© The Author(s) 2018

Murat A. YülekHow Nations Succeed: Manufacturing, Trade, Industrial Policy, and Economic Developmenthttps://doi.org/10.1007/978-981-13-0568-9_5

5. The ‘Why’ of Manufacturing

Murat A. Yülek¹  

(1)

Istanbul Commerce University, Istanbul, Turkey

 

 

Murat A. Yülek

Is there anything special about manufacturing? If so what is it? If there is anything special about manufacturing, then appropriate industrial policies supporting the naissance or growth of manufacturing—especially in developing countries—may have merit. This chapter discusses the merit of the manufacturing sector. It starts by reviewing a well-known trend, that of the falling share of manufacturing in total Gross Domestic Product (GDP, which measures the total value of production of all goods and services across the economy). If manufacturing is a ‘good’ sector, why does its share in total economic activity go down? We tackle the possible answers at the end of the chapter.

The chapter then examines ‘which’ countries manufacture? Using recent statistics, it identifies who manufactures and exports: Are they poor or rich countries? In other words, is manufacturing a poor man’s business? Then, we examine the world trade. Trade is what enriches countries and drives growth. What, then, constitutes the world trade? Anything other than manufactures (exception is relatively small share of food and energy)? And any country other than the rich (the main exception is China)?

The link between the GDP growth and growth of manufacturing is then discussed. Backward and forward linkages of the manufacturing sector are the key to its social benefits. Lastly, the smile curve phenomenon is reviewed with a view to discern the level of (private) benefits from high- and low-value-added manufacturing.

5.1 The ‘Fall’ of Manufacturing

Let’s start by reviewing a well-known trend. As per capita income rises in a country, the share of manufacturing in total output (GDP) first increases and then decreases. In the aggregated world economy, the share of manufacturing in total output went down from around 27% in the 1970s to around 17% after 2010. In developed economies, the share of manufacturing and overall industry in the GDP has steadily diminished over recent decades (Fig. 5.1). It currently ranges between 10% and 20%. In Germany and Japan, which are among the developed countries which are still ‘manufacturing’ nations, the ratio is close to 20%. In the USA, it is low; around 12%. Nevertheless, the fall in Japan and Germany is also illustrative; the share of manufacturing fell from 35% and 32% in the 1970s and went down to less than 25% and 20%, respectively, of the total output.

Fig. 5.1

Decline of manufacturing: selected developed economies (share of manufacturing in total output). (Source: World Bank)

In developing countries, manufacturing has continued to increase its share in total output until recently, after which it started to fall (Fig. 5.2). In Brazil, the fall is prominent since the 1980s, and in Turkey since the 1990s. In Malaysia the fall started in the mid-2000s. In China, which is the largest manufacturer in the world, the fall—from high levels—is ongoing prominently since the mid-2000s, although the level is still high. In India the share of manufacturing in GDP has been low (in the band of 15–20% of the total output) but not falling further; India seems to have experienced a premature industrialization earlier than other developing countries in contrast to its north-eastern peer China.

Fig. 5.2

Decline of manufacturing: selected developing economies (share of manufacturing in total output). (Source: World Bank)

The share of manufacturing in total employment has diminished in parallel with its share in total output. In the USA, for example, the share of manufacturing employment in total went down from 40% in the 1940s to 8% in 2015 (Fig. 5.3). That was the result of two divergent trends. While total employment increased from 38 million in 1940 to 145 million in 2015, manufacturing employment went down from 12.8 million to 12.2 million (after reaching the 19 million bars in the 1970s). So, in the USA, the fall in manufacturing employment is apparent not only in terms of its share but also in terms of absolute numbers.

Fig. 5.3

Manufacturing employment in the USA. (Source: Bureau of Labour Statistics. Employment data is seasonally adjusted)

The falling share of manufacturing in total output and similarly falling numbers of manufacturing employment led to the arguments of ‘de-industrialization.’ In the developing economies, the turning point from rising to falling share of manufacturing has been named ‘premature de-industrialization’; that is, the share of manufacturing started to fall much earlier than expected. In Fig. 5.2, the turning points for selected developing countries are apparent.

In the USA, 2016 presidential elections gave way to Donald Trump becoming the 44th president of the country, supported by his campaign arguments of de-industrialization and his policies to increase domestic industrial production. So, industrialization and de-industrialization continues to be a topic of heated public discussion even in the richest countries of the world. In fact, as we will see in a moment, the USA is still by far a huge industrial economy.

5.2 Is Manufacturing a Poor Man’s Business?

The falling trend in manufacturing’s share in output and employment, especially in the developed economies, led to a widespread misconception among some economists and decision makers (i.e. statesmen): that manufacturing was a poor man’s business or something similar. In more official words, the misconception goes as follows: As countries develop, they get away (or, rather, they have to get away) from manufacturing.

This misconception seems even to lead the decision (policy) maker to wrongly feel that if the country is to prove that it is now richer, its statistics have to show low production of manufacturing and high production of services.

