2. Strengths and challenges in the rural development of Colombia

The chapter provides an overview of the main challenges and strengths of Colombian rural regions and outlines their economic, social and environmental trends vis-à-vis comparable Latin American and OECD countries. The chapter first revises the classification of rurality in Colombia vis-à-vis the OECD territorial methodology. It then examines the national context under which rural development is taking place. After this, the chapter outlines the main economic and demographic patterns of Colombian rural regions. Finally, it analyses different drivers of well-being for rural regions.

To enable a meaningful comparison of Colombian’s performance against a relevant benchmark, the analysis in the chapter makes use of two country groups, one based on comparable Latin American countries and the other composed of all OECD countries:

  • The Latin American countries are made up of the four Latin American economies: Argentina, Brazil, Chile and Mexico.

  • OECD country average: international comparability with the OECD’s 38 member countries has been established through a selection of indicators.

OECD countries have moved away from traditional definitions of rural as simply the remaining “leftover” space that is not urban, to delimitations of rurality that recognise different types of rural regions and thus help design policy responses best suited to their different challenges (OECD, 2020[1]).

In 1997, Colombia developed a methodology that identified urban areas as the main territorial criteria while defining rural areas as the residual. At the time, a Land Management Plan was approved (Law 388 of 1997) to distinguish urban municipalities as the territorial units that had road infrastructure and primary energy, water and sanitation networks. Instead, it defined rural as the land that was not suitable for urban use or was used for agricultural, livestock, forestry, natural resource exploitation and similar activities.

In 2014, an officially appointed commission of experts on rural development in Colombia (Mission for the Countryside) proposed a new territorial classification, which moved away from the urban-rural dichotomy and recognised different types of rural areas (DNP, 2014[2]) (Box 2.1). The suggested classification identifies four categories of municipalities, based on the size of the population, the population density and the proportion of the rural population, which was very much in line with OECD standards at the time (OECD, 2006[3]). The four territorial categories identified are: i) cities and agglomerations; ii) intermediate cities; iii) rural; and iv) dispersed rural. With this classification, the share of the rural population reached 30.4% in 2014, above the figure of 23.7% identified with the 1997 classification (Box 2.1). By 2014, the results revealed that 117 municipalities belonged to the category of “cities and agglomerations”, 314 to “intermediate cities”, 373 to “rural” and 318 to “dispersed rural”. Overall, this methodology improves on the previous one as it establishes a more differentiated view of rurality and adopts an urban-rural continuum.

Yet, the 2014 definition has not been fully integrated into national statistics nor across all sectoral policies. The National Department of Statistics (DANE) still relies on the 1997 definition for its statistical data collection process to classify between “rural land” and “urban land”. It mainly displays urban and rural statistics in two ways: urban as urban centres (cabeceras in Spanish) and rural as dispersed rural and populated centres (centros poblados y rural disperse in Spanish). According to this approach, the rural definition is based solely on qualitative criteria: those areas that are characterised by their dispersed disposition of houses and farms. For example, populated centres are defined as a concentration of at least 20 neighbouring or semi-detached houses located in the rural area of a municipality or a departmental township.

This approach leads to dichotomous territorial demographic and economic statistics that do not allow for differentiated trends among different types of rural areas or the recognition of different potential. For example, official statistics do not allow measuring the capacity of rural areas close to big cities to attract people and businesses. Equally important is the fact that this traditional definition is also embedded in certain policy plans, strategies and investment allocation (e.g. strategy to deploy broadband), which can lead to a homogeneous view on rural issues and policies for rural communities with different characteristics. Moreover, the existence of both types of rural definitions undermines consensus on what rurality represents across different levels of government and other stakeholders.

Colombia can thus benefit from homogenising its rural definition for policy and statistical uses. The 2014 methodology to define rurality sets a good basis for a definition to be adopted across all levels of government and standardise the way society and local actors refer to rurality. This definition could eventually be further finetuned by moving away from the qualitative and administrative criteria as the starting point of the definition (e.g. municipalities with regional relevance in the case of intermediate areas) and by including a sense of proximity, distance or accessibility (e.g. distance to cities).

On top of the 2014 categorisation of the degree of rurality at the municipal level, in the framework of the National Development Plan 2018-2022, the National Planning Department (DNP) carried out a territorial division exercise based on regional functionality, in line with the OECD regional classification (Box 2.2). The development of this territorial definition under a functional criterion was pivotal in order to transcend administrative and political criteria and to deliver policies to municipalities that operate as functional regional units.

The result was 101 sub-regions, which serve as a basis for the development of statistics and analysis not only at the national level but now also at the international level, as they are covered by the regional development framework of the OECD and its 38 member countries. All in all, Colombia made significant progress in defining functional sub-regions (TL3), which set the ground for the categorisation of regions in terms of the degree of rurality and in line with the OECD Principles on Rural Policy adopted by member countries in 2019 (OECD, 2019[6]) (see Principle 2: Organise policies and governance at the relevant geographic scale in Chapter 3).

