Manufacturing and Inclusive Growth: The Experience in the Rest of the World. 1

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1 Manufacturing and Inclusive Growth: The Experience in the Rest of the World. 1 By Robert Z Lawrence Albert L Williams Professor Harvard Kennedy School, Senior Fellow Peterson Institute for International Economics & Senior Fellow Mastercard Center for Inclusive Growth and Danial Lashkari Harvard University. 1 Support from the MasterCard Center for Inclusive Growth for this research is gratefully acknowledged. This is the second phase report on our project on the global role of manufacturing in inclusive growth. The first phase examined the US experience. 1

2 Overview. This report describes some of the results from the second phase of the research project on the role of manufacturing in inclusive growth. The first phase of the project examined the US experience. In the second phase, undertaken by Robert Lawrence and Danial Lashkari a graduate student in the Harvard Department of Economics, the scope of the analysis has broadened to explore the experience of manufacturing employment growth in the rest of the world. The first section of this report looks at the experience in other advanced economies. We find that all of today s industrial countries are experiencing declines in the share of manufacturing employment that are similar in their percentage declines to that experienced by the United States between 1973 and The explanation for this deindustrialization in these advanced economies is similar to that in the United States. Two forces are in operation. The first is that at higher incomes, consumers allocate increasing shares of their expenditures to purchase services. The second is that as a result of relatively rapid technological change, the prices of manufactured goods have declined relative to services, but the response of consumers to these lower prices is relatively small i.e. inelastic. The combination of these low income and price elasticities has led the share of consumption spending on goods to decline and the share of employment to follow. While international trade has played a role in some countries in leading to higher levels of manufacturing employment in countries with trade surpluses in manufacturing, and somewhat lower levels in countries with trade deficits -- the impact of trade balances has not offset the underlying forces of rapid technological change combined with low income and price elasticities. Thus regardless of whether they have trade deficits or surpluses in manufactured goods, the percentage declines in manufacturing employment in industrial countries have been quite similar. Since the percentage of workers in the agricultural sector is small, the analysis of recent deindustrialization in developed countries can ignore the sector, but developments in agriculture are part of the explanation for the humped shaped relationship that 2

3 characterizes the relationship between manufacturing employment and per capita income growth in less advanced countries. For understanding the behavior of this share, it is necessary, therefore to use a three-sector model, i.e., one that includes agriculture, manufacturing and services. In the initial phases of development, the share of agriculture declines and the shares of manufacturing and services both grow. As the economy becomes richer it reallocates labor away from agriculture to both manufacturing and services. However, over time the size of the agricultural sector diminishes and thus the magnitude of this source of growth for manufacturing and services diminishes. At the same time, however, the share of employment in services continues to grow relative to manufacturing. Thus, boosted by inflows from agriculture, manufacturing s share initially grows, but later contracts as the gains from agriculture are more than offset by the shift from manufacturing to services. It is noteworthy that the relationship between this humped effect and per capita incomes has shifted over time. In particular it appears that compared with the industrial countries, countries that have industrialized later, especially outside of Asia, have experienced their peak in manufacturing employment at lower levels of income. Moreover, the peak share of manufacturing employment has been lower and the rate of growth of these has also economies has been slower than early industrializers. This early deindustrialization (sometimes called premature deindustrialization ) has major implications for the achievement of inclusive growth because it diminishes the role that manufacturing can play both as a source of employment and income opportunities for relatively less educated workers as well as a generator of economic growth. Our analysis of this process indicates that the ability of a country to experience a deep process of industrialization crucially hinges on its ability to go through a very fast transition out of agriculture and into manufacturing. Since services constantly draw workers out of both agriculture and manufacturing, the manufacturing sector has a short window of opportunity for deepening. We find that the key factor explaining the early deindustrialization among developing countries may be their relatively slow reallocation of workers from agriculture to manufacturing. If the industrialization process happens too 3