This is in a way more dangerous, as this may involve the level of production rather than its share in total output. The immediate question is why this happens. There may be a number of explanations and they will be taken up in a subsequent section of this chapter, while the remainder of this section will look at the relationship between manufacturing value added and per capita GDP. The former, the manufacturing value added, is defined as the addition of value by the manufacturing firms on their primary inputs such as agricultural or mining products. A simple example is an iron smelter transforming iron ore into iron bars. Manufacturing sector comprises firms that produce subsequent products in the value chain. Iron ore is transformed into steel in the steel factory and steel is transformed into cars in the automobile plant. Thus, during all those stages, manufacturing firms ‘add value’ to the inputs they receive while transforming them into their final products. Manufacturing value added in a country is the sum of all the added values by manufacturing firms in a year in the country. Thus, it is a good indicator of the level of production of the manufacturing industry.

The latter, per capita GDP, is the total production value of goods and services in the country per head, which is equal to the average income generated by the productive factors in the country. Thus, a country with a higher per capita GDP is one which is able to produce more income for its citizens. Unless an economy is dominated by natural resources, higher per capita income means a more complex and possibly a more diversified one.

Thus, a positive association between per capita income and manufacturing value added would indicate that manufacturing is the business of the rich men (country) not the poor, and vice versa. Figure 5.4 confirms this conjecture of a positive relationship. Richer countries (those with higher per capita income) are bigger manufacturers.

Fig. 5.4

Manufacturing and per capita income: selected countries. (Source: IMF [WEO] and World Bank [reproduced from PGlobal, 2016])

That may be considered a natural result of numbers rather than a surprise. High per capita income may point to a larger economy given the population, and in a large economy, the (added) value of production of manufactured goods is likely to be high anyway even when the share of manufacturing in total output has declined.

This is a valid objection requiring further evidence for our hypothesis. A good second indicator can be per capita manufacturing value added (i.e. total manufacturing value added in the country divided by the country’s population). If manufacturing is a poor man’s activity, in a rich country, per capita manufacturing value added should be small. Table 5.1 ranks world countries in terms of per capita manufacturing value added. Disregarding smaller and resource-based countries¹ at the top of the list, Switzerland is the most industrialized country in the world. The top 20 countries consist entirely of rich countries. Per capita manufacturing value added, however crude, is probably the most meaningful indicator of the level of industrialization of a country available. A visual scan of the table shows that the industrialized countries are generally the rich ones, supporting our earlier conclusion.

Table 5.1

Industrialization ranking of countries (2014)

Rank	Country	Per capita manufacturing value added ($)	Per capita GDP ($)
1	Switzerland	15,903	86,606
2	Ireland	11,038	55,899
3	Singapore	10,038	56,336
4	Germany	9893	48,043
5	Qatar	8809	86,853
6	Sweden	8647	59,180
7	Austria	8569	51,733
8	Korea, Rep,	7645	27,811
9	Japan	7505	38,096
10	Denmark	7418	62,549
11	Finland	7265	49,915
12	Brunei Darussalam	6698	41,531
13	Norway	6604	97,200
14	United States	6499	54,599
15	Belgium	5956	47,379
16	Luxembourg	5935	119,225
17	Iceland	5628	52,473
18	Netherlands	5350	52,157
19	Canada	5007	50,440
20	Italy	4925	35,397
21	New Zealand	4873	44,503
22	Slovenia	4787	24,202
23	Czech Republic	4775	19,745
24	Israel	4544	37,540
25	France	4353	42,955
25	United Kingdom	4143	46,783

Source: World Bank (World Development Indicators)

Another interesting ranking can be on total manufacturing value added. Table 5.2 shows that China , the USA, Japan, Germany, and South Korea are the largest manufacturers in the world. Top manufacturers in the world are mostly rich countries. The USA, which has faced long debates of ‘de-industrialization,’ is the second largest manufacturer in the world in nominal terms. Thus, the USA, the largest economy in the world (with a high per capita income), has actually not abandoned manufacturing at all—despite low share in total GDP. China, the second largest economy in the world, which is now a middle (per capita) income country, is the largest manufacturer in the world.

Table 5.2

Largest manufacturers of the world (2015)

Rank	Country	Manufacturing value added (billion $)
1	China	3250
2	United States	2142
3	Japan	892
4	Germany	700
5	Korea, Rep.	374
6	India	315
7	Italy	262
8	United Kingdom	258
9	France	250
10	Mexico	204
11	Brazil	182
12	Indonesia	180
13	Russian Federation	168
14	Spain	154
15	Turkey	143
16	Switzerland	121
17	Thailand	110
18	Ireland	99
19	Australia	85
20	Argentina	85

Source: World Bank (World Development Indicators)

On the other end of the spectrum, take Switzerland, an industrialized and rich country; while Switzerland is generally considered as a chocolate, tourism, and banking nation, it is actually among the most industrialized countries in the world. So is Holland; another small but rich country.

5.3 World Trade and Manufactures: What Do Rich and Poor Countries Export?

Trade drives incomes. International trade provides livelihoods to many people. More importantly, firms in exporting countries generate employment and value added by producing more than their countries’ population can buy. On the importing side, people get access to goods which are more competitive (in price or features) than those locally produced, indicating a positive impact on their welfare.

Macroeconomic sustainability requires a country to balance its international trade at least over the long run. This can happen by building the capacity to increase competitive exports to the world. More than half of world trade comprises manufactured materials (Table 5.3). For countries seeking exporting opportunities (and lacking abundant natural resources and primary goods) the only way to achieve sustainability in international trade is building competitiveness in manufacturing industries.

