The nexus between structural transformation and poverty reduction in Maghreb region. A sectoral value-added analysis

O nexo entre a transformação estrutural e a redução da pobreza na região do Magrebe. Uma análise do valor agregado setorial

ABSTRACT

The study examined the relationship between structural transformation and poverty reduction in the Maghreb region. The study looked at the value-added proportion of gross domestic product in the manufacturing, service, agricultural, and industrial sectors. The research adopted the autoregressive distributed lag model. Data were sourced from World Bank from 1990 to 2022. The ARDL results showed that the agricultural sector led to an increase in poverty levels in the short run. In the long run, it was ascertained that the service sector and manufacturing were useful for poverty reduction in the short run and long run.

KEYWORDS:
Economic growth; industrialisation; poverty; structural transformation; sustainable development goals

RESUMO

O estudo examinou a relação entre a transformação estrutural e a redução da pobreza na região do Magrebe. O estudo analisou a proporção do valor agregado do produto interno bruto nos setores manufatureiro, de serviços, agrícola e industrial. A pesquisa adotou o modelo de defasagem distribuída autorregressiva. Os dados foram provenientes do Banco Mundial de 1990 a 2022. Os resultados do ARDL mostraram que o setor agrícola levou a um aumento dos níveis de pobreza no curto prazo. No longo prazo, verificou-se que o sector dos serviços e a indústria transformadora foram úteis para a redução da pobreza no curto e no longo prazo.

PALAVRAS-CHAVE:
Crescimento econômico; industrialização; pobreza; transformação estrutural; metas de desenvolvimento sustentável

INTRODUCTION

This paper examines how the Maghreb region can use structural transformation for poverty reduction. The Maghreb region of Africa, which comprises of Algeria, Libya, Mauritania, Morocco, and Tunisia, struggles with low levels of industrialisation and high poverty levels (Tregenna, 2023). Poverty reduction and socioeconomic development are key goals that many nations strive to achieve (Neves & Da Silva, 2023). Structural transformation entails a shift from labor-intensive methods to capital-intensive methods, which ultimately increase productivity, and helps in poverty reduction as well as improving the socioeconomic development of any economy (Oyelaran-Oyeyinka & Lal, 2016). Structural transformation helps in poverty reduction through a non-deterministic mechanism where expansion of industries leads to employment creation, and it can be assumed that such employment benefits the poor people hence poverty is reduced as people earn incomes and also support their families with some remittances. The results from the study will be used to prove this assumption. Structural transformation is crucial for achieving high industrialization levels and poverty reduction, which are enunciated in Sustainable Development Goals 9 and 1, respectively (Enongene, 2022).

Nearly half (42.6%) of Algerians are exposed to acute poverty, which manifests in the form of a lack of nutritious food, water, sanitation, and quality healthcare (Alsamara et al., 2022; United Nations, 2017). In 2021, Libya had over 765,000 multidimensionally poverty-stricken people (United Nations Development Program, 2023). Morocco and Tunisia also suffer from the same predicament of poverty, which manifests in the form of poor access to education (Kokas et al., 2021; United Nations Development Program, 2020). These statistics show the high levels of poverty in the Maghreb region. The research’s central argument is premised on examining how the Maghreb region can use structural transformation for poverty reduction. In particular, the study assesses on the value-added proportion of gross domestic product (GDP) in the manufacturing, service, agricultural, and industrial sectors of the Maghreb region from 1990-2022. The objectives of the study are stated below:

1. To examine the effect of structural transformation on poverty reduction in the Maghreb region.

2. To preview and describe the trends of the manufacturing, service, agricultural, and industrial sectors of the Maghreb region.

Low productivity in the agricultural sector may not have meaningful effects in poverty reduction and this calls for structural transformation to ensure that resources are moved to high productive sectors. Barbier & Hochard (2018) explained that the reason why growth has had a limited effect on reducing poverty in developing nations is that a greater proportion of the population resides on less productive agricultural land. The Maghreb region is more reliant on the labor-intensive agricultural sectors, and they do not attain high levels of efficiency in production due to the lack of capital-intensive methods of production (Messaouda, Rayane, & Hadjer, 2021). For example, the World Bank (2023) ascertained that Morocco’s rate of structural transformation is very low. Roufaye et al. (2023) indicated that structural change that would transfer resources from low-productivity industries, like agriculture, to high-productivity industries, such as manufacturing and services, would help reduce poverty.

Value-added shares, employment value-added shares, and final consumption expenditure shares are the three most widely used metrics to assess structural changes at the sectoral level (Herrendorf et al., 2014). To determine value-added share as a percentage of gross domestic product (GDP), this study will examine value-added contribution in the manufacturing sector (MVA), service sector (SVA), industrial sector (IVA), and agricultural sector (AVA). The article is structured as follows: The next section is based on the literature review. The endogenous growth theory and the Clark-Fisher model were used in this research. The research methodology, results, and conclusion will follow. In terms of the research methodology, the research adopts the panel auto-regressive distributed lag, and the data includes the years 1989-2022. The research findings prove that the AVA sector had a negative effect on Human Development Index (HDI) levels in the short run. In the long run, it was ascertained that the SVA and MVA were useful for poverty reduction in the short run and long run.

THEORETICAL LITERATURE REVIEW

Endogenous Growth Theory

According to (Lucas, 1988; Romer, 1990) endogenous growth theory states that long-term growth is influenced by internal, autonomous reasons rather than by external forces. The theory, which was developed by Romer, Arrow, & Lucas, (1998) is based on the notion that “human capital investment, technological innovation, and knowledge are significant contributors to economic growth” (Romer, 1990). This implies that an economy should be able to use the aforementioned factors for increased economic growth. Romer (1990) further posited that investment in human capital enables the acquisition of knowledge. Such knowledge should be used to shift economic resources to productive sectors of the economy. This theory is important to this research because the study is premised on the arguments that there is a need for Maghreb region to focus on structural transformation and this can be achieved by using technologically innovative ways and the use of human capital in the different sectors of the economy (Habiyaremye et al., 2019). This will help to increase economic growth and such growth, if it is accompanied by job creation and expansion of industries, more jobs will be created and as people get employed, they get incomes that help them to earn a living, hence poverty reduction.

