Description
This is report on effort to find out the extent to which income inequity is being affected by various macroeconomic factors.
2009
Group 10 Shrirang Jadhav (09FT-064) Jasvir Singh (09FT-067) Lakshey Bhalla (09FT-074) Sameer Mahajan (09FT-078) Naval Jain (09FT-093) Rahul Aggarwal (09FT-111)
MEPP- ANALYSIS OF WEALTH DISPARITY IN INDIA
Table of Contents
Acknowledgement ........................................................................................................................... 3 INTRODUCTION ........................................................................................................................... 4 Section: 1 Macro economic factors included in the analysis .......................................................... 5 MACRO ECONOMIC indices ........................................................................................................ 6 Macro economical factors ............................................................................................................... 8 Section-2 Observations and analysis ............................................................................................... 9 Measures of wealth: ....................................................................................................................... 10 Summary Measures: ..................................................................................................................... 10 Means and medians: .................................................................................................................. 10 Inequality measure: ................................................................................................................... 11 Section: 3 Explanations & conclusions ......................................................................................... 15 Bibliography .................................................................................................................................. 18
Acknowledgement
It is our heartfelt pleasure and deepest sense of gratitude to offer our humble and sincere thanks towards our project guide, Prof. V.P. Ojha. His precious suggestions, academic excellence, incisive criticism, ineffable faith in us motivated us to work towards our goal. We would also like to thank all our friends, whose help and support made this project come to life.
INTRODUCTION
INDIA, a name established among the fastest growing economies at annual GDP rate of 5.8% for last two decades transformed itself from an era of red-tapism to an era of Globalization, Liberalization and Privatization. There were reforms in all the sectors that have lead to an increase in wealth signified by increase in GDP. India is often characterized as an emerging economic super power. The process of integration of India into the global market is progressing fast. Almost all economic indicators are showing healthy trend and India is one of the fastest growing major economies of the world. The powerful Indian Diaspora can work as a potential facilitator for the smooth transition of India into a world economic power. The high quality engineering and management talent of India is universally acclaimed. Home grown Indian companies have been entering international arena to set new trends of mergers and acquisitions. On contrary, Indians still constitute 17 percent of the world population where we account for 35 percent of the poor and 40 percent of the illiterates. With economic reforms the wealth disparity has been on an increase, while metros and medium cities have experienced matchless growth, the rural areas are suffering from economic stagnation. This rise in income inequality has posed a challenge in front of policy makers across the world because this may reflect a lack of economic opportunity that can limit the growth by not allowing all economic agents to fully exploit the new opportunities created by globalization and can act as an impediment to productive capacity by not matching labor and capital efficiency. So social and economic development holds a symbiotic relationship that mutually benefits each other because it is the economic gains that help to hone the social skills which directly or indirectly contributes to economic gains which forms a vicious circle that extends from generation to generation. This paper deals with the analysis of this whole concept of increase in wealth disparity with rise in income levels on various summary measures It is an effort to find out the extent to which income inequity is being affected by various macroeconomic factors. Our study is based on secondary data from various sources of government of India. One limitation of our study is that though some data were available annually other data were available five-yearly or decennially and hence total effect of all the factors could not be studied at the same time.
Section: 1
Macro economic factors included in the analysis
MACRO ECONOMIC indices
Gini coefficient:
Gini’s Index (Human Development Report, 2005 by Planning Commission) introduced by Italian Statistician Corrado Gini (1912) arguably or unarguably is the most commonly used inequity metrics. It measures the extent to which the distribution of income (or consumption) among individuals or households within a country deviates from a perfectly equal distribution. A value of 0 represents perfect equality, a value of 100 perfect inequalities.
Gini’s Coefficient is mathematically expressed as: ?? = Where, G is the Gini’s coefficient n = number of individuals (households) in a group xi = income (expenditure) of ith individual (household)
?? ??=1
2?? ? ?? ? 1 ???? ?? ?? ???? ??=1
LORENZ CURVE:
A Lorenz curve plots the cumulative percentage of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and the hypothetical line of absolute equality, expressed as the percentage of maximum area under the line.
