An empirical analysis of petroleum demand for Indonesia: An application of the cointegration approach

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An empirical analysis of petroleum demand for Indonesia: An application of the cointegration approach Suleiman Sa’ad Surrey Energy Economics Centre (SEEC), Department of Economics, University of Surrey, Guildford, Surrey GU2 7XH, UK

a r t i c l e in fo

abstract

Article history: Received 18 November 2008 Accepted 21 May 2009 Available online 21 June 2009

This paper uses selection criteria from various models in a bounds testing approach to cointegration to estimate the price and income elasticities of demand for total petroleum products (gasoline and diesel) and gasoline share in total products in Indonesia. The results suggest that both total products and gasoline share estimates are more responsive to changes in income than changes in the real price of petroleum products. These results have important policy implications as they suggest that policy makers may need to use market-based pricing policies and other policies such as public enlightenment in addition to regulations like minimum energy efficiency standards to promote efficiency and conservation and curb the rising consumption of petroleum products in Indonesia. & 2009 Published by Elsevier Ltd.

Keywords: Petroleum demand Bounds tests Time-series analysis

1. Introduction and background The dynamics of the petroleum industry in the Indonesian economy make the study of demand for petroleum in Indonesia very important. Indonesia has been producing and exporting oil since the 1870s and is one of the few petroleum producing and exporting countries in Southeast Asia. In fact, Indonesia is the only member of OPEC in this region. Over the years, consumption of petroleum products in Indonesia has been growing significantly; as a result, by 2004, Indonesia had become a net-importer of both crude oil and refined products. The rapid depletion of Indonesia’s oil and increases in oil consumption have implications for future energy security in Indonesia. It seems that significant resources are used for importation of petroleum, at the expense of other vital social services in Indonesia. The annual production and consumption of petroleum in Indonesia in thousands of barrels per day (tb/d) during the period from 1970 to 2005 presented in Fig. 1 shows that the production of petroleum initially increased rapidly, from about 0.93 tb/d in 1970 to 1680 tb/d in 1980, it declined to about 1537 tb/d by 1990 and continued to drop, reaching about 1147 tb/d in 2007. Consumption of oil during this same period increased from about 235 tb/d in 1970 to about 580 tb/d in 1980, then increased further to about 1036 tb/d in 1990 before peaking at about 1200tb/d in 2005. These trends in petroleum production and consumption are due to interplay between several factors. On the production side, rapid depletion in oil fields and a dearth of refining capacity are

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among the major factors that contributed to decline in petroleum production in Indonesia. For example, the recent decline in Indonesia’s oil production is due to declining production at Indonesia’s large, mature oil fields (Energy Information Administration, 2006). Similarly, the Petroleum Report of Indonesia (2006) suggests that the gross output of refined products from Indonesia’s nine existing refineries is insufficient to meet internal demand; therefore, Indonesia must import oil to meet its domestic needs. On the consumption side, high growth in per capita GDP is one of the most important factors in stimulating ownership of personal vehicles, leading to a consequent increase in the transportation sector’s demand for petroleum products in Indonesia. For example, Dahl and Kurtubi (2001) attributed the growth of petroleum demand in Indonesia to growth in incomes. Relatively low oil prices are another factor that encourages increases in the consumption of oil in Indonesia. Over the years, prices of petroleum products have been heavily subsidized by the government of Indonesia as a deliberate policy; for example, even with rapid depletion of domestic petroleum resources, the Indonesian government spent 59.2 trillion Rupiah on fuels subsidies in 2004 (Petroleum Report of Indonesia, 2006, p. 26). In addition to the economic factors enumerated above, structural factors such as population growth and urbanization also play an important role in petroleum consumption in Indonesia. In addition to having the fourth-largest population in the world, Indonesia is among the countries in Southeast Asia that witnessed a rapid growth in urban areas due to migration from rural areas to cities; today, Jakarta has more people than any other city in Southeast Asia. A study by the Asia and Pacific Energy Centre (2006) suggested that the level of urbanization in

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Thousands of Barrels per Day

1800

Production Consumption

1600 1400 1200 1000 Indonesia becomes net oil importer in 2004

800 600 400 200 1970

1976

1982

1988

1994

2000

2006

Year Fig. 1. Indonesia’s Oil Production and Consumption: 1970–2005. Data source: International Energy Agency, 2008.

