Place-based approach to rural development: Ethiopia in context

Wazza, Melkamu Tadesse; Ayele, Seife; Shano, Berhanu Kuma Article Place-based approach to rural development: Ethiopia in context Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Wazza, Melkamu Tadesse; Ayele, Seife; Shano, Berhanu Kuma (2025) : Placebased approach to rural development: Ethiopia in context, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 3, pp. 1-29, https://doi.org/10.3390/economies13030061 This Version is available at: https://hdl.handle.net/10419/329341 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Academic Editor: Richard J. Cebula Received: 12 December 2024 Revised: 3 February 2025 Accepted: 4 February 2025 Published: 22 February 2025 Citation: Wazza, M. T., Ayele, S., & Shano, B. K. (2025). Place-Based Approach to Rural Development: Ethiopia in Context. Economies,13(3), 61. https://doi.org/10.3390/ economies13030061 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Place-Based Approach to Rural Development: Ethiopia in Context Melkamu Tadesse Wazza 1,2,* , Seife Ayele 3and Berhanu Kuma Shano 4 1Department of Rural Development, Wolaita Sodo University, Sodo P.O. Box 138, Ethiopia 2Department of Project Management, PBTAfrica College, Addis Ababa P.O. Box 62045, Ethiopia 3Institute of Development Studies, University of Sussex, Brighton BN1 9RH, UK; [email protected] 4Department of Agricultural Economics, Wolaita Sodo University, Sodo P.O. Box 138, Ethiopia; [email protected] *Correspondence: [email protected] or [email protected] Abstract: Place-specific socioeconomic features are unique and, unlike first-nature geography, are shaped and reshaped by human and institutional interactions. In Ethiopia, however, policy thinking has not progressed much beyond first-nature geography, overlooking the multidimensional socio-spatial formations of rural areas. This study, based on nationally representative socioeconomic panel data from 2018/19 and 2021/22, used a place-based framework to explore the complex nature of rural development and its relationship with multidimensional, place-specific key determinants, namely rurality and entrepreneurial ecosystems. Indices for the key variables were developed by reducing their dimensions using Principal Component Analysis to measure multidimensional variables, including rural development, and undertake subsequent examinations. The study examines the effects of the key determinants on rural development using the Fixed Effects Instrumental Variables–Two-Stage Least Squares regression model, owing to endogeneity concerns with the key determinants. The study shows significant effects of both rurality and entrepreneurial ecosystems on rural development. It offers insights into the complex socio-spatial formations and explanatory power of rural contexts and contributes to the understanding of a place-based approach to rural development. The study also contributes to national and sub-national strategies to address rural challenges in Ethiopia and beyond. Keywords: socio-spatial; place-based; rurality; entrepreneurial ecosystem; FE-2SLS 1. Introduction Africa is primarily rural (Christiaensen & Maertens,2022), yet it struggles with critical policy challenges in rural development (RD). Particularly, Sub-Saharan countries lack a clear focus on RD, and often, there is a narrow emphasis on agricultural productivity. They also inadequately adapt to changing socioeconomic conditions, such as high rural fertility, ruralto-urban migration and regional inequalities (OECD,2016;OECD/PSI,2020). Moreover, they overlook growth potential through a place-based approach (OECD,2016, p. 181). For instance, Côte d’Ivoire and Tanzania present a compelling case. Côte d’Ivoire focused on agricultural productivity and has achieved impressive results in farming. Likewise, Tanzania pursued an explicit RD strategy and achieved notable economic growth since 2000. However, Côte d’Ivoire still confronts extreme poverty and low human development, and Tanzania has struggled to translate growth into improved rural socioeconomic welfare. While the labour force is growing rapidly, African countries including Ethiopia are not offering adequate job opportunities (OECD,2016;OECD/PSI,2020). Economies 2025,13, 61 https://doi.org/10.3390/economies13030061 Economies 2025,13, 61 2 of 29 With 130 million inhabitants, and being 77% rural, Ethiopia is the second most populous and the fifth most rural country in Africa (OECD/PSI,2020;Trading Economics, 2024). It implemented the world’s most prominent Chilalo Agricultural Development Unit (CADU) and Wolaita Agricultural Development Unit (WADU) rural programmes five decades back (Cohen,1987). However, existing rural development strategies in Ethiopia often overlook the diverse socioeconomic contexts of rural areas (OECD/PSI,2020), relying on the defective conceptualization of rural areas that classify areas as non-urban spaces, mainly using population threshold (FAO,2018). While rural areas are shaped and reshaped by complex interactions between human and institutional factors affecting their development trajectories (Gaddefors & Anderson,2019;Lazaro et al.,2019;Zahra et al.,2014), policy thinking in the country has not progressed much beyond first-nature geography, overlooking the multidimensional socio-spatial formations of rural areas, making it a key study country for this topic. Against this background, this study aims to understand the complex socio-spatial nature of rural development and its relationship with key multidimensional, place-specific determinants, namely rurality and the entrepreneurial ecosystem, to inform effective policymaking. Drawing from the concepts, theories, and empirical evidence and using the Living Standards Measurement Study (LSMS) data for Ethiopia, it tests a central hypothesis (H) that multidimensional, place-specific features are key determinants of rural development (RD), expounded with two specific hypotheses: H 1 : rurality (RUR) of a place has a statistically significant effect on RD, and H 2 : the entrepreneurial ecosystem (EE) of a place has a statistically significant effect on RD. In associating these determinants directly with RD and quantifying their effects, to the best of the authors’ knowledge, there are no substantive studies, particularly in Ethiopia. The existing knowledge base in understanding such socio-spatial dimensions in Ethiopia is limited. The failure to improve this may result in difficulty in addressing the persistent socio-spatial micro-level challenges such as inequalities and migration. Most studies on economic growth in Ethiopia are not directly related to the rural context; instead, they differ from the present study in various common aspects. They often focus on the macro-economy, rely on (real) GDP as a singular indicator of economic performance, and have no substantive concern in distinguishing urban and rural spatial settings (Alemu et al., 2019;Negera,2021;Tesfaye & Bekana,2020;Rao & Bedada,2017). Studies from Africa also primarily emphasize macroeconomic determinants that underplay the detrimental role played by local communities and multidimensional development aspects (Nguyen,2023; Sendi et al.,2022;Thaddeus et al.,2024). Though GDP is a popular economic indicator, it falls short of assessing RD as it emphasizes monetary values (Kaˇcar et al.,2016). In rural economies like Ethiopia, important economic activities such as subsistence farming, community services and environmental concerns are vital for livelihoods but are often ignored in GDP measurements. Thus GDP overlooks the essential and multidimensional aspects of RD. This study addresses the drawbacks of GDP by directly measuring and analyzing RD. Moreover, GDP data are unavailable at the grassroots (micro) level for many Sub-Saharan countries, including Ethiopia, where one cannot find them even at a regional level, usually due to a lack of statistical capacity (Gates,2013). Thus, direct measurement has an additional benefit in tackling data unavailability. The micro-level evidence available in Ethiopia tends to emphasize individual factors or was merely collected at the sub-national level, or without concern for endogeneity problems (see Kruseman et al.,2006;Ogunleye,2010;Panahi,2015;Yilmaz et al.,2010). The empirical representation of place-specific factors is commonly limited to simple and single-dimensional variables such as market access (Hausmann et al.,2021). Unlike some experiences in objectively analyzing rurality and entrepreneurial ecosystems (Leendertse Economies 2025,13, 61 3 of 29 et al.,2021;Li et al.,2015;Sánchez-Mateos,2018;Yilmaz et al.,2010), the present study identifies these indicators as