Productive Specialization and Factor Endowments in Emerging Municipalities: A Comparative Analysis of Tunja and Chiquinquirá (2017–2021)

Abstract

Despite the growing relevance of subnational development strategies in emerging economies, the literature lacks empirical applications of classical trade models to territorial productive specialization. This study addresses this gap by adapting the Heckscher–Ohlin–Samuelson (HOS) model to identify optimal specialization patterns in intermediate municipalities with asymmetric factor endowments. Using data from 2017 to 2021 for Tunja and Chiquinquirá (Colombia), we estimate capital-to-labor ratios and sectoral factor intensities to detect specialization aligned with local comparative advantages. The results show that Tunja exhibits capital-abundant conditions favoring specialization in sectors such as real estate, construction, and financial services, while Chiquinquirá demonstrates labor-intensive dynamics suitable for tourism and service industries. Methodologically, the study extends the HOS model to subnational scales, offering a robust analytical tool for regional policy formulation. This article contributes to the academic debate by bridging international trade theory and regional development, and it provides empirical evidence to support place-based industrial policies. Our findings emphasize the importance of aligning productive strategies with structural endowments to foster inclusive and sustainable development in emerging territories.

1. Introduction

In recent decades, sustainable development has become a central objective in global economic agendas, particularly within emerging municipalities [1,2]. These nations face complex challenges in balancing economic growth with social inclusion and environmental preservation [3]. International frameworks such as the United Nations 2030 Agenda and the Sustainable Development Goals (SDGs) emphasize the importance of fostering inclusive, equitable, and regionally balanced development [4,5]. In this context, productive specialization is increasingly seen as a strategy for territorial transformation, enabling regions to strengthen their economic base, increase competitiveness, and integrate into global value chains [6].

At the subnational level, significant disparities persist in terms of resource endowments, institutional capacity, and productive capabilities [7]. Intermediate cities and peripheral regions often struggle with low diversification, limited technological sophistication, and weak innovation ecosystems [8]. These constraints limit their ability to compete effectively in a knowledge-based global economy [9]. Addressing such challenges requires a strategic understanding of how local productive structures align with the relative abundance of economic factors—primarily capital and labor—and how these alignments can guide efficient and sustainable development [10,11,12].

Theoretical debates on productive specialization have evolved considerably over time [13,14]. While classical and neoclassical trade theories focused on static efficiency based on comparative advantage, more recent perspectives emphasize the dynamic accumulation of capabilities, institutional learning, and knowledge-based growth [15,16,17]. Despite this evolution, foundational models like the Heckscher–Ohlin–Samuelson (HOS) framework remain analytically powerful for assessing how relative factor endowments shape specialization patterns [7,8]. The HOS model posits that regions tend to specialize in the production of goods that intensively use their most abundant factors, offering useful logic to analyze subnational productive configurations [18].

However, the empirical application of the HOS model at the subnational level remains underexplored in the academic literature [19,20]. Most existing research focuses on international comparisons or national-level trade dynamics, overlooking the internal heterogeneity of emerging countries [21]. There is a clear research gap concerning how well the HOS model can explain patterns of specialization in intermediate cities with asymmetric development trajectories [22,23]. Moreover, few studies combine this theoretical approach with empirical evidence drawn from detailed sectoral and demographic data at the municipal level [19,24].

This study seeks to address that gap by adapting the HOS model to the subnational scale [25,26]. It focuses on two intermediate municipalities in Colombia—Tunja and Chiquinquirá—that exhibit contrasting endowments of capital and labor. By calculating capital-to-labor ratios and analyzing sectoral intensities across time, we identify whether each municipality’s productive specialization aligns with its structural factor endowments. This alignment provides empirical insights into the rationality and sustainability of their current development trajectories and informs policy decisions aimed at promoting more effective territorial planning [27,28,29].

The relevance of this research lies in its dual contribution. First, it operationalizes a classical trade model in a novel territorial setting, demonstrating its analytical utility beyond international trade. Second, it provides empirical evidence to support place-based policies grounded in structural realities, thereby contributing to smarter, more differentiated, and sustainable development strategies [30]. In doing so, it also aligns with broader debates on economic resilience, inclusive growth, and the role of territorial intelligence in public decision-making [30,31].

