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The 2019-2024 World Outlook for Iron Foundries

The 2019-2024 World Outlook for Iron Foundries

  • January 2018
  • 271 pages
  • ID: 1992637

Summary

Table of Contents

This study covers the world outlook for iron foundries 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 iron foundries. 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 iron foundries 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 iron foundries 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 iron foundries. 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 iron foundries. 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 iron foundries.

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 iron foundries 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 iron foundries. 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 iron foundries. 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 iron foundries.

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 iron foundries 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 iron foundries 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 iron foundries as defined by the North American Industrial Classification system or NAICS (pronounced "nakes").

The NAICS code for iron foundries is 331511. It is for this definition that aggregate latent demand estimates are derived.

Iron foundries is specifically defined as follows:
331511 This U.S. industry comprises establishments primarily engaged in pouring molten pig iron or iron alloys into molds to manufacture castings, (e.g., cast iron man-hole covers, cast iron pipe, cast iron skillets). Establishments in this industry purchase iron made in other establishments.
3315111 Ductile iron pressure pipe and fittings
33151111 Ductile iron pressure pipes and iron-fittings, all sizes
3315111106 Ductile iron pressure pipe, 14 in. or more (inside diameter)
3315111107 Ductile iron pressure pipe, 14 in. to 24 in.
3315111108 Ductile iron pressure pipe, 30 in. and over
3315111111 Ductile iron fittings, less than 14 in.

(inside diameter)
3315111116 Ductile iron fittings, 14 in. or more (inside diameter)
3315111117 Ductile iron fittings, 14 in. to 24 in.
3315111118 Duticle iron fittings, 25 in. and over
33151112 Ductile iron pressure pipe, less than 14 in.

(inside diameter)
3315111201 Ductile iron pressure pipe, less than 14 in. (inside diameter)
3315113 Other ductile iron castings
33151131 Other ductile iron castings for automotive uses
3315113101 Other ductile iron castings for automotive uses
33151132 Other ductile iron castings for all other uses
3315113206 Other ductile iron castings for construction and utility uses
3315113211 Other ductile iron castings for valve uses
3315113216 Other ductile iron castings for machinery uses
3315113221 Other ductile iron castings for all other uses, including electric and electronic equipment uses, heat-resistant parts (including coke oven door parts)
3315115 Cast iron pressure pipe and fittings
33151151 Cast iron pressure pipe fittings, all sizes
3315115101 Cast iron pressure pipe, less than 14 in. (inside diameter)
3315115106 Cast iron pressure pipe, 14 in.

or more (inside diameter)
3315115111 Cast iron pressure pipe fittings, less than 14 in. (inside diameter)
3315115116 Cast iron pressure pipe fittings, 14 in. or more (inside diameter)
3315115121 Gray iron pressure pipe and fittings, under 14 in.

(inside diameter)
3315115125 Gray iron pressure pipe and fittings, 14 in. and over
3315117 Cast iron soil pipe and fittings (incl special fittings), all sizes
33151171 Cast iron soil pipe fittings, all sizes
3315117101 Cast iron soil pipe, 3 in. or less (inside diameter)
3315117106 Cast iron soil pipe, more than 3 in.

up to but not including 5 in. (inside diameter)
3315117111 Cast iron soil pipe, 5 in. or more (inside diameter)
3315117114 Gray iron soil pipe
3315117116 Cast iron soil pipe fittings (including special fittings), 3 in.

or less (inside diameter)
3315117121 Cast iron soil pipe fittings (including special fittings), more than 3 in. up to but not including 5 in. (inside diameter)
3315117126 Cast iron soil pipe fittings (including special fittings), 5 in. or more (inside diameter)
3315119 Other gray iron castings
33151191 Other gray iron castings for all other uses
3315119101 Gray iron rolls for rolling mills
3315119111 Other gray iron castings for construction and utility uses
3315119116 Other gray iron castings for all other uses
33151192 Other gray iron castings for automotive uses
3315119206 Other gray iron castings for automotive uses
331511A Standard malleable iron castings
331511A1 Standard malleable iron castings
331511A100 Standard malleable iron castings
331511C Pearlitic malleable iron castings
331511C3 Pearlitic malleable iron castings
331511C300 Pearlitic malleable iron castings
331511E Molds and stools for heavy steel ingots
331511E2 Molds and stools for heavy steel ingots
331511E200 Molds and stools for heavy steel ingots
331511M Miscellaneous receipts
331511P Primary products
331511S Secondary products
331511SM 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 iron foundries 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 iron foundries 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 iron foundries 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 iron foundries). 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.

Consider, again, the category "satellite launch vehicles." Clearly, there are no launch pads in most cities of the world. However, the core benefit of the vehicles (e.g. telecommunications, etc.) is "consumed" by residents or industries within the world’s cities. Without certain cities, in other words, the world market for satellite launch vehicles would be lower for the world in general. One needs to allocate, therefore, a portion of the worldwide economic demand for launch vehicles to regions, countries, and cities.

This report takes the broader definition and considers, therefore, a city as a part of the global market. I allocate latent demand across areas of dominant influence based on the relative economic importance of cities within its home country, within its region, and across the world total.

Not all cities are estimated within each country as demand may be allocated to adjacent areas of influence. Since some cities have higher economic wealth than others within the same country, a city’s population is not generally used to allocate latent demand. Rather, the level of economic activity of the city is used vis-à-vis others.

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