Fecha de recepción: 1 de septiembre de 2025
Fecha de aceptación: 21 de julio de 2026
Disponible en línea: 2 de septiembre de 2026
Vol. 13 N.° 2
Julio - Diciembre del 2026
pp. 1- 27
HISTORIA ECONÓMICA,EMPRESARIAL Y DEL PENSAMIENTO
TIEMPO & ECONOMÍA
Sugerencia de citación:
Yangailo, T.
(2026). Sectoral Value Added and Labor
Market Outcomes in Zambia: An
Empirical Analysis.
tiempo&economia, 13(2), 1-27.
https://doi.org/10.21789/24222704.2185
DOI:
https://doi.org/10.21789/
24222704.2185
Sectoral Value Added and Labor
Market Outcomes in Zambia:
An Empirical Analysis
Valor añadido sectorial y resultados del
mercado laboral en Zambia:
un análisis empírico
Tryson Yangailo
Independent Researcher
ytryson@yahoo.com
https://orcid.org/0000-0002-0690-9747
ABSTRACT
This study examines the impact of value added of the Agriculture,
Forestry and Fishing, Manufacturing, and Services sectors on Zambia’s
unemployment rates and employment-to-population ratio. Using World
Bank data from 1994 to 2023, the analysis employs Jamovi software for
descriptive statistics, correlation analysis, and regression modeling. The
findings indicate that the value added in the Agriculture, Forestry and
Fishing sector is positively and significantly associated with the
unemployment rate, implying that growth in this sector does not
necessarily translate into job creation, possibly due to low productivity and
the prevalence of informal employment. In contrast, expansion in the
Services sector is positively associated with the employment-to-population
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ratio, indicating its stronger capacity to generate employment. The
Manufacturing sector exhibits a weaker relationship with labor market
indicators, suggesting its limited direct contribution to employment
outcomes. In addition, the study reveals distinct sectoral dynamics:
agricultural growth is associated with lower employment levels, whereas
expansion in the services sector corresponds to higher employment rates.
These findings highlight the need for policies that enhance agricultural
productivity while fostering the expansion of the services sector to improve
labor market performance. By providing new evidence on the relationship
between sectoral growth and employment in Zambia, this study offers
practical insights for policymakers seeking to strengthen sectoral
contributions to inclusive employment growth.
Keywords: sectoral contributions; labor market outcomes;
unemployment rates; employment-to-population ratio: economic
development; economic history.
JEL Codes: J64; O14; O55; R11; E24
RESUMEN
Este estudio examina el impacto del valor agregado de los sectores de
Agricultura, Silvicultura y Pesca, Manufactura y Servicios en las tasas de
desempleo y la relación empleo-población de Zambia. Utilizando datos del
Banco Mundial de 1994 a 2023, el análisis emplea el software Jamovi para
estadísticas descriptivas, análisis de correlación y modelado de regresión.
Los resultados indican que el valor agregado en el sector de Agricultura,
Silvicultura y Pesca está asociado de manera positiva y significativa con la
tasa de desempleo, lo que implica que el crecimiento en este sector no
necesariamente se traduce en creación de empleo, posiblemente debido a
la baja productividad y la prevalencia del empleo informal. Por el contrario,
la expansión en el sector de Servicios está asociada positivamente con la
relación empleo-población, lo que indica su mayor capacidad para generar
empleo. El sector de Manufactura muestra una relación más débil con los
indicadores del mercado laboral, lo que sugiere su limitada contribución
directa a los resultados de empleo. Además, el estudio revela dinámicas
sectoriales distintas: el crecimiento agrícola se asocia con menores niveles
de empleo, mientras que la expansión en el sector de servicios corresponde
a tasas de empleo más altas. Estos hallazgos resaltan la necesidad de
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políticas que mejoren la productividad agrícola y, al mismo tiempo,
fomenten la expansión del sector servicios para optimizar el desempeño del
mercado laboral. Al aportar nuevas evidencias sobre la relación entre el
crecimiento sectorial y el empleo en Zambia, este estudio ofrece
perspectivas prácticas para los responsables políticos que buscan fortalecer
la contribución sectorial al crecimiento inclusivo del empleo.