Table 5.3

Composition of world exports (2015)

	Value ($ billion)	%
Merchandise	15,464	76
Of which manufactured goods	11,289	55
Services	4808	24
Total world exports	20,272	100

Source: WTO (World Trade Statistical Review 2017)

The export pattern of a country reveals a lot of things about the sophistication of its economy. Three economists, Ricardo Hausman et al. (2007), contend that the products exported by higher-income countries should be more sophisticated than those exported by middle- or low-income countries. If a product is exported by a high-income country, its companies must be making more money even after paying high salaries to employees and bearing other costs than those in low-income countries. In other words, those are ‘worthwhile’ products specialized in by high-income countries.

So, what do high-income countries export? And what do middle- and low-income countries export? Tables 5.4, 5.5 and 5.6 show the following:

• High-income countries primarily export three categories of products: electrical and non-electrical machinery, chemical products, and transport equipment (automobiles, locomotives, tramways and railway rolling stock, ships, boats, etc.). Combined, these product categories constitute typically around 40–60% of total exports. Their exports show that these economies are not dependent on their natural resource base.

• Low-income countries primarily export primary and resource-based products (such as basic agricultural and food products, fuels [refined or unrefined], minerals and metals, wood, etc.) and textile and clothing products. Exports of these countries almost fully depend on their natural resource base.

• In high- to middle-income countries an important export item is machinery, but its total value and share in total exports are not as high as in high-income countries. Exports of these countries show that their economies are still somewhat dependent on their resource base.

• In low-income countries, exports are constituted by primary products (agriculture, minerals, etc.) as well as some manufactured products (textiles, machinery, etc.). In countries such as India and Egypt, chemicals also represent some respectable share in exports. Depending on the country itself, there is still a significant amount of dependence on the natural resource base.

Table 5.4

Exports of selected high-income countries (2013–2014)

		Countries
		Belgium	Denmark	France	Germany	Italy	Japan	S. Korea	United Kingdom	USA
Product group (HS 1988/92)	01–05_Animal	2.2	10.3	3.0	1.7	1.3	10.3	0.3	1.6	1.9
	28–38_Chemicals	22.8	11.2	15.1	12.2	9.7	11.2	6.9	13.3	10.1
	16–24_FoodProd	4.9	6.8	6.6	3.0	4.9	6.8	0.7	4.1	2.8
	64–67_Footwear	1.2	0.7	0.6	0.4	2.4	0.7	0.1	0.5	0.1
	27–27_Fuels	11.4	6.6	3.9	2.7	3.7	6.6	9.7	10.8	9.6
	41–43_HidesSkin	0.4	1.6	1.4	0.3	2.9	1.6	0.3	0.4	0.4
	84–85_MachElec	10.2	23.0	19.3	26.4	25.9	23.0	34.6	20.2	23.7
	72–83_Metals	7.1	6.0	6.9	7.5	9.7	6.0	8.2	5.6	4.9
	25–26_Minerals	0.6	0.2	0.2	0.2	0.3	0.2	0.1	0.2	0.7
	90–99_Miscellan	7.0	16.6	7.8	11.8	8.4	16.6	7.4	9.5	17.7
	39–40_Plastic&Rubber	8.0	2.8	5.1	5.3	5.0	2.8	7.1	3.2	4.8
	68–71_StoneGlas	5.9	1.0	2.3	2.0	4.8	1.0	1.1	11.1	4.7
	50–63_TextCloth	3.3	4.9	2.9	2.3	6.9	4.9	2.8	2.6	1.6
	86–89_Transport	10.1	3.4	19.0	20.6	9.5	3.4	19.8	14.3	9.6
	06–15_Vegetable	2.8	2.6	3.5	1.2	2.4	2.6	0.2	0.9	4.8
	44–49_Wood	2.2	2.2	2.3	2.6	2.3	2.2	0.7	1.9	2.5
	01–99_All Products	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0

Source: World Integrated Trade Solutions Database 2016

Table 5.5

Exports of selected middle-income countries (2013–2014)

		Upper-middleincome countries						Lower middle income countries
		Brazil	China	Malaysia	Mexico	South Africa	Turkey	India	Indonesia	Egypt
HS 1988/92 Product Group	01–05_Animal	7.8	0.8	0.6	0.9	1.2	1.4	3.5	2.0	1.8
	28–38_Chemicals	5.1	4.7	4.3	2.8	6.3	3.3	10.5	4.8	11.6
	16–24_FoodProd	11.6	1.2	2.8	2.7	4.4	4.8	2.1	3.6	5.0
	64–67_Footwear	0.6	3.0	0.1	0.2	0.3	0.5	1.0	2.5	0.1
	27–27_Fuels	9.2	1.5	22.1	10.6	10.5	3.7	19.6	29.1	23.4
	41–43_HidesSkin	1.4	1.5	0.1	0.2	0.5	0.6	1.2	0.3	0.7
	84–85_MachElec	7.5	41.4	37.9	35.3	10.1	14.8	7.1	8.9	8.2
	72–83_Metals	7.2	7.9	4.9	4.4	13.1	13.2	8.1	5.3	7.2
	25–26_Minerals	13.0	0.2	0.5	1.3	13.5	2.5	1.1	1.2	1.5
	90–99_Miscellan	3.2	9.8	5.3	7.7	2.0	4.4	1.8	2.3	2.7
	39–40_PlastiRub	2.5	3.9	6.2	2.8	2.4	5.5	2.6	5.6	6.6
	68–71_StoneGlas	2.1	4.8	1.9	2.9	16.0	7.1	13.7	3.2	6.3
	50–63_TextCloth	1.1	12.3	1.5	1.8	1.4	18.4	12.2	7.2	11.3
	86–89_Transport	7.2	4.5	1.4	22.9	10.7	12.6	8.2	3.4	0.5
	06–15_Vegetable	16.5	0.9	8.1	2.9	5.3	5.3	6.8	15.2	11.3
	44–49_Wood	4.3	1.7	2.4	0.7	2.5	2.0	0.6	5.5	1.9
	01_99_All Products	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0

