Rebuilding Somalia's Manufacturing Industry: Assessing Critical Determinants and Pathways for Economic Development

2.1 Industrialization (Industrialization and economic development)

The introduction and expansion of industries in a specific location, region, or country is referred to as industrialization [10]. It is a state in which a variety of industries are recognized in different sections of the country. As a country develops its manufacturing, it produces a wide range of goods.

The Industrial Revolution has made industrialization a central aspect of structural changes, resulting in steady increases in production and employment levels, leading to unparalleled wealth development. Therefore, fostering the growth of the industrial sector can play a crucial role in attaining sustainable development. There is a well-established correlation in the growth and development literature between the expansion of manufacturing production and the increase in GDP [11].

Following the industrial revolution in the eighteenth century that brought about rapid growth in many Western countries, many governments regarded industrialization as a panacea for accelerated growth [12]. The researcher perceives the manufacturing industry as the primary catalyst for economic expansion. In the near future, a comprehensive analysis can be provided regarding the industrialization efforts undertaken by several East Asian nations, which have propelled them to achieve remarkable economic growth and earned them the moniker of the Asian Tigers (2021). The process of industrialization plays a crucial role in driving economic growth, leading governments to implement various policies aimed at fostering the development of the industrial sector [13].

In a more comprehensive perspective, industrialization can be defined as the increase of the value contributed by sectors other than agriculture and services to the Gross Domestic Product (GDP). To be more precise, it refers to the augmentation in the value added by the secondary sector. However, it is more common for industrialization to be used interchangeably with the manufacturing industry [14].

From a historical context rooted in an agrarian society, the subsequent stage of progression that can be anticipated is the industrial phase. Nevertheless, it appears that Africa has bypassed the industrial phase and transitioned directly to the service phase. This is generally inconsistent with the developmental trajectory pursued by most Western and Asian countries. The aforementioned phenomenon, commonly referred to as the skip, however, has been contended to have detrimental effects on the region due to its neglect of potential benefits, including technology innovation, policy experimentation, and knowledge acquisition. Furthermore, owing to the substantial dependence on foreign capital for both public and private investment, as well as the enduring deficits in manufacturing imports, employment is generated to support population growth, enhance wages and working conditions, and sustain persistent balance-of-payments deficits [15].

According to the IMF report of 2019, the Somalia conflict that lasted for three decades has delayed the country's growth. However, the manufacturing industry has demonstrated growth and resilience, emerging as a crucial sector in the country's economy.

Before the outbreak of the civil war in 1991, approximately 53 state-owned small, medium, and large manufacturing businesses were encountering difficulties, and many of the remaining industries were subsequently destroyed by the ensuing bloodshed. Nevertheless, several of these small-scale plants have been reopened, and newer ones have been established, primarily as a result of significant local investments made by the Somali diaspora. The latter category encompasses factories involved in the production of pasta, mineral water, confections, plastic bags, fabric, hides and skins, detergent and soap, aluminum, foam mattresses and pillows, fishing boats, carry-out packaging, and stone processing, in addition to approximately 25 factories located in the Mogadishu region [16]. Despite the industry is seeing fast growth, partially driven by the inflow of remittances from the diaspora. The lack of resources, significant security concerns, and non-competitive characteristics have impeded the growth and restricted the expansion of industries nationwide [17]. Moreover, according to the Ministry of Commerce and Industry Report in 2018, the manufacturing sector in Somalia contributes 10% to the GDP, employs 2% of the formal workforce, and accounts for 0.01% of the country's exports. Additionally, in Mogadishu, a total of 33 prominent industrial facilities specialize in the production of various goods including mineral water, plastic bags, foam mattresses and pillows, detergent and soap, aluminum, stone processing, and fishing boats. This information is based on the official listing provided by the Ministry of Commerce and Industrialization in 2018.