Palabras clave: contribuciones sectoriales; resultados del mercado
laboral; tasas de desempleo; ratio empleo-población; desarrollo
económico; historia económica.
Códigos JEL: J64; O14; O55;
Introduction
Zambia’s economy comprises a diverse set of sectors, including
Agriculture, Forestry and Fishing, Manufacturing, and Services, each
making distinct contributions to the country’s economic development.
Agriculture, traditionally a cornerstone of Zambia’s economy, continues to
play a critical role by providing raw materials, employment, and food,
despite its declining share of GDP (Alston & Pardey, 2014). The
manufacturing sector, historically a key driver of economic growth, remains
important for job creation and industrial development (Szirmai, 2013;
Haraguchi et al., 2017). Meanwhile, the service sector has become an
increasingly important component of Zambia’s economy, reflecting a
broader global shift toward service-oriented economies (Ndulo & Chanda,
2016). Understanding how these sectors influence employment metrics is
essential for explaining how labor markets respond to economic change. As
of 2023, Zambia’s unemployment rate stood at 5.91 percent (World Bank,
2024). However, this figure may not fully capture the extent of labor market
challenges due to factors such as underemployment and the prevalence of
informal unemployment. Therefore, examining how sectoral contributions
influence employment metrics is essential for informing effective economic
planning and policymaking.
The existing literature often overlooks the distinct ways in which
different sectors of the economy influence labor market outcomes, such as
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unemployment rates and the employment-to-population ratio. Although
previous studies have emphasized the importance of sectoral
contributions, they have not fully examined how these sectors interact to
shape labor market indicators, particularly in the Zambian context (Ketu &
Ningaye, 2024; Herrmendorf et al., 2014). This gap highlights the need for
a more detailed examination of the relationship between sectoral
contributions and labor market indicators, providing deeper insights into
the underlying dynamics.
Research Objectives
The primary objectives of this study are:
1. To examine the impact of value added by the agriculture,
manufacturing, and services sectors on unemployment rates in
Zambia.
2. To examine the influence of sectoral value added on the
employment-to-population ratio in Zambia.
3. To examine the interactions among the Agriculture,
Manufacturing, and Services sectors and their collective effects on
employment dynamics in Zambia.
Significance of the Study
The results of this study will have significant implications for
policymaking, economic planning, and labor market strategies in Zambia.
By providing insights into how sectoral shifts impact employment
outcomes, the study can inform strategies designed to improve labor
market performance and support sustainable economic development. The
findings will be valuable to policymakers and economic planners seeking to
address labor market challenges and strengthen the contributions of the
agriculture, manufacturing, and services sectors to economic growth.
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Literature Review
Theoretical Framework
Economic theories related to sectoral contributions and labor markets
provide a foundation for understanding economic growth and employment
dynamics. The dual-sector model explains economic growth through the
movement of labor from the agricultural sector to the industrial sector,
where expanding industries absorb surplus labor (Moloi & Marwala, 2020).
This model emphasizes labor mobility as a key driver of economic
development. Similarly, the concept of structural transformation describes
the shift from agriculture to industry and services, driven by factors such as
globalization, technological change, and economic policy (Herrendorf et
al., 2014).
Empirical Studies on Sectoral Impact
Previous research indicates that advanced services and manufacturing
are primary drivers of economic growth, whereas the role of traditional
agriculture has gradually declined (Ketu & Ningaye, 2024). For example, the
manufacturing sector continues to play a critical role in economic growth
and job creation despite its declining share of GDP (Haraguchi et al., 2017).
Evidence from other regions suggests that strengthening the
manufacturing sector through reindustrialization can promote economic
growth and reduce unemployment (Moyo & Jeke, 2019).
Sector-Specific Insights
The Agriculture, Forestry and Fishing sector remains essential to many
livelihoods despite technological advances that have reduced its
contribution to the economy (Alston & Pardey, 2014; Johnson, 1997).
Although agriculture continuous to play an important role in employment
and productivity, its overall economic significance has declined relative to
the expanding manufacturing and services sectors. Manufacturing has long
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been associated with economic growth and productivity gains, particularly
in middle-income countries, and continuous to play a key role despite the
expansion of the service sector (Szirmai, 2013; Haraguchi, 2017). In Zambia,
the Service sector has become a major driver of economic growth, although
challenges persist in areas such as energy and telecommunications (Ndulo
& Chanda, 2016).
