Risks and Resilience of Emerging Economies

This paper develops a macro-theoretic framework suitable for India to examine the impact that the outbreak of COVID-19 is likely to produce on the land holding of the small producers. It shows that the occurrence of the pandemic, the restrictions and the lockdown it brings about, the distress and the penury it causes and the switch from offline to online purchase of goods and services it induces generates a process that leads to a permanent, large and cumulative loss of land by the small producers to the capitalists.

Goods and Services Tax (GST) is the most significant reform in the history of indirect taxation in India. It was introduced in India on 1st July, 2017. After the implementation of GST, a large part of the states’ revenues from indirect taxes got subsumed under GST. In this paper, we have carried out an inter-state comparison of the impact of GST on the indirect tax revenue of the General Category States (GCS) in India with special reference to West Bengal. Indicators such as Tax to GSDP ratio & Tax Buoyancy have been used to measure tax revenue performance in pre-GST and post-GST era for each GCS. In addition, we have carried out a comparative analysis of the extent of dependency on GST compensation among the GCS and found that for Punjab, Chhattisgarh, Karnataka, Gujarat, Odisha, Haryana, Tamil Nadu, Madhya Pradesh and Maharashtra, the dependency is not only high but also strictly increasing at an increasing rate. Further, we have examined whether GST has benefitted net consumption states because GST is designed to be a consumption-based tax, not an origin-based tax. Besides, we have estimated the impact of COVID-19 on the GST collections of the states taken together and found that GST collection of all states has declined in the first quarter of FY 2020–2021 over the first quarter of the previous fiscal year. Further, we have examined the impact of GST on formalization of the economy of GCS. We have analysed indicators such as the change in number of Micro, Small and Medium Enterprises (MSMEs) registrations and number of registered persons on payroll to assess the extent of formalization of economy of GCS.

Entrepreneurship can boost the economic health of a country and hence is considered to be a driver of growth. The advent of digitalization or digital transformation has transformed the entrepreneurial process. The diffusion of technology enhances competitiveness and drives entrepreneurial growth. This study identifies the determinants of digital adoption required for digital or technological entrepreneurship. Data related to the digital platform economy index of 116 countries are collected from The Global Entrepreneurship and Development Institute. Data is collected for the year 2020 and GNI (Gross National Income) per capita data is collected from the World Bank. Data analysis is done in three phases—descriptive analysis, correlation analysis, and path analysis using Smart PLS 3.0. The study confirms that digital access, digital literacy, and financial facilitation contribute to the digital technology entrepreneurship of a country. Digital technology entrepreneurship impacts the economic well-being of a country measured through GNI per capita. The study further establishes digital access, digital literacy, and financial facilitation affect the GNI per capita of a country through digital entrepreneurial activities. However, these effects depend on the county type categorized on the basis of GNI per capita. The study has practical implications, especially in the wake of the Covid-19 pandemic. Countries need to embrace technology that can enhance technology entrepreneurship leading to economic well-being.

The present study measures volatility in output growth and price growth by using ARCH/GARCH method of modern time series analysis. Furthermore, this paper estimates the long-run and short-run relationship between the above two series in the case of rice considering four major rice-producing Indian states such as Andhra Pradesh, Punjab, Uttar Pradesh and West Bengal, over the period 1963–64 to 2018–19 applying the Panel-VECM model. The result demonstrates that, Andhra Pradesh and Punjab show large (small) changes in the current year’s volatility in output growth, which is due to large (small) changes in its own variance of the previous year and the lagged value of the variance of the random term of the previous period (i.e. other variables). There exists significant volatility in the price growth for all the four states out of which volatility is large for Andhra Pradesh, Uttar Pradesh and West Bengal. The results indicate that a large (small) changes in the volatility are due to the large (small) changes in their own respective variances of the previous year and the variances of random terms of the previous period. The results of the Panel-VECM clearly points out that the volatility in price growth is significantly influenced by the volatility in output growth both in the long-run and short-run. Moreover, it can be concluded that the long-run effect of volatility in price growth on the volatility in output growth is more than the reverse case.

Global value chains (GVCs) popularly refer to international production sharing, where production is broken into activities and tasks carried out in different countries and traded globally. East and Southeast Asian countries, namely, South Korea, China, Taiwan, Thailand, Malaysia, Vietnam, etc., have successfully leveraged their open trade and investment policy to integrate into the global production networks. India, while moving in the same direction, is yet to become a major GVC-oriented economy. In other words, India’s GVC participation is still in a nascent stage and it is imperative for India to enhance its participation in GVCs. For increased integration into GVCs, a combination of initiatives linked with domestic and international policies are important. These include improvement in trade facilitation, open FDI regime, developing domestic capacities to build ecosystems in specific sectors, and good governance principles including timely policy response. This chapter makes an attempt to analyse the role that trade facilitation can play in strengthening India’s GVC participation. This chapter draws in some sections from the author’s previous work entitled “India’s emerging connectivity with Southeast Asia: Progress and prospects” ADBI Working Paper, No. 507 Asian Development Bank Institute (ADBI), Tokyo.

