How Happy is our Economy? An Overview of India
by Aishwary Trivedi, Ashwin Katyal and Karan Mishra
Gross domestic product has always been a dependable instrument that is used to determine the health of an economy and the well-being of a nation. However, GDP has had ambiguous results when it is used to illustrate the welfare of the people. As an economic tool, it estimates the average standard of living, which may vary across the socioeconomic spectrum of a country. Moreover, a higher standard of living does not necessarily correspond to increased welfare; overall welfare covers various domains including but not limited to nutrition and health, mental well-being, cultural pliability, social tolerance, and pro-environmental behaviour. Well-being of a country has been treated synonymously with income levels. But with the neoteric intensification of quantitative tools in economics, the definition of well-being needs to be revisited adding a heterogeneous range of factors impacting overall welfare.
There is a large literature, using data from the United States, Canada, Europe and developing countries where researchers have regressed some well-being measures against economic growth or income growth. Some researchers like Kahneman, Krueger, Schkade, Schwarz, and Stone (2005) argue that economic growth is only one of the indicators of social well-being and after a certain satiation point, income growth does not matter. Others like Ingelhart and Klingemann, 2000; Graham, 2005; Layard, 2005; Leigh and Wolfers, 2006, have found a positive correlation between income and happiness in the long run.
This position is in contrast to the more widely accepted view, associated with Sen (1999), which is that ‘human well-being depends on a range of functions and capabilities that enable people to lead a good life, each of which needs to be directly and objectively measured and which cannot, in general, be aggregated into a single summary measure.’
Data and Methodology
In this paper we have used a new dataset developed by World Gallup Poll (WGP), World Bank and World Development Indicators to describe the patterns of Life Satisfaction scores across regions and groups. Our dataset is an unbalanced panel consisting of 165 cross sections (countries) and 11 time periods on average, with approximately 1600 observations. Panel data regression has been used to regress Life Satisfaction on multiple variables using the following regression equation:
The dependent variable Life Satisfaction is based on the life evaluation score of the Cantril ladder, which asks the survey respondents to rate the current status of their lives on a scale or a “ladder” ranging from 0-10, where 0 represents the worst life possible and 10 represents the best life possible. This variable is also interchangeably referred to as Happiness in the discussion of results.
There are 8 independent variables in the regression equation:
· Log of GDP per capita (PPP) - in terms of purchasing power parity adjusted to constant 2011 international dollars taken from the World Development Indicators (WDI) database.
· Undernutrition - the percentage of population below the minimum level of dietary energy consumption. Another variable considered as an indicator for health outcomes is healthy life expectancy at birth, defined as the number of healthy life years a person is expected to live. However, there was a high negative correlation between this variable and undernutrition which is why only undernutrition was retained to avoid bias arising out of multicollinearity.
· Social support- is the national average of the binary responses (0 or 1) to the WGP question - “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?”
· Freedom to make life choices- is the national average of binary responses to the WGP question “Are you satisfied or dissatisfied with your freedom to choose what you do with your life?”
· Generosity- is the residual of regressing the national average of WGP responses to the question “Have you donated money to a charity in the past month?” on GDP per capita.
· Perception of corruption- is the average of binary answers to two GWP questions: “Is corruption widespread throughout the government or not?” and “Is corruption widespread within businesses or not?” Where the data for government corruption is missing, the perception of business corruption is used.
· Pollution- is the average exposure to PM 2.5 levels in the country.
· Unemployment- is the percentage of labour force that is without work but available for and seeking employment.
The following tests were conducted on the panel regression model and based on the significance results, a time fixed effect model was used. Low p-value in the BPLM test indicates time effects in the dataset. F-test was performed to choose between pooled-OLS and fixed effects model. With the low p-value, we reject the null hypothesis that pooled-OLS is the appropriate model. For the Hausmann test, the fixed effect model is significant at the 10% level but not at the 5% level.
Note: *** p < 0.001, ** p < 0.01, * p < 0.05
The R-squared value for the model was 0.76. The results of the regression model are presented as graphical analyses in the following section.
Discussion of Results
1. Log GDP per capita: GDP per capita is a measure of the country’s economic output that divides the country’s gross domestic product by its total population. Well-being and economic aspects of life are quite closely related and our findings show that per capita income and well-being are positively related. Countries like Denmark, Norway, Australia, Singapore, etc. have relatively high per capita levels of income and have a correspondingly high happiness score.
