I. Deindustrialization: Some Conceptual Issues

Clark’s account of structural change is notable for the weight it assigns to income elasticities of demand in explaining what happens to the output of manufacturing in the course of development. The income elasticity of demand for manufactures is high in poor countries, but low in rich countries, and this explains why the share of manufacturing in output and employment rises at first and falls later on. While there is some empirical basis for this hypothesis (see below), a purely demand-based explanation of deindustrialization is incomplete because it neglects the influence of productivity and prices on the structure of demand, and hence on output and employment. As noted above, labor productivity grows faster in manufacturing than it does in the economy as a whole, and hence the relative price of manufactured goods declines as the economy develops. This in turn encourages the substitution of manufactured goods for other items, especially those services whose relative cost is rising because of the relatively slower growth of productivity in those activities. In the earlier stages of development, the effect of such substitution is to boost the already rapid growth in demand for manufactures, while later on the substitution effect helps to stimulate an otherwise flagging demand for manufactured goods.

From a theoretical point of view, the effect of productivity growth on manufacturing employment is ambiguous. As noted above, on the one hand, the faster growth of productivity in this sector makes manufactured goods relatively cheap, thereby stimulating demand for them. On the other hand, less labor is required to manufacture any given volume of output. How these two influences net out in their effect on manufacturing employment is an empirical question that cannot be settled theoretically. As we shall see below, the evidence suggests that the labor-saving impact of faster productivity growth in the manufacturing sector outweighs the demand-creating effect of lower prices, so the net effect is to reduce the share of employment in this sector.

Figure 1 provides a schematic illustration of what happens to the manufacturing sector as per capita incomes rise. For convenience, units are chosen so that the shares of manufacturing in real output and in employment are initially the same. The curve labeled “hypothetical” shows how these shares would evolve if productivity growth were uniform across sectors and if relative prices were to remain unchanged through time. Under these conditions, the shares of manufacturing in real output and employment would remain equal, and their evolution would be determined solely by the income elasticity of demand for manufactures. The hypothetical curve is at first upward sloping because the income elasticity of demand for manufactures is greater than unity in the initial stages of economic development, and it later slopes downward when this elasticity falls below unity in the more advanced stages of economic development. In practice, neither output nor employment shares follows this hypothetical curve. Faster than average productivity growth in the manufacturing sector causes the relative price of manufactured goods to fall, thereby stimulating demand, raising their share in real output, and causing this share to follow a path indicated by the upper curve in the diagram. It also causes the amount of labor required per unit of manufacturing output to fall rapidly, so that the share of manufacturing in employment follows a much lower trajectory, which normally lies well below the hypothetical curve.

Evolution of the Manufacturing Share

Citation: IMF Staff Papers 1999, 003; 10.5089/9781451974553.024.A002

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Evolution of the Manufacturing Share

Citation: IMF Staff Papers 1999, 003; 10.5089/9781451974553.024.A002

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Evolution of the Manufacturing Share

Citation: IMF Staff Papers 1999, 003; 10.5089/9781451974553.024.A002

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The above exposition assumes that the income elasticity of demand for manufactures is less than unity in advanced economies. How does one reconcile this with the findings of studies such as those of Summers (1985) and Falvey and Gemmel (1996) that services as a whole have an income elasticity of demand close to unity? The answer is as follows. In advanced economies, the share of manufactures in output and expenditure is small (and conversely, the share of services is large). Under these conditions, with an income elasticity of demand for services only marginally greater than unity, the income elasticity of demand for manufactures may be well below unity. For example, with an income elasticity of demand for services equal to 1.1. the income elasticity of demand for manufactures in the typical advanced economy will he around 0.7. This issue is explored in more detail in the appendix.

Regarding external factors, foreign trade can affect the internal structure of an economy in various ways. One involves international specialization between manufactures. and other goods and services. A country can become a “workshop” economy. generating a large trade surplus in manufactures that is used to help finance a substantial deficit in nonmanufactured items, such as food, fuels, or services. This is the situation in Germany or Japan, and was also true at one time of the United Kingdom. Alternatively, like Australia, Canada, or the United Kingdom today, a country may have a trade deficit in manufactures that is financed partly through the export of non-manufactured items such as food, minerals, or services. Such trade patterns have obvious implications for the relative size of the manufacturing sector. Other things being equal, a more positive trade balance in manufactured goods implies a larger share of domestic manufacturing in output and employment.