multidimensional place-specific regressors and qualifies them as key explanatory powers. The authors are unaware of a study that applied multidimensional regressors as factors directly affecting RD, or if so, at least empirically established to what extent such entities explain RD, anywhere or at least in Ethiopia. This study leverages quantitative panel data and effectively confronts the challenges of place heterogeneities and endogeneities, offering considerable technical advantages (see Greene,2020; Wooldridge,2020). In line with the shift in the concepts of RD to a territorial, “bottom-up” approach that acknowledges rural places (Beer et al.,2020;Cattaneo et al.,2022), this study applies place-based development modelling and offers insight into the rural context’s complex socio-spatial formations. It further underscores the explanatory nature of the multidimensional socio-spatial features in driving socioeconomic changes. The study informs policymakers and practitioners of “what” and “how” to target the fundamental sociospatial concerns feasibly. The analytical framework enables the effective identification and measurement of place-specific features and highlights the spatial interplay that can help the growing conceptualization of a place-based approach. Adopting a place-based approach and integrating it into broader regional and national strategies will help address the unique rural challenges and enhance policy effectiveness in Ethiopia and beyond. The rest of the paper includes five parts. First, a short account of the place-based approach is presented. Following this, the determinants of RD, data and methods, key findings, discussions, and conclusions are elaborated on. 2. Place-Based Approach and Determinants of Rural Development 2.1. Place-Based Approach to Rural Development: Spatial Concepts and Empirical Evidence Rural development (RD) is a broad concept that incorporates agricultural and nonagricultural activities, with due emphasis on improving the living standards of rural society (Singh & Shishodia,2023). The approaches in addressing RD have shifted from their simplistic approaches to tackle the ‘falling’ rural areas with urbanization, modernization, and industrialization into the broader concept of a territorial model and more toward community and place-based approaches (Beer et al.,2020;Cattaneo et al.,2022) that emphasize concepts of space, place, and territory. Space is the basic layer embracing territory and place. It is geographically conceptualized with location, territory, and place (Duarte,2017). Both territory and place are slices of space with which people’s (individual, group, or social) meaningful interactions and values are ascribed. The place is a specific geographic location where people live, and it exhibits affective and subjective values. Territory on the other hand has enforced values that tend to govern the occupants (pp. 4, 79). The place-based approach to rural development (PBRD) is an approach to development that seeks to achieve desired socioeconomic changes in a given geographic place such as local rural areas or regions (Beer et al.,2020). Though connoted as place-based, it is ultimately designed to improve the well-being of rural people, making people–place duality unnatural (Byron,2010). The underlying principle of the place-based approach is equity and efficiency. While the equity concern is due to commonly seen uneven spatial development, the efficiency concern is due to market failure (Duranton & Venables,2021). In this regard, the PBRD approach recognizes two essential elements: (i) the unique identity and sense of place inherent in communities, and (ii) the tailoring of local strategies with regional and national policies. The first element focuses on the thematic concerns of a place. This enables leveraging tangible and intangible unique endowments and bottom-up planning. The emphasis thus is on the needs and pressing concerns of the local (regional) development (Manioudis & Economies 2025,13, 61 4 of 29 Angelakis,2023). In a heterogeneous context of a place, the approach goes beyond the traditional sectors such as manufacturing or finance to further promote the growing sector of alternative economies such as creative economies such as craft, gaming, and media. These sectors differ from traditional capitalist models that intensify inequalities and environmental challenges. This economy relies less on natural resources and notably offers job opportunities for young citizens and women compared to traditional sectors and helps efficiently drive development in local areas (Healy,2020;Manioudis & Angelakis,2023). The second element, i.e., tailoring, emphasizes the importance of the interaction and interdependence between local and extra-local factors, and active community engagement (Manioudis & Angelakis,2023). Consequently, the place-based approach encourages a socio-spatial understanding of both local and broader factors. Between the local and the broader national and global factors are regional factors serving as a part of a larger national strategy aimed at addressing socioeconomic challenges, including regional inequalities, economic stagnation, and unemployment. Place-oriented regional planning thus recognizes the significance of place-specific features such as rurality and local entrepreneurship and innovation (Beer et al.,2020). Entrepreneurship is usually a regional phenomenon and when connecting and interacting with other components to form an EE, it has a strong and effective capacity to mobilize the capital, labour, and resources of places (O’Connor et al.,2018). Such mechanisms urge harnessing the untapped local potential and addressing social exclusion in a specific (regional) context. Thus, the PBRD approach through its equity and efficiency concerns promotes sustainable (regional) development (Almusaed & Almssad,2023). The prominent proponents of place-based policymaking include the EU, OECD, the Australian government, and AfDB (African Development Bank) (AfDB/OECD,2015; Geatches et al.,2023;OECD/PSI,2020). With the place-based approach, while the EU addresses continental challenges such as population decline, ageing, and limitations in social services (Barca et al.,2012;European Union,2022), the Australian government deals with area marginalization and regional inequalities (Geatches et al.,2023). The application of the approach is growing in African countries such as Nigeria and South Africa (Abagna et al.,2024;AfDB/OECD,2015;Pugalis & Gray,2016). In Nigeria, for instance, while Lawal and Osayomi (2021) applied cross-sectional place-based modelling for COVID-19 social vulnerability analysis, KTN (2021) conducted a place-based innovation audit in a rural state. Using a three-decade (1990–2020) household dataset from 10 African countries, Abagna et al. (2024) also showed the effectiveness of a place-based policy (Special Economic Zones—SEZs) on household wealth 1 . An OECD study on Ethiopia argued against the rural–urban divide and recommended shifting away from a sectoral-based policy to a PBRD (OECD/PSI,2020, pp. 22, 102) that strengthens rural–urban linkage and addresses growing regional inequalities (p. 162). 2.2. Determinants of Rural Development Determinants of economic development are complex and vary across and within countries (Nguyen,2023). They are generally grouped into economic (e.g., resources and capital) and noneconomic (e.g., institutions) categories (Jhingan,2016). Within this broad framework, several factors affect the level of RD such as resource endowments, human resources, available capital, cutting-edge technology, and the effectiveness of institutions and organizations (Agarwal et al.,2009;Banakar & Patil,2018;Singh & Shishodia,2023; Tae-Hwa & Seung-Ryong,2016). Understanding the interactions between each of these various factors in spatial and social settings is essential for a successful policy (Gaddefors & Anderson,2019;Lazaro et al.,2019). Economies 2025,13, 61 5 of 29 Recent empirical research on factors affecting RD was performed in different parts of the world such as Turkey (Yilmaz et al.,2010), Iran (Panahi,2015), and Nigeria (Ogunleye,2010), and review work in Czech (Straka & Tuzová,2016), among others. Evidence from Turkey’s rural province indicated that RD is affected by natural, land-use, demographic, infrastructure, and socioeconomic structures (Yilmaz et al.,2010). In determining development in a rural context, i.e., RD, resource endowments such as land and livestock; technology such as improved seed, fertilizers, and machinery; and human capital such as general and technical education are important factors (Singh & Shishodia,2023). Local organizations and