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The 2019-2024 World Outlook for Manufacturing Resin, Synthetic Rubber, and Artificial Synthetic Fibers and Filaments

The 2019-2024 World Outlook for Manufacturing Resin, Synthetic Rubber, and Artificial Synthetic Fibers and Filaments

  • January 2018
  • 289 pages
  • ID: 1993252

Summary

Table of Contents

This study covers the world outlook for manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments across more than 190 countries. For each year reported, estimates are given for the latent demand, or potential industry earnings (P.I.E.), for the country in question (in millions of U.S. dollars), the percent share the country is of the region, and of the globe.

These comparative benchmarks allow the reader to quickly gauge a country vis-à-vis others. Using econometric models which project fundamental economic dynamics within each country and across countries, latent demand estimates are created.

This report does not discuss the specific players in the market serving the latent demand, nor specific details at the product level. The study also does not consider short-term cyclicalities that might affect realized sales.

The study, therefore, is strategic in nature, taking an aggregate and long-run view, irrespective of the players or products involved. This study does not report actual sales data (which are simply unavailable, in a comparable or consistent manner in virtually all of the countries of the world).

This study gives, however, my estimates for the worldwide latent demand, or the P.I.E., for manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments. It also shows how the P.I.E. is divided across the world’s regional and national markets. For each country, I also show my estimates of how the P.I.E. grows over time (positive or negative growth). In order to make these estimates, a multi-stage methodology was employed that is often taught in courses on international strategic planning at graduate schools of business.

1.3 THE METHODOLOGY
In order to estimate the latent demand for manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments on a worldwide basis, I used a multi-stage approach. Before applying the approach, one needs a basic theory from which such estimates are created.

In this case, I heavily rely on the use of certain basic economic assumptions. In particular, there is an assumption governing the shape and type of aggregate latent demand functions.

Latent demand functions relate the income of a country, city, state, household, or individual to realized consumption. Latent demand (often realized as consumption when an industry is efficient), at any level of the value chain, takes place if an equilibrium is realized.

For firms to serve a market, they must perceive a latent demand and be able to serve that demand at a minimal return. The single most important variable determining consumption, assuming latent demand exists, is income (or other financial resources at higher levels of the value chain). Other factors that can pivot or shape demand curves include external or exogenous shocks (i.e., business cycles), and or changes in utility for the product in question.

Ignoring, for the moment, exogenous shocks and variations in utility across countries, the aggregate relation between income and consumption has been a central theme in economics. The figure below concisely summarizes one aspect of problem.

In the 1930s, John Meynard Keynes conjectured that as incomes rise, the average propensity to consume would fall. The average propensity to consume is the level of consumption divided by the level of income, or the slope of the line from the origin to the consumption function.

He estimated this relationship empirically and found it to be true in the short-run (mostly based on cross-sectional data). The higher the income, the lower the average propensity to consume.

This type of consumption function is shown as "B" in the figure below (note the rather flat slope of the curve). In the 1940s, another macroeconomist, Simon Kuznets, estimated long-run consumption functions which indicated that the marginal propensity to consume was rather constant (using time series data across countries). This type of consumption function is show as "B" in the figure below (note the higher slope and zero-zero intercept).

The average propensity to consume is constant. For a general overview of this subject area, see Principles of Macroeconomics by N. Gregory Mankiw, South-Western College Publishing; ISBN: 0030340594; 2nd edition (February 2002).

Is it declining or is it constant? A number of other economists, notably Franco Modigliani and Milton Friedman, in the 1950s (and Irving Fisher earlier), explained why the two functions were different using various assumptions on intertemporal budget constraints, savings, and wealth. The shorter the time horizon, the more consumption can depend on wealth (earned in previous years) and business cycles.

In the long-run, however, the propensity to consume is more constant. Similarly, in the long-run, households, industries, or countries with no income eventually have no consumption (wealth is depleted).

While the debate surrounding beliefs about how income and consumption are related and interesting, in this study a very particular school of thought is adopted. In particular, we are considering the latent demand for manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments across some 190 countries.

The smallest have fewer than 10,000 inhabitants. I assume that all of these counties fall along a "long-run" aggregate consumption function.

