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CHAPTER 4 Volatility in External Demand: Indonesia’s Commodity Boom and Overall Competitiveness

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Thomas Rumbaugh
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January 2012
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GUSTAVO ADLER

Indonesia experienced a remarkable export boom in the years preceding the 2008–09 global economic and financial crisis, driven mainly by surging commodity exports. While helping to sustain high economic growth, the commodity boom and its accompanying real exchange rate appreciation have raised questions about the effect on the manufacturing sector (Dutch disease) and the country’s growing vulnerability to volatile commodity prices. This chapter takes an in-depth look into recent trade patterns to assess the extent of such concerns. The analysis finds that (a) there is no strong evidence of Dutch disease; (b) so far, weak performance in some sectors does not appear to be linked to the commodity boom; and (c) although further reliance on commodities has increased Indonesia’s vulnerability to export price volatility, the terms of trade have actually been relatively stable because import and export prices move together markedly, mitigating such potential vulnerability.

BACKGROUND

Indonesia went through an impressive period of export growth in the five years preceding the 2008–09 global crisis. Following a period of stagnation in the early 2000s, exports accelerated sharply, increasing by about 120 percent from 2003 to 2008 (until the global financial crisis). This remarkable growth was noticeably stronger than the already impressive performance seen in the early 1990s. After being interrupted by the collapse in trade in late 2008 and early 2009, the export boom seems to have resumed despite a still modest recovery in global activity, with exports rebounding to near precrisis levels by late 2009 (Figure 4.1).

Figure 4.1Exports of Goods

Sources: CEIC Data Co., Ltd.; and IMF staff calculations.

Much of this extraordinary export performance reflected a surging commodity sector (Figure 4.2). Commodity exports grew by 180 percent during this period (2003–10)—notably faster than manufacturing (75 percent). This is significantly different from the export boom of the early 1990s, which was driven solely by rapidly growing manufacturing exports. Both renewable resource commodities (primarily vegetable oils and rubber) and nonrenewable resource commodities (mainly oil and gas) contributed to the sharp increase in exports, although the former resulted from a combination of rapidly increasing volumes and prices, while the latter reflected a sharp increase in prices. 1,2 Manufacturing exports, meanwhile, displayed decent but significantly lower volume growth (Figure 4.3).

Figure 4.2Commodity Exports

Sources: CEIC Data Co., Ltd.; and IMF staff calculations.

Figure 4.3Export Performance by Main Products, 1990–2010

Sources: CEIC Data Co., Ltd.; and staff estimates.

The increase in both renewable and nonrenewable resource commodity prices (Figure 4.4) was accompanied by sharply accelerating demand from neighboring countries. Most noticeable was the quadrupling of exports to India, the tripling of exports to China and, because of its already high starting level, the doubling of exports to Japan (Figure 4.5). As a result, exposure to neighboring emerging-market countries increased markedly while the overall exposure to the region remained broadly stable. (Box 4.1 discusses recent trends and potential vulnerabilities related to the composition of trading partner countries.)

Figure 4.4Deflators of Main Export Products, 1990–2010

Sources: CEIC Data Co., Ltd.; and IMF staff calculations.

Figure 4.5Indonesia Exports to Main Destinations

Sources: IMF, Direction of Trade Statistics; and IMF staff calculations.

BOX 4.1Trading Partner Diversification

The composition of Indonesia’s trading partner countries has changed markedly in recent years, shifting toward rapidly growing emerging and developing economies. The overall exposure (export share) to advanced countries fell from 73 percent in 2003 (and an average of 81 percent during the 1990s) to 65 percent in 2008. Although this has helped diversify export destinations somewhat and increase market share in rapidly growing economies, partner concentration has remained relatively high, with five countries accounting for more than 50 percent of total exports. A group of 10 countries has retained about 75 percent of export share for more than a decade, although China, India, and Malaysia have gained ground at the expense of Japan and the United States. The result has been a sustained downward trend in Indonesia’s Herfindahl index of trading partner concentration (Box Figure 4.1.1). Despite the sharp acceleration in demand from neighboring countries, exposure to the region as a whole has increased only marginally, from 60 percent of total exports in 2003 to 62 percent in 2008, as increases in the share of exports to China, India, and Malaysia have been offset by a declining share to Japan (Box Figure 4.1.2).

Figure 4.1.1Herfindahl Index on Export Markets

Sources: IMF, Direction of Trade Statistics; and IMF staff calculations.

Figure 4.1.2Shares of Main Export Destinations

Sources: IMF, Direction of Trade Statistics; and IMF staff calculations.

