Retail Industry impact on the Economy
Retail Industry impact on the Economy
“Make a customer, not a sale”
-Katherine Barchetti, Founder, K. Barchetti Shops
Executive Summary
The developed economies have an established and fiercely competitive global retail sector. This research is conducted to identify the retail sector’s impact on the country’s economy. Moreover, statistical techniques are applied, and the retail industry dynamics are explored to better understand this industry’s importance.
Introduction
All entity that offers goods and services are added to the retail sector. In retail, products are intended to be sold at a single point of sale, such as a physical or online store. Statista said the US retail industry reached 6.5 (T) dollars in 2021. Furthermore, the global retail sector earned 26 (T) U.S. dollars, and the below graph shows the total retail sales in the US for the last ten years until 2021.
The US is the largest retail industry in the world. The below graph shows the world’s key companies in the retail industry with their revenue. So, the top three companies are located in the US, whereas Europe has fourth place in the list. Moreover, in the below graph, the US country is on the highest as compared to the other countries. The only Asian company on the list is JD.com Inc, located in China with below 100 billion USD in revenue.
The National Retail Federation’s 2018 report took into account three different sorts of economic impacts: induced, indirect, and direct. The GDP, labor income, and jobs that the retail sector directly affects are considered under the heading of direct impact. The retail sector also has an indirect impact on other firms. In other words, household spending from money received directly or indirectly causes the induced effect. According to the research, the US retail business supports 52 million jobs, or 25.8% of all US jobs, make 2.3 trillion dollars in labor income, or 18.7% of the state’s total labor income, and contributes 3.9 trillion dollars to the state’s GDP, or 18.7% of overall GDP.
Trends in Retail Industry
Consumer behavior significantly impacts the retail sector. Their behavior shapes the market and increases the competition among retailers. Some of the trends shaping the retail industry are mentioned below:
E-commerce In Retail Industry
According to Gray, e-commerce has become unmanageable in the retail industry, and the annual growth rate of sales is 15%. Amazon alone is responsible for over 40% of all online sales in the United States. Consequently, the graph below shows the retail e-commerce sales forecasted up to 2026.
Source: Statista
Self-Checkout
Some of the benefits of self-checkout, described by Magestore, are
- fewer queues, quicker checkout,
- increased customer satisfaction,
- better shop capacity,
- increased output from the employees
The report by Toshiba highlights the checkout methods are most popular among customers, and the self-checkout is the 2nd most popular among all.
Artificial Intelligence
World Economic Forum defines some benefits of utilizing artificial intelligence in the retail industry, i.e.,
- Artificial intelligence can help retail firms run more profitably and efficiently.
- Automation, loss avoidance, and feasibility.
- Costs can be reduced, supply chains can be improved, and customer happiness can rise.
Augmented Reality
Shopify says a 94% greater conversion rate was seen while dealing with AR-enhanced products than those without such features. According to a Google survey, 66% of participants are interested in employing augmented reality to assist them in buying. According to PR Newswire, the two most common augmented reality (AR) technologies utilized in retail are virtual fitting rooms and visualizing software, and it is the main augmented reality technology used in retail, accounted for 61.19 percent of the global market in 2019 and had a market value of roughly 734 million USD.
D2C
In a direct-to-consumer (D2C) retail sales strategy, a company creates, sells, and ships a product directly to the customer. Diffusion, in its article, says that nearly 23% of Americans think that DTC companies are the ultimate arbiters of what’s hip and in style. 4% of Americans think DTC firms deliver better quality goods at lower prices than their traditional rivals.
Research Methodology
After a detailed understanding of the retail industry and its dynamics, we move on to our research. We have explored the retail firm’s impact on the economy of the US. The time series analysis has been performed, and data is collected from OECD (Organisation for Economic Co-operation and Development) and FRED (Federal Reserve Economic Data) websites. We have analyzed the data for 20 years from 2002-2021. Further, the predictor variables involved in the study are CCI (Consumer Confidence Index) and GDP (Gross Domestic Product), and the regressor is the revenue of different retail sectors of the US.
First, we carried out a descriptive analysis of our data set; then, the ADF (Augmented Dickey-Fuller) test was used to determine the unit root. The last ARDL (Autoregressive Distributed Lag) model was applied to measure the long-run impact of the Profitability of different sectors on the country’s GDP and CCI.