The Clark-Fisher model

The Clark-Fisher model is another important theory that supports the current study. According to Fisher (1939) and Clark (1940), economic development depends on the labor force’s reallocation among the three economic sectors which are the primary, secondary, and tertiary sectors (Clark, 1940; Fisher, 1939). The theory further posits that a significant percentage of the labor force is employed in the service sector as the economy shifts from the primary to the tertiary sector due to the high-income elasticity of demand for services like leisure (Aiginger, 2001; Regan, 1963). Based on the key aspects of this theory it can be concluded that sectors are interdependent, that is from primary, secondary, and tertiary sectors. Contextualizing this to the current study, there is a need for structural transformation in the Maghreb region by focusing on the key sectors which are the agricultural sector, manufacturing industrial, and service sector. Such transformation should lead to the attainment of SDG 1 of poverty reduction.

EMPIRICAL LITERATURE REVIEW

This section presents the literature review, which is structured as follows: The first section addresses the effect of the agriculture sector on poverty reduction, followed by a review of the literature on the role of the manufacturing sector on poverty. Literature on the role of industrialization in poverty reduction will be presented next, and finally, the role of the service sector in poverty alleviation will be presented.

Effect of agriculture on poverty reduction

High productivity in an economy’s agricultural, manufacturing, and service sectors can be helpful for poverty reduction ceteris paribus. Enongene (2022) researched structural transformation and poverty alleviation in Sub-Saharan African countries. The findings show that, in the short and long term, value-added in agriculture, manufacturing industrial value-added, and services all significantly and favorably reduce poverty (Enongene, 2022). From these findings, it can be concluded that economies can reduce poverty if they invest in efficient sectors. Miranda, Pusra & Seftarita (2021) found similar results in a study that focused on Indonesia. Miranda et al. (2021) ascertained that the agricultural sector, construction sector, trade sector, and manufacturing sector contributed to poverty reduction by 0,71%, 0,48%, 0,51%, and 0,67%, respectively.

The agricultural sector can provide a platform or channel for poverty reduction through the productivity levels that enable employment creation. Ogundipe et al. (2016) support the above sentiment in their research that focused on agricultural productivity and poverty reduction in Africa. Results from the study proved that agricultural value added per worker had a positive effect on poverty reduction. These research findings are similar to those of Christiaensen et al. (2006) who concluded that the impact of poverty reduction is significantly greater when the poor are involved in the growth of the agricultural sector, particularly in low-income nations. According to Chandrarekha et al. (2022), GDP per worker in India reduced poverty by 0.11%, whereas GDP per worker in non-agricultural sectors reduced poverty by 0.04%. This implies that the expansion of agriculture in India has a greater effect on reducing poverty. Thus, Chandrarekha et al. (2022) and Ogundipe et al. (2016) share similar results on the role of the agricultural sector on poverty reduction.

Gildas et al. (2020) found a moderate correlation between the decline in agricultural employment and the decrease in poverty in Sub-Saharan Africa. However, to better understand how agriculture contributes to the fight against poverty in Kenya, Eichsteller et al. (2022) used a panel survey to carry out the research. They found that there is uncertainty about the relationship between asset accumulation and poverty escape, that it can be difficult for low-income households to turn agricultural practices into a profit, and that shocks related to climate change exacerbate these challenges.

Role of the manufacturing sector on poverty

The structural transformation and growth of the manufacturing sector are positively and significantly correlated with the elimination of poverty, according to Erumban & De Vries (2021). Sharafat Ali et al. (2014) examined manufacturing sector employment and multidimensional poverty in Pakistan. The study concluded that in Punjab province, Pakistan, jobs in the manufacturing sector and human capital (healthcare and education) had a mitigating effect on poverty. These research findings tally with the results from UNIDO (2017), which concluded that sustainable industrial development is a crucial factor that helps in poverty alleviation.

Expansion of industries enables employment creation and assuming that the new jobs created benefit the poor, poverty will be reduced. Karahasan (2023) supports the above assertion and realized that economic expansion in the Global South directly lowers poverty, but industrialization’s mediating role is what gives the full effect. To add to the findings, it was also shown that employment in manufacturing accounts for over 50% of the influence on poverty. This implies that the efficient functioning of the manufacturing sector helps in poverty alleviation, provided that the efficiency allows job creation, which is necessary for providing income to people. These research findings are in line with the views of Justin & Miaoli (2019), who found out that structural transformation and high efficiency in the manufacturing sector enabled employment creation and poverty reduction in China.

Role of industrialisation on poverty reduction

To eradicate poverty by 2030, Memedocvi (2020: 10) proposed that “inclusive and sustainable industrial development is associated with job creation, sustainable livelihoods, innovation, technology and skills development, food security, and equitable growth”. Mulok et al. (2012) realized that the creation of jobs may be correlated with high levels of economic growth, and industrialization can aid in the reduction of poverty. This is consistent with the findings of Bokosi (2022), who found that industrialization and economic growth in the Southern African Development Community (SADC) were positively correlated.

Industrialization allows the expansion of industries and employment creation, as well as an increase in wages or ensuring for formally employed and informally employed workers. Erumban & De Vries (2021) researched the effects of industrialization on poverty. Research results showed a strong correlation among structural change, manufacturing productivity increase, and poverty reduction in developing economies. This should also be supported by the movement of capital and labor from low-productivity sectors to highly productive sectors of an economy (International Labour Organization, 2020; Tregenna, 2015). In the context of the current study, this implies that economies in the Maghreb region need to improve their industrialization levels for effective poverty reduction.

Role of service sector in poverty alleviation

The United Nations Conference on Trade and Development (2017) realized that because the services sector and infrastructural services can supply intermediary inputs for all economic activity, services make a substantial contribution to economic growth and poverty reduction. The expanding significance of services across all economic sectors, or “servicification”, makes it easier to produce and export goods throughout the productive process, and this enables employment creation, which is key for poverty reduction (United Nations Conference on Trade and Development, 2017). These research findings are on par with the views of Antai et al. (2016) and Mujahid & Alam (2014), who realized that the service sector of an economy helps in improving economic growth.