Table 1 Review of studies on demand for petroleum in developing countries. Study

Model/estimation techniques

Country

Price elasticity

Income elasticity

Dahl (1994) Eltony and Al-mutairi (1995) Hunt et al. (1999) Ramanathan (1999) Dahl and Kurtubi (2001) Dahl and Kurtubi (2001) Belhaj (2002) Alves and Bueno (2003) Ahmadian et al. (2007) Akinboade et al. (2008) Sa’ad (2008)

Survey Two-step cointegration Two-step cointegration Two-step cointegration Two-step cointegration Partial adjustment OLS OLS Two-step cointegration Structural time-series models ARDL bounds test Structural time-series models

Developing countries GCC countries Honduras India Indonesia Indonesia Morocco Brazil Iran South Africa Indonesia and S. Korea

0.36 0.46 0.24 0.32 0.59 0.68 0.30 0.46 0.74, 0.63 0.47 0.16

2.20 0.92 1.58 2.68 1.35 1.34 0.50 0.12 1.25 0.36 0.95

Note: Only long-term prices and income elasticities are reported in the table.

Indonesia is projected to increase from 44% in 2002 to 68% in 2030. Over the period of 2030, 18 cities in Indonesia are projected to have populations between 1 and 5 million, which will lead to higher demand for mobility and corresponding increase in demand for oil in transport among others. Rini and Sutomo (2006) show that, after Indonesia recovered from the 1997/98 currency crisis, Jakarta recorded an annual growth in vehicles of 11% per annum, while the infrastructure grew at a rate of only 1% per annum during this same period. These factors have implications for petroleum demand in Indonesia. This study attempts to investigate these issues by using a timeseries model developed by Pesaran et al. (2001), referred to as the ARDL/bounds testing approach to cointegration, to estimate the price and income elasticities of demand for total products (gasoline and diesel) and market share of gasoline in Indonesia. The next section of the paper briefly discusses previous studies on demand for petroleum products in developing countries and is followed by a section outlining the study methodology. Section 4 presents empirical results, and the final section summarizes and draws conclusions for the paper.

2. Literature review Since the two global oil shocks of 1973 and 1979, interest in demand for petroleum products in both developed-OECD and developing countries has grown significantly. This has led to number of studies that seek to investigate the relationships between real income, real prices and the consumption of petroleum products. These studies include Baltagi and Griffin (1983), Dunkerley and Hoch (1987), Gately and Rappoport (1988), Dahl and Sterner (1991), Gately (1992), Eltony (1994) and Eltony

and Al-mutairi (1995). Ghouri (2001), and yet, few studies have estimated the demand for oil in Indonesia, despite the importance of the country in the global oil market. Some of these few include Dapice (1984) and McRae (1994), who studied eleven Asian developing countries including Indonesia; Dahl and Kurtubi (2001), who focused on Indonesia; and Sa’ad (2008), who looked at South Korea and Indonesia. This section provides a brief review of some selected studies of petroleum demand in developing countries; existing studies are summarized in Table 1. A survey of petroleum demand in developing countries by Dahl (1994) showed that the average price elasticity of demand for developing countries was 0.36, while income elasticity was 2.20, suggesting that the demand for petroleum products is more responsive to changes in income than changes in real prices. Hunt et al. (1999) applied the Engel–Granger procedure to data for Honduras and reported that the long-term price elasticity of gasoline demand was 0.24 and long-term income elasticity was 1.59. In studies of Indonesia, Dahl and Kurtubi (2001) used the Engel–Granger two-step and partial adjustment models to estimate demand for petroleum. Under the former, long-term price and income elasticities were 0.59 and 1.35. Using a partial adjustment model, they estimated a long-term price elasticity of 1.34 and a price impact of 0.68. Using a cointegration model, Alves and Bueno (2003) reported long-term price and income elasticities of demand of 0.46 and 1.27, respectively, for gasoline in Brazil. Belhaj (2002) study of demand for gasoline in Morocco found a long-term price elasticity of 0.30 and a long-term activity elasticity of 0.50 of demand for gasoline, respectively. Ramanathan (1999) used a cointegration model and found that the demand for petroleum products in India is more elastic to changes in income than real petroleum prices. He reported a longterm price elasticity of 0.319 and a long-term income elasticity

