Search results

Many aspects of well-being depend critically on individual-level expenditure and consumption. The Millennium Development Goals include the promotion of gender equality and the empowerment of women, which partly have to do with women’s access to resources within households. Many important questions in labour, public and development economics also hinge on the intra-household distribution of resources. This paper aims to estimate the resource shares within a household in the rural and urban sectors of West Bengal through a collective household model, where each household member has a specific utility function. The sharing rule parameters, that determine the apportionment of resources between members within a household, are estimated in an intra-household collective framework. The analysis is based on a system of log-quadratic Engel curves estimated using the 68th round (2011–2012) household-level consumption expenditure data of the Indian National Sample Survey Office (NSSO) for rural and urban sectors separately for the state of West Bengal.

The sharing rule parameters (that determine the apportionment of resources between members) within a household are estimated in an intra-household collective framework as suggested by Dunbar et al. (2013). The analysis is based on a Quadratic Almost Ideal Demand System (QUAIDS) estimated using the 68th round (2011–2012) household-level consumption expenditure data of the Indian NSSO.

In this paper, the authors estimate the sharing rule of total household expenditure between couples in a household in the state of West Bengal. They use a modified version of the QUAIDS and the 68th round (2011–2012) household-level consumer expenditure data provided by the NSSO. From the exercise, it emerges that on an average, the resource shares between husband and wife in a household is about 66:34% in the rural sector and about 60:40% in the urban sector. Based on a classification of households by the distribution of resource shares, where higher resource share for the husband is classified as “Husband dominated” and the reverse as “Wife dominated”, the percentage of “Husband dominated” households is much more in both sectors. This unequal distribution of resources may have far-reaching consequences on allocation of expenditure on the children of the household. The authors leave this exercise as a future project.

This paper is an attempt to estimate the sharing rule for households using NSSO consumption expenditure data. This paper also highlights the intra household unequal resource allocation through the sharing rule. They use a modified version of the QUAIDS and the 68th round (2011–2012) household-level consumer expenditure data provided by the NSSO. From the exercise, it emerges that on an average, the resource shares between husband and wife in a household is about 66:34% in the rural sector and about 60:40% in the urban sector. Based on a classification of households by the distribution of resource shares, where higher resource share for the husband is classified as “Husband dominated” and the reverse as “Wife dominated”, the percentage of “Husband dominated” households is much more in both sectors. This unequal distribution of resources may have far-reaching consequences on allocation of expenditure on the children of the household. The authors leave this exercise as a future project.

The provision of fair and remunerative prices to farmers through government intervention is one of the key debates to address the farmers' distress in India. This article identifies how different marketing channels are responsible for higher price realization over the officially announced minimum support price (MSP).

The study uses the NSSO-SAS, 2012–13 and NSSO-SAS, 2018–19 for Aggregate level data and Unit Level Data on the Situation Assessment Survey of Farmers' households. It uses logit regression to determine the factors responsible for better price realization.

Our major findings indicate that two factors importantly determine better price realization than MSP. Firstly, government agencies provide better prices for crops covered by MSP, such as paddy, wheat and cotton. However, the probability of receiving higher prices increases for some crops if the farmers belong to the upper land size classes and upper social category. Secondly, jowar, bajra, maize and ragi, other important crops that don't benefit from government agencies, may require higher levels of procurement at the state level.

The present study only analyzes selected major crops. Distance is an important factor in choosing a marketing channel that is not incorporated due to unavailability in NSS Data.

The study is based on the latest original empirical evidence and sheds light on the variation in price realization in different agricultural marketing channels in India.

The present century is an age of knowledge-based economy. Higher education is in the process of transformation and thereby challenging the traditional system of education in India. The present paper reviews the current conditions of ICT use by the students in higher education in India. The major objectives of the study are (1) assessment of the use of computer and Internet by the students of higher education in India and (2) to find the determinants of use of ICT by the students in India. The study uses the NSSO 71st Round Unit Level Data on Social Consumption: Education Survey (71st Round, 2014). The present paper is based on 6,035 students from all the regions in India out of which 3,127 students were from the rural area and 2,908 from the urban area. The findings from logit analysis suggest that the determinants of ICT use by the students in higher education in India are regional disparities, gender, education levels of households, type of courses pursued by the students, type of institutions, access to computer and Internet facility, consumption levels of households, and students' residence type.

The issue of dropout looms large in the context of school education in India despite various flagship programs that have been initiated in school education. According to U-DISE report (2019–2020), girls drop out more than boys at the upper primary level. An analysis of the dropout problem demands probing deeper into intrahousehold dynamics that involves bargaining at the household level on investment decision. These decisions are often influenced by the social context in which the girl child in the family faces discrimination which gets reflected in terms of dropout of girl children. Apart from the issue of gender, birth order also determines which child is more likely to drop out. Using NSSO data (2017–2018), we observe that not all children of a household are equally susceptible to the dropout problem. Moreover, the eldest sibling is found to be more susceptible to the dropout problem and dropout rate goes down with the other younger siblings in the same household. First-born girl children drop out more than their male counterparts showing gender bias. The chapter concludes that the factors pushing a child to drop out become more effective for the eldest sibling. The major reason is the family structure of India as the eldest sibling is expected to be more responsible and look after other younger siblings. Consequently, a certain number of the younger siblings try to follow the elder siblings and discontinue going to school.

