How Policymakers can Utilize Input-Output Analytics to Tackle the Consequences of Urbanization

As urbanization increases rapidly around the world, humanity is pushed towards the forefront of one of its greatest changes in functionality. Local, state, and federal governments are trying their best to tackle the environmental issues that come with urbanization. In this pursuit, policymakers’ greatest asset comes from foundational economic theory that’s intertwined with modern data science: input-output analytics.

Introduction to Our Models

Input-output analytics encompass the assumptions and implications of two distinct models that analyze different aspects of the relationship between input (raw resources) and output (the production of goods and services). The first is rooted in visual modeling postulated by British mathematician and social scientist, Alan Wilson, and the second is based on macroeconomic theory developed by Soviet-American Nobel laureate, Wassily Leontief. Wilson’s family of spatial interaction models discusses an overarching notion of the movement of goods, ideas, people, and things that can fundamentally transform an environment or region. Leontief’s framework, which differentiates between open and closed economies, details how consumption and production are directly related within the economy of a particular area or country. Together, these two techniques can be utilized to develop functional hypotheses and suggestions regarding how governments can accelerate and revitalize stagnant or negative economic growth.

Case Study #1: Spain

Our first case study is conducted by researchers from Malaysia and Barcelona, and it focuses on how production of goods and services within Spain has unfortunate side effects such as environmental damage. The study addresses input-output analytics from a comprehensive, demand-based approach by analyzing different economic sectors such as transportation, consumption, and housing.

A. Impacts of Urbanization

Researchers think of the economy that results from urbanization as a product of urban metabolism. One can compare this to how metabolism within an organism fuels it with energy to function and carry out all its activities. Due to urbanization, an economy grows more robust and active, but increased production of certain goods such as motor vehicles may cause certain negative externalities such as an influx in carbon dioxide emissions. To make this relationship clearer, the researchers utilize a methodology called the Economic Input-Output Life Cycle Assessment which “estimates the materials and energy resources required, and the environmental emissions resulting from activities in the economy” (Gabarrell et al. 29).

As stated earlier, economic output increases Gross Domestic Product (GDP) and Gross National Output (GNP), but the production of certain goods and services exacerbate environmental concerns. Using Leontief’s framework, the researchers derive the “Inverse Leontief” matrix by observing the relationship between household consumption and gross output, from which you can derive regional demand.

Figure 1: The inverse matrix above visualizes how much money (in millions of Euros) each “branch of products” produces correlated products in order to meet consumer demand (Gabarrell et al. 31).

B. An Influx of Emissions

This, in tandem with the Statistical Institute of Catalonia’s Greenhouse Gases Emissions for Catalonia Inventory, explains how particular sectors produce significant amounts of emissions annually based on the equilibrium point between supply and demand. As seen in the graph below, the manufacturing sector creates the largest amount of emissions probably due to industries such as steel, petroleum, and consumer electronics.

Figure 2: Economic Activities in Terms of CO2 Emission: As seen in the horizontal bar graph above, which depicts how different economic sectors and activities within those sectors emit carbon dioxide, the manufacturing sector contributes the most to CO₂ emissions (Gabarrell et al. 33).

According to the study, Barcelona’s daily emissions averaged 24.44 kg CO2eq./cap, a number similar to a Portuguese city, Aveiro, which had an urban metabolism rate of 25.8 CO2eq./cap per day. However, it’s important to note that Barcelona’s population is 14 times the size of Aveiro’s population but the city also has higher emission density, meaning that Aveiro does have more overall emissions per capita per day.

C. Data Discrepancy and Implications

Despite these numbers though, the study also denotes that the data used varied between regional and national sources which each have their own metrics to follow. They also have different standards of classification towards economic sectors, resulting in some overlapping figures or a lack of acknowledgement towards certain industries overall.

In conclusion, the research conducted in Barcelona makes it clear that input-output analysis is key in urban planning because it enables possible alteration or continuation of production practices within a locality. Here, it was referenced in relation to harmful environmental impacts, which are becoming increasingly important to analyze in order to prevent or at least delay global warming.

Case Study #2: China

Our second case study shifts our focus from the environment to geographic disparity, honing in on how urban areas of China differ vastly in labor practices and resource allocation from their rural counterparts.

A. The Batey-Madden Model

This is a more novel application of the input-output model, which until the past half decade, linked economic analysis primarily with environmental concerns. The author of this study which was published in the Journal of Economic Structures in 2021, Nobuhiro Okamoto, applies the Leontief framework and Wilson’s spatial interaction models to the Batey-Madden model, which extends input-output analysis to economic-demographic forecasting. It is comprised of four core components:

1. Economic Interaction Submatrix
2. Demographic Interaction Submatrix
3. Economic-Demographic Interaction Submatrix
4. Demographic-Economic Interaction Submatrix

Both the economic interaction submatrix and the demographic interaction submatrix regard certain variables pertaining to their name as constant, so that they can measure how other variables are affected by them. For example, economic characteristics such as GDP, purchasing power, monetary flow are all things that can be held constant in the economic interaction submatrix. It would measure how consumer demographic attributes such as race, gender, and marital status could affect household consumption and spending, which then affects GDP, purchasing power, and fiscal balances. Vice versa is true for the demographic interaction submatrix.

Once you understand the notion behind the first two submatrices, it becomes more intuitive to learn about the remaining two. The economic-demographic interaction submatrix measures the impact of changes in output on employment, while the demographic-economic interaction submatrix measures how fluctuations in employment affect production.