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of 2.68. Using a similar methodology, Eltony and Al-mutairi (1995) reported an average long-term price elasticity of demand for gasoline of 0.46 in Gulf cooperation countries; long-term income elasticity was found to be 0.919. Recently, Ahmadian et al. (2007), using structural time-series models, reported a longterm price elasticity for gasoline of 0.74 and a long-term income elasticity of 1.25 in Iran. Using the same models, Sa’ad (2008) reported a long-term price elasticity of transportation demand for total petroleum products in Indonesia of 0.19 and a long-term activity elasticity of 0.95. Finally, in bounds tests for cointegration, Akinboade et al. (2008) reported a long-term price elasticity of demand for gasoline of 0.47 and an income elasticity of 0.36 in South Africa. As this brief review of past studies in developing countries shows, demand for petroleum is more responsive to changes in income than changes in the real prices of petroleum in these countries.

3. Methodology and model specification Of the studies reviewed in Section 2, Eltony and Al-mutairi (1995), Hunt et al. (1999), Ramanathan (1999), Dahl and Kurtubi (2001) and Alves and Bueno (2003) all employed the Engel–Granger two-steps procedure. Akinboade et al. (2008) applied a bounds test for cointegration in ARDL in their study. This study follows the approach adopted by Akinboade et al. (2008) for Indonesian demand for petroleum by using the unrestricted error correction model (UECM), referred to as the ARDL/bounds approach to cointegration analysis, developed by Pesaran et al. (2001). This method has some advantages over Engle and Granger (1987) procedure used by the studies reviewed. First, the UECM approach is applicable irrespective of whether all the series are I(0) or integrated I(1) (Amarawickrama and Hunt, 2008). Therefore, the need to pre-test for the integrational properties of the series associated with the other forms of cointegration analysis is avoided. Additionally, Narayan (2005) argue that the small sample properties of this procedure are superior to that of multivariate cointegration. However, the major drawback of this procedure is that it is inappropriate when there are more than two cointegration relationships. The total demand for petroleum products (total and gasoline share) can be specified as a function of real prices of total petroleum products (gasoline and diesel), real per capita GDP and a trend variable representing annual constant growth of energy efficiency. This can be modeled as follows: ln et ¼ b0 þ b1 t þ b2 ln yt þ b3 ln pt þ t

(1)

Eq. (1) has two parts; the total demand for gasoline and diesel per capita combined (ln et) is estimated as a log-linear function of per capita real GDP (lnyt), weighted average of real gasoline and diesel prices (lnpt), a linear trend variable (t) representing annual constant growth of energy efficiency and et(iid), a white noise error term, which is assumed to follow all classical assumptions and l is the polynomial lag operator. According to economic theory, b2 and b3 are expected to have positive and negative signs, respectively; however, b1 can assume any sign (positive or negative). Furthermore, a market share of gasoline consumption is estimated; in the market share model, per capita gasoline consumption is defined as (GC/TC) ¼ 100exp[ln(GC/TC)]. In addition, the prices of gasoline and diesel are incorporated as a difference (i.e., GPDP) to avoid the problem of using two separate prices moving in parallel. In testing for long-term cointegration relationships and estimating the long- and short-term elasticities, a bounds tests approach to cointegration will be applied by estimating an

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unrestricted model in Eq. (2). However, as stated previously, knowledge of the levels of integration of the series is not a priori in this approach; therefore, a unit root test is not conducted for the series.

