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Every nation's economy is significantly impacted by international trade. It makes it possible to meet the requirements of the populace and promotes the nation's internal growth. The exchange of commodities and services between nations is known as international trade. The study examines developments of specialization in industrial production and global commerce from 1980 to 2013. In order to determine whether these patterns are consistent with the three different strands of trade theories—the traditional Heckscher-Ohlin theory, the "new" trade theories based on increasing returns to scale, and the economic geography theories based on vertical linkages between industries—it looks into whether specialization has increased in international trade. I discover that there is evidence of growing specialization in global trade, and that there is some evidence to support each of the three strands of tradetheories.

Trend ofmanufacturing sector(1980-2013)%perannum

Note: Figures in parenthesis are the average share of individual sector in total manufacturing. The growth rates are the simpleaverage of annual growth over the respective periods. Production data is based on real net value added, base 2004‐05=100Source: Author’s calculation based on data collected from ASI (CSO), various issues.

The overall trend is comparable to the pattern seen at the aggregate level, despite the significant variety in sectoral growth rates. For example, we can see that the growth rates of 13 sectors are comparatively larger during period I compared to period II.

Twelve industries saw double-digit growth rates during period III, indicating a significant improvement in growth rates. But the recovery in growth was short-lived, as the last month saw a dramatic slowdown in ten industries. Over the whole duration, nine manufacturing sectors—leather (11%), coke & petroleum (21%), chemicals (10%), rubber and plastics (13%), non-metallic minerals (10%), electrical machinery (10%), medical, precision, and optical instruments (19%), and transport equipment (10%)—saw double-digit growth rates. The manufacturing sector's value added share distribution shows that, with a 17 percent share, the chemicals sector is the biggest contributor, followed bymachinery, comprising textile segments (10%), basic metals (13%), electrical and non-electrical components (14%). Throughout the reference period, the proportion of non-traditional technology-intensive industries, such as chemicals, petroleum, and coke, has steadily increased. Conversely, the proportion of conventional and lower-technology industries, such tobacco, food & beverage, textiles, wood & paper products, rubber & plastics, has decreased or been unchanged over the whole period. This shows how sectoral specialization is evolving from a traditional manufacturing segment to a more sophisticated or knowledge-intensive manufacturing activity. Generally, the capital goods and chemicals divisions have been the most successful in organized manufacturing over the past few decades, both in terms of size distribution and strong growth rates.

Table 3 shows the manufacturing trade pattern for a few chosen industrial sectors. Over the course of the study, the manufacturing export shares that are most significant include textiles (20%), chemicals (15%), food and beverage (14%), coke and refined petroleum (10%), and machinery (9%). The proportion of non-traditional export industries, like machinery, coke and petroleum, chemicals, transport equipment, etc., has increased and maintained its relative sizes. However, throughout the same time period, the relative export share of the majority of traditional export industries, including food and beverage, textiles, leather, etc., has been steadily declining. In a similar vein, the mix of imports during this time period indicates a strong reliance on technology-intensive goods. Coke and petroleum, chemicals, basic metals, and machinery equipment are some of the main import categories.

The manufacturing export growth profile indicates that most industries have seen double-digit growth rates during different sub-periods. Since most of them had a significant slowdown in the 1990s, the growth was somewhat less remarkable during that time. Nonetheless, during the third quarter, practically all sectors' exports have rebounded, much like the production scenario. With the exception of the food and beverage, textile, leather, and paper product sectors, every industry saw a slowdown during period IV, suggesting that the decline in global demand may have had an impact on export prospects. Over the whole time, manufacturing exports increased at a rate of 18% annually.IncaseofFigureTrendin manufacturing trade(1980-2013)%perannum

Note: Im = Imports, Ex = Exports. Based on basic average annual rates of trade volumes in US dollars, the growth is calculated. The average shares in percentage, calculated annually, are indicated by figures in parenthesis. Source: The author's computation using information gathered from UN Comtrade and DGCIS.

Methods of Econometric Analysis

We suggest applying an econometric approach to evaluate the effect of global trade on the productivity of the organized manufacturing sector. Our hypothesis is that higher levels of trade exposure abroad will boost manufacturing productivity in India. The econometric framework15 establishes a relationship between the relative import price (RP), import penetration ratio (IMP), export intensity (EXI), capital intensity (CI), and capacity utilisation (CU) and productivity growth (P) in manufacturing. That is,

Whereas J stands for the manufacturing industry, and t for the years 1980–2013. The purpose of the first three trade-related variables is to offer proof of the theoretical channels. Furthermore, we incorporate CI and CU as supplementary explanatory variables (control variables). One key factor that influences the rise of productivity is the amount of capital per worker. It is anticipated that the capacity utilisation variable will regulate the industrial productivity's procyclical nature.