2. Theoretical Framework

2.1. Economic Growth Theory

The evolution of economic thought regarding growth has progressively shifted from exogenous neoclassical models, such as that of [32], to endogenous approaches that place knowledge, innovation, and human capital accumulation at the center of sustained economic development [33,34]. This theoretical transition has had significant implications for public policy design, particularly in regions with limited natural resources or poorly diversified productive structures [35,36].

In the context of subnational territories, the knowledge-based growth approach is especially relevant [10,36]. This framework posits that long-term economic growth depends not solely on the accumulation of physical capital or labor force expansion, but on the economic system’s ability to generate, disseminate, and apply knowledge in productive processes [11]. Accordingly, total factor productivity becomes a key channel for growth, influenced by education, innovation, technological absorption capacity, institutional structure, and the quality of interactions among economic and academic actors [37,38].

2.2. Productive Specialization

Productive specialization refers to the concentration of a country’s, region’s, or territory’s productive efforts in a relatively limited set of economic sectors or activities, based on relative advantages arising from factor endowments, technological capabilities, or institutional conditions [39,40].

In classical and neoclassical traditions, specialization is seen as a rational response to trade incentives: countries should concentrate on those activities in which they hold comparative advantages, either through relative productivity or factor abundance [6,13]. This logic aims to maximize resource efficiency and global welfare through exchange [41]. Thus, the specialization pattern is determined by the relationship between factor endowments (capital, labor, land) and the intensity with which different activities use those factors [42].

However, this framework has received several criticisms. First, assuming complete specialization can overlook the importance of diversification for macroeconomic stability and the development of technological capabilities [43,44]. Moreover, traditional theories treat factor endowments as exogenous, ignoring the possibility of modifying them through investment, public policy, and knowledge accumulation [45]. Additionally, classical models fail to account for dynamic effects that certain activities generate on overall productivity, leading to alternative theories that emphasize productive structure composition over static efficiency [15,46].

In response to these limitations, new approaches redefine specialization not only as efficient factor allocation, but as a deliberate strategy for productive transformation [47]. Productive specialization literature argues that territories should focus their efforts on activities where latent advantages exist, grounded in knowledge, innovation, and local entrepreneurial capacity [47,48]. This approach suggests that specialization should emerge from entrepreneurial discovery processes, where public and private actors identify opportunity areas based on existing capacities, articulating a competitive and sustainable development vision [49].

Furthermore, productive specialization can also be understood as a manifestation of organizational and territorial learning [50]. Following the logic of agglomeration economies and technological trajectories, territories develop dynamic advantages through path dependence—accumulating specific capabilities that are reinforced over time through collective learning processes, knowledge networks, and institutional infrastructure [51]. In this sense, specialization does not imply rigidity but rather the intelligent deepening of productive niches that may serve as a basis for adjacent diversification [41].

From a structuralist perspective, specialization is also conditioned by a territory’s position within the global production hierarchy [14]. Peripheral or developing economies tend to specialize in primary or low value-added sectors due to historical constraints, technological dependence, or subordinate insertion in global value chains [52]. Therefore, promoting specialization in strategic sectors requires active productive development policies, strengthening endogenous capabilities, and deliberate structural transformation [53,54].

2.3. Comparative Advantage and the HOS Model as a Methodological Approach

The core principle of comparative advantage posits that each economy should concentrate on producing goods or services it can generate at a lower relative opportunity cost compared to other economies [48]. This simple yet powerful rule underpins the logic of efficiency that has guided economic analysis and the formulation of trade, industrial, and territorial policies [55].

One of the most relevant and applicable developments of this principle is the model formulated by [56] and [57] known as the Heckscher–Ohlin (H–O) model and later extended by [58] as the Heckscher–Ohlin–Samuelson (HOS) model. This theoretical framework provides a formal representation of how the relative endowments of production factors—primarily capital and labor—shape a territory’s comparative advantages [59]. According to this logic, economies tend to specialize in the production of goods that intensively use the factor in which they are relatively more abundant [60]. Thus, a capital-abundant economy specializes in capital-intensive goods, while a labor-abundant economy focuses on labor-intensive production [53,61].