Results and Findings
Descriptive Statistics
| CCI | GDP | TFS | BMS | FHF | BWL | CLA | GRO | AAT | GAS | PDS | EAS | |
| Mean | 99.74896 | 16457215 | 410360.9 | 23377.75 | 8758.5 | 3757.95 | 19094.7 | 47262.95 | 6903.35 | 37712.7 | 19283.65 | 8210.3 |
| Median | 99.90696 | 15926851 | 392389.5 | 22950 | 8794.5 | 3607.5 | 18731.5 | 46273.5 | 6964 | 37809.5 | 19217 | 8316.5 |
| Maximum | 101.6014 | 23315081 | 619974 | 34705 | 11856 | 5844 | 24339 | 65209 | 9319 | 48625 | 26675 | 9195 |
| Minimum | 96.63922 | 10929108 | 288369 | 18073 | 7073 | 2495 | 14359 | 35024 | 5217 | 20885 | 12828 | 6332 |
| Std. Dev. | 1.455269 | 3503916 | 85237.18 | 4221.877 | 1178.302 | 932.7099 | 2768.314 | 8911.309 | 1081.979 | 7766.508 | 3879.574 | 680.4224 |
| Skewness | -0.43249 | 0.251352 | 0.670911 | 1.023355 | 0.641015 | 0.634061 | 0.039144 | 0.449829 | 0.272939 | -0.67731 | 0.077243 | -0.7884 |
| Kurtosis | 2.031329 | 2.115111 | 2.914804 | 3.81122 | 3.482127 | 2.745653 | 2.035033 | 2.251687 | 2.435189 | 2.679405 | 2.130484 | 3.988531 |
| Jarque-Bera | 1.405437 | 0.863116 | 1.506455 | 4.039248 | 1.563372 | 1.394023 | 0.781076 | 1.141132 | 0.514162 | 1.614823 | 0.649936 | 2.886266 |
| Probability | 0.495237 | 0.649497 | 0.470844 | 0.132705 | 0.457634 | 0.498072 | 0.676693 | 0.565205 | 0.773306 | 0.446011 | 0.72255 | 0.236187 |
| Sum | 1994.979 | 3.29E+08 | 8207217 | 467555 | 175170 | 75159 | 381894 | 945259 | 138067 | 754254 | 385673 | 164206 |
| Sum Sq. Dev. | 40.23838 | 2.33E+14 | 1.38E+11 | 3.39E+08 | 26379537 | 16529009 | 1.46E+08 | 1.51E+09 | 22242895 | 1.15E+09 | 2.86E+08 | 8796518 |
| Observations | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 | 20 |
The null hypothesis of the Jarque-Bera test is that the data is normally distributed. The results show that the p-value of all variables is greater than 0.05; hence we retain the null hypothesis for all variables.
ADF Test for the Unit Root
| Variables | ADF Model | ADF Model (With Intercept) | ADF Model (With Intercept and Trend) |
| (a) ADF test for unit root on the level series | |||
| GDP | 4.31NS | 0.86NS | -1.45NS |
| CCI | -0.36NS | -1.65NS | -1.87NS |
| TFS | 3.53NS | 1.47NS | -0.40NS |
| BMS | 0.94NS | 2.43NS | 0.23NS |
| FHF | 1.20NS | -0.11NS | -0.75NS |
| BWL | 6.07NS | 2.70NS | 0.74NS |
| CLA | 1.26NS | -1.96NS | -4.34* |
| GRO | 3.08NS | 3.16NS | 2.07NS |
| AAT | 3.74NS | 0.90NS | -2.64NS |
| GAS | 0.76NS | -2.21NS | -2.57NS |
| PDS | 7.72NS | 0.82NS | -1.02NS |
| EAS | -0.71NS | -1.96NS | -2.89NS |
| (b) ADF test for unit root on the first difference | |||
| GDP | -0.62NS | -4.44** | -4.3* |
| CCI | -3.97** | -3.91** | -3.79* |
| TFS | -0.70NS | -2.04NS | -2.23NS |
| BMS | -0.74NS | -1.08NS | -1.42NS |
| FHF | -2.13* | -2.28NS | -2.34NS |
| BWL | 1.43NS | 0.72NS | 0.07NS |
| CLA | -6.39** | -6.74** | -7.00** |
| GRO | 1.22NS | 0.46NS | -2.85NS |
| AAT | -0.68NS | -2.52NS | -2.32NS |
| GAS | -3.83** | -4.02** | -3.91* |
| PDS | -0.63NS | -2.51NS | -2.53NS |
| EAS | -3.99** | -3.83* | -3.96* |
| (c) ADF test for unit root on the Second difference | |||
| GDP | -5.57** | -5.33** | -4.27* |
| CCI | -5.98** | -5.83** | -5.02** |
| TFS | -2.87** | -2.79NS | -2.75NS |
| BMS | -3.64** | -3.55* | -4.05* |
| FHF | -3.18** | -3.04NS | -2.85NS |
| BWL | -0.81NS | -1.08NS | -7.98** |
| CLA | -8.25** | -7.96** | -7.41** |
| GRO | -3.62** | -3.86* | -4.42* |
| AAT | -3.23** | -3.16* | -3.13NS |
| GAS | -4.53** | -4.34** | -4.17* |
| PDS | -5.26** | -5.21** | -5.21** |
| EAS | -4.27** | -4.10** | -3.94* |
The ADF test results show that some factors become stationary after the 1st order difference. However, all the variables have achieved stationarity after the 2nd order difference.