Shifting resources to high productive sectors boosts economic growth and enables job creation. According to the United Nations Conference on Trade and Development (2014), greater ties between competitive services have aided Asia’s growth since 1990 as a result of structural shifts from low-productivity to high-productivity industries and other economic sectors, particularly the manufacturing sector. Similarly, Rifa’i & Listiono (2021) asserted that improving the efficiency of an economy’s service sector helps reduce poverty. However, Pham & Riedel (2019), established contrary results in the case of Vietnam, where improvement in the functioning of the service sector led to increased poverty levels. Given the above discussion, which shows how the agricultural, manufacturing, industrial, and service sectors can impact poverty reduction. It is hypothesized that structural transformation helps in poverty reduction.

METHODOLOGY

The methodology section illustrates how structural transformation and poverty reduction are linked, drawing on arguments from endogenous growth theory and the Clark-Fisher model. The variables that were employed are briefly described, and an econometric model is presented. Poverty, the dependent variable, was assessed using the Human Development Index (HDI). Because the index considers factors like income, education, and health, it helps measure poverty. HDI generates accurate estimations when it comes to measuring poverty (Bejar, 2021; Korankye, Wen, Nketia, & Kweitsu, 2020). Furthermore, one of the most reliable proxies for measuring poverty is the multidimensional poverty index, which includes the health and education components found in the HDI (United Nations Development Programme, 2022; Vollmer & Alkire, 2022). The research used four independent variables, which were: agricultural value added (AVA), manufacturing value added (MVA), industrial value added (IVA), and service value added (SVA). This research uses a sectoral analysis approach, and these sectors were used in the study. These variables were added to the study because (Enongene, 2022; Rifa’i & Listiono, 2021; Erumban & De Vries, 2021; Karahasan, 2023) realized that they have an impact on poverty alleviation. Their inclusion in this study is based on the argument that the research seeks to examine how structural transformation can be achieved for effective poverty reduction in the Maghreb region.

MODEL

To examine how the structural transition affects poverty, the study used the autoregressive distributed lag model (ARDL), which was developed by Pesaran and Smith in 1995. The research spanned the years 1990 through 2022. Because it allows for the estimation of both short- and long-term parameters and is thought to be a helpful model in econometric analysis, this model was chosen (Mamvura & Sibanda, 2020; Shin & Greenwood, 2014; Kripfganz & Schneider, 2016). Due to its ability to lessen the likelihood of spurious regression, ARDL was utilized in the study (Gholami, Sang-Vong Tom, & Heshmati, 2005). The ARDL is applicable when the variables are integrated into orders 1 and 0 (Giles, 2013; Mamvura & Sibanda, 2020). The use of ARDL is robust for research that has a small sample size, and this research covers a 32-year period which is considered small (Kripfganz & Schneider, 2018). The general model is stated below:

$\Delta Y_{i,t}={\alpha }_1+\sum _{i=1}^p{\beta }_i\Delta Y_{i,t-i}+\sum _{i=0}^q{\delta }_i\Delta X_{i,t-i}+{\phi }_1{Y}_{i,t-1}+{\phi }_2{X}_{i,t-1}+{\epsilon }_{it}$ (1)

Where ∆Y _it represents a vector of (kx1) representing poverty measured through HDI, ∆ captures differences in operator, X ₁, y ₁, are the independent variables for every i=1 which were AVA, MVA, IVA and SVA.

β_i and δ_i represent the short-run coefficients of the model explaining the short-run relationships between the variables, φ₁, φ₂ represent the long-run relationship, and ε_it represent the lags of the dependent variable and the independent variables respectively and is the error term.

Summary of dataset

This section presents the summary of the dataset. Data was sourced from the World Bank from 1990 to 2022. The variable column shows each variable, followed by the indicator used and the description of the variable. The last column shows the data source for each variable used in the research.

Chart 1
Summary of dataset

Variable	Indicator	Variable description	Data source
Poverty (PO)	Human Development Index (HDI)	The absence of opportunities, coupled with high levels of undernourishment, hunger, illiteracy levels, lack of access to education, physical and mental diseases, and socio-economic instability	World Bank (2023)
Agricultural Value Added (AVA)	Percentage of GDP	Value added is the net output of the agricultural sector after adding up all outputs and subtracting intermediate inputs.	World Bank (2023)
Manufacturing Value Added (MVA)	Percentage of GDP	Value added is the net output of the manufacturing sector after adding up all outputs and subtracting intermediate inputs.	World Bank (2023)
Industrial value added (IVA)	Percentage of GDP	Value added is the net output of the industrial sector after adding up all outputs and subtracting intermediate inputs. It comprises value added in mining, manufacturing (also reported as a separate subgroup), construction, electricity, water, and gas.	World Bank (2023)
Service Value Added (SVA)	Percentage of GDP	Value added is the net output of the service sector after adding up all outputs and subtracting intermediate inputs. It includes value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal services such as education, health care, and real estate services. Also included are imputed bank service charges, import duties.	World Bank (2023)

• Source: Researcher’s Construct (2023).

RESULTS

The results presented below are for the agricultural sector, the manufacturing sector, the industrial sector, and the services sector. Period 1 refers to the years 1990-2000; period 2: 2001-2010; and period 3: 2011-2022.

Figure 1 shows the Maghreb agricultural value added as a percentage of GDP. In period 1, Algeria recorded a slight increase in AVA, from 5% to an estimated 10%. Libya had almost constant levels of AVA. Mauritania recorded a steady decrease in AVA levels, from 15% to 10%. For the same period, Morocco had notable fluctuations in AVA of 15%-10%. Tunisia recorded a decrease in AVA from 15% to about 9%. In period 2, Algeria, Libya, Mauritania, Morocco, and Tunisia recorded sharp fluctuations in AVA levels. In period 3, Mauritania recorded a sharp increase in AVA levels, while the other 4 economies registered almost constant levels of AVA. Overall, the five countries in the Maghreb region showed a decline in AVA levels between 1990 and 2022. This implies that the agricultural sector’s contribution as a percentage to GDP decreased, and it was below 30%.