Figure 1: Graphical representation of Gini’s Index and Lorenz curve
0.7000 0.6000 0.5000 Year 1951 0.4000 0.3000 0.2000 0.1000 0.0000 Year 1961 Year 1970 Year 1983 Year 1990
_
0
0.2
0.4
0.6
0.8
1
1.2
Figure2: Lorenz curve for India in some selected years In 2001, Cornia and Court found out that this increase in inequality is due to many factors of globalization, liberalization and technological developments besides urbanization and other factors. Our objective is to look into the various economic factors affecting unequal distribution of wealth in India. Gini’s Index has been fairly steady for India; however, it has increased post globalization period as shown in the graph below. In Indian context, whenever the economy moves from agricultural to manufacturing sector the inequity i.e. Gini c coefficient against the development i.e. per capita income takes a Ushape as explained by Kuznet’s Theory and validated by Sinha (2004).
Figure 1: Gini's index (1950-2004)
45.00 40.00 35.00 30.00 25.00 20.00 15.00 10.00 5.00 0.00 1940 1950 1960 1970 1980 1990 2000 2010
Source: Datt, Gaurav, Income Inequality in India, The World Bank, Washington DC, mimeo. 1995.
Macro economical factors
Gross domestic product: India’s Gross Domestic product from the year
1951-2004 has been steadily increasing and as from the curve the growth has been very sharp in the 1990’s during the post-liberalization period.
1400000 1200000 1000000 800000 600000 400000 GDP
200000
0 -2000001940 1960 1980 2000 2020
Source: Economic Survey, 2000 Figure3: GDP in Rs. (thousand crores) 1950-2000
The Gross Domestic product has negative relationship with Gini’s coefficient. The equation is given as Gini’s Coefficient = 59.8581 + -2.16938 loge(GDP)
.
Section-2
Observations and analysis*
*The micro-data used in this paper are extracted from the all-India debt and investment survey (AIDIS) (in 1991-92 and 2002-03) collected by the National Sample Survey Organization (NSSO).
Measures of wealth:
Total household assets:
The NSS defines total household assets as comprising ?physical assets like land, buildings, livestock, agricultural machinery and implements, non-farm business equipment, all transport equipment, durable household goods and financial assets like dues receivable on loans advanced in cash or in kind, shares in companies and cooperative societies, banks, etc, national saving certificates and the like, deposits in companies, banks, post offices and with individuals?
Net worth: Net worth is defined as the total household assets net of the indebtedness
of households (also provided in the surveys). Debt is defined by the NSS as consisting of cash loans payable as of June 30, 2002 and June 30, 1991 for the two years respectively and subsequently, net worth is total assets less debt.
PROBLEMS IN THE SURVEYS DONE BY THESE AGENCIES:
The sampling methodology followed by the agencies does not follow a two-stage sampling process where the first stage units were the census rural and urban blocks and the second, was the households. So, this methodology does not over sample the wealthy because a large concentration of wealth is at the top end of the population and this is problematic since a few large values can impact the summary measures like the mean and the median. Generally all the respondents under-report their wealth holdings. So, inequality in asset holding is underestimated in the survey. Difficulty in obtaining the market prices for various kinds of assets.
-
To counter these problems some consumption based deflators were used. Consumer Price Index (CPI) - for agricultural workers in order to deflate wealth data and for urban workers to deflate urban data.
Summary Measures:
Means and medians:
If we calculate some basic statistics of the level and distribution of wealth at the per capita level in 1991 and 2002 the following observations can be made: - Overall Per Capita assets have increased by 35%. - Overall net worth has increased by 39%. - Ratio of average per capita assets in urban areas to average per capita assets in rural areas is relatively constant at 1.5.
-
If we analyze per capita net worth in the same segment i.e. rural and urban, we find that growth is faster in urban areas reflecting greater reported in debt in rural areas.
While there is an increase in assets, in real terms there is a decrease in the average values of livestock assets and durable assets in urban areas. This discrepancy is due to readily available and consistent price deflators for all categories.