Gaps in the Literature
Despite extensive research on sectoral impacts, important gaps
remain, particularly in understanding how sectoral shifts influence labor
market outcomes in Zambia. Existing studies often lack a focused analysis
of this issue within the Zambian context, highlighting the need for further
research. This study addresses these gaps by providing a detailed
examination of how sectoral interactions and contributions influence
employment metrics in Zambia.
Methodology
Research Design
This study employs a quantitative research design to examine the
impact of the agriculture, manufacturing, and service sectors on
unemployment and employment outcomes in Zambia. The analysis focuses
on how sectoral contributions influence labor market outcomes, using
secondary data and statistical software to provide a comprehensive
assessment.
Data Collection
The primary data source for this study is the World Bank database,
which provides comprehensive statistics on economic indicators relevant to
Zambia. The dataset includes key variables, such as sectoral GDP value
added (Agriculture, Forestry and Fishing, Manufacturing, and Services),
unemployment rates, and employment-to-population ratios covering the
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period from 1994 to 2023. The World Bank database was selected because
of its reliability and comprehensive coverage of the economic indicators
essential to this analysis
Data Analysis
Data analysis was conducted using Jamovi, a statistical software
package for data analysis and visualization. Jamovi was selected for its
user-friendly interface and robust analytical capabilities, which support the
detailed examination and interpretation of the relationships between
sectoral value added and labor market outcomes.
Descriptive Statistics: The initial analysis involves calculating
descriptive statistics, including means, standard deviations, and correlation
coefficients to summarize the data. These statistics provide an overview of
sectoral contributions and labor market outcomes.
Regression Analysis: Multiple regression models are used to examine
the effects of sectoral contributions on unemployment rates and
employment-to-population ratio. Separate models are estimated for
agriculture, manufacturing, and service sectors to assess their individual
effects.
Triangulation
Data Triangulation: By relying on data from the World Bank, this study
draws on a reputable source with extensive historical records, allowing the
findings to be compared with those of previous studies and other datasets.
Methodological Triangulation: The combination of descriptive
statistics, regression analysis, and interaction effect analysis provides a
comprehensive assessment of sectoral impacts. This approach helps
mitigate the limitations of any single analytical technique and enhances the
robustness of the findings.
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Theoretical Triangulation: The study integrates insights from different
theoretical frameworks, including the dual-sector model and structural
transformation theory, to interpret the findings within established
economic theories and provides a more comprehensive understanding of
the relationships under investigation.
Ethical Considerations
All data used in the study are secondary and publicly available,
ensuring compliance with ethical standards related to privacy and
confidentiality.
Limitations
Although this study provides valuable insights, several limitations
should be acknowledged. First, the reliance on secondary data may
introduce limitations related to data accuracy and completeness. Second,
the analysis is restricted to indicators available from the World Bank, which
may not capture all factors relevant to labor market outcomes.
Conclusion
The methodology outlined in this study provides a structured
approach to examining the effects of sectoral contributions on
unemployment and employment outcomes in Zambia. By combining
statistical analysis using Jamovi with triangulation techniques to
strengthen the reliability and validity of the findings, the study provides a
comprehensive understanding of the sectoral dynamics shaping Zambia’s
labor market.
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Results
Descriptive Statistics on Agriculture, Forestry and fishing,
Manufacturing, Services, and Unemployment
The descriptive statistics presented in Table 1 provide an overview of
the contribution of the agriculture, manufacturing, and service sectors to
Zambia’s GDP, along with the unemployment rate. The analysis focuses on
four variables: Agriculture, Forestry and Fishing, Manufacturing, Services,
and Unemployment.
Table 1. Descriptive Statistics for Agriculture, Forestry and Fishing,
Manufacturing, Services, and Unemployment
Beginning with the Agriculture, Forestry and Fishing sector, the mean
value added to GDP is 10.4%. This sector exhibits considerable variability,
with a standard deviation of 4.99 percentage points. It’s contribution to GDP
ranges from 2.86% to 18.2%, reflecting the volatile nature of agricultural
productivity and its sensitivity to factors such as climate conditions, market
forces, and government policies.