A second avenue through which trade may affect the structure of employment in advanced economies is international specialization within manufacturing production. In recent decades, there has been an evolution in the division of labor whereby advanced economies of the north export skill-intensive manufactured goods in return for labor-intensive manufactures, such as clothing or toys, from developing countries in the south. To manufacture the former goods requires the employment of a modest number of skilled workers, whereas to produce the same value of labor-intensive goods would require the employment of a much greater number of unskilled workers. The effect of such trade should therefore be to reduce manufacturing employment in the north and to alter the skill composition of the manufacturing workforce. Low-wage imports may also reduce employment in the manufacturing sector of advanced economies by increasing competition and forcing firms to utilize their labor more efficiently. Note that aggregate statistics for the manufacturing sector make no distinction between a shift into higher value-added products and greater efficiency in the creation of existing products, and both will show up as an increase in labor productivity.

The above discussion describes the evolution of the manufacturing sector under the impact of rising incomes, differential productivity growth, relative price changes. and foreign trade. Superimposed on this evolution is the influence of other factors such as the share of fixed investment in total spending. Investment expenditure is skewed toward manufactured goods, such as machinery and building materials, so that a higher rate of investment will increase the share of manufactured goods in total demand, and thereby raise the share of manufacturing in real output and employment.

II. Econometric Estimations

To examine the above relationships empirically, we use annual data from a panel of 18 industrial countries over the period 1963-94. for which a total of 510 observations are available.² This sample was derived by dropping Ireland, Portugal, and Switzerland from the slightly larger sample of countries used in our previous study—Rowthorn and Ramaswamy (1997). Their exclusion was necessitated by the absence of sectoral data on prices, output, and productivity for these countries. The present sample also differs from the one used in our earlier study because the data now include observations for every year.³

The equations we estimate are of the following type:⁴

Productivity:

where RELPROD is relative labor productivity in manufacturing as compared to labor productivity in the economy as a whole, Y is per capita income, and the Zs are additional variables reflecting the influence of foreign trade and other factors.

Prices:

where RELPRICE is the relative price of manufacturing goods as compared to the price of national output as a whole.

Output:

where OUTSHARE is manufacturing value added as a share of real GDP

With appropriate units of measurement, the following equation holds identically:

where EMPSHARE is the share of manufacturing in total employment.

Eliminating the relative price variable from equation (3) and using the above identity, we obtain equations of the following type:

The preceding discussion implies that coefficients have the following signs;

As we shall see. the estimated coefficients satisfy these inequalities.

Most of the estimated equations include other variables in addition to those explicitly identified above. Dummy variables are used for individual countries to correct for international differences in measurement practices and other unexplained “fixed” effects. In some cases a time trend or time dummies are also included. To examine the influence of international trade on economic structure, we use two main variables, TRADEBAL and LDCIMP. The former is the overall trade balance in manufactured goods (total exports minus total imports); the latter is equal to manufactured imports from developing countries. Both are expressed as a percentage of GDP measured in U.S. dollars at purchasing power parity.⁵

The role of TRADEBAL is to capture the effect of overall manufacturing trade performance on the structure of employment. The variable LDCIMP is designed to capture the effects of competition from low-wage countries on labor productivity in northern economies. These effects include increased efficiency in activities that compete directly with low-wage producers, together with shifts in the composition of northern manufacturing toward higher value-added, skill-intensive, or capital-intensive activities. Such effects may occur even when trade is balanced between the north and the south, and are in addition to those north-south effects captured by the variable TRADEBAL.

Finally, there is the variable FIXCAP. which is gross domestic fixed capital formation expressed as a percent of GDP at constant prices. As noted earlier, the rationale for using this variable is that capital investment is manufacturing intensive, and a change in the rate of investment will therefore have a greater impact on the demand for manufactured goods than on the demand for the output of other sectors.

Regression Results: Productivity and Prices

Table 1 reports the results of pooled regressions in which the dependent variables are log y and log RELPROD. Both these variables have a strong upward trend, which accounts for the finding in regression equation (3) that rising per capita income is associated with increasing relative labor productivity in manufacturing. The variable LDCIMP is included to quantify the impact of low-wage imports on manufacturing productivity. In equation (4). the coefficient of this variable is quite large and highly significant. It implies that an increase of 1 percentage point in the ratio of low-wage imports to GDP will cause manufacturing productivity to rise by 8.5 percent as compared to productivity in the economy as a whole. To examine the robustness of this finding, we experimented with two other equations: (4 AR), which corrects for first-order autocorrelation, and (5), which includes a time trend. In each case, the coefficient of LDCIMP turns out to be smaller than in the original formulation, but is still statistically significant.