Source: World Integrated Trade Solutions Database 2016

Table 5.6

Exports of selected low-income countries (2013–2014)

		Benin	Cambodia	Madagascar	Mali	Mozambique	Nepal	Niger	Uganda	Zimbabwe
HS 1988/92 Product group	01–05_Animal	1.1	0.0	5.9	4.5	1.1	1.4	1.6	6.5	0.3
	28–38_Chemicals	0.8	0.0	2.8	6.5	1.5	4.8	0.1	3.5	0.6
	16–24_FoodProd	2.1	1.2	7.4	0.4	11.6	9.5	3.7	16.0	29.5
	64–67_Footwear	0.0	4.1	0.1	0.0	0.4	3.4	0.0	0.2	0.1
	27–27_Fuels	10.9	0.0	4.5	0.6	33.5	0.0	27.0	6.4	1.0
	41–43_HidesSkin	0.0	0.2	1.1	0.8	0.0	1.8	0.0	2.7	1.0
	84–85_MachElec	11.8	3.0	1.3	2.1	2.7	0.6	2.0	5.4	0.8
	72–83_Metals	8.3	0.4	23.6	0.8	28.2	17.1	0.0	6.3	18.7
	25–26_Minerals	4.5	0.0	7.0	0.6	4.0	1.1	45.9	4.5	11.4
	90–99_Miscellan	1.6	0.5	1.5	0.3	1.7	5.9	9.2	2.4	1.1
	39–40_PlastiRub	0.3	2.2	0.3	0.2	0.2	2.0	0.1	1.5	0.5
	68–71_StoneGlas	2.3	0.1	1.4	65.8	0.0	0.8	0.0	0.8	27.8
	50–63_TextCloth	31.9	55.2	24.9	15.0	2.7	38.5	3.0	2.2	3.6
	86–89_Transport	11.2	4.5	2.4	1.3	6.9	0.0	1.0	4.4	0.8
	06–15_Vegetable	11.8	3.2	13.8	1.1	4.1	11.8	6.3	35.4	1.8
	44–49_Wood	1.4	25.3	1.9	0.1	0.1	1.5	0.1	1.7	1.2
	01_99_All Products	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0

Source: World Integrated Trade Solutions Database 2016

The upshot of the exercise is simple; as the average income level of the country goes up, the country more and more becomes an exporter of manufactured products. This is akin to a ‘sophistication ladder’ (Fig. 5.5). As the sophistication of a country’s economy increases, it exports more and more manufactured goods.

Fig. 5.5

Export sophistication ladder

The conclusion that follows from this section is simple; manufacturing is generally the business of richer countries. Richer, more developed countries export automobiles, airplanes, and electrical, electronic, and mechanical machinery and devices. Indeed, the top ten exporters of manufactured goods which source 82% of total world exports of such products mostly comprise developed economies such as the European Union (EU) countries, the USA, Japan, Singapore, and Canada (Table 5.7).

Table 5.7

Top ten manufactured goods exporters of the world (2015)

	Value (billion USD; 2015)	Share in world exports of manufactured goods (%)
		1980	1990	2000	2015
European Union (28)	4239	–	–	43.0	36.6
Extra-EU (28) exports	1601	–	–	14.1	13.8
China	2153	0.8	1.9	4.7	18.6
USA	1126	13.0	12.1	13.8	8.7
Japan	545	11.2	11.5	9.6	4.7
S. Korea	470	1.4	2.5	3.3	4.1
Hong Kong, China	437	–	–	–	–
Domestic exports	5	1.2	1.1	0.5	0.0
Re-exports	432	–	–	–	–
Mexico	312	0.4	1.1	3.0	2.7
Singapore	266	0.8	1.6	2.5	2.3
Chinese Taipei	240	1.6	2.6	3.0	2.1
Canada	208	2.7	3.1	3.7	1.8
Top ten exporters	9445	–	–	87.0	81.6
World exports of manufactured goodsa	11,572	100.0	100.0	100.0	100.0
Memo: World merchandise exports	16,482

Note: ^aWTO statistics for manufactured products exclude the large export items of manufactured arms and armaments

Source: WTO (World Trade Statistical Review 2016)

5.4 Manufacturing and Growth²

Nicholas Kaldor (1966, 1967, 1975), a well-known Cambridge economist, has described the manufacturing sector as the engine of growth. According to him, the manufacturing sector not only enjoys higher productivity growth rates but also drives up productivity in service and agricultural sectors.