The HOS model stands as a robust and elegantly structured methodological tool that establishes a direct relationship between a region’s factor endowment and its optimal specialization pattern. This relationship is analytically expressed through factor ratios—such as the capital/labor ratio (K/L)—which quantify relative abundance, and through the classification of economic activities based on their factor intensity. This articulation makes the HOS model a powerful approach for guiding economic policy decisions, as it identifies which sectors are most efficient under the structural conditions of each territory.

The applicability of the HOS model transcends international trade analysis and offers a precise analytical framework for examining productive specialization at the subnational level [56]. In the context of intermediate territories or regions within emerging economies, the model allows for the identification of productive vocations that align with local endowments, thereby supporting rational resource allocation and strengthening territorial capacities [24]. This methodological utility is particularly relevant in settings where strategic decision-making requires tools grounded in quantitative evidence to inform sectoral development [59].

Also, the HOS model provides a solid conceptual foundation for planning territorial economic development strategies by linking a region’s potential factor to the sectors with the highest comparative efficiency [19]. Such alignment is essential to promote sustainable growth, productive diversification, and the creation of quality employment [20].

3. Research Approach

This study adopts a quantitative approach aimed at identifying optimal patterns of productive specialization at the subnational level through the adaptation of the Heckscher–Ohlin–Samuelson (HOS) model. This methodological choice is grounded in the need to integrate empirical evidence with structural factor analysis to inform territorial development policies, especially in emerging economies characterized by significant regional disparities [19,59]. The unit of analysis corresponds to the municipalities of Tunja and Chiquinquirá, selected for their economic relevance within the department of Boyacá. The observation period spans from 2017 to 2021, allowing for the capture of changes in the productive structure resulting from shifts in factor endowments. The variables of capital and labor are operationalized using financial data from formal enterprises (total assets) and labor records (number of employees and economically active population) provided by the Chamber of Commerce of Tunja.

Analytical Procedure

The analysis unfolds in three sequential stages, each supported by specialized literature on factor-based development economics.

Step 1. Estimation of relative factor abundance (structural factor endowment). The capital-to-labor ratio (K/L) is calculated for each municipality and year, following the methodology proposed by [24] and refined by [19], who emphasizes the importance of quantifying relative factor endowments as a basis for determining comparative advantages. This step identifies which factor—capital or labor—is relatively more abundant in each territory and how this relationship evolves over time. The stability or variability of this endowment is key to interpreting specialization possibilities.

Step 2. Categorization of sectoral factor intensity (relative productive structure). The K/L ratio is calculated by the economic sector for each municipality, with the objective of classifying activities according to their factor intensity. This procedure follows the conceptual framework of [18] and has been applied in recent studies on smart specialization [15,48]. An activity is classified as capital-intensive if its K/L ratio exceeds the intersectoral average; otherwise, it is considered labor-intensive. This analysis is conducted longitudinally to detect structural persistence or change in the relative use of factors.

Step 3. Identification of factor alignment and optimal specialization (strategic matching): In the final stage, results from the previous steps are cross-referenced to identify sectors that intensively use the most abundant factor in each municipality. According to the HOS model, these sectors constitute optimal candidates for efficient productive specialization [59,62]. This strategic alignment between factor endowments and productive structure informs the allocation of public and private resources toward activities with the highest potential for sustainable growth, territorial resilience, and value generation [47].

Additionally, a temporal dimension is incorporated to assess whether changes in capital or labor flows have led to transformations in the local productive structure. This dynamic perspective is fundamental in emerging economies, where comparative advantages may evolve due to investment, technological shocks, or regulatory changes [36,43]. The analysis assumes that municipalities operate as open economic units without internal trade restrictions, thereby legitimizing the subnational application of the HOS model, as supported by [24], in comparative studies on occupational inequality across OECD countries.

4. Results

As previously established, according to the Heckscher–Ohlin–Samuelson (HOS) model, trade patterns are determined by two fundamental elements: the relative abundance of productive resources and the factor intensity associated with specific economic activities. Given that municipalities operate as autonomous administrative units, they may be analytically treated as equivalent to countries without internal trade barriers, following the logic of international trade theory [63,64]. Accordingly, this section presents and interprets the results derived from applying the HOS model using the available data. Capital is proxied by the total assets of formally registered enterprises, while labor is represented both by the economically active population (EAP) and the number of employees reported per productive unit [65,66].