Table 1.

Pooled Estimates of Relative Productivity and Prices, 1963-94

Equation Number (in parentheses) and Dependent Variable

Notes: The constant term is not reported. Absolute t-values are in parentheses. Two asterisks and one asterisk denote statistical significance at the 1 percent and the 5 percent level, respectively. The data sample consists of 18 OECD countries, for which a total of 510 observations are available. Equation (4A R) assumes an AR(1) error process. The prefix “log” denotes the natural logarithm. Y = per capita income in 1986 U.S. dollars at purchasing power parity. RELPROD = real value added per worker in manufacturing ÷ real value added per worker in the whole economy (index 1990 = 100). LDCIMP = imports of manufactures from developing countries (U.N. definition) as a percent of GDP at purchasing power parity. YEARS = years elapsed since 1963. There is a separate time dummy for each year except 1994 and a separate country dummy for each country except the United States.

Table 1.

Pooled Estimates of Relative Productivity and Prices, 1963-94

Equation Number (in parentheses) and Dependent Variable

		(i)	(2)	(3)	(4)	(4AR)	(5)
		logY	log	log	log	log	log
			RELPROD	RELPROD	RELPROD	RELPROD	RELPROD
Explanatory variables
	log Y	…	…	0.41**	0.30**	0.40**	-0.07
				(22.40)	(12.60)	(7.84)	(1.34)
	YEARS	0.024**	0.012**	…	…	…	0.012**
		(60.37)	(27.26)				(8.41)
	LDCMP	…	…	…	0.085**	0.029**	0.028**
					(7.12)	(3.57)	(2.15)
Country dummies exist?		yes	yes	yes	yes	yes	yes
R2		0.93	0.75	0.87	0.88	.022	0.77

Notes: The constant term is not reported. Absolute t-values are in parentheses. Two asterisks and one asterisk denote statistical significance at the 1 percent and the 5 percent level, respectively. The data sample consists of 18 OECD countries, for which a total of 510 observations are available. Equation (4A R) assumes an AR(1) error process. The prefix “log” denotes the natural logarithm. Y = per capita income in 1986 U.S. dollars at purchasing power parity. RELPROD = real value added per worker in manufacturing ÷ real value added per worker in the whole economy (index 1990 = 100). LDCIMP = imports of manufactures from developing countries (U.N. definition) as a percent of GDP at purchasing power parity. YEARS = years elapsed since 1963. There is a separate time dummy for each year except 1994 and a separate country dummy for each country except the United States.

View Table

Table 1.

Pooled Estimates of Relative Productivity and Prices, 1963-94

Equation Number (in parentheses) and Dependent Variable

		(i)	(2)	(3)	(4)	(4AR)	(5)
		logY	log	log	log	log	log
			RELPROD	RELPROD	RELPROD	RELPROD	RELPROD
Explanatory variables
	log Y	…	…	0.41**	0.30**	0.40**	-0.07
				(22.40)	(12.60)	(7.84)	(1.34)
	YEARS	0.024**	0.012**	…	…	…	0.012**
		(60.37)	(27.26)				(8.41)
	LDCMP	…	…	…	0.085**	0.029**	0.028**
					(7.12)	(3.57)	(2.15)
Country dummies exist?		yes	yes	yes	yes	yes	yes
R2		0.93	0.75	0.87	0.88	.022	0.77

Notes: The constant term is not reported. Absolute t-values are in parentheses. Two asterisks and one asterisk denote statistical significance at the 1 percent and the 5 percent level, respectively. The data sample consists of 18 OECD countries, for which a total of 510 observations are available. Equation (4A R) assumes an AR(1) error process. The prefix “log” denotes the natural logarithm. Y = per capita income in 1986 U.S. dollars at purchasing power parity. RELPROD = real value added per worker in manufacturing ÷ real value added per worker in the whole economy (index 1990 = 100). LDCIMP = imports of manufactures from developing countries (U.N. definition) as a percent of GDP at purchasing power parity. YEARS = years elapsed since 1963. There is a separate time dummy for each year except 1994 and a separate country dummy for each country except the United States.