This shift has been accompanied by heightened vulnerability to foreign demand shocks, reflecting primarily increased co-movement across trading partners (Box Figure 4.1.3). A measure of foreign demand volatility was constructed as a weighted average of trading partners’ domestic demand volatility, with weights given by the trading partner’s share in Indonesia’s exports.a This index, depicted below, points to a sharp increase in foreign demand volatility in the last few years following a prolonged period of very low volatility (consistent with the phenomenon often referred to as the “Great Moderation”). Heightened volatility of foreign demand, also affecting other countries, reflects mainly a sharp increase in correlations across trading partners rather than their changing shares in Indonesia’s exports (as suggested by the negligible gap between the index with fixed and moving weights in Box Figure 4.1.4). At the same time, covariance with trading partners picked up recently, after a long period of negative or zero correlation, although, again, the changing structure of trading partners has played no significant role.

Figure 4.1.3Volatility of Foreign Demand, 2000–10

Source: IMF staff estimates.

Figure 4.1.4Covariance of Indonesia’s GDP and Trading Partners’ Domestic Demand, 2000–10

Source: IMF staff estimates.

a The index is computed as σ0=Σinvar(di)wi2+2ΣinΣjincovar(djidi)wiwj,, where di denotes domestic demand in country i, wi is country i’s share in Indonesia’s total exports, and σ0 is the variance of overall foreign demand (D). Weights are fixed or moving depending on the desired information. The variance is measured on a seasonally adjusted and (Hodrick-Prescott filter) detrended series of domestic demand.

But an appreciating real exchange rate and a sharp pickup in imports have raised questions about the effect on other sectors and the increasing vulnerability to external shocks. Driven mainly by marked upward pressures on the rupiah, the real exchange rate appreciated about 14 percent from late 2003 to mid-2008. The sharp depreciation following the collapse of Lehman Brothers reversed most of the previous appreciation, but the subsequent rebound quickly raised the real exchange rate above precrisis levels and 20 percent above the levels before the commodity boom (Figure 4.6). Notwithstanding appreciating pressures, fast-growing exports allowed Indonesia to maintain trade (and current account) surpluses for a prolonged period. However, after a lengthy time of sluggish import growth, a rapid catch up in 2007–08, mainly reflecting capital and intermediate goods, led to trade deficits for the first time in more than a decade (Figure 4.7). Against this backdrop of weakening external balances, the increased reliance on commodity exports could be a source of vulnerability because commodity prices tend to be highly volatile, potentially exposing the economy to large terms-of-trade shocks that could rapidly translate into mounting external imbalances. This situation is of particular concern if the commodity boom occurred at the expense of growth in the manufacturing sector (a phenomenon often referred to as Dutch disease)—manufacturing is unlikely to recover quickly in the event of a reversal in commodity exports. The next section assesses the evidence for Dutch disease in Indonesia.

Figure 4.6Nominal and Real Exchange Rates, 1995–2010

Source: IMF, Information Notice System.

Figure 4.7Trade Balance, 1990–2010

Source: CEIC Data Co., Ltd.

DUTCH DISEASE IN THE MAKING?

Commodity booms often lead to Dutch disease. As extensively documented, first by Corden and Neary (1982) and by Corden (1984), and later by an extensive literature, 3 commodity booms—resulting from sharp increases in production (e.g., following the discovery of new sources) or in prices—often have pervasive effects on other sectors (see Box 4.2). Dutch disease is normally associated with (a) real exchange rate appreciation; (b) a slowdown in manufacturing exports, output, and employment; and (c) an increase in wages.

BOX 4.2The Dutch Disease Hypothesis

The Dutch disease phenomenon is normally associated with two main effects:

  • A resource movement effect that refers to the reallocation of factors from other sectors of the economy (e.g., manufacturing) to the booming natural resource sector. This effect is caused by increased demand for production factors in the resource-intense sector, which tends to attract labor from other sectors of the economy by means of higher wages. That is, if labor is mobile across sectors, higher wages in the booming export sector would cause labor to move toward this sector, leading to lower output in the other sector (if the economy is operating at full capacity). This process of resource reallocation also often leads to an appreciating real exchange rate. Lower productivity in the nonbooming sector (including nontradables) results in a loss of production, giving rise to excess demand for nontrad-ables, and leading to an increase in the relative price of nontradables (thus, the real exchange rate).
  • A spending effect that relates to the appreciation of the real exchange rate as a result of increased spending of (at least part of) the booming sector’s extra income. Increased demand for nontradables leads to exchange rate appreciation because nontradable goods prices need to adjust upward to induce higher production in response to higher demand. The magnitude of this effect normally depends on the propensity to consume nontradable goods. That propensity tends to be higher when a large part of the additional income is received by the government because the government tends to have a high propensity to consume non-tradable goods.

Combining the two effects, the Dutch disease hypothesis generates three unambiguous predictions: (a) because the relative price of nontradable goods increases, the real exchange rate appreciates; (b) manufacturing output and employment fall as a result of factor reallocation; (c) the overall wage level increases (possibly starting with higher wages in the booming sector) in response to higher demand for labor. The combined effect on output and employment in the nontradable sector is ambiguous because the spending and resource movement effects push in opposite directions.