2.2 Assessing critical determinants and pathways for economic development

The elements determining manufacturing transformation have been investigated by most researchers, including Joumard et al. [18], Ford and Despeisse [19], as well as Mehta and Rajan [20]. Additionally, manufacturing change is influenced by politics, quality, and logistical infrastructure, as indicated by Sahoo and Yadav [21]. In this section, an examination was conducted of the four possible critical determinants of industrial transformation: infrastructure, government role, human capital, and economic development.

2.2.1 Infrastructure

In the development of economic growth theory, infrastructure is generally regarded as an endogenous component that enhances economic performance. For instance, in resource-constrained settings, the importance of infrastructure in agricultural development is underscored, as it enables the overcoming of limitations imposed by an unfavorable geographic location and local natural endowment [22]. By making alternate production methods more viable, agricultural production costs and trade expenses can be reduced. However, whether infrastructure can be perceived as a production element or if it impacts agricultural production costs depends on the ease with which infrastructure can be substituted or added, as noted by Christoffersen [23].

In the late 1970s, a total road infrastructure length of 19,380 kilometers, encompassing all categories from paved to gravel and tarmac, existed. According to the African Competitiveness Report from 2015, despite some deterioration in some areas of the nation, the proportion of roads that Somalis paved and maintained during the civil war period is still the same as in Kenya and Ethiopia and significantly higher than in Tanzania. In 1991, the disintegration of the Central Somalia government occurred, leading to the eruption of civil war throughout the country, resulting in the destruction of all forms of infrastructure, including highways, airports, seaports, and power plants. The manufacturing industry relies significantly on infrastructure [24]. Infrastructure has significantly impeded the growth of the Indian planning sector. According to Sridevi and Kumar [25], ongoing infrastructure-related issues like poor road conditions, inadequate connectivity, constrained air, and seaport capacity, and the underdevelopment of transportation modes like railways as well as alternatives like inland water transport and domestic aviation have been persistent sources of concern.

Although Ethiopia possesses the capability to generate sufficient electricity for its current and prospective manufacturing facilities, the country's power distribution system and infrastructure are outdated, thereby limiting the supply of industrial power. Inadequate road networks, insufficient air and seaport facilities, and underdeveloped railway networks all hinder the expansion of this sector. Consequently, product delivery to clients is characterized by delays and inefficiencies [26].

In Somalia, the manufacturing sector boomed prior to the Civil War era, with a total of fifty-three firms of varying sizes operating in the industry. The country had the potential to manufacture a variety of products such as juices, textiles, canned meat, fruits, fish, food items, sugar, alcohol, cosmetics, beverages, and candies, among others. Presently, the present number has significantly diminished in comparison to both historical records and neighboring countries in the region. According to Somalia customs data, there has been a growing importation of capital machinery for manufacturing and raw materials into Somalia during the past three years. This indicates that Somalia's future in manufacturing is promising due to the entrepreneurial tendencies of its society [27].

In prior studies, most research efforts focused on investigating and reporting the challenges confronting the existing industrial sector. In contrast, this study has examined the reestablishment of Somalia's industrial businesses: measuring key drivers and approaches for economic development.

2.2.2 Quality of human capital

Human capital encompasses the knowledge, skills, attitudes, physical abilities, and managerial efforts necessary for the management of capital, technology, and land to produce goods and services for human consumption, among other purposes. Numerous studies have investigated the influence of human capital on the development, productivity, and innovation within the manufacturing industry. However, it is worth noting that certain studies have posited that the role of human capital during the Industrial Revolution was relatively limited [28]. Additionally, there have been studies emphasizing the ineffectiveness of human capital in the manufacturing industry.

As indicated by Adejumo et al. [29], the unorganized manufacturing industry experiences diminished productivity and efficiency, primarily stemming from a substantial workforce composed of low-skilled and less-educated laborers who utilize inferior technology and receive meager wages. This observation underscores that enhancing the human capital base within a state exerts a positive impact primarily within the formal manufacturing industry, given the pronounced demand for skilled labor in that context.