Based on 17 cross-section industrial sectors selected at the 2-digit level and monitored between 1980–81 and 2013–14, the empirical study is conducted. The statistical conclusions are grounded in normal panel regression estimation methodology, and the panel is balanced. By combining intra- and inter-sectoral dynamics with robust inference of parameters, panel regression helps manage the problem of omitted variable biases resulting from unobserved heterogeneity in the regression model (Hsiao, 2003).

The methodology describes a panel econometric estimating strategy that we utilize to analyze the trade-productivity growth nexus. Using data from 33 years of observations, we use the conventional longitudinal regression methods to 17 cross-section units. The "within" variation is found to be greater than the "between" variation for all variables. The variables related to import penetration and export intensity have the lowest mean deviations, whereas the relative import price variable has the highest total variance. The general pattern of import penetration, export intensity, relative import pricing, and capacity utilization.The trade-related variables are also given a lag structure of one to two periods since the effects of trade exposure on productivity can persist for multiple periods. Based on Hausman specification tests41, the random effect technique40 is used to estimate the econometric model. The Huber-White standard errors technique is employed to calculate standard errors, and it has been demonstrated to be resilient against panel level heteroscedasticity and autocorrelation of an unknown type. There are two subsections that discuss the econometric results. Part (a) displays the TFP-based results, while Part (b) displays the LP-based estimation results.The coefficient of capacity utilisation (ΔCU) is positive and significantly significant at the one percent level across all estimations. This supports our hypothesis that productivity in the manufacturing sector is pro-cyclical. Likewise, it is discovered that the coefficient of capital intensity (ΔCI) influences LP in a positive and statistically significant way, both immediately and over time. The coefficient is positive for TFP but not statistically significant. The findings offer solid proof of the beneficial effects of more mechanization on labor productivity.

Trade and Productivity Growth Econometric Results: Two-digit Manufacturing Sector Sample

Table displays the results of the random panel estimation for TFP and LP. Three columns representing the lag length are used to display the findings. There is no lag in the first period, and we have 561 observations. We have 544 observations for the second period (with a one-year lag) and 527 observations for the last period (with a two-year lag).

Panel Regression of productivity growth (1980-2013)

The current study aims to investigate how global commerce affects the growth in productivity of India's organized manufacturing sector. The study's reasoning is based on a number of theoretical claims that contend that engaging in international trade may increase productivity development through factors such as reallocation, economies of scale, competitiveness, and spillover. We attempted to evaluate the relative benefits of these channels using a number of trade-related indicators, such as relative import prices, import penetration, and export intensity, in contrast to the empirical research that are currently available on India. The empirical evaluation is predicated on a panel econometric estimation of 17 two-digit sectors from 1980 to 2013, an age of significant economic trade openness.

According to the descriptive study, there has been a discernible upsurge in the production, productivity, and trading pattern of organized industry. There is evidence of a shift in the pattern of specialization, with highly technology- and skill-intensive modern industries like the chemical and engineering sectors replacing less technology-intensive, traditional manufacturing sectors like food and beverage, tobacco, and wood. In comparison, domestic output has a greater compositional shift. Both TFP and LP productivity have increased along with output increases, particularly in the 2000s. Expert-intensive manufacturing has also become a more significant part of trade composition, even while labor-intensive and historically competitive industries like textiles continue to lead the export basket. The panel econometric results for TFP and LP demonstrate evidence of trade-induced increases in manufacturing productivity. There is some indication of negative economies of scale predominating due to increasing competition during the brief era. Nonetheless, there is compelling evidence of trade-induced productivity improvements that occur through imports, particularly through tightening the channels of competition. After a year, it appears that the benefits of competition, reallocation, and spillover channels from imports are clearly strong. Conversely, however, there is less proof of a long-term boost in productivity via reallocation, economies of scale, and spillover effects through exports, as it was only discovered to be noteworthy during the short period. Instead of only pure learning effects after entry, the contemporaneous association may be caused by the entry of relatively high productivity enterprises in the export market with the expectation of recovering initial sunk costs. Ultimately, the empirical research shows that there is a dynamic rather than a static relationship between foreign trade and productivity growth in Indian manufacturing.

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- Available at: https://www.theguardian.com/ business/2017/jan/27/us-economic-growth-slows-to - lowest-rate-since-2011