Building on the efforts of territorial categorisation in Colombia, this review adopts the OECD regional classification (Territorial Level 2 [TL2] and Territorial Level 3 [TL3]) to better understand rural trends and facilitate regional comparability across OECD countries (Box 2.3). This classification identifies two levels of geographic units within OECD countries: i) large regions (TL2), which represent the first administrative tier of subnational government that in the case of Colombia is Departments (interchangeably referred to as regions in this review); and ii) small regions (TL3) that for Colombia is based on the efforts to build provinces or sub-regions (RIMISP-DNP, 2018[7]; CEPAL, 2016[8]; DANE, n.d.[9]). Both levels of regions encompass the entire national territory.

The OECD regional approach identifies the degree of rurality of regions based on density, population thresholds and their level of accessibility to cities (understood as Functional Urban Areas-FUAs). It then takes into account the relative location of rural places with respect to FUAs and introduces the idea of spatial continuity between urban and rural. TL3 regions cover the entire territory within countries, while FUAs only capture a sub-sample of the territory. This territorial methodology is used for the 38 member countries, allowing international comparability based on territorial functionality (presence of and access to FUAs) (Box 2.3). While this does not intend to replace Colombia’s 2014 definition or the recent definition of functional sub-regions, it can provide a classification of international comparability based on similar criteria with other OECD countries.

According to the OECD regional methodology, Colombia’s share of the population living in rural regions (42.1% by 2021) is slightly higher than the OECD average (41.4%) and other OECD Latin American countries (Chile, 30% and Mexico, 34.7%). This OECD methodology depicts a territorial classification that looks at rurality through an internationally comparable regional lens where Colombia is relatively more rural than territorial classifications with a municipal focus, such as the proposed 2014 rural definition (30.4%) and DANE’s statistical definition (24% by 2021). Nevertheless, when using territorial methodologies at a most granular level, Colombia remains a relatively high-urbanised country in international comparisons, as identified through other international and national definitions of urbanisation (OECD, 2022[12]).1

Most of the rural population in Colombia locates within remote rural regions (16.9%), almost twice the level of concentration in these types of regions across the OECD (8.9%). Regions with/near a small/medium size city concrete the second greatest share of the rural population (14.2%), also above the share of OECD rural population located in these types of regions. Table 2.1 shows a comparison between the OECD and Colombia of the share of the population living in each type of region.

When looking at the administrative areas, 88% of municipalities are classified as rural, with an important share of municipalities in remote rural regions (51%). Table 2.2 shows the distribution of municipalities by the different types of regions according to the OECD typology explained:

In Colombia, departments (TL2 regions) are the territorial level with the largest volume of information and data. Therefore, this report uses regional data (TL2) to identify some of the rural development trends in the country, when the information at the provincial (TL3) level is not available. To do so, this review classifies the degree of rurality of Colombian regions (TL2) according to the share of people living in rural TL3 regions (provinces) (Figure 2.4). The result displays a degree of rurality at the regional level from regions in which 100% of people live in rural provinces (e.g. Guainía, Vaupés) to regions like Bogotá where everyone is considered urban.

Colombia is a country located in the north-western part of South America, with a land surface area of 1 141 748 km², which makes it the 5th largest country across both Latin America and OECD countries. It borders in the east with Brazil and Venezuela, in the south with Ecuador and Peru, and in the west with Panama, and is the only South American country with coasts on the Pacific Ocean and the Caribbean Sea.

Colombia is the second most populated country in South America, reaching 51 million inhabitants in 2021, with 7.18 million inhabitants in its capital Bogotá (DANE, 2022[13]). The 1991 Constitution establishes Colombia as a unitary and decentralised republic that is divided administratively and politically into 33 divisions: 32 departments (TL2 regions), which are governed from their respective capital cities, and a capital district, Bogotá (also included as TL2 region in this report). Colombia is a highly concentrated country, both demographically and economically.

The country has a unique diversity, ranking as the 3rd most biodiverse country in the world (15% of the world’s total) thanks to its great variety of climatic areas. Colombia’s thermal floors are: paramo, cold, temperate and warm, which in addition to their particular climate, are also characterised by the fact that each thermal floor is home to unique species and agricultural production. Colombia has one of the most representative ethnic presences in South America. Amerindians, African immigrants and Hispanic immigrants from the colonial era are the predecessors of the 5 recognised ethnic groups: Indigenous (4.4% of the national population), Afro-Colombians (6.7%), Raizal (in San Andrés and Providence and Santa Catalina Islands, 0.05%), Palenquero (mainly located in the municipality of San Basilio de Palenque, 0.01%) and gypsy (0.0006%) populations inhabit the territory and make up Colombia’s ethnic diversity (DANE, 2018[14]; Ministry of Health and Social Protection, 2020[15