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Exploring The Influence of Kwacha Fluctuations on Medium Small
and Micro Enterprises Mmsmes Profitability: A Case of Choma
District Southern Zambia
Temwani Zulu*
Ministry of Education, Zimba. Zambia
DOI: https://dx.doi.org/10.47772/IJRISS.2025.91100274
Received: 10 November 2025; Accepted: 20 November 2025; Published: 06 December 2025
ABSTRACT
This study investigated the impact of kwacha fluctuations on the financial performance of Micro, Small and
Medium Enterprises (MSMEs) in Choma District, Zambia, specifically on how demographic characteristics,
sectoral distribution, and managerial strategies influence enterprise resilience to currency volatility. Using both
descriptive and correlation analyses, the study found that MSME operators are mmostly male (59.6%) and the
majority aged between 36 and 45 years, with most possessing only primary or secondary education. This
demographic structure significantly constrains financial literacy and the capacity to apply sophisticated risk
management tools such as hedging. The findings revealed that 98% of MSMEs are import-driven, making
them acutely vulnerable to kwacha depreciation, which inflates procurement costs and erodes profitability.
Retail and manufacturing sectors exhibited the highest exposure, showing large fluctuations in net profit
margins (NPM) and profit growth rates (PGR), while service-oriented sectors such as education and finance
displayed relative stability. The study further established that most MSMEs depend on informal coping
mechanisms primarily inventory management and reactive pricing adjustments rather than formal financial
instruments, due to limited financial literacy, weak supplier networks, and restricted access to capital markets.
Correlation analysis confirmed significant relationships between currency volatility, pricing strategies, import
reliance, and profitability, as well as a positive association between education level, enterprise size, supplier
relationships, and hedging adoption. The findings affirm theoretical perspectives from the Resource-Based
View (RBV) and Purchasing Power Parity (PPP), emphasizing that internal capacity and market exposure
determine enterprise resilience.
The study concludes that MSMEs in Choma District face systemic vulnerabilities to exchange rate instability
due to import dependence, weak financial management practices, and limited macroeconomic awareness. It
recommends targeted financial literacy programmes, improved access to hedging instruments, promotion of
local value chains, and macroeconomic stability as crucial pathways for enhancing MSME resilience and
sustainable growth in Zambia’s volatile economic environment.
Keywords: MSMEs, exchange rate volatility, kwacha depreciation, financial performance, hedging, Zambia,
Choma District
INTRODUCTION
Micro, Small, and Medium Enterprises (MMSMEs ) are a cornerstone of Zambia’s economic development,
contributing substantially to employment creation, innovation, and inclusive growth. Despite their vital role,
MMSMEs face numerous external and internal challenges that undermine their profitability and sustainability.
Among these, exchange rate fluctuations particularly the volatility of the Zambian Kwacha represent a critical
external factor affecting costs, revenues, and overall business performance.
Globally, several studies have explored the effects of currency fluctuations on business performance, a study
by (Belghitar, et al., 2021) examined post-Brexit currency effects on MSMEs in the United Kingdom, while
similar investigations have been undertaken within the European (European Union, 2015), and India
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(Kahunde, et al., 2021), (Kahunde, et al., 2021). These studies predominantly emphasize macroeconomic
outcomes and global trade dynamics, with limited focus on firm-level profitability within MMSMEs .
Regionally, (Metumara, 2023) explored the impact of Naira volatility on SME profitability in Nigeria, while
local investigations by (Kuntashula, 2020), (Nyirenda, 2020) and (Lungu & Kaubi, 2017) examined the effects
of exchange rate fluctuations on trade and SME operations in Zambia. Additional work by (Chilufya &
Mwewa, 2022 ) analyzed exchange rate volatility and SME performance, and (Chitambala, 2019) examined
currency depreciation and economic growth. Despite these contributions, there remains a gap in understanding
how Kwacha fluctuations specifically influence MSME profitability in Choma District a region characterized
by high import dependency and a growing base of small enterprises operating on narrow profit margins. The
current study seeks to address this knowledge gap.
The statement of the problem is that The Zambian Kwacha has experienced pronounced volatility in recent
years (Sikabbwele, 2024), driven by inflationary pressures, shifts in economic policy, and fluctuations in
global commodity prices (Kuntashula, 2020). Such instability introduces uncertainty for MSMEs, often
resulting in rising input costs, inconsistent revenues, and challenges in maintaining competitive pricing
(Nyirenda, 2020). Despite the centrality of MSMEs to Zambia’s local economic development, little empirical
evidence exists on how Kwacha fluctuations affect their profitability, particularly in Choma District, where
import reliance amplifies exposure to currency risk.
This study aligns with several United Nations Sustainable Development Goals (SDGs) which includes: SDG 8
(Decent Work and Economic Growth); The study supports the promotion of sustained and inclusive economic
growth by examining how currency stability can enhance MSME performance and employment generation;
SDG 1 (No Poverty): By improving understanding of MSME profitability, the study contributes to poverty
reduction through enhanced income stability and job creation; SDG 9 (Industry, Innovation, and
Infrastructure): The findings provide insights into financial access and resilience among MSMEs operating in
volatile environments. SDG 17 (Partnerships for the Goals): The study informs partnerships that mobilize
financial and technical support for MSME development in Zambia.
The paper is organized as follows: Section One introduces the study; Section Two presents the literature
review; Section Three outlines the theoretical and conceptual frameworks; Section Four discusses the research
methodology; Sections Five and Six present the results and discussion, while Section Seven concludes and
provides recommendations.
LITERATURE REVIEW
General Overview.
Exchange rate volatility significantly influences macroeconomic stability, trade performance, and business
operations. Previous research by (Chilanga & Kunda, 2025) (Chilufya & Mwewa, 2022 ). has shown that
exchange rate volatility is associated with asset price distortions, financial uncertainty, and credit risks. In
Zambia, key macroeconomic fundamentals including inflation, interest rates, GDP growth, and balance of
payments are the principal drivers of exchange rate movements. Broadly, currency instability affects both
developing and developed economies by disrupting business activity and reducing competitiveness.
Impact of Exchange Rate Fluctuations on operational aspects of SME
Notably exchange rate fluctuations has a positive impact on Small and Medium-sized Enterprises (MSMEs ),
which are not only experienced in developing but also developed countries the major key areas of business that
are normally impacted includes procurement, pricing strategies, and profit margins.
Impact on Procurement
In import-dependent economies like Zambia, currency depreciation raises the cost of imported goods and raw
materials. Studies across sub-Saharan Africa show that MSMEs experience sharp increases in production costs,
reduced import volumes, and disrupted production continuity during depreciation periods. Local evidence
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confirms similar trends in Zambia, where rising import costs compress profit margins and destabilize
operations., (Chitambala, 2019)
Effects on Pricing Strategies
Currency instability undermines MSMEs’ ability to maintain consistent pricing. As a consequence, weak
market power, most firms cannot fully pass on increased costs to consumers. Thus, they frequently adjust
prices to remain competitive, although such dynamic pricing often reduces customer loyalty. A Regional study
by (Kuntashula, 2020) indicate that firms resort to short-term contracts and ad-hoc pricing as a way of coping
with mechanisms, which contributee to revenue unpredictability.
Impact on Profit Margin
Profitability is one of the most affected components of MSME performance. Rising input costs and pricing
instability reduce margins and erode capital bases, discouraging reinvestment. Empirical evidence from
Zambia and other African countries consistently shows a negative relationship between exchange rate volatility
and MSME profitability, with increased operating costs restricting both growth and sustainability (Lakuma &
Muhumuza, 2019), (Chilufya & Mwewa, 2022 ).
Sectoral Effects of Kwacha Fluctuations
The extent of impact varies by sector depending on the level of import dependency, export orientation, and
access to foreign currency. Exchange rate depreciation can function as either a constraint or an opportunity.
Real Estate Sector
A depreciating Kwacha can attract foreign investors due to relatively cheaper property values. However, local
developers face higher costs of imported building materials, narrowing profit margins despite increased foreign
interest (Kuntashula, 2020).
Information and Communication Technology (ICT) Sector
Short-term depreciation may attract outsourcing contracts, but over time, increased import costs for hardware
and software reduce profitability. Inflation erodes consumer purchasing power and makes long-term pricing
difficult, creating revenue uncertainty (Lungu & Kaubi, 2017).
Manufacturing Sector
Manufacturing is highly exposed due to reliance on imported machinery and raw materials. Depreciation
inflates production costs, reduces production volumes, and compresses margins. Although exporters may gain,
most small manufacturers lack access to hedging and remain negatively affected (Lungu & Kaubi, 2017).
Agricultural Sector
For export-oriented crops, depreciation enhances international competitiveness. However, farmers dependent
on imported fertilizers, pesticides, and equipment faces escalating costs. Smallholder farmers are particularly
vulnerable due to limited credit access and inability to manage price volatility (Mwansa, 2020).
Retail and Wholesale Trade Sector
Retailers and wholesalers many of whom import from South Africa, China, and Tanzania (Common Market
for Eastern and Southern Africa, 2020). Who face immediate cost increases during depreciation. Passing these
costs to consumers reduces demand, while absorbing them leads to liquidity challenges. Profit margins in this
sector are among the most volatile.
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Tourism and Hospitality Sector
Depreciation can make Zambia a more affordable destination for international tourists. Nonetheless, higher
costs of imported supplies and operational inputs reduce profitability. The net effect depends on whether
tourism inflows outweigh increased operating expenses, (Africa Economic Outlook, 2021).
Mining and Energy Sector
Depreciation increases the local currency value of foreign-denominated revenues, temporarily boosting
earnings. However, it simultaneously raises the cost of imported machinery, fuel, and spare parts. Prolonged
volatility also complicates energy pricing and discourages new investment (Mbao, 2021).
Education and Health Sectors
Though less trade-intensive, these sectors are affected through the rising cost of imported educational
materials, laboratory equipment, and pharmaceuticals. Increased service fees reduce accessibility, particularly
for lower-income households. Currency volatility also complicates tuition and operational planning for private
institutions. (Hambayi, 2020).
CONCEPTUAL AND THEORETIC FRAMEWORK
Conceptual Framework
The conceptual framework of the study characterizes the comprehension of factors of kwacha fluctuation that
influences profitability of MMSMEs , encompassed in the conceptual framework representation is: The
Independent Variable (IV), which classically shows Kwacha Exchange Rate Fluctuations,
Depreciation/Appreciation Volatility and Uncertainty and the Rate of fluctuation (Chigozie, 2021); Moderating
Variables, which includes operations such as procurement, pricing, revenue; Internal Capabilities (RBV)
which depicts intrinsic factors such as Financial management, skills, Use of hedging, risk mitigation and
business diversification (Kuntashula, 2020); Behavioural Factors includes Risk perception; Overreaction or
underreaction, Financial literacy and Herd behaviour Contextual/Contingency Factorsincludes; Business size
Sector and market type (rural/urban); Dependent Variable (DV), includes MSME Profitability such as Net
profit margin, Cash flow performance, Business growth or decline and Sustainability/continuity. Thus, the
conceptual framework is important in the study because it enables the Identifcation of a cause-effect
relationships, it characteristically explains the dissimilarity in impact across MSMEs , together with
highlighting the role of firm-level responses and resilience, the framework can also be used to informs how to
support MSMEs during macroeconomic shocks.
