All subsequent adjustments to the 2002 I-O tables are as described in this study. Applying the Canadian trade margin structure entails a 6.7 percentage point increase in the value added share of the oil and gas sector in Kazakhstan (from 12.7 to 19.3). The value added of the trade sector decreased by the same 6.7 points accordingly from 12.3 to 5.6 percent of value added.

The base scenario in Chapter 5 involves an 18 percent exogenous increase in the export of oil, which corresponds to the 5 year real average growth of the oil export between 2001 and 2005. The results show that on average between 2001 and 2005, 4 out of 10 percent of GDP growth was due to the oil sector. Selected results of repeating the same scenario, but using the adjusted for transfer pricing Input-Output table and the SAM instead are reported in Table 6.1. along with the original results.

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Table 6.1. Macro-results of the CGE Model with Adjusted and Unadjusted Input-Output Tables (% change).

Unadjusted Adjusted

Oil Output 12.5 11.7

Total Domestic Output 3.0 3.6

Public Spending 6.1 7.1

Private Spending 2.9 3.8

Export 5.4 6.6

Import 5.5 6.7

GDP 4.0 5.2

Real exchange rate

(- means real appreciation) -0.9 -1.0

Real wage 1.8 2.2

One can see that the results are similar to the original ones with the oil sector spillover being more profound when the adjusted Input-Output tables were used. Since the absolute value of the output of oil is greater in the adjusted table, the relative increase to support an 18 percent export rise is smaller. All other macro-variables however experienced a bigger positive boost in the adjusted case compared to the case when official data were used.

When transfer pricing has been appropriately accounted for in the national statistics, estimated contribution to the GDP growth of the oil industry could be as high as 5.2 percent annually out of a 10 percent average annual growth over the studied period.

6.11. Conclusions

Transfer pricing if not dealt with appropriately may significantly distort the GDP structure.

This problem is especially evident in the oil and gas industry in Kazakhstan. This paper developed a framework that allows the adjusting of the GDP structure distorted by the transfer pricing when it was not carried out by the national statistical agencies. The data required for the adjustment are the SUT and IO tables, which are available from the national statistical offices. Moreover if one is interested only in the value added structure

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the amount of calculations and data can be reduced greatly. Results for Kazakhstan show that the value added share of the oil and gas sector in 2001 was significantly underestimated. In 2005 the correction is smaller and suggests a gradual improvement and implementation of internationally accepted practices in Kazakhstan’s statistics. The higher value added share of the energy industry means a bigger contribution from this sector to the post-soviet economic recovery and recent high levels of economic growth than that implied by the official statistics.

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CONCLUSION

Chapter 1 provides economic background of Kazakhstan since the collapse of the FSU and subsequent gain of independence in 1991 with focus on the energy sector. Since then, the importance of the oil and gas industry for the economy has been growing rapidly. By 2007 the level of oil output tripled reaching 1.5 million barrels per day and the share of the oil in total industrial output reached 50 percent compared to 2 percent in 1990. In 2008 oil and gas accounted for about 70 percent of all Kazakhstan’s export compared to 9 percent just before the Soviet collapse. At the same time, GDP and living standards of the population were rising rapidly in more recent years, thus making the Resource Curse arguments irrelevant in Kazakhstan’s case. When transition started, without heavy subsidies and artificial trade links that prevailed in the Soviet era most firms appeared internationally uncompetitive. Those firms had to close or restructure and by the time Russian financial crisis of 1998 was over, the oil and metals industries emerged as virtually the only internationally competitive sectors.

The aim of Chapter 2 is to provide a background for construction of a CGE model and a review of existing literature on the subject. The first chapter also defines the area of the model application. It highlights theoretical foundation, general structure and some interesting findings of the previously conducted studies that employed CGE modelling technique. Modern CGE modelling tool has a long history of development. It gradually progressed from purely theoretical concepts developed by Debreu (1959) to a powerful applied tool first pioneered by Scarf (1967) and later extended by Johansen, Shoven, Taylor and others. In the present day, when computational ability is growing exponentially the CGE apparatus presents a unique tool in which various economic forces can be contained and analysed in a consistent economy-wide framework. Chapter 2 established how CGE models are effectively applied to cases in energy economics and economics of transition countries. It concluded, however, that CGE studies are more likely to influence policy decisions when their results are supported by alternative modelling techniques and analysis. Kazakhstan’s economy is rather underrepresented in the general pool of CGE studies, and those few papers which do exist focus on the impact of trade liberalisation and Dutch Disease in the form of exchange rate appreciation where the latter studies concur that real appreciation is inevitable in the presence of oil windfall revenues, although policies aimed to restrain it may prove to be counterproductive.

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Chapter 3 developed a small CGE model for the analysis of minerals-driven economic growth in Kazakhstan. It discussed all aspects of building a CGE model such as the fundamental assumptions, the derivation of model equations, the estimation of parameters and model balancing. The extensions to the basic model in Chapter 3 include several household types and multiple closure rules which allow for poverty and inequality analysis and bring some long run features to an essentially static framework. The structure of transactions within the model closely resembles the Kazakhstan’s SAM constructed in Chapter 4.

Consequently, the construction of a SAM for Kazakhstan is described in Chapter 4. The chapter has two main objectives. First, it develops an appropriate dataset for the CGE model in Chapters 3 and 5. Second, it provides a general framework for building a SAM based on the 1993 UN system of National Accounts and Input-Output tables. A SAM is a snapshot of the whole economy over a certain period of time. A detailed and carefully constructed SAM can be a valuable tool for policy analysis. The chapter begins with building a macro-SAM using primarily the national accounts data. Detailed references to the standard 1993 UN national accounts source ensures that it can be easily replicated for other periods and countries. At this stage the SAM has only one production sector and a single representative household.

