Measuring and relating aggregate and subaggregate total factor productivity change without neoclassical assumptions. From this perspective, it is interesting to look at how productivity enhances welfare by considering the relationship between productivity, poverty reduction, and inequality. The impact of productivity on poverty reduction can come through multiple channels, including its effect on stimulating growth-enhancing structural change (Christiaensen et al., 2011; Ravallion and Datt, 1996; Datt and Ravallion, 1992), or the appropriation of productivity gains between various agents. The large-scale liberalisation measures have shown a positive impact in the contribution of services to India’s trade flows. Figure 11.2 below shows that services accounted for 51.7 per cent of India’s gross exports in 2015 (OECD-TiVA, 2018), although this is lower than the OECD average of 54 per cent. Despite the impressive performance of services trade in India, it is important to note that exports of services remain constrained by domestic and external barriers and regulatory aspects.
- NAS provides data only for nine broad sectors, while we have 27 industries, which necessitated further splitting of some of the NAS sectors.
- Before doing so, industries in China and India are first ranked according to their TFP growth.
- The estimates of China’s growth and productivity have been subject to fierce debate, amid the widespread conviction that the official growth rates overstated the economy’s real growth (Maddison and Wu, 2008; Bardhan 2010; Wu 2013; Wu 2014).
- Undoubtedly, the India KLEMS database and the productivity report demonstrate substantial improvements in the data and analysis of the Indian economy.
- Gross output series and gross value added series are mostly formed from national accounts data.
Other symbols could have been used, for instance, C for capital stock and N for number of persons employed. The difficulty is that for being truthful to the underlying production function, use of K and L in equation 9.1 above is correct. However, if different symbols are used to specify the estimable equation, then clarity will be lost, and additional questions may be raised on why the labour and capital quality variables are not included in the estimable equation. Considering all these, this limitation of the analysis has been allowed to continue in the interest of ease of exposition.
Dieppe (2020, p.i8) notes that “labour reallocation towards higher-productive sectors has historically accounted for about two-fifths of overall productivity growth in EMDEs”. He however points out that the mechanism of structural change gets weakened through shocks, such as the global financial crisis and COVID-19 pandemic, by restricting the mobility of people, which slows geographical and sectoral labour reallocation. Legalists argue that stringent regulations that restrict the organised sector also perpetuate inefficiencies in the unorganised sector. However, there is some consensus that the lack of legislation that often characterises the informal sector also plays an important role in its perpetuation. One common way in which the formal and informal segments of the manufacturing sector interact is through sub-contracting.
2.5. Economics of Scale
Since, among various sectors of the economy, the agriculture sector is relatively more labour intensive, a structural shift away from agriculture is obviously likely to reduce labour intensity and raise capital intensity. As the India KLEMS capital service estimation involves imputing the rental price or user cost of capital, it is also possible to evaluate the trends in the wage rental ratio or the relative price of capital. The importance of the relative price of capital for economic growth is well established in the literature (e.g., Jones, 1994). One would expect a negative relationship between the relative price of capital, especially equipment capital that drives positive composition effect, and economic growth. Table 9.6 provides the indices of wage rental ratio,151 measured by dividing the average wage rate and the average rental price of the aggregate capital stock.
India KLEMS database consists of industry-level estimates of output and inputs—primary inputs and intermediate inputs, since 1980 for 27 industries. This chapter discussed the approach we take in constructing these variables and the main data sources used. The database provides estimates of gross output and value-added measures of output, and estimates of employment, capital, and intermediate inputs as inputs.
The disappointing growth between 1955 and 1978, often marked as a failure of Nehru-Mahalanobis growth strategy, was due to the deepening of import substitution and industrial regulation, a reduced role of the market and some exogenous shocks such as oil prices and wars with neighbouring countries. The subsequent gradual acceleration in growth, especially since the early 1980s, has attracted a lot of attention from economists and policy analysts and a significant amount of research has been carried out on this theme. India’s economic growth since the 1980s has been attributed to different sets of factors by different authors.
The richer countries seem to be benefitting more from their higher focus on equipment capital. 70 The report points out that various dimensions of human capital improved with economic development and the progress has been slightly faster in low-income countries with lower levels of health and education because of the catching up. 43 Accounting for asset heterogeneity is an important feature of capital service measures compared to capital stock measures. The capital stock measures account only for the external economies accrue due to differences in various vintages of capital assets, while the capital services also account for the differences in marginal productivities, approximated by capital service prices, across different asset types. The Table gives the estimates of the four segments and the respective population for the major rounds which have been used to get the total employment by industry. If productivity gains are allocated in favour of labour, the poverty-reducing impact of productivity is likely to be more prevalent.
