Overview of economic effects using the CGE model: fall in oil prices
June 15, 2020
One of our latest products is the CGE (computable general equilibrium) economic model adapted for Kazakhstan. As a basic computable model of general equilibrium, this tool allows to assess the influence of external factors and internal policies on the state of economy in general terms.
In more detail, the CGE is a macroeconomic model that combines economic theory and real economic data to determine the effects on the economy from external shocks and policies.
Kazakhstan's CGE model KAZORANI is an adapted version of the Australian model ORANI-G, developed in 2000. Policy Research Centre at the University of Victoria. The model data is based on country input-output tables (IOT) for the last available year (2018) and global data from the GTAP database (Global Trade Analysis Project, 2015).
KAZORANI covers 68 sectors of the economy and has a regional expansion (17 regions). It is possible to group industries according to different characteristics: level of manufacturability, type of employment, localization/export-orientation. Thus, it is possible to distinguish the effects of shocks on groups of interest to policy makers and businesses (for example, "economy of simple things", high-tech sectors of manufacturing industry, etc.).
Shocks and policies can take different forms: changes in taxation, in the preferences of the population, export demand and government spending, technological progress, and tariff policies. This article will describe a real example of an external shock - a change in export demand for oil due to a fall in the world price – and its potential long-term effects on the Kazakh economy.
Example Of Shock:
Fall In Oil Prices
One of the most prevailing external shocks for Kazakhstan was the reduction in export demand for oil due to the fall in the world price. With the coronavirus pandemic and subsequent oil market flooding in the first quarter of 2020, the world has already experienced a sharp drop in oil prices. Starting in 2020 with a price of $ 68 per barrel, by the end of April the world was seeing a price of $ 20 per barrel. Taking into account the recovery with the easing of quarantine measures, the average oil price in 2020 will be 30-35 dollars per barrel, which is 2 times lower than the price at the beginning of the year.
For the country, the fall in the oil price is certainly a shock, since it does not affect world prices (price-taker) and can not have an effect on the market supply side. In this case, the shock of falling oil prices becomes an external factor that does not depend on the state and connections in the local economy, and enters the country's economy from the outside.
In the KAZORANI model, the impact of a shock, like any other, is interpreted as a shift in the economy to a new equilibrium. As shown in Figure 1 before the impact of external shocks and policies the economy moves along the baseline scenario from point A to point B. In this scenario, all variables grow steadily, i.e. without sudden jumps or policy interventions.
When an external shock or policy is added to the model, i.e. when a certain variable is artificially changed, restructuring of the economy and the transition to the point C state occur. This scenario is an alternative and describes the movement of the economy taking into account shocks.
It is possible to interpret the effect on the economy in the model in terms of various indicators. These include macro indicators (nominal and real GDP, employment, exports, etc.), regional indicators (GVA, output, employment, etc.), and general scalar indicators (exchange rate, etc.).
The effect takes the form of a percentage change, which means that the indicator changes from its base value to an alternative one. Since the model covers the answers to the question "what if", the effect is interpreted as the incremental difference between the indicator at the basic growth (point B) and at the growth taking into account the shock (point C).
It is important to note that the model is atemporal. In Figure 1, the "T" time scale is a symbol. In fact, there are two types of time intervals in the CGE model: short-run (short-term) and long-run (long-term). According to theoretical logic, short-run is such a short period of time that it is impossible to completely rebalance all factors of production (labor and capital are not mobile). Long-run also indicates a period during which all factors are mobile and have time to flow between industries and form a new balance. The model also roughly indicates the time periods that correspond to the empirical periods of short-run (3-5 years) and long-run (more than 5 years) scenarios, i.e. the period of time during which, on average, changes in the economy take place . This article uses the results of a long-term scenario with a conditional length of 12 years (2018-2030).
Figure 1. Interpretation of effects in the KAZORANI model.
Thus, a shock in the form of a reduction in export demand due to the fall in the oil price has an effect on indicators in the base state (this article shows the results of the base state in 2030) of the economy and reconstructs them to an alternative state (alternative state for year 2030).
For the baseline scenario until 2030, the following assumptions are inherent (for a 12-year period from 2018):
– Growth in total employment at 1% per year;
– Growth in the number of households at 1% per year;
– Growth in labor and land productivity by 2% per year;
– Growth in export demand by 2% per year.
The following sections will consider the effects of shock on macroeconomic indicators and other specific indicators of the economy in its base state both by the country and in terms of regions.
Effects On The Country
And Regional Indicators
Among the indicators that are affected by the effects of shocks in the model, there are the following classic macro indicators :
The shock in the form of a 50% fall in the world price of oil (and, accordingly, the export demand for oil) may lead to the following (hypothetical, "what if") effects on macro indicators, namely, the next shortfall in the growth of indicators until 2030 :
Table 1. Effects of shock on macro indicators.
Group of industries
The KAZORANI model makes it possible to consider the impact of shock on certain groups of industries that are interesting for policy makers and businesses. For example, in addition to the direct effect of the loss of demand for products from the extractive sector, related groups of industries will also suffer. All types of services, especially local and social ones, are closely linked to oil revenue through distribution in the form of labor remuneration and state transfers.
Thus, in case of a decline in oil demand, the increase in the output of services will also be much lower than the base figure.
The construction and trade and logistics sectors, linked to investment demand and production in the extractive sector, will also suffer from income redistribution and a decline in industrial production.
An interesting observation in the model is a greater increase in output of manufacturing industries in the alternative state, especially in the medium- and high-tech sectors of the core operations. The effects are explained by a redistributive feature of the model in the conditional long-run time period. In this case, labour and capital flows from the extractive sector, which has experienced a sharp drop in demand, to substitute manufacturing sectors. At the same time, due to high labor remuneration in the extractive sector, labor will move to no less competitive sectors with high wages (which explains the largest growth in high-tech production).