4.1. Factor Abundance

Based on the definition of relative abundance, the identification of a territory’s factor depends on the proportion of one resource relative to another and its comparative weight with respect to other economies [67]. As noted by [68], this concept reflects an economic reality in which many countries can compete internationally by leveraging their abundant factor endowments, which allows them to produce at lower relative costs. This rationale helps explain, for example, the sustained economic growth experienced by countries such as China and India, whose comparative advantage is rooted in an abundant labor force [69,70]. In the present study, we analyze data for the municipalities of Tunja and Chiquinquirá over the 2017–2021 period.

Table 1. Abundance of productive factors in Tunja and Chiquinquirá.

Abundance		Capital (Millions)	Work (PEA)	K/L	L/K
2017	Tunja	2,865,682	81,672	35.09	0.03
	Chiquinquirá	169,950	25,023	6.79	0.15
2018	Tunja	2,904,924	83,026	34.99	0.03
	Chiquinquirá	181,927	25,442	7.15	0.14
2019	Tunja	4,115,690	85,328	48.23	0.02
	Chiquinquirá	186,983	26,045	7.18	0.14
2020	Tunja	4,423,248	86,833	50.94	0.02
	Chiquinquirá	197,402	26,569	7.43	0.13
2021	Tunja	3,765,257	88,363	42.61	0.02
	Chiquinquirá	229,343	27,035	8.48	0.12

presents a longitudinal comparison of the capital-to-labor (K/L) and labor-to-capital (L/K) ratios in Tunja and Chiquinquirá, revealing a persistent divergence in their relative factor endowments. Tunja consistently displays a high K/L ratio, indicating a capital-intensive productive structure that benefits from sustained investment in fixed assets. In contrast, Chiquinquirá maintains an elevated L/K ratio, reflecting a structural dependence on labor and a comparatively limited capital base. These distinct profiles align with the theoretical expectations of the HOS model: Tunja is structurally positioned to specialize in capital-intensive sectors such as construction, finance, and real estate, while Chiquinquirá exhibits conditions more favorable to labor-intensive activities like tourism, hospitality, and personal services. The temporal consistency of these patterns, even during macroeconomic disruptions, reinforces the argument that they are not transitional but reflect stable economic trajectories. The evidence calls for differentiated policy approaches tailored to each municipality’s endowment structure. Rather than applying uniform development strategies, regional planning should leverage local comparative advantages to enhance inter-municipal complementarities. Tunja and Chiquinquirá exemplify how productive specialization, if strategically guided, can serve as a foundation for balanced and sustainable territorial development.

4.2. Intensity of Values

Capital-to-labor (K/L) and labor-to-capital (L/K) ratios are calculated and ranked by economic activity to identify the sectors that are intensive in one factor or the other. This classification reveals relatively comparative advantages at the sectoral level based on the efficiency in the use of local resources. Unlike the general analysis of factor endowments, in this case, labor is measured by the number of formal employees per sector—according to records from the Chamber of Commerce of Tunja—rather than by the total economically active population. This methodological refinement enhances the precision of the analysis by capturing the actual intensity of labor use within the formal productive structure, allowing for a more rigorous identification of specialization patterns [71,72].

Table 2. Intensity of productive factor use by economic activity, 2017.

Activity	K Tunja	L Tunja	K/L Tunja	L/K Tunja	K Chiqui	L Chiqui	K/L Chiqui	L/K Chiqui
Agriculture, livestock, and others	13,246	132	100.35	0.01	13,325	72	185.07	0.01
Mining and quarrying	17,026	132	128.98	0.01	866	66	13.12	0.08
Manufacturing industry	66,793	1015	65.81	0.02	4137	242	17.09	0.06
Water distribution and others	56,608	563	100.55	0.01	479	24	19.97	0.05
Construction	333,463	1125	296.41	0.00	13,935	56	248.84	0.00
Vehicle trade and repair	394,428	5859	67.32	0.01	67,245	1407	47.79	0.02
Transport and storage	33,399	1268	26.34	0.04	23,992	335	71.62	0.01
Accommodation and meals	10,400	1258	8.27	0.12	2067	230	8.99	0.11
Information and communications	6251	950	6.58	0.15	435	50	8.69	0.12
Financial activities and insurance	370,175	3187	116.15	0.01	27,405	176	155.71	0.01
Real estate activities	102,043	177	576.52	0.00	1538	16	96.11	0.01
Professional, scientific, and technical activities	50,888	1049	48.51	0.02	3031	82	36.96	0.03
Administrative services activities	48,501	3135	15.47	0.06	305	62	4.93	0.20
Education	15,781	194	81.35	0.01	378	22	17.17	0.06
Health care activities	107,049	1577	67.88	0.01	3377	69	48.95	0.02
Artistic, entertainment, and recreation activities	33,066	348	95.02	0.01	59	37	1.59	0.63
Other service activities	46,474	1308	35.53	0.03	7370	167	44.13	0.02