This study uses dynamic panel data technique to estimate the exchange rate pass-through (ERPT) elasticity of import prices for a sample of five South Asian countries (Bangladesh, India, Iran, Pakistan, and Sri Lanka). The pass through is found to be imperfect in the short term. This suggests that firms can absorb most of the exchange rate shock and do not pass all the fluctuations on to prices. The dynamic panel results show that marginal costs, trade openness, and country-specific domestic demand are statistically significant variables that explain import price variability. In addition, pass-through elasticity also depends on the depreciation or appreciation of each country’s domestic currency. Insights into exchange rate transmission have important implications for imports, especially intermediate imports, and the use of exchange rates as an important policy tool for controlling inflation.

The present paper investigates into the firm-level heterogeneity and its impact on export performances. There are three major hypotheses in the existing literature on the export performance: hypothesis of self-selection theory, hypothesis of learning-by-exporting theory and sunk cost hysteresis hypothesis. The present cross-sectional study uses ASI unit level data for 2011–12 across 30 states and sixty six manufacturing industries. Here 11,188 firms are considered for the analysis. The study estimated a Heckman Selection model to identify the determinants of export decision (the selection equation) as well as the level of export to gross output (outcome equation). The firm-level heterogeneity can be explained in terms of technical efficiency, spatial location, ownership pattern, technological advancement, quality standard, and skill intensity. Firm-level technical efficiency is estimated using Stochastic Frontier Analysis. Ownership pattern has diverging impacts on export decision and export performances across the manufacturing units. Technical efficiency, standardization of quality, transfer of sophisticated technology in terms of usage of imported input, and urban location improve the export performance of the manufacturing unit.

This study advocates the use of non-radial Data Envelopment Analysis (DEA) in order to evaluate productive efficiency of banks. We present a comparison of non-radial DEA and conventional radial DEA models in the context of Indian banks. It further investigates the impact of the global financial crisis (GFC) of 2007 and 2008 on efficiency of banking sector. Technical efficiency scores related to both radial and non-radial DEA models have been calculated for a three-input three-output technology set, under the variable returns to scale assumption. A balanced panel of 26 public sector banks (PSB) and 19 private banks (PVB) for 13 years (2004–05 to 2016–17) has been utilized for the purpose. Kendal’s Tau-b statistic is used to calculate the rank correlation amongst the TE scores. Second-stage bootstrapped truncated regression is conducted to find the factors affecting TE. Results of DEA indicate a lot of scope for improvement in resource utilization if non-radial efficiency measure is employed. The disaggregation of efficiency reveals that physical capital, labor, and other incomes are main contributors to inefficiency. This insight into the components leading to inefficiency will assist managerial decision-making for performance improvement. The study reveals that the non-radial efficiency has a U-shaped relationship with the size of bank. Further, the GFC has a positive impact on efficiency of PSB but has an adverse effect on the same for PVB.

Financial inclusion allows broader access to financial services and access to faster and more equitable growth. MFIs can play a significant role in facilitating inclusion, as they are uniquely positioned in reaching out to the rural poor. This paper tries to analyse the impact of Government effectiveness and regulatory framework of a country on the performance of MFIs using the data of 91 MFIs from selected seven Asian countries. This study has taken into account a period of 2004–2013. These selected Asian countries have different backgrounds of evolution of MFIs. To analyse the data of this period, longitudinal multilevel model has been used. This study has found that regulatory quality has negative impact on Return on Asset (ROA) in case of Bangladesh. In India and Vietnam, government effectiveness has positive impact on profit margin of MFIs.

The decade of 2001–2011 has seen a magnificent growth of small towns, named as ‘Census Towns’(CTs), in India. These are the towns governed by the rural local governments having a population of 5000 or more, with a population density of at least 400/km² and 75% of its male main workforce working in non-farm sector. There are three different kinds of theoretical argument that attempt to explain the dynamics of formation of the CTs. The first is related to the impact of developments in the large city in the neighborhood; the second is to development of transport infrastructure connecting the villages that are identified as ‘would be CTs’; and third is the local development at the ‘would be CTs’. Since, these factors cause each other, this paper performs a Principal Component Analysis (PCA) and identifies some uncorrelated variables that can potentially affect the formation of CTs. It uses the data from the state of West Bengal, India which has seen the birth of maximum number of CTs during this period, to find out that in all the districts of West Bengal, the presence of highways within 5 km radius of a village played an important role. In the districts bordering Kolkata, the capital city of the state, the population density at the nearest city had been important.