institutions generally apply to the optimum utilization of production factors (Jhingan,2016). In rural Poland, for instance, increased institutional quality was associated with higher socioeconomic development (Bartkowiak-Bakun,2018). Moreover, RD is affected not only by local factors but also by extra-local factors (Kim,2024) such as urban agglomeration (Frick & Rodríguez-Pose,2017), continental road network (Pavel & Moldovan,2019), and globalization (Beer et al.,2020). In understanding the determinants of rural development, the socio-spatial conceptualization of rural areas is also helpful as socio-spatial factors are among the major determinants of RD (Agarwal et al.,2009). Beyond being a geographic entity, space is where socioeconomic phenomena are put together and shaped. Such socioeconomic interactions produce a place with distinct features that act as explanatory factors (Beer et al.,2020; Coe et al.,2013). The distinctiveness of a place (within and between territories) emerges from its physical nature (e.g., resource endowment, touristic and settlement landscape) as well as from human activity (e.g., forms of government, culture, religion, wealth status, built environment, social relationships, etc.), and the interaction between each of these. A place is formed of historical and existing changes within an area and elsewhere (Coe et al., 2013). Moreover, a place is a secure portion of space, and one experiences space through a sense of place—a factor in geographic identity that is acknowledged as intangible capital within a geographic space (a rural place) that can affect labour availability, entrepreneurship, and commodity supply (Bolton,1992). Thus, a place with socio-spatial features and interactions affects RD (Beer et al.,2020;Coe et al.,2013;Gaddefors & Anderson,2019; Zahra et al.,2014). The major socio-spatial features influencing rural development include rurality (RUR) and the rural entrepreneurial ecosystem (EE). Rurality embodies a unique socioeconomic identity shaped by a strong sense of place, socioeconomic interactions, and historical context (Woods,2011). The rural entrepreneurial ecosystem represents the interactions among entrepreneurial players—entrepreneurs, businesses, government organizations, and research institutions—that can either support or hinder entrepreneurial performances (Stam,2015;World Economic Forum,2013). While entrepreneurship is often driven by individual initiative, it is meaningfully affected by unique rural contexts, which, with its marked socio-spatial feature, shapes rural entrepreneurship differently than general/urban entrepreneurship (Muñoz & Kimmitt,2019). Rural areas are characterized by strong social interaction and are sensitive to market demands, but they face challenges such as limited access to finance, skills, technology, infrastructure, and distribution channels. Unlike urban entrepreneurship, which focuses on seizing opportunities, rural entrepreneurship primarily relies on available resources (Asmit et al.,2024;Pato & Teixeira,2014). 2.3. Rural Development in Ethiopia Since the mid-1990s, “agriculture and rural development (ARD)” has been the focus of Ethiopia’s development strategy, guided by the ADLI’s (Agricultural Development-Led Industrialization) framework. The ADLI’s main objective is to increase the agricultural productivity and the productive capacity of smallholder farmers. ADLI helped to bring Economies 2025,13, 61 6 of 29 forth several subsequent development plans including the Sustainable Development and Poverty Reduction Program (SDPRP) and the Growth and Transformation Plans (GTP I and II) (OECD/PSI,2020). Within the ADLI framework, the most notable RD strategy is the “Rural Development Policy and Strategies”, endorsed in 2003; though it considers agroecological variations (MoFED,2003, p. 16), it fails to acknowledge the overall socioeconomic heterogeneities of rural areas. Following the design and implementation of those plans and strategies, albeit with uneven outcomes, the country has experienced significant economic growth, particularly since 2004. The current regime, in place since 2019, has made no major change to the approach. Recently, the country implemented a ten-year perspective plan of the country (2021–2030). However, the plan continues to prioritize smallholder agriculture and transformation into commercial farming, although private sector involvement remains limited. The plan does not comprehensive rural development model. Rather, there is a high emphasis on urban development (see PDC,2020). The overall policy directions, though with registered GDP growth, failed to achieve economic transformation and the much-required quality growth in Ethiopia. A recent OECD/PSI study identified three main challenges for Ethiopia: demographic, economic, and spatial. The ongoing population growth, particularly in rural areas, poses significant labour market challenges. Ethiopia is experiencing a decline in agricultural GDP and employment, yet agriculture still employs a remarkable percentage of the workforce (73%). For most rural households, crop farming is essential, contributing over 70% of their income, while the non-farm sector plays a minimal role. The OECD/PSI study underscored that the ADLI framework struggles to address the current issues (OECD/PSI,2020). 3. The Analytical Framework for Place-Based Rural Development This study applies a place-based analytical framework which structures the different socio-spatial factors affecting rural development. It frames the factors in a specific place taking account of the formation of multidimensional, place-specific determinants with local and extra-local interplay. In this context, endogenous (local/micro-level) and exogenous (extra-local/macro) forces interact within and between rural areas. Such interactions usually generate multidimensional factors unique to a place. As illustrated in Figure 1, these forces influence rural development (RD), empowering grassroots communities. In this study, the unique features of rural places—specifically rurality (RUR) and entrepreneurial ecosystems (EEs)—are assumed to be key socio-spatial determinants. Thus, rural areas are not merely first-nature geography or non-urban homogenous spaces. They are complex socio-spatial contexts integral to development processes. Because economic development has a spatial dimension (Komor,2020) and places, with their varying socio-spatial features and interactions, affect RD (Beer et al.,2020;Coe et al.,2013;Gaddefors & Anderson,2019; Zahra et al.,2014), a rural place with a spatial element acts as an important factor affecting RD (Komor,2020). Here, it is important to underscore the basic notion underlying RUR and RD. RUR represents the unique socio-spatial characteristics of a rural place, and RD, on the contrary, reflects the level of development achieved. It is the outcome of the development process shaped by various place-specific factors, including RUR. While RUR can be quantified through aspects such as agricultural engagement, population size, or coverage of communal spaces, measuring RD requires a different set of indicators. These include evaluating productivity levels (of labour or land) and consumption levels (both food and non-food). Ultimately, the concept of RD seeks to capture the results of a development process, while RUR aims to define the attributes that characterize a specific area. Economies 2025,13, 61 7 of 29 Economies 2025, 13, x FOR PEER REVIEW 7 of 31 Figure 1. A place-based analytical framework for the proposed RD process (authors’ presentation). Here, it is important to underscore the basic notion underlying RUR and RD. RUR represents the unique socio-spatial characteristics of a rural place, and RD, on the contrary, reflects the level of development achieved. It is the outcome of the development process shaped by various place-specific factors, including RUR. While RUR can be quantified through aspects such as agricultural engagement, population size, or coverage of communal spaces, measuring RD requires a different set of indicators. These include evaluating productivity levels (of labour or land) and consumption levels (both food and nonfood). Ultimately, the concept of RD seeks to capture the results of a development process, while RUR aims to define the aributes that characterize a specific area. 4. Data and Methods 4.1. The Study Area and Data This study was conducted in Ethiopia, a federal country in East Africa. With over 130 million people (in 2024), it is Africa’s second most populous nation, and with a 76.84% rural population, it is the fifth most rural country (Trading Economics, 2024). Known for its diverse geography, culture, and livelihood (Lie & Mesfin, 2018; OECD/PSI, 2020), Ethiopia’s economy relies heavily on agriculture, with smallholder farmers accounting for 90% of production. However, the country faces significant challenges, particularly land fragmentation; 40% of landholders cultivate less than 0.5 hectares each (OECD/PSI, 2020). This study employs nationally representative panel datasets from the Ethiopian Socioeconomic Panel Survey (Panel II: ESPS 4 for 2018/19 and ESPS 5 for 2021/22), which is produced by the Ethiopian Statistical Service (ESS, formerly the Central Statistical Agency), in partnership with the Living Standards Measurement Study (LSMS) of the World Bank. The sample units are the same within the panel. Data were collected from predefined rural and urban centres, including small, medium, and large towns, covering community, household, and agricultural activities, with specific aention to crops and Figure 1. A place-based analytical framework for the proposed RD process (authors’ presentation). 