offers a disaggregated view of the factor intensity of economic sectors in Tunja and Chiquinquirá, providing empirical granularity to the macro-level divergence observed in Table 1. In Tunja, sectors such as real estate, construction, mining and quarrying, and financial services consistently exhibit the highest capital-to-labor (K/L) ratios. These sectors are typically associated with long-term capital commitments, fixed infrastructure, and institutionalized financial mechanisms, characteristics commonly found in urban centers with advanced service economies. The prevalence of such activities reinforces the city’s comparative advantage in capital-intensive production structures. In contrast, Chiquinquirá demonstrates a sectoral composition with markedly higher labor-to-capital (L/K) ratios in industries such as administrative and support services, accommodation and food services, and arts and cultural activities.

These sectors exhibit low capital requirements but high labor absorption potential, often operating in informal or semi-formal settings with limited barriers to entry. This aligns with the characteristics of service-based, labor-reliant local economies, particularly those embedded in tourism and cultural sectors. The clear factorial duality between the two municipalities substantiates the theoretical premise of the Heckscher–Ohlin–Samuelson (HOS) model: regions should specialize in the production of goods and services that intensively use their relatively abundant factor. The data provides actionable guidance for territorial policy: Tunja’s strategy should leverage capital-intensive sectors to attract investment and reinforce infrastructure, while Chiquinquira should focus on enhancing labor productivity and upgrading service quality in labor-intensive domains.

Table 3. Intensity of productive factors by economic activity, 2021.

Activity	K Tunja	L Tunja	K/L Tunja	L/K Tunja	K Chiqui	L Chiqui	K/L Chiqui	L/K Chiqui
Agriculture, livestock, and others	67,518	146	462.46	0.00	5332	234	22.78	0.04
Mining and quarrying	19,993	295	67.77	0.01	2352	98	24.00	0.04
Manufacturing industry	83,792	1317	63.62	0.02	6488	481	13.49	0.07
Water distribution and others	76,142	322	236.47	0.00	963	295	3.27	0.31
Construction	545,728	2156	253.12	0.00	25,972	197	131.84	0.01
Vehicle trade and repair	416,964	8277	50.38	0.02	102,699	2527	40.64	0.02
Transport and storage	37,969	5853	6.49	0.15	22,507	2718	8.28	0.12
Accommodation and meals	135,924	2601	52.26	0.02	2596	671	3.87	0.26
Information and communications	23,513	6724	3.50	0.29	659	65	10.14	0.10
Financial activities and insurance	474,419	7257	65.37	0.02	34,451	120	287.09	0.00
Real estate activities	103,807	197	526.94	0.00	2861	25	114.42	0.01
Professional, scientific, and technical activities	104,794	1313	79.81	0.01	5505	136	40.48	0.02
Administrative services activities	67,069	35,063	1.91	0.52	1019	1072	0.95	1.05
Education	28,058	773	36.30	0.03	628	79	7.96	0.13
Health care activities	163,939	4264	38.45	0.03	5492	150	36.61	0.03
Artistic, entertainment, and recreation activities	33,066	348	95.02	0.01	59	37	1.59	0.63
Other service activities	46,474	1308	35.53	0.03	7370	167	44.13	0.02

refines the comparative analysis by ranking sectors according to their intensity in capital or labor use, highlighting the structural orientation of each municipality. In Tunja, the top-ranked sectors by capital intensity replicate those from Table 2, confirming the stability and coherence of the local productive matrix. Moreover, the presence of specialized activities such as professional, scientific, and technical services within the upper K/L tier signals the city’s potential to diversify within high value-added services, consolidating its position as a capital-driven urban economy. In Chiquinquirá, the top-ranked labor-intensive sectors reflect a distinct productive logic. The prominence of cultural, recreational, and hospitality-related services suggests a specialization pathway rooted in the symbolic and experiential economy. Unlike Tunja’s infrastructural dependence, Chiquinquirá productive logic appears to rely on endogenous human capital, social interaction, and tourism flows.

