Building a Sustainable Economic Growth Model
By: Luis F Manrique
Tags: Machine Learning, Data Science, Economics, Sustainability, Data Visualization

Photo by Fas Khan on Unsplash

Introduction

Sustainable economic growth means a rate of growth that can be maintained without creating other significant economic problems, especially for future generations. The following article is created with the premise that the Gross Domestic Product (GDP) and Profit are the fundamental measures of economic success; however, they do not necessarily measure sustainable economic growth. Therefore, it endorses the Vibrant Economy Index from Grant Thornton. This index has the potential to measure sustainable economic growth through social and environmental characteristics and ranks the economic vibrancy of towns, counties, and regions.

The Vibrant Economy Index can be created as an arithmetic average of a region’s performance across six ‘baskets’.

Prosperity — Are we producing wealth and creating jobs? — Components: Total GVA; GVA per job; Average workplace earnings; Employment in knowledge-driven sectors (%); Businesses with turnover over $1 million (%); Businesses with turnover over $100 million (%); Foreign-owned businesses (%). Dynamism and Opportunity — Are we developing an entrepreneurial and innovative culture to drive future growth? — Components: Business formation rates; Patent applications granted (per 100,000 population); Residents qualified to NVQ +4 (degree level); Share of knowledge workers (%); Percentage of pupils who achieved grade 9–5 at GCSE level (%); Employment in higher education (%); Employment in research and development (%). Inclusion and Equality — Are individuals benefiting from economic growth? Components: Indices of Multiple Deprivation (average score); Inequality score; Child poverty (score); Housing affordability; Employment rate (%); Total Income ($); Fuel-poor household (%); Unemployment over five years (%); Working-age population claiming benefits (%); Housing benefits claimants (%); Homelessness; NEETs; Unemployment inequality (ethnicity). Health, Wellbeing, and Happiness — Are our people living healthy, active, and fulfilling lifestyles? — Components: Sports participation; Life expectancy at birth (male and female combined); Diabetes prevalence (%); Obesity in adults (%); Child obesity in Year 6; Happiness (score); Anxiety (score); Life satisfaction (score); Life worthwhile (score); Mean hours. Resilience and Sustainability — Are we creating places that people want to live in and are not damaging our natural environment? — Components: Air quality score; Waste recycled; Per capita CO2 emissions; Energy consumption (all fuels); Household on the local authority waiting list (%); Total dwelling completions; Total planning applications; Proportion of new residential addresses created in a national flood zone. Community, Trust, and Belonging — Are people engaged with their community and living vibrant and creative cultural lives? — Components: Valid votes turnout (%); Violent crimes (per 1,000 population); Living alone, aged over 65 years old (%); Cultural amenities score; Community asset score; Ethnic diversity score.

Each basket holds a set of socio-economic and/or business indicators, and such is an index own right. The Vibrant Economy Index is therefore based on a three-layer model of:

1. Individual Indicators.

2. Basket of Indicators.

3. Overall Vibrant Economy Index.

Methodology

Step 1: Select Indicators

The indicators are collected from locally available data sets. Analytics researchers from Grant Thornton propose the initial suites of indicators. They are based on the reliability of data sources, spatial coverage, and their coherence to other indicators within a wider basket. The selection of indicators was then refined through discussion between Vibrant Economy Commission, Grant Thornton Partners, and inputs from the general public.

Step 2: Normalize indicators with the min-max formula

The first stage from the normalization process is to ensure a ‘fair’ comparison by creating denominators for indicators that are unduly influenced by an area’s size — for example, there will be more businesses formed in areas with larger business populations, but this does not necessarily mean that they are more entrepreneurial.

The second stage from the normalization process is to ensure that indicators on different scales (percentages, scores, actual numbers) are measured on a notionally common scale. To achieve this, the min-max formula is applied so that each area receives a score between 0 to 1. In applying the min-max formula, indicators that relate to positive outcomes are normalized to a score between 0 and 1. Indicators that relate to a negative outcome are normalized inversely to reflect this.

Step 3: Apply threshold to uplift zero entries

The Vibrancy Economy Index is based on the benchmarking principle. Therefore, in order to ensure that there are not ‘zero’ entities, values are capped to be finitely small. Given that the analysis is undertaken across local authority areas and the variability between the values for each indicator, a minimum threshold scaled to one-tenth (1/10) of the second lowest value is applied. This step ensures that the geometric mean is calculated, as it removes any zero values from the data and recognizes the gap between each pair of the lowest values to each indicator.

Step 4: Calculate the geometric mean using the normalized scores to obtain basket scores

All indicators are grouped together under their respective baskets, and the geometric mean for each is calculated. The geometric mean provides a better reflection of the intrinsic differences between indicators compared to a simple average.

Step 5: Scale the basket scores

A scale score is created to enable a comparison between different spatial areas. This calculation enables all scores to be scaled against the national average of 100 and a standard deviation of 5. The local authority areas are then ranked accordingly to their scores in each basket.

Step 6: Calculate the Vibrant Economy Index

The final step is to create the arithmetic average of all six scales basket scores. This yields a location’s overall Vibrant Economy Index Score. The index ranking is based on this score.

The current article supports the development of the Vibrant Economic Model. It acknowledges limitations from proxies of wider indicators, collection methodologies, and constrains from applications; yet, data collection can be consistent on a location basis and enable place-based measurements and comparisons. Additionally, It can serve as a framework that stimulates discussions, as an evidence base to identify (and measure) strategic priorities or investments, and a tool to support investment decisions.