4. Data and Methods 4.1. The Study Area and Data This study was conducted in Ethiopia, a federal country in East Africa. With over 130 million people (in 2024), it is Africa’s second most populous nation, and with a 76.84% rural population, it is the fifth most rural country (Trading Economics,2024). Known for its diverse geography, culture, and livelihood (Lie & Mesfin,2018;OECD/PSI,2020), Ethiopia’s economy relies heavily on agriculture, with smallholder farmers accounting for 90% of production. However, the country faces significant challenges, particularly land fragmentation; 40% of landholders cultivate less than 0.5 hectares each (OECD/PSI,2020). This study employs nationally representative panel datasets from the Ethiopian Socioeconomic Panel Survey (Panel II: ESPS 4 for 2018/19 and ESPS 5 for 2021/22), which is produced by the Ethiopian Statistical Service (ESS, formerly the Central Statistical Agency), in partnership with the Living Standards Measurement Study (LSMS) of the World Bank. The sample units are the same within the panel. Data were collected from predefined rural and urban centres, including small, medium, and large towns, covering community, household, and agricultural activities, with specific attention to crops and livestock production. Enumeration Areas (EAs), the smallest local-level sampling units, served as the study’s unit of analysis (ESS and LSMS,2024). In this study, the EA is a specifically defined geographic section of a community representing a rural place. EAs effectively represent rural communities, allowing for detailed micro-level analysis and the consideration of broader sub-national effects. A two-stage probability sampling technique was used for data collection, with EAs selected in proportion to their size within regions, followed by household selection. The focus on rural development (RD) restricts the analysis to EAs in rural areas and small towns, excluding medium and large urban centres, ensuring the findings are relevant to rural contexts and enhancing this study’s applicability (see Table 1). Economies 2025,13, 61 8 of 29 Table 1. Survey datasets with their basic statistics. Description Planned Observation Observations in the Study a Year ESPS-4 ESPS-5 ESPS-4 ESPS-5 Number of Enumeration Areas: Rural 297 223 262 b223 Urban 244 215 — — Total 541 438 262 223 Number of Households: Rural 3239 2325 2841 2325 Urban 3655 2674 — — Total 6894 4999 2841 2325 Note: a Some EAs in the country and the whole Tigray region were not included in the ESPS-5 due to security reasons, b Because follow-up (ESPS-5) data from Tigray region were not available, Tigray EAs from ESPS-4 were also excluded. Source: authors’ presentation. 4.2. Variables and Measurement Using Principal Component Analysis The major variables employed are as follows. (1) Dependent variable: rural development (RD) This study constructed the dependent variable, RD, as a composite variable by adapting the dimensions and variables presented by Singh and Shishodia (2023). The status of RD was measured by including potential outcome variables, making it a “result index” (see Table 2). According to Tae-Hwa and Seung-Ryong (2016), a result index is a component value made of different indicator domains to investigate the level of development. Development in Ethiopia is also explained by improvement in agricultural productivity and the level of investment in public infrastructure (Dube et al.,2019). Table 2. Domains and indicators employed in the index construction for rural development. No. Domains and Indicators Description and Unit of Measurement A Agricultural productive efficiency 1 Land (agricultural) productivity Crop harvested in quintals per farm size (crop yield) 2 Labour (agricultural) productivity a Total crop harvested in kilogram per total man-hours (adult equivalent adjusted) (labour activity post-planting and post-harvesting season) B Workforce diversification 3 Percent non-agricultural workforce Total off-farm employment per working-age population) × 100 C Rural educational and health infrastructure 4 Primary and secondary schools Number 5 Primary health centres Number 6Electrified facilities: primary and secondary schools Number D Rural amenities 7 Rural HHs with drinking water (HH members using clean drinking water in a place/population size) ×100 (%) 8 Rural HHs with electricity connection (HH members with electricity utilities in a place/population size) ×100 (%) 9 Rural HHs with toilets (HH members with toilet facilities in a place/population size) ×100 (%) Economies 2025,13, 61 15 of 29 socioeconomic factors like education, employment, safe water, and sanitation, Kuznar discusses the rural–urban gap. However, this study uncovers remarkable disparities within rural areas. Given regions in Ethiopia are structured by ethnic criteria, inequalities in line with the ethnicity of Kuznar’s findings also have implications for regional disparities. Economies 2025, 13, x FOR PEER REVIEW 15 of 31 This study showed marked regional inequalities in the mean score values of RD, RUR, and EE across regions (Figure 4). Rural Oromia, Gambela, and Harari rank highest in development, and Somali, Amhara, and Afar score the lowest in descending order. While Oromia is leading in RD, Afar is at the boom. Additionally, the Somali, Afar, and Benishangul-Gumuz regions exhibited the highest rurality, with Somali having the most. In terms of EE, Oromia ranks highest, followed by Amhara and the former SNNP (Southern Nations, Nationalities, and Peoples) region. These findings reveal substantial performance differences among rural areas11. The regional inequalities found agree with earlier findings in Ethiopia by Argaw (2017) and Kuznar (2019), who aributed disparities to differences in opportunities, socioeconomic status, and ethnic diversities. While Argaw focuses on socioeconomic factors like education, employment, safe water, and sanitation, Kuznar discusses the rural–urban gap. However, this study uncovers remarkable disparities within rural areas. Given regions in Ethiopia are structured by ethnic criteria, inequalities in line with the ethnicity of Kuznar’s findings also have implications for regional disparities. Figure 3. Paerns of RD, RUR, and EE over time. Authors’ representation. Figure 3. Patterns of RD, RUR, and EE over time. Authors’ representation. Economies 2025, 13, x FOR PEER REVIEW 16 of 31 Figure 4. The extent of RD, RUR, and EE in the regional states of Ethiopia. Authors’ representation. Such disparities in development levels call for urgent, place-based interventions (Morei, 2022). Remote rural areas are struggling with population decline in Europe (Castillo et al., 2023) and economic stagnation in China (Li et al., 2015). The regional differences make private and public service delivery difficult, worsening regional inequalities (Castillo et al., 2023). Place-based strategies are generally more relevant to specific challenges and marginalization, as they utilize local social dynamics and networks (Winterton et al., 2014). Initiatives for PBRD are gaining recognition in Europe (SánchezZamora & Gallardo-Cobos, 2020), Australia (Geatches et al., 2023), the USA (Parker et al., 2022) and Africa (AfDB/OECD, 2015), with Ethiopia alerted to adopt similar solutions (OECD/PSI, 2020, pp. 22, 104). The PBRD approach aligns with Ethiopia’s constitutional commitment to federation and decentralization that aims for inclusive growth. Also, by addressing local needs, the approach can enhance the low entrepreneurship and strengthen the weak rural–urban linkages in the country. 