Dlet ¼ a0 þ

m X

a1 Dlet1 þ

i0

þ

m X

m X

a2i Dlyt1

i¼0

a3 Dlpt1 þ Z1 lyt1 þ Z2 lpt1 þ Z3 let1 þ t

(2)

i¼0

where D is the difference operator and et1 is the error correction term. Other variables are as defined earlier. The URECM/bounds test for cointegration proposed by Pesaran et al. (2001) is based on two procedures; the first step involves using an F-test or Wald test to test for joint significance of the no cointegration hypothesis (HO: Z1 ¼ Z2 ¼ Z3) against an alternative hypothesis of cointegration, (H1:a1aa2aa3). This test is performed using Eq. (2). Pesaran et al. provides two sets of critical values for a given significance level with and without time trend. One assumes that the variables are I(0), and the other assumes that the variables are I(1). If the computed F-values exceed the upper critical bounds value, then HO is rejected; alternatively, if the computed F is below the critical bounds values, we fail to reject HO. Finally, if the computed F-statistic falls within the boundary, then the result is inconclusive. However, since the sample size in this study is relatively small, critical values from Narayan (2005) are used. After establishing cointegration, the second step involves estimation of the long-term elasticities and error correction model.

4. Empirical results 4.1. Data The petroleum products consumption data for Indonesia are sourced from the International Energy Agency (IEA) beyond 2010 Energy Statistics and Balances (2005). The GDP data and GDP deflators are from various issues of the Asian Development Bank Key Indicators of Developing Asian and Pacific Countries. Energy prices from 1973 to 1992 are sourced from the Energy Indicators of Developing Member Countries of Asian Development Bank and those from 1993 to 2003 are from various issues of Energy Prices and Taxes from the IEA. Per capita real income is the GDP at 1973 constant prices (billions of Ruppiah) divided by annual population. Per capita total petroleum products is the consumption of gasoline and diesel in thousand tons of oil equivalents consumed in Indonesia during the period of this study divided by the annual population. The index of the weighted average of real petroleum product prices is the nominal prices of diesel and gasoline in Ruppiah/tons of oil equivalents paid by end user sectors, deflated by the GDP implicit deflator and indexed to 1973 as the base year. Table 2 presents the results of the bounds test for cointegration for total petroleum products and gasoline share equation. The critical values for the test are extracted from Narayan (2005). The Table 2 Bounds test for cointegration test results. Dependent variable

DLTPC DLGC a b

F-statistics

a

6.20 4.85b

Critical bounds at 5% and 10% Lower bound I(0)

Upper bound I(1)

4.05 3.37

5.09 4.27

Indicates significance at the 5% level. Indicates significance at the 10% level.

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results show that the computed F-statistics are larger than the upper critical values at 5% for the total petroleum products and 10% for the gasoline share equation. The results therefore suggest that the null hypothesis of no cointegration can be rejected for both total petroleum products and gasoline share. Having established the existence of cointegration between the series, the second step is to estimate the long- and short-term elasticities. Various information criteria, including adjusted R2, and the AIC, SBC and HQIC are used for estimation. In the estimation, maximum lag lengths of two are used for both the total and share equation models. The results of the estimated long- and short-term models and their respective diagnostic tests are presented in Tables 3 and 4. The results show that all the estimated elasticities from the models are both statistically significant and have the correct expected signs. 4.2. Model selection strategy The strategy for the selection of the preferred equation is based on the following criteria:

 The preferred model among the various models is the one that passes all the diagnostic tests.

 In case where all the specifications have passed the entire diagnostics, the models that have all its estimated coefficients consistent to the underlying economic theory (that is negative price and positive income elasticity) will be chosen as the preferred one. The results shown in Table 2 demonstrate that the SBC specification passes a diagnostic test and therefore does not suffer from any statistical problems. However, the other three specifications failed the functional test for misspecification. Therefore, it appears that there is misspecification problem with

the other three models. From a statistical point of view, the SBC model is more robust and acceptable than the other models. Thus, based on the first selection criteria, SBC is the preferred specification for total petroleum products demand. The estimated long-term elasticities of demand for total petroleum are similar when drawn from AIC, HQIC and R2, with a value of 0.86; however, the result of SBC is slightly larger with a value of 0.88. The coefficient of the long-term price impact is also the same for the three models, 0.15. The long-term price elasticity under the SBC model is 0.16. The estimated coefficient of the trend variable is 0.35 in SBC specification and 0.34 in the other three specifications. The estimated long-term elasticities from all the specifications suggest that the demand for total petroleum products in Indonesia is more responsive to changes in per capita income than changes in the real price of petroleum products. The estimated long-term income and price elasticities from this study are within the range of the previous elasticities reported for developing countries. The results of the short-term dynamic models show that the estimated coefficient of the error correction term is statistically significant and has a negative sign. In the SBC specification, the coefficient of the adjustment is 0.52, indicating 52% annual adjustment in long-term disequilibrium. The other three models are slightly smaller with an annual adjustment of 50%. Furthermore, the estimated coefficients of the lagged-dependent variable from AIC, HQIC and R2 are also statistically significant. Table 4 presents the results for the gasoline market share equation based on the various lags criteria used; the results of the diagnostics tests indicate that all models show no evidence of serial correlation, non-normality, functional misspecification or heteroskedasticity. The estimated coefficients of long-term income elasticities for all the specifications are statistically significant and in a positive direction. However, there are some slight disparities between the elasticities; while AIC and HQIC indicate a long-term income