This long-run function applies despite some of these countries having wealth; current income dominates the latent demand for manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments. So, latent demand in the long-run has a zero intercept. However, I allow firms to have different propensities to consume (including being on consumption functions with differing slopes, which can account for differences in industrial organization, and end-user preferences).

Given this overriding philosophy, I will now describe the methodology used to create the latent demand estimates for manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments. Since ICON Group has asked me to apply this methodology to a large number of categories, the rather academic discussion below is general and can be applied to a wide variety of categories, not just manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments.

1.3.1 STEP 1. PRODUCT DEFINITION AND DATA COLLECTION
Any study of latent demand across countries requires that some standard be established to define "efficiently served". Having implemented various alternatives and matched these with market outcomes, I have found that the optimal approach is to assume that certain key countries are more likely to be at or near efficiency than others.

These countries are given greater weight than others in the estimation of latent demand compared to other countries for which no known data are available. Of the many alternatives, I have found the assumption that the world’s highest aggregate income and highest income-per-capita markets reflect the best standards for "efficiency".

High aggregate income alone is not sufficient (i.e., China has high aggregate income, but low income per capita and cannot be assumed to be efficient). Aggregate income can be operationalized in a number of ways, including gross domestic product (for industrial categories), or total disposable income (for household categories; population times average income per capita, or number of households times average household income per capita).

Brunei, Nauru, Kuwait, and Lichtenstein are examples of countries with high income per capita, but not assumed to be efficient, given low aggregate level of income (or gross domestic product); these countries have, however, high incomes per capita but may not benefit from the efficiencies derived from economies of scale associated with large economies. Only countries with high income per capita and large aggregate income are assumed efficient. This greatly restricts the pool of countries to those in the OECD (Organization for Economic Cooperation and Development), like the United States, or the United Kingdom (which were earlier than other large OECD economies to liberalize their markets).

The selection of countries is further reduced by the fact that not all countries in the OECD report have industry revenues at the category level. Countries that typically have ample data at the aggregate level that meet the efficiency criteria include the United States, the United Kingdom, and in some cases France and Germany.

Is it declining or is it constant? A number of other economists, notably Franco Modigliani and Milton Friedman, in the 1950s (and Irving Fisher earlier), explained why the two functions were different using various assumptions on intertemporal budget constraints, savings, and wealth. The shorter the time horizon, the more consumption can depend on wealth (earned in previous years) and business cycles.

In the long-run, however, the propensity to consume is more constant. Similarly, in the long-run, households, industries, or countries with no income eventually have no consumption (wealth is depleted).

While the debate surrounding beliefs about how income and consumption are related and interesting, in this study a very particular school of thought is adopted. In particular, we are considering the latent demand for manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments across some 190 countries.

The smallest have fewer than 10,000 inhabitants. I assume that all of these counties fall along a "long-run" aggregate consumption function.

This long-run function applies despite some of these countries having wealth; current income dominates the latent demand for manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments. So, latent demand in the long-run has a zero intercept. However, I allow firms to have different propensities to consume (including being on consumption functions with differing slopes, which can account for differences in industrial organization, and end-user preferences).

Given this overriding philosophy, I will now describe the methodology used to create the latent demand estimates for manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments. Since ICON Group has asked me to apply this methodology to a large number of categories, the rather academic discussion below is general and can be applied to a wide variety of categories, not just manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments.

1.3.1 STEP 1. PRODUCT DEFINITION AND DATA COLLECTION
Any study of latent demand across countries requires that some standard be established to define "efficiently served". Having implemented various alternatives and matched these with market outcomes, I have found that the optimal approach is to assume that certain key countries are more likely to be at or near efficiency than others.

These countries are given greater weight than others in the estimation of latent demand compared to other countries for which no known data are available. Of the many alternatives, I have found the assumption that the world’s highest aggregate income and highest income-per-capita markets reflect the best standards for "efficiency".

High aggregate income alone is not sufficient (i.e., China has high aggregate income, but low income per capita and cannot be assumed to be efficient). Aggregate income can be operationalized in a number of ways, including gross domestic product (for industrial categories), or total disposable income (for household categories; population times average income per capita, or number of households times average household income per capita).