Source: Author 2025
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THEORETIC FRAMEWORK
The study applied the following theories in exploring the influence of kwacha fluctuations on Medium Small
and Micro enterprises MMSMES profitability.
Purchasing Power Parity Theory
The Purchasing Power Parity (PPP) was developed by Gustav Cassel a Swedish economist in the aftermath of
World War I (Cassel, 1918). It is thus an economic theory that establishes that exchange rates between
currencies should adjust to ensure that identical goods cost the same in different countries when priced in a
common currency (Dornbusch, 1987), (Dornbusch, 1987). The theory is important to the current study,
because kwacha fluctuations has an impact on input costs, where numerous MSMEs in Zambia depend on
imported raw materials, equipment, or finished goods, thus if the Kwacha depreciates, the cost of these imports
increases, reducing the purchasing power of the Kwacha, and thereby increasing business expenses, the
common notion with this theory is that a Kwacha leads to comparatively higher prices for imported goods,
which increase prices, consequently affecting customer demand and reduce competitiveness.
Transaction Exposure Theory
The transactional exposure theory was discoursed by Lawrence (1976) (Lawrence, 1976), It was however
expanded by modern financial scholars Madura (2008) and Shapiro (2006) (Madura, 2008), (Shapiro, 2006).
The theory contends that companies are exposed to exchange rate movements, in events where they commit to
future foreign-currency denominated transactions as a consequence when the domestic currency depreciates,
importers pay more, and exporters receive more in local currency therefore affecting profitability (Madura,
2008). The theory is important to the present study because most Zambian MSMEs import raw materials,
goods, or services, which are quoted and priced in foreign currencies, as a result the depreciation in the
Zambian Kwacha (ZMW) has a likelihood of increasing local cost of these transactions, thus squeezing profit
margins, additional Most MSMEs in Zambia do not hedge against foreign exchange risks, perhaps because of
limited financial literacy or lack of access to financial instruments, as a consequence this makes them highly
susceptible to transaction exposure.
Resource Based Theory
The resource based theory traces its roots from Birger Wernerfelt, who introduced the term Resource-Based
View (Wernerfelt, 1984), Jay Barney further ddeveloped the VRIN framework (Barney, 1991 ), further Edith
Penrose added the Theory of the Growth of the Firm (Peteraf, 1993). The theory stresses that a firm’s
capability to attain and sustain competitive advantage lies principally in the internal resources it holds as
opposed to external market positioning. The theory shifts emphasis from external exchange rate shocks to
internal strategic capacityy. The theory is applicable in the present study because the fluctuations of the
kwacha is an external shocks, thus MSMEs with strong internal capabilities such as financial acumen,
supplier networks, inventory control systems are better equipped to adapt and remain profitable, additionally
MSMEs that have currency risk management skills, access to forex accounts, or diversified supply chains may
uphold stable profit margins in spite of exchange rate changes, it is also important because it shows the
importance of financial literacy to MMSMEs , because those MMSMEs who understand how to fine-tune
pricing, source locally, or hedge currency risk are more likely to survive Kwacha depreciation.
RESEARCH METHODS
Research Design
This study adopted a mixed-method research design that incorporated both quantitative and qualitative
approaches. Quantitative data were gathered through structured questionnaires to measure the relationship
between Kwacha exchange rate fluctuations and MSME profitability. Additionally, qualitative data were
collected using semi-structured interviews to gain deeper insights into business owners’ perceptions and lived
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experiences regarding currency volatility. This design provided both statistical reliability and contextual
understanding, thereby enriching the analysis of how exchange rate movements influence MSME performance.
Study Area
The study was conducted in Choma District, which lies between longitudes 26°30′ and 27°30′ East of
Greenwich, and latitudes 16° and 17°45′ South of the equator, situated on the plateau of Zambia’s Southern
Province. Choma serves as the provincial capital and a commercial hub, hosting a diverse range of MMSMEs
engaged in retail, agro-processing, hospitality, manufacturing, and service delivery. The district has
experienced notable economic fluctuations linked to currency volatility, making it a suitable site for examining
the effects of Kwacha exchange rate movements on MSME performance
Source: (Choma Municipal Council, 2021)
Target Population
The target population comprised Micro, Small, and Medium Enterprises (MMSMEs ) operating in Choma
District. These included both formal and informal businesses across various sectors such as agriculture, retail,
services, and manufacturing. The primary respondents were business owners, managers, and financial officers,
as they possess relevant knowledge and experience regarding business operations and financial management.
Sampling Techniques
To ensure inclusivity the study used three distinct sampling techniques, which are Stratified sampling, which
was applied to ensure representation across the three enterprise categories which are micro, small, and
medium, Purposive sampling this was used in the selection of key informants such as experienced business
owners, officials or financial officers, and Simple random sampling, within each stratum to reduce prejudice
(Nanjundeswaraswamy & Divakar, 2021).
Sample Size Calculation
The Cochran’s Formula was used to determine the appropriate sample size, as the total number of MMSMEs
in Choma was unknown and potentially large. The formula is expressed as:
n
0
=
z
x
2
p
x
q
(
2
)
Where:
n₀ = sample size
Z = standard normal deviation corresponding to a 95% confidence level (1.96)
p = estimated proportion of the population with the attribute (0.4)
q = 1 - p = 0.6
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e = desired level of precision (0.04)
Calculation:
Numerator = 3.8416 × 0.24 = 0.921984
Denominator = e² = 0.0016
Therefore, n₀ = 0.921984 / 0.0016 = 576
Thus, the target sample size was 576 participants. However, 297 respondents successfully participated,
representing a 52% response rate.
Data collection Instruments
Primary data were collected using structured physical questionnaires administered to MSME owners and
finance managers. The questionnaires focused on exchange rate awareness, cost structures, sales trends,
sourcing of inputs, and perceived impacts on profitability. Qualitative data were collected through semi-
structured interviews with purposively selected business owners to gain deeper explanatory insights into how
exchange rate fluctuations influence business operations.
Validity and Reliability Test
A pilot test involving 2040 MSME respondents was conducted to assess the clarity, consistency, and
comprehensibility of the questionnaire. Triangulation methods in accessing questionnaires, interviews, and
secondary documents enhanced validity.
Reliability was assessed using Cronbach’s Alpha in SPSS version 26, yielding a coefficient of 0.527 with an
average inter-item covariance of 0.252. Although this value reflects low internal reliability, it was considered
sufficient for exploratory research. The low alpha may be attributed to the small number of items measuring
certain constructs, weak inter-item correlations, or heterogeneity in respondents’ interpretations. Future studies
may improve reliability by refining or expanding the scale items.
Data Analysis
Quantitative data were analyzed using SPSS Version 21. Descriptive statisticsmeans, standard deviations,
frequencies, and percentages were used to summarize the dataset. Inferential statistics, particularly correlation
analysis, were employed to examine the relationships between exchange rate fluctuations and MSME
profitability. Trends in currency movements were also analyzed using Microsoft Excel to visualize sector-
specific effects.
To integrate qualitative and quantitative evidence, a convergent mixed-methods design was adopted.
Qualitative data from interviews were transcribed, coded, and organized into thematic categories that captured
participants’ perceptions, coping mechanisms, and experiences with exchange rate volatility. These emergent
themes were subsequently compared with the quantitative results to explain, validate, or contextualize the
observed statistical patterns.
PRESENTATION OF FINDINGS.
Demographic Characteristics of Participants
The study sought to describe the demographic characteristics of participating Micro, Small and Medium
Enterprise (MSME) operators. Table 1 presents the background information of respondents, including age,
gender, marital status, and education level.
The results indicate that the majority of respondents were aged between 36 and 45 years (36.4%, n=108),
followed by those aged 2635 years (25.3%, n=75) and 4655 years (23.2%, n=69). Only 4% (n=12) were
below 25 years, while 11.1% (n=33) were above 56 years. This suggests that most MSME owners and
managers fall within the economically active age group, characterized by entrepreneurial experience and
managerial maturity (Chirwa & Odhiambo, 2017)
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Gender distribution shows that 59.6% (n=177) of respondents were male, whereas 40.4% (n=120) were
female, indicating a gender imbalance in MSME ownership within the study area. Similar trends have been
reported in other studies, which attribute this disparity to unequal access to start-up capital and sociocultural
norms that limit women’s participation in entrepreneurship (World Bank, 2022). (World Development Report,
2022).
Marital status analysis revealed that the majority of respondents were married (65.7%, n=131), followed by
single (18.9%, n=37), widowed (7.5%, n=15), and divorced (6.0%, n=12), while (2.5%, n=5) were separated.
Regarding education, most respondents had attained primary education (41.3%, n=83), followed by senior
secondary school (33.8%, n=68), and certificate or diploma qualifications (12.6%, n=26). A small proportion
reported no formal education (8%, n=16), while only (0.5%(n=1) possessed an undergraduate degree.
These results imply that the majority of MSME operators have modest educational backgrounds, which may
constrain their financial literacy and management capabilities. This observation is consistent with findings by
Banda and Phiri (2020), who argue that educational attainment plays a pivotal role in effective business
recordkeeping and financial decision-making.
Table 1 Demograhic Chareterlistics
F(n)
%
AGE
<25
12
4
26 35
75
25.3
36- 45
108
36.4
46-55
69
23.2
56+
33
11.1
GENDER
Male
177
59.6
Female
120
40.4
MARITAL STATUS
Single
37
18.9
Married
131
65.7
Divorced
12
6.0
Widowed
15
7.5
Separated
05
2.5
LEVEL OF EDUCATION
No Education Level
16
8.0
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Primary Education
83
41.3
Senior Secondary School
68
33.8
Certificate/Diploma
26
12.6
Degree
01
0.5
Business Dynamics of MSMEs
Table 2 presents the distribution of MSMEs by sector, size, operational focus, and trade orientation. The
majority of MSMEs operate within the retail sector (32.7%, n=96), followed by food processing (21.4%,
n=63), technology services (11.1%, n=33), and liquor business (9.2%, n=27). Other sectors include
manufacturing (5.1%, n=15), healthcare products and services (5.1%, n=15), motor vehicle services (6.1%,
n=18), education (3.1%, n=9), financial services (2.0%, n=6), hospitality (2.0%, n=6), Agro-business (2.0%,
n=6), and music and culture (1.0%, n=3).
In terms of size, macro enterprises accounted for 40.4% (n=120), medium enterprises for 37.4% (n=111), and
established small businesses for 22.2% (n=66). This distribution reflects a vibrant yet unevenly structured
MSME landscape, where most enterprises remain in the growth or stabilization phase, who are the most
impacted by currency fluctuation.