Research on gender-based occupational segregation and its costs in developing countries like India is an under-researched area. This paper estimates the level of gender-based occupational segregation in India, decomposes the estimate into endowment effect and “unexplained” component (referred to as discrimination), and estimates the cost of gender-based segregation in terms of wage loss of female workers. The study is based on the two rounds of NSSO data (61st and 68th rounds). The results reveal an increase in occupational segregation across gender at the all-India level and for most states. About 30% (in the 61st round) to 22% (in the 68th round) of the segregation index may be due to unexplained factors. Such gender-based segregation has resulted in the lowering of GDP by 11% in the 61st round and 1% in the 68th round. The study advocates encouraging educational programmes for school-going female students to promote the choice of atypical occupations, encouraging vocational training programs for female workers and addressing gender biases in institutional practices like selection, recruitment, and promotion.

It is well-known that school enrolment and retention of female students in most countries of South Asia are abysmally poor. While some recent improvements have been made possible via direct transfers to attract female students in school, the factors that can make participation natural and widespread remain elusive. This chapter tries to link school enrolment to prospects in industry and service sectors in these countries. We find that rise in industrial jobs for women invariably attracts female students to secondary school enrolment, which is not similar to rise in job prospects in services. Female school enrolment is also strongly influenced by peer-effects. We explore male industrial jobs and related interaction effects in establishing the proposed relation for eight South Asian countries between 1994 and 2018 via use of dynamic panel estimations.

In the last few decades, educational enrolment at different levels of schooling has increased. The socio-economic disparity in education participation has also reduced. However, students’ learning levels have been low, and the trends in learning outcomes have been stagnant. Against this backdrop, this paper investigates how regional disparity in learning outcomes has changed over time. Using multiple rounds of nationally representative data on test scores and schooling inputs, we apply the methodology of convergence to study temporal changes in the distribution of learning outcomes across the Indian districts over a decade. Our findings reveal the existence of absolute convergence, which seems to be driven mainly by a fall in learning outcomes among initially better-performing districts. Our analysis of conditional convergence shows the importance of having equality of opportunity in reducing regional disparity in learning over time. Our study highlights the need for having policy measures targeted toward underperforming regions, especially in the current times when the COVID-19 pandemic has disrupted the education system and caused significant learning loss among students from disadvantaged backgrounds.

India has achieved a decent rate of economic growth in past decades except the COVID pandemic period. However, such growth fails to ensure gender equality in various aspects of life. This paper tries to investigate the gender disparity in literacy and enrolment rate across 24 states over 4 time points. The major focus of this paper is to examine the spatial influence of gender differential in educational outcomes along with conventional socio-economic variables in a panel data framework. We find that gender disparity of net enrolment is found to be negative which means females are better off compared to males. However, disparity prevails in respect of literacy rate though over time it is found to be declining in all the states. We also find that net enrolment is space neutral but the literacy rate is space dependent. The Global Moran Index (GMI) values of gender disparity in literacy in different time points appear to be statistically significant; this implies the existence of spatial clustering of states (high-high and low-low) in gender disparity in literacy rate. The spatial panel data regression results of gender differential of literacy clearly prove the influence of neighbouring effect; only two explanatory variables like urbanization and per capita social sector expenditure have appeared to be statistically significant.

Scheduled castes and tribes have been historically discriminated, excluded and marginalized in India. To promote an egalitarian and inclusive society, the Government has imposed reservation policy to extend opportunities to these “backward” classes in higher education, employment and political representation. In this chapter, we aim to explore the efficacy of such reservation in higher education. While the data shows that enrolment has increased over the years, we aim to investigate intergenerational inertia in educational achievement. We have used transition matrix and regression analysis on the data collected from IHDS data from 2011 to 2012. We build on the methodology in papers by Majumder and Ray (Development and exclusion: Intergenerational stickiness in India (MPRA Paper 71182). University Library of Munich, Germany, 2016) and Long and Ferrie (The American Economic Review 103:1109–1137, 2013). Our analysis suggests that there has been vertical mobility across generations. Third generation is more mobile than the second generation in terms of higher education. With regard to the regression analysis, we obtain that if the father’s educational attainment is in category 2 and beyond, then caste certificate does not have a significant contribution toward upward mobility. So, the possession of caste certificate is crucial in bringing about educational mobility but it is not necessary but it has to be coupled with socioeconomic opportunities like expansion of income, provision of educational infrastructure to facilitate higher education. (a) Identification of exact father–son pairs which was not done in previous research; (b) calculation of distance between two contingency tables using Long and Ferrie (The American Economic Review 103:1109–1137, 2013), Lodh et al. (Indian Economic Review 56(1), 2021), and Altham and Ferrie (Historical Methods: A Journal of Quantitative and Interdisciplinary History 40(1), 2007) methodology and (c) exploring the situation in higher education where the instance of reservation policy is more pronounced.