Inequality measure:
As from the data we see that there is an increase of inequality across various parameters.
The observations that can be made using data from above tables are: - Gini coefficient has seen an increase of about 2 percent for per capita net worth as well as about 1 percent for per capita assets. - From the values of per capita shares and per capita loans we see that tremendous focus is given to the health of stock market and the movement of corporate asset.
So as to answer the wealth concentration levels, examination of shares and cumulative shares by decile for both total per capita assets and per capita net worth. From the table below we find that 10 percent of the individuals possess a little over half of the total country’s wealth.
while the bottom 10 per cent possess a mere 0.4 per cent of the total wealth. The bottom 50 per cent of the population own less than 10 per cent of the total wealth. The wealthiest have tended to consolidate between the two surveys (the top 10 per cent owned 51.94 per cent of wealth in 2002 versus 50.79 per cent in 1991), while the asset-poor, i.e. bottom 10 per cent have only lost their share (0.21 per cent in 2002 versus 0.22 per cent in 1991).
The above two tables extends the observation to average wealth holdings by decile of mean per capita monthly expenditure and we see that the growth rate of assets accumulation is highest in the top decile. This analysis is further substantiated from Table 8 where we see that there is a sharp increase in the holdings at the very top end of the distribution. The ratio of assets held by the individual at the 95th percentile to the assets held by the median individual
rose from 758 per cent to 814 per cent, while the corresponding ratio for net worth rose from 766 per cent to 824 per cent. When we examine these figures with the reference point of the individual at the 99 percentile, the ratio rose from 1851 per cent to 1958 per cent for assets and 1886 per cent to 2012 per cent for net worth.
for every decile (except, of course for the last one), the cumulative share of net worth per capita in 1991 is higher than (although close to) the corresponding figure for 2002. This would indicate that if we construct Lorenz curves based on deciles, the Lorenz curve for 1991 would be close to but lie above the Lorenz curve for 2002. In the context of wealth inequality, another tool that is useful is the absolute Lorenz curve, which can be derived from the standard Lorenz curve.10 Figure 2 presents the absolute Lorenz curves for per capita net worth for 1991 and 2002 and we can observe that the absolute Lorenz curve for 1991 lies above the same for 2002. This indicates an
unambiguous increase in inequality from 1991 to 2002 according to all standard absolute inequality measures. We can also note that the difference across these two points in time between the absolute Lorenz curves is much more pronounced compared to the difference between the standard Lorenz curves. Figure3 provides a scatter plot of the growth rate in real per capita state domestic product (SDP) and the change in the wealth Gini by state. As is evident, there is a definite positive correlation (correlation coefficient, r=0.3) between these variables. The numbers tell a stark story, with the middle income and rich states experiencing much faster asset growth rates annually than the poor states. It is interesting to note too that growth in asset holdings has been fastest in the urban areas of the middle income states, regions which include dynamic urban centers such as Hyderabad and Bangalore. The 2002 survey data shows that there are large differences in wealth holdings among religious groups. Muslims with average per capita asset holdings of about Rs 20,250 are the poorest, compared to the somewhat wealthier Hindus with per capita asset holdings of about Rs 30,500 and Jains with Rs 1, 03,900 being the wealthiest. Educational and occupational differences also are strongly correlated with average wealth holdings. Unsurprisingly, wealth levels rise with the educational level of the head of the household. Individuals from the households with a graduate head have about twice the average wealth as those from one with a head who has a secondary school certificate (Rs 91,200 vs. Rs 49,500) and nearly five times that of individuals from a household with an illiterate head.