Figure 2. Effects on output by group of industries, % growth until 2030 at the baseline and alternative scenarios .
In the regional context, similarly, we can observe effects on macro indicators in the form of GRP, employment and labor remuneration (non-exclusive list).
The direct effect of fall in oil prices is reflected in GRP of oil regions, but to a different extent. For example, the largest negative difference between the alternative and the base value would be observed in Mangystau region and the West Kazakhstan region, due to their extremely low current diversification. Atyrau, Kyzylorda and Aktobe regions would be less, but also strongly affected by the fall in GRP.
It should be noted that along with the oil regions, they would have received significantly less GRP growth until year 2030 Nur-Sultan and Almaty. This can be explained by the fact that now the capital is a hub for redistribution of oil revenues through the state apparatus, and together with Almaty, the two cities are locations of the head and administrative offices of the extractive sector companies.
The positive difference in growth between the alternative and baseline scenarios in East Kazakhstan region, Pavlodar and Karaganda regions reflects the flow of resources to regions with specialization in medium- and high-tech sectors of OP. In turn, the positive difference in GRP growth in Shymkent indicates a hypothetical growth in the sectors of the "economy of simple things" (labor-intensive B2C industries), which is similar in Akmola region, where resources flow to agriculture.
Figure 3. Effects on GRP of regions, % growth until 2030 at the baseline and alternative scenarios.
In terms of employment, there is a similar pattern. The same regions have the highest positive growth in the alternative scenario compared to the baseline scenario, but the difference is much greater. It is important to note that with a negative difference in GRP growth, other regions still have a positive difference in employment growth (with the exception of oil). Both observations can be explained by low labor productivity, which does not provide a comparable increase in GRP with a significant increase in employment.
Figure 4. Effects on GRP by region, % growth until 2030 at the baseline and alternative scenarios.
Regional labor remuneration (nominal) will not be received in all regions until 2030, but mainly in oil and in the cities of Nur-Sultan and Almaty (the largest difference between the baseline and alternative growth). Despite the positive difference in GRP growth in regions with a predominance of the core operations and agriculture sectors, the difference in labor remuneration growth in these regions will be negative if oil prices fall. The reason for this may be a significant share of employees in public sectors (education, healthcare, public administration, etc.), whose labor remuneration does not depend on regional specialization of production. Due to the reduction of oil revenues for distribution by the 'center', the level of labor remuneration of public sector employees should be significantly reduced (hypothetically, without taking into account interference in the form of state policies).
Figure 5. Effects on labor remuneration by region, % growth until 2030 at the baseline and alternative scenarios.
In terms of industries, it is possible to see many effects, but let's focus on the most general ones. Due to the fact that the model covers all 68 branches of the country's input-output table, it is advisable to focus on the most extreme effects.
For example, the increase in employment until 2030 in oil production and technical services in the extractive sector at the alternative scenario is at negative peak (let us recall, under condition that there are no other counter-shocks).
It is interesting to note that among other industries, the largest decline will be observed in the sector of steam supply, auxiliary financial services and household facilities (service). The reason for this may be the links in the form of reduced intermediate demand from the oil sector for these services.
Figure 6. Extreme effects on employment by industry (industries with the largest decline in the alternative scenario), % increase until 2030 at the baseline and alternative scenarios.
As noted earlier, employment spillovers with declining oil revenues will benefit mainly the core operations sectors, and especially the high-tech sectors. Thus, the greatest positive growth in employment with falling oil prices would be observed in the production of computers and electronics, finished goods, leather goods and chemical industry.
Figure 7. Extreme effects on employment by industry (industries with the largest decline at the alternative scenario), % increase until 2030 at the baseline and alternative scenarios.
When interpreting the model results, some extreme and weakly realistic values can be observed, so it is important to take into account a number of assumptions underlying the model in order to facilitate calculations.
First, the model is based on a number of standard macroeconomic assumptions inherent in many CGE models:
Secondly, the Kazakhstan model does not yet take into account the geographical localization of capital and production. For example, the model does not specify the locations of electric power infrastructure by region.
Third, the model calculations are based on the latest available data, which may not accurately describe the current state of the economy. The intersectoral relationships underlying the model can change in a short period of time, and national statistics provide data with a 1.5-2-year behind the schedule, which throws into question the model's basis on country input-output tables data.
Fourth, the model is static, which makes it impossible to see the distribution of shock effects over time and take into account cyclical phenomena and uncertainty in the economy (for this purpose, the dynamic general equilibrium model is used).
Fifth, the effects described above suggest that employment in a country is exogenous (assuming a long-term scenario), i.e. the number of people employed does not grow over time, but moves from one industry and region to another (which is similar to capital). However, the assumption may not reflect real demographic cycles and the entry of new labor force.
In general, this digest is an example of what effects the CGE model can describe without taking into account additional calculations. As noted in our early posts on the model, its purpose is to describe the overall scale of the economy's response to external shocks and policies, rather than to predict real effects. The hypothetical component of the model is still valuable in terms of expanding the understanding of the links in the economy and industry and regional specialization.
Thus, the article makes it clear that the oil sector is not a separate sector, occupying only 18% of Kazakhstan's GDP. A better understanding of intersectoral relations helps to see which industries depend on the oil sector and through which channels (including regional ones) oil revenues are redistributed. Despite the extreme values, it is useful to understand the scale of the economy's response to this shock.
We are open to feedback and comments from the expert community of economists.
Committee on Statistics under the Ministry of National Economy, 2018
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Author: Aigerim Kushumbayeva