Kaldor’s three laws have been formulated as follows³ (order changed purposefully):

• The second law of Kaldor: Productivity drives the growth of the manufacturing sector; also known as Verdoorn’s (1949) law.

• The third law of Kaldor: The productivity of the non-manufacturing sector is positively related to the growth of the manufacturing sector.

• The first law of Kaldor: The manufacturing sector is the engine of GDP growth.

Kaldor’s ideas can be summarized and extended to classify the manufacturing sector as the hotbed of productivity growth and causality running from growth in the manufacturing sector to others (Fig. 5.6). Manufacturing is the pioneering driver of growth; it is the sector in which the productivity and its growth are relatively high and which raises the growth of the sector. That in turn leads to productivity increases (and hence growth) and growth in the non-manufacturing sector, importantly including services.

Fig. 5.6

Manufacturing and growth. (Source: Yülek 2017)

Growth effects of manufacturing have been the subject of numerous research studies at the national and regional levels.⁴ The results of research generally confirm the growth effects of the manufacturing sector.

The fact that productivity growth is high in the manufacturing sector is one of the prime reasons for the secular trend of a falling share of the sector in total GDP and total employment in countries with higher per capita GDP figures. The share of manufacturing in the GDP probably will converge to a certain level in richer countries. In the USA, for example, where a popular and academic discussion on ‘de-industrialization’ exists, the share of manufacturing in the GDP has been resilient in the recent decades (Baily and Bosworth 2014); moreover, the USA is the second largest manufacturer in the world. This level may be different in different countries enjoying different conditions and policies.

Prices of services are less sensitive to GDP growth, while unit prices of manufactured goods tend downward. Consequently, economic research has documented secularly growing relative prices of services with respect to manufacturing goods. This can be due to higher productivity growth in the manufacturing sector⁵ and higher tradability of manufactured goods compared to most services, which are generally non-tradable (the Balassa-Samuelson theorem). The fact that most manufactured goods are not differentiated products is also likely to play a role.

Following this brief discussion, it can be said that manufacturing can contribute to the growth performance in both developed and developing economies. In developing economies, through both capital accumulation and productivity gains, the manufacturing sector can increase the GDP growth rates. In developed economies, manufacturing can still play the role of a growth engine through overall total factor productivity (TFP) growth as well as supporting growth in the backward regions.⁶

Figure 5.7 shows the relationship of average growth manufacturing production and GDP over the period of 2000–2014. The figure does not necessarily indicate causality running from manufacturing to overall GDP. However, the selected nations, including developing as well as developed economies, indicate that there is an obvious association between the growth of manufacturing and overall income.

Fig. 5.7

Growth of manufacturing and growth of GDP (2000–2014). (Source: IMF [WEO] and World Bank [reproduced from PGlobal, 2016])

5.5 Linkages of the Manufacturing Industry

The concept of forward and backward linkages of a firm or a sector put forward by the economist Alfred Hirschman is well known. Along the value chain, a firm (or a sector) receives raw or intermediate inputs from other firms (and sectors). Forward linkages refer to the firms (or sectors) receiving inputs from the firm (or sector) in question; backward linkages are firms/sectors that supply inputs to the firm. The forward and backward linkages determine the impact on sectoral production following an increase in the final demand; depending on the international supply chain, the impact may go beyond the national borders. Figure 5.8 presents the estimations of backward linkages for an increase in the final demand for automobiles in Japan for an equivalent of 10 billion yens.⁷

Fig. 5.8

Automobile manufacturing: backward linkages and value-added generation. (Source: Inomata 2013)

With a further tool—Wassily Leontief’s input-output methodology—one can quantify the intersectoral flows of economic value and thus the backward and forward linkages of each sector. Such quantifications allow the calculation of ‘multipliers,’ showing how much the production in a sector triggers production in other sectors. Likewise, they show how much an increase in final demand for a sectoral product triggers the production of supplying sectors (in terms of value added), on a dollar-for-dollar basis.

Manufacturing has strong backward linkages and thus high multiplier effects. That is, an increase of $1 in the final demand of manufactured goods generates a high increase in the total output. A study⁸ revealed that in the USA an increase of $1 in the ultimate demand for manufactured goods generates $1.33 of increase in total output. For agriculture the multiplier is $1.11 and for retail sector it is calculated as 0.86 (Fig. 5.9). In general, the multiplier for the manufacturing sector is significantly higher than for the service sector. This is how East Asian countries recorded very high growth rates in recent decades; increasing global demand for manufactured products helped these countries, as they concentrated on the production and exports of manufactured goods such as electronics, automobile, and ships.

Fig. 5.9

Multiplier effects in the USA (change in the total output in response to a $1 change in final demand for the product of the sector, 2012). (Source: U.S. Bureau of Economic Analysis, Annual Input-Output Tables, Manufacturing Institute)

Moreover, some manufacturing subsectors have higher multiplier effects than others. Thus, higher demand for their products triggers production in other subsectors. That implies that increased production in those subsectors generates higher ‘social returns’ (economic return to the entire society) than immediate private returns (economic returns to the firm or the subsector). As we will see in Chap. 12, this can be an important factor in the selection of economically ‘strategic’ sectors in industrial policy.