Table 3 ARDL analysis results for total products consumption. Dependent variable LTPC Panel A: long-term results Regressors LY Lp Trend Intercept

AIC ARDL (2,3,2)

SBC ARDL (1,3,2)

HQ ARDL (1,0,0)

R2ARDL (2,3,2)

0.86 0.15 0.34 3.84

(6.22) (5.92) (5.88) (31.8)

0.88 (6.37) 0.16 (6.99) 0.35 (6.14) 3.91 (33.92)

0.86 (6.21) 0.15 (5.92) 0.34 (5.88) 3.84 (31.8)

0.86 (6.21) 0.15 (5.92) 0.34 (5.88) 3.84 (31.8)

AIC ARDL 0.19 0.41 0.09 0.04 0.25 0.50

(2,3,2) (1.46) (4.26) (0.76) (3.49) (1.95) (4.88)

SBC ARDL (1,3,2) NA 0.39 (3.95) 0.53 (0.00) 0.02 (2.15) 0.42 (2.15) 0.52 (4.99)

HQ ARDL (1,0,0) 0.19 (1.46) 0.41 (4.26) 0.09 (0.76) 0.04 (3.49) 0.25 (1.95) 0.50 (4.88)

R2ARDL (2,3,2) 0.19 (1.46) 0.41 (4.26) 0.09 (0.76) 0.04 (3.49) 0.25 (1.95) 0.50 (4.88)

AIC ARDL R42 0.83 DW 2.08 w2SC 0.17 w2FC 10.3 w2N 0.32 w2H 0.20

(2,3,2)

SBC ARDL (1,3,2) R42 0.82 DW 1.81 w2SC 0.29 [0.58] w2FC 5.48 [0.02] w2N 0.21 [0.89] w2H 0.08 [0.77]

HQ ARDL (1,0,0) R42 0.83 DW 2.08 w2SC 0.17 [0.68] w2FC 10.3 [0.00] w2N 0.32 [0.85] w2H 0.20 [0.65]

R2ARDL (2,3,2) R42 0.83 DW 2.08 w2SC 0.17 [0.68] w2FC 10.3 [0.00] w2N 0.32 [0.85] w2H 0.20 [0.65]

Panel B: short-term results

DLTCt1 DLY DLYt1 DLP DLPt1 ecmt1 Diagnostic tests

[0.68] [0.00] [0.85] [0.65]

Notes: t-squares in parentheses, p-values in square brackets. R42 is the coefficient of determination, DW is the Durbin Watson statistic, w2SC is the Lagrange multiplier test for serial correlation, w2FC is the functional form test for misspecification, w2N is the test for normality based on skewness and kurtosis of residuals and w2H is the test for heteroskedasticity based on squared residuals of fitted values.  Significance level is 10%.

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Table 4 ARDL analysis result for gasoline share equation. Dependent variable LGC Panel A: long-term results Regressors LY Lp Trend Constant

AIC ARDL (3,0,0) 0.16 0.016 0.014 0.39

(4.63) (1.23) (5.79) (5.63)

SBC ARDL (1,0,0) 0.18 0.007 0.15 0.42

(3.88) (0.44) (4.86) (4.54)

HQ ARDL (3,0,0) 0.16 0.016 0.014 0.39

(4.63) (1.23) (5.79) (5.63)

R2ARDL (3,0,3) 0.11 0.019 0.13 0.59

(2.70) (0.69) (6.64) (3.82)