Therefore, the second step involves matching the Input-Output tables and household budget survey data with the macro-SAM entries. Finally, various sources are combined in a micro-SAM using a powerful optimisation procedure, which allows to efficiently remove all potential inconsistencies between the different data sources. The acquired micro-SAM represents a detailed reflection of the Kazakhstan’s economy in 2002. It is fully consistent with published national accounts and has 57 sectors, 10 household types (cohorts defined according to their income) and complex tax and transfer structure. Finally, the chapter describes what adjustments are necessary for the SAM to be compatible with the CGE model.

Chapter 5 analyses the role of the oil industry in Kazakhstan’s economy. Recent Kazakhstan’s economic development fits the framework developed by North (1955). High revenues and demand generated by the highly successful industry, which exports goods or services help to maintain a high level of spending and investment, and, as a result, sustain domestic production of a wide range of services and goods. Those tertiary industries, in turn, has a potential to expand internationally in the future as in the case of the banking sector.

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The results of the CGE simulations imply that since 2001 the oil sector contributed about 40 percent of total GDP growth. In other words, the oil industry generated 4 out of 10 percent average GDP growth in Kazakhstan between 2001 and 2005. If there was no oil industry, Kazakhstan’s economy would have performed worse than most transition economies which do not have rich energy resources. This estimate is more substantial than the one presented by the IMF (2005) study, which estimated broad oil sector contribution to GDP growth at 3 percent. Financial services and construction have strong links with the oil industry and benefit most from its expansion while other mining industries and some manufacturing are among sectors which would have been better off without it, as they essentially compete for the same resources and do not enjoy the additional demand created by the oil sector.

Increased oil export revenues have put an upward pressure of about 1 percent annually on the real exchange rate. However, running the same simulation while holding the real exchange rate fixed shows a worse overall economic performance with the GDP growth of only 2.5 percent. A moderate exchange rate of appreciation ensures cheaper imports for import-intensive domestic producers and private consumers, which outweigh the benefit of protecting the economy from the Dutch Disease via restraining the exchange rate appreciation.

Finally, there was a consistent improvement in living standards for all population groups.

Over 5 years, between 2001 and 2005, the poverty headcount index fell from 20 to 7 percent. It should be noted, however, that in rural areas poverty remains high. Inequality, on the other hand, as measured by the Gini coefficient, fell only slightly during the same period from 35.1 to 33.4, with the richest 10 percent (of population?) receiving about 27 percent of all income. Combining the results from the CGE simulations and micro household survey data it was estimated that the expansion of the oil sector contributed about 38 percent of the total annual poverty reduction. Results for inequality showed no significant change suggesting proportional increase in income for the rich and poor.

Chapter 6 concludes this study of Kazakhstan and its energy sector by developing an analytical framework correcting the national statistics for transfer pricing in the extracting industry. Trade margins in the oil and gas industry are disproportionably large in

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Kazakhstan compared to other developed energy-exporting countries. This relates to a widely discussed issue of transfer pricing and also suggests that national statistical authorities do not correct the statistics for it. As a result, the oil and gas sector is undervalued and the services sector is artificially inflated. When the framework developed to adjust for the transfer pricing is utilised, the value added share of the oil and gas sector almost doubles, increasing from 8.5 percent to 16.1 percent in 2001. In 2005 the adjustment was less dramatic and represents an increase of 3.2 percentage points from 17.3 percent to 20.5 of the value added. These results indicate that, particularly in the earlier years, the size of the oil and gas sector and its contribution to the Kazakhstan’s economic development was far greater than implied by the official numbers.

The corrections for transfer pricing introduced to the Input-Output tables and the value added structure may alter the results of economic models that rely on this data. The CGE modelling exercise in Chapter 5 would be particularly exposed to such risks given the construction of the SAM in Chapter 4. Comparing simulations with two different social accounting matrixes, the original and the one adjusted for transfer pricing Input-Output table, shows that the oil sector spillover is more profound when adjusted Input-Output tables are used. The larger absolute value of output of oil in the adjusted table entails smaller increase in oil output to support an 18 percent export rise compared to the unadjusted case. All other macro-variables however experienced a more significant positive boost in the adjusted case as compared to the case when official data were used.

When transfer pricing is appropriately accounted for in the national statistics, an estimated contribution to the GDP growth of the oil industry can be as high as 5.2 percent annually out of 10 percent average annual growth over the studied period.

Finally, two research directions deserve further investigation, outside the scope of this thesis. Firstly, macroeconomic models such as CGE are well suited for the analysis of economic growth, exchange rates, relative prices and wages, however they do not capture micro-level effects such as the distribution of personal income, migration decisions or labour market participation decisions which can only be addressed effectively in a micro, household-level survey-based framework. This current study offers an assessment of the impact of the oil sector expansion on population using changes in consumption as the transmission mechanism for the oil shock. More complicated transmission mechanisms

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between micro and macro models would be needed in order to address the question in greater depth. This would therefore contribute to the growing body of literature on micro-macro simulation frameworks for ex ante analysis of the income distribution and poverty effects of macroeconomic trends.

Secondly, it was shown how transfer pricing in the oil and gas industry distorts the apparent structure of GDP and makes international comparison misleading. Transfer pricing is only one of the factors that might affect the domestic prices which serve as the basis for the compilation of national accounts. To account for various other distortions such as hidden subsidies and informal trade barriers one would be required to recalculate GDP at world prices. This exercise would give a more realistic picture of the economic structure and provide a stronger basis for any policy modelling and analysis.

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