Brunei, Nauru, Kuwait, and Lichtenstein are examples of countries with high income per capita, but not assumed to be efficient, given low aggregate level of income (or gross domestic product); these countries have, however, high incomes per capita but may not benefit from the efficiencies derived from economies of scale associated with large economies. Only countries with high income per capita and large aggregate income are assumed efficient. This greatly restricts the pool of countries to those in the OECD (Organization for Economic Cooperation and Development), like the United States, or the United Kingdom (which were earlier than other large OECD economies to liberalize their markets).

The selection of countries is further reduced by the fact that not all countries in the OECD report have industry revenues at the category level. Countries that typically have ample data at the aggregate level that meet the efficiency criteria include the United States, the United Kingdom, and in some cases France and Germany.

Latent demand is therefore estimated using data collected for relatively efficient markets from independent data sources (e.g. Euromonitor, Mintel, Thomson Financial Services, the U.S. Industrial Outlook, the World Resources Institute, the Organization for Economic Cooperation and Development, various agencies from the United Nations, industry trade associations, the International Monetary Fund, and the World Bank).

Depending on original data sources used, the definition of manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments is established. In the case of this report, the data were reported at the aggregate level, with no further breakdown or definition.

In other words, any potential products and/or services that might be incorporated within manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments fall under this category. Public sources rarely report data at the disaggregated level in order to protect private information from individual firms that might dominate a specific product-market.

These sources will therefore aggregate across components of a category and report only the aggregate to the public. While private data are certainly available, this report only relies on public data at the aggregate level without reliance on the summation of various category components.

In other words, this report does not aggregate a number of components to arrive at the "whole". Rather, it starts with the "whole", and estimates the whole for all countries and the world at large (without needing to know the specific parts that went into the whole in the first place).

Given this caveat, this study covers manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments as defined by the North American Industrial Classification system or NAICS (pronounced "nakes").

The NAICS code for manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments is 3252. It is for this definition that aggregate latent demand estimates are derived.

Manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments is specifically defined as follows:
3252 Resin, Synthetic Rubber, and Artificial Synthetic Fibers and Filaments Manufacturing
32521 This industry comprises establishments primarily engaged in one or more of the following: (1) manufacturing synthetic resins, plastics materials, and nonvulcanizable elastomers and mixing and blending resins on a custom basis; (2) manufacturing noncustomized synthetic resins; and (3) manufacturing synthetic rubber.
325211 This U.S. industry comprises establishments primarily engaged in (1) manufacturing resins, plastics materials, and nonvulcanizable thermoplastic elastomers and mixing and blending resins on a custom basis and/or (2) manufacturing noncustomized synthetic resins.
3252111 Thermoplastics resins and plastics materials
32521111 Thermoplastic resins and plastics materials
3252111100 Thermoplastics resins and plastics materials
3252111110 Thermoplastic resins and plastics materials, polyethylene
3252111120 Thermoplastic resins and plastics materials, polypropylene
3252111130 Thermoplastic resins and plastics materials, polystyrene
3252111140 Thermoplastic resins and plastics materials, polyvinyl chloride
3252111150 Thermoplastic resins and plastics materials, polyester
3252111160 Other thermoplastic resins and plastics materials
3252114 Thermosetting resins and plastics materials
32521141 Thermosetting resins and plastics materials
3252114100 Thermosetting resins and plastics materials
3252114110 Thermosetting resins and plastics materials, phenolic
3252114120 Thermosetting resins and plastics materials, urea
3252114130 Thermosetting resins and plastics materials, polyester
3252114140 Thermosetting resins and plastics materials, epoxy
3252114150 Other thermosetting resins and plastics materials
325211M Miscellaneous receipts
325211P Primary products
325211S Secondary products
325211SM Secondary products and miscellaneous receipts
325212 This U.S. industry consists of establishments primarily engaged in manufacturing synthetic rubber.
3252120 SYNTHETIC RUBBER (VULCANIZABLE ELASTOMERS)
32521201 Styrene-butadiene rubber (SBR), excluding latex
3252120111 Styrene-butadiene rubber (SBR), excluding latex
32521202 Styrene-butadiene rubber (SBR), latex
3252120211 Styrene-butadiene rubber (SBR), latex
32521203 Butyl, polychloroprene, and stereo polyisoprene elastomers, and nitrile rubber, including latex
3252120311 Nitrile rubber, including latex
3252120321 Butyl, polychloroprene, and stereo polyisoprene elastomers, including latex
32521204 Stereo polybutadiene elastomers, including latex
3252120411 Stereo polybutadiene elastomers, including latex
32521205 Ethylene-propylene elastomers, including latex
3252120511 Ethylene-propylene elastomers, including latex
32521206 Silicone elastomers
3252120611 Silicone elastomers
32521207 Other elastomers, excluding thermoplastic elastomers, including latex
3252120711 Other elastomers, excluding thermoplastic elastomers, including latex
32521208 Thermoplastics elastomers
3252120811 Thermoplastics elastomers
3252129 Synthetic rubber, inc. SBR, ethylene propylene, & all other synthetic elastomers
325212M Miscellaneous receipts
325212P Primary products
325212S Secondary products
325212SM Secondary products and miscellaneous receipts
32522 This industry comprises establishments primarily engaged in (1) manufacturing cellulosic (i.e., rayon and acetate) and noncellulosic (i.e., nylon, polyolefin, and polyester) fibers and filaments in the form of monofilament, filament yarn, staple, or tow or (2) manufacturing and texturing cellulosic and noncellulosic fibers and filaments.
325221 This U.S. industry comprises establishments primarily engaged in (1) manufacturing cellulosic (i.e., rayon and acetate) fibers and filaments in the form of monofilament, filament yarn, staple, or tow or (2) manufacturing and texturizing cellulosic fibers and filaments.
3252210 RAYON, ACETATE, AND LYOCELL MANUFACTURED FIBERS
32522101 Rayon, acetate, and lyocell manufactured fibers
3252210111 Rayon, acetate, and lyocell monofilament yarn, including strip
3252210121 Rayon, acetate, and lyocell group (multi) filament yarn, including strip
3252210131 Rayon, acetate, and lyocell textured yarn (including strip), made by filament yarn producers
3252210139 Rayon, acetate, and lyocell yarn (including strip), monofilament and group (multi) filament, made by filament yarn producers
3252210141 Rayon, acetate, and lyocell staple, tow, and salable waste
3252213 Rayon and acetate fibers
325221M Miscellaneous receipts
325221P Primary products
325221S Secondary products
325221SM Secondary products and miscellaneous receipts
325222 This U.S. industry consists of establishments primarily engaged in (1) manufacturing noncellulosic (i.e., nylon, polyolefin, and polyester) fibers and filaments in the form of monofilament, filament yarn, staple, or tow, or (2) manufacturing and texturizing noncellulosic fibers and filaments.
3252221 Nylon and other polyamide fibers
32522211 Nylon and other polyamide manufactured fibers
3252221111 Industrial nylon and other polyamide fiber yarns, including strip, made by filament yarn producers
3252221121 Nylon and other polyamide fiber carpet yarns, including strip, made by filament yarn producers
3252221131 Nylon and other polyamide fiber monofilament and fewofilament textile yarns, including strip, made by filament yarn producers
3252221141 Nylon and other polyamide fiber staple
3252221145 Nylon and other polyamide fiber staple, tow, and waste
3252221151 Nylon and other polyamide fiber tow and waste
3252223 Polyester
325222311 Yarn, except producer textured (industrial and textile)
325222332 Staple, tow and waste (fiberfill and other)
3252224 Polyolefin
32522241 Polyolefin manufactured fibers
3252224111 Polyolefin monofilament yarn, including strip, made by filament yarn producers
3252224121 Polyolefin group (multi) filament yarn, including strip
3252224125 Polyolefin group (multi) filament and film, including strip
3252224131 Polyolefin film yarn, including strip
3252224141 Polyolefin staple
3252224145 Polyolefin staple, tow, and waste
3252224151 Polyolefin tow and waste
3252225 Other noncellulosic manmade fibers (except glass, carbon, and graphite)
3252226 Producer textured noncellulosic manmade fibers
3252227 POLYESTER MANUFACTURED FIBERS
32522271 Industrial polyester yarn, including strip, made by filament yarn producers
3252227111 Industrial polyester yarn, including strip, made by filament yarn producers
32522272 Polyester textile yarn, including strip, made by filament yarn producers
3252227211 Polyester textile yarn, including strip, made by filament yarn producers
32522273 Polyester fiberfill staple and tow
3252227311 Polyester fiberfill staple and tow
32522274 Other polyester staple and tow and polyester fiber salable waste
3252227411 Other polyester staple and tow
3252227421 Polyester fiber salable waste
325222A OTHER MANUFACTURED NONCELLULOSIC FIBERS (EXCLUDING GLASS, CARBON, AND GRAPHITE)
325222A1 Other manufactured noncellulosic fibers (excluding glass, carbon, and graphite)
325222A111 Other manufactured noncellulosic fibers monofilament yarn, including strip (except glass, carbon, and graphite)
325222A115 Other manufactured noncellulosic fibers, yarn (including strip), monofilament and group (multi) filament, made by filament yarn producers
325222A121 Other manufactured noncellulosic fibers group (multi) filament yarn, including strip (except glass, carbon, and graphite)
325222A131 Other manufactured noncellulosic fibers staple, tow, and salable waste (excluding glass, carbon, and graphite)
325222D PRODUCER-TEXTURED MANUFACTURED NONCELLULOSIC FIBERS
325222D1 Nylon and other polyamide textured fibers, made by noncellulosic fiber producers
325222D111 Nylon and other polyamide textured fibers, made by noncellulosic fiber producers
325222D2 Polyester, polyolefin, and other noncellulosic textured fibers, made by noncellulosic fiber producers
325222D211 Polyester textured fibers, made by noncellulosic fiber producers
325222D221 Polyolefin textured fibers, made by noncellulosic fiber producers
325222D231 Other noncellulosic textured fibers, made by noncellulosic fiber producers
325222M Miscellaneous receipts
325222P Primary products
325222S Secondary products
325222SM Secondary products and miscellaneous receipts