Indian households having the below poverty line (BPL) ration card receive rice, wheat, sugar and kerosene from the Indian Targeted Public Distribution System (TPDS) at subsidized rates. The paper uses the National Sample Survey Organization's consumption expenditure survey for the 61st round to study the causal effect of the BPL ration card on BPL households' calorie consumption. The paper aims to discuss these issues.

This causal effect is estimated by comparing per-capita-per-day calorie consumption of the BPL households having BPL card with that of a matched counterfactual BPL household from the same state not having BPL card, using stratified propensity score matching.

The BPL ration card was found to increase calorie consumption from cereals and decrease calorie consumption from non-cereal food items without affecting the overall calorie consumption of household. Thus, TPDS induces households to consume more cereals and less non-cereal without significantly changing the overall calorie consumption.

The research methodology controls for selection bias due to observable variables. Further, research needed to devise experimental set up to control for the selection bias due to unobserved variables.

The paper uses the targeting error in identifying BPL households in TPDS as a quasi-experiment set up to study the causal effect of the BPL ration card.

The purpose of this paper is to examine the performance of small manufacturing enterprises (SMEs) in India during the pre‐reforms (prior to 1991) and reforms period (1991 onwards) with focus on 15 major states from different levels of development.

In order to capture variation across different categories of states, 15 major states in India have been classified into high‐, middle‐ and low‐income states. Further, to capture productivity growth in the sector during the pre‐reforms and reforms period, both partial factor productivity and total factor productivity method (growth accounting approach) have been adopted. The analysis is based on different rounds of nationwide survey conducted by the National Sample Survey Organization (NSSO) of the Government of India during 1978‐2001.

The findings of the study reveal erosion in growth of output in the SMEs during the reforms period as compared to the pre‐reforms period with variation across different categories of states. The decline in growth of output during the reforms period can be primarily on account of fall in growth of employment and investment. The total factor productivity growth has also declined during the reforms period suggesting the need to enhance the level of technical efficiency and skills of the labour force in the sector. This is noticed in spite of major role played by the SMEs in providing employment (80 per cent of the total manufacturing sector employment) opportunities and in generating output (contributes 60 per cent of net domestic product) in the country.

On account of non‐availability of annual data, the study relied on data collected by the NSSO of the Government of India periodically. In addition, the study did not examine the factors that explain decline in productivity growth in the sector.

There is a large body of literature on regional growth and productivity in the Indian manufacturing sector but most of the studies have considered only the organized manufacturing sector. This study contributes to the literature by analyzing the inter‐state variation in growth and productivity performance of SMEs in the pre‐reforms and reforms periods.

This paper aims to compute total factor productivity (TFP) growth for India as well as for its 19 major states and to explore the determinants of TFP at the state level by considering the spillover effects.

TFP growth has been obtained using growth accounting equation. Further, the TFP growth estimates were used to derive TFP levels using the translog index procedure. Given the policy focus on building infrastructure and expanding financial access, we have estimated the impact of irrigation, electricity, road, health, education and financial depth on TFP using the Spatial Durbin Model to account for spillover effects.

Computing TFP growth for two sub periods, namely, 2001-2008 and 2009-2015, the study finds a deterioration in TFP growth for India as well as for 10 of the 19 states under study in the post global financial crisis period. The author find that TFP is positively impacted by irrigation, health and road infrastructure. While financial depth and education were statistically insignificant, installed capacity of electricity had a negative impact on state level TFP.

'The author provides rationale for the empirical findings considering the country context. The findings of this study act as pointers for shaping higher growth on a sustained basis in India. The study helps to assess the productivity growth in the new states, namely, Jharkhand, Chhattisgarh and Uttarakhand, that were carved out in 2000 vis a vis their parent states. This assessment is useful especially for the states of Jharkhand and Chhattisgarh which were created to address economic backwardness in certain pockets of the parent states.

First, it provides TFPG estimates for India as well as 19 major states during the 2000-2015 period. Second, this study helps to understand how TFPG for India as well as each of the 19 states have behaved in the post global financial crisis period. Third, the study helps to assess the productivity growth in the three newly created states in 2000 vis a vis their parent states. Fourth, this is the first attempt which considers the spatial interdependence among the states to estimate the impact of financial and infrastructural development on productivity in the Indian states.