The Manufacturing sector contributes an average of 8.63% to GDP. Its
standard deviation of 1.37 percentage points indicates substantially less
variability than that of the Agriculture, Forestry and Fishing sector, with
contributions ranging from 6.02% to 10.9%. This relatively low variation
suggests a more stable industrial sector, although manufacturing accounts
for a smaller share of the GDP than agriculture and services.
The Services sector is the largest contributor to GDP, with a mean value
added of 50.0%. The standard deviation is 4.26 percentage points indicates
moderate variability, with the sector’s contribution to GDP ranging from
39.6% to 56.2%. The prominence of the services sector underscores its crucial
role in Zambia’s economy, possibly reflecting factors such as urbanization,
technological change, and the expansion of the tertiary sector.
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Finally, the unemployment rate has a mean of 10.7%, with a standard
deviation of 3.60 percentage points. The unemployment rate ranges from a
minimum of 5.03% and a maximum of 16.8%, highlighting the challenges
facing the labor market, possibly reflecting structural economic changes,
population growth, and the transition from agriculture to manufacturing and
services.
In summary, the descriptive statistics provide valuable insights into the
sectoral composition of Zambia’s GDP and unemployment rate. The
agricultural sector exhibits the greatest variability, whereas the services
sector is the largest contributor to the economy. The unemployment rate
shows moderate variability, reflecting persistent challenges in the labor
market.
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Table 2. Correlation Matrix on Agriculture, Forestry and fishing, Manufacturing,
Services, and Unemployment
The correlation matrix in Table 2 provides a comprehensive view of the
relationships among the value added of the agriculture, forestry and
fishing, manufacturing, and services sectors , as well as the unemployment
rate in Zambia.
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The first relationship to note is between Agriculture, Forestry and
Fishing, and Manufacturing. The Pearson correlation coefficient (r) is 0.767,
indicating a strong positive correlation between these two sectors. This
suggests that as the contribution of agriculture to GDP increases, the
contribution of the manufacturing sector also increases, possibly reflecting
the interrelated nature of these sectors in the Zambian economy. The p-
value of less than 0.001 confirms that this relationship is statistically
significant. Similarly, Spearman’s rho, a non-parametric measure, is 0.791,
also indicating a strong, statistically significant positive correlation.
Conversely, the correlation between Agriculture, Forestry and Fishing,
and services is negative, with a Pearson’s correlation coefficient (r) of -
0.706. This strong negative correlation suggests that an increase in the
contribution of agriculture sector to GDP is associated with a decrease in
the contribution of services sector. The p-value is again less than 0.001,
indicating that this relationship is statistically significant. The Spearman's
rho confirms this finding with a value of -0.786, further emphasizing the
inverse relationship between these two sectors.
The Manufacturing sector also shows a strong negative correlation
with the Services sector, with a Pearson’s correlation coefficient (r) of -
0.780. This suggests that as the contribution of the manufacturing sector to
GDP increases, the contribution of services sector tends to decrease. This
inverse relationship is statistically significant, as indicated by the p-value of
less than 0.001. Spearman’s rho also supports this finding, with a value of -
0.819.
Finally, the relationship between unemployment and the other
sectors reveals interesting patterns. There is a very strong positive
correlation between Unemployment and Agriculture, Forestry and Fishing,
with a Pearson's correlation coefficient (r) of 0.869. This suggests that
higher unemployment rates are associated with a greater contribution of
the agriculture sector to GDP. This relationship is highly statistically
significant, with a p-value of less than 0.001. The Spearman’s rho value of
0.812 supports this finding.
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Unemployment also has a strong positive correlation with
Manufacturing, with a Pearson's correlation coefficient (r) of 0.745,
indicating that higher unemployment rates are associated with a greater
contribution of the manufacturing to GDP. This relationship is statistically
significant with a p-value of less than 0.001. Spearman's rho is 0.750 further
confirms this positive correlation.
However, the correlation between Unemployment and Services is
negative, with a Pearson’s correlation coefficient (r) of -0.729. This suggests
that higher unemployment rates are associated with a lower contribution
of services to GDP. The p-value of less than 0.001 indicates that this
negative correlation is statistically significant, and the Spearman's rho of -
0.735 confirms this finding.