4 slowly, most of the migration from agriculture moves into, and remains in, services employment. Thus manufacturing loses the chance to constitute a sizable share of the economy. Having identified the importance of this rapid transition our research has now begun to try to explain it. Our first efforts have eliminated the most straightforward explanation. We have explored the simplest mechanisms that can deliver the humped relationship between the share of manufacturing employment and growth by considering an economy in which the size of international trade is not large enough to significantly alter the sectoral composition of employment. Without trade, employment in each sector would be determined by the size of domestic demand for goods produced by that sector and the productivity of that sector. We find however that the standard forces of price and income elasticity alone are not able to rationalize early deindustrialization. In further research, therefore, we will explore alternative explanations for the more sluggish recent transition from agriculture to manufacturing. One possibility is that today s late developing countries face more challenges in promoting export led-growth and in competing with manufactured goods imports because East Asian countries such as China have now made the markets for these products more competitive and difficult to enter. A second possibility is that at any given level of income today, the manufacturing technologies used by developing countries may require fewer inputs per unit of output and thus relatively less labor per unit of output than before. In addition, more modern technologies may also require workers that are more highly skilled. A third possibility is, whether slow growth itself associated with low shares of investment in GDP and thus less demand for industrial equipment and construction could also be part of the explanation. We intend to systematically investigate each of these possibilities in order to explain early deindustrialization and then to able to draw policy implications. In what follows, in this report we present our initial findings in greater depth. The report contains five sections. In Section 1 we provide a brief review of the literature on structural change; in Section 2 we discuss deindustrialization in advanced countries besides the 4

5 United States. In section 3 we present evidence that demonstrates how deindustrialization in poorer countries has taken place with lower employment shares, lower per capita incomes and slower income growth. In section 4 we then highlight the key role played by the pace of the shift from agriculture to manufacturing in the deindustrialization process. In section 5 we report on the inability of the simple closed economy structural model to account for early deindustrialization and outline our ideas for further research. Finally, in Section 6 we lay out our plan for the future work on this topic. Section 1: A Brief Review of the Literature. The hump-shaped evolution of manufacturing is part of a broader set of structural transformations that mark the process of economic growth, namely, a continuous decline in the economy-share of agriculture and a corresponding rise in services. The study of these transformations have a long history in economics and go back to the decades following World War II (Kuznets 1973). For the simple reason that the deindustrialization in developing countries did not begin until the last few decades, the earlier literature mainly focused on the study of the evolution of manufacturing shares in richer countries (for a recent review of this literature, see (Herrendorf, Rogerson, and Valentinyi 2014)). At this earlier stage of deindustrialization, the scale of world trade had not reached the levels that could reasonably explain the magnitude of structural transformations observed in the sectoral shares of output and employment. Moreover, the majority of this trade occurred among developed countries exchanging different varieties of products, as they were simultaneously going through similar patterns of structural change. Therefore, a general consensus emerged that domestic forces of supply and demand were the key to the earlier trend toward deindustrialization (see, e.g., (Rowthorn and Ramaswamy 1999) and the references therein). The main supply-side force that can drive structural transformation is heterogeneity in sectoral rates of technical growth. Empirical estimates for the rates of productivity growth significantly vary across sectors (Martin and Mitra 2001; Nordhaus 2008; Duarte and Restuccia 2010). If sectoral demand is not very responsive to lower prices (i.e., is inelastic) 5

