Consumer Behaviour and Macro Economy

By: Dripto Mukhopadhyay
The economic situation and policies of the states has a direct bearing on economic welfare, and hence has significant impact on consumption demand. Although hitherto neglected, it can easily explain the fluctuations in consumption pattern for consumer and other goods, and help in the planning of business strategy.
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Microeconomic volatility, one of the most crucial indicators for business, has hardly been studied in India (Garda & Ziemann, 2014). Reflected through fluctuations in the economic situation of households, microeconomic volatility has a direct bearing on economic welfare. Yet, it has remained an issue untouched by research aimed at understanding India’s consumer market.

Consumer Behaviour Figure 1

Macroeconomic situation can be linked to household income distribution. This article presents an analysis of two wheeler demand and its linkage in three states—Maharashtra, Tamil Nadu (TN) and Uttar Pradesh (UP). The states were selected based on the premise that Maharashtra and TN are the two top economic performers in the country; UP and Maharashtra are both large in terms of population and size, as compared to TN; and, in UP and Maharashtra, rural-urban contrasts and regional inequalities are more prominent as compared to TN.

Consumer Behaviour Figure 2

Secondary data, gathered from the Society of Indian Automobile Manufactures (SIAM), National Accounts Statistics, employment data from Nielsen’s Market Skyline, a proprietary database of Nielsen prepared by its Micromarketing and Economics (MME) team database, has been used for this analysis, which aims to:

  • Understand the sales pattern of two wheelers and the changes therein from 2001 to 2014.
  • Analyse state gross domestic product (SGDP) data by components, as given in National Accounts Statistics from 2001, to identify the key sectors and the changes that took place over the years. These are captured by the share of sectors to total GDP in the three states.
  • Understand employment scenario of the states from 2001 to 2013 and identify the changes that occurred, in correlation with the state GDP.
  • Understand the household distribution across income groups and the changes over time, in a bid to capture volatility in household incomes.

Consumer Behaviour Figure 3

Sales of two-wheelers

Two-wheeler sales data for the last 15 years was used to understand the demand, and hence analyse consumer behavioural patterns. Figure 1 shows that the pattern of two-wheeler sales and the price trend becomes discernible. The price has been represented by the wholesale price index (WPI) for the entire two-wheeler industry. The correlation between WPI and sales data for the states suggests that price did not affect sales significantly.

One can also see the distinct phases for two-wheeler demand in the selected states. The entire time span considered here is divided into four phases:

  • Phase 1: During 2001-02 to 2006-07, the total sales in Maharashtra and UP were similar. Although TN followed the same trajectory of growth, the sales were lower than in the other two states.
  • Phase 2: It can be marked distinctly between 2006-07 and 2008-09. The period of pre-global recession and global recession. In both Maharashtra and UP, sales figures dipped significantly during this period, but TN saw sales almost at similar level.
  • Phase 3: This phase between 2008-09 and 2011-12 saw significant improvement in sales. In the later part of this phase, the sales in all three states were at almost similar level.
  • Phase 4: One can see a drastic change after 2011-12. UP showed a phenomenal growth in two-wheeler sales whereas Maharashtra maintained a slow growth. In contrast, sales declined significantly in TN. It is an inexplicable phenomenon keeping in mind that TN is economically one of the best performing states in the country.

To take care of the population factor in the analysis, an index of sales per lakh households in all three states for 2004-05, 2009-10 and 2014-15 is presented in Figure 2. The chart shows that even in terms of per household two-wheeler sales, TN was way behind the other two states.

The industry and services sectors together contribute more than 94 per cent to TN’s total GDP, which is a little less than 93 per cent of Maharashtra’s GDP and about 77 per cent of UP’s total GDP.

Services are undoubtedly the most crucial sector for all three states. UP has seen much higher growth in the services sector as compared to Maharashtra and TN from 2011-2004. The overall share of the industry sector has declined significantly in UP, primarily because of a slowdown in manufacturing although the construction sector has gained significantly over the years. Maharashtra has seen fluctuations in the overall share of the industry sector with the manufacturing sector showing a continuous decline since 2006-07 onward. However, the construction sector’s share has largely increased. In the case of TN, the decline in share of the manufacturing sector is from 2011 onwards. However, that is just one perspective. The shares, for instance, tend to change with variations in GDP in different sectors. The magnitude of income generated is more important than the change in shares.

Consumer Behaviour Figure 4

Change in employment scenario

The employment scenario is one of the best yardsticks to measure the economy. Changes experienced, though, are sometimes immediate. Yet, it is a clear indication of the movement of the economy and its correlates. The share of employment in key sectors for four time points is shown through Figures 3, 4 and 5. The absolute changes in each sector provide some interesting and contrasting characteristics for each state. The most crucial ones that have a bearing on observed auto sales are:

  • In TN, employment in the construction sector between 2009 and 2013 stood at over 25 lakhs. For transport, storage and communication sectors, employment stood at 10 lakhs. An increase of about 3.5 lakh people was observed in the manufacturing sector in 2013 over 2009. Another important point to be noted is that in the banking, financial services and insurance (BFSI) sector, the actual increase in employment in 2009-13 was only 11,000, as against 31,000 in 2005-09.
  • In Maharashtra, the increase in employment in the trade, hotel and restaurant sector was about 18 lakhs during 2009-13. In the construction sector the same is about 6 lakhs. In manufacturing, employment declined by 3 lakhs during 2009-13. The BFSI sector has seen an increase of more than 4,00,000 persons as compared to only 11,000 for TN in 2009-13.
  • In UP, apart from the construction sector, the transport and trade sectors showed significant change in numbers employed from 2009 to 2013. One of the most important points to highlight is the increased employment of more than 4.5 lakh in the BFSI sector in this period. There is large scale migration from UP, particularly to Noida, Ghaziabad and Meerut. This large population residing in UP and working in the National Capital Region (NCR) creates incongruity in the economy and employment as well as demand for goods.