However, just two sectors—agriculture and non-market services—registered positive labour productivity in India, and it was primarily non-market services that saved the aggregate economy from a massive slowing of productivity growth. India’s productivity growth in the post-crisis years appears to have been driven by the government rather than the private sector. In particular, there was a significant drop in India’s manufacturing productivity, which it had raised in the preceding period.
As a firm increases its scale of operations, it can properly be linked to various production processes more efficiently. For example, in order to obtain the advantage in a linkage process, both editing and printing of newspapers are generally carried out in the same premises. One driver and one conductor may be needed, whether it is a double decker or a single decker bus. Technical economies are those, which accrue to a firm from the use of better machines and techniques of production.
External economies accrue due to ________
Existing evidence also points to the role of economic reforms in India in intensifying the productivity differential between formal and informal firms, with the latter being relatively less efficient (Kathuria et al., 2013). The KLEMS database has not formally considered the dual structure of the Indian economy while analysing productivity in various industries. Indeed, attempts to do basic productivity analysis for the manufacturing sector, distinguishing between the formal and informal segments, have been made by the KLEMS researchers in the past years . It is very important for India to understand the trends in intangible investment, especially given the recent fall in tangible investment to GDP ratio . For instance, if investment in intangible assets complement the weakening tangible investment, we are missing an important part of the capital in our measurement (see Decker et al., 2020).
Among the two asset categories, investment in construction grew at about 4 per cent in the economy in the 1980s, which increased to 8.5 per cent in the 1990s. The rise was primarily a result of a massive increase in construction and market services, but other sectors such as agriculture, manufacturing, and non-market services also showed an uptick. While the Indian economy seems to have seen a construction boom in the 1990s, the overall growth rate of construction investment fell to 5.4 per cent in the 2000s.
Explain the concept of scarcity, choice and opportunity cost with the help of Production possibility curve.
It remains to be seen, however, whether growth in services will lead to inclusive overall growth in India. This chapter is an attempt to understand India’s service sector productivity growth dynamics observed since the 1980s. Computation of total energy use for the purpose of constructing the energy intensity series is based on the concept of final use, i.e., the concept of ‘final consumption of energy commodity’ is used rather than ‘total primary energy supply’. Out of the total consumption of each specific petroleum products in the manufacturing sector, the part that is consumed by industry as feedstock (e.g., the consumption of naphtha in fertiliser and petrochemicals), rather than as fuel, is excluded.
The study provides evidence that structural changes apparently underline the slowing productivity growth rates at the macro level. In the United States, most of the positive contribution to productivity growth is coming from the digital-manufacturing sector. The Euro Area and the United Kingdom show larger productivity contributions from the digital-using sectors.
The scale of decline in TFPG for over half of China’s industries made its aggregate performance poorer than India’s since 2008. Our analysis of the industry pattern of aggregate TFPG in China and India further reveals that both experienced an uneven pattern of aggregate TFP growth with TFPG in some industries growing beyond their relative importance in the economy and others https://1investing.in/ underperforming. Although in general, the output share of industries with productivity expansion has been high in China throughout the entire period, that dominance has eroded substantially since the global financial crisis. The magnitude of decline in more than half of Chinese industries’ TFPG has made China’s aggregate TFP performance poorer than India’s since 2008.
However, it is interesting to analyse whether India’s underlying industrial anatomy underwent a change following the GFC. Such analysis is possible by defining the required sub-periods and utilising the industry production accounts developed in this section. In column 4 of Table 7.1, we provide the contributions of intermediate inputs to output growth, which is the aggregate of Energy , Material and Services inputs. The industry average contribution of intermediate inputs was 4.16 per cent, while the industry median was higher at 4.66 per cent. Thus, contrary to the case of primary inputs, we identify a negative skewness in the frequency distribution of intermediate input contribution—indicating that majority of industries saw higher than average contributions from intermediate inputs.
In a framework of the economy where organised and unorganised segments are present, the outcome on labour productivity and the reallocation of labour would depend upon the linkage or interaction145 of the two segments. But since the two segments experience different level of productivity, it is therefore unavoidable to have some reallocation of labour both within the segments and between the two segments over time. One may notice from equation 8.4 that the extent of the drag depends on the difference between the labour productivity of the unorganised and the organised segment, and on the share of the former within the sector .
Capital intensity, or capital deepening, measured as capital stock per worker, has been increasing quite rapidly in the aggregate economy, especially since 2008 (Figure 9.1). This has been true for both equipment capital stock as well as non-equipment capital stock . At the same time, the capital-output ratio, measured as capital stock per unit of real value-added, had been falling until the early 1990s and then started increasing. Despite some decline in the last two years since 2015, the general trend has been of growth. While the capital to output ratio has been falling for non-equipment assets, it has been expanding for equipment, indicating the increasing mechanisation of production in the economy. The analysis of growth in real GVA , employment and productivity for the 27 KLEMS industries presented in the previous chapters did not examine the differences in the performances of the formal and informal segments of the industries.