2. Social Support: Social support is defined as the perception that one has assistance available and belongs to the society (sense of belongingness) or the degree to which a person is integrated in a social network. Social assistance can come from many sources including friends, family, neighbours and government. According to the model there is a positive relationship between well-being and social support which shows that as people receive more social assistance from society, nations are better off in terms of happiness and well-being. Countries like France, Ireland, Germany, Italy, etc. provide help to unemployed population. In Nordic countries 100% of salary is received from day one for up to a year.
3. Freedom to make life choices: According to the model, there is a positive relationship between well-being and freedom to make life choices. Freedom to make life choices plays an important part in the lives of people which makes the positive relationship with happiness justified. Countries like New Zealand, Australia, Ireland, Luxembourg, etc. have done better according to a report by Freedom House on political rights, civil liberties and have a correspondingly high happiness score.
4. Generosity: Generosity makes people believe that they are capable of making a difference in the world by actively addressing the needs of those around them and shaping their community into a healthier one. Community service, donations, social responsibility and positivity towards people depict generosity in general. Our model shows a positive relationship between well-being and generosity. Generous people report being happier and more content with life than others. According to World Giving Index, most generous countries are Ireland, Australia, Unites States of America, New Zealand, etc. and they have a correspondingly high happiness score.
5. Undernutrition: Health and nourishment are the basic necessities that most governments try to provide to their citizens which is a base for building human capabilities as pointed out by Sen (1999). In our model we have regressed undernutrition on happiness, depicting a negative relationship, that is as undernutrition increases it causes a decrease in subjective well-being of the nation. High levels of undernutrition are persistent in many African countries like Chad, Uganda, Congo, Zambia, etc. and they have a correspondingly low happiness score.
6. Perceptions of corruption: Perceptions of corruption measures the prevailing corruption in the businesses running in various nations. The model shows a negative relationship between well-being and perceptions of corruption. As corruption increases, it acts as an impediment in the path of development which eventually leads to a decline in the overall well-being of the nation. African countries like Egypt, Kenya, Congo, Nigeria, etc. have high levels of corruption and have a correspondingly low happiness score.
7. Unemployment: Unemployment is a situation where a person who is capable of working is actively searching for a job but is unable to find one. The model depicts a negative relationship between well-being and unemployment. As unemployment increases, the inability of workers to contribute to the productive output of the economy increases which adversely affects the well-being and life satisfaction of the residents of a nation. Countries like Afghanistan, Sudan, Mozambique, Iraq, etc. have high levels of unemployment and have a correspondingly low happiness score.
8. Pollution: Pollution here is measured by the average exposure to PM 2.5 levels in the country. Our model shows a negative relationship between well-being and pollution that is, as pollution increases, it impacts human health in the short term and is directly linked to the deaths of elderly people and those already suffering from chronic diseases. The adverse impact on human health is directly related to sustainability and the well-being of the nation. Countries like India, Nepal, Afghanistan, Nigeria, etc. have high levels of pollution and have a correspondingly low happiness score.
The analysis presented above is subject to the following constraints:
· The analysis is not causal in nature and country-level fixed effects have not been included.
· Countries have not been segregated into income-wise categories to analyse inter-income differences.
· In generosity and social support, numerous qualitative factors had to be ignored because they cannot be measured directly.
· Variables like senior secondary education, women safety etc. could not be included due to paucity of data.
· Graphical analysis is valid for the timeline under consideration.
· The slopes may tilt in the future as tastes and preferences of countries change.
An overview of India
Since the dawn of independence, India’s policy formulations were majorly driven by the goal of achieving economic growth. Many researchers have argued that Indian planning was inspired by the Soviet planning literature, that is, it took a socialist approach but has not successfully provided healthcare and education for all. Even though India’s GDP doubled after 1991 reforms, socio-economic indicators lagged behind.
The following table summarises the indicators discussed in the previous section for India against the world average. Unemployment data has not been included as the data for India was not available from World Bank for many years and generosity has not been included because it is obtained as a residual of regressing responses; averaging it to obtain a single value would not allow for correct interpretation.
From the above table it is evident that India has performed poorly in almost every aspect. Data on population density has been sourced from World Bank, log GDP per capita from World Development Indicators and remaining variables from World Gallup Poll.
According to World Happiness Report, India slipped 7 places from 133rd to 140th out of 156 countries in 2019 and became one of the worst performing countries when it comes to happiness. Our average happiness score has gone down by about 1.2 points even though there has been no recession or huge natural calamity in recent years. We are one of the fastest growing major economies of the world but that may mean little to our 1.3 billion strong population, many of whom still continue to struggle when it comes to the matter of well-being.
Panel 1: Despite an increase in the log GDP per capita, happiness has been declining.