Section: 3
Explanations & conclusions
Conclusions:
1. These surveys conducted across various axes bring out two patterns in the Indian wealth distribution that apply for most classifications. First, the majority of the population, divided along axes such as caste, size-distribution or occupation has, on an average, witnessed increases in its absolute wealth levels during the period of liberalization. Second, these impressive increases in wealth levels have been unequal across different groups and axes. Even with significant under-reporting and undersampling problems at the very top, there seems to be a clear trend of the wealthiest 20 per cent diverging away from the rest of the population. In particular, the top 1 per cent is making solid gains relative to the rest of the population. These add up to the inference that the first decade of reforms witnessed an impressive increase in wealth as well as a rise in its concentration, especially at the upper end. 2. Even though average economic well-being has increased considerably over time, the degree of inequality in economic outcomes over the past three decades has increased as well. Economists continue to grapple with the reasons for this trend. But as best we can tell, the increase in inequality probably is due to a number of factors, notably including technological change that seems to have favoured higher skilled workers more than lower-skilled ones. In addition, some economists point to increased international trade and the declining role of labour unions as other, probably lesser contributing factors. 3. To the extent that rising inequality may reflect a lack of economic opportunity, it may itself limit the growth potential of economies by not allowing all economic agents to fully exploit the new opportunities created by globalization and limiting the productive capacity of an economy by not matching capital and labour as efficiently as possible. Moreover, to the extent that economies are periodically subject to shocks of various kinds that limit growth in the short term, greater inequality makes a greater proportion of the population vulnerable to poverty. Finally, rising inequality if not addressed can also lead to a backlash against economic liberalization and protectionist pressures, limiting the ability of economies to benefits from globalization. 4. Nevertheless, at the same time average real incomes of the poorest segments of the population have increased across all regions and income groups. Our analysis finds that increasing trade and financial globalization have had separately identifiable and opposite effects on income distribution. Trade liberalization and export growth are found to be associated with lower income inequality, while increased financial openness is associated with higher inequality.
5. However, combined contribution of financial & trade liberalization to rising inequality has been much lower than that of technological change, both at a global level and especially markedly in developing countries. The spread of technology is, of course, related to increased globalization, but technological progress is nevertheless seen to have a separately identifiable effect on inequality. The disequalizing impact of financial openness mainly felt through foreign direct investment (FDI)—and technological progress appear to be working through similar channels by increasing the premium on higher skills, rather than limiting opportunities for economic advancement. Consistent with this, increased access to education is associated with more equal income distributions on average. 6. For a given level of technology, greater access to education would be expected to reduce income inequality by allowing a greater share of the population to be engaged in high-skill activities. Both educational variables considered in the analysis have tended to increase across all regions, but with considerable cross-country variation. 7. In developing countries, a move away from the agricultural sector to industry is expected to improve the distribution of income by increasing the income of lowearning groups. Similarly, increase in the relative productivity of agriculture is expected to reduce income disparities by increasing the income of those employed in this sector. The sectoral distribution of employment is measured by the shares of employment in agriculture and in industry. 8. The effects of agriculture, manufacturing, and services exports are statistically not significantly different from one another, but agricultural exports have the largest coefficient and are statistically significant. The coefficient on exports thus seems to reflect the fact that in many developing countries a lot of the poor are still employed in the agricultural sector, so that an improvement in the export prospects of this sector tends to reduce inequality. Tariff reductions on average also seem to benefit the poor relatively more than the rich, suggesting that on average they affected goods which are disproportionately consumed by the poor and/or formal sectors where the better off part of the population is employed. 9. The share of agriculture employment tends to increase inequality, while the share of industry employment reduces it. This is consistent with the idea that labour shifts from agriculture to industry raise the productivity of the agricultural sector where most poor are employed and decrease productivity in industry.
Bibliography
- Widening economic & social disparities: Implications for India – N.J. Kurian. - Jayadev.A.Motiram.S and-Vakulbharanam.V(2007)-Wealth Disparities in India. - The unequal effects of Liberalization: Evidence rom dismantling the License Raj from India –Philippe Aghion, Robin Burgess ,Stephen Redding and Fabrizio Zilibotti. - IMF-Report- Rising Income Inequality : Technology or trade and financial globalization? – Florence Jaumotte, Subir Lall and Chris Papageorgiou. - Inequality and growth in India – An article by Gulzar Natarajan (Mint newspaper). - Trade openness, Poverty and Inequality in India: Literature and empirics at the sub-national level.- Rongili Biswas and Alice Sindzingre
doc_903159493.pdf
This is report on effort to find out the extent to which income inequity is being affected by various macroeconomic factors.