The results further show that 98% (n=291) of the businesses are import-driven, while only 2% (n=6) are
export-oriented, this directly indicates that most MSMEs have high dependence on foreign-sourced materials
and finished goods. Congruently, 72.7% (n=216) reported importing raw or finished products, while 27.3%
(n=81) did not. The dominance of import-driven enterprises suggests that currency volatility affects
procurement and pricing decisions (Bank of Zambia, 2021).
Furthermore, 89.9% (n=267) of respondents transact primarily in Zambian Kwacha, while 10.1% (n=30) use
multiple currencies. Most MSMEs (96%, n=285) engage in domestic trade, while only 4% (n=12) operate
internationally. This limited exposure to export markets reflects both the structural and institutional challenges
faced by MSMEs in integrating into global value chains (International Trade Center, 2022).
Table 2 Business Dynamics
BUSINESS DYNAMICS
N %
BUSINESS TYPE
Manufacturing
15
5.1
Technology Service
33
11.1
Food Processing
63
21.4
Retailing
96
32.7
Education
9
3.1
Financial Services
6
2.0
Health Care Products and services
15
5.1
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Music and Culture
3
1.0
Liquor Business
27
9.2
Motor Vehicle service
18
6.1
Hospitality
6
2.0
Agro-Business
6
2.0
BUSINESS SIZE
Macro
120
40.4
Medium
111
37.4
Established
66
22.2
CORE BUSINESS
Import Driven
291
98.0
Export Driven
6
2.0
CURRENCY
Kwacha
267
89.9
Various Currencies
30
10.1
IMPORTATION OF
GOODS/MATERIALS
Yes
216
72.7
No
81
27.3
Type of Trade
Domestic
285
96.0
International
12
4.0
TOTAL
297
Financial Management and the Effects of Currency Fluctuations
Table 3 presents findings related to financial reporting practices and the perceived impact of currency
fluctuation on MSME performance. The results reveal that most MSMEs do not prepare formal financial
statements (M = 3.35; Mode = 4), this shows that there is massive lack of compliance with basic accounting
standards. In the same way, the majority do not report currency losses (M = 3.09; Mode = 4), highlighting
weak financial control systems and limited risk monitoring practices, which may directly impact business
profitability.
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On the other hand, the results indicate that currency fluctuations significantly increase procurement costs (M =
2.12; Mode = 2), thereby constraining profitability. Moreover, macroeconomic factors particularly inflation
and exchange rate variability were found to influence MSME profitability (M = 2; Mode = 1). The findings
further demonstrate that exchange rate fluctuations have pronounced effects on pricing strategies (M = 2.08;
Mode = 2) and profit margins (M = 1.53; Mode = 1), affecting both short-term and long-term performance (M
= 1.77; Mode = 1).
These findings align with previous research emphasizing that exchange rate instability in developing
economies increases business uncertainty, reduces purchasing power, and undermines enterprise
competitiveness (Belghitar, et al., 2021), (Chitambala, 2019), (Mwansa, 2020) and (World Development
Report, 2022).
Table 3: Financial Management and Effects to currency fluctuations
Median
statistic
Mode
Statistics
Mean
Statistic
Remarks
3.35
4
4
The median, mode and mean
DISAGREES” That most MMSMEs
do not prepare financial statements.
3.09
4
4
The median, mode mean and standard
deviation “Disagree”. That MMSMEs
do not report Currency losses.
2.12
2.00
2.00
The median, mode and standard
“AGREES” that Kwacha Fluctuation
spikes procurement costs of goods and
services.
2
1
2
The median, and mean “AGREES” that
Macro Economic factors influence
profitability of MSMEs
2.08
1
2.00
The median, mean and mode Suggests
that fluctuation of the Kwacha has an
influence on pricing strategies.
1.53
1
1
The median, mean and mode Suggests
that fluctuation of the Kwacha has an
impact on profit margins.
1.77
2
1
The median, mean and mode Suggests
that kwacha fluctuation impacts short-
term and long-term profitability
Procurement and Financial Adjustment Strategies
Table 4 relates to finding on procurement and financial adjustment strategies it was found that to mitigate
exchange rate risks, MSMEs employ various procurement adjustment strategies. The findings indicate that
inventory management is the most common adjustment mechanism (M = 2.00; Mode = 1), while strategies
such as supplier relationship management (M = 3.59; Mode = 4) and hedging (M = 3.59; Mode = 4) are less
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commonly practiced. This preference suggests that MSMEs rely more on operational flexibility than on
complex financial instruments to manage currency risks
Table 4: Procument and Financial Adjustment strategies
Median
statistic
Mode
Statistics
Mean
Statistic
Remarks
3.59
4
4
The median, mode and mean “DISAGREES” That
they do not adjust their procurement procedure
through supplier relationship.
3.59
4
4
The median, mode mean “Disagree”. That MMSMEs
adjust their procurement strategies through Hedging.
2
2.00
1
The median, mode and standard “AGREES” most
MMSMEs adjust their procurement strategies
through inventory management
Financial Strategies
Table 5 relates to finding of financial strategies that most MMSMEs implement, the findings reveal most
MSMEs do not use hedging instruments (M = 3.59; Mode = 4) or foreign accounts and derivatives (M = 4;
Mode = 4). These findings mirror those of (Lungu & Kaubi, 2017) and (Chitambala, 2019) who observed that
the adoption of formal financial risk management mechanisms among MSMEs remains low due to limited
financial literacy, lack of access to derivative markets, and high transaction costs.
Table 5: Financial Strategies
Median
statistic
Mode
Statistics
Mean
Statistic
Remarks
3.59
3.52
4
The median, mean suggest that most MMSMEs
do not either use hedging as a financial strategy
“.
4
4
3.62
The median, mode and standard “DISAGREES”
most MMSMEs most MMSMES do not use
foreign accounts derivatives as a financial
strategy
Sectoral Trends
The dataset captured in table 8 shows financial performance indicators for MSMEs across different sectors
which includes: Manufacturing; Technology Services; Food Processing; Retailing; Education, Financial
Services;Workshop; Health Care; Music and Culture;Liquor Businesss;, Motor Vehicle Service, and
Hospitality, highlighting major key variables such as Gross Profit Margin (GPM), Net Profit Margin (NPM),
Operating Profit Margin (OPM), Return on Assets (ROA), and Profit Growth Rate (PGR), which all aided in
showing how kwacha variation (currency instability) has affected SME performance, The findings were
presented according to sector by sector.
Manufacturing sector shows that the Average GPM is High (5090%), the Average NPM is Very volatile,
which ranged from + 60% to - 100% respectively, the ROA: is generally positive but inconsistent which range
(17% to 90%).
Technology Services sector it has been revealed that Average GPM lies 6080% NPM shows Mostly 2050%,
with a few negative outliers. The ROA is Positive in most cases (20130%), indicating efficient asset
utilization, while the PGR reveals Mixed results with several negative growth rates.
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The Food Processing sector reveals that the GPM Ranges widely (3095%), while the NPM vary Often
between 2060%, with outliers below zero, The ROA is Mostly positive, irregularly above 100%, while the
PGR is Highly inconsistent (100% to +300%).
The Retailing sector reveals a GPM of 4080%, with NPM which has revealed a Frequency of 2060%,
despite of some business recording losses, the ROA is Very high in several cases which reveals (>200%),
indicative of rapid turnover. On the other hand, the PGR is Wide spread (90% to +1400%), showing
volatility.
The Education and Financial Services recorded a GPM of 100% because there are no COGS, whereas the
NPM varies Between 2080%. The ROA is Very high (up to 800%), which can be attributed to low asset
intensity, on the other hand the PGR Fluctuated based on fee collection cycles or loan repayment trends.
Finally, Workshops, Motor Services, and Hospitality recorded a GPM which is High (7095%), an NPM
which varied between 3080%. ROA Often where high (>200%), indicating fast asset turnover, The PGR on
the other hand was generally negative, suggesting profitability declined despite strong margins.
The cross sectoral trends can be summarized table 6
Table 6: cross sectoral trends
Indicator
Observation
Implication
Gross Profit Margin
(GPM)
Generally strong across MSMEs (50
90%)
Indicates robust revenue generation before
accounting for costs.
Net Profit Margin
(NPM)
Highly volatile; negative in many cases
Currency instability increases costs, reducing
profitability.
ROA
Positive but inconsistent; some sectors
show extreme values
Efficiency varies widely; MSMEs with better
cost control fare better.
PGR (Profit
Growth Rate)
Extreme variation (100% to +1400%)
Kwacha fluctuations directly disrupt growth
stability.
The finding suggests that Kwacha variation exerts a dual effect on MSMEs , where Negative impact Import-
dependent MSMEs (manufacturing, retail, hospitality) suffer from increased input costs, reduced margins, and
erratic growth, and Positive/neutral impact where Service-oriented and locally sourcing MSMEs (education,
finance, workshops) exhibited stronger margins and resilience to currency shocks.