Panel 2: Happiness has been declining over the years
Subjective well-being consists of several major components, some of which are unemployment, undernourishment, education, health, pollution, population density and corruption. The following statistics indicate India's performance across some of these components:
· Unemployment rate stood at 6% in the 2017-18 fiscal year compared to 3.8% in 2012-2013. Unemployment rate rose to a 33-month high of 7.8% in the month of June 2019. For the first time in India’s history, employment dropped by 9 million between these years.
· 22 of the top 30 most polluted cities in the world are in India.
· The 2019 Global Hunger Index (GHI) report brings somber tidings this year. India’s poorer neighbours — Bangladesh, Nepal, and Pakistan — have overtaken India in the battle against hunger. The GHI report ranks India at a low 102 out of 117 countries listed. India still has a higher proportion of undernourished children than almost any other country in the world.
· Only 3.4% of total federal spending was budgeted for education, down from 3.74% the previous year and from 4.3% in 2014. 10.6% of the total amount in the Interim Budget is allocated to defence, while only 2.2% is allocated to healthcare.
· Despite various advancements in the healthcare sector in recent times, in line with India’s relentless pursuit of reforms, the government still remains short of its goal to increase public health spending to 2.5% of GDP. At present, health spending is only 1.15-1.5% of GDP.
· The Corruption Perception Index ranks 180 countries and territories by the given levels of public sector corruption according to experts and businesspersons, using a scale of 0 to 100, where 0 is highly corrupt and 100 is very clean. More than two-thirds of countries score less than 50 on this year’s CPI, with an average score of just 43. India’s score here is 41 out of 100 and was ranked 78/100 in 2018.
· According to another recent survey (conducted between May to July 2018) by the Pew Research Centre (US), 76 percent Indians are unhappy due to lack of employment opportunities and 73 percent are unhappy due to rise in prices.
In addition to the above factors, rapid urbanization, congestion in cities, problems of commuting, law and order, women's safety, etc. are possible reasons why Indians are unhappy than before despite higher incomes. Moreover, growing inequality and diminishing egalitarianism where the lifestyles are in stark contrast with rich and famous people also contributes to people being unhappy. An Oxfam survey in 2017 has revealed that India’s richest 1 percent has cornered almost 73 per cent of the total wealth created in the country.
Policy Implications and Conclusion
Results suggest that the explanation for the subjective well-being lies in a wide range of factors and not only Gross Domestic Product which has been the popular belief.
Acknowledging that there is a problem is the first step towards finding a solution. Our empirical and theoretical analysis depicts that income affects well-being only to a certain extent and the other important factors have a significant impact on the overall well-being.
Some of these sufferings are entirely preventable. Given appropriate public policies —sensitively designed, adequately resourced and effectively implemented — the country has both the wealth and the human resource to tackle these problems.
Two major policies that could be considered to ramp-up overall well-being of India are: Measuring Gross National Happiness (GNH) and introducing Well-Being Budget. Gross National Happiness is an index used by Bhutan which takes a holistic approach towards development by measuring the collective happiness and well-being of the population. GNH index considers nine domains including psychological well-being, health, education, cultural diversity and resilience, good governance, ecological diversity and resilience, time use, community vitality and living standards. If implemented in India, GNH could support policy making at the micro and macro level.
The well-being budget is a new concept which focuses on people instead of traditional parameters including changes in tax rates and tariffs. New Zealand is the first country in the world to introduce well-being budget on May 30, 2019. They designed the entire budget around well-being priorities and Ministers were instructed to formulate policies to improve life satisfaction. The budget requires all new spending to go towards five specific well-being goals: bolstering mental health, reducing child poverty, supporting indigenous peoples, moving to a low-carbon-emission economy, and flourishing in a digital age. The effectiveness of this budget is yet to be tested given its recent adoption but the idea is promising.
In addition to the above prospects, some policies that could be implemented to move towards a higher level of welfare could be inclining towards a ‘pro-poor’ economic growth, raising household incomes as well as significant improvements in “nutrition-sensitive” sectors. Instilling a sense of generosity and voluntary social service through formation of community self-service groups and non-government organizations, both in rural and urban societies is essential for development. Additionally, education and health are the major sectors that demand attention from the government and the society. Also, there is an urgent requirement for a legally enforceable right to healthcare, clean air and measures to combat pollution. The ongoing Covid-19 pandemic has reinstated the need to reboot the public healthcare system of India.
We can thus conclude that higher income leads to higher levels of subjective well-being only up to a certain level of income and well-being must be addressed on multiple fronts so as to move towards a path of growth and a more prosperous society.
The authors are Economics students at Dyal Singh College, University of Delhi.
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