The determinants of relative prices are examined in Table 2 The coefficient of log RELPROD is large, negative, and highly significant, which is consistent with the notion that movements in labor productivity are the major factor influencing the behavior of relative prices. The coefficient of LDCIMP is close to zero and statistically insignificant, suggesting that competition from low-wage imports has had little enduring effect on domestic producer prices once we control for productivity. It may be that competition from such imports does affect producer prices by squeezing profit margins, but this effect must either be small or short lived, and manufacturers are probably able to restore their profit margins by becoming more efficient or shifting into higher value-added products.

Table 2.

Pooled Estimates of Relative Prices, 1963-94

(Dependent variable = log RELPRICE)

Notes: RELPRICE = implicit price of manufactures ÷ implicit price of GDP (index 1990 = 100). See also the notes to Table 1.

Table 2.

Pooled Estimates of Relative Prices, 1963-94

(Dependent variable = log RELPRICE)

		Equation Number
		(6)	(7)
Explanatory variables
	Log RELPROD	-0.88**)	0.89**
		(36.91)	(28.97)
LDCIMP		…	-0.008
		…	-0.085
Country dummies exist?		yes	yes
R²		0.80	0.80

Notes: RELPRICE = implicit price of manufactures ÷ implicit price of GDP (index 1990 = 100). See also the notes to Table 1.

View Table

Table 2.

Pooled Estimates of Relative Prices, 1963-94

(Dependent variable = log RELPRICE)

		Equation Number
		(6)	(7)
Explanatory variables
	Log RELPROD	-0.88**)	0.89**
		(36.91)	(28.97)
LDCIMP		…	-0.008
		…	-0.085
Country dummies exist?		yes	yes
R²		0.80	0.80

Notes: RELPRICE = implicit price of manufactures ÷ implicit price of GDP (index 1990 = 100). See also the notes to Table 1.

Output

Table 3 analyzes how the share of manufacturing in real output is determined; three different equations are presented. Equation (8) is based on the standard OLS approach applied to the pooled sample containing all 510 observations. The residuals of this equation exhibit strong autocorrelation, which is largely eliminated in equation (8A R) by assuming an AR(1) error process. Equation (8AVG) is derived by grouping the original data by taking three-year averages centered on the years 1964, 1968, 1972. and so on. While this method sacrifices information, it has the advantage of largely eliminating autocorrelation without having to make explicit assumptions about the nature of the error process.⁶

Table 3.

Pooled Estimates of the Share of Manufacturing in Real Output, 1963-94

(Dependent variable = log OUTSHARE)

Notes: OUTSHARE = manufacturing value added as a percent of GDP at 1990 prices. Equation (8A R) assumes an AR(1) error process; equation (8AVG) is based on three-year averages centered on the years 1964, 1968, 1972, 1976, 1980, 1984, 1988, and 1992. FIXCAP = gross domestic fixed capital formation as a percent of GDP at 1990 prices. The turning point is the value of Y at which the dependent variable starts to fall with increasing Y. See also notes for Tables 1 and 2.

Table 3.

Pooled Estimates of the Share of Manufacturing in Real Output, 1963-94

(Dependent variable = log OUTSHARE)

		Equation Number
		(8)	(8AR)	(8AVG)
Explanatory variables
	Log Y	6.32**	5.01**	6.55**
		(13.63)	(3.95)	(7.02)
	(Log Y)2	-0.350**	-0.269**	-0.363**
		(13.82)	(4.00)	(7.09)
	Log RELPRICE	-0.589**	-0.265**	-0.611 **
		(18.37)	(6.14)	(9,82)
	TRADEBAL	0.019**	0.004	0.020**
		(12.96)	(3.50)	(6.39)
	LDCIMP	0.000	-0.009	-0.003
		(0.01)	(1.16)	(0.14)
	F1XCAP	0.016**	0.007**	0.018**
		(11.71)	(5.89)	(6.16)
Country dummies exist?		yes	yes	yes
R²		0.84	0.36	0.84
Turning point		$8,390	$10,983	$8,276

Notes: OUTSHARE = manufacturing value added as a percent of GDP at 1990 prices. Equation (8A R) assumes an AR(1) error process; equation (8AVG) is based on three-year averages centered on the years 1964, 1968, 1972, 1976, 1980, 1984, 1988, and 1992. FIXCAP = gross domestic fixed capital formation as a percent of GDP at 1990 prices. The turning point is the value of Y at which the dependent variable starts to fall with increasing Y. See also notes for Tables 1 and 2.