6 fast productivity growth (and the ensuing fall in prices) in a given sector can result in declining shares of spending and employment in that sector. Since productivity growth in manufacturing has historically exceeded that in the service sector, we can use this channel to explain the declining share of manufacturing in rich countries with high productivity manufacturing sectors (Ngai and Pissarides 2007). This phenomenon is often referred to as the Baumol s disease, whereby economic activity shifts from industries with faster productivity growth into more stagnating ones (Baumol 1967). An alternative view of structural transformation focuses on demand-driven forces in explaining the shift away from manufacturing. Economists have long studies systematic variations in the sectoral composition of demand as a function of income through the concept of Engel curves. Typically, income elasticities are higher for services compared to manufacturing products. Therefore, one can use Engel curves as a way to explain the shift of consumer spending from manufacturing into services, which can be observed both in real and nominal terms (Kongsamut, Rebelo, and Xie 2001; Foellmi and Zweimüller 2008; Boppart 2014; Comin, Lashkari, and Mestieri 2015). The recent rise in globalization has further impacted the dynamics of employment shares through patterns of international specialization. Such forces may be stronger for smaller developing countries that cannot plausibly influence world prices and practically act as price takers in world markets. Theory suggests that by disentangling domestic technical growth from domestic demand, international trade can create patterns of structural change distinct from the ones already discussed (Ventura 2005; Matsuyama 2009). Recent studies of the evolution of industrialization have documented much heterogeneity in patterns observed across a wide array of developing countries, attributing them to differential responses to trade. 2 2 Several recent studies have investigated the sectoral impact of international trade on manufacturing employment using micro level data. For instance, (Utar and Ruiz 2013) studies the effect of Chinese competition on Mexican manufacturing plants, while (Menezes-Filho and 6

7 A recent line of work has focused attention on deindustrialization across developing countries, documenting pronounced trends toward earlier deindustrialization in recent decades (Dasgupta and Singh 2006). Providing a theory for this so-called premature deindustrialization, Rodrik (2015) has argues for the existence of a fundamental divergence in the mechanisms shaping deindustrialization between developing and developed countries. He focuses on a supply-driven account of structural transformation as the core driver in the earlier phases of deindustrialization in richer countries, whereby the decline of manufacturing stems from the faster productivity growth in this sector relative to the competing service sectors. Since the demand for services is price-inelastic, the resulting rise in the relative price of services results in a rise in the share of expenditures (and therefore employment) for services and a corresponding fall for manufacturing. Rodrik (2015) points out that in a world interconnected by trade, a rise in the productivity of manufacturing in a small developing nation does not influence world prices. Rather, it can improve its comparative advantage in this sector, drawing in workers from other sectors to help sustain increasing exports to foreign markets. He suggests the possibility that deindustrialization may have been imported through globalization, but the exact nature of this channel remains unexplained. Section 2: Evidence from Advanced Economies. Much of the discussion about deindustrialization in the United States focuses on policies and practices that are specifically American, with the presumption that had these been different, the United States might have avoided the shrinking share of manufacturing employment. It is useful therefore to compare the US experience with that of other industrial countries. This exercise shows that the US is by no means unique. Declining Muendler 2011) study the impact of tariff cuts on manufacturing employment in Brazil. The recent rise in the volume of Chinese exports have been large enough to have some impacts on the manufacturing employment in the US and other European countries (for micro level studies, see (Autor, Dorn, and Hanson 2013; Pierce and Schott 2012)). 7

8 employment shares in manufacturing and declining share of spending goods in overall consumption are evident in all major industrial countries. In 2010 the US employment share in manufacturing was actually quite typical of an industrial country. It was the same as in Canada (10.3), and the Netherlands (10.6), somewhat higher than Australia (8.9), and lower than Sweden (12.7) and France (13.1). The US decline in the share of manufacturing employment over the period was also quite typical. All major industrial countries experienced declines between 1973 and As shown in Table 1, these were typically on the order of 15 percentage points. Table 1: Share of employment in manufacturing (percent) Country Change (1) (2) (3) (3) - (1) United States Canada Australia Japan France Germany Italy Netherlands United Kingdom Source: Bureau of Labor Statistics. Average Change It is also noteworthy as reported in Table 2 as an average of GDP, several of these countries have run large trade surpluses in their manufacturing trade. In 2010 these include Germany (9.9 percent of GDP), Japan (6.45) and the Netherlands (5.52). Their surpluses do help explain why countries like Germany and Japan have had high manufacturing employment shares. But it is striking that the surpluses have not mitigated these countries declining trends in the manufacturing employment share. These data underscore our argument in the previous section that while reducing the US deficit in manufactured goods could deliver a one-time boost to manufacturing employment, it would not permanently alter the declining trend in the manufacturing employment share. 8