2009
Group 10 Shrirang Jadhav (09FT-064) Jasvir Singh (09FT-067) Lakshey Bhalla (09FT-074) Sameer Mahajan (09FT-078) Naval Jain (09FT-093) Rahul Aggarwal (09FT-111)
MEPP- ANALYSIS OF WEALTH DISPARITY IN INDIA
Table of Contents
Acknowledgement ........................................................................................................................... 3 INTRODUCTION ........................................................................................................................... 4 Section: 1 Macro economic factors included in the analysis .......................................................... 5 MACRO ECONOMIC indices ........................................................................................................ 6 Macro economical factors ............................................................................................................... 8 Section-2 Observations and analysis ............................................................................................... 9 Measures of wealth: ....................................................................................................................... 10 Summary Measures: ..................................................................................................................... 10 Means and medians: .................................................................................................................. 10 Inequality measure: ................................................................................................................... 11 Section: 3 Explanations & conclusions ......................................................................................... 15 Bibliography .................................................................................................................................. 18
Acknowledgement
It is our heartfelt pleasure and deepest sense of gratitude to offer our humble and sincere thanks towards our project guide, Prof. V.P. Ojha. His precious suggestions, academic excellence, incisive criticism, ineffable faith in us motivated us to work towards our goal. We would also like to thank all our friends, whose help and support made this project come to life.
INTRODUCTION
INDIA, a name established among the fastest growing economies at annual GDP rate of 5.8% for last two decades transformed itself from an era of red-tapism to an era of Globalization, Liberalization and Privatization. There were reforms in all the sectors that have lead to an increase in wealth signified by increase in GDP. India is often characterized as an emerging economic super power. The process of integration of India into the global market is progressing fast. Almost all economic indicators are showing healthy trend and India is one of the fastest growing major economies of the world. The powerful Indian Diaspora can work as a potential facilitator for the smooth transition of India into a world economic power. The high quality engineering and management talent of India is universally acclaimed. Home grown Indian companies have been entering international arena to set new trends of mergers and acquisitions. On contrary, Indians still constitute 17 percent of the world population where we account for 35 percent of the poor and 40 percent of the illiterates. With economic reforms the wealth disparity has been on an increase, while metros and medium cities have experienced matchless growth, the rural areas are suffering from economic stagnation. This rise in income inequality has posed a challenge in front of policy makers across the world because this may reflect a lack of economic opportunity that can limit the growth by not allowing all economic agents to fully exploit the new opportunities created by globalization and can act as an impediment to productive capacity by not matching labor and capital efficiency. So social and economic development holds a symbiotic relationship that mutually benefits each other because it is the economic gains that help to hone the social skills which directly or indirectly contributes to economic gains which forms a vicious circle that extends from generation to generation. This paper deals with the analysis of this whole concept of increase in wealth disparity with rise in income levels on various summary measures It is an effort to find out the extent to which income inequity is being affected by various macroeconomic factors. Our study is based on secondary data from various sources of government of India. One limitation of our study is that though some data were available annually other data were available five-yearly or decennially and hence total effect of all the factors could not be studied at the same time.
Section: 1
Macro economic factors included in the analysis
MACRO ECONOMIC indices
Gini coefficient:
Gini’s Index (Human Development Report, 2005 by Planning Commission) introduced by Italian Statistician Corrado Gini (1912) arguably or unarguably is the most commonly used inequity metrics. It measures the extent to which the distribution of income (or consumption) among individuals or households within a country deviates from a perfectly equal distribution. A value of 0 represents perfect equality, a value of 100 perfect inequalities.
Gini’s Coefficient is mathematically expressed as: ?? = Where, G is the Gini’s coefficient n = number of individuals (households) in a group xi = income (expenditure) of ith individual (household)
?? ??=1
2?? ? ?? ? 1 ???? ?? ?? ???? ??=1
LORENZ CURVE:
A Lorenz curve plots the cumulative percentage of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and the hypothetical line of absolute equality, expressed as the percentage of maximum area under the line.
