Does the S-curve demonstrate an asymmetrical response to fluctuations in exchange rates?
| Date | 13 August 2024 |
| Pages | 170-192 |
| DOI | https://doi.org/10.1108/JCEFTS-12-2023-0072 |
| Published date | 13 August 2024 |
| Author | Sareer Ahmad,Javed Iqbal,Misbah Nosheen,Nikhil Chandra Shil |
Does the S-curve demonstrate an
asymmetrical response to
fluctuations in exchange rates?
Sareer Ahmad
School of Economics, Quaid-I-Azam University, Islamabad, Pakistan
Javed Iqbal
School of Economics, Quaid-I-Azam University, Islamabad, Pakistan and
Fulbright Postdoc Fellow, University of Nebraska Oamah,
Omaha, Nebraska, USA
Misbah Nosheen
Department of Economics, Hazara University, Mansehra, Pakistan and Fulbright
Postdoc Fellow, University of Nebraska Oamah,
Omaha, Nebraska, USA, and
Nikhil Chandra Shil
Department of Business Administration, East West University, Dhaka, Bangladesh
Abstract
Purpose –This study aims to examine the asymmetric S-curve betweenthe trade balances of Pakistan and
China at the commoditylevel using disaggregated data.
Design/methodology/approach –This study focuses on Pakistan and China bilateral trade based on
commodity-leveldata. This study delves into the S-curve phenomenaby examining time series data from 1980
to 2023 across 32 three-digitindustries/commodities.
Findings –The findings show significantevidence in favor of the “asymmetric S-curve”in 27 out of the 32
industries studied.This study confirms that the devaluation of home currency is not a viable solutionalways to
improve tradebalance.
Research limitations/implications –This study considers 32 three-digit industries limiting the
generalizability of findings. Due to data unavailability,the authors fail to consider other industries. In the
absence of quarterly data on industry-level trade between Pakistan and China, annual d ata from 1980 to
2023 were used in generating the cross-correlation functions. Previous literature frequently resorted to
the general consumer price index with its inherent aggregation issues, whereas this study has opted for
commodity price indices to overcome the shortcomings in the estimation of S-curves at the commodity
level.
Practical implications –The findings have practical relevance in guiding policy decisions regarding
commodity trade, whereas the industry-wiseanalysis enriches the understanding of the short-term effects of
currencydepreciation on trade balance dynamics.
Originality/value –The S-curvehypothesis predicts a negative cross-correlationbetween a country's current
exchange rate andits past trade balance and a positive cross-correlationbetween the current exchange rate and
its future trade balance. Previousempirical S-curve studies had the limitation of assuming symmetryin cross-
correlationwith both current and future trade balance values.
Keywords S-curve, Trade balance, Exchange rates, Pakistan, China
Paper type Research paper
JCEFTS
17,2/3
170
Journalof Chinese Economic and
ForeignTrade Studies
Vol.17 No. 2/3, 2024
pp. 170-192
© Emerald Publishing Limited
1754-4408
DOI 10.1108/JCEFTS-12-2023-0072
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1754-4408.htm
1. Introduction
The exchange rate, which frequentlyplays a critical role in the formulation of the economic
policy of a country, has a significant influence on the behavior of key economic variables.
Following the breakdown of the Bretton Woods system in 1973, research into exchange rate
underpinnings gained popularityand momentum. Several studies (Kale, 2001;Meade, 1951;
Harberger, 1950; Bahmani, 1985; Mundell, 1968) have examined the impact of exchange
rate changes on trade balance (TB). Accordingto Magee (1973), the relationship between the
exchange rate and the TB is not instantaneous;rather, there is a lag between the two variables
due to short-term contracts and time lag considerations.
In response to a devaluation, the short-run movement of trade patterns has been studied
using two approaches. Magee (1973) theoretically introduced the J-curve approach, which
stated that due to currency depreciation,the TB was initially worse but improved over time.
The price elasticity of demandis inelastic in the short run due to country contracts, time lags
and wage rigidity; however, inthe long run, when previous contractsexpire, export and
import elasticities exceed one (elastic). In subsequent studies, Bahmani-Oskooee (1985)
investigated it empirically usingvarious estimation techniquesand regression approaches
(Bahmani-Oskooee and Malixi, 1992;Bahmani-Oskooee and Ratha, 2004;Bahmani-
Oskooee and Hegerty, 2010; and Bahmani-Oskooee et al.,2013). The second approach is the
S-curve approach that was introduced by Backus et al. (1994). Unlike the regression
approach, this approach is based upon a cross-correlation function between past and future
values of the TB and the current terms of trade or the real exchangerate.