View Table

Table 3.

Pooled Estimates of the Share of Manufacturing in Real Output, 1963-94

(Dependent variable = log OUTSHARE)

		Equation Number
		(8)	(8AR)	(8AVG)
Explanatory variables
	Log Y	6.32**	5.01**	6.55**
		(13.63)	(3.95)	(7.02)
	(Log Y)2	-0.350**	-0.269**	-0.363**
		(13.82)	(4.00)	(7.09)
	Log RELPRICE	-0.589**	-0.265**	-0.611 **
		(18.37)	(6.14)	(9,82)
	TRADEBAL	0.019**	0.004	0.020**
		(12.96)	(3.50)	(6.39)
	LDCIMP	0.000	-0.009	-0.003
		(0.01)	(1.16)	(0.14)
	F1XCAP	0.016**	0.007**	0.018**
		(11.71)	(5.89)	(6.16)
Country dummies exist?		yes	yes	yes
R²		0.84	0.36	0.84
Turning point		$8,390	$10,983	$8,276

Notes: OUTSHARE = manufacturing value added as a percent of GDP at 1990 prices. Equation (8A R) assumes an AR(1) error process; equation (8AVG) is based on three-year averages centered on the years 1964, 1968, 1972, 1976, 1980, 1984, 1988, and 1992. FIXCAP = gross domestic fixed capital formation as a percent of GDP at 1990 prices. The turning point is the value of Y at which the dependent variable starts to fall with increasing Y. See also notes for Tables 1 and 2.

The key points to note in Table 3 are as follows. There is strong evidence of a hump-shaped relationship between log OUTSHARE and log Y. This implies that the income elasticity of demand for manufactures is well above unity when a country is poor, and falls below unity when a country becomes rich. This outcome is found no matter what method of estimation is used. Table 3 also shows the “turning point,” which is the level of per capita income at which the income elasticity of demand for manufactures is equal to unity. An interesting feature of these estimations is the coefficient on relative prices. In equations (8) and (8AVG), this coefficient is highly significant and is about 0.6, suggesting that the price elasticity of substitution between manufactures and nonmanufac-tures is in the region of 0.6.⁷ In the case of equation (8A R), the price coefficient is less than half this value, but remains highly significant. Thus, while there is strong evidence that, as economies develop, the demand for manufactures is stimulated by their falling relative price, there is some uncertainty about the magnitude of this effect.

As expected, the real output share of manufactures is boosted by a positive manufacturing trade balance and by a high level of fixed capital formation, but the estimated size of these effects is lower when the AR version of the equation is used. In all output equations, the coefficient of LDCIMP is very small and insignificant, which is to be expected since this variable captures the impact of low-wage imports on productivity rather than output.

Employment

Tables 4 and 5 examine how the share of manufacturing employment is determined. The former table uses pooled data, while the latter uses cross-sectional data. There is strong evidence of a hump-shaped relationship, with the employment share of manufacturing rising in the earlier stages of economic development and falling back at high levels of per capita income. The estimated turning point varies somewhat between equations, with the cross-sectional equations yielding a somewhat higher turning point than the pooled equations. However, the latter are probably a better guide to the intertemporal process of structural change since they make use of time-series data for individual countries. They suggest a turning point of about $9,000 (1986 purchasing power parity) per capita, which most OECD countries had reached by 1970, and some well before. A number of East Asian economies have also reached or surpassed this point, and the share of manufacturing employment in most of them is now falling; thus the estimated turning point is consistent with the East Asian experience.⁸

Table 4.

Pooled Estimates of the Share of Manufacturing in Employment, 1963-94

(Dependent variable = log EMPSHARE)

Notes: EMPSHARE = employment in manufacturing as a percent of total employment. For other notes see Tables 1 and 2.

Table 4.

Pooled Estimates of the Share of Manufacturing in Employment, 1963-94

(Dependent variable = log EMPSHARE)

		Equation Number
		(9)	(9AR)	(9AVG)	(10)
Explanatory variables
	Log y	11.78**	12.02**	11.65**	9.72**
		(22.14)	(8.70)	(10.92)	(18.31)
	(Log y)2	-0.649**	-0.667**	-0.643**	-0.510**
		(22.39)	(8.97)	(11.01)	(17.05)
	TRADEBAL	0.012**	0.000	0.015**	0.012**
		(7.11)	(0.14)	(4.16)	(5.88)
	LDCIMP	-0.041**	-0.035**	-0.041	-0.041**
		(3.79)	(4.36)	(1.57)	(3.54)
	FIXCAP	0.013**	0.008**	0.014**	0.002**
		(8.68)	(6.65)	(4.26)	(0.21)
Country dummies exist?		yes	yes	yes	yes
Time dummies exist?		no	no	no	yes
R²		0.90	0.70	0.91	0.93
Turning point		$8,790	$9,421	$8,673	$13,846

Notes: EMPSHARE = employment in manufacturing as a percent of total employment. For other notes see Tables 1 and 2.