In Indonesia, the recent commodity boom has been accompanied by significant real exchange rate appreciation, although there is no evidence of overvaluation. Marked appreciation in recent years followed rapid income and productivity gains—mainly earlier in the first decade of the 2000s—and served to reverse much of the overshooting experienced during the 1997–98 Asian financial crisis. As a result, today the exchange rate is broadly in equilibrium with economic fundamentals, as suggested by the different Consultative Group on Exchange Rates methodologies (Box 2 in IMF, 2010).

Evidence of effects on manufacturing exports is mixed, with significant heterogeneity within the group. Although manufacturing exports have been noticeably outpaced by commodity exports in recent years, growth in that sector has still been robust at an aggregate level (Figure 4.3). Some traditional industries (mainly textiles, wood manufactures, and paper products) have performed poorly, whereas others (e.g., chemicals and machinery and apparatus) have shown remarkable growth. Still, most of the sectors that witnessed sluggish growth in recent years seem to have been on that path long before the commodity boom manifested itself (Figure 4.8).

Figure 4.8Manufacturing Exports by Main Products, 1990–2009

Sources: CEIC Data Co., Ltd.; and IMF staff calculations.

GDP data confirm that sectoral performance has been uneven, and weak output does not appear to be linked to the recent commodity boom. A long-term view of sectoral output reveals that sectors that have been sluggish in recent years were displaying weak performance long before the commodity boom, suggesting that real exchange rate appreciation may not have been the main factor behind these sectoral weaknesses (Figure 4.9). This is particularly clear for textiles, wood manufactures, and iron and steel industries, which have shown sluggish growth since 2000. At the same time, sectors closely linked to commodities (e.g., food; fertilizers, chemicals, and rubber; and cement) have witnessed decent, although slowing, economic performance in recent years. And other capital-intensive industries (e.g., automotive and machinery) have actually been booming recently—similar to their performance in the period preceding the commodity boom—arguably suggesting that long-standing labor market frictions may have been a constraint on growth in some labor-intensive industries. 4

Figure 4.9Real GDP by Manufacturing Sector, 1993–2010

Sources: CEIC Data Co., Ltd.; and IMF staff calculations.

Only recently have wage pressures started to appear in the manufacturing sector. 5 Consistent with the Dutch disease hypothesis, wages in the commodity sectors (particularly in mining) have grown rapidly and outpaced those in other sectors in recent years. Employment in these sectors (except in agriculture, forestry, and fishery) has also grown fast, and faster than in the manufacturing sector. Still, until 2008, wage pressures on the manufacturing sector had not materialized, partly reflecting one-time reforms that helped to lower wages in the sector.6 In fact, wages in this sector decreased about 15 percent in real terms during 2003–08, along with somewhat smaller decreases in service sectors, while real wages in the mining sector grew by 19 percent (Figure 4.10). More recently, however, wage pressures have appeared in the service and manufacturing sectors, with the latter showing real wage increases of about 13 percent during 2009 alone.

Figure 4.10Employment and Wage by Sector, 2003–09

Sources: CEIC Data Co., Ltd.; and IMF staff calculations.

Note: Employment data not available for personal services.

Econometric analysis also suggests little evidence of Dutch disease. A vector autoregression model was estimated to gauge the effect of commodity price shocks on the output of key manufacturing sectors. The model was estimated for each sector with monthly data for the period 1993–2008 (until the Lehman Brothers crisis). Sectoral output was measured by the corresponding industrial production index. To control for possible correlation between commodity and sectoral manufacturing prices, the sectoral export price deflator was also included in the model. Finally, an index of total imports in trading partners—weighted by their share in Indonesia’s exports—was introduced to control for external demand shocks. Results (Figure 4.11) suggest that only the textile sector may have been affected by commodity price shocks (as indicated by the negative and statistically significant impulse response and the 35 percent of the variance explained by commodity prices). Other sectors do not display any statistically significant evidence of Dutch disease. In fact, if any, the effect of higher commodity prices seems to be positive, reflecting some correlation between commodity and industry-specific export prices, as well as likely inter-sectoral complementarities (e.g., machinery and chemical industries are closely linked to production in the commodity sector), although the role of commodity prices in explaining the variance in output is limited.

Figure 4.11Vector Autoregression: Impulse Responses and Variance Decomposition (Relative to commodity export prices)

Source: IMF staff estimates

IS INCREASED RELIANCE ON COMMODITY EXPORTS A SOURCE OF CONCERN?