More specifically,the concept implies that the correlation between past values of the
balance of trade and the present exchange rate may be negative, whereas the correlation
between future TB and the present exchange rate may be positive, resulting in an S-curve
shape. The available literatureon the S-curve is divided into three categories. The first group
of studies used aggregate-level trade data between one country and the rest of the globe,
which ended up with mixed findings (Bahmani-Oskooee et al., 2008a,2008b;Parikh and
Shibata, 2004;Bahmani-Oskooeeet al., 2008a,2008b;Senhadji, 1998;Backus et al.,1994).
However, these studies tend to embody an aggregation bias, hence Bahmani-Oskooee and
Ratha (2007a,2007b,2011) used bilateral trade levels between the two bilateral trading
partners. However, the findings of these studies are also supposed to have a second
aggregation bias, as the empirical results are supposed to vary at the industry level. As a
result, the third group of studies investigatedthe S-curve at the commodity level between the
two countries to check for evidence in support of the S-curve. It includes the study of
Bahmani-Oskooee and Ratha (2008,2009,2010), Bahmani-Oskooee and Zhang (2013) and
Bahmani-Oskooee and Xu (2014), however, the empirical results are supposed to be
industry-specificand vary from country to country.
The present study focuses on investigating the S-curve between Pakistan and China.
China is the largest trading partner [1] of Pakistanin terms of imports. Trade between China
and Pakistan increasedfrom US$1.2bn in 2003 to US$16.2bn in 2018 showing an increaseof
18.9%. Because of better connectivity–both planned and realized –in the context of CPEC,
and because of the implementation of the China–Pakistan free trade agreement, the volume
of trade between the two countrieshas increased relatively. Compared to its imports, Chinese
exports to Pakistan have risen at a fasterrate. Exports from China to Pakistan grew by 19.8%
per year, whereas Pakistan'sexports to China grew at a rate of 13.6% each year. Pakistan's
exports to China totaled $1.75bnin 2018, whereas China's exports to Pakistan totaled $14bn.
Textiles, vegetables and food production constitute a major share of Pakistan's exports to
China, whereas machinery, chemicals and metals comprise Pakistan’s main imports from
China.
Journal of
Chinese
Economic and
Foreign Trade
Studies
171
Get this document and AI-powered insights with a free trial of vLex and Vincent AI
Get Started for FreeUnlock full access with a free 7-day trial
Transform your legal research with vLex
-
Complete access to the largest collection of common law case law on one platform
-
Generate AI case summaries that instantly highlight key legal issues
-
Advanced search capabilities with precise filtering and sorting options
-
Comprehensive legal content with documents across 100+ jurisdictions
-
Trusted by 2 million professionals including top global firms
-
Access AI-Powered Research with Vincent AI: Natural language queries with verified citations
Unlock full access with a free 7-day trial
Transform your legal research with vLex
-
Complete access to the largest collection of common law case law on one platform
-
Generate AI case summaries that instantly highlight key legal issues
-
Advanced search capabilities with precise filtering and sorting options
-
Comprehensive legal content with documents across 100+ jurisdictions
-
Trusted by 2 million professionals including top global firms
-
Access AI-Powered Research with Vincent AI: Natural language queries with verified citations
Unlock full access with a free 7-day trial
Transform your legal research with vLex
-
Complete access to the largest collection of common law case law on one platform
-
Generate AI case summaries that instantly highlight key legal issues
-
Advanced search capabilities with precise filtering and sorting options
-
Comprehensive legal content with documents across 100+ jurisdictions
-
Trusted by 2 million professionals including top global firms
-
Access AI-Powered Research with Vincent AI: Natural language queries with verified citations
Unlock full access with a free 7-day trial
Transform your legal research with vLex
-
Complete access to the largest collection of common law case law on one platform
-
Generate AI case summaries that instantly highlight key legal issues
-
Advanced search capabilities with precise filtering and sorting options
-
Comprehensive legal content with documents across 100+ jurisdictions
-
Trusted by 2 million professionals including top global firms
-
Access AI-Powered Research with Vincent AI: Natural language queries with verified citations
Unlock full access with a free 7-day trial
Transform your legal research with vLex
-
Complete access to the largest collection of common law case law on one platform
-
Generate AI case summaries that instantly highlight key legal issues
-
Advanced search capabilities with precise filtering and sorting options
-
Comprehensive legal content with documents across 100+ jurisdictions
-
Trusted by 2 million professionals including top global firms
-
Access AI-Powered Research with Vincent AI: Natural language queries with verified citations