View Table

Table 4.

Pooled Estimates of the Share of Manufacturing in Employment, 1963-94

(Dependent variable = log EMPSHARE)

		Equation Number
		(9)	(9AR)	(9AVG)	(10)
Explanatory variables
	Log y	11.78**	12.02**	11.65**	9.72**
		(22.14)	(8.70)	(10.92)	(18.31)
	(Log y)2	-0.649**	-0.667**	-0.643**	-0.510**
		(22.39)	(8.97)	(11.01)	(17.05)
	TRADEBAL	0.012**	0.000	0.015**	0.012**
		(7.11)	(0.14)	(4.16)	(5.88)
	LDCIMP	-0.041**	-0.035**	-0.041	-0.041**
		(3.79)	(4.36)	(1.57)	(3.54)
	FIXCAP	0.013**	0.008**	0.014**	0.002**
		(8.68)	(6.65)	(4.26)	(0.21)
Country dummies exist?		yes	yes	yes	yes
Time dummies exist?		no	no	no	yes
R²		0.90	0.70	0.91	0.93
Turning point		$8,790	$9,421	$8,673	$13,846

Notes: EMPSHARE = employment in manufacturing as a percent of total employment. For other notes see Tables 1 and 2.

Table 5.

Cross-Sectional Estimates of the Share of Manufacturing in Employment

(Dependent variable = log EMPSHARE)

Notes: Equations are estimated using data averaged over a three-year period centered on the year shown. For other notes, see Tables 1 to4.

Table 5.

Cross-Sectional Estimates of the Share of Manufacturing in Employment

(Dependent variable = log EMPSHARE)

		Equation Number (In Parentheses), Year, and Number of Countries
		(11)	(12)	(13)	(14)	(15)	(16)	(17)	(18)
		1964	1968	1972	1976	1980	1984	1988	1992
		14	15	15	16	18	18	16	16
Explanatory variables
	log Y	15.61**	14.59**	19.51**	22.15*	16.70	18.54	11.60	-1.09
		(5.18)	(3.30)	(2.65)	(2.21)	(1.55)	(1.58)	(1.07)	(0.09)
	(Log Y)²	-0.855**	-0.783**	-1.049**	-1.189*	-0.894	-0.990	-0.623	0.031
		(5.03)	(3.21)	(2.64)	(2.21)	(1.55)	(1.59)	(1.09)	(0.05)
	TRADEBAL	0.019**	0.020**	0.021**	0.014**	0.014**	0.017**	0.018**	0.022**
		(4.24)	(3.67)	(4.03)	(3.33)	(3.20)	(2.80)	(3.80)	(3.61)
	R²	0.88	0.77	0.69	0.54	0.42	0.39	0.50	0.44
	Turning point	$9,208	$11,094	$10,916	$11,108	$11,447	$11,645	$11,053	…
	(Standard error)	($638)	($1,147)	($788)	($832)	($1,136)	($1,114)	($2,588)	…

Notes: Equations are estimated using data averaged over a three-year period centered on the year shown. For other notes, see Tables 1 to4.

View Table

Table 5.

Cross-Sectional Estimates of the Share of Manufacturing in Employment

(Dependent variable = log EMPSHARE)