Figure 1
Maghreb Agricultural Value-Added Trend

Figure 2 shows the service value-added trend, and in periods 1-3, Algeria saw a fair increase, which was characterized by some fluctuations. Libya also had significant fluctuations, from 40% in period 1 to an estimated 22% by the end of period 3. Mauritania recorded minor fluctuations in SVA levels in all periods; at the end of 2022, it was at 40%. Morocco recorded a fairly high increase in SVA levels, and at the end of 2023, it was constant. Finally, Tunisia registered a fair increase in SVA levels in all three periods. Overall, the performance of these five economies was below 60% of their GDP levels.

Figure 2
Maghreb Service Valued Added Trend

Figure 3 shows the manufacturing value-added trends of the Maghreb region. In period 1, Algeria recorded a sharp increase in MVA levels; however, in period 2, there were minor fluctuations, and in Period 3, there was a sharp decline in MVA levels from an estimated 33% to 28%. Libya and Mauritania had similar MVA trends, and they both recorded a sharp decline in MVA levels by the end of period 3. Morocco and Tunisia registered an insignificant decline in MVA levels from 1990 to 2022. In general, the entire region had poor performance as far as MVA levels were concerned.

Figure 3
Maghreb Manufacturing Value Added Trend

Figure 4 shows that from period 1 to period 4, Algeria recorded an increase in IVA levels of 27%, though there were some fluctuations, and at the end of 2022, it was on. Libya registered a sharp increase in IVA levels from period 1 to period 2, but there was a sharp decline in IVA levels up to the end of 2022. Mauritania recorded fluctuations in IVA levels from 1990-2022, and the highest recorded value was 43%. For Morocco and Tunisia from period 1 to period 3, there were no significant changes in IVA levels, and they were below 30%. Overall, these 5 economies showed a slight increase in IVA levels.

Figure 4
Maghreb Industrial value added Trends

Descriptive statistics

This section presents the descriptive statistics.

Table 1 above shows that the mean HDI was 0.62, the median was 0.66, the maximum was 0.74, and the minimum was 0.39. AVA had a mean of 11.41%, a median of 10.38%, a maximum of 28.64%, and a minimum of 1.28%. SVA had a mean of 44.79%, a median of 44.59%, a maximum of 93.62%, and a minimum of 12.67%. MVA had a mean of 15.08%, a median of 14,61%, a maximum of 49,87%, and a minimum of 0.65%. IVA had a mean of 36.30%, a median of 29.35%, a maximum of 86.66%, and a minimum of 10.36%.

Table 1
Descriptive statistics

Variable	HDI	AVA	SVA	MVA	IVA
Mean	0.62	11.41	44.79	15.08	36.30
Median	0.66	10.38	44.59	14.61	29.35
Maximum	0.74	28.64	93.62	49.87	86.66
Minimum	0.39	1.28	12.67	0.65	10.36

Correlation analysis

This section presents the correlation analysis.

Table 2 shows the correlation analysis. HDI was negatively correlated with AVA at 80%, while the relationship among SVA, MVA, and IVA was positive at 26%, 15%, and 43%, respectively. AVA was negatively correlated with SVA, MVA, and IVA at 7%, 3%, and 52%, respectively. SVA was negatively associated with MVA and IVA at 25% and 41%, respectively. MVA was positively correlated with IVA at 1%. Overall, it can be concluded that there was no severe multicollinearity as all values were below 0.8 (Duda, 2022).

Table 2
Correlation analysis

Variables	HDI	AVA	SVA	MVA	IVA
HDI	1.00
AVA	-0.80	1.00
SVA	0.26	-0.07	1.00
MVA	0.15	-0.03	- 0.25	1.00
IVA	0.43	-0.52	-0.41	0.01	1.00

Unit root tests

The Augmented Dickey-Fuller test and Phillips-Perron test were used to test for unit root in the research.

Table 3
Unit root test results

Variable	ADF test		PP test
	Level	1st Diff	Level	1st Diff
HDI	0.00 (18.35)	0.00 (34.76)	0.00 (19.44)	0.00 (77.53)
AVA		0.00 (74.19)		0.00 (119.26)
SVA
MVA		0.00 (62.55)		0.00 (94.26)
IVA		0.00 (58.19)		0.00 (116.56)

• () represents t-statistics.

The results above show that HDI, AVA, MVA and IVA were stationary after the first difference. SVA was stationary at level. This suffices the condition of running an ARDL because some variables are integrated in order 1 and some at level.

Lag length selection

The VAR model was used to determine the optimal lag length in this study.

The Akaike criterion was used for decision-making purposes on the appropriate lag length. Table 4 shows that Lag 2 was selected because it had the lowest AIC value of 12.54.

Table 4
Lag Length results

Lag	LogL	LR	FPE	AIC	SC	HQ
0	-1580.97	NA	72132	25.37	25.48	25.42
1	-772.98	1538.42	0.26	12.84	13.52	13.12
2	-728.80	80.57	0.19*	12.54*	13.78*	13.04*

COINTEGRATION TEST

To test for cointegration in this research a Johansen cointegration test was employed. The results are presented below.

Table 5 shows that at none, the p-value is less than 5%, thus the null hypothesis is rejected. Similarly, the critical value of Max-Eigen at 49.80 is of less value than the trace static of 66.50. At equations 1 and 4, the p-values are also below the 5% level and statistically significant, so the null hypothesis is rejected. However, for equations 2 and 3, the p-values are above the 5% level, hence the null hypothesis is accepted. It can be concluded that there is a long-term relationship among the variables used in the study, which were AVA, SVA, MVA, and IVA. This answers the third research objective of this study. There are at most three cointegrating equations. Since there is a long-run relationship among variables used in the study, the short-run and long-run dynamics will be estimated.

Table 5
Johansen cointegration test

Hypothesized No of CEs	Trace Statistics	Prob	Max-Eigen test	Prob
None	66.50	0.00	49.80	0.00***
At most 1	29.00	0.00	23.58	0.00***
At most 2	12.16	0.27	4.14	0.94
At most 3	15.39	0.11	10.52	0.39
At most 4	23.61	0.00	23.61	0.00***

• ***, ** and * stand for significance at the 1%, 5% and 10% levels, respectively

Granger Causality Tests

The study employed the Granger Causality Test to determine causal relationship among variables.