This distinction calls for differentiated development instruments and sector-specific support strategies that are context-sensitive and factor-aligned. The comparative sectoral ranking reinforces the relevance of adopting specialization policies tailored to structural realities rather than normative goals. In both cases, the sectoral profiles indicate stable endowment-based specialization patterns that can be used to guide investment priorities, human capital development, and inter-municipal complementarities.

Table 4. More capital-intensive activities in Tunja.

Activity	2017	2018	2019	2020	2021
Real estate activities	576.52	796.85	628.72	759.71	526.94
Construction	296.41	300.91	315.10	269.93	253.12
Mining and quarrying	128.98	152.95	59.50	48.04	67.77
Financial activities and insurance	116.15	325.95	245.18	468.15	65.37
Water distribution and others	100.55	355.99	137.10	150.59	236.47
Agriculture, livestock, and others	100.35	75.86	118.54	89.52	462.46
Other service activities	35.53	49.75	70.64	93.95	80.79

reveals a consistent pattern of capital-intensiveness in Tunja’s productive structure between 2017 and 2021. Real estate and construction activities firmly occupy the top positions in the K/L ranking throughout the period, followed closely by financial services, and—on a more intermittent basis—by water distribution and certain agricultural subsectors. This configuration reflects a structurally capital-oriented economy, driven by sectors that demand sustained investment in fixed assets, infrastructure, and regulatory compliance, rather than labor-intensive processes. Such a pattern is not only aligned with the characteristics of a departmental capital undergoing steady urban expansion and institutional densification but also signals a path-dependent specialization dynamic. The recurrence of the same sectors across multiple years reinforces the notion of a locked-in productive configuration, one that favors capital accumulation and scale economies over workforce expansion.

This trajectory, while potentially limiting labor absorption, is consistent with the core tenets of the Heckscher–Ohlin–Samuelson (HOS) model: territories should deepen their specialization in sectors that maximize the use of their most abundant factor—in this case, capital. From a policy standpoint, the evidence underscores the need for strategic support aimed at enhancing productivity, innovation, and vertical integration within these capital-intensive sectors. Rather than diversifying for its own sake, Tunja may benefit more from reinforcing its existing comparative advantage through targeted investment in infrastructure, financial intermediation, and advanced business services. Such a strategy not only aligns with the municipality’s structural profile but also offers stronger prospects for long-term economic scalability and fiscal sustainability.

Table 5. Most labor-intensive activities in Chiquinquirá.

Activity	2017	2018	2019	2020	2021
Artistic, entertainment, and recreation	0.63	0.41	0.86	0.47	0.58
Administrative services activities	0.20	0.15	0.35	0.26	1.05
Information and communications	0.12	0.07	0.08	0.10	0.10
Accommodation and meals	0.11	0.10	0.18	0.14	0.26
Mining and quarrying	0.08	0.02	0.05	0.07	0.04
Transport and storage	0.01	0.07	0.01	0.01	0.12
Water distribution and others	0.05	0.04	0.36	0.05	0.31
Education	0.06	0.07	0.16	0.16	0.13

highlights the consistent predominance of labor-intensive sectors in Chiquinquirá’s economic structure between 2017 and 2021. Activities related to arts, entertainment, and recreation, as well as administrative and support services and hospitality, repeatedly occupied the upper ranks in labor-to-capital (L/K) ratios. These sectors demonstrate a high capacity for labor absorption with minimal capital requirements, underscoring the municipality’s economic reliance on human-intensive production processes.

Rather than emerging as a short-term pattern, this configuration appears to reflect a stable trajectory of specialization rooted in the structural composition of Chiquinquirá’s factor endowments. The prevalence of sectors that depend heavily on labor rather than fixed capital investment reinforces the theoretical expectation that territories will gravitate toward production structures that optimize their most abundant input. Within this framework, Chiquinquirá’s profile is illustrative of a local economy shaped by endogenous labor dynamics and an ecosystem favorable to flexible service provision.

