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Characteristic Study of
Manufacturing Output and Global Commerce |
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| Paper Id :
18354 Submission Date :
25/12/2023 Acceptance Date :
16/01/2024 Publication Date :
20/01/2024
This is an open-access research paper/article distributed under the terms of the Creative Commons Attribution 4.0 International, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. DOI:10.5281/zenodo.10848472 For verification of this paper, please visit on
http://www.socialresearchfoundation.com/innovation.php#8
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| Abstract |
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. |
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| Keywords | Specialization, Trade Theories, Economic Geography, and International Trade. | ||||||
| Introduction | A descriptive statistical
assessment of industrial production and trade patterns during the reference
period is given in this section. Simple statistical methods like average shares
and annual growth rates serve as the foundation for the investigation. The
entire time is subdivided as follows for brevity: 1980–1990 was Period I;
1991–2000 was Period II; 2001–2007 was Period III; and 2008–2013 was Period IV.
The first phase includes the 1980s import deregulation and first liberalization
period. The extensive liberalization regime of the 1990s began during the
second period. The third phase includes additional trade liberalization up
until the 2008 external demand shock brought on by the world economic crisis.
Period IV describes a setting with significantdemand shock and external
uncertainty brought on by the start of the global financial crisis. The actual value added
increase of India's manufacturing production at the sectoral level, calculated
at 2004–05 prices, is shown in the table. The corresponding sectoral shares in
total manufacturing for a specific time period are shown in figures in
parenthesis. The average annual growth rate of organized industrial production
during the whole period (from 1980 to 2013) is approximately 8%. During the
1980s (period I), manufacturing production increased at this rate; but, during
the 1990s (period II), it fell to 6% annually. Nonetheless, production showed
significant improvement in the subsequent period, with a double-digit growth
rate of 15% annually between 2007–08. After that, the growth rate fell to what
was seen in the 1990s. |
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| Objective of study |
The aim of study this paper is Characteristic Study of Manufacturing Output and Global Commerce. |
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| Review of Literature | For this paper many
books, research papers, newspapers and online websites has been reviewed and
mentioned in the complete paper. |
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| Main Text | 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.
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| Methodology | 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). |
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| Result and Discussion |
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)
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| Conclusion |
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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