From all these, a few key issues that can help understand microeconomic volatility from the right perspective can be identified as:

  • In TN, there is a significant increase in services GDP, but a substantial decline in employment in other sectors, and almost no increase in employment in the BFSI sector. It is clear then, that most employment opportunities in the services sector are for the highly skilled.
  • Though manufacturing GDP at TN declined substantially, employment in the manufacturing sector has increased. This is only possible when employment is generated at the unskilled and lower levels.
  • The rise in construction sector employment is much more than the increase in GDP in the sector. This is because the construction sector is labour intensive with the workforce mainly comprising low-level unskilled workers earning low or daily wages.
  • The economic situation in TN has triggered an increase in lower level jobs during the last 4-5 years. This may change the earlier income distribution scenario.
  • In the case of Maharashtra, notwithstanding an economic downturn, productivity levels remained constant. An increase in employment in the BFSI and trade sectors suggests significant business activities in the State and lesser possibility of shifting to less remunerative jobs.
  • In UP, the increase in services sector GDP, lesser dependence on manufacturing and substantial increase in trade, transport and BFSI sector employment together have prevented the population from being adversely affected by the lack in momentum in GDP growth.
  • The shifting of several households to districts adjacent to Delhi or part of the NCR such as Noida and Ghaziabad, has prevented income levels getting affected by the UP’s economy, especially over the last five years or so.

Consumer Behaviour Figure 5

Change in income distribution pattern

Market Skyline 2014-15, has been used for this analysis. Since the household distribution data is available for three years; 2004-05, 2009-10 and 2014-15, a database was created for all the three states to understand the changes in household income distribution at 2014-1015 constant prices. While estimating household distribution across income groups, the consumer price index was considered separately for urban and rural areas.

The household distribution for three income groups, that is, those with incomes less than INR 3 lakh per annum, INR 3 lakh to INR 10 lakh per annum and greater than INR 10 lakh per annum, at 2014-15 prices was taken into consideration, with the intention of determining the groups that are more likely to purchase two-wheelers. Discussion with industry players suggests that households with income between INR 3 lakh to INR 10 lakh are the most likely group opting for two wheelers, as per the affordability perspective.

To show how income distribution has changed over time, Table 1 indicates actual change in number of households in each of these income groups for the periods 2004-09 and 2009-14.   Table 1 and Table 2 clearly show that the number of households in the low income group, that is, less than INR 3 lakh per annum was the least in TN if it is compared between 2004-09 and 2009-05. Therefore, the segment more inclined to purchase two wheelers, increased least in TN as compared to the other two states. But the increase in high income households in TN was almost similar to UP, though it was higher in Maharashtra.

Consumer Behaviour Figure 6

This is even more evident if we look at the share of incremental households in the three income groups. The total increase in middle income households was lowest in TN, that is, the gradual transition of households from the lower to upper income group was least experienced in urban TN. Tables 3 and 4, indicate a similar trend in rural TN.

Thus, in both rural and urban TN, as compared to UP and Maharashtra, the number of middle income households did not increase enough to drive the sales of several products, including two wheelers. Thus, microeconomic volatility, that is instability in income distribution across income groups, is an important factor that explains the decline in two wheeler sales in TN as compared to other states.

 

Endnote

Household income distribution data across income groups in real terms suggests that TN experienced higher levels of inequality in income distribution in the last five years as compared to Maharashtra and UP. This was reflected in the consumption behaviour of households, and two-wheeler sales. Hence, studying household income distribution scenario to foresee consumption patterns can foster better business planning and strategy in different parts of the country.

 

References

National Accounts Statistics, Central Statistical Organization, Government of India. (2016). Ministry of statistics and programme implementation. Retrieved from http://mospi.nic.in/Mospi_New/site/home.aspx#.

Government of India. (2016). National sample survey organization, employment rounds. Retrieved from http://www.mospi.nic.in/Mospi_New/site/home.aspx.

Nielsen. (2016). Market skyLine (MSL) database. Garda, P., & Ziemann, V. (2014). Economic policies and microeconomic stability: A literature review and some empirics. OECD Economics Department Working Papers, p1115.

Sarel, M. (1997). How macroeconomic factors affect income distribution: The cross-country evidence. International Monetary Fund.

Giovannoni, O. (2014). Income distribution macroeconomics. Working Paper No. 807. New York: The Levy Economics Institute.

Jones, I.C. (2015). Pareto and Piketty: The macroeconomics of top income and wealth inequality.  Journal of Economic Perspectives, 29(1), pp29–46.

Roy, D. (2000). What’s new in development economics. The American Economists, Sage Journals, 44(2).

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