Figure 1: Graphical representation of Gini’s Index and Lorenz curve
0.7000 0.6000 0.5000 Year 1951 0.4000 0.3000 0.2000 0.1000 0.0000 Year 1961 Year 1970 Year 1983 Year 1990
_
0
0.2
0.4
0.6
0.8
1
1.2
Figure2: Lorenz curve for India in some selected years In 2001, Cornia and Court found out that this increase in inequality is due to many factors of globalization, liberalization and technological developments besides urbanization and other factors. Our objective is to look into the various economic factors affecting unequal distribution of wealth in India. Gini’s Index has been fairly steady for India; however, it has increased post globalization period as shown in the graph below. In Indian context, whenever the economy moves from agricultural to manufacturing sector the inequity i.e. Gini c coefficient against the development i.e. per capita income takes a Ushape as explained by Kuznet’s Theory and validated by Sinha (2004).
Figure 1: Gini's index (1950-2004)
45.00 40.00 35.00 30.00 25.00 20.00 15.00 10.00 5.00 0.00 1940 1950 1960 1970 1980 1990 2000 2010
Source: Datt, Gaurav, Income Inequality in India, The World Bank, Washington DC, mimeo. 1995.
Macro economical factors
Gross domestic product: India’s Gross Domestic product from the year
1951-2004 has been steadily increasing and as from the curve the growth has been very sharp in the 1990’s during the post-liberalization period.
1400000 1200000 1000000 800000 600000 400000 GDP
200000
0 -2000001940 1960 1980 2000 2020
Source: Economic Survey, 2000 Figure3: GDP in Rs. (thousand crores) 1950-2000
The Gross Domestic product has negative relationship with Gini’s coefficient. The equation is given as Gini’s Coefficient = 59.8581 + -2.16938 loge(GDP)
.
Section-2
Observations and analysis*
*The micro-data used in this paper are extracted from the all-India debt and investment survey (AIDIS) (in 1991-92 and 2002-03) collected by the National Sample Survey Organization (NSSO).
Measures of wealth:
Total household assets:
The NSS defines total household assets as comprising ?physical assets like land, buildings, livestock, agricultural machinery and implements, non-farm business equipment, all transport equipment, durable household goods and financial assets like dues receivable on loans advanced in cash or in kind, shares in companies and cooperative societies, banks, etc, national saving certificates and the like, deposits in companies, banks, post offices and with individuals?
Net worth: Net worth is defined as the total household assets net of the indebtedness
of households (also provided in the surveys). Debt is defined by the NSS as consisting of cash loans payable as of June 30, 2002 and June 30, 1991 for the two years respectively and subsequently, net worth is total assets less debt.
PROBLEMS IN THE SURVEYS DONE BY THESE AGENCIES:
The sampling methodology followed by the agencies does not follow a two-stage sampling process where the first stage units were the census rural and urban blocks and the second, was the households. So, this methodology does not over sample the wealthy because a large concentration of wealth is at the top end of the population and this is problematic since a few large values can impact the summary measures like the mean and the median. Generally all the respondents under-report their wealth holdings. So, inequality in asset holding is underestimated in the survey. Difficulty in obtaining the market prices for various kinds of assets.
-
To counter these problems some consumption based deflators were used. Consumer Price Index (CPI) - for agricultural workers in order to deflate wealth data and for urban workers to deflate urban data.
Summary Measures:
Means and medians:
If we calculate some basic statistics of the level and distribution of wealth at the per capita level in 1991 and 2002 the following observations can be made: - Overall Per Capita assets have increased by 35%. - Overall net worth has increased by 39%. - Ratio of average per capita assets in urban areas to average per capita assets in rural areas is relatively constant at 1.5.
-
If we analyze per capita net worth in the same segment i.e. rural and urban, we find that growth is faster in urban areas reflecting greater reported in debt in rural areas.
While there is an increase in assets, in real terms there is a decrease in the average values of livestock assets and durable assets in urban areas. This discrepancy is due to readily available and consistent price deflators for all categories.