5.3. Findings from the Principal Component Analysis After varimax rotation, four components (Comp1, Comp2, Comp3, and Comp4) were extracted in the PCA of RD with proportions of 0.2188, 0.1490, 0.1406, and 0.1371 in the first survey year 2018/19; and 0.1939, 0.1860, 0.1557 and 0.1124 in the second survey year 2021/22 (Table 4). The first and the overall components have jointly explained a high percentage of the total variation in the original dataset of both survey years. While the first component value was 21.9% and 19.4%, for ESPS-4 and ESPS-5, respectively, the overall variation in the data explained by the four components was 64.55% and 64.8%, respectively, which implies that the variation explained is adequate to determine the structure of RD (Finch, 2013), and the model, thus, is acceptable (Hair et al., 2019). Table 4. Initial component extraction of rural development, Statistics for Varimax Rotated Components. Figure 4. The extent of RD, RUR, and EE in the regional states of Ethiopia. Authors’ representation. Such disparities in development levels call for urgent, place-based interventions (Moretti,2022). Remote rural areas are struggling with population decline in Europe (Castillo et al.,2023) and economic stagnation in China (Li et al.,2015). The regional differ- Economies 2025,13, 61 16 of 29 ences make private and public service delivery difficult, worsening regional inequalities (Castillo et al.,2023). Place-based strategies are generally more relevant to specific challenges and marginalization, as they utilize local social dynamics and networks (Winterton et al.,2014). Initiatives for PBRD are gaining recognition in Europe (Sánchez-Zamora & Gallardo-Cobos,2020), Australia (Geatches et al.,2023), the USA (Parker et al.,2022) and Africa (AfDB/OECD,2015), with Ethiopia alerted to adopt similar solutions (OECD/PSI, 2020, pp. 22, 104). The PBRD approach aligns with Ethiopia’s constitutional commitment to federation and decentralization that aims for inclusive growth. Also, by addressing local needs, the approach can enhance the low entrepreneurship and strengthen the weak rural–urban linkages in the country. 5.3. Findings from the Principal Component Analysis After varimax rotation, four components (Comp1, Comp2, Comp3, and Comp4) were extracted in the PCA of RD with proportions of 0.2188, 0.1490, 0.1406, and 0.1371 in the first survey year 2018/19; and 0.1939, 0.1860, 0.1557 and 0.1124 in the second survey year 2021/22 (Table 4). The first and the overall components have jointly explained a high percentage of the total variation in the original dataset of both survey years. While the first component value was 21.9% and 19.4%, for ESPS-4 and ESPS-5, respectively, the overall variation in the data explained by the four components was 64.55% and 64.8%, respectively, which implies that the variation explained is adequate to determine the structure of RD (Finch,2013), and the model, thus, is acceptable (Hair et al.,2019). Table 4. Initial component extraction of rural development, Statistics for Varimax Rotated Components. Survey Data: ESPS-4 ESPS-5 Component Variance Proportion Cumulative Component Variance Proportion Cumulative Comp1 1.9695 0.2188 0.2188 Comp1 2.32653 0.1939 0.1939 Comp2 1.3409 0.1490 0.3678 Comp2 2.23201 0.1860 0.3799 Comp3 1.2654 0.1406 0.5084 Comp3 1.8688 0.1557 0.5356 Comp4 1.2337 0.1371 0.6455 Comp4 1.3483 0.1124 0.6480 Comp5 0.89720 0.0997 0.7452 Comp5 0.90972 0.0758 0.7238 Comp6 0.69803 0.0776 0.8228 Comp6 0.70951 0.0591 0.7829 Comp7 0.63911 0.0710 0.8938 Comp7 0.65126 0.0543 0.8372 Comp8 0.54929 0.0610 0.9548 Comp8 0.57741 0.0481 0.8853 Comp9 0.40681 0.0452 1.0000 Comp9 0.48477 0.0404 0.9257 Comp10 0.44418 0.0370 0.9627 Comp11 0.31252 0.0260 0.9888 Comp12 0.13495 0.0112 1.0000 ESPS-4 ESPS-5 Number of observations 255 218 Number of components 9 12 Kaiser–Meyer–Olkin measure of sampling adequacy (KMO) (overall): 0.6278 0.6454 Determinant of the correlation matrix 0.338 0.026 Bartlett test of sphericity a Chi-square = 271.204 775.493 p-value = 0.000 0.000 Note: aH0: Variables are not intercorrelated. The final best model is obtained from several trials and errors, which ended in 9 and 12 variables in the ESPS-4 and ESPS-5, respectively. See Table 5for component loadings (eigenvectors of the eigenvalues) which inform the strength of association between the loading variables and the extracted principal components (PCs). It is usually recommended Economies 2025,13, 61 17 of 29 to include component loading values greater than 0.3 (WinCross,2022). The components loading on the identified variables were significant and thus, they explain RD (Finch,2013). This loading allows us to obtain an RD score for each community involved in the study. Table 5. Component loadings for rural development (for both survey years). 1Variable ESPS-4 ESPS-5 Comp1 Comp2 Comp3 Comp4 Comp1 Comp2 Comp3 Comp4 2Rural HH with drinking water (%) 0.5406 0.446 3 HH with toilet facilities (%) 0.5998 0.591 4 HH with electric utilities (%) 0.5861 5Effective literacy rate (read/write: 0.636 0.696 6 School enrollment (mean) 0.642 0.611 7Primary and secondary schools (number) 0.366 0.564 0.463 8Non-agricultural workforce (%) 0.737 0.522 9Expenditure on non-food items 0.692 0.558 10 Annual expenditure on education 0.720 0.547 11 Electrified facilities, schools; number 0.557 12 Percent rural HHs with modern household amenities 0.611 13 Annual expenditure on utilities 0.515 14 Accessibility to commercial banks −0.406 Component interpretation: ESPS-4 ESPS-5 Comp1: Environmental hygiene and sanitation Environmental hygiene and sanitation Comp2: Literacy Non-food consumption Comp3: Non-food consumption Infrastructure Comp4: (Public) Infrastructure Literacy Significant loading: >0.3; empty cells: loadings less than 0.3. Source: authors’ computation. Based on the results in Table 5, there was significant positive loading on Comp 1 for variables such as percent rural households with drinking water, toilet facilities, and electric utilities. From the nature of the variables, it is evident that the component is “Environmental Hygiene and Sanitation”. Component 2 has a significant loading on effective literacy rate (household members aged over or equal to 5 and who can read or write), mean school enrollment, and the total number of primary and secondary schools in the community. This component is thus interpreted as “Literacy”. Comp3 has a significantly high loading on annual expenditure on non-food items and education. This component is labelled as “Non-food consumption”. In the fourth component of the first survey, the important loadings were on the number of primary and secondary schools and the percentage of non-agricultural workforce in the communities. This is thus “non-farm employment”. In all four components, the higher the score for the component loadings, the stronger the RD would be and vice versa, i.e., a direct relationship. In ESPS-5 (2021/22), Comp1 has a high and significant loading on the percent of rural households with drinking water, toilet facilities, and total modern household amenities. This component is similar to the findings in the previous survey. It is interpreted as Economies 2025,13, 61 18 of 29 “Environmental Hygiene and Sanitation”. Component 2 has a high positive loading on annual expenditure on non-food items, education, and utilities. Hence, it is “Non-food consumption”. Component 3 has high positive loading on the total number of primary and secondary schools in the community, the percentage of non-agricultural workforce, the total number of electrified facilities in the community (primary and secondary schools), and negative loading on accessibility to commercial banks (distance in km). This is interpreted as public infrastructure. Component 4 has a high and positive loading on effective literacy rate and mean school enrollment. All the components in the ESPS-5 have a direct relationship with RD. From the above component analysis, RD in Ethiopia as an outcome can be explained mainly in terms of environmental sanitation and amenity levels, literacy rate and school enrollment, non-food consumption, and public expenditure. Outputs for the two key regressors can be found in the Supplementary Materials Tables S1 and S2 for RUR and Tables S3 and S4 for EE. 5.4. Key Findings Using FE IV-2SLS Regression This study constructed indices for the key multidimensional dependent variable of rural development (RD) and its key endogenous regressors: rurality (RUR) and the rural entrepreneurial ecosystem (EE). Drawing on theoretical insights and empirical research, the study tested the hypothesis that these multidimensional, place-specific features are vital determinants of RD in Ethiopia. The panel fixed effects method enhances our analysis, while the 2SLS method significantly reduces