Table 7 Key for Sectoral Trend Table
Code
Variable
NP
Net Profit
PP
Present Profit
CP
Current Profit
GP
Gross Profit
GPM
Gross Profit Margin (%)
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NPM
Net Profit Margin (%)
OP
Operating Profit
OPM
Operating Profit Margin (%)
ROA
Return on Assets (%)
PGR
Profit Growth Rate (or Performance
Growth Rate)
Table 8: Sectoral Trend Table
BUSINESS TYPE
NP
PP
CP
GP
GPM
NPM
OP
OPM
ROA
PGR
Manufacturing
345000
184600
175,244
425000
75.8929
61.6071
345000
61.607
57.5
-5.07
Manufacturing
100200
67890
99878
198655
56.7586
28.6286
100200
28.629
42.64
47.12
Manufacturing
71826
45670
66870
197615
49.1739
17.8729
71826
17.873
46.05
46.42
Manufacturing
40955
120000
989567
940000
47
2.04775
40955
2.0478
0.819
724.6
Manufacturing
56556
567859
23400
94056
60.5695
36.4205
56556
36.421
87.59
-95.9
Manufacturing
29868
41780
33216
59546
56.5898
28.3852
29868
28.385
11.63
-20.5
Manufacturing
129800
86251
26400
257780
96.2261
48.4527
129800
48.453
66.56
-69.4
Manufacturing
-4913
28900
30500
218221
93.0497
-2.0949
-4913
-2.095
-4.81
5.536
Manufacturing
150200
95678
231456
239100
92.7139
58.2419
150200
58.242
75.05
141.9
Manufacturing
1985
34678
28700
37111
29.792
1.59352
1985
1.5935
0.296
-17.2
Manufacturing
-145903
29500
44560
75553
62.3256
-120.36
-145903
-120.4
-17.2
51.05
Manufacturing
41034
123600
21570
77898
79.4878
41.8714
41034
41.871
90.73
-82.5
Manufacturing
154010
23490
12220
176110
79.4075
69.4427
154010
69.443
62.71
-48
Manufacturing
-238890
23310
21650
-12190
-
36.3881
-713.1
-238890
-713.1
-52.2
-7.12
Manufacturing
-265190
145600
225670
-11190
-
4.77002
-113.04
-265190
-113
-757
54.99
Manufacturing
-41586
78960
22789
124203
35.9311
-12.031
-41586
-12.03
-33
-71.1
Technology Service
25110
24500
19660
52000
68.4211
33.0395
25110
33.039
45.32
-19.8
Technology Service
36790
35780
22690
53580
59.8258
41.0786
36790
41.079
66.89
-36.6
Technology Service
36890
24560
34780
92780
87.8016
34.9106
36890
34.911
102.5
41.61
Technology Service
18770
22450
35180
48550
60.6875
23.4625
18770
23.463
18.77
56.7
Technology Service
43220
87950
45670
66110
57.0455
37.294
43220
37.294
64.51
-48.1
Technology Service
115685
75690
66980
155535
60.5454
45.0329
115685
45.033
74.2
-11.5
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Technology Service
29230
22500
35980
42120
62.8844
43.6399
29230
43.64
48.88
59.91
Technology Service
53540
82500
34560
80320
63.746
42.4921
53540
42.492
155.2
-58.1
Technology Service
25820
35690
26780
53710
71.045
34.1534
25820
34.153
24.52
-25
Technology Service
-3000
13500
17890
32890
31.0928
-2.8361
-3000
-2.836
-25
32.52
Technology Service
25360
17690
25670
44810
66.2968
37.5203
25360
37.52
201.3
45.11
Technology Service
50712
45100
28100
102401
80.0696
39.6528
50712
39.653
33.79
-37.7
Technology Service
27655
21780
22190
49665
30.6991
17.0942
27655
17.094
27.52
1.882
Technology Service
56342
101267
89760
122131
68.2677
31.4936
56342
31.494
2.809
-11.4
Technology Service
89300
78960
29340
179060
58.5623
29.2059
89300
29.206
133.5
-62.8
Technology Service
16234
15680
14570
26884
75.116
45.359
16234
45.359
64.45
-7.08
Technology Service
6021
25790
21890
81910
54.281
3.99006
6021
3.9901
20.22
-15.1
Technology Service
7250
21220
25900
41030
63.5238
11.2246
7250
11.225
15.83
22.05
Technology Service
680430
450000
779819
736110
94.1582
87.036
680430
87.036
136.1
73.29
Technology Service
3110
29800
15980
25890
56.6893
6.80972
3110
6.8097
7.974
-46.4
Technology Service
29980
69800
78240
58880
46.8529
23.8561
29980
23.856
5.996
12.09
Technology Service
12070
13960
18900
26770
58.6162
26.4287
12070
26.429
63.53
35.39
Technology Service
22100
18000
19890
39990
58.8175
32.5048
22100
32.505
96.55
10.5
Technology Service
6620
22780
14560
33400
81.068
16.068
6620
16.068
51.8
-36.1
Technology Service
2810
15690
16100
13020
65.7576
14.1919
2810
14.192
10.08
2.613
Technology Service
5200
22900
15600
32100
18.0337
2.92135
5200
2.9213
19.48
-31.9
Technology Service
68410
78000
198000
98210
78.568
54.728
68410
54.728
19.51
153.8
Technology Service
28544
22900
10900
44244
66.3328
42.7946
28544
42.795
106.9
-52.4
Technology Service
13110
18900
20600
28000
49.3218
23.0932
13110
23.093
84.04
8.995
Technology Service
80940
86700
22560
96610
76.9188
64.4427
80940
64.443
40.47
-74
Technology Service
-96400
19800
21600
71400
42.7545
-57.725
-96400
-57.72
-54.2
9.091
Technology Service
172690
66800
59980
195670
64.0135
56.4956
172690
56.496
38.38
-10.2
Technology Service
31960
260000
12356
133250
86.1345
20.6593
31960
20.659
15.15
-95.2
Technology Service
54600
20500
21500
69100
72.0542
56.9343
54600
56.934
47.48
4.878
Food Processing
2100
15600
10500
4900
21.7778
9.33333
2100
9.3333
67.74
-32.7
Food Processing
6200
15800
17800
20300
44.2266
13.5076
6200
13.508
59.05
12.66
Food Processing
6900
6700
4800
14700
49.6622
23.3108
6900
23.311
65.09
-28.4
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Food Processing
2000
2300
1400
5700
54.2857
19.0476
2000
19.048
66.67
-39.1
Food Processing
9700
2500
6800
11200
58.9474
51.0526
9700
51.053
312.9
172
Food Processing
166800
255000
125600
205700
57.7971
46.8671
166800
46.867
33.36
-50.7
Food Processing
66100
125800
22190
79600
47.1844
39.182
66100
39.182
33.05
-82.4
Food Processing
94800
20600
40800
117400
46.96
37.92
94800
37.92
82.65
98.06
Food Processing
12300
15900
10600
22800
63.8655
34.4538
12300
34.454
58.57
-33.3
Food Processing
30900
11280
14000
45500
67.1091
45.5752
30900
45.575
283.5
24.11
Food Processing
60100
12190
50199
100100
82.2514
49.3837
60100
49.384
30.05
311.8
Food Processing
76200
20000
21300
94000
81.3149
65.917
76200
65.917
38.29
6.5
Food Processing
83092
91000
63400
129000
51.4765
33.1572
83092
33.157
68.33
-30.3
Food Processing
73300
22600
19600
130000
82.9611
46.7773
73300
46.777
94.22
-13.3
Food Processing
50500
45600
60100
106100
51.58
24.5503
50500
24.55
16.83
31.8
Food Processing
122742
22100
86000
201642
93.0512
56.6414
122742
56.641
24.55
289.1
Food Processing
-40255
80000
76900
74645
24.8448
-13.398
-40255
-13.4
-6.71
-3.88
Food Processing
1294400
750000
1259000
1500000
30
25.888
1294400
25.888
18.49
67.87
Food Processing
382200
450000
766900
536100
95.9034
68.3721
382200
68.372
63.7
70.42
Food Processing
54320
250900
140600
110120
32.8129
16.1859
54320
16.186
7.884
-44
Food Processing
435199
225100
145780
550099
70.8983
56.0896
435199
56.09
435.2
-35.2
Food Processing
10300
6000
7000
18100
80.0885
45.5752
10300
45.575
153.7
16.67
Food Processing
16671
30000
36000
50471
50.1351
16.56
16671
16.56
13.89
20
Food Processing
14900
16000
14000
19400
84.7162
65.0655
14900
65.066
115.5
-12.5
Food Processing
26539
22900
4899
44439
66.5364
39.7356
26539
39.736
74.55
-78.6
Food Processing
4510
5000
4500
19110
53.2312
12.5627
4510
12.563
57.82
-10
Food Processing
406800
250000
450000
565800
84.726
60.9164
406800
60.916
45.2
80
Food Processing
-2200
22900
0
37000
41.7607
-2.4831
-2200
-2.483
-1.47
-100
Food Processing
76500
30000
45000
99000
62.6979
48.4484
76500
48.448
38.25
50
Food Processing
8500
28000
10000
16500
74.6606
38.4615
8500
38.462
170
-64.3
Food Processing
1599000
1600000
1600000
3389000
59.6865
28.1613
1599000
28.161
159.9
0
Food Processing
0
16000
22000
44600
66.8666
0
0
0
0
37.5
Food Processing
244701
200000
255000
422701
94.8611
54.9149
244701
54.915
48.94
27.5
Food Processing
31100
18900
18900
47800
71.6642
46.6267
31100
46.627
111.1
0
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Food Processing
90800
12100
200000
190800
85.0267
40.4635
90800
40.463
30.27
1553
Food Processing
24700
168000
7800
37300
59.3005
39.2687
24700
39.269
54.89
-95.4
Food Processing
9300
14600
17900
153900
86.9492
5.25424
9300
5.2542
4.047
22.6
Food Processing
56300
30000
45600
111900
55.95
28.15
56300
28.15
16.09
52
Food Processing
18330
129000
113200
32930
53.1729
29.5979
18330
29.598
14.66
-12.2
Food Processing
-106800
14600
17600
37900
56.745
-159.9
-106800
-159.9
-534
20.55
Food Processing
-14000
14400
22100
-7500
-
6.60211
-12.324
-14000
-12.32
-7
53.47
Food Processing
50090
49000
45000
120090
60.018
25.0337
50090
25.034
74.87
-8.16
Food Processing
639100
78000
80000
651500
97.6762
95.8171
639100
95.817
127.8
2.564
Food Processing
683990
89900
89000
762100
96.5906
86.6907
683990
86.691
684
-1
Food Processing
588900
78000
77311
744400
82.8031
65.5061
588900
65.506
560.9
-0.88
Food Processing
20000
89900
10600
37000
36.0273
19.4742
20000
19.474
13.33
-88.2
Food Processing
101810
10100
55800
119700
61.4792
52.2907
101810
52.291
95.33
452.5
Food Processing
199100
100600
150000
366100
65.8453
35.8094
199100
35.809
33.18
49.11
Food Processing
546100
121000
200000
681100
89.6184
71.8553
546100
71.855
94.94
65.29
Food Processing
106000
35000
45000
151000
88.3041
61.9883
106000
61.988
82.17
28.57
Food Processing
260390
22780
144000
283790
80.9695
74.2931
260390
74.293
391.6
532.1
Food Processing
35300
66500
25800
45800
68.5629
52.8443
35300
52.844
186.8
-61.2
Food Processing
75700
180000
228999
131700
80.3539
46.1867
75700
46.187
75.7
27.22
Food Processing
125211
174000
189000
143961
86.4735
75.2108
125211
75.211
62.61
8.621
Food Processing
32900
22800
10500
100900
82.7728
26.9893
32900
26.989
26.17
-53.9
Food Processing
194500
88900
102300
214400
32
29.0299
194500
29.03
19.45
15.07
Food Processing
1281400
166800
225700
1511000
59.4882
50.4488
1281400
50.449
320.4
35.31
Food Processing
6330500
225100
300000
6552100
98.1147
94.7963
6330500
94.796
5510
33.27
Food Processing
610000
125000
141600
1399000
86.4114
37.6776
610000
37.678
10.76
13.28
Food Processing
2975820
224800
115000
3275820
93.5681
84.9991
2975820
84.999
1807
-48.8
Food Processing
94580
161200
132900
138890
86.16
58.6725
94580
58.672
11.26
-17.6
Food Processing
131000
221700
21100
117700
34.5364
38.439
-13300
-3.903
69.09
-90.5
Food Processing
433200
223000
144800
466400
67.5942
62.7826
433200
62.783
228.1
-35.1
Food Processing
320100
115100
20705
541800
81.1321
47.9335
320100
47.934
278.6
-82
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Retailing
57000
221900
400000
156000
43.9437
16.0563
57000
16.056
34.55
80.26
Retailing
46500
20000
200000
68700
40.7232
27.5637
46500
27.564
39.81
900
Retailing
130800
212900
40100
149500
56.3938
49.3399
130800
49.34
114
-81.2
Retailing
91630
11400
221900
101600
53.5865
48.3281
91630
48.328
407.2
1846
Retailing
10689
14490
22700
27589
23.641
9.15938
10689
9.1594
13.7
56.66
Retailing
75500
20000
20000
98000
59.3939
45.7576
75500
45.758
50.33
0
Retailing
32700
212300
22600
96800
81.413
27.5021
32700
27.502
113.1
-89.4
Retailing
70900
22100
33100
95000
43.8394
32.718
70900
32.718
59.63
49.77
Retailing
126450
15680
22780
142050
86.0909
76.6364
126450
76.636
508
45.28
Retailing
40000
22900
121000
73000
39.5236
21.6567
40000
21.657
47
428.4
Retailing
50000
65000
91000
1000000
40
2
50000
2
7.143
40
Retailing
845000
600000
750000
1000000
28.5714
24.1429
845000
24.143
18.78
25
Retailing
610000
125000
114800
1260000
86.8966
42.069
610000
42.069
119.6
-8.16
Retailing
400000
1266000
1289000
1845000
92.25
20
400000
20
1.6
1.817
Retailing
125000
2251000
1264000
250000
50
25
125000
25
20.83
-43.8
Retailing
58100
51200
165700
124900
35.6857
16.6
58100
16.6
46.11
223.6
Retailing
129000
12260
99800
206600
62.3792
38.9493
129000
38.949
28.91
714
Retailing
91700
15560
10000
214200
66.9166
28.6473
91700
28.647
22.59
-35.7
Retailing
64700
22100
336100
110600
35.6085
20.8307
64700
20.831
15.53
1421
Retailing
27571
46700
221300
83471
26.936
8.89714
27571
8.8971
6.037