This report was prepared from a variety of sources including excerpts from documents and official reports or databases published by the World Bank, the U.S. Department of Commerce, the U.S. State Department, various national agencies, the International Monetary Fund, the Central Intelligence Agency, various agencies from the United Nations (e.g. ILO, ITU, UNDP, etc.), and non-governmental sources, including ICON Group Ltd., Euromonitor, the World Resources Institute, Mintel, the U.S. Industrial Outlook, and various public sources cited in the trade press.

1.3.2 STEP 2. FILTERING AND SMOOTHING
Based on the aggregate view of manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments as defined above, data were then collected for as many similar countries as possible for that same definition, at the same level of the value chain. This generates a convenience sample of countries from which comparable figures are available.

If the series in question do not reflect the same accounting period, then adjustments are made. In order to eliminate short-term effects of business cycles, the series are smoothed using a 2-year moving average weighting scheme (longer weighting schemes do not substantially change the results).

If data are available for a country, but these reflect short-run aberrations due to exogenous shocks (such as would be the case of beef sales in a country stricken with foot and mouth disease), these observations were dropped or "filtered" from the analysis.

1.3.3 STEP 3. FILLING IN MISSING VALUES
In some cases, data are available for countries on a sporadic basis. In other cases, data from a country may be available for only one year.

From a Bayesian perspective, these observations should be given the greatest weight in estimating missing years. Assuming that other factors are held constant, the missing years are extrapolated using changes and growth in aggregate national income.

Based on the overriding philosophy of a long-run consumption function (defined earlier), countries which have missing data for any given year are estimated based on historical dynamics of aggregate income for that country.

1.3.4 STEP 4. VARYING PARAMETER, NON-LINEAR ESTIMATION
Given the data available from the first three steps, the latent demand in additional countries is estimated using a "varying-parameter cross-sectionally pooled time series model".

The interested reader can find longer discussions of this type of modeling in Studies in Global Econometrics (Advanced Studies in Theoretical and Applied Econometrics V. 30), by Henri Theil, et al., Kluwer Academic Publishers; ISBN: 0792336607; (June 1996), and in Principles of Econometrics, by Henri Theil John Wiley & Sons; ISBN: 0471858455; (December 1971), and in Econometric Models and Economic Forecasts by Robert S. Pindyck, Daniel L.

Rubinfeld McGraw Hill Text; ISBN: 0070500983; 3rd edition (December 1991). Simply stated, the effect of income on latent demand is assumed to be constant across countries unless there is empirical evidence to suggest that this effect varies (i.e., the slope of the income effect is not necessarily the same for all countries).