9 Table 2: Manufacturing Trade Balance as Share of GDP Ave rage Australia Canada De nmark France Ge rmany Italy Japan Ne the rland Kore a UK USA Source: UN Statistics; World Bank GDP Data We have also gathered data on the share of overall consumption that is spent on goods in a number of OECD countries. In the US in which consumption spending accounts for about three quarters of overall spending on goods, between 1970 and 2010, the share of goods in consumption fell from a half to just over a third -- implying an annual average decline in the share of 0.42 percent. As shown in the Table 3 below this is almost exactly equal to the average annual decline in the overall sample. While the pace of the US decline has been quite similar to that in other countries, the share of goods in US expenditure is also the lowest in the sample. 9

10 Table 3 Share of Consumption Spending on Goods in Total Consumption (%) Annual Change Australia Canada Denmark France Italy Korea Netherlands UK USA Average Source: OECD National Income Accounts. The international experience in other industrial countries is therefore consistent with the conclusions we have drawn with respect to the United States. Relatively faster productivity growth in the production of manufactured goods has led to declining relative goods prices. In response, however, consumers have chosen to devote more of the money they save to the purchase of services. In addition, richer people have a lower propensity to purchase goods as opposed to services. The result has declining shares of expenditure and employment in the production of goods. Larger trade surpluses or smaller trade deficits are associated with larger shares of manufacturing employment, but over the long run do not change the impact of the forces driving down the overall employment share in manufacturing. 10

11 Figure 1 Section 3: Deindustrialization across the Developing World Figure 1 shows the evolution of the share of manufacturing in the total market economy employment as a function of per capita income for a number of high, middle, and lowincome countries. 3 As we can see, the dynamics of the employment share of manufacturing 3 Since our focus is on the long-run trends, we apply a Hodrick-Prescott filter with λ = 100 to the logarithm of each variable, and use the filtered data for all our analyses. We then find the maximum employment share in each country in the smoothed data to minimize the effect of business cycles and outlier observations. We then use the time series of the employment share in each country to identify and exclude all countries whose employment share in manufacturing either has not yet peaked, or has peaked before beginning of the dataset. We use USA as the reference country. 11

12 in a typical country has a hump-shape pattern. The industrialization process initially results in the rise of the manufacturing share. As income continues to grow, the share of manufacturing in most countries first plateaus and then begins to fall. 4 Figure 2 illustrates how this pattern is not limited to employment, and appears in another measures of the size of the manufacturing sector, the production value added. 5 This rise-and-fall pattern for manufacturing has long been known as an essential component of economic growth (Kuznets 1973). Figure 2 4 Note that the peak of the manufacturing sector in the US happens right around the beginning of the data in We use other sources of data to determine this peak (Buera and Kaboski 2009). 5 In this paper, we focus our attention on employment as the leading measure of the size of each sector, but most of the results remain intact if we perform the analyses reported here on production value added. 12

13 We can also see in Figure 1 and Figure 2 that countries show some degree of heterogeneity in terms of the maximum size of their manufacturing sector and the level of income in which they reach this maximum size. In addition, we note that not every country necessarily follows this exact pattern. While economic and political turmoil in some countries such as Argentina and Nigeria have resulted in major deviations from the standard hump-shape, other countries like China have managed to sustain a high share of their economic activity manufacturing for a long time. Figure 3 Figure 3 presents a key regularity observed in the cross-country variations in the evolution of the manufacturing sector, namely, a trend toward deindustrialization at lower levels of income. The figure focuses on the set of countries for which we observe a peak in the share of manufacturing and we have available data in It shows the income of each country at the point in time that they reached the peak of their manufacturing on the y-axis and 13