B. Input-Output Analysis Using Batey-Madden

The study denotes how these four components of the Batey-Madden model have provided a measure of the relationship between labor input and household expenditure, or simply the input-output relationship, in various regions across China. Building upon the inverse matrices that are derived from Leontief’s work, the model puts forth three unique equations that characterize urbanization with regards to vectors of activity level and inputs.

Please note the following special terms:

a column vector comprised of average consumption coefficients

a column vector of consumption coefficients for unemployed individuals

a row vector representing employment-production functions

There are also two constants, em and ue, which represent the amount of employed workers and unemployed workers, respectively.

C. Urban Multiplier

Solving the inverse matrix with values acquired through federal accounts (primarily China’s National Bureau of Statistics) of the labor force, national production, and consumption leads to the calculation of an urban multiplier. In practice, the multiplier helps determine the impacts of a growing population on regional economies, which can be explored to a great extent in a country such as China which has seen not only rapid boosts in economic productivity, but also the development of densely-populated cities along coastlines as a result of foreign direct investment.

Contrasting the multiplier in bustling, metropolitan hubs to the slow and stagnant urbanization of rural areas makes it clear that the migration of workers from rural to more urban areas has led to a bigger labor force, increasing employment opportunities until the point of job market saturation. At this point, spatial friction is treated as a constant of 1, meaning that there is free movement between rural and urban areas and if someone wishes to migrate, they can. The economy is also considered to be at its optimal production point and should retain similar levels of productivity for maximum growth. However, the same opportunity is not given to rural areas which lose chunks of their labor force and must adjust to meeting regional demand by over-working.

In a more realistic setting, spatial friction is not negligible; for example, not all job vacancies can be immediately filled by workers from villages. Even if people would like better, higher-paying job opportunities, they may not be able to relocate for work due to family reasons or a lack of financial independence. As a result, they undergo emotional stressors and internal challenges that further diminishes their quality of life. In this scenario, the urban economy would not be at its optimal point due to job vacancies, but it is reasonable to assume that a majority of vacancies would be filled since the population is so large that everyone is looking for a shot at employment. This means that urban economies, at the very least, would be slowly growing, while rural areas are left to bite the dust.

Case Study #3: Dubai

This last case study’s purpose is to help navigate and pinpoint the goal of urban planning practices which aim to transform urban centers such as Dubai into smart cities. Using the input-output model, the paper guides policymakers to better understand the implementation of “smart” practices that can mitigate issues such as traffic congestion and air pollution. Such implementation can be especially challenging because the emergence and growth of smart cities is a relatively new phenomenon that began roughly a decade ago.

Figure 3: Created using a sample size of 6475 articles, this line graph shows the recent increase in academic research on “smart” cities and “low carbon” cities, beginning around 2011 and 2012 (Noori et al. 72).

A. Smart City Planning

The first target of researchers is to analyze current production on a local level and encourage manufacturers to adopt smart city initiatives that practice cleaner, more efficient methods. Such initiatives include researching contemporary Information and Communications Technology (ICT) infrastructure, building mobile-friendly applications to streamline digital transactions, and producing innovation management techniques.

None of this planning is possible without deeply examining what each sector is currently contributing in terms of meeting demand, overall economic growth, and other possible externalities. For example, if the international investment sector is identified as contributing heavily to fiscal outflow within Dubai, then policymakers ought to consider finding ways to maximize that profit. One idea they pursued is the creation of separate entrepreneurship zones where solely foreign ownership is encouraged at higher interest rates, or where corporate investment is allowed but with appropriate income taxes. Not only does this boost economic growth in regards to increasing foreign investment and taxes, but it also creates competitive markets for innovative ideas to grow–a foundational characteristic of a smart city where ideas are encouraged.

Figure 4: Visualizing the process of creating a “smart” city, the flowchart depicts how input in the technology sector is used by government and bureaucratic management to yield positive applications and externalities (Noori et al. 77).

B. Public Policy Initiatives Using Input-Output Analysis

After brainstorming possible initiatives, analysts were able to assess the “smartness” of Dubai with the help of Key Point Indicators (KPI’s) from 2015 onwards under the Dubai and the International Telecommunication Union agreement. In this deal, policymakers implemented an Internet of things (IoT) platform controlled under Dubai Pulse, the “digital backbone” powering Dubai. KPIs helped track the success of the rollout, and with the aid of public data published online by Dubai Pulse as well as other entities, the transformation of the smart city began. Soon enough, the city saw the growth of advances in other sectors such as healthcare and clean energy, too.

C. Setting a Precedent

It is important to note that the main driver of the transformation was The Dubai Smart City Office, which took responsibility for the planning and execution of smart programs undertaken by subsidiary entities such as Dubai Pulse. Because of the centralized leadership, Dubai continues to serve as a flagship example of how quickly and effortlessly a city can adopt newer, technological initiatives to boost the welfare of their residents and enhance environmental sustainability as well.

The Key Takeaways of Input-Output Analytics

The benefits of data analysis can evidently be applied to a myriad of topics: economics, environmental issues, healthcare, technology, etc. We’ve observed how mathematical models derived from public and private data enable researchers to visualize and evaluate intraregional and interregional trade, supporting the notion that input-output analytics are key to understanding city growth and development. These models, in turn, allow policymakers to reach decisions regarding smart planning, ways to address inequality within the labor market, or the general forecasting of trends such as climate change impacts. Whatever it may be, modern-day data science can help us find solutions to problems that were thought of as impossible-to-solve before.