This chapter tries to capture the disparity in expenditure on primary education based on gender among the religious groups (Hindu, Muslim, and Christian) in rural India. The gender gap in education expenditure for a certain demographic group is calculated using the Oaxaca–Blinder decomposition approach. Further, we tried to identify the various household-related factors which might influence the decision of spending on a child's education. We used the 75th-level National Sample Survey Office (NSSO) unit-level dataset of July 2017 to June 2018 (one academic year) to obtain data on education expenditure and other household factors which play a manifesting role in the gender gap in expenditure on education. Our finding suggests that the total differential (log mean boys education expenditure-log mean girls education expenditure) is positive among all religious groups signifying the gender bias in education expenditure. We also found that the magnitude of the “Unexplained Effect” component is higher compared to the “Explained Effect” component signifying that the treatment of characteristics by students differs by their sex at elementary education. Household size and if household members are employed on a casual basis, then their expenditure on education falls on the other hand income of the household, a household with computer availability and household member engaged in regular wage/salary earning plays a positive role in expenditure on primary education in rural India.

World economies including India have been moving toward recession. To combat this recession more employment generation through investment is required in a highly populated economy like India. Since unorganized manufacturing enterprises (UMEs) provide employment to a huge mass in India, therefore its growth and productivity is a matter of concern in the Indian economy. The present study analyzes the growth and productivity of UMEs on the basis of the latest two rounds of NSSO unit level data incorporating all states and union territories (UTs) of India. It reveals that the growth of UMEs, employment, gross value added (GVA) and fixed assets widely varied across states/UTs, and these growth rates were substantially high in a number of states during 2010–11 and 2015–16. In most of the states/UTs the labor productivity of UMEs has increased significantly but not the capital productivity. Our analysis supports the theoretical relationship among growth of employment, GVA, and labor productivity. Therefore, the government has to make deliberate attempts to increase the growth of UMEs on one side and raise productivities of UMEs through skill developments on the other side to overcome the problem of unemployment in particular and expedite the growth of the Indian economy in general to combat the global economic recession.

Enrolling in an academic discipline or selecting the college major choice is a dynamic process. Very few studies examine this aspect in India. This paper makes a humble attempt to fill this gap using NSSO 71st round data on social consumption on education. The purpose of this paper is to use multinomial regression model to study the different factors that influence course choice in higher education. The different factors (given the availability of information) considered relate to ability, gender, cost of higher education, socio-economic and geographical location. The results indicate that gender polarization is apparent between humanities and engineering. The predicated probabilities bring out the dichotomy between the choice of courses and levels of living expressed through consumption expenditures in terms of professional and non-professional courses. Predicted probabilities of course choices bring in a clear distinction between south and west regions preferring engineering and other professional courses, whereas north, east and NES prefer humanities.

The present paper follows the same approach as that of Turner and Bowen (1999). The Multinomial regression is specified as P ( M i = j ) = ( exp ( β j × X i ) / ∑ j − 1 5 exp ( β j × X i ) ) , where P (M_i=j) denotes the probability of choosing outcome j, the particular course/major choice that categorizes different disciplines. This response variable is specified with five categories: such as medicine, engineering, other professional courses, science and humanities. The authors’ primary interest is to determine the factors governing an individual’s decision to choose a particular subject field as compared to humanities. In other words, to make the system identifiable in the MLR, humanities is treated as a reference category. The vector X_i includes the set of explanatory variables and β_j refers to the corresponding coefficients for each of the outcome j. From an aggregate perspective, the distribution of course choices is an important input to the skill (technical skills) composition of future workforce. In that sense, except humanities, the rest of the courses are technical-intensive courses; hence, humanities is treated as a reference category.

The results indicate that gender polarization is apparent between humanities and engineering. The predicated probabilities bring out the dichotomy between the choice of courses and levels of living expressed through consumption expenditures in terms of professional and non-professional courses. Predicted probabilities of course choices bring in a clear distinction between south and west regions preferring engineering and other professional courses, whereas north, east and NES prefer humanities.

Predicted probabilities of course choices bring in a clear distinction between south and west regions preferring engineering and other professional courses, whereas north, east and NES prefer humanities. This course and regional imbalance need to be worked with multi-pronged strategies of providing both access to education and employment opportunities in other states. But the predicted probabilities of medicine and science remain similar across the board. Very few research studies on the determinants of field choice in higher education prevail in India. Research studies on returns to education by field or course choices hardly exist in India. These evidences are particularly important to know which course choices can support student loans, which can be the future area of work.

The research evidence is particularly important to know which course choices can support student loans, which can be the future area of work, as well as how to address the gender bias in the course choices.

The paper has social implications in terms of giving insights into the course choices of students. These findings bring in implications for practice in their ability to predict the demand for course choices and their share of demand, not only in the labor market but also across regions. India has 36 states/UTs and each state/UT has a huge population size and large geographical areas. The choice of course has state-specific influence because of nature of state economy, society, culture and inherent education systems. Further, within the states, rural and urban variation has also a serious influence on the choice of courses.

The present study is a value addition on three counts. First, the choice of courses includes the recent trends in the preference over market-oriented/technical courses such as medicine, engineering and other professional courses (chartered accountancy and similar courses, courses from Industrial Training Institute, recognized vocational training institute, etc.). The choice of market-oriented courses has been examined in relation to the choice of conventional subjects. Second, the socio-economic background of students plays a significant role in the choice of courses. Third, the present paper uses the latest data on Social Consumption on Education.