Panel B: short-term results

DLGC1 DLY DLY1 DLP DLP1 ecm1

AIC ARDL (3,0,0) 0.48 (2.81) 0.019 (3.69) N/A 0.01 (1.13) N/A 0.68 (3.70)

SBC ARDL (1,0,0) 0.46 (2.70) 0.09 (3.28) N/A 0.004 (0.43) N/A 0.51 (3.39)

HQ ARDL (3,0,0) 0.48 (2.81) 0.019 (3.69) N/A 0.01 (1.13) N/A 0.68 (3.70)

R2ARDL (3,0,3) 0.60 (3.25) 0.092 (2.84) N/A .003 (0.31) 0.028 (1.27) 0.81 (3.81)

AIC ARDL (3,0,0) R42 0.41 DW 2.19 w2SC 3.6 [0.56] w2FC 3.54 [0.60] w2N 0.76 [0.68] w2H 4.08 [0.04]

SBC ARDL (1,0,0) R42 0.38 DW 2.10 w2SC 0.94 [0.33] w2FC 3.66 [0.56] w2N 0.27 [0.87] w2H 2.67 [0.10]

HQ ARDL (3,0,0) R42 0.41 DW 2.19 w2SC 3.6 [0.56] w2FC 3.54 [0.60] w2N 0.76 [0.68] w2H 4.08 [0.04]

R2ARDL (3,0,3) R42 0.44 DW 2.13 w2SC 1.49 [0.22] w2FC 3.15 [0.76] w2N 0.34 [0.84] w2H 4.19 [0.41]

Diagnostic tests

Notes: t-squares in parentheses, p-values in square brackets. R42 is the coefficient of determination, DW is the Durbin Watson statistic, w2SC is the Lagrange multiplier test for serial correlation, w2FC is the functional form test for misspecification, w2N is the test for normality based on skewness and kurtosis of residuals and w2H is the test for heteroskedasticity based on squared residuals of fitted values.  Not statistically significant.

elasticity of 0.16, R2 and SBC suggest elasticities of 0.11 and 0.18, respectively. Furthermore, the estimated coefficients of long-term price elasticity for AIC, SBC and HQIC are in the expected negative direction, but the R2 specification has a positive sign for the coefficient of long-term price elasticity. The long-term price elasticity under SBC and R2 specifications are not statistically significant. Therefore, based on economic theory considerations, these two specifications are rejected in favor of AIC and HQIC. The estimated coefficient of the linear trend variable is statistically significant in all four specifications and has a negative sign. The estimated results of the short-term dynamic model indicate that the coefficient of the error correction term for all lag selection criteria are statistically significant and in a negative direction. However, the coefficients differ in size across the various specifications; AIC and HQIC find the coefficient of error term to be 0.68. With SBC, the estimated coefficient is 0.51, and the estimated coefficient of error term under R2 is 0.81. In addition, the estimated coefficients of the lagged-dependent variable for all specifications are statistically significant. The estimated short-term income elasticities for all specifications are also statistically significant with positive signs; their values range from 0.019 in the AIC and HQIC specifications to 0.09 under the SBC specification.

than changes in product prices. This result supports previous studies in Indonesia as well as previous studies of petroleum products demand in other developing countries. In terms of policy implications, this result suggests that the Indonesian government needs to promote energy efficiency and conservation through market-based pricing and taxation, minimum energy efficiency standards and informing the public to curb the present trend in both consumption and importation. Otherwise, the consumption of petroleum products is likely to continue to grow at a significant rate as long as per capita income continues to grow.

Acknowledgements Critical comments and recommendations from Lester C. Hunt, the Director of the Surrey Energy Economics Centre, are gratefully acknowledged. Furthermore, useful suggestions on how to improve the earlier version of the paper by the unanimous referee of the Journal are highly appreciated. All views expressed in this paper and any errors or omissions are solely the responsibility of the author. References

5. Summary and conclusion This paper briefly reviewed trends in petroleum consumption and imports and estimated the energy demand functions for total petroleum products and gasoline share in total products in Indonesia over the period from 1970 to 2005. The bounds testing approach to cointegration developed by Pesaran et al. (2001) is used to estimate price and income elasticities. The results of the estimated price and income elasticities suggest that total products demand tends to be more responsive to changes in real income

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