This assumption applies across countries along the aggregate consumption function, but also over time (i.e., not all countries are perceived to have the same income growth prospects over time and this effect can vary from country to country as well). Another way of looking at this is to say that latent demand for manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments is more likely to be similar across countries that have similar characteristics in terms of economic development (i.e., African countries will have similar latent demand structures controlling for the income variation across the pool of African countries). This approach is useful across countries for which some notion of non-linearity exists in the aggregate cross-country consumption function.

For some categories, however, the reader must realize that the numbers will reflect a country’s contribution to global latent demand and may never be realized in the form of local sales. For certain country-category combinations this will result in what at first glance will be odd results. For example, the latent demand for the category "space vehicles" will exist for Togo even though they have no space program.

The assumption is that if the economies in these countries did not exist, the world aggregate for these categories would be lower. The share attributed to these countries is based on a proportion of their income (however small) being used to consume the category in question (i.e., perhaps via resellers).

1.3.5 STEP 5. FIXED-PARAMETER LINEAR ESTIMATION
Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function. Because the world consists of more than 200 countries, there will always be those countries, especially toward the bottom of the consumption function, where non-linear estimation is simply not possible.

For these countries, equilibrium latent demand is assumed to be perfectly parametric and not a function of wealth (i.e., a country’s stock of income), but a function of current income (a country’s flow of income). In the long run, if a country has no current income, the latent demand for manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments is assumed to approach zero.

The assumption is that wealth stocks fall rapidly to zero if flow income falls to zero (i.e., countries which earn low levels of income will not use their savings, in the long run, to demand manufacturing resin, synthetic rubber, and artificial synthetic fibers and filaments). In a graphical sense, for low-income countries, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage of the estimation procedure, low-income countries are assumed to have a latent demand proportional to their income, based on the country closest to it on the aggregate consumption function.

1.3.6 STEP 6. AGGREGATION AND BENCHMARKING
Based on the models described in Chapter 1, latent demand figures are estimated for all countries of the world, including for the smallest economies. These are then aggregated to get world totals and regional totals.

To make the numbers more meaningful, regional and global demand averages are presented. Figures are rounded, so minor inconsistencies may exist across tables.

1.3.7 STEP 7. LATENT DEMAND DENSITY: ALLOCATING ACROSS CITIES
With the advent of a "borderless world", cities become a more important criteria in prioritizing markets, as opposed to regions, continents, or countries. This report also covers the world’s top 2,000 cities.

The purpose is to understand the density of demand within a country and the extent to which a city might be used as a point of distribution within its region. From an economic perspective, however, a city does not represent a population within rigid geographical boundaries.

To an economist or strategic planner, a city represents an area of dominant influence over markets in adjacent areas. This influence varies from one industry to another, but also from one period of time to another.

Similar to country-level data, the reader needs to realize that latent demand allocated to a city may or may not represent real sales. For many items, latent demand is clearly observable in sales, as in the case for food or housing items.


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2018 Future of Global Ethylene-Propylene Elastomers (EP elastomers) Market to 2025- Growth Opportunities, Competition, Trends and Outlook of Ethylene-Propylene Elastomers (EP elastomers) Across Applications and Regions Report

2018 Future of Global Ethylene-Propylene Elastomers (EP elastomers) Market to 2025- Growth Opportunities, Competition, Trends and Outlook of Ethylene-Propylene Elastomers (EP elastomers) Across Applications and Regions Report

  • $ 4580
  • August 2018

The global demand for Ethylene-Propylene Elastomers (EP elastomers) is forecast to report strong growth driven by consumption in major emerging markets.More growth opportunities will turn up between 2018 ...

2018 Future of Global Polybutadiene Elastomers Market to 2025- Growth Opportunities, Competition, Trends and Outlook of Polybutadiene Elastomers Across Applications and Regions Report

2018 Future of Global Polybutadiene Elastomers Market to 2025- Growth Opportunities, Competition, Trends and Outlook of Polybutadiene Elastomers Across Applications and Regions Report

  • $ 4580
  • August 2018

The global demand for Polybutadiene Elastomers is forecast to report strong growth driven by consumption in major emerging markets.More growth opportunities will turn up between 2018 and 2025 as compared ...


ref:plp2018

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