In summary, the correlation matrix reveals significant relationships
between the value added and unemployment in Zambia. The positive
correlations between Unemployment and the Agriculture, Forestry and
Fishing and Manufacturing sectors suggest that these sectors may not be
absorbing labor effectively. In contrast, the negative correlation between
Unemployment and the Services sector suggests that a stronger services
sector could help reduce unemployment.
Linear Regression Analysis
Table 3 presents the overall model fit measures for the regression
analysis examining the relationships between sectoral value added and the
unemployment rate. The model has an R value of 0.885, indicating a strong
correlation between the predictors and the unemployment rate. The R²
value of 0.784 suggests that approximately 78.4% of the variance in the
unemployment rate is explained by the model. The adjusted R² of 0.757
accounts for the number of predictors in the model, providing a more
accurate measure of model fit. The F-statistic of 29.0, with degrees of
freedom (df1 = 3, df2 = 24) and a p-value of less than 0.001 confirms that the
model is statistically significant and provides a good fit to the data.
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Table 3. Model Fit Measures
Table 4. Omnibus ANOVA Test
Table 5. Model Coefficients - Unemployment, total (% of total labor force)
(modeled ILO estimate
Table 4 presents the results of the omnibus ANOVA, which assesses
the contribution of each predictor to the overall model. The variable
Agriculture, Forestry and Fishing has a sum of squares of 60.661, with a
mean square of 60.661 and an F-value of 19.247. The p-value of less than
0.001 indicates that this predictor contributes significantly to the model. In
contrast, Manufacturing and Services have much lower F-values of 0.209
and 1.467, respectively, with p-values of 0.652 and 0.238, indicating that
these predictors do not contribute significantly to the model. The residuals,
with a sum of squares of 75.639 and a mean square of 3.152, represent the
variance not explained by the model.
Table 5 presents the regression coefficients for predicting
unemployment based on sectoral value added. The intercept is estimated
at 11.918, but it is not statistically significant (p = 0.218). Among the
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predictors, Agriculture, Forestry and Fishing has a significant positive
relationship with unemployment, with an estimate coefficient of 0.486, a
standard error of 0.111, and a t-value of 4.387. The p-value of less than 0.001
confirms its statistical significance, and the standardized estimate of 0.6736
indicates a strong effect. In contrast, Manufacturing and Services do not
show statistically significant effects on unemployment, with estimated
coefficients of 0.209 and -0.161, standard errors of 0.457 and 0.133, and p-
values of 0.652 and 0.238, respectively. The standardized coefficients for
these predictors are 0.0794 and -0.1909, reflecting their small effects
compared with Agriculture, Forestry and Fishing.
In summary, the analysis shows that Agriculture, Forestry and Fishing
is a significant predictor of unemployment, whereas Manufacturing and
Services are not significant predictors of the unemployment rate. Overall,
the model fits the date well, with the significant contribution of Agriculture,
forestry and Fishing highlighting its important role in shaping employment
dynamics in the Zambian economy.
Descriptive on Agriculture, Forestry and Fishing,
Manufacturing, Services, and Employment to
population ratio
Table 6. Descriptive Statistics on Agriculture, Forestry and Fishing,
Manufacturing, Services, and Employment-to-population ratio
Table 6 presents the descriptive statistics for sectoral value added and
employment-to-population ratio for individuals aged 15 and older. The
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table summarizes the mean, median, standard deviation, minimum, and
maximum values for these variables.
For Agriculture, Forestry and Fishing, the mean contribution to GDP is
10.4%, with a median of 11.5%. This sector exhibits considerable variability,
as indicated by a standard deviation of 4.99 percentage points. Its
contribution to GDP ranges from a minimum of 2.86% to a maximum of
18.2%, reflecting the sector’s fluctuating performance and its sensitivity to
external factors such as weather conditions and market demand.
In the Manufacturing sector, the mean value added to GDP is 8.63%,
with a median of 8.67%. The sector exhibits relatively less variability
compared to Agriculture, Forestry and Fishing, with a standard deviation of
1.37 percentage points. Its value added ranges from 6.02% to 10.9%,
indicating a more stable, although smaller, contribution to total GDP.
