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BE_coeff <- input_coefficient_matrix_create(data_table = BE)## Columns and rows of CPA_U are all zeros and will be removed.I_be <- leontieff_inverse_create(BE_coeff)Inter-industry linkages show the strength of economic relationships among industries of a given economy, which can be understood as Belgian national economy or the economy of the European Union. (The creation of regional SIOTs is possible, but difficult and has many limitations, as large companies do not place their entire production or sales in a region.)
From an economic policy perspective, a both linkages are desirable. In a development economics scenario, usually foreign investments into less developed countries are seen better if they have strong backward linkages – i.e. they source their ingredients locally, which usually leads to technology transfer and higher quality standards, too. In an advanced economy like Belgium, forward linkages are equally important, because they create opportunities for further value added with domestic inputs. From an economic policy perspective, you would like to weight the backward linkages, because a small industry has little gravity – a relatively large order compared to a small industry will remain relatively small. For antitrust or environmental impact analysis, the weighting is usually not required.
The weighted case may be useful, though, even in antitrust impact or enviornmental impact analysis when we want to make a risk-assessment of methane emission growth, habitat loss riks, or risk of vertical antitrust problems originating from the market power of a particular industry.
Backward linkages show the buying linkages towards suppliers, and often understood as the strength to pull the supplier base when a given industry is growing. Industries with a strong pull tend to create many purchasing orders when they are growing within the same economy.
## Selecting by label| Code | Linkage | Industry |
|---|---|---|
| CPA_C16 | 3.174476 | Wood and of products of wood and cork, except furniture; articles of straw and plaiting materials |
| CPA_N79 | 3.098058 | Travel agency, tour operator and other reservation services and related services |
| CPA_C13-15 | 2.978716 | Textiles, wearing apparel, leather and related products |
| CPA_H50 | 2.918551 | Water transport services |
| CPA_H52 | 2.394841 | Warehousing and support services for transportation |
| CPA_G45 | 2.297246 | Wholesale and retail trade and repair services of motor vehicles and motorcycles |
| CPA_G46 | 2.275559 | Wholesale trade services, except of motor vehicles and motorcycles |
| CPA_J61 | 2.223591 | Telecommunications services |
| Note: | ||
| Only the 8 strongest linkages are shown (relative to industry). For policy use, you could weight the backward pull with the size of the industry in question within the Belgian national economy. |
BE_output_coeff <- output_coefficient_matrix_create(BE)## Columns and rows of CPA_U are all zeros and will be removed.BE_fw <- forward_linkages(BE_output_coeff)
names(BE_fw) <- c("code", "value")Forward linkages show the supply side effects when the industry in question is growing. The abundance of supply, with normal goods associated with falling prices, creates more opportunities within the same economy for users of this intermediate product. Industries with a strong push tend accumulate many purchasing orders from others.
I think that these values are flawed, I will check the code.
## Selecting by label| Code | Linkage | Industry |
|---|---|---|
| CPA_C13-15 | -0.6556602 | Textiles, wearing apparel, leather and related products |
| CPA_N79 | -3.4169100 | Travel agency, tour operator and other reservation services and related services |
| CPA_G45 | -8.6114565 | Wholesale and retail trade and repair services of motor vehicles and motorcycles |
| CPA_H50 | -8.7334201 | Water transport services |
| CPA_G46 | -12.4338897 | Wholesale trade services, except of motor vehicles and motorcycles |
| CPA_J61 | -18.1400827 | Telecommunications services |
| CPA_C16 | -21.7941900 | Wood and of products of wood and cork, except furniture; articles of straw and plaiting materials |
| CPA_H52 | -32.4365450 | Warehousing and support services for transportation |
| Note: | ||
| Only the 8 strongest linkages are shown (relative to industry). For policy use, you could weight the forward push with the size of the industry in question within the Belgian national economy. |
From an antitrust perspective, firms with market power can accumulate monopolistic buying power if they have relatively high (unweighted) backward linkages. They are important purchasers within the economy (national or EU level) and their anti-competitive behaviour may have negative upstream effects. Similarly, strong forward linkages may lead to monopolistic supply. Too many economic actors rely on the industry, which, if it starts acting in an anti-competitive way, can cause problems.
An interesting hypothesis is to check weather antitrust law breeches, or disapprovals of mergers are associated with high-risk industries, i.e., industries with strong forward linkages. Monopsony cases are relatively rare, but from a risk-assessment point of view, you can associate them with high backward linkages.
| Use | Short Description |
|---|---|
| Competition Data Observatory | Systematic unweighted backward and forward linkage values for all developed countries and several years. They can be checked against SCP variables, and antitrust activity in national and EU/US level markets. |
| Green Deal Data Observatory | Systematic, pollutant weighted backward and forward linkage values for all developd countries, plus EU/US levels, to see the risk of spillovers changing over time and geography. For example, methane output is very particular to the type of agriculture in a certain geographical area |
| Eviota | Creating objective benchmarks for double materiality for cost and buyer groups. |
| Note: | |
| Only the 8 strongest linkages are shown (relative to industry). For policy use, you could weight the forward push with the size of the industry in question within the Belgian national economy. |
From a sustainable economy or sustainable finance point of view, a similar analysis can be made, if we use auxiliary tables related to the typical material flows of economic activities, such as emissions of carbon dioxide or methane, or potential harm to natural habitats. It is easy to rank industries by their CO2 emissions for each country, but this would not give a full picture. The new non-financial disclosure rules of the EU therefore want to create more transparency with the entire value chain, including suppliers and buyers. If we define the total impact of the industry as its own emission, plus emissions of its suppliers and users, we get a much better picture.
Again, industries with a strong backward pull to the industry may increase demand, which may lead to material economic activities that have a risk of adverse effect on climate change, water, or marine resource, and so on. A good risk-metric for the likely increase in emissions of methane is a weighted forward or backward linkage vector of industries. Industries with a high, weighted backward will have a strong pull for methane pollution. Industries with a high, weighted forward linkage will have induce further activities in the economy that will lead to strong methane output.
From an environmental policy point of view, the backward pull and forward push for more methane makes no difference. In this case, we can use the methane indicators and the methane multipliers of the input-output system. The methane indicators show industries with a high, own methane output, whilst methane multipliers show the total impact, including methane emissions of suppliers and buyers.
It is worth noting that in the input-output system the households (domestic end-users of Belgium or the entire EU) can be analysed directly. Forward linkages make no sense for business-to-consumer transactions. Industries that have a very small forward linkage usually sell their products or services directly to the households. When the household buys a piece of furniture, the total emissions impact (not counting recycling or disposal at the end of the useful life) is the embedded emissions of the furniture manufacturers and its supply chain of raw material producers, transport services, banks, insurance companies, advertisers, lawyers, and so on. Some products sold to the households, such as cars, will continue to have an adverse effect on the environment throughout their useful life. These effects can be accounted for, but not within input-output system, which only records the production side of the economy.