Although no clear evidence of Dutch disease is apparent, greater reliance on commodities raises questions about increasing vulnerability to terms-of-trade shocks (Figure 4.12). Despite increasing diversification within manufacturing exports, and to some extent within the commodity group as well, overall diversification has remained broadly constant for more than a decade—as indicated by the overall Herfindahl index of product concentration 7 —reversing the trend seen in the 1990s and early 2000s. This is reflected by the fact that greater within-group diversification has been offset by increased reliance on commodities, which have a lower degree of diversification (higher Herfindahl index) than the manufacturing sector (Figure 4.13).

Figure 4.12Product Concentration, 1990–2010 (Share of natural resource-intense products in total exports)

Sources: CEIC Data Co., Ltd.; and IMF staff calculations.

Figure 4.13Product Concentration, 1990–2010

Sources: CEIC Data Co., Ltd.; and IMF staff calculations.

Note: Herfindahl index varies from 0 to 1 with higher value representing greater concentration.

Export price volatility has increased significantly, reflecting increased volatility in international prices as well as increased concentration in price-volatile products. This analysis constructs an index of export price volatility that tracks the variance of main export prices over time, weighting them by their share in total exports (Figure 4.14). 8 As expected, the index shows that volatility increased significantly in the last few years, even before the sharp and generalized fall in commodity prices associated with the collapse in trade (after Lehman Brothers collapsed). This increase is explained by both the heightened variance of underlying prices and the increased correlation among them. As suggested by the difference between the index with fixed weights and the index with moving weights, increased reliance on commodity exports has contributed significantly to Indonesia’s export price volatility, precisely because commodities display higher volatility than do manufacturing products (even after detrending and seasonally adjusting them).

Figure 4.14Volatility of Export Prices, 1991–2010 (Moving and fixed weights)

Sources: CEIC Data Co., Ltd.; and IMF staff estimates.

However, Indonesia’s terms of trade have displayed limited volatility. Indonesia is also a significant importer of raw materials, thus, its import prices have also fluctuated sharply in recent years, and moved closely with export prices (Figure 4.15). As a result, terms of trade have actually remained stable during the commodity boom period, as well as during the commodity price bust of the 2008–09 global crisis. This suggests that, despite significant exposure to commodities, Indonesia’s external balances are likely to remain resilient to external price shocks.

Figure 4.15Terms of Trade, 1990–2010

Sources: CEIC Data Co., Ltd.; and IMF staff estimates.

CONCLUSIONS

The unprecedented export boom preceding the 2008–09 global crisis, and the associated marked real exchange rate appreciation and import boom, have raised questions about the impact of the commodity boom on other sectors of the economy, given that a reversal in commodity prices (and demand) could quickly lead to mounting external imbalances. An in-depth look at recent trade patterns, sectoral output, and labor markets, however, does not point to an obvious case of Dutch disease at this point, although some evidence indicates accumulating pressures in some sectors. Although there is evidence of stagnation in some manufacturing sectors, it does not seem to relate to the recent commodity boom, suggesting that other known structural factors (e.g., infrastructure bottlenecks and labor market frictions) may have played a role. Increasing reliance on commodity exports, however, could make the economy vulnerable to external price shocks, although a high correlation between export and import prices (reflecting a high commodity content in imports) goes a long way toward mitigating price shocks. Addressing structural problems will be the key to fostering growth in the manufacturing sector and diversifying the economy away from commodities, while still exploiting its comparative advantage.

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*Research assistance in the preparation of this chapter was provided by Agnes Isnawangsih.
1Commodities labeled as renewable resources refer mainly to agriculture-, animal-, and fishery and forestry–related activities; nonrenewable resource commodities refer mainly to mining activities.
2Volume and deflators are estimated by IMF staff, based on official value and volume data for export and import groups corresponding to Standard Industrial Trade Classification (SITC) 2-digit-level disaggregation because aggregate official statistics on volumes do not weight subgroups by their economic values.
3Recently studied cases of Dutch disease include Bolivia (Cerutti and Mansilla, 2008), Russia (Oomes and Kalcheva, 2007), and many oil exporting countries (Ismail, 2010).
4In particular, high severance payments have been a major constraint on labor-intensive industries (see Thacker, 2005).
5Serious limitations (low coverage and frequency) of the data on wages and employment prevented more in-depth analysis of sectoral trends.
6Minimum wage setting was decentralized to the provinces, giving rise to interprovince wage competition.
7The Herfindahl index of product concentration is constructed from SITC 2-digit disaggregation groups.
8Based on the construction of the overall export deflator (P=Σi1n[(PiWi)]), the vulnerability index is computed as σp=Σvar(Pi)wi2+2ΣiΣjicovar(PjPi)WiWj, where P is the export deflator, Pi is product i’s price deflator, wi is product i’s share in total exports and σp is the variance of P. As before, weights are fixed or allowed to move over time depending on the desired information. Variances and covariances are measured on seasonally adjusted and (Hodrick-Prescott filter) detrended series of export deflators.

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