14 their income in 1950 on the x-axis (the year in which, roughly speaking, the US reached its peak of manufacturing size). 6 It shows that the countries that were poorer in the 1950 cross section achieve the peak of their manufacturing sector at lower levels of income relative to the point at which the US peaked its share of manufacturing. 7 Error! Reference source not found. shows this relationship with respect to the 1970 cross section, for which we have data available from a broader set of countries. Again, a strong positive relationship between the income in the cross section and income at the peak of the share of manufacturing sector exists here. 6 Ideally, we should have chosen the cross section of income in the year in which the US has peaked its share of manufacturing (which is 1948 according to our calculation). Unfortunately, we do not have data for most development countries that goes that far back in time, and therefore, we choose the 1950 cross section, a year for which the data provides broader coverage. 7 We emphasize that this pattern is robust with respect to the measure we use to determine the peak of the share of manufacturing. Using value added produces a very similar relationship. 14

15 Figure 4 Figure 5 shows the highest share of the manufacturing sector reached by each country, again as a function of their relative income in the 1970 cross section. Although the relationship is weaker here, we still observe a negative relationship between the cross section of income and the peak share of manufacturing in total employment. Thus, countries that are poorer in 1970 not only reach their peak at lower levels of income, but they also begin the process of deindustrialization at lower levels of industrial penetration into the labor force. It is the combination of these two facts that has come to be referred to as premature deindustrialization in developing countries (Lawrence and Edwards 2013; Rodrik 2015). The idea is that if all countries were following an identical path of development, we would expect the relationship between the industrializationdeindustrialization process and the country s income to remain intact over time. The trends observed here suggest that, in so far as we are concerned with the role of manufacturing, the trajectories of development evolve over time. 15

16 Error! Reference source not found. and Error! Reference source not found. starkly illustrate why the industrial penetration matters for countries. They show a strong relationship between the maximum size of the manufacturing sector, on the one hand, and the income of the country in both level and growth terms, on the other. These figures suggest that historical data indeed corroborates the view, widely held among policy makers and commentators concerned with growth, that early deindustrialization might be detrimental to development. Figure 5 16

17 Figure 6 Figure 7 17

18 Section 4: A 3-Sector Framework for Understanding Deindustrialization We now turn to exploring potential determinants of the patterns we observed in the previous section. To this end, we will consider the evolution of manufacturing and income in the context of broader structural transformations that countries experience along the process of development. In particular, as we discussed in the introduction, the process of industrialization and deindustrialization is commonly rationalized in a 3-sector framework: the industrialization and deindustrialization phases involve movement of labor from agriculture into manufacturing, and out of manufacturing into services, respectively. Therefore, we will first study how the combination of these two forces determines the timing of deindustrialization in a given country. Figure 8 18

19 For the same group of 16 countries we saw in Figure 1, Error! Reference source not found. and Figure 9 show the trends in the size of employment in agriculture and services, respectively, both relative to the manufacturing size of employment. Error! Reference source not found. shows that the size of employment relative to agriculture almost universally falls throughout the second half of the twentieth century. Roughly speaking, we can interpret this trend as the rate at which the relative size of manufacturing grows relative to agriculture by drawing workers from that sector. 8 The downward trend is the strongest among countries that have experienced very fast episodes of industrialization, countries like Mauritius or East Asian countries such as Korea and Taiwan. While we might find more exceptions in Figure 9, the trends here also appear common across most countries: employment in services grows relative to that in manufacturing. In other words, a net growth in the size of services relative to manufacturing over time characterizes the evolution of sectors at all levels of income. The major exceptions are temporary periods in countries such as Korea, Mauritius, or more recently Kenya, where the economy experiences an accelerated industrialization. 9 8 Of course, these size trends do not have to involve direct movement of workers from agriculture into manufacturing. For instance, one can imagine a situation whereby the relative shrinking of agriculture is solely driven by a trend out of agriculture into services that is faster relative to a parallel trend out of manufacturing, without a direct agriculturemanufacturing flow of workers. However, the statement made here still holds in net terms. In addition, our interpretation is broadly reflective of a large body of work documenting the migration of rural agricultural workers into urban centers of manufacturing plants, as countries industrialize. 9 In the original unsmoothed data, a regression of the logarithm of relative employment in the two sectors on time, accounting for a constant effect for each country, gives an estimate of for agriculture, and for services. The robust standard errors for the estimates for agriculture and services are and 0.001, respectively, where the errors are clustered at the country level. 19