373.9
Retailing
75090
55100
66300
95600
48.8503
38.37
75090
38.37
51.71
20.33
Retailing
11600
44110
46200
22500
49.8891
25.7206
11600
25.721
51.56
4.738
Retailing
21500
20000
21000
34000
52.1472
32.9755
21500
32.975
59.72
5
Retailing
45100
22600
33500
66700
40.012
27.0546
45100
27.055
56.02
48.23
Retailing
3100
2100
4500
4400
41.9048
29.5238
3100
29.524
91.18
114.3
Retailing
2345
2000
2000
3300
73.3333
52.1111
2345
52.111
78.69
0
Retailing
490
2500
3100
600
28.5714
23.3333
490
23.333
490
24
Retailing
8100
2100
1900
9600
32.4324
27.3649
8100
27.365
81
-9.52
Retailing
40000
2100
3100
66100
74.2697
44.9438
40000
44.944
330.6
47.62
Retailing
3110
1400
2100
6310
39.6855
19.5597
3110
19.56
140.7
50
Retailing
36100
15000
15000
40600
54.0613
48.0692
36100
48.069
135.2
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Retailing
28944
30000
29000
44000
66.1654
43.5248
28944
43.525
12.58
-3.33
Retailing
7100
12500
14510
9000
46.1538
36.4103
7100
36.41
338.1
16.08
Retailing
6600
6800
4000
10600
47.9638
29.8643
6600
29.864
314.3
-41.2
Retailing
2140
1500
1500
4140
35.8131
18.5121
2140
18.512
142.7
0
Retailing
1900
1400
1260
4000
34.4828
16.3793
1900
16.379
146.2
-10
Retailing
6290
4510
2100
8590
68.2288
49.9603
6290
49.96
251.6
-53.4
Retailing
6830
10000
10000
8790
52.9837
41.1694
6830
41.169
30.82
0
Retailing
6100
2100
2300
12000
54.2986
27.6018
6100
27.602
40.67
9.524
Retailing
3500
3100
2100
10600
70.1987
23.1788
3500
23.179
29.17
-32.3
Retailing
3200
2000
4500
4700
70.1493
47.7612
3200
47.761
94.12
125
Retailing
4400
9100
8000
5600
56
44
4400
44
40.74
-12.1
Retailing
14200
2100
4100
16300
77.619
67.619
14200
67.619
346.3
95.24
Retailing
8500
2100
2500
10600
70.1987
56.2914
8500
56.291
850
19.05
Retailing
7000
7500
10500
9500
45.2381
33.3333
7000
33.333
280
40
Retailing
6200
5000
5000
7600
72.381
59.0476
6200
59.048
79.49
0
Retailing
5800
2100
5600
7900
71.8182
52.7273
5800
52.727
82.86
166.7
Retailing
7900
10000
15000
10400
49.5238
37.619
7900
37.619
98.75
50
Retailing
40000
65000
75000
85000
38.4615
18.0995
40000
18.1
330.6
15.38
Retailing
3300
2500
3100
5600
44.0945
25.9843
3300
25.984
220
24
Retailing
12200
19000
22000
15700
46.8657
36.4179
12200
36.418
58.1
15.79
Retailing
11000
15600
22100
14100
90.3846
70.5128
11000
70.513
85.94
41.67
Retailing
10700
26500
21450
13200
59.7285
48.4163
10700
48.416
62.57
-19.1
Retailing
23100
22100
4650
42000
53.9846
29.6915
23100
29.692
104.5
-79
Retailing
1300
5600
12590
1800
58.0645
41.9355
1300
41.935
260
124.8
Retailing
1900
2600
2600
4000
29.6296
14.0741
1900
14.074
126.7
0
Retailing
2040
1000
1000
8740
41.0329
9.57746
2040
9.5775
9.067
0
Retailing
10200
10000
56000
22600
36.2761
16.3724
10200
16.372
32.9
460
Retailing
670550
44900
22600
672000
96.4132
96.2052
670550
96.205
3034
-49.7
Retailing
827430
225000
450000
1473430
86.7744
48.7297
827430
48.73
827.4
100
Retailing
989920
560000
750000
1035820
82.3648
78.715
989920
78.715
447.7
33.93
Retailing
949801
225600
114700
1395000
86.3243
58.7748
949801
58.775
658.7
-49.2
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Retailing
1064801
144900
22190
1234651
84.5767
72.9416
1064801
72.942
469.3
-84.7
Retailing
61315
251000
521000
83445
40.5142
29.7696
61315
29.77
8.891
107.6
Retailing
25600
21670
24560
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28.2249
15.1479
25600
15.148
73.14
13.34
Retailing
111700
121300
121690
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87.0079
62.8234
111700
62.823
22.34
0.322
Retailing
525800
221001
121000
647600
96.6567
78.4776
525800
78.478
78.72
-45.2
Retailing
251000
221780
143600
476000
68.2927
36.0115
251000
36.011
36.93
-35.3
Retailing
219430
221450
359000
444430
66.5314
32.8488
219430
32.849
49.19
62.11
Retailing
69690
235679
129090
136690
83.9104
42.7808
69690
42.781
261
-45.2
Retailing
108440
225600
114900
120000
84.4476
76.3125
108440
76.312
1033
-49.1
Retailing
74360
226100
145900
94360
80.8569
63.7189
74360
63.719
70.16
-35.5
Retailing
335709
127000
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1780709
88.7294
16.7277
335709
16.728
215.8
-90
Retailing
37900
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48.7493
22.0477
37900
22.048
3.357
-5.06
Retailing
1122500
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1237400
84.8115
76.9363
1122500
76.936
499.1
11.27
Retailing
72900
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81.8704
59.8031
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59.803
8.1
1359
Retailing
75300
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46.1321
35.5189
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35.519
59.81
8.202
Retailing
32800
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245700
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51.4201
13.9042
32800
13.904
32.8
6.826
Retailing
161600
345100
448900
935900
80.8902
13.9672
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68.366
73.12
30.08
Retailing
283200
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65.2995
43.5023
283200
43.502
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-26.9
Retailing
144231
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93.5479
41.435
144231
41.435
64.36
53.25
Retailing
121580
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133150
85.539
78.1061
121580
78.106
99.57
-77.6
Retailing
124510
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92.652
83.173
124510
83.173
562.6
-93.3
Retailing
133500
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55.3942
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55.394
60.41
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Retailing
48100
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73.9953
56.8558
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56.856
5.726
61.9
Retailing
11770
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19.7848
9.59485
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9.5948
14.01
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Retailing
41000
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29.214
17.9039
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17.904
41
-24
Retailing
7850
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41.5138
36.0092
7850
36.009
61.33
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Retailing
1145
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33.3333
25.4444
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25.444
38.17
16.67
Retailing
4700
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10.307
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10.307
23.5
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Retailing
3660
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13.6567
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13.657
17.43
-5.56
Retailing
87800
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80.9937
69.2429
87800
69.243
127.8
49.77
Retailing
88660
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44120
109960
90.7261
73.1518
88660
73.152
132.8
99.37
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168220
21090
23100
183000
89.2248
82.0185
168220
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9.531
Education
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Education
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27.961
34.76
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Education
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Education
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Education
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76.8
Education
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39.036
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Education
153886
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92.0548
61.0901
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61.09
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-86.4
Education
162809
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61.6663
54.1759
162809
54.176
111.6
-4.98
Financial Services
775000
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77.5
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77.5
139.6
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Financial Services
1000000
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20
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Financial Services
1204400
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68.82
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Financial Services
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81.776
1022200
81.776
813.9
10.86
Financial Services
300000
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19.1083
300000
19.108
13.3
7.064
Workshop
164010
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85.3153
65.5358
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65.536
105.4
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Workshop
83510
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49.1814
83510
49.181
83.68
45.31
Workshop
72750
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57.3964
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57.396
47.99
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Workshop
100810
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83.3701
74.1795
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74.18
61.06
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Workshop
115100
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67.2313
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Workshop
77620
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49.878
141.6
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Workshop
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85.0299
55.3892
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55.389
117.5
12.31
Workshop
32800
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48.3063
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48.306
145.1
843.4
Workshop
75100
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84.0487
48.1102
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48.11
134.1
563.7
Workshop
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69.9119
87320
69.912
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249.1
Workshop
129900
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54.8029
46.5591
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46.559
240.6
298.5
Workshop
60500
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67.5325
28.0612
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28.061
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Workshop
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168
-41.7
Workshop
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50.5905
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50.591
251
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Workshop
86500
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57.2848
86500
57.285
75.09
-27.6
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Workshop
96500
21670
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92.0809
48.2138
96500
48.214
428.9
-46.9
Workshop
66040
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81.4936
56.6867
66040
56.687
56.69
-46.3
Workshop
62400
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44.0989
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70.19
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Workshop
45700