The services sector is the largest contributor to GDP, with a mean
value of 50.0% and a median of 50.1%. The standard deviation is 4.26
percentage points, indicating moderate variability. Its contribution to GDP
ranges from a minimum of 39.6% to a maximum of 56.2%, highlighting its
dominant role in the economy and the relative stability of its contribution.
The employment-to-population ratio for individuals aged 15 and older
has a mean of 52.9% and a median of 53.0%. The standard deviation is 2.33,
indicating moderate variability in the employment-to-population ratio. The
ratio ranges from a minimum of 49.2% to a maximum of 57.2%, reflecting
variations in employment levels relative to the total population in this age
group.
In summary, while the Services sector remains the largest contributor
to GDP and exhibits moderate variability, Agriculture, Forestry and Fishing
shows substantial fluctuations. The Manufacturing sector is more stable
but contributes a smaller share of GDP. The employment-to-population
ratio reflects the proportion of the working-age population that is
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employed, with a relatively narrow range of values indicating moderate
stability in employment levels.
Table 7. Correlation Matrix on Agriculture, Forestry and Fishing, Manufacturing,
Services, and Employment-to-population ratio
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Table 7 presents the correlation statistics between sectoral value
added and the employment-to-population ratio for individuals aged 15 and
older. Specifically, the table examines the relationships among Agriculture,
Forestry and Fishing, Manufacturing, Services, and the employment-to-
population ratio.
The correlations reveal several statistically significant relationships.
The Manufacturing sector has a strong positive correlation with
Agriculture, Forestry and Fishing, with a Pearson’s correlation coefficient (r)
of 0.767 and a Spearman’s rho of 0.791. Both correlations are statistically
significant with p-values of less than 0.001. This suggests that increases in
the value added of the agriculture sector is closely associated with increases
in the value added of the manufacturing sector.
In contrast, the Manufacturing sector has a strong negative correlation
with the Services sector. The Pearson’s correlation coefficient (r) is -0.780,
and the Spearman’s rho is -0.819, both of which are statistically significant,
with p-values of less than 0.001. This indicates that as the value added by
the manufacturing sector increases, the value added of the services sector
to GDP tends to decrease, and vice versa.
The services sector is also negatively correlated with Agriculture,
Forestry and Fishing, with Pearson’s correlation coefficient (r) of -0.706 and
a Spearman’s rho of -0.786. Both correlations are statistically significant,
with p-values of less than 0.001, indicating an inverse relationship between
the value added of these sectors.
Regarding the employment-to-population ratio, there are notable
correlations with each sector. The employment-to-population ratio has a
strong negative correlation with Manufacturing, with Pearson’s correlation
coefficient (r) of -0.696 and a Spearman’s rho of -0.729, both of which are
statistically significant, with p-values of less than 0.001. This suggests that
as the added value of the manufacturing sector increases, the employment-
to-population ratio tends to decrease.
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The employment-to-population ratio also has a strong negative
correlation with Agriculture, Forestry and Fishing, with Pearson’s
correlation coefficient (r) of -0.866 and a Spearman’s rho of -0.823. This
strong inverse relationship suggests that higher value added of the
agricultural sector is associated with a lower employment-to-population
ratio.
Conversely, the employment-to-population ratio is positively
correlated with Services, with a Pearson’s correlation coefficient (r) of 0.703
and a Spearman’s rho of 0.734. Both correlations are statistically significant
with p-values of less than 0.001, suggesting that an increase in value added
of the services sector is associated with a higher employment-to-
population ratio.
In summary, the correlations reveal complex relationships among
these variables. Manufacturing and Agriculture, Forestry and Fishing are
positively correlated with each other but negatively correlated with
Services sector and the employment-to-population ratio. In contrast, the
services sector is negatively correlated with Agriculture, Forestry and
Fishing and Manufacturing, but positively correlated with employment-to-
population ratio. These findings provide insights into how different sectors
of the economy are associated with employment outcomes in Zambia.
Linear Regression Analysis
Table 7 presents the model fit measures for the linear regression
analysis examining the relationship between sectoral value added and the
employment-to-population ratio for individuals aged 15 and older. The
model shows a good overall fit, with an R value of 0.876, indicating a strong
correlation between the predictors and the employment-to-population
ratio. The R² value of 0.767 suggests that approximately 76.7% of the
variance in the employment-to-population rate is explained by the model.