In countries where domestic suppliers of manufacturing products are strong, the multiplier for the manufacturing sector is higher. Where domestic manufacturing suppliers are weak, manufacturing imports lead to lower multipliers. In Cambodia, for example, a study⁹ found “positive and statistically significant backward links for the services sector, but we did not notice statistically significant backward linkages for the manufacturing sector. This is likely due to the lack of strong domestic industries to form linkages with multinationals.”

5.6 Why Then Does the Share of Manufacturing in Output Falls as Economies Grow?

It is an obvious truth, which has been taken notice of by many writers, that population must always be kept down to the level of the means of subsistence; but no writer that the Author [Thomas Malthus] recollects has inquired particularly into the means by which this level is affected: and it is a view of these means which forms, to his mind, the strongest obstacle in the way to very great future improvement of society. (Thomas Malthus¹⁰)

That economics is a ‘dismal science’ is a view attributed to Malthus, an early economist. Living at a time when the British population exploded, and noticing that the size of arable land was finite, he argued that the production of food could not keep up with the growth of population. Time proved him wrong; agricultural productivity increased quite impressively; increasing population led to increasing food production. And economics became a quite ‘joyful’ science, if it can be called a science and not debating its use and precision.

How about manufacturing? Let’s come back to the question at the beginning of this chapter: Why does the share of manufacturing fall as per capita income rises after a threshold in development is surpassed? And does that mean that the importance of manufacturing is diminished?

It is apparent from the experience of developed and developing economies that the share of manufacturing in total output in a country shows a hump shape over the course of economic development (Fig. 5.10). It starts at a relatively low level when industrialization starts and proceeds to peak to a level, after which it starts to fall or flatten. In countries like Germany or South Korea, the peak has been higher and more long-standing than others. In countries like India, the peak has been low and shorter—deserving the claims of premature de-industrialization. That is, in some developing countries, manufacturing started to fall before it reached the usual peak levels (in terms of share in output or employment) in the history of other nations.

Fig. 5.10

Share of manufacturing in total output over the course of economic development

Why does this inverse-U-shaped course of manufacturing happen then? One plausible explanation using a Kaldorian framework may be as follows. As industrialization starts and intensifies, productivity of the labour within the manufacturing rises, pushing up average productivity and thus income in the overall economy. At the same time, mechanization (mainly through imported machinery and equipment) pushes up the productivity in the agricultural and service sectors. More industrialization also generates managerial and blue-collar skills that spill over to the non-manufacturing sectors (agricultural and more importantly to the services). This is an additional factor in the increase in overall productivity in the economy. Thus, high productivity growth in manufacturing tends to increase the supply beyond the growth of the needs of the people. Think of Apple and Samsung introducing major new models every six months to a saturated global market. Less and less manufacturing workers can meet the demand of billions of people.

So, the share of employment in manufacturing tends to go down, while the supply of manufactured goods increases. In the USA, while manufacturing employment fell to about 12 million in 2015 from about 19 million in 1970s and the peak of more than 20 million in the late 1970s, manufacturing output increased seven times in real terms between the 1950s and 2015 (Figs. 5.11 and 5.12).

Fig. 5.11

Evolution of employment in the USA (1950–2015; seasonally adjusted). (Source: Bureau of Labour Statistics)

Fig. 5.12

Employment and real output in the US manufacturing sector. (Source: Bureau of Labour Statistics and Bureau of Economic Analysis)

At the same time, production of more manufactured (and agricultural) goods gives way to more services, which may become more and more sophisticated. In time, mechanization (some imported, some local) in the non-manufacturing sectors ushers in a faster transfer of employment to those sectors. Thus, at more developed stages of an economy, the same real amount of manufacturing production leads to higher and higher amounts of services output and employment.

It is important to note that the growth of productivity in the non-manufacturing sector continues to remain largely behind that of the manufacturing sector. That has been the case even in the USA, a quite developed economy, during the last three decades (Table 5.8). One reason for this is that innovations that we know of largely concern the manufacturing sector either directly or indirectly.

Table 5.8

Productivity in non-farm services and manufacturing sectors in the USA

	Services sector	Manufacturing sector
1990–2000	2.2	4.1
2000–2007	2.6	4.7
2007–2016	1.1	1.7a

Note: ^a2007–2015

Source: Bureau of Labour Statistics https://​www.​bls.​gov/​lpc/​prodybar.​htm (last accessed 17 February 2017)

So, as the economy becomes more and more sophisticated, manufacturing remains the hotbed of productivity and innovation and services become and remain hotbeds of employment. The employment in the manufacturing sector continues to go down (at a slower pace), approaching to a band of 10–15% (Table 5.9).