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162.5
Workshop
71900
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56.838
80.25
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Workshop
61200
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30.3571
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54.8
-80.6
Workshop
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37.0134
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Workshop
91500
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44.6124
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-33.2
Workshop
74600
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33.14
10.88
Workshop
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35.3823
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Workshop
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386
-24.4
Workshop
92500
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Workshop
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27.3177
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Workshop
69900
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59.4388
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-46
Workshop
74230
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36.7839
74230
36.784
82.75
-28.5
Workshop
35200
216100
104500
37900
100
92.876
35200
92.876
1408
-51.6
Health Care Products
and services
45500
37500
45700
68100
40.7784
27.2455
45500
27.246
68.22
21.87
Health Care Products
and services
25600
22750
56700
45600
22.664
12.7237
25600
12.724
142.2
149.2
Health Care Products
and services
44100
66700
79800
66600
30.7337
20.3507
44100
20.351
257.6
19.64
Health Care Products
and services
30410
75600
75900
49150
40.5528
25.0908
30410
25.091
289.6
0.397
Health Care Products
and services
30300
17800
21600
67300
29.8978
13.4607
30300
13.461
137.1
21.35
Health Care Products
and services
3910
21000
22000
5910
35.7965
23.6826
3910
23.683
558.6
4.762
Health Care Products
and services
6700
23000
22500
9200
23.4694
17.0918
6700
17.092
209.4
-2.17
Health Care Products
and services
18200
17650
22100
31000
21.3499
12.5344
18200
12.534
97.59
25.21
Health Care Products
and services
49780
22800
49700
80400
26.6755
16.5163
49780
16.516
401.5
118
Health Care Products
and services
250000
225100
1415780
500000
25
12.5
250000
12.5
55.56
529
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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Health Care Products
and services
120600
125700
267300
187300
62.4333
40.2
120600
40.2
536
112.6
Health Care Products
and services
242000
223150
145700
462000
38.1188
19.967
242000
19.967
171.4
-34.7
Health Care Products
and services
23400
22570
14800
46100
28.598
14.5161
23400
14.516
16.57
-34.4
Health Care Products
and services
26730
220400
116350
38950
63.128
43.3225
26730
43.323
17.68
-47.2
Health Care Products
and services
33700
22010
114600
56800
31.9101
18.9326
33700
18.933
234.8
420.7
Health Care Products
and services
63000
225100
116700
90800
44.2064
30.6719
63000
30.672
54.22
-48.2
Health Care Products
and services
17300
144000
14400
35300
24.4291
11.9723
17300
11.972
76.89
-90
Music and Culture
105400
26700
102100
129000
100
81.7054
105400
81.705
92.29
282.4
Music and Culture
14000
22100
26100
37100
32.0657
12.1003
14000
12.1
14.03
18.1
Music and Culture
54200
221200
114700
77000
65.9811
46.4439
54200
46.444
47.5
-48.1
Music and Culture
115400
21020
115600
206100
100
55.9922
124900
60.602
52.17
450
Liquor Business
6300
25600
14500
20800
31.3253
9.48795
6300
9.488
14.13
-43.4
Liquor Business
76900
21500
29500
101500
47.0561
35.6514
76900
35.651
605.5
37.21
Liquor Business
16370
21500
218700
26770
55.8057
34.1255
16370
34.125
88.01
917.2
Liquor Business
45600
221200
114700
66700
30.9226
21.1405
45600
21.14
27.18
-48.1
Liquor Business
59300
28700
22500
103800
34.0216
19.4363
59300
19.436
222.1
-21.6
Liquor Business
62150
24600
11470
77700
77.8557
62.2745
62150
62.275
291.8
-53.4
Liquor Business
8100
14800
17650
25900
22.6795
7.09282
8100
7.0928
36.65
19.26
Liquor Business
72780
22100
15100
95480
81.8166
62.365
72780
62.365
491.8
-31.7
Liquor Business
2600
26000
15000
12200
45.6929
9.73783
2600
9.7378
56.77
-42.3
Liquor Business
8700
22560
17680
155700
87.4719
4.88764
8700
4.8876
9.898
-21.6
Liquor Business
5600
22100
17100
7900
50.3185
35.6688
5600
35.669
39.72
-22.6
Liquor Business
-500
8500
2500
1700
16.1905
-4.7619
-500
-4.762
-8.33
-70.6
Liquor Business
49600
16800
10750
88800
41.1492
22.9842
49600
22.984
221.4
-36
Liquor Business
53130
14700
92030
80.5867
46.5236
53130
46.524
234.1
-100
Liquor Business
83800
21500
201100
106500
48.1465
37.8843
83800
37.884
72.74
835.3
Liquor Business
52500
66909
22199
97000
30.7254
16.6297
52500
16.63
99.43
-66.8
Liquor Business
68700
26400
88500
111500
50.9831
31.4129
68700
31.413
88.3
235.2
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Liquor Business
252750
218400
18700
275450
95.6756
87.7909
252750
87.791
221.3
-91.4
Liquor Business
3700
22500
22799
6700
31.6038
17.4528
3700
17.453
5.481
1.329
Liquor Business
82160
22100
17100
94630
81.0188
70.3425
82160
70.342
71.94
-22.6
Liquor Business
28200
22600
21700
48200
42.2067
24.6935
28200
24.694
190.5
-3.98
Liquor Business
6431
9200
6900
8900
50
36.1292
6431
36.129
13.95
-25
Liquor Business
64870
14700
17800
85000
42.2465
32.2416
64870
32.242
287
21.09
Liquor Business
4200
22100
4700
5600
31.4607
23.5955
4200
23.596
62.69
-78.7
Liquor Business
54180
14000
28000
64280
81.6773
68.8437
54180
68.844
238.7
100
Motor Vehicle service
151100
16499
114200
200000
92.2935
69.7277
151100
69.728
994.1
592.2
Motor Vehicle service
5900
15200
21400
7900
38.7255
28.9216
5900
28.922
7.564
40.79
Motor Vehicle service
77200
22600
13500
99900
46.0157
35.5596
77200
35.56
412.8
-40.3
Motor Vehicle service
7910
14700
12000
10120
46.8519
36.6204
7910
36.62
101.4
-18.4
Motor Vehicle service
12900
10500
15800
31000
37.9902
15.8088
12900
15.809
58.37
50.48
Motor Vehicle service
29000
25800
14400
46600
27.623
17.1903
29000
17.19
185.9
-44.2
Motor Vehicle service
27800
14100
86100
42400
62.8148
41.1852
27800
41.185
24.34
510.6
Motor Vehicle service
6700
22100
14300
18800
17.9904
6.41148
6700
6.4115
40.12
-35.3
Motor Vehicle service
3300
14700
26000
10800
48.8688
14.9321
3300
14.932
14.6
76.87
Motor Vehicle service
36500
22599
18799
59400
40.4082
24.8299
36500
24.83
207.4
-16.8
Hospitality
186200
221100
23100
201000
93.0125
86.1638
186200
86.164
33.25
-89.6
Hospitality
313800
201700
34100
333900
95.1282
89.4017
313800
89.402
31.38
-83.1
Hospitality
120700
219000
147999
165200
87.4074
63.8624
120700
63.862
38.29
-32.4
Hospitality
97100
216700
106700
153200
86.5537
54.8588
97100
54.859
8.503
-50.8
Hospitality
93900
221000
216700
180600
88.7906
46.1652
93900
46.165
81.79
-1.95
Agro-Business
113500
221200
15600
129100
36.8752
32.4193
113500
32.419
256.8
-92.9
Agro-Business
47500
29200
14000
67600
75.3623
52.9543
47500
52.954
254
-52.1
Agro-Business
53190
19100
10500
79290
39.3109
26.3708
53190
26.371
374.6
-45
Agro-Business
18100
20500
18100
38200
17.6688
8.37188
18100
8.3719
112.4
-11.7
Agro-Business
47480
22201
18700
59300
29.4585
23.5867
47480
23.587
401.7
-15.8
Correlation Analysis
Correlation analysis was conducted to examine the relationship between key variables, including
macroeconomic factors, business type, size, and strategic responses. The correlation analysis yielded several
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significant relationships that provide deeper insights into how MMSMEs respond to macroeconomic
dynamics, particularly currency fluctuations.
HO
1
tests the relationship between Macroeconomic factors business type which were insignificantly correlated
(r = -0.009, p > 0.05), the analysis underscores that MMSMEs across different sectors experience relatively
uniform exposure to macroeconomic shocks.
HO
2
tests the relationship between currency fluctuation and pricing strategies The analysis found a positive
and statistically significant correlation between the two where (r = 0.040, p < 0.05), the analysis suggests that
exchange rate volatility exerts a direct influence on how MMSMEs set or adjust their prices.
HO
3
tests the relationship between exchange rate volatility and business type where a negative correlation
between exchange rate volatility and business type (r = 0.0856, p < 0.05), was observed which indicates that
the impact of currency fluctuations on profitability differs by sector.
HO
4
tests the relationship between hedging and supplier relationships where the findings reveal a strong
positive correlation (r = 0.42, p < 0.005),the analysis suggests that firms that maintain strong supplier networks
are more likely to employ financial hedging mechanisms
HO
5
tests the relationship between hedging and business size, where the test reveals a positive and significant
relationship (r = 0.1623, p < 0.005) the analysis suggests that, that larger MMSMEs are more likely to employ
hedging as a financial risk management tool compared to smaller enterprises.
HO
6
tests the relationship between level of education and hedging. The results indicated a positive correlation
(r = 0.0856, p = 0.011), suggesting a weak but statistically significant association between the two variables at
the 0.05 significance level. This implies that as the level of education increases, the likelihood of engaging in
hedging practices also tends to rise, although the strength of this relationship is relatively low.
Table 9: Correlation Analysis
Variables Correlated
Correlation
Coefficient
Sig (2
Tailed)
Significate at
Comment
HO
1
Business type and
Macroeconomic Factors influence
profitability
-.009
.089
0.05
Insignificant Reject null
hypothesis.
HO
2
fluctuation influence
pricing strategies and Businesses
0.040
0.046
0.05
Significant accept null
hypothesis.
HO
3
- Importation of Materials
and
fluctuation influence pricing
strategies
0.36
0.00
.05
significant accept
alternative null
hypothesis.
HO
4-
Hedging & Supplier
Relationships
0.42
0.050
.005
significant accept
alternative null
hypothesis.
HO
5 -
Hedging & Business Size
0.1623
0.005
0.005
significant accept
alternative null
hypothesis.
HO
6
Level of Education and
Heding.
0.0856
0.011
.0 5
Significant accept
alternative null
hypothesis.
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DISCUSSION OF FINDINGS
Demographic Characteristics and Managerial Implications
The study constituted of a verity of age groups, the majority of respondents were aged between 36 and 45
years (36.4%), followed by 2635 years (25.3%) and 4655 years (23.2%), similar to the findings of (Chirwa
& Odhiambo, 2017). As such this demographic profile was significant, as it influenced strategic responses to
kwacha fluctuations.
The study revealed a Gender distribution more predominant of male operators (59.6%) compared to females
(40.4%), these findings are consistent with (Common Market for Eastern and Southern Africa, 2020),
(Ministry of Finance, 2020), and (World Development Report, 2022) who all reported gender imbalance
consistent with sociocultural and financial access constraints. Gender disparities in access to start-up capital
and business networks has the likelihood of influencing MSMEs’ capacity to adapt to currency shocks. Male-
dominated sectors such as manufacturing, retail, and workshops might have better access to capital, which
could explain their ability to sustain high gross profit margins (GPM) despite net profit volatility.