20 We rely on our observations in Error! Reference source not found. and Figure 9 to derive predictions about when a country begins to deindustrialize. If at all levels of income, the employment in services grows relative to manufacturing and that of agriculture falls, how would the evolution of the share of manufacturing look like along the path of development? Early in the process, when the employment share of agriculture is large, employment gains of manufacturing relative to agriculture (resulting form the movement of labor from agriculture to manufacturing) is larger than the employment losses to services. This is the industrialization phase whereby the overall share of value added and employment rises for manufacturing. The deindustrialization process begins at the point where the relative employment in services relative to agriculture grows enough such that the losses of manufacturing to services surpasses its gains from agriculture. The discussion above suggests that there should be a threshold on the size of service sector employment relative to that of agriculture, such that reaching the threshold triggers the beginning of the deindustrialization process. This is the point in which the employment gains from agriculture equates the losses to manufacturing, that is, ( 1 ) Rate of EMP Decline Agr/Man Agr Share of EMP = Rate of EMP Growth Ser/Man Ser Share of EMP The discussion above leads us to focus on the evolution of the gap in employment between services and agriculture as the key force determining the timing of the deindustrialization process. In particular, in any cross section of time, poor and rich countries show gaps both in income and in their relative size of the service sector. Whether or not the income of a poor country at its peak of the manufacturing sector is the same as that of a richer country crucially depends on the rate at which the two gaps are closed. 20

21 Figure 9 Error! Reference source not found. shows how Equation ( 1 ) holds in the data. On the x- axis, we have shown the share of employment in services relative to agriculture in the year in which each country begins to deindustrialize, while on the y-axis, we have shown the ratio of the rate of decline of employment in agriculture to manufacturing by the rate of growth of employment in services relative to manufacturing. 10 Given that the relationship is a mechanical equality, it is not surprising that, as the figure shows, it indeed holds fairly accurately in the data. 11 Therefore, we conclude that this relation provides a precise characterization of the timing of the beginning of the deindustrialization process. 10 We compute the rate of growth over a 10-year window around the time when the country peaks its share of employment in manufacturing. 11 We have removed Singapore from this figure, for which both of these ratios are much higher relative to all other countries. In 1984, the year in which Singapore reaches the height of its industrialization, the share of employment in services relative to agriculture 21

22 Figure 10 Consider now a country that in a given cross section, say the year 1950, is characterized by an initial level of income and an initial size of services relative to agriculture. The relative size of services to agriculture is larger in richer countries in this cross section. From Error! Reference source not found. and Figure 9 we know that this relative size grows over time for all countries. Equation ( 1 ) then suggests that there is a country-specific constant threshold on the relative size of services to agriculture such that, upon reaching this level, the share of manufacturing in the country s employment begins to fall. 51.7, while the relative growth rates are The reason for this outlier behavior is simply the fact that Singapore does not have enough land endowment to allow for a sizable agricultural sector. We remove Singapore from all the results reported in this section. 22

23 Figure 11 Error! Reference source not found. provides another crucial fact about the heterogeneity across countries in deindustrialization. We can see that at the peak of their industrialization, countries show wide variations in the ratio of the rate of decline of agricultural employment relative to manufacturing to the rate of growth of services employment relative to manufacturing. This gives us an important clue about the determinants of heterogeneity in industrialization experiences across countries. We will next investigate the degree to which this heterogeneity is related to the heterogeneity in the timing of deindustrialization. 23

