Trade and Employment From Myths to Facts - International Labour ...

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Chapter 7: Trade diversification: Drivers and impacts Kee (2008) were the first to test empirically the importance of the reverse mechanism — from export diversification to productivity. They do so by estimating simultaneously a GDP function derived from a heterogeneous-firm model and a TFP equation where the number of export varieties (i.e. of exporting firms) is correlated with aggregate productivity through the usual selection effect. On a sample of 48 countries, they find that the doubling of product varieties observed over 1980–2000 explains a 3.3 per cent cumulated increase in country-level TFP. Put differently, changes in export variety explain 1 per cent of the variation in TFP across time and countries. The explanatory power of product variety is particularly weak in the between-country dimension (0.3 per cent). Thus, product variety does not seem to explain much of the permanent TFP differences across countries, but an increase in export diversification — for example, due to a decrease in tariffs — seems to trigger non-negligible selection effects. To recall, this selection effect means that the least efficient firms exit the domestic market when trade expands, raising the average productivity of remaining firms. Still, even in the within-country dimension, two-thirds of the variation in productivity is explained by factors other than trade expansion. While the determinants of diversification have been studied in the previous section, we now turn to the other side of the causality and investigate the effect of export diversification on growth, starting with the well-known “natural resource curse”. 7.5.2 The “natural-resource curse” The “natural resource curse” hypothesis found support with Sachs and Warner (1997) empirical findings that a large share of natural-resource exports in GDP is statistically associated, ceteris paribus, with slow growth. Since then the discussion on the existence of such a curse has been fierce. Building on Sachs and Warner (1997), Auty (2000, 2001) also found a negative correlation between growth and natural-resource exports concentration. Prebisch (1950) provides a set of possible explanations for this phenomenon: deteriorating terms of trade, excess volatility, and low productivity growth. A host of other growth-inhibiting syndromes associated with natural-resource economies are discussed in Gylfason (2008). As we will see, each potential channel has been a subject of controversy; moreover, the very conjecture holds only when looking at natural-resource dependence, which is endogenous to a host of influences. Endowments of natural resources, by contrast, do not seem to correlate negatively with growth. In this section, we thus review the main arguments for and against the conjecture that concentrating on a few natural resources leads to lower growth. The notion that the relative price of primary products has a downward trend is known as the Prebisch-Singer Hypothesis. Verification of the Prebisch-Singer hypothesis was long hampered by a (surprising) lack of consistent price data for primary commodities, but Grilli and Yang (1988) constructed a reliable price index for 24 internationally traded commodities between 1900 and 1986. The index has later been updated by the IMF to 1998. The relative price of commodities, calculated as the ratio of this index to manufacturing unit-value index, indeed showed a downward log-linear trend of -0.6 per cent per year, confirming the Prebisch-Singer hypothesis. 279
Trade and Employment: From Myths to Facts However, Cuddington, Ludema and Jayasuriya (2007) showed that the relative price of commodities has a unit root, so that the Prebisch-Singer hypothesis would be supported by a negative drift coefficient in a regression in first differences, not in levels (possibly allowing for a structural break in 1921). But when the regression equation is first-differenced, there is no downward drift anymore. Thus, in their words, “[d]espite 50 years of empirical testing of the Prebisch-Singer hypothesis, a long-run downward trend in real commodity prices remains elusive” (page 134). The second argument in support of the natural resource curse has to do with the second moment of the price distribution. Easterly and Kraay (2000) regressed income volatility on terms-of-trade volatility and dummy variables marking exporters of primary products. The dummy variables were significant contributors to income volatility over and above the volatility of the terms of trade. Jansen (2004) confirms those results with variables defined in a slightly different way. Combining these results with those of Ramey and Ramey (1995), who showed that income volatility is statistically associated with low growth, suggests that the dominance of primary-product exports is a factor of growth-inhibiting volatility. Similarly, Collier and Gunning (1999), Dehn (2000) and Collier and Dehn (2001) found significant effects of commodity price shocks on growth. However, these results must be nuanced. Using vector autoregressive (VAR) models, Deaton and Miller (1996) and Raddatz (2007) showed that although external shocks have significant effects on the growth of low-income countries, together they can explain only a small part of the overall variance of their real per-capita GDP. For instance, in Raddatz (2007), changes in commodity prices account for a little more than 4 per cent of it, shocks in foreign aid about 3 per cent, and climatic and humanitarian disasters about 1.5 per cent each, leaving an enormous 89 per cent to be explained. Raddatz’s interpretation is that the bulk of the instability is home-grown, through internal conflicts and economic mismanagement. Although this conclusion may be a bit quick (it is nothing more than a conjecture on a residual), together with those of Deaton and Miller, Raddatz’s results suggest that the effect of commodityprice volatility on growth suffers from a missing link: although it is a statistically significant causal factor for GDP volatility and slow growth, it has not been shown yet to be quantitatively important. A third line of arguments runs as follows. Suppose that goods can be arranged along a spectrum of something that we may loosely think of as technological sophistication, quality or productivity. Hausmann, Hwang and Rodrik (2005) proxy this notion by an index they call PRODY. For each good, this index is the weighted average of the income of countries that export that good where the weight corresponds to a Balassa’s index of revealed comparative advantage for each good-country pair. The central idea is that a good mainly exported by highly developed countries has higher technology or quality content. They show that countries with a higher average initial PRODY (across their export portfolio) have subsequently stronger growth, suggesting, as they put it in the paper’s title, that “what you export matters”. As primary products typically figure in the laggards of the PRODY scale, diversifying out of them may accelerate subsequent growth. In addition, according to the so-called 280
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Trade and Employment From Myths to
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Trade and Employment From Myths to
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PREFACE Trade negotiations -bilater
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Trade and Employment: From Myths to
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EDITORS AND CONTRIBUTORS EDITORS Ma
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ACKNOWLEDGEMENTS The editors wish t
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NEW EVIDENCE ON TRADE AND EMPLOYMEN
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Chapter 2: New evidence on trade an
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Region Annualized percentage change
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Region 1980 1985 1990 1995 2000 200
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Figure 2.4.b: Long-run association
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20 See Harrison and Rodriguez-Clare
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ASSESSING THE IMPACT OF TRADE ON EM
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Chapter 3: Assessing the impact of
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p * Chapter 3: Assessing the impact
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APPPENDIX 3.A: TABLES Chapter 3: As
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TRADE AND THE INFORMAL ECONOMY 4.1
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Chapter 4: Trade and the informal e
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For more than two decades, quantita
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GENDER ASPECTS OF TRADE 5.1 INTRODU
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and income-earning opportunities ge
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Figure 5.1: Description of gender i
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Chapter 5: Gender aspects of trade
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A third view is that the type of jo
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REFERENCES Chapter 5: Gender aspect
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