The study constituted of more respondents married (65.7%), followed by single (18.9%), widowed (7.5%),
and divorced or separated (8.5%). The findings are significant because Marital stability provides a more
supportive environment for business continuity and risk management, enabling owners to withstand short-term
financial pressures caused by kwacha depreciation.
Most respondents had primary (41.3%) or senior secondary education (33.8%), while only a small proportion
possessed tertiary qualifications (12.6% certificate/diploma, 0.5% undergraduate). Which may Suggests that
many MSME operators have modest educational backgrounds, which may constrain financial literacy and
advanced managerial decision-making (Chilufya & Mwewa, 2022 ). The observed volatility in NPM and profit
growth rates (PGR), particularly in import-dependent sectors such as manufacturing, retail, and hospitality,
may partially reflect limited capacity to implement complex hedging strategies or forecast the financial impact
of kwacha fluctuations. Conversely, service-oriented sectors with simpler operational models, like education
and finance, displayed more stable profitability despite similar demographic characteristics, indicating that
sectoral context interacts with managerial capacity to influence resilience to currency shocks.
Sectoral Distribution and Implications
The majority of MMSMEs in the study operate in the retail sector (32.7%), followed by food processing
(21.4%) and technology services (11.1%), with smaller shares in manufacturing, healthcare, motor services,
and other sectors. This sectoral composition reflects the trends observed in the Choma District study, where
retail and food-based enterprises dominate the MSME landscape similar to the findings of (Chigozie, 2021)
and (Lungu & Kaubi, 2017), Notwithstanding that the Retail and food processing MSMEs are highly sensitive
to kwacha fluctuations due to their reliance on imported goods and raw materials, which inflates cost of goods
sold (COGS) during depreciation periods (Chilufya & Mwewa, 2022 ).
Enterprise Size and Growth Phase
The distribution of enterprise size macro (40.4%), medium (37.4%), and small (22.2%) echoes a dynamic but
uneven MSME ecosystem. This is so because so often medium and small enterprises, are often in the growth
or stabilization stages, are principally susceptible to kwacha volatility (Hambayi, 2020), This is so because
limited capital buffers and exposures to foreign input costs which implies that these enterprises experience
higher erraticism in profitability metrics, as evidenced by extreme fluctuations in net margins and PGR in
import-dependent sectors The findings are consistent with the present study where smaller and medium
enterprises bear the brunt of currency-induced financial stress.
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Trade Orientation and Foreign Exchange Exposure
A striking 98% of the MMSMEs are import-driven, while only 2% are export-oriented, dissimilar to other
localities to Ghana and South Africa (Abor & Quartey, 2010), the findings infer that there is a very high
dependence on foreign-sourced materials which augments the sensitivity of MMSMEs to kwacha
depreciation, as equally noted by (Chilufya & Mwewa, 2022 ) and (Nyirenda, 2020), which thus affects
procurement, pricing decisions, and eventually net profitability. Furthermore, 89.9% of MMSMEs transact
primarily in Zambian kwacha, with only a small proportion using multiple currencies, similar to the findings of
(Chitambala, 2019), While local currency transactions protect firms from the risk of foreign exchange
receivables, they do not insulate them from increased input costs caused by depreciation (Kahunde, et al.,
2021), Correspondingly, the limited exposure to export markets (4%) indicates structural barriers that
constrain MMSMEs from capitalizing on favorable exchange rates for export-oriented revenue, a trend
highlighted in the Choma study and supported by (International Trade Center, 2022).
Financial Reporting and Risk Management
The majority of MMSMEs in the dataset do not prepare formal financial statements (M = 3.35; Mode = 4) and
fail to report currency losses (M = 3.09; Mode = 4), which is a major trend with traditional MSMEs in Zambia
and Africa at Large (Chigozie, 2021), (Eze & Okpala, 2015 ), (Kuntashula, 2020), and (Ngugi, et al., 2019),
The findings stresses that there is significant lack of compliance with basic accounting standards and limited
internal financial control mechanisms. Thus, these deficiencies hinder the ability of MSME operators to
precisely track costs related with foreign currency exposure, undermining their capacity to respond effectively
to kwacha depreciation. The present study correspondingly underscores that poor financial record-keeping and
weak accounting practices limit MMSMEs capacity to monitor financial performance and manage risks
associated with exchange rate fluctuations.
Impact of Currency Fluctuations on Costs and Profitability
The data further indicate that currency fluctuations suggestively increase procurement costs (M = 2.12; Mode
= 2),similar to the findings of (Lakuma & Muhumuza, 2019) which directly constrain profitability. More
particularly true because MMSMEs in Choma operate in import-dependent sectors, such as retail,
manufacturing, and hospitality, which are principally affected, as depreciation of the kwacha increases the
cost of imported raw materials and finished goods, which erodes their profitability.
Exchange Rate Effects on Pricing and Profit Margins
The results revealed that exchange rate fluctuations have an influence on pricing strategies (M = 2.08;
Mode = 2) and profit margins (M = 1.53; Mode = 1) affecting both short-term and long-term performance (M
= 1.77; Mode = 1). ), similar to the findings of (Lungu & Kaubi, 2017), This confirms that MMSMEs must
adjust sales prices frequently to cope with cost variability, a practice that may not always be feasible due to
competitive pressures and demand elasticity. Consequently, profitability becomes highly unpredictable,
consistent with findings in the present study, where fluctuating input costs and inability to hedge against
exchange rate risk led to erratic financial outcomes for MMSMEs .
Macro-Economic Context
Macroeconomic factors such as inflation and exchange rate variability were also identified as influential on
MSME profitability (M = 2; Mode = 1). This reflects the broader economic reality in Zambia, where currency
instability, combined with rising prices, erodes purchasing power and imposes additional operational
challenges for small and medium enterprises. Prior research corroborates these observations, emphasizing that
currency instability in developing economies increases business uncertainty, reduces competitiveness, and
undermines enterprise sustainability (Belghitar, et al., 2021), (Chitambala, 2019), (Mbao, 2021) (World
Development Report, 2022).
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Procurement and Financial Adjustment Strategies
The findings on procurement and financial adjustment strategies provide important insights into how
MMSMEs in Choma District respond to kwacha fluctuations, reflecting both operational realities and
constraints in financial sophistication. The results indicate that MMSMEs predominantly rely on inventory
management (M = 2.00; Mode = 1) as a mechanism to mitigate exchange rate risk, whereas strategies such as
supplier relationship management and financial hedging (both M = 3.59; Mode = 4) are less commonly
practiced, all similar to the finding of (Chigozie, 2021) and (Lungu & Kaubi, 2017). The over dependance on
inventory management suggests that MMSMEs prioritize operational flexibility over formal financial
instruments when addressing currency volatility (Belghitar, et al., 2021) by adjusting inventory levels, firms
attempt to buffer against sudden increases in procurement costs caused by kwacha depreciation. The findings
further asserts that MMSMEs often adopt pragmatic, short-term measures such as stockpiling imported inputs
or delaying purchases to reduce exposure to foreign exchange fluctuations, (Chitambala, 2019), as such very
few MMSMEs do not use complex hedging mechanisms that require higher financial literacy and access to
capital markets, which can be attributed to poor financial literacy (Kuntashula, 2020).
Also, the minimal engagement with supplier relationship management and hedging strategies highlights
structural and capacity constraints within the MSME sector. Hedging instruments, including forward contracts
or currency swaps, are underutilized due to limited awareness, expertise, and access to formal financial
services (Banda, 2025) In the same way, supplier relationship management as a strategic approach is
constrained by the transactional nature of many MMSMEs operations and the prevalence of informal supply
chains. These limitations are in line with the studies by (Sikabbwele, 2024)and (Chilufya & Mwewa, 2022 )
who all noted that most MMSMEs operate in environments characterized by informal procurement practices
and limited integration into global value chains, making advanced financial risk mitigation strategies largely
inaccessible.
Financial Strategies
The analysis of financial strategies among MMSMEs highlights a pronounced reliance on operational
measures rather than formal financial instruments to mitigate the effects of kwacha fluctuations. The findings
indicate that most MMSMEs do not employ hedging instruments (M = 3.59; Mode = 4) or maintain foreign
currency accounts and derivatives (M = 4; Mode = 4). This low adoption of formal financial risk management
mechanisms aligns with the observations of (Lungu & Kaubi, 2017) and (Chitambala, 2019) who reported that
MMSMEs in Zambia and similar developing economies face structural barriers, including limited financial
literacy, restricted access to derivative markets, and high transaction costs.
The preference for operational strategies such as inventory management over financial instruments reflect the
practical realities of MSME operations, as established by (Chigozie, 2021) (Ngugi, et al., 2019) (Nyirenda,
2020), many MMSMEs lack the technical knowledge and institutional support required to effectively utilize
hedging or derivatives, leaving them vulnerable to exchange rate volatility. The implication of the findings is
that most MMSMEs frequently adopt short-term, pragmatic responses to currency fluctuations rather than
long-term, formal risk management strategies, which overexposes then to the effects of currency fluctuation.
Sectoral Analysis
The sectoral analysis revealed that the impact of Kwacha fluctuations on MSME financial performance varies
significantly across industries.
Manufacturing MSMEs exhibited high gross profit margins (5090%) but extremely volatile net profit margins
(100% to +60%) and profit growth rates. This pattern indicates strong revenue-generating capacity but high
vulnerability to input cost shocks. The manufacturing sector in Choma is particularly sensitive to currency
depreciation, which raises the cost of imported raw materials and reduces net margins, thereby contributing to
erratic profit performance. Furthermore, because most manufacturing firms in the district do not export, they
do not fully benefit from favorable exchange-rate movements.
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Technology MSMEs demonstrated comparatively higher resilience. Their moderate net margins and positive
return on assets (ROA) suggest less exposure to currency risks. This resilience is attributable to the sector’s
limited reliance on imported physical inputs and its service-driven income structure. Although foreign
exchange volatility can affect the cost of software licenses and imported equipment, most technology MSMEs
in Choma are not yet at a scale where such imports dominate their cost structure. Consequently, they
experience more stable growth relative to other sectors.
The food processing sector showed moderate exposure to Kwacha fluctuations, with reduced net margins and
volatile profit growth. This is driven by the use of imported packaging materials, additives, and certain food
inputs. However, firms that rely more heavily on locally sourced raw materials tend to perform better, as
depreciation increases operational costs but may benefit exporters (Mwansa, 2020).
The study found that retail MSMEs are the most severely affected by Kwacha depreciation. Though they
exhibit substantial turnover (ROA > 200%), their profit growth rates are extremely volatile (90% to +1400%).