The null hypothesis of no causal correlation between AVA and HDI and HDI and AVA cannot be rejected because the p-values of 0.34 and 0.25, respectively, are over 5% level and insignificant as shown in table 6. The same applies to the relationship between SVA and HDI and vice versa; the p-values are above 5%, hence it can be concluded that there is no causal relationship between AVA and HDI, SVA and HDI. The null hypothesis of no causal correlation between MVA and HDI can be rejected because the p-value of 0.01 is statistically significant. This implies that there is unidirectional causality, and this means that the performance of the MVA sector leads to high HDI levels, and such high levels imply low levels of poverty. However, the null hypothesis of no causal correlation between HDI and MVA is accepted because the p-value of 0.58 is above the 5% level.

Table 6
Granger Causality Tests

Null Hypothesis	F-statistics	Probability
AVA does not granger cause HDI	1.07	0.34
HDI does not granger cause AVA	1.39	0.25
SVA does not granger cause HDI	0.84	0.43
HDI does not granger cause SVA	22.97	1.97
MVA does not granger cause HDI	4.71	0.01
HDI does not granger cause MVA	0.53	0.58
IVA does not granger cause HDI	3.02	0.05
HDI does not granger cause IVA	23.24	1.60
SVA does not granger cause AVA	0.60	0.54
AVA does not granger cause SVA	0.52	0.59
MVA does not granger cause AVA	0.08	0.91
AVA does not granger cause MVA	1.16	0.31
IVA does not granger cause AVA	0.84	0.43
AVA does not granger cause IVA	1.19	0.30
MVA does not granger cause SVA	0.15	0.85
SVA does not granger cause MVA	1.95	0.14
IVA does not granger cause SVA	0.52	0.59
SVA does not granger cause IVA	4.85	0.00
IVA does not granger cause MVA	0.36	0.69
MVA does not granger cause IVA	0.66	0.51

The null hypothesis of no causal correlation between IVA and HDI can be rejected because the p-value of 0.05 is statistically significant. This implies that there is unidirectional causality, and this means that the performance of the IVA sector leads to high HDI levels, and such high levels imply low levels of poverty. However, the null hypothesis of no causal correlation between HDI and IVA is accepted because the p-value of 1.60 is above the 5% level.

The null hypothesis of no causal correlation between SVA and AVA and AVA and SVA cannot be rejected because the p-values of 0.54 and 0.59, respectively, are over 5% level and insignificant. Thus, there is no causality between these two variables. The same applies to MVA and AVA, IVA and AVA, MVA and SVA, and IVA and AVA. In both scenarios, the p-values were above the 5% level, hence there was no causality. Finally, the null hypothesis of no causal correlation between IVA and SVA cannot be rejected because the p-value of 0.59 is greater than 5% and insignificant. However, for SVA and IVA, the p-value was statistically significant because it was below the 5% level, and it can be concluded that there is unidirectional causality between the SVA and the IVA.

Short run Autoregressive Distributed Lag Model Results

This section presents the short run results of the autoregressive distributed lag model.

The results above shown in table 7 prove that AVA and SVA were statistically significant, as their p-values were below the 5% level. In the short run, a 1% increase in AVA leads to a 6% decrease in HDI levels, ceteris paribus. A decrease in HDI levels implies an increase in poverty levels. This implies that there is a negative relationship between AVA and HDI levels in the Maghreb region. A 1% increase in SVA leads to a 4% increase in HDI levels in the short-run ceteris paribus. This implies that there is a positive relationship between SVA and HDI levels in the Maghreb region.

Table 7
Short-run ARDL Results

Variable	Coefficient	T-statistic	Probability
AVA	-0.06	-3.87	0.00***
SVA	0.04	8.87	0.00***

• ***, ** and * stand for significance at the 1%, 5% and 10% levels, respectively

LONG RUN ARDL RESULTS

This section presents the long run results of the auto regressive distributed lag model.

Table 8 shows that AVA and MVA were statistically significant at 10% and 5%, respectively. A 1% increase in AVA leads to a 4% decrease in HDI levels in the long run. This means that AVA has a negative relationship with HDI, and in practical terms, an increase in the performance of the AVA sector increases poverty levels. These results are contrary to the findings of other authors who indicated the importance of the agricultural sector in poverty reduction ((Chandrarekha et al., 2022; Christiaensen et al., 2006; Ogundipe et al., 2016). These results can be explained from the viewpoint that, high performance of the AVA sector can be achieved through the use of modern machinery. Increased usage of sophisticated machinery in agriculture production leads to reduction of human labour. As workers get replaced by machinery, they become unemployed and get exposed to poverty. Therefore, high production levels may be achieved in the agricultural sector due to the use of modern technology and, machinery and use of minimal labour (Radic et al., 2022). For example, the use of a combine harvester can replace hundreds of farm laborers and these workers get exposed to poverty because their source of income from the farm ceases to exist.

Table 8
Autoregressive distributed lag long run results

Variable	Coefficient	T-statistic	Probability
AVA	-0.04	1.81	0.07*
MVA	0.11	2.01	0.04**

• ***, ** and * stand for significance at the 1%, 5% and 10% levels, respectively

A 1% increase in the MVA leads to an increase in HDI levels of 11% in the long run. This means that there is a positive relationship between MVA and HDI. Thus, an increase in HDI levels means a reduction in poverty levels; hence, it can be concluded that an increase in the efficiency of the MVA sector is useful for poverty reduction in the Maghreb region. The AVA sector had a negative effect on HDI levels both in the short run and long run. This implies that the agricultural sector in the Maghreb region does not help to reduce poverty levels; rather, it increases poverty levels. These study conclusions are comparable to those of Le and Pham (2012), who found that between 1998 and 2008, Vietnam’s poverty rate grew as the country’s share of the agricultural sector increased. Additionally, Moukpe et al. (2022) discovered that value-added agriculture has a detrimental impact on African economic growth.