Inequality measure:
As from the data we see that there is an increase of inequality across various parameters.
The observations that can be made using data from above tables are: - Gini coefficient has seen an increase of about 2 percent for per capita net worth as well as about 1 percent for per capita assets. - From the values of per capita shares and per capita loans we see that tremendous focus is given to the health of stock market and the movement of corporate asset.
So as to answer the wealth concentration levels, examination of shares and cumulative shares by decile for both total per capita assets and per capita net worth. From the table below we find that 10 percent of the individuals possess a little over half of the total country’s wealth.
while the bottom 10 per cent possess a mere 0.4 per cent of the total wealth. The bottom 50 per cent of the population own less than 10 per cent of the total wealth. The wealthiest have tended to consolidate between the two surveys (the top 10 per cent owned 51.94 per cent of wealth in 2002 versus 50.79 per cent in 1991), while the asset-poor, i.e. bottom 10 per cent have only lost their share (0.21 per cent in 2002 versus 0.22 per cent in 1991).
The above two tables extends the observation to average wealth holdings by decile of mean per capita monthly expenditure and we see that the growth rate of assets accumulation is highest in the top decile. This analysis is further substantiated from Table 8 where we see that there is a sharp increase in the holdings at the very top end of the distribution. The ratio of assets held by the individual at the 95th percentile to the assets held by the median individual
rose from 758 per cent to 814 per cent, while the corresponding ratio for net worth rose from 766 per cent to 824 per cent. When we examine these figures with the reference point of the individual at the 99 percentile, the ratio rose from 1851 per cent to 1958 per cent for assets and 1886 per cent to 2012 per cent for net worth.
for every decile (except, of course for the last one), the cumulative share of net worth per capita in 1991 is higher than (although close to) the corresponding figure for 2002. This would indicate that if we construct Lorenz curves based on deciles, the Lorenz curve for 1991 would be close to but lie above the Lorenz curve for 2002. In the context of wealth inequality, another tool that is useful is the absolute Lorenz curve, which can be derived from the standard Lorenz curve.10 Figure 2 presents the absolute Lorenz curves for per capita net worth for 1991 and 2002 and we can observe that the absolute Lorenz curve for 1991 lies above the same for 2002. This indicates an
unambiguous increase in inequality from 1991 to 2002 according to all standard absolute inequality measures. We can also note that the difference across these two points in time between the absolute Lorenz curves is much more pronounced compared to the difference between the standard Lorenz curves. Figure3 provides a scatter plot of the growth rate in real per capita state domestic product (SDP) and the change in the wealth Gini by state. As is evident, there is a definite positive correlation (correlation coefficient, r=0.3) between these variables. The numbers tell a stark story, with the middle income and rich states experiencing much faster asset growth rates annually than the poor states. It is interesting to note too that growth in asset holdings has been fastest in the urban areas of the middle income states, regions which include dynamic urban centers such as Hyderabad and Bangalore. The 2002 survey data shows that there are large differences in wealth holdings among religious groups. Muslims with average per capita asset holdings of about Rs 20,250 are the poorest, compared to the somewhat wealthier Hindus with per capita asset holdings of about Rs 30,500 and Jains with Rs 1, 03,900 being the wealthiest. Educational and occupational differences also are strongly correlated with average wealth holdings. Unsurprisingly, wealth levels rise with the educational level of the head of the household. Individuals from the households with a graduate head have about twice the average wealth as those from one with a head who has a secondary school certificate (Rs 91,200 vs. Rs 49,500) and nearly five times that of individuals from a household with an illiterate head.