The adjusted R² of 0.738 accounts for the number of predictors in the
model, providing a more accurate measure of its explanatory power. The F-
statistic of 26.4, with degrees of freedom (df1 = 3, df2 = 24) and a p-value of
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less than 0.001 confirm that the model is statistically significant and fits the
data well.
Table 7. Model Fit Measures
Table 8. Omnibus ANOVA Test
Table 9. Model Coefficients - Employment to population ratio, 15+, total (%)
(modeled ILO estimate)
Table 8 presents the results of the Omnibus ANOVA, which assesses
the contribution of each predictor to the overall model. Agriculture,
Forestry and Fishing has a sum of squares of 31.8984, with a mean square
of 31.8984 and an F-value of 22.4181. The p-value of less than 0.001
indicates that this predictor contributes significantly to the model. In
contrast, Manufacturing and Services have much lower F-values of 0.0454
and 1.5086, respectively, with corresponding p-values of 0.833 and 0.231.
These results suggest that Manufacturing and Services do not contribute
significantly to the model, indicating that their association with the
employment-to-population ratio is weaker than that of Agriculture,
Forestry and Fishing.
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Table 9 presents the regression coefficients for predicting the
employment-to-population ratio based on the sectoral value added. The
intercept is estimated at 50.5352, with a standard error of 6.3260 and a t-
value of 7.988, which is statistically significant (p-value of less than 0.001).
This represents the estimated employment-to-population ratio when all
predictors are equal to zero.
Among the predictors, Agriculture, Forestry and Fishing has a
significant negative association with the employment-to-population ratio,
with an estimate of -0.3523, a standard error of 0.0744 and a t-value of -
4.735. The p-value of less than 0.001 confirms its statistical significance, and
the standardized coefficient of -0.7538 indicates a strong inverse
relationship. This suggests that higher value added of the agricultural
sector is associated with a lower employment-to-population ratio.
In contrast, Manufacturing and Services are not significant predictors
of employment-to-population ratio. The estimated coefficient for
Manufacturing is 0.0654, with a standard error of 0.3070 and a t-value of
0.213, and a p-value of 0.833, indicating no statistically significant
association. Similarly, the estimated coefficient for Services is 0.1099, with
a standard error of 0.0895, and a t-value of 1.228, and a p-value of 0.231,
indicating no significant association.
In summary, the analysis indicates that Agriculture, Forestry and
Fishing is a significant predictor of the employment-to-population ratio,
whereas manufacturing and Services are not significant predictors. The
strong model fit and significant contribution of Agriculture, Forestry and
Fishing highlight its important role in shaping employment dynamics in the
Zambian economy.
Discussion
The analysis provides a comprehensive understanding of how
different economic sectors —Agriculture, Forestry and Fishing,
Manufacturing, and Services— are associated with unemployment and
employment outcomes in Zambia. The results reveal several key insights
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into the relationships among these sectors and labor market outcomes. The
findings indicate that the Agriculture, Forestry and Fishing has a significant,
positive association with the unemployment rate. As agricultural value
added increases, unemployment rates also tend to increase. This suggests
that growth in the agricultural sector may not translate effectively into job
creation, possibly because of factors such as low productivity or the large
informal sector. This finding is consistent with that of Katongo et al. (2024),
who reported that although agriculture plays an important role in Zambia’s
economy, its capacity to absorb labor and generate employment remains
limited.
In contrast, Manufacturing and Services are not significant predictors
of unemployment. The lack of statistically significant associations for these
sectors suggests that their value added does not explain variations in the
unemployment rates. This observation differs from that of Moyo and Jeke
(2019), who reported a positive correlation between manufacturing value
added and economic growth in Africa. Their results suggest that although
manufacturing remains an important driver of economic growth, its
relationship with unemployment rates may be less direct or shaped by
other structural factors.
Employment-to-Population Ratio
The study also highlights the relationship between these sectors and
the employment-to-population ratio. There is a strong negative association
between the value added by Agricultural, Forestry and Fishing and the
employment-population ratio. This suggests that higher agricultural value
added is associated with a lower employment-to-population ratio,
indicating that growth in the agricultural sector may not translate into
higher employment levels as expected. This finding is consistent with that
of Alston and Pardey (2014), who emphasized that despite advances in
agriculture, its declining share of the global economy has a limited role in
job creation.