The findings of the survey have unveiled pivotal components of employability skills deemed essential by employers operating in the industrial sector. These skills have been subjected to ranking, revealing that, in the context of the Malaysian manufacturing industry, seven skills are considered crucial: basic skills, thinking skills, sourcing skills, resource skills, system and technology skills, and personal qualities skills. In contrast, informational skills are deemed to hold moderate importance [29].

2.2.3 Government role

The role of the government in industrialization and its connection to political instability has been examined in several studies. In this study, an attempt is made to examine the importance of a stable political environment in the re-establishment of Somalia's industrial sector. The re-establishment of the industrial sector is reliant on good governance. Ekpo [30] discussed various industrial policies and industrialization techniques, such as import substitution, export promotion, and foreign private investment-led industrialization, as well as policy reform measures like the indigenization policy and structural adjustment program. These policies and measures have been devised and implemented. It is worth noting that in Somalia, the manufacturing industry is predominantly based on massive state investment.

According to Ekpo [30], industrial core projects (ICPS) were established by the government. These projects included an iron and steel plant in Ajaokuta, steel rolling mills in Warri, Kaduna, and Oshogbo, an aluminum smelter plant in Ikot Abasi, crude oil refineries in Port Harcourt and Kaduna, and petrochemical and fertilizer factories in Port Harcourt, among others. Initiatives in the paper industry at Oku Iboku, cement industries at Calabar and Nkalagu, a machine tools company, sugar plants, and marble industries were also put into place by the government. Import-substitution industrialization (ISI) policies can also be enacted by the government, exerting pressure on local and foreign firms, particularly Somali importers, to establish local plants. Additionally, the encouragement of exports can be promoted by the government. Nigeria's export-oriented industrial policy aimed to expedite the country's industrial development.

Nigeria's 1989 new industrial policy and debt conversion (equity swap) program, along with the new export promotion policy and incentives, resulted in the abolition of export license requirements for manufactured goods under the new export promotion law. Additionally, export credit guarantees and insurance schemes were introduced, commodity boards were abolished to allow for a more active role of market forces, and export-free zones were established at various locations throughout the country [31]. Political unrest and governance issues were undoubtedly barriers to industrialization. Weak policy execution, poor entrepreneurship, political instability, corrupt government institutions, and a lack of technical know-how have been identified as contributing factors [10, 32]. The positive impact of the policy was observed in the increase in the number of medium- and large-scale industrial plants from 150 at independence to 380 in 1965, as well as the elevation of the manufacturing industry's contribution to GDP from 4.2 percent to 6.1 percent in 1964 [33].

Despite Somalia's little industrial contribution to the GDP, particularly in comparison to the livestock sector, it was substantially more advanced than neighboring countries in terms of development [34]. According to UNIDO [35], Somalia's manufacturing sector contributed 6.3 percent to the country's GDP from 1977 to 1980. Nevertheless, the proportion of GDP experienced a minor decrease to 5.6 percent over the period of 1981-1985. In the mid-80s, there was a decline in the growth rate of manufacturing output, which dropped from 2.4 percent to 2 percent. Additionally, capacity utilization was reduced to 26 percent. This decline can be attributed to a shift in government policy focus from the private sector to the public sector. Before 1991, Somalia had more than 50 state-owned manufacturing companies of various sizes. Mogadishu, the capital city, served as the main industrial hub, hosting bottling plants, factories producing spaghetti, cigarettes, matches, and boats, a petroleum refinery, a small tractor-assembly workshop, and small businesses engaged in the production of construction materials. Kismayo boasted a meat-tinning factory, a tannery, and a state-of-the-art fish factory [36]. Two sugar refineries were located in the vicinity: one near Jilib, situated on the lower section of the Jubba River, and another at Jawhar, positioned on the middle section of the Shabelle River. Nevertheless, with the commencement of the civil war in 1991, the majority of these establishments ceased operations due to the destruction of infrastructure and widespread looting of enterprises, resulting in the complete annihilation of the intellectual and material advancements made in the preceding three decades [37].