24 Figure 12 If the rates of relative employment growth were the same across countries, potential variations would have been driven solely by the sectoral composition in the initial cross section. 12 According to this scenario, countries that show early deindustrialization are those that start with a larger service sector relative to agriculture. Figure 11 and Figure 12 clearly refute this hypothesis. In both figures, we show for the cross section of 1950, the 12 Consider, for instance, a model whereby the trends in relative size of employment are driven by relative states of technology across sectors. Furthermore, the frontier technology diffuses from rich to poor countries in all sectors with a constant time lag. In such a model, even though Mexico in 1950 has the same level of income as the US in 1900, the sectoral composition of Mexico in 1950 is different from the US in In particular, since the sectoral technological gap in Mexico in 1950 reflects that of the US, services are already much larger in 1950 in Mexico compared to the US in As a result, starting from 1950, it takes Mexico less than 50 years to reach its peak of manufacturing. 24

25 residual of a regression of the logarithm of the size of services (relative to agriculture) on log income on the x-axis. While Figure 11 shows countries that begin with a larger size gap in fact reach the maximum earlier, this difference is not reflected in their maximum share of manufacturing, as we see in Figure 12. Therefore, we conclude that among the terms contributing to Equation ( 1 ), the rates of employment growth are more likely than the initial sizes of the sectors to reflect key structural parameters that drive the deindustrialization process. Figure 13 25

26 Figure 14 Next, we investigate whether the heterogeneity reflected in Error! Reference source not found. also matters for explaining the income at the peak and the highest level of industrialization achieved. Figure 13 and Figure 14 document that this is indeed the case. Countries where the rate of decline of agriculture/manufacturing employment is larger relative to the rate of growth of services/manufacturing employment are exactly those that experience a deeper penetration of industrialization (Figure 14). We then unpack this effect by separately exploring the relationship for the rate of decline of the relative agriculture/manufacturing employment and the rate of growth of the relative services/manufacturing employment. Figure 15 and Figure 16 explore these relationships. While we do not find a strong relationship between the maximum penetration of manufacturing and the growth rate of relative services/manufacturing employment, the relationship appears very strong for the rate of decline of the relative agriculture/manufacturing employment. 26

27 Figure 15 Figure 15 captures the key result of our investigation in this section. It captures the following simple intuition: the ability of a country to experience a deep process of industrialization crucially hinges on its ability to go through a very fast transition out of agriculture and into manufacturing. In other words, the trends toward premature deindustrialization may be due to the fact that developing countries outside East Asia have not been able to experience an accelerated shift from agriculture into manufacturing. Since services continue to draw workers out of agriculture at all levels of income, a slow industrialization process results in a situation whereby agriculture shrinks by losing its workers to services rather than manufacturing. So far, we have solely focused on patterns of relative employment and have abstracted away from deeper forces that have shaped the observed rates of growth. In the next section, we briefly overview some of these potential drivers the forces that give rise to systematic reallocation of employment and value added across sectors as we have seen in this section. 27

28 Figure 16 Section 5: Sectoral Price and Income Elasticity We now turn our attention to the determinants of the patterns we observed in the previous section. As we saw, the share of the manufacturing sector in employment and production value added for the most part move in tandem. Most of the patterns appear qualitatively the same whether we express them in terms of employment and value added. This suggests that the trends in factors such as the sectoral labor share of output or sectoral wages may not make a large contribution to the patterns of deindustrialization. 13 Therefore, we follow 13 To see this point more clearly, consider the identity αy = wl where Y and L denote the value added and employment at the sectoral level, α stands for the share of labor in output, and w stands for wages. The fact that most of our patterns of interest appear similarly in Y and L suggests that the variations over time and across sectors in labor share and the wages may not be first order. We will investigate this point in more detail in the appendix. 28