		Equation Number (In Parentheses), Year, and Number of Countries
		(11)	(12)	(13)	(14)	(15)	(16)	(17)	(18)
		1964	1968	1972	1976	1980	1984	1988	1992
		14	15	15	16	18	18	16	16
Explanatory variables
	log Y	15.61**	14.59**	19.51**	22.15*	16.70	18.54	11.60	-1.09
		(5.18)	(3.30)	(2.65)	(2.21)	(1.55)	(1.58)	(1.07)	(0.09)
	(Log Y)²	-0.855**	-0.783**	-1.049**	-1.189*	-0.894	-0.990	-0.623	0.031
		(5.03)	(3.21)	(2.64)	(2.21)	(1.55)	(1.59)	(1.09)	(0.05)
	TRADEBAL	0.019**	0.020**	0.021**	0.014**	0.014**	0.017**	0.018**	0.022**
		(4.24)	(3.67)	(4.03)	(3.33)	(3.20)	(2.80)	(3.80)	(3.61)
	R²	0.88	0.77	0.69	0.54	0.42	0.39	0.50	0.44
	Turning point	$9,208	$11,094	$10,916	$11,108	$11,447	$11,645	$11,053	…
	(Standard error)	($638)	($1,147)	($788)	($832)	($1,136)	($1,114)	($2,588)	…

Notes: Equations are estimated using data averaged over a three-year period centered on the year shown. For other notes, see Tables 1 to4.

As expected, fixed capital formation exerts a positive influence on manufacturing employment, but its impact varies between equations. There is also evidence that the overall trade balance in manufactures has a major impact on manufacturing employment. In equations (9) and (9AVG) the coefficient of TRADEBAL is quite large and statistically significant: this is also true of the cross-sectional estimates shown in Table 5 However, in the autoregressive formulation (9AR) the coefficient of TRADEBAL is small, which most likely reflects the fact that this variable plays a much greater role in explaining cross-country differences in the structure of employment than it does in explaining intertemporal developments.

The coefficient of the other trade variable, LDCIMP, differs somewhat in magnitude and significance between equations. Equations (9), (9A R), and (9AVG) use pooled data, and in all of these equations the coefficient of LDCIMP is negative and of similar size, although it is not always statistically significant. Interestingly, when additional dummies are included to allow for “fixed” time effects, the coefficient of LDCIMP increases in significance, indicating that this variable is not simply acting as a proxy for unidentified time effects. Note that in the cross-sectional regressions, the coefficients of LDCIMP are either small and insignificant or of the wrong sign. This suggests that although imports from the south have influenced the evolution of economic structure through time, they do not account for the large persistent differences in structure between countries. Such contrasts are explained primarily by the pattern of trade surpluses and deficits as reflected in the variable TRADEBAL.

III. Accounting for Deindustrialization

In this section we quantify the influence of the various factors responsible for the declining share of manufacturing employment in the industrial countries since 1970. For this purpose we use the regression results shown in Table 4. This table contains a number of equations, all of which yield unbiased estimates, so there is a question as to which is the most appropriate. Equation (9) has autocorrelated residuals, but yields plausible coefficients. Equation (9A R) has the advantage that the residuals are virtually uncorrelated, but the coefficient for TRADEBAL is implausibly low, whereas equation (9AVG) also has uncorrected residuals but ignores much of the available information. We have chosen the estimates in equation (9) as the basis for the decompositions, but the results would have been virtually the same whichever equation was used.

Table 6 decomposes the changes in manufacturing employment into various components using the regression coefficients shown in equation (9). The headings in the table are self-explanatory with the exception of the component labeled “normal growth.” This component covers all of the effects that would normally he associated with rising per capita income in a closed economy, and thus takes into account both the income elasticity of demand and the influence of normal productivity and price changes. It is estimated from the coefficients of log Y and (log Y)² in equation (9), Note that this component excludes the effect of abnormal price and productivity changes, in particular the abnormal productivity growth induced by competition from low-wage imports. The latter is included under the heading “north-south trade.”

Table 6.

Explaining Deindustrialization, 1970-94

Notes: This table decomposes changes in the employment share of manufacturing. The estimates shown here are based on regression equation (9) in Table 4. The column showing the residuals includes the interaction effects due to the nonlinear (logarithmic) form of the estimated equation. “Normal growth” refers to the income effect estimated from the income coefficient in equation (9); it includes the effect of those productivity and price changes that are normally associated with rising incomes. It excludes the effect of abnormal price and productivity changes, in particular the effects of abnormal productivity growth arising from competition with low-wage countries. The latter is included under the heading “north-south trade.”

Table 6.