Table 5.9

Employment and output by major sectors in the USA (2015)

	Employment (million)	Output ($ thousand)	Output/employee ($ thousand)
Manufacturing	12.4	2170	175.60
Services	101.3	12,293	121.41
Agriculture	6.9	175	25.23
Government	22.1	2338	105.80
Mining	0.7	328	441.18
Construction	6.6	732	110.46
Total	150.0	18,037	120.22

Source: Bureau of Economic Analysis, Bureau of Labour Statistics and the author’s calculations

This is what we have seen so far in different industrialized economies. But it is likely that combined shares of the primary sector (agriculture, forestry, minerals, and fuels) and the manufacturing sector (perhaps including also construction) may ultimately fall to 10%. A study of the economic impact of fashion in the UK showed that value of manufacturing in total bill to consumers constituted around 10% only. The rest is the share of all services that relayed the product to the consumer.¹¹ In other words, 10 units of manufacturing output have given way to 90 units of services output in the British fashion industry.

The plausible policy prescription is that the services sector can be used to drive employment, and manufacturing can be used to drive growth, productivity, and innovation. In the earlier stages of development, manufacturing becomes a destination for otherwise unproductive labour in addition to pushing overall labour productivity up. In the later stages of development, the value of manufacturing as a source of employment diminishes vis-à-vis services, although continuing as a source of productivity and innovation. At these stages, services take over as the more important source of employment.

So, the decision on the ‘mix’—how much manufacturing versus how much service to encourage—should be based on the stage of the development and the needs of the economy. In Hong Kong, a small high-income country, manufacturing has a very small share in total output (in the order of 2%). In Singapore, a similar small economy, the share of manufacturing is much higher at 20% of GDP as a result of a deliberate policy decision.

A manufacturing sector is critical if a country is to reach or break out of the middle -income trap. Beyond the middle-income level, high-wage employment would become a more important policy goal. The manufacturing industry can also help reduce regional economic growth differences and discrepancies.¹² The relationship between manufacturing, industrial policy, and the middle-income trap will be discussed in more detail in Chap. 11.

5.7 The Smile Curve and the ‘New’ Product Cycle: Does All Manufacturing Always Make Money?

Not all manufacturing always makes good money for the manufacturer. For example, subcontracting manufacturers, whether of automobiles, mobile phones, or apparel, make pennies, while the bulk of the profits accrue to the principal. In other words, if not combined with a monopolistic competition strategy, including product differentiation, proprietary technology, or a patent protection, manufacturing may generate relatively low value. This phenomenon has been named the smile curve (Fig. 5.13) by Stan Shih, the founding CEO of the Taiwanese IT company Acer. Some research studies have confirmed Shih’s view.¹³

Fig. 5.13

The smile curve

The smile curve can be considered as a progression curve that describes value generation along the process of developing, designing, branding, manufacturing, marketing, and selling a product. It implies that manufacturing an undifferentiated product that is invented, developed, and designed (and/or marketed) by third parties may yield very little to the manufacturer (assembler) and to the economy. The reason is simple; this manufacturer is either a producer or an assembler—among many others—of a standard (undifferentiated) product. In either case, a producer of an undifferentiated product cannot command a ‘non-zero’ profit in its market, as raising its price will lead its clients to momentarily shift to one of its many competitors.

An illustrative example is the manufacturing costs of Apple iPhone 6s 16GB NAND flash memory. Launched in 2015 and sold for $749¹⁴ it was assembled by Foxconn (China), which is still one of the largest electronics companies of the world, with over 1.3 million employees. Foxconn received only 0.6% of total manufacturing cost against the assembly (Table 5.10). In 2013, Foxconn employed 1 million people worldwide and manufactured 500,000 iPhones in only one of its facilities in China (Zhengzhou).¹⁵

Table 5.10

Manufacturing costs of Apple iPhone 6s 16GB NAND flash memory

Component	$	% of bill of materials	% of launching price
NAND Flash + DRAM	15	7.0	2.0
Display and touchscreen	52.5	24.4	7.0
Processor-AP	20	9.3	2.7
Camera(s)	12.5	5.8	1.7
Wireless section – BB/RF/PA	33	15.3	4.4
User interface and sensors	22	10.2	2.9
BT/WLAN	4.5	2.1	0.6
Power management	7	3.2	0.9
Battery	4.6	2.1	0.6
Mechanical/Electro-mechanical	35	16.2	4.7
Box contents	5	2.3	0.7
Manufacturing cost	4.5	2.1	0.6
Total manufacturing cost	215.6	100.0	28.8

Source: Calculations based on data from Macdailynews.com (2014)

As an assembler, it is not surprising that assembly manufacturing does not earn Foxconn much on a unit basis. The conclusion is that manufacturing does not automatically make the firm or country rich or bring value added. The practical result of Foxconn’s position versus that of Apple’s is simple. In 2016 Apple recorded $214 billion in revenues and $61 billion in pre-tax profits. For Foxconn, the same figures were; $136 billion in revenues and $4.9 billion in net profit.

For value creation, manufacturing activities require to be complemented with those other crucial value chain activities of invention, innovation, design, branding, and/or marketing. That earmarks a recent phenomenon that can be called servicification of manufacturing, signifying a convergence of manufacturing and service activities. The firms such as Apple achieving this convergence increase profitability and value generation.

Another example can facilitate the debate. By 2012, Turkey had a quite glorious automobile manufacturing sector, with some 1.3 million automobiles manufactured per year. However, the sector is characterized by manufacturing automobiles under the licences of global brands such as Ford, Renault, Fiat, and Toyota. The shareholding of the manufacturers themselves is mostly dominated by the same firms. The ultimate result was the direct economic impact of the automobile manufacturing sector (excluding spare part industry) on the total GDP of the country and was limited to less than 1% of the GDP.¹⁶ A significant part of the profits of the foreign investor is repatriated abroad, reducing the value added left to the host economy.