Based on these results, it can be concluded that the SVA and MVA are useful for poverty reduction in the short and long run. These research findings are on par with those of Erumban & De Vries (2021), Sharafat Ali et al. (2014), Antai et al. (2016), & United Nations Conference on Trade and Development (2017). These authors established that the service sector and the manufacturing sector help to increase economic growth, industrial expansion, job creation, and poverty reduction. This implies that the Maghreb region needs to ensure the efficient running of these sectors for effective poverty reduction through structural transformation.

Post-estimation tests

Normality test

To test for normality, the Jarque-Bera test was used and the probability value of 0.72 was obtained as shown in table 9. The p-value of 0.72 is greater than the significance level of 5%. Therefore, the null hypothesis of having a normal distribution is accepted. This implies that the data used was normally distributed.

Table 9
Post-estimation tests results

Type of Test	P-value
Normality test	0.72
White Heteroscedasticity Test	0.23
Breusch-Godfrey LM test	0.31

Heteroscedasticity Tests

To test for heteroscedasticity, the White Heteroscedasticity Test was used. Results proved that the p-value of 0.23 is greater than at=0.05, hence there was no heteroscedasticity. This implies that there was homoscedasticity.

Serial Correlation test

The model was tested for serial correlation using the Breusch Godfrey Serial Correlation LM test and a p-value of 0.31 was obtained as shown in Table 9. It was concluded that the model was free from serial correlation as the p-value of 0.31 was above the 5% level.

POLICY RECOMMENDATIONS

The Maghreb region needs to prioritize the development of the service sector and the manufacturing sector. This is crucial because the good performance of these sectors helps to improve the capacity of the Maghreb region to achieve industrialization levels and enable poverty reduction, which are sustainable development goals 1 and 9, respectively. High productivity in these sectors will enable increased economic growth levels to be achieved, and this will help in poverty reduction through the multiplier effect.

CONCLUSION

The study examined the effect of structural transformation on poverty reduction in the Maghreb region. In the long run, it was ascertained that the SVA and MVA are useful for poverty reduction in the short run and long run. In terms of policy recommendations, it was suggested that the Maghreb region needs to ensure that high productivity levels are attained in the service and manufacturing sectors for effective poverty reduction.