Explaining Deindustrialization, 1970-94

	Change in	Change Due to:
	Share or
	Manufacturing	Normal		Tota	North-south	Other	Total
	Employment	growth	Investment	InternaL	trade-	trade-	rade	Residual
Industrial countries	8.7	-6.9	-1.5	-8.4	-1.6	0.1	1.5	1.2
United States	-10.4-	7.8	-0.6	-8.4	-2.0	-0.5	-2.5	0.6
European Union	-9.5	-6.0	-2.0	-8.0	-1.6	0.2	-1.4	-0.1
Japan	-3.3	-8.0	-1.9	-10.0	-0.9	1.3	0.4	6.2

Notes: This table decomposes changes in the employment share of manufacturing. The estimates shown here are based on regression equation (9) in Table 4. The column showing the residuals includes the interaction effects due to the nonlinear (logarithmic) form of the estimated equation. “Normal growth” refers to the income effect estimated from the income coefficient in equation (9); it includes the effect of those productivity and price changes that are normally associated with rising incomes. It excludes the effect of abnormal price and productivity changes, in particular the effects of abnormal productivity growth arising from competition with low-wage countries. The latter is included under the heading “north-south trade.”

View Table

Table 6.

Explaining Deindustrialization, 1970-94

	Change in	Change Due to:
	Share or
	Manufacturing	Normal		Tota	North-south	Other	Total
	Employment	growth	Investment	InternaL	trade-	trade-	rade	Residual
Industrial countries	8.7	-6.9	-1.5	-8.4	-1.6	0.1	1.5	1.2
United States	-10.4-	7.8	-0.6	-8.4	-2.0	-0.5	-2.5	0.6
European Union	-9.5	-6.0	-2.0	-8.0	-1.6	0.2	-1.4	-0.1
Japan	-3.3	-8.0	-1.9	-10.0	-0.9	1.3	0.4	6.2

Notes: This table decomposes changes in the employment share of manufacturing. The estimates shown here are based on regression equation (9) in Table 4. The column showing the residuals includes the interaction effects due to the nonlinear (logarithmic) form of the estimated equation. “Normal growth” refers to the income effect estimated from the income coefficient in equation (9); it includes the effect of those productivity and price changes that are normally associated with rising incomes. It excludes the effect of abnormal price and productivity changes, in particular the effects of abnormal productivity growth arising from competition with low-wage countries. The latter is included under the heading “north-south trade.”

The main conclusion from our decomposition is that the bulk of deinduslrialization since 1970 is due to internal factors. In most countries in our sample, between one-half and two-thirds of the relative decline of manufacturing employment is explained by the normal process of economic growth—via changing preference patterns. differential productivity growth, and associated price changes. In addition, there has been a substantial fall in the ratio of investment to GDP, which has helped to skew demand away from manufactured goods. Our calculations suggest that this has had quite a large impact on manufacturing employment in some countries, and on average accounts for about one-sixth of deindustrialization. For the average country in the sample, this is similar to the effect of north-south trade. Note that these findings do not depend on the specific equation used for our decomposition; similar results are obtained with equation (9A R) or (9AVG).

For most countries in the sample, our decomposition explains with reasonable accuracy what has happened to manufacturing employment since 1970. There is, however, a striking exception. As can be seen from Table 6, the residual for Japan is both large and positive, indicating that Japanese manufacturing employment has declined by much less than predicted. To understand what might be responsible for this anomaly, we examined the behavior of output, productivity, and prices over the period. The data indicate that the share of manufacturing in real output in Japan has risen substantially since 1970, whereas in most other countries this share has fallen. The unusual experience of Japan may be explained by the behavior of relative prices. Since 1970, profit margins have declined sharply in Japanese manufacturing industry, causing the relative price of manufactured goods to fall by far more than is predicted by productivity growth and by far more than in other industrial countries. This has stimulated the demand for manufactured goods in Japan, and helps to explain why the constant price share of manufactured goods in national production has risen since 1970. and hence why the share of manufacturing employment has fallen so little.

IV. Conclusion

The main conclusion of this paper is that deindustrialization is explained mainly by factors that are internal to the advanced economies—i.e., as a result of the interactions among changing preference patterns between manufactures and services, the faster growth of productivity in manufacturing as compared to services, and the associated relative decline in the price of manufactures. North-south trade has. on average, contributed less than 20 percent to the relative decline in manufacturing employment in the advanced economies. Moreover, the impact of north-south trade on deindustrialization has been mainly through its effect in stimulating labor productivity in the manufacturing sector of the advanced economies; it has had little effect on manufacturing output in the advanced economies. The decline in the ratio of investment to GDP in the advanced economies has also skewed demand away from manufacturing output. The decline in the investment ratio has caused almost one-sixth of total deindustrialization—which is roughly similar to the effect of north-south trade on deindustrialization.