ARDL Long Run Form and Bounds Test
Dependent Variable: GDP
| TFS | BMS | FHF | BWL | CLA | GRO | AAT | GAS | PDS | EAS | |
| Coefficient | 46.30 | -1282.06 | 5796.08 | 8350.30 | 837.03 | 410.88 | 3433.64 | 29.98 | 937.37 | -631.97 |
| Prob. | 0.0000 | 0.3710 | 0.2390 | 0.5630 | 0.0006 | 0.0000 | 0.0000 | 0.9150 | 0.0000 | 0.4070 |
| F-statistic | 24.44 | 14.85 | 35.63 | 4.30 | 45.18 | 15.31 | 40.77 | 17.59 | 14.93 | 186.74 |
| Lower Bound (at 1%) | 4.94 | 4.94 | 4.94 | 4.94 | 4.94 | 4.94 | 4.94 | 4.94 | 4.94 | 4.94 |
| Upper Bound (at 1%) | 5.58 | 5.58 | 5.58 | 5.58 | 5.58 | 5.58 | 5.58 | 5.58 | 5.58 | 5.58 |
The above table shows the value of long-term coefficients. According to trade and food services (TFS), clothing and clothing accessory (CLA), grocery (GRO), automotive parts, accessory, and tire (AAT), and pharmacies and drug (PDS) industries’ revenue has a significant impact on GDP in the long run.
Further, the unit change in these industries’ revenue would increase the GDP by 46.30, 837.03, 410.88, 3433.64, and 937.37 times, respectively.
In contrast, building materials and supplies (BMS), furniture and home furnishings (FHF), beer, wine, and liquor (BWL), gasoline (GAS), and electronics and appliance (EAS) industries’ revenue have an insignificant impact on GDP in the long run.
The F-statistic values of all the variables except BWL are greater than the upper bound value (at 1%), this shows all factors are cointegrated with the GDP, but BWL does not show cointegration.
Dependent Variable: CCI
| TFS | BMS | FHF | BWL | CLA | GRO | AAT | GAS | PDS | EAS | |
| Coefficient | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Prob. | 0.8818 | 0.7819 | 0.8182 | 0.2700 | 0.3119 | 0.1239 | 0.8088 | 0.7644 | 0.8288 | 0.7868 |
| F-statistic | 2.29 | 2.35 | 2.31 | 3.00 | 2.92 | 4.53 | 3.98 | 2.33 | 2.73 | 2.32 |
| Lower Bound (at 1%) | 4.94 | 4.94 | 4.94 | 4.94 | 4.94 | 4.94 | 4.94 | 4.94 | 4.94 | 4.94 |
| Upper Bound (at 1%) | 5.58 | 5.58 | 5.58 | 5.58 | 5.58 | 5.58 | 5.58 | 5.58 | 5.58 | 5.58 |
The above table shows the value of long-term coefficients. The probability value shows that all the sectors have an insignificant impact on CCI in the long run. Also, the values of the F-statistic are less than the lower bound, which shows no cointegration.
Conclusion
The retail industry is the fastest-growing industry worldwide. The results and findings analyze the impact of retail industries’ revenue on the GDP. Hence, the time series analysis was performed, representing that all the industries with a significant impact on GDP are positively affected.
The results explain the influence of revenues in the long run, whereas some industries do not account for the influence on GDP in the long run. On the other hand, retail industries’ revenue was neither significant nor cointegrated with CCI.
Overall it is concluded that the growth of the retail sector is cointegrated with the country’s GDP. Also, the trends in the retail industry depict customer behavior toward buying products and services, which could be interpreted as highly volatile. Therefore, it’s important for companies to move towards technological advancement and modernization rapidly and to flow with the trend.
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