REFERENCES

• Alsamara, T., Farouk, G., & Halima, M. (2022). Public health and the legal regulation of medical services in Algeria: Between the public and private sectors. South African Journal of Bioethics and Law, 15(2), 60-64. https://doi.org/10.7196/SAJBL.2022.v15i2.817
  » https://doi.org/10.7196/SAJBL.2022.v15i2.817
• Aiginger, K. (2001). Speed of Change and Growth of Manufacturing, Structural Change and Economic Growth, Austrian Institute of Economic Research, Vienna, pp. 53-86.
• Barbier, E. & Hochard, J. (2018). Land degradation and poverty. Retrieved from: https://www.researchgate.net/publication/328914896_Land_degradation_and_poverty
  » https://www.researchgate.net/publication/328914896_Land_degradation_and_poverty
• Bejar, E. (2021). Human Development Index and Multidimensional Index. Retrieved from: https://ideas.repec.org/p/pra/mprapa/108501.html
  » https://ideas.repec.org/p/pra/mprapa/108501.html
• Bokosi, K. (2022). The Effects of Industrialisation on Economic Growth: Panel data evidence for SADC countries. African Journal of Economic Review, 10(3). Retrived from: https://www.ajol.info/index.php/ajer/article/view/226626
  » https://www.ajol.info/index.php/ajer/article/view/226626
• Chandrarekha, C., Guledagudda, S. S., Kulkarni, G. N., Biradara, N., & Yeledhalli, R. A. (2022). Nexus between Agriculture Growth and Poverty Reduction in India. Asian Journal of Agricultural Extension, Economics & Sociology, 157-163. https://doi.org/10.9734/ajaees/2022/v40i121777
  » https://doi.org/10.9734/ajaees/2022/v40i121777
• Christiaensen, L., Demery, L., & Kühl, J. (2006). The Role of Agriculture in Poverty Reduction An Empirical Perspective. http://econ.worldbank.org
  » http://econ.worldbank.org
• Duda, S. (2022). Identifying and Addressing Multicollinearity in Regression Analysis. Doi: https://scottmduda.medium.com/identifying-and-addressing-multicollinearity-in-regression-analysisca86a21a347e#:~:text=A%20quick%20way%20to%20identify,just%20a%20rule%20of%20thumb
  » https://scottmduda.medium.com/identifying-and-addressing-multicollinearity-in-regression-analysisca86a21a347e#:~:text=A%20quick%20way%20to%20identify,just%20a%20rule%20of%20thumb
• Eichsteller, M., Njagi, T. and Nyukuri, E. (2022). The role of agriculture in poverty escapes in Kenya - Developing a capabilities approach in the context of climate change. Doi: https://www.researchgate.net/publication/355111642_The_role_of_agriculture_in_poverty_escapes_in_Kenya_-_Developing_a_capabilities_approach_in_the_context_of_climate_change
  » https://www.researchgate.net/publication/355111642_The_role_of_agriculture_in_poverty_escapes_in_Kenya_-_Developing_a_capabilities_approach_in_the_context_of_climate_change
• Enongene, B. (2022). Structural transformation and poverty alleviation in Sub-Saharan Africa countries: sectoral value-added analysis. Retrieved from: https://www.emerald.com/insight/content/doi/10.1108/JBSED-12-2022-0128/full/html
  » https://www.emerald.com/insight/content/doi/10.1108/JBSED-12-2022-0128/full/html
• Erumban, A. A., & De Vries, G. J. (2021). WIDER Working Paper 2021/172-Industrialization in developing countries: is it related to poverty reduction? https://doi.org/10.35188/UNU-WIDER/2021/112-9
  » https://doi.org/10.35188/UNU-WIDER/2021/112-9
• Fisher, A.G. (1939). Primary, secondary and tertiary production, Economic Record, Vol. 15 No. 6, pp. 24-38. Retrieved from: https://onlinelibrary.wiley.com/doi/10.1111/j.1475-4932.1939.tb01015.x
  » https://onlinelibrary.wiley.com/doi/10.1111/j.1475-4932.1939.tb01015.x
• Giles, D. (2013). ARDL Models - Part II - Bounds Tests. Retrieved from: https://davegiles/2013/06/ardl-models-part-ii-bounds-tests.html
  » https://davegiles/2013/06/ardl-models-part-ii-bounds-tests.html
• Gildas, D., Joshua, M., Justin, N. & David, N. (2020). Structural transformation in sub-Saharan Africa, World Bank Publications - Reports 33327, The World Bank Group, pp. 1-4.
• Gholami, R., Sang-Vong Tom, S., & Heshmati, A. (2006). The Causal Relationship Between Information and Communication Technology and Foreign Direct Investment. The World Economy, Wiley Blackwell, 29(1), 43-62, January. Retrieved from: https://econpapers.repec.org/article/blaworlde/v3a293ay3a20063ai3a13ap3a43-62.htm
  » https://econpapers.repec.org/article/blaworlde/v3a293ay3a20063ai3a13ap3a43-62.htm
• Gniniguè, M. & Abalo, B.F.A. & Paroubénim, T. & Heyou, M. R. (2022). The Impact of Agricultural Structural Transformation on Economic Growth in Africa, African Journal of Economic Review, vol. 10(2), March.
• Habiyaremye, A., Kruss, G. & Booyens, I. (2020) Innovation for inclusive rural transformation: the role of the state, Innovation and Development, 10: 2, 155-168, DOI: 10.1080%2F2157930X.2019.1596368
  » https://doi.org/10.1080%2F2157930X.2019.1596368
• Herrendorf, B., Rogerson, R. & Valentinyi, A. (2014). Growth and structural transformation, Handbook of Economic Growth, Vol. 2, pp. 855-941. Retrieved from: https://econpapers.repec.org/bookchap/eeegrochp/2-855.htm
  » https://econpapers.repec.org/bookchap/eeegrochp/2-855.htm
• International Labour Organisation (2020). Poverty alleviation through social and economic transformation. Retrieved from: https://www.ilo.org/wcmsp5/groups/public/---dgreports/---cabinet/documents/publication/wcms_771116.pdf
  » https://www.ilo.org/wcmsp5/groups/public/---dgreports/---cabinet/documents/publication/wcms_771116.pdf
• Justin, Y. & Miaojie, Y. (2019). Industrial Structural Upgrading and Poverty Reduction in China 1. Trade Openness and China’s Economic Development, 1st ed., Routledge, London, pp. 1-37.
• Karahasan, B.C. (2023). To make growth reduce poverty, industrialize: Using manufacturing to mediate the effect of growth on poverty. Retrieved from: https://onlinelibrary.wiley.com/doi/epdf/10.1111/dpr.12689
  » https://onlinelibrary.wiley.com/doi/epdf/10.1111/dpr.12689
• Kokas, D., Lahga, A.-R. El, & Lopez-Acevedo, G. (2021). Poverty and Inequality in Tunisia: Recent Trends. http://www.iza.org
  » http://www.iza.org
• Korankye, B., Wen, X., Nketia, E. B., & Kweitsu, G. (2020), The Impact of Human Development on the Standard of Living in Alleviating Poverty: Evidence from Africa. European Journal of Business and Management Research, 5(5), 1-5. https://doi.org/10.24018/ejbmr.2020.5.5.511
  » https://doi.org/10.24018/ejbmr.2020.5.5.511
• Kripfganz, S. & Schneider, D.C. (2018), “Ardl: estimating autoregressive distributed lag and equilibrium correction models”, London Stata Conference September, Vol. 7, pp. 1-44, 2018.
• Kripfganz, S. & Schneider, D.C. (2016). Ardl: stata module to estimate autoregressive distributed lag models, Stata Conference, pp. 1-20.
• Le, H. & Pham, H. (2012). Sectoral composition of growth and poverty reduction in Vietnam, VNU. Journal of Science, Economics and Business, Vol. 28 No. 2, pp. 75-86.
• Mamvura, K., & Sibanda, M. (2020). Modelling short-run and long-run predictors of foreign portfolio investment volatility in low-income Southern African Development Community countries. Journal of Economic and Financial Sciences, 13(1), 1-11. https://doi.org/10.4102/jef.v13i1.559
  » https://doi.org/10.4102/jef.v13i1.559