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Conversely, the Services sector is positively associated with the
employment-to-population ratio, suggesting that higher value added in the
services sector is associated with higher employment levels. This finding is
consistent with that of Ndulo and Chanda (2016), who reported that the
services sector has been a major driver of economic growth and
employment in Zambia. In contrast, Manufacturing is not a significant
predictor of the employment-to-population ratio, suggesting that its value
added is not significantly associated with this labor market indicator. This
finding contrasts with that of Haraguchi et al. (2017), who highlights the
importance of the manufacturing sector in driving economic growth,
although its relationship with employment may vary.
Sectoral Interactions
The relationship among the sectors reveals important labor market
dynamics. For example, higher agricultural value added is associated with
lower employment-to-population ratios, whereas higher value added in the
Services sector is associated with higher employment-to-population ratios.
This contrast highlights the different ways in which these sectors are
associated with labor market outcomes. The negative association between
Agriculture, Forestry and Fishing and the employment-to-population ratio
may reflect structural challenges or limited labor absorptive capacity,
consistent with Herrendorf et al.’s (2014) analysis of structural
transformation and sectoral contributions. In contrast, the positive
association between Services and the employment-to-population ratio
may reflect the sector’s greater potential to generate diverse employment
opportunities, a finding consistent with Szirmai (2013), who emphasized
the growing importance of the services sector in economic development.
Policy Implications
The findings have important implications for policy and economic
strategy. Policies aimed at increasing agricultural productivity and
diversification may help address high unemployment, particularly in
regions heavily dependent on agriculture. Improving the efficiency of the
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agricultural sector may also help mitigate adverse labor market
outcomes. This recommendation is consistent with Ketu and Ningaye
(2024), who advocate agricultural mechanization to facilitate movement
of labor into more productive sectors. In addition, expanding the Services
sector may contribute to improved employment outcomes, suggesting
that policies supporting service-based industries could represent an
effective strategy for job creation. This recommendation is consistent
with that of Ndulo and Chanda’s (2016), who emphasized strengthening
the Services sector to promote economic growth and employment.
Overall, the analysis highlights the need for a balanced approach to
economic planning that takes into account the different relationships
between each sector and labor market outcomes. A strategic focus on
increasing productivity in Agriculture, Forestry and Fishing while
supporting growth in the Services sector may provide a more
comprehensive approach to addressing Zambia’s labor market
challenges. This recommendation is consistent with the findings of
Szirmai (2013) and Haraguchi et al. (2017), who emphasized the
importance of sectoral development in improving labor market
outcomes.
Future Research Directions
Future research should examine the factors underlying the negative
association between Agriculture, Forestry and Fishing and the
employment-to-population ratio, as well as the mechanisms through which
the Services sector is associated with this labor market indicator. In
addition, future studies could examine the role of technological progress
and sectoral innovation in shaping labor market outcomes. Research could
also be expanded to include comparative analysis across countries to
validate these findings and provide a broader understanding of the
relationship between sectoral value added and labor market outcomes,
consistent with Grabowski and Self’s (2021) call for roader regional
comparisons to better understand economic dynamics.
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Conclusion
The analysis underscores the distinct relationships between
Agriculture, Forestry and Fishing, Manufacturing, and Services and
Zambia’s labor market outcomes. The positive association between
agricultural value added and the unemployment rate suggests that growth
in the agricultural sector may not translate effectively into job creation,
possibly due to low productivity or the large informal sector. The positive
association between Services and the employment-to-population ratio
highlights the sector’s potential contribution to employment, reflecting the
broader global shift toward service-oriented economies. In contrast,
Manufacturing is not a significant predictor of either the unemployment
rate or the employment-to-population ratio, suggesting that its current
contribution is not significantly associated with this labor market
indicators. The findings underscore the need for targeted policies to
improve agricultural productivity and support the expansion of the Services
sector. Future research should examine the mechanisms underlying these
relationships and explore the role of innovation and technological progress
in shaping labor market outcomes. Overall, this study provides valuable
insights for policymakers and economic planners seeking to optimize
sectoral development and improve labor market outcomes in Zambia.
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