These findings suggest that policy interventions aimed at improving workforce skills, supporting small-scale service entrepreneurship, and consolidating tourism-related value chains could enhance the productivity and resilience of Chiquinquirá’s labor-intensive sectors. Rather than redirecting its economic model, the municipality would benefit from deepening its existing specialization while fostering innovation and inclusion within its current comparative advantage.

5. Discussion and Conclusions

This study aimed to assess the applicability of the Heckscher–Ohlin–Samuelson (HOS) model to analyze subnational productive specialization in emerging economies, using the cases of two intermediate municipalities in Colombia: Tunja and Chiquinquirá. The empirical findings confirm the core hypothesis: the alignment between local factor endowments and sectoral factor intensities leads to stable specialization trajectories and more efficient resource allocation at the territorial level.

Tunja exhibits a progressive accumulation of capital, supporting specialization in capital-intensive sectors such as real estate, financial services, construction, and professional activities. In contrast, Chiquinquirá maintains a labor-intensive structure, favoring tourism, hospitality, and recreational services. This persistent divergence in factor proportions is consistent with the HOS model’s predictions and demonstrates that even within the same region, territories can follow differentiated and complementary paths, provided their productive vocations are recognized and deliberately strengthened.

From a critical standpoint, while the HOS model provides a robust theoretical framework linking factor endowments to specialization patterns, its application at the subnational level raises several analytical challenges. The model rests on assumption perfect competition, factor mobility, and institutional neutrality—which are rarely met in intermediate territories marked by market failures, informality, and governance asymmetries. Moreover, the static nature of the model does not account for dynamic processes such as technological learning, institutional evolution, or human capital upgrading, all of which are central to contemporary development trajectories [43,73].

Methodologically, the approach employed—based on the triangulation of relative factor abundance, sectoral factor intensity, and strategic alignment—enabled the construction of an empirically grounded diagnostic framework. Beyond its explanatory value, this methodology offers a replicable tool for territorial planning in emerging economies, particularly those characterized by structural disparities and sectoral concentration [74].

5.1. Policy Implications

The findings have significant implications for public policy. First, they underscore the importance of adopting territorially differentiated development strategies [2]. One-size-fits-all interventions fail to reflect the diversity of local capacities [75]. For Tunja, policy should focus on strengthening high value-added, technology-intensive sectors, attracting investment, and reinforcing financial infrastructure and human capital.

In contrast, Chiquinquirá would benefit from policies aimed at enhancing labor-intensive sectors with strong employment potential, such as cultural tourism and hospitality services. These efforts must be supported by targeted investment in education, infrastructure, and local coordination mechanisms.

Furthermore, the model’s application provides an analytical foundation for designing cluster-based development strategies and productive specialization policies. Local and regional governments can use this evidence to align public investment with structural efficiency criteria, fostering endogenous development and increasing territorial resilience. Smart specialization, when anchored in local comparative advantages, can become a driver of inclusive growth and long-term competitiveness.

5.2. Limitations and Future Research Directions

This research is not without limitations. First, capital was proxied by total assets of formal enterprises, which may underestimate available capital in areas with high informality. Likewise, employment data reflects formal labor only, omitting informal dynamics and the quality dimension of jobs. The static character of the HOS model restricts its ability to capture gradual transformations linked to innovation, institutional reform, or entrepreneurial evolution. Finally, the study’s scope—limited to two municipalities—precludes broader generalization, though the methodology remains transferable to other territorial contexts.

To overcome these limitations, future studies should incorporate dynamic elements and institutional variables. Integrating indicators of capital and labor quality, innovation networks, and governance capabilities would enhance the explanatory power of the framework. Mixed-method approaches combining quantitative analysis with qualitative fieldworks such as interviews with key stakeholders or social network mapping could yield deeper insights into the mechanisms behind specialization.

Moreover, extending the analysis to a broader set of municipalities or conducting cross-regional comparisons would enable the assessment of inter-territorial synergies or conflicts and identify broader specialization patterns. Future research may also explore the interaction between productive specialization and sustainability, social inclusion, or technological diffusion, thus enriching the scope of territorial development analysis.