• McMillan, M. & Rodrik, D. (2011). Globalization, structural change and productivity growth, Making Globalization Socially Sustainable. Retrieved from: https://www.wto.org/english/res_e/booksp_e/glob_soc_sus_e_chap2_e.pdf
  » https://www.wto.org/english/res_e/booksp_e/glob_soc_sus_e_chap2_e.pdf
• Messaouda, M. & Rayane, R. & Hadjer, S. (2021). Sustainable Agriculture in Some Arab Maghreb Countries (Morocco, Algeria, Tunisia). Retrieved from: https://www.researchgate.net/publication/355186659_Sustainable_Agriculture_in_Some_Arab_Maghreb_Countries_Morocco_Algeria_Tunisia
  » https://www.researchgate.net/publication/355186659_Sustainable_Agriculture_in_Some_Arab_Maghreb_Countries_Morocco_Algeria_Tunisia
• Memedovic, O. (2020). Industrialisation in Africa and Least developed Countries. Retrived from: https://www.researchgate.net/publication/339078095_Industrialization_in_Africa_and_Least_Developed_Countries
  » https://www.researchgate.net/publication/339078095_Industrialization_in_Africa_and_Least_Developed_Countries
• Miranda Pusra, C., & Seftarita, C. (2021). Effect of Selected Economic Sectors on Poverty. International Journal of Business, Economics and Social Development, 2(1), 37-49.
• Neves, O. J. F., & Da Silva, A. M. R. (2023). The effects of multidimensional well-being growth on poverty and inequality in Brazil over the periods of 2004-2008 and 2016-2019*. Brazilian Journal of Political Economy, 43(2), 358-379. https://doi.org/10.1590/0101-31572023-3428
  » https://doi.org/10.1590/0101-31572023-3428
• Ogundipe, A. A., Oduntan, E. A., Ogunniyi, A. I., & Olagunju, K. O. (2016). Agricultural Productivity, Poverty Reduction and Inclusive Growth in Africa: Linkages and Pathways. https://ssrn.com/abstract=2856449Electroniccopyavailableat:https://ssrn.com/abstract=2856449
  » https://ssrn.com/abstract=2856449Electroniccopyavailableat:https://ssrn.com/abstract=2856449
• Oyelaran-Oyeyinka, O. & Lal, K. (2016). Structural transformation in developing countries: cross regional analysis, p. 36, HS/018/16E, available at: http://unhabitat.org/books/structuraltransformation-in-developing-countries-cross-regional-analysis
  » http://unhabitat.org/books/structuraltransformation-in-developing-countries-cross-regional-analysis
• Pham, T.H. & Riedel, J. (2019). Impacts of the sectoral composition of growth on poverty reduction in Vietnam, Journal of Economics and Development, Vol. 21 No. 2, pp. 213-222. Retrieved from: https://www.emerald.com/insight/content/doi/10.1108/JED-10-2019-0046/full/html
  » https://www.emerald.com/insight/content/doi/10.1108/JED-10-2019-0046/full/html
• Radic, V., Radić, N. and Cogoljević, V. (2022). New technologies as a driver of change in the agricultural sector. Doi: https://www.researchgate.net/publication/359832366_New_technologies_as_a_driver_of_change_in_the_agricultural_sector
  » https://www.researchgate.net/publication/359832366_New_technologies_as_a_driver_of_change_in_the_agricultural_sector
• Regan, W. (1963). Economic Growth and Services. Retrieved from: https://www.jstor.org/stable/2351112
  » https://www.jstor.org/stable/2351112
• Rifa’i, A., & Listiono, L. (2021). Structural Transformation And Poverty Eradication In East Java (A Panel Data Approach Of 38 Counties). Journal of Developing Economies, 6(1), 114. https://doi.org/10.20473/jde.v6i1.23080
  » https://doi.org/10.20473/jde.v6i1.23080
• Romer, P.M. (1990). Endogenous technological change, Journal of Political Economy, Vol. 98 No. 5, pp. 71-102. Retrieved from: https://web.stanford.edu/~klenow/Romer_1990.pdf
  » https://web.stanford.edu/~klenow/Romer_1990.pdf
• Roufaye, S. W., Nafiou, M. M., & Ouedraogo, I. M. (2023). Structural Transformation and Poverty in the WAEMU. European Journal of Development Studies, 3(3), 24-33. https://doi.org/10.24018/ejdevelop.2023.3.3.256
  » https://doi.org/10.24018/ejdevelop.2023.3.3.256
• Sharafat Ali, B., Asghar, M., Anjum, S., Abbas Kalroo, R., & Ayaz Bahauddin Zakariya University Multan, M. (2014). Manufacturing Sector Employment and Multidimensional Poverty in Pakistan: A Case Study of Punjab Province. http://ssrn.com/abstract=2426115https://creativecommons.org/licenses/by-nc/3.0/
  » http://ssrn.com/abstract=2426115https://creativecommons.org/licenses/by-nc/3.0/
• Tregenna, F. (2023). Can Africa Run? Industrialisation and Development in Africa. Africa Development. 48, 2(Aug. 2023). DOI: https://doi.org/10.57054/ad.v48i2.5078
  » https://doi.org/10.57054/ad.v48i2.5078
• Tregenna, F. (2015). Deindustrialisation, structural change and sustainable economic growth, MERIT Working Papers 2015-032, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
• UNIDO (2017). Industrial development board’s input to the 2017 HLPF, (Issue 12).
• United Nations Conference on Trade and Development (2014). The Least Developed Countries Report 2014 (United Nations publication), Sales No. E.14.II.D.7, New York and Geneva.
• United Nations Conference on Trade and Development (2017). The role of the services economy and trade in structural transformation and inclusive development. Retrieved from: https://unctad.org/system/files/official-document/c1mem4d14_en.pdf
  » https://unctad.org/system/files/official-document/c1mem4d14_en.pdf
• United Nations Development Program (2023). Briefing note for countries on the 2023 Multidimensional Poverty Index. Retrieved from: https://hdr.undp.org/sites/default/files/Country-Profiles/MPI/LBY.pdf
  » https://hdr.undp.org/sites/default/files/Country-Profiles/MPI/LBY.pdf
• United Nations Development Program (2020). Poverty in the Arab world successes and limits of Morroco’s experience. Retrieved from: https://www.undp.org/arab-states/publications/poverty-arab-world-successes-and-limits-morrocos-experience
  » https://www.undp.org/arab-states/publications/poverty-arab-world-successes-and-limits-morrocos-experience
• United Nations Development Programme (2022). 2022 Global Multidimensional Poverty Index (MPI). Retrieved from: https://hdr.undp.org/content/2022-global-multidimensional-poverty-index-mpi#/indicies/MPI
  » https://hdr.undp.org/content/2022-global-multidimensional-poverty-index-mpi#/indicies/MPI
• United Nations (2017). Country Background Paper Multidimensional Poverty in Algeria. Retrieved from: https://archive.unescwa.org/sites/www.unescwa.org/files/page_attachments/multidimensional_poverty_in_algeria.pdf
  » https://archive.unescwa.org/sites/www.unescwa.org/files/page_attachments/multidimensional_poverty_in_algeria.pdf
• Vollmer, F., & Alkire, S. (2022), Consolidating and improving the assets indicator in the global Multidimensional Poverty Index. World Development, 158, 105997. https://doi.org/10.1016/j.worlddev.2022.105997
  » https://doi.org/10.1016/j.worlddev.2022.105997
• World Bank (2023). Employment prospects for Moroccans. Retrieved from: https://www.worldbank.org/en/news/feature/2021/03/30/employment-prospects-for-moroccans-diagnosing-the-barriers-to-good-jobs
  » https://www.worldbank.org/en/news/feature/2021/03/30/employment-prospects-for-moroccans-diagnosing-the-barriers-to-good-jobs

• JEL Classification:
  I32; L16; L52.

Publication Dates

• Publication in this collection
  24 Mar 2025
• Date of issue
  2025

History

• Received
  13 Dec 2023
• Accepted
  11 June 2024