29 the literature on structural change and focus our attention on the drivers of demand for the size of different sectors, and in particular the sectoral prices. Let us begin by applying the logic of the price-based theories of structural change to our problem. First, let us assume that the size of trade is not large enough to make an impact on the sectoral composition of the economy. Thus, the size of each sector is determined by the total demand for the output of that particular sector from the consumers in each country. Demand for sectoral goods is in turn shaped by the evolution of sectoral prices and income. The relationship between the trends in relative demand and the trends in relative prices crucially hinges on the elasticity of substitution across sectors. If sectoral demand is elastic (inelastic) and sectoral goods are highly substitutable (complementary), the relative demand for a sector whose relative prices are rising falls (rises). Most studies have taken the view that the demand elasticity of substitution across broadly defined sectoral goods is below unity, implying inelastic demand (Ngai and Pissarides 2007; Acemoglu and Guerrieri 2008; Buera and Kaboski 2009). The estimates reported rarely, if ever, exceed 1 (Stockman and Tesar 1995; Herrendorf, Rogerson, and Valentinyi 2013; Comin, Lashkari, and Mestieri 2015). Therefore, the available evidence point to positive relationship between relative prices and relative demand (and therefore relative employment). 29

30 Figure 17 Figure 18 30

31 Figure 19 The trends in relative prices in the US match this picture: relative (production) prices of agriculture to manufacturing falls at an average rate of 1% between , while the relative (production) prices of services to manufacturing goods rise at an average rate of 0.9% within the same period. 14 Therefore, trends in relative employment are aligned with the trends in relative prices for the US. However, Error! Reference source not found. and Figure 18 show that the trends in sectoral relative prices show a larger degree of heterogeneity across countries when contrasted with patterns in relative employment growth in Error! Reference source not found. and Figure We define the price for each sector as the ratio of nominal value added to real value added in constant (2005) prices. The growth rates are found by regressing the logarithm of relative prices by time, and the t-statistics for the two estimates are 7.55 and 24.71, respectively. 31

32 Figure 20 If the trends in relative prices in fact drive the trends in relative employment, they should also explain the patterns we observed in maximum share of employment in the previous section. Figure 19 suggests that this is not the case. It shows that the rate of growth of the relative prices of agriculture to manufacturing around the beginning of the deindustrialization process does correlate with the peak size of the manufacturing sector. 15 Another demand-side factor that can potentially influence the trends in relative employment is the income elasticity of sectoral goods. As discussed above, a series of recent papers have highlighted the fact that demand nonhomotheticities do not have to be 15 Similarly, Figure 20 shows that relative sectoral productivity growth may not play a crucial role in this process. Here, we have defined sectoral productivity by dividing real value added by employment in each sector. 32

33 a transient feature of consumer preferences (Boppart 2014; Comin, Lashkari, and Mestieri 2015). However, we cannot use variations in income elasticity of demand to explain the patterns observed in the previous section. Most reliable estimates of income elasticities imply that the income elasticities are the highest for the service sector and the lowest for agriculture. Depending on the specifications of demand, either the income elasticity of relative demand falls with income (Kongsamut, Rebelo, and Xie 2001; Herrendorf, Rogerson, and Valentinyi 2013) or remains stable (Boppart 2014; Comin, Lashkari, and Mestieri 2015). Neither case can explain why countries that have higher income at the peak of their manufacturing also show a faster rate of decline in their size of agriculture relative to manufacturing. We conclude that the standard theories of structural change that rely on demand-side forces operating through price and income elasticity may not rationalize the patterns observed in the previous section. Section 6: Future Work A adopting a 3-sector framework can help us derive predictions about the timing of deindustrialization. We have shown that the root causes of premature deindustrialization may be tied to the relatively slow movement of workers from agriculture into manufacturing in developing countries. However, a brief overview of the traditional demand-side drivers of structural change suggested that they cannot readily explain the patterns identified here. Therefore, our future research will explore the alternative causes that may explain the relatively slower process of industrialization among developing countries. One key force that we have not discussed here is international trade. Rodrik (2015) argues that trade competition has played a major role in curbing the growth of manufacturing in Latin America and Africa. We find evidence that the growth of manufacturing can be driven by short episodes of fast industrialization, as can be seen in the case of East Asian growth miracles. Even in the United States, the rate of decline of agriculture relative to 33

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