potential endogeneity issues. Residual plots indicate no serious concerns regarding non-linearity, outliers, or heteroskedasticity, with variance around zero appearing uniformly distributed. The output from the model without and with control variables is presented in Table 6 . The final results refer to the prediction with the FE IV-2SLS model including all the control variables with cluster robust standard error 12 (see columns C and D). Given the large clusters in the study, the clusters’ robust standard errors remain consistent, even with heteroskedasticity or autocorrelation (Cameron & Miller,2015) 13 . The estimation is validated by weak instrument and over-identification tests, confirming the relevance and validity of the instruments (see the output at the bottom of Table 6). The first-stage regression demonstrates a statistically significant relationship between the instruments and the endogenous regressors (see Table 6, bottom). Instrument validation is further supported by robust empirical evidence (see Section 5.7). It was found that both the RUR and EE statistically significantly affect RD (see details in the sections below). Moreover, key variables related to technology and capital, such as the percentage of households using improved crop seeds (p< 0.1) and the number of farm tools per hectare (p< 0.01), are also statistically significant explaining the role played by agriculture in RD (Table 6). Table 6. FE IV-2SLS estimation output. AaB C Db Dependent Variable: Rural development (RD) FE IV-2SLS Coeff (without control vars and non-robust std. err.) (-xtivregcommand) FE IV-2SLS Coeff (control variable and non-robust std. err.) (-xtivregcommand) FE IV-2SLS Coeff (control vars and cluster-robust sd err) c (-xtivregcommand) FE IV-2SLS Coeff (with control variables and cluster-robust sd err (final model) (-xtivreg2command) Economies 2025,13, 61 19 of 29 Table 6. Cont. AaB C Db Endogenous explanatory (instrumented) variables Rurality (RUR) −0.261 (0.117) ** −0.270 (0.1266) ** −0.270 (0.1121) ** −0.270 (0.1106) ** Rural entrepreneurial ecosystem (EE) 0.294 (0.165) * 0.456 (0.1724) *** 0.456 (0.1748) ** 0.456 (0.1726) *** Exogenous control variables (included instruments) Percent HH utilizing improved crop seed (agricultural technology) — 0.208 (0.0806) ** 0.208 (0.1225) * 0.208 (0.1210) * Cost of fertilizer (global price) — 0.000035 (0.00001) −0.00035 (0.00082) −0.0003 (0.00008) TLU total (resources) — 0.0007 (0.0008) 0.0007 (0.0006) 0.0007 (0.0006) Farm tools per farm holding (farm capital) —−0.001 (0.004) ** −0.001 (0.003) *** −0.001 (0.003) *** Land certificate (rural institution) — 0.096 (0.0638) 0.096 (0.0701) 0.096 (0.0692) Constant 0.227 *** 0.123 (0.0549) ** 0.123 (0.0630) * — No. of Obs 390 337 337 252 Prob > F 0.080 0.0029 0.0000 0.0085 Test of overidentifying restrictions (using the -xtoveridcommand) (Outright) d Sargan–Hansen statistic 4.406 2.274 2.462 2.462 (J) e Chi-sq(2) p-value = 0.1104 0.3208 0.2921 0.2921 First-stage regression results Instrumental Variables (Excluded Instruments) Dependent Variable: Rurality (RUR) Dependent Variable: Entrepreneurial Ecosystem (EE) Coefficient (robust standard errors) Percent bushland 0.015 (0.0045) *** −0.03 (0.0047) Percent forest land 0.07 (0.0014) *** 0.026 (0.0016) Licence payment (non-farm) −0.01 (0.003) *** 0.015 (0.0029) *** Urban distance 0.01 (0.002) *** −0.017 (0.029) *** Tests for the final model reported, FE IV-2SLS. Underidentification test (Kleibergen–Paap rk LM statistic) f:9.766 Chi-sq(3) p-val = 0.0207 Weak identification test (Kleibergen–Paap rk Wald F statistic): g9.904 Stock–Yogo weak ID test critical values: 5% maximal IV relative bias 11.04 10% maximal IV relative bias 7.56 20% maximal IV relative bias 5.57 30% maximal IV relative bias 4.73 10% maximal IV size 16.87 15% maximal IV size 9.93 20% maximal IV size 7.54 25% maximal IV size 6.28 Economies 2025,13, 61 20 of 29 Table 6. Cont. AaB C Db NB: Critical values are for Cragg–Donald F statistic and i.i.d. errors. Endogeneity test of endogenous regressors: 6.091 Chi-sq(2) p-val h=0.046 Note: a A model without the control variables is good for examining the estimates with respect to the subsequent full model. However, one cannot stop there as it suffers from omitted variables bias that may mislead interpretation, b Final coefficient estimates in column D are reported using the user’s command of -xtivreg2- (Schaffer,2010). Column C is the output using Stata’s official command (-xtivreg-). No difference in the coefficient estimates except for the absence of a constant in the -xtivreg2-. The -xtivreg2command has the advantage of the outright presentation of the various post-estimation checks, c Cluster-robust standard errors: robust to heteroscedasticity and correlation of errors within the indicated panel identification (id) clusters and serial correlation, d The -xtivreg2user command presents the test result outright, i.e., without the -xtoveridcommand, e Hansen J statistics; Ho: the instruments are valid (i.e., uncorrelated with the error term), f Ho: The matrix of reduced form coefficients has rank = K1-1 (underidentified), g Ho: The equation is weakly identified, h Ho: The specified endogenous regressors are exogenous (using endog option in -xtivreg2command). *** p< 0.01; ** p< 0.05; * p< 0.1. Source: authors’ computation. 5.5. Effect of Rurality on Rural Development This section presents the test result of the hypothesis that rurality (RUR) has a statistically significant effect on RD. It was found that a 1-unit decrease in RUR results in a statistically significant increase of 0.27 units in RD (p< 0.05), holding all other variables constant (Table 6). The result confirmed the inverse relationship exhibited between RD and RUR in Section 5.2. The result agrees with findings from Europe (Castillo et al.,2023) and China (Li et al.,2015) where areas with higher rurality—such as remote rural areas— experience lower levels of economic development. By viewing RUR as a multidimensional, place-specific factor affecting development, this study agrees with Goltzsche (2022) and Chigbu (2013). Although these studies approached the issue differently, they highlight the need to recognize rurality (or rural typology) in development discussions. The evidence from China further confirms the link between rurality and RD (Li et al.,2015). Additionally, the findings go along with studies by Kruseman et al. (2006) in Northern Ethiopia and Hausmann et al. (2021) in Mexico. Distinguishing the differences and similarities with the earlier studies helps understand place-specific factors. Kruseman et al. (2006) identified traditional factors like market access, agricultural potential, and population density to affect production systems and development in Northern Ethiopia. They emphasize that realizing traditional place-specific factors is important in understanding heterogeneity and policy targeting. However, applying several traditional indicators may confine policy targeting. The present study, instead, presents an innovative approach by integrating the various individual place-specific indicators into a single index which agrees with the findings by Hausmann et al. (2021). The latter is a recent study by Hausmann et al. (2021) in Chiapas, Mexico’s poorest region. It underscores the importance of multidimensional, place-specific features in development. Their findings show that the Economic Complexity Index 14 , the multidimensional place-specific factor they examined, significantly affected wage and employment growth at the municipal level. The inclusion into the model of this variable increased the explanatory power of the model by far greater than that of individual factors like education and traditional place-specific factors such as road infrastructure and credit markets. The use of such multidimensional factors is more manageable than trying to address the many individual place-specific factors. Their effects are notably stronger in rural areas as these economies rely heavily on natural resources and climate (Goltzsche,2022). Places are specific geographic locations. The finding on RUR would imply the effect of geography. Geography shapes economic development through factors like agricultural productivity, resource availability, and climate (Boldeanu & Constantinescu,2015). However, this study Economies 2025,13, 61 21 of 29 goes beyond traditional understanding to provide compelling evidence that a single, multifaceted characteristic—such as rurality—holds significant explanatory power. This reveals the deep connections between spatial entities, human dynamics, and institutions. Rurality entails more than a simple geographic entity. Beyond this, it is marked by unique material and social capital shaped through interactions between people and a place forming dynamic, place-specific features. Such understanding compels acknowledging rural areas as vibrant centres of economic activity and social advancement in a way that enhances both rural development and in situ urbanization. 