4.1 Demographic analysis

Table 1. Demographic

Sources: computed by authors (2024)

A total of 41 female participants made up 20.20% of the total, while 162 male participants represented 79.8% of the total. Regarding age distribution, 41.9% of the participants were below 25 years old, 54.2% fell within the 25-40 age range, and 3.90% were above 40. Marital status-wise, 40.4% of the participants were married, and 59.6% were single. In terms of qualifications, 4.90% of the participants held a diploma, 66.5% possessed a bachelor's degree, and 28.8% had attained a master's degree. Concerning work experience, 63.1% of the participants had less than 5 years of experience, 25.1% had between 1 and 10 years of experience, and 11.8% had over 10 years of experience (Table 1).

4.2 Outer model evaluation (Model measurement)

In the pursuit of meticulous data analysis and the examination of intricate interrelations among variables, sophisticated statistical methodologies were deployed, notably incorporating the application of structural equation modeling techniques, including both Partial Least Squares Structural Equation Modeling (PLS-SEM) and Covariance-Based Structural Equation Modeling (CB-SEM). These analyses were executed utilizing the Smart PLS software platform.

Reliability was determined using Alpha Cronbach, factor loadings, composite reliability, and average variance through PLS-SEM and CB-SEM techniques. Factor loadings exceeding 60% were categorized as high, while those less than 40% were considered low [38]. Concerning Alpha Cronbach, reliability values greater than 0.90 were deemed excellent, those greater than 0.80 were considered fine, values exceeding 0.70 were regarded as adequate, those surpassing 0.60 raised doubts, and values less than 0.60 were deemed unsatisfactory [39]. Furthermore, composite reliability and average variance should both be greater than 70% and 50%, respectively. Thus, for the factor loadings to exceed 0.50, the threshold value for this study should have been greater than 0.6.

Table 2. Reliability and assessment

Sources: computed by authors (2024)

Table 2 shows the results, confirming that all items measuring the constructs had factor loadings greater than 0.60. Moreover, the findings presented in the tables demonstrate that the Alpha Cronbach and composite reliability values exceeded the benchmark of 0.70, indicating good internal consistency for most constructs. However, it is worth noting that the construct "Government Role" obtained a score of 0.664 when assessed using the second method of CB-SEM, as shown in Table 2. While this score falls slightly below the recommended threshold, it is still considered acceptable as it closely approaches the desired level of 0. 70. Additionally, all mixed AVE scores exceeded the cutoff value of 0.50. This evidence supports the conclusion that the measurements of variables are valid and reliable for both PLS-SEM and CB-SEM methods.

Furthermore, the values of the variance inflation factor (VIF) for the constructs of human capital, infrastructure, government role, and economic development are all less than 10 (1.00), indicating that the PLS outer model used does not suffer from multicollinearity issues. Moreover, Table 3 presents the results of the HTMT-Matrix, which was conducted to assess discriminant validity after processing the data using PLS. According to the recommendation by Henseler et al. [40], if the HTMT value is less than or equal to 0.90, discriminant validity between two constructs has been established. Since the value of each construct in the HTMT matrix is less than 0.90, it can be asserted that all variables in the study are fully valid and reliable. Furthermore, the study instrument meets the Fornell-Larcker criterion for the validity test, as shown in Tables 3 and 4.

Table 3. HTMT matrix

Sources: computed by authors (2024)

Table 4. Fornell-Larcker criterion-

Sources: computed by authors (2024)

Inner model evaluation (Structural Model)

The meticulous analysis of data and exploration of complex relationships among variables involved the use of advanced statistical methods. Specifically, structural equation modeling techniques, such as PLS-SEM and CB-SEM, were employed. These analyses were conducted using the Smart PLS software platform.