This volatility is rooted in their heavy dependence on imported merchandiseincluding electronics, clothing,
groceries, and household goodswhose costs rise immediately when the currency weakens. Despite high
turnover driven by fast-moving inventory, retail firms operate with thin margins, limited pricing power, and
frequent exposure to exchange-rate-driven stock price adjustments. When the Kwacha depreciates, retailers
cannot always pass on increased costs to consumers, resulting in sharp swings in profitability. Consequently,
retail MSMEs in Choma remain particularly vulnerable to currency shocks.
The education and financial services sectors demonstrated the highest resilience to Kwacha fluctuations. These
sectors consistently recorded positive ROA and stable net profit margins, reflecting mature management
practices, largely local revenue streams, and minimal reliance on imported inputs. Their reduced exposure to
exchange-rate risk stems from their dependence on domestic demand, although inflationary pressures from
depreciation may still influence operational costs and real income (Chitambala, 2019).
Workshops, Motor Services, and Hospitality exhibited extremely high volatility ratios (>200%). These sectors
depend heavily on imported consumables and equipment. Motor service providers rely on imported spare
parts, lubricants, and automotive tools, all of which become more expensive when the currency weakens,
thereby increasing operational costs and compressing margins. Hospitality firms depend on imported
foodstuffs, beverages, cleaning supplies, and equipment, while also being susceptible to fluctuations in tourist
demanddemand that typically declines under inflationary conditions. Although these sectors record high
asset turnover (>200%), the combination of high imported input costs, demand sensitivity, and elevated
operating expenses drives extreme profit volatility.
Overall, the high volatility observed in retail, workshops, motor services, and hospitality is driven by a
combination of:
Heavy reliance on imported inputs, making them highly sensitive to exchange-rate movements; Thin and
competitive margins, limiting their ability to adjust prices in response to rising costs; High turnover but
unstable cost structures, amplifying profit fluctuations; Demand variability, especially in hospitality, where
consumer spending changes rapidly during inflationary periods; and Limited hedging mechanisms or financial
buffers, which increases vulnerability to macroeconomic shocks (Banda, 2025)
Correlation Analysis
Consistent with the Purchasing Power Parity (PPP) Theory, the Pearson correlation coefficient for HO
1
indicated an insignificant relationship between macroeconomic factors and business type. This suggests that all
business categories, regardless of their nature, are adversely affected by Kwacha fluctuations. As the Kwacha
depreciates, the cost of imports, labor, and general living expenses increases, thereby reducing purchasing
power and raising operational costs. This finding aligns with (Chilufya & Mwewa, 2022 ) who similarly
observed that MSMEs across various regions experience relatively uniform exposure to macroeconomic
shocks. The insignificant correlation further confirms that currency volatility and inflationary pressures are
pervasive, cutting across all business types without discrimination.
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Aligned with Transactional Exposure Theory, the results for HO
2
and HO
3
demonstrated positive and
significant correlations between currency fluctuations, pricing strategies, and the importation of materials.
These findings imply that exchange rate volatility directly influences how MSMEs set or adjust their prices. As
the local currency depreciates, transaction costs rise, compelling MSMEs to revise prices upward to offset
increased procurement and input expenses. Such reactive pricing behavior reflects short-term responses to
external macroeconomic pressures rather than strategic long-term planning. Similar evidence is reported by
(Lungu & Kaubi, 2017)and (Mwansa, 2020), who note that exchange rate instability in developing economies
often forces firms to make frequent price adjustments, reducing market competitiveness and weakening
consumer purchasing power. This is consistent with the present study, which found that import-dependent
MSMEs experience heightened cost pressures during periods of Kwacha depreciation, resulting in variability
in net profit margins and profit growth.
The results for HO
4
and HO
5
revealed strong positive correlations between hedging practices, supplier
relationships, and business size. These findings suggest that MSMEs with stronger supplier networks are more
likely to adopt hedging mechanisms, while larger enterprises are better positioned to employ formal financial
risk-management tools. This pattern is consistent with the resource-based theory, which posits that firms with
greater internal capabilities and external linkages are better able to deploy strategic resources such as hedging
instruments. The analysis further indicates that import reliance, pricing adjustments, and sectoral
characteristics directly influence profitability, and that businesses with stronger supplier networks and more
substantial resource bases are better equipped to manage currency risk. These findings correspond with the
work of (Nyirenda, 2020).
HO
6
revealed a weak but statistically significant association between education level and hedging practices.
This implies that as the educational attainment of entrepreneurs increases, the likelihood of adopting hedging
mechanisms also rises, although the strength of the relationship remains modest. A plausible explanation is that
more educated entrepreneurs tend to possess higher financial literacy, better awareness of market instruments,
and enhanced analytical capacities to assess currency risks (Mensah, et al., 2021). Consequently, they are more
inclined to use formal hedging tools such as forward contracts, futures, or currency diversification.
The study also found a strong positive correlation between hedging and supplier relationships (r =0.42, p<
0.005), indicating that firms with robust supplier networks are structurally better positioned to employ
financial hedging mechanisms. Strong supplier relationships can provide favorable payment terms,
opportunities for risk-sharing, and access to timely market information, all of which support informed hedging
decisions. This finding aligns with OECD (2022), which underscores the role of collaborative supplier
networks in strengthening MSME financial resilience.
Integrated Theoretical Contribution
This study strengthens its theoretical contribution by systematically applying three core theories Purchasing
Power Parity (PPP), Transactional Exposure Theory, and the Resource-Based View (RBV) to measurable
variables and empirically tested hypotheses (HO
1
HO
6
). Each theory guided variable selection, the expected
direction of relationships, and the interpretation of statistical outcomes.
Purchasing Power Parity (PPP)
PPP posits that exchange rate movements influence relative prices of goods and inputs. This theory informed
the analysis of macroeconomic factors, pricing strategies, cost of inputs, and business type exposure. The
empirical results supported PPP-based expectations: HO1 revealed an insignificant relationship between
macroeconomic factors and business type (r=0.009), suggesting that exposure to macroeconomic shocks is
broadly uniform across sectors. Conversely, HO2 showed a positive association between currency fluctuations
and pricing strategies (r = 0.040, p< 0.05), consistent with PPP’s prediction that exchange rate changes shape
price-setting behavior. These findings demonstrate how PPP manifests in MSME cost structures and pricing
dynamics within the Zambian context.
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The Transactional Exposal theory
Transactional Exposure Theory explains how exchange rate volatility translates into domestic price, cost, and
profitability shifts. This framework informed the analysis of pricing strategies, profitability effects, and sector-
specific exposure (HO
2
HO
3
). The positive correlation between currency fluctuations and pricing strategies in
HO
2
confirms partial transactional exposure, indicating that firms adjust prices when exchange rates move.
HO
3
showed a negative correlation between exchange rate volatility and business type (r =0.0856, p< 0.05),
highlighting sectoral differences in exposure. These findings illustrate that Zambian MSMEs experience
uneven transactional exposure, reflected in sector-dependent pricing and profitability responses.
Resourced Based View Theory
RBV emphasizes that firm performance is shaped by internal resources and capabilities. This theory guided
hypotheses HO
4
HO
6
, focusing on hedging strategies, supplier relationships, business size, and
owner/manager education. The strong positive correlation between hedging and supplier relationships (r=0.42)
supports RBV’s emphasis on relational capital as a strategic resource. HO
5
showed that larger firms are more
likely to adopt hedging practices (r = 0.1623), reflecting the role of resource availability. HO
6
demonstrated a
positive relationship between education level and hedging (r = 0.0856), highlighting human capital as a key
determinant of risk management capability. Collectively, these findings extend RBV by showing that MSME
hedging behavior in Zambia is shaped by internal resources, including knowledge, firm size, and network
strength.
CONCLUSION
The study examined the impact of kwacha fluctuations on the financial performance of MSMEs in Choma
District, with concentration on how demographic, sectoral, and managerial characteristics influence firms
responses to currency volatility. The present study revealed that demographic variable such as age, gender,
marital status, and education play a critical role in shaping managerial decision-making and resilience to
macroeconomic shocks. The majority of MSME operators have modest educational backgrounds, which
constrains their capability to interpret financial risks, implement hedging strategies, and make data-driven
managerial decisions.
The Study further established that the MSME landscape in Choma is dominated by import-dependent
enterprises, mostly in retail and food processing sectors, thus making them most exposed to kwacha
depreciation. The study also found that while manufacturing enterprises generate high gross profit margins,
they experience volatile net profits due to increased import costs. On the contrary, service-oriented sectors
such as education and financial services exhibited greater stability and resilience, mainly due to their reliance
on local inputs and abridged exposure to exchange rate variations.
Furthermore, the study revealed that MSMEs rely principally on informal and operational strategies such as
inventory management to cope with currency volatility, where as the adoption of formal financial risk
management tools such as hedging, currency swaps, or forward contracts are tremendously limited. This low
uptake is greatly ascribed to inadequate financial literacy, restricted access to capital markets, and weak
supplier relationships. The correlation analysis further accentuated those larger enterprises and those with
stronger supplier networks are more likely to employ hedging mechanisms, supporting the resource-based
theory that firm capacity and access to resources determine strategic resilience.
Overall, the findings highlight that MSMEs in Choma District face systemic vulnerabilities to kwacha
fluctuations as a result of import dependence, weak financial management practices, and limited
macroeconomic awareness. Consequently, profitability and growth remain erratic, with only a few sectors
demonstrating sustained financial performance amid currency volatility.
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RECOMMENDATIONS
Based on the findings, the study proposes the following recommendations:
The Ministry of Small and Medium Enterprise Development, in partnership with financial institutions and
local training centres, should implement targeted financial literacy programs, which basically focus on
exchange rate risk management, budgeting, and the use of formal financial instruments such as forward
contracts and hedging tools.
The Government and financial institutions in Zambia should create simplified and affordable hedging facilities
tailored for MSMEs ,which may include pooled hedging schemes or SME friendly derivative products to
protect against foreign currency exposure.
MSMEs should be encouraged to build long-term partnerships with suppliers to negotiate favourable payment
terms and joint risk-sharing arrangements. Reinforced supplier networks enhance bargaining power and reduce
susceptibility to currency shocks. Additionally, to reduce import dependence, MSMEs particularly in retail and
food processing sectors should be incentivized to source raw materials locally through tax rebates, production
grants, or cooperative purchasing schemes, which may reduce foreign exchange exposure and stabilize input
costs.
Capacity-building interventions should prioritize the adoption of formal accounting systems and financial
reporting standards amongst MSMEs. The enhancement of improved record-keeping will permit firms to
monitor costs, assess exposure to currency risks, and make informed business decisions. Policymakers should
design targeted interventions that address sectoral differences in vulnerability, which can be done through,
forex access support or tax incentives and or the enhancement of digital infrastructure investments to sustain
growth.
Banks should adopt more inclusive financial models that recognize the unique needs of small enterprises by
providing flexible credit facilities, foreign currency accounts, and advisory services aimed at improving
financial resilience. The Government of Zambia should pursue stable monetary and fiscal policies to minimize
exchange rate volatility. Maintain macroeconomic stability which will improve business confidence, reduce
operational uncertainty, and enhance MSME sustainability.
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