5.6. Effect of Entrepreneurial Ecosystem on Rural Development This section presents the test results of the hypothesis that the entrepreneurial ecosystem (EE) has a statistically significant effect on RD. It is important to note that the EE analysis is limited by the availability of data for local-level indicators for certain enterprise variables. For instance, information is usually unavailable on high-tech firms that help gain deeper insights into EE. Data inadequacy in measuring and analyzing entrepreneurial ecosystems is not uncommon (see Li et al.,2023;Spigel et al.,2020). While national-level information better exists, data for local and regional analyses are often inadequate (Stam, 2017). The issue is particularly evident in Sub-Saharan African countries like Ethiopia where most firms face technological constraints (Cirera et al.,2023). The study, however, effectively utilized data from extra-local sources on large-scale enterprises. Approaching such local data limitations is also not uncommon (see Mainlevel Consulting,2024). This is because entrepreneurial ecosystems are usually formed with the interaction between local and extra-local stakeholders’ actions where there are chances for large and high-tech firms to interact (Li et al.,2023). Thus, this study carefully constructed proxy indicators for such missing variables (see Table S2). Accordingly, the results presented in Figure 3and Table 6 show a direct correlation between RD and EE, and Figure 4illustrates regional inequalities in EE. A unit increase in EE was associated with a statistically significant 0.46-unit increase in the RD (p< 0.01), holding all other variables constant. Entrepreneurship is often seen as an individual endeavour, but it is deeply affected by the surrounding spatial context. The connections between entrepreneurs, enterprises, and stakeholders—including the market and enabling environment—are crucial (Korsgaard & Tanvig,2015). A rural area is beyond a physical location shaped by dynamic socio-cultural interactions between people and place. This understanding acknowledges the view of “rural” as an essential “spatial dimension” in the development process in general and entrepreneurship in particular. Entrepreneurship is a key driver of innovation and economic growth. Deeper than ever, RD has a strong and strategic association with entrepreneurship (Kulkarni & Narkhede,2016). With the current socioeconomic landscape featuring high rural fertility and significant rural-to-urban migration in the country, urban areas face high unemployment rates, largely due to this migration and limited farmland in rural regions (OECD/PSI,2020). The uneven entrepreneurial levels across the country (see Figure 4) exacerbate these challenges. In such cases, a place-based vibrant EE has tremendous potential for job creation (Kulkarni & Narkhede,2016), poverty alleviation (Naminse et al.,2019), and transformative solutions for employment and resource management (Priya & Mohanasundari,2024). 5.7. Post-Estimation Checks 5.7.1. Weak Instruments and Overidentification Tests Two instruments for each endogenous regressor were examined to ensure robust findings. Percentages of land covered by bush and forest in a community are pivotal for assessing rurality (RUR). Additionally, annual licencing fees paid by non-farm enterprises, which serve as a proxy for the regulatory framework, and the distance to the nearest major Economies 2025,13, 61 22 of 29 urban centre inform the entrepreneurial ecosystem (EE). Rurality can be defined by primary economic activities like farming and forestry (FAO,2018), making land cover an essential feature. The proportions of bush and forest land coverages not only characterize the rural areas but are strongly correlated with rurality. Likewise, the development of rural regions is influenced further by local institutional factors (WBG,2016) and extra-local factors such as nearby urban agglomerations (Cattaneo et al.,2022) or distance from the nearest urban centres (Lavesson,2018). Non-farm licencing fees and distance to urban centres promote the entrepreneurial ecosystem by impacting business start-up opportunities (WBG,2016). The first-stage regression output (2SLS) confirms the relevance of these instruments, showing a significant statistical relationship with their respective endogenous regressors ( p < 0.0000), as presented in Table 615. 5.7.2. Robustness Check In validating the estimates, the endogeneity test requires more than statistical checks for robustness (Clarke & Matta,2018). The examinations across various scenarios showed consistent and robust coefficient estimates, supporting their reliability. First, they were checked using a different sample, removing the pastoral representatives. The pastoral system, covering 61% of the country’s territory, reflects distinct livelihoods and cultures (Gebeye,2016). From pastoral samples (regions and zones), 127 Enumeration Areas (EAs) were excluded, focusing solely on the highland sample. The results showed consistent estimates for the key coefficients (see Table S7). Also, the control variables were examined in various ways to assess their effect on RD. By excluding all control variables, and reintroducing them one at a time—an approach supported in the literature (see Kafka, 2024)—the key coefficients remained consistent in sign and magnitude (see Table 6, column A). More importantly, the plausible exogeneity test was assessed based on Conley et al. (2012) to challenge the exclusion restriction of the instrumental variable assumption. Such a procedure is not uncommon practice (Nguyen-Phung & Le,2024;Zheng et al.,2023). The results indicate that when instruments are not strictly exogenous, the socio-spatial determinants still significantly affect RD (Figure 5). Economies 2025, 13, x FOR PEER REVIEW 23 of 31 Figure 5. The 95% confidence intervals for the effect of rurality on rural development using the localto-zero (LTZ) method of the plausible exogeneity assumption. Source: authors’ representation. 6. Conclusions and Implications This study aims to understand the complex socio-spatial nature of RD and its relationship with key multidimensional, place-specific determinants, namely rurality and the entrepreneurial ecosystem. Using a place-based framework, it structured the socio-spatial factors of rural localities to underscore the explanatory power of socio-spatial determinants. It applied instrumental variables estimation to deal with the endogeneity problem associated with these place-specific determinants. The estimation results using the fixed effects model with instrumental variables showed statistically significant effects of both rurality (negative) and the entrepreneurial ecosystem (positive) on RD. Rurality is a distinct socioeconomic characteristic of a place. The present finding depicts that rural areas are beyond first-nature geography and they have unequal development levels. Thus, their embedded unique features are composed of several local factors such as geography, institutions, and governance, which interact primarily with local and extra-local features, to shape their unique development, and in turn, drive each in a different development trajectory. Likewise, a unique entrepreneurial ecosystem emerges within the rural context. While entrepreneurial activity entails individual or group efforts, it is shaped by the rural context that leads to a unique entrepreneurial ecosystem in a rural locality or region. This ecosystem nurtures enterprise opportunities and addresses rural needs in a way that significantly impacts RD. This study also found a statistically significant effect of farm capital and technology on RD. This implies the important role agricultural development plays in successful RD. However, the study goes beyond sectoral outlook to socio-spatial concerns. It underpins a considerable significance of the inherent and blended formation of natural and human factors in a rural place. The finding thus emphasizes the fundamental influence of the second-nature geography (human and institutional effect) on the first nature, supporting a need to address the multidimensional place-specific factors as a unique explanatory power. Ethiopia is a predominantly rural and heterogeneous country facing pressing chal0 Estimated β Figure 5. The 95% confidence intervals for the effect of rurality on