Common Method Assessment

If the Variance Inflation Factors (VIFs) within the inner model, as determined through a comprehensive collinearity assessment, are equal to or below 3.3, it indicates that the model is devoid of common method bias [41]. Hence, all VIF values of this model are below 3.3 as indicated in Table 5, thus, the common method bias is generally avoided, and the model results in acceptable levels of collinearity.

Table 5. Common method assessment

Sources: computed by authors (2024)

Table 6. Regression (R-square)

Sources: computed by authors (2024)

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Figure 1. Structural model

Sources: computed by authors (2024)

Evaluating the inner model in this study involved two main aspects. Firstly, the tables mentioned earlier were utilized to assess the quality of the proposed model, particularly regarding its reliability and validity. Secondly, another part was dedicated to testing the model's goodness or fitness. It is crucial to evaluate the model's capacity to elucidate and forecast the relationships between constructs, and this was achieved through the examination of R-square values.

Table 6 reveals that economic development has an R-square of 0.221 and an adjusted R-square of 0.209. The R-square value indicates weak predictive accuracy, as it is less than 0.50. However, it is noteworthy that Figure 1 demonstrates that every single path leads to a confidence level exceeding 95%. Consequently, it can be argued that the model, to a sufficient extent, can predict its dependent variables with accuracy.

4.3 Evaluation of research hypotheses

Table 7 provides a compelling quantitative analysis of the relationship between various constructs and economic development. Each construct is evaluated based on standard deviation, T statistics, P values, and a corresponding decision regarding the hypothesis's support. The constructs in question are 'Human Capital -> Economic Development', 'Government Role -> Economic Development', and 'Infrastructure -> Economic Development'. This analysis leverages standard deviation to assess data variability, T statistics for hypothesis testing, and P values for inferential statistical significance.

Firstly, examining the 'Human Capital -> Economic Development' construct, the standard deviation (STDEV) of 0.098 is moderately low, indicating a relatively tight clustering of data points around the mean. The T statistic 2.723 is notably above the conventional threshold of 2 for statistical significance in many social sciences studies, implying a robust deviation from the null hypothesis. The P value of 0.006, well below the typical alpha level of 0.05, reinforces the statistical significance of this finding. The decision to support the hypothesis aligns with the quantifiable evidence suggesting a significant positive correlation between human capital and economic development with similar researchers such as Wirajing et al. [42] and Li et al. [43]. This conclusion is consistent with the economic theory that emphasizes the role of human capital in driving economic growth through increased productivity and innovation.

In the second construct, 'Government Role -> Economic Development', the findings are similarly supportive but with nuanced differences. The STDEV is slightly higher at 0.099, implying a marginally more significant data spread. The T statistic, while slightly lower at 2.714, remains comfortably above the threshold for deeming a result statistically significant. The P value of 0.007, although higher than the human capital construct, is still well below the 0.05 cut-off, affirming the hypothesis's support with similar researchers [44], These results underscore the significance of government intervention in stimulating or regulating economic development, aligning with theories that acknowledge the government's multifaceted role in shaping economic environments.

Conversely, the 'Infrastructure -> Economic Development' construct presents a contrasting scenario. The STDEV is lower at 0.078, suggesting less variability in the data set. However, the T statistic of 1.492 falls below the threshold for statistical significance, indicating that the observed effect needs to be sufficiently strong to reject the null hypothesis confidently. This is further corroborated by a P value of 0.136, which exceeds the conventional alpha level. As a result, the hypothesis is rejected. This outcome suggests that within the context of this study, infrastructure may not have a statistically significant direct impact on economic development. These findings challenge specific economic development models that heavily weigh physical infrastructure, suggesting the need for a more nuanced understanding of the role of infrastructure in the broader economic context.

Table 7. Direct effect

Sources: computed by authors (2024)