Section: 3
Explanations & conclusions
Conclusions:
1. These surveys conducted across various axes bring out two patterns in the Indian wealth distribution that apply for most classifications. First, the majority of the population, divided along axes such as caste, size-distribution or occupation has, on an average, witnessed increases in its absolute wealth levels during the period of liberalization. Second, these impressive increases in wealth levels have been unequal across different groups and axes. Even with significant under-reporting and undersampling problems at the very top, there seems to be a clear trend of the wealthiest 20 per cent diverging away from the rest of the population. In particular, the top 1 per cent is making solid gains relative to the rest of the population. These add up to the inference that the first decade of reforms witnessed an impressive increase in wealth as well as a rise in its concentration, especially at the upper end. 2. Even though average economic well-being has increased considerably over time, the degree of inequality in economic outcomes over the past three decades has increased as well. Economists continue to grapple with the reasons for this trend. But as best we can tell, the increase in inequality probably is due to a number of factors, notably including technological change that seems to have favoured higher skilled workers more than lower-skilled ones. In addition, some economists point to increased international trade and the declining role of labour unions as other, probably lesser contributing factors. 3. To the extent that rising inequality may reflect a lack of economic opportunity, it may itself limit the growth potential of economies by not allowing all economic agents to fully exploit the new opportunities created by globalization and limiting the productive capacity of an economy by not matching capital and labour as efficiently as possible. Moreover, to the extent that economies are periodically subject to shocks of various kinds that limit growth in the short term, greater inequality makes a greater proportion of the population vulnerable to poverty. Finally, rising inequality if not addressed can also lead to a backlash against economic liberalization and protectionist pressures, limiting the ability of economies to benefits from globalization. 4. Nevertheless, at the same time average real incomes of the poorest segments of the population have increased across all regions and income groups. Our analysis finds that increasing trade and financial globalization have had separately identifiable and opposite effects on income distribution. Trade liberalization and export growth are found to be associated with lower income inequality, while increased financial openness is associated with higher inequality.
5. However, combined contribution of financial & trade liberalization to rising inequality has been much lower than that of technological change, both at a global level and especially markedly in developing countries. The spread of technology is, of course, related to increased globalization, but technological progress is nevertheless seen to have a separately identifiable effect on inequality. The disequalizing impact of financial openness mainly felt through foreign direct investment (FDI)—and technological progress appear to be working through similar channels by increasing the premium on higher skills, rather than limiting opportunities for economic advancement. Consistent with this, increased access to education is associated with more equal income distributions on average. 6. For a given level of technology, greater access to education would be expected to reduce income inequality by allowing a greater share of the population to be engaged in high-skill activities. Both educational variables considered in the analysis have tended to increase across all regions, but with considerable cross-country variation. 7. In developing countries, a move away from the agricultural sector to industry is expected to improve the distribution of income by increasing the income of lowearning groups. Similarly, increase in the relative productivity of agriculture is expected to reduce income disparities by increasing the income of those employed in this sector. The sectoral distribution of employment is measured by the shares of employment in agriculture and in industry. 8. The effects of agriculture, manufacturing, and services exports are statistically not significantly different from one another, but agricultural exports have the largest coefficient and are statistically significant. The coefficient on exports thus seems to reflect the fact that in many developing countries a lot of the poor are still employed in the agricultural sector, so that an improvement in the export prospects of this sector tends to reduce inequality. Tariff reductions on average also seem to benefit the poor relatively more than the rich, suggesting that on average they affected goods which are disproportionately consumed by the poor and/or formal sectors where the better off part of the population is employed. 9. The share of agriculture employment tends to increase inequality, while the share of industry employment reduces it. This is consistent with the idea that labour shifts from agriculture to industry raise the productivity of the agricultural sector where most poor are employed and decrease productivity in industry.
Bibliography
- Widening economic & social disparities: Implications for India – N.J. Kurian. - Jayadev.A.Motiram.S and-Vakulbharanam.V(2007)-Wealth Disparities in India. - The unequal effects of Liberalization: Evidence rom dismantling the License Raj from India –Philippe Aghion, Robin Burgess ,Stephen Redding and Fabrizio Zilibotti. - IMF-Report- Rising Income Inequality : Technology or trade and financial globalization? – Florence Jaumotte, Subir Lall and Chris Papageorgiou. - Inequality and growth in India – An article by Gulzar Natarajan (Mint newspaper). - Trade openness, Poverty and Inequality in India: Literature and empirics at the sub-national level.- Rongili Biswas and Alice Sindzingre
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