rural development using the local-to-zero (LTZ) method of the plausible exogeneity assumption. Source: authors’ representation. Economies 2025,13, 61 23 of 29 6. Conclusions and Implications This study aims to understand the complex socio-spatial nature of RD and its relationship with key multidimensional, place-specific determinants, namely rurality and the entrepreneurial ecosystem. Using a place-based framework, it structured the socio-spatial factors of rural localities to underscore the explanatory power of socio-spatial determinants. It applied instrumental variables estimation to deal with the endogeneity problem associated with these place-specific determinants. The estimation results using the fixed effects model with instrumental variables showed statistically significant effects of both rurality (negative) and the entrepreneurial ecosystem (positive) on RD. Rurality is a distinct socioeconomic characteristic of a place. The present finding depicts that rural areas are beyond first-nature geography and they have unequal development levels. Thus, their embedded unique features are composed of several local factors such as geography, institutions, and governance, which interact primarily with local and extra-local features, to shape their unique development, and in turn, drive each in a different development trajectory. Likewise, a unique entrepreneurial ecosystem emerges within the rural context. While entrepreneurial activity entails individual or group efforts, it is shaped by the rural context that leads to a unique entrepreneurial ecosystem in a rural locality or region. This ecosystem nurtures enterprise opportunities and addresses rural needs in a way that significantly impacts RD. This study also found a statistically significant effect of farm capital and technology on RD. This implies the important role agricultural development plays in successful RD. However, the study goes beyond sectoral outlook to socio-spatial concerns. It underpins a considerable significance of the inherent and blended formation of natural and human factors in a rural place. The finding thus emphasizes the fundamental influence of the second-nature geography (human and institutional effect) on the first nature, supporting a need to address the multidimensional place-specific factors as a unique explanatory power. Ethiopia is a predominantly rural and heterogeneous country facing pressing challenges such as regional inequalities. Yet, it has implemented a policy biased towards homogeneity and urban way, i.e., designing RD in a way that promotes an “urbanely” approach. This urban dominance risks high rural-to-urban migration that outpaces job creation in urban centres, resulting in urbanization without growth. Thus, with these sociospatially interwoven rural challenges, the findings in this study boil down to adopting a place-based rural approach to development (PBRD) that necessitates targeting a place, and the unique place features thereof. It also helps further leverage extra-local gains to improve the welfare of the people. A PBRD promotes an approach that acknowledges the rural lifestyle, maximizing the benefit of a sense of place. This consequently enhances development, reducing the cost of migration and allowing a simultaneous nurturing of farm and non-farm activities. Such an approach aligns with the Sustainable Development Goals, which promote RD without subsuming it within urban development, leading to inclusive and sustainable growth. Neglecting place-specific factors in policymaking could thus lead to failure in sustainable development. In line with the PBRD, this study contributes to the theory and practices of RD in two essential ways. First, it provides a vivid depiction of the complex socio-spatial context of areas for policy targeting and implementation. The multidimensional features presented are context-applicable so that RD policymakers and practitioners working in governmental and non-governmental sectors can catch hold of and courageously utilize them in designing and implementing a targeted PBRD. Second, the analytical framework enables the effective identification and measurement of multidimensional place-specific features in a complex socio-spatial interplay. This greatly helps the growing conceptualization of a place-based approach in refining the role of place in local and regional development. Adopting a Economies 2025,13, 61 24 of 29 PBRD and integrating it into the broader national and sub-national strategies thus will help address the unique rural challenges and enhance policy effectiveness in Ethiopia and beyond. The study is constrained by obtaining local-level indicators of some variables, particularly for enterprise analysis such as data on high-tech firms. The latter is the nature of the firms in the country, where the technology is limited. However, the study included data from the extra-local levels on large-scale enterprises by constructing possible proxies, thus enabling analysis of the complex support services and business interactions. Supplementary Materials: The following supporting information can be downloaded at https:// www.mdpi.com/article/10.3390/economies13030061/s1. Table S1. Domains and indicators employed in the index construction of rurality; Table S2. Domains and indicators employed in constructing the EE index (entrepreneurial ecosystem); Table S3. Initial component extraction of rurality; Table S4 . Component loadings for rurality; Table S5. Initial component extraction of the entrepreneurial ecosystem; Table S6. Component loadings for the entrepreneurial ecosystem; Table S7. Sample size and the estimation output for an alternative sample. Author Contributions: Conceptualization, M.T.W., S.A. and B.K.S.; Methodology, M.T.W.; Data Curation, M.T.W.; Formal analysis, M.T.W.; Software, M.T.W.; Writing—original draft, M.T.W.; Writing— review & editing, M.T.W., S.A. and B.K.S.; Visualization, M.T.W.; Supervision, S.A. and B.K.S. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Informed Consent Statement: Not applicable. Data Availability Statement: Data available in a publicly accessible repository. Acknowledgments: We would like to express our gratitude to the World Bank and CSA of Ethiopia (now known as ESS) for permitting us to use the LSMS data. We also appreciate valuable comments and suggestions from academic editor, three anonymous reviewers and our academic colleagues during the drafting process. The research paper reflects the authors’ professional judgments. Conflicts of Interest: The authors declare no conflict of interest. Notes 1Egypt, Ethiopia, Ghana, Kenya, Mali, Mozambique, Nigeria, Tanzania, Uganda and Zambia. 2TLU: tropical livestock unit. 3Allows us to keep those components with eigenvalues greater than unity (>1). 4Tables S1–S7 are Supplementary Materials. 5H0: No heterogeneity: all Ui = 0. 6H0: The random effects model is preferred; H1: the fixed effects model is preferred. 7Ho: The specified endogenous regressors are exogenous. 8The panel data used in this study are unbalanced. 9ESPS (Ethiopian Socio-Economic Panel Survey, 2018/19 and 2021/2022) is the main data used in the present study. 10 Each measured on a continuous scale of a minimum of 0 and maximum of 1. 11 Due to prevailing conflict in the country, data for the Tigray region were unavailable in the latest follow-up survey. Also, the recently formed four regions were considered together with their former parent region (SNNP) per the study data. 12 The final model specification (column D, Table 6) excludes a time dummy (year-fixed effects) due to the limited nature of the panel data, which cover only two periods, and the use of several local, regional, and global variables. The numerous local and extra-local variables resulted in collinearity with the time dummy, making the core variables difficult to identify. However, incorporating global and regional indicators offered more advantages. As the time dummy assumes uniform effects across heterogeneous rural communities, it is less suited for the micro-level analysis. Internal conflicts in Ethiopia, especially since 3 November 2020, have intensified, particularly in the Tigray region, which is excluded from the survey data. The Amhara region is also experiencing active conflict but remained largely stable during data collection, aside from a brief incident. Figure 3also clearly shows that the dependent variable (RD) has consistently increased since 2016 and remained stable during the survey period (2018/19–2021/22). Statistical tests for year dummies, using the -testparmcommand, indicate their joint insignificance (p> 0.5) in the model, further supporting the decisions made in our specification.