As importantly, the sector (again excluding the spare parts exports) recorded significant net imports, although it was among the largest exporting sectors. This could be contrasted with South Korea, whose automobile manufacturing sector flourished through industrial policies. In the 1970s, South Korea prioritized local branding, indigenous technology, and establishment of Korea-centric value chains in the sector. Korean policies led to a significant generation of value add and net exports from the sector.¹⁷

Repeating the key takeaways from this section, not all industrialization and not all manufacturing add the same level of value to the GDP and economic development. The smile curve phenomenon is quite relevant to the assessment of the economic impact of industrialization; the value added is generated by the activities at the two ends of the smile curve. The two ends enable the manufacturing firm to produce differentiated rather than undifferentiated products (and assembly) and generate higher value added. Nevertheless, assembly can be useful as a first step to a future rise on the productive and technological learning curves, or up both ends of the smile curve. That requires policies at the firm and government levels to make sure that the firm is not trapped as an eternal low-value manufacturer.

Product life cycle is another important concept. It shows evolution of the sales (market penetration) of a product (or an industry), consisting of several phases (Fig. 5.14):

• Pre-production/pre-market investment phase. At this stage the firm starts incurring costs of physical (such as buildings, machinery, licences), human and institutional capital (such as management and quality systems, training) investments as well as R&D and product development costs.

• Market launch and production kick-off

• Growth of sales and production. The growth can be slow or fast, determining the steepness of the product life cycle curve. If no further development is necessary, the firm incurs only production and administrative costs during this phase.

• Maturation of sales

• Fall in sales as demand fades and/or alternative products and technologies emerge. This phase may end up with withdrawal from the market. Bar soap sales suffered but did not disappear after the entry of liquid soaps into the market (Fig. 5.15). However, mobile phones almost totally erased pagers from the market.

Fig. 5.14

Product life cycle

Fig. 5.15

Alternative cases of product life cycle

In parallel with rapid technological progress, an important phenomenon became more and more visible recently: shortening product life cycles. Prior to the nineteenth century product life cycles lasted hundreds (tobacco) or even thousands of years (wheel). Now product life cycles can be as short as a few years or less. A good example is the pager. The pager was invented in 1949 by Al Gross. Commercially viable launching could be made only in 1974 by Motorola. By 1980 the pager users reached 3.2 million in the USA. The number of users soared to 22 million in 1990 and 61 million in 1994. In the second half of the 1990s however, with the advent of cellular phones, pagers started to diminish and by the 2000s they virtually disappeared from the market.¹⁸ The shortening of product life cycles is also reflected on firm life cycles, as demonstrated by Kodak’s withdrawal from the market or Nokia’s difficulties in shifting to Android.

The subcontractor which suffers from being at the bottom of the smile curve benefits from not incurring the value-added costs at the upper-left portion. By evading the development costs, it saves a significant chunk of the pre-market investment expenditures. However, once the product declines, its original physical investment costs necessitate the subcontractor to shift to the production of another standard product for the same or another principal. The same applies to the manufacturer of any undifferentiated industrial product.

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Footnotes

1

The table does not include very small countries such as Monaco, Lichtenstein, Iceland, and Nauru and highly resource-based countries such as Qatar, which show high per capita manufacturing output due to the existence of few number of dominating manufacturing industries such as oil refining.

 

2

This section draws largely on Yülek (2017).

 

3

Thirlwall (1983).

 

4

Arisoy (2013), Atesoglu (1993), Bautista (2003), Bairam (1991), Bernat (1996), Felipe (1998), Guo et al. (2012), Fingleton and McCombie (1998), Hansen and Zhang (1996), Harris and Lau (1998), Leon-Ledesma (2000), Metcalfe and Hall (1983), Necmi (1999), Pons-Novell and Viladecans-Marsal (1999), Rayment (1981), Szirmai et al. (2013), Szirmai (2012), Thirlwall (2015), Celebi and Ozdeser (2016), Andreosso-O’Callaghan, B., & Lenihan, H. (2011), Paci & Pigliaru (1999), Beheshti & Sadighnia (2006), Drakopoulos & Theodossiou (1991), Vaciago (1975), Szirmai, A. (2013).

 

5

In the services, productivity growth remains relatively low; unless robots do the restaurant serving or haircuts, employment in services and thus wages do not fall, while in manufacturing rising productivity means less worker hours are necessary to increase the output, let alone keeping the production level unchanged.

 

6

See, for example, Pons-Novell and Viladecans-Marsal (1999).

 

7

Inomata (2013).

 

8

Manufacturing Institute (undated).

 

9

UNDP (2014).

 

10

Malthus (1798: vii).

 

11

British Fashion Council (2010); Yülek et al. (2015).

 

12

See, for example, Guo et al. (2013).

 

13

See, for example, Shin et al. (2012).

 

14

Macdailynews (2014).

 

15

Elmer-DeWitt (2013).

 

16

Yülek et al. (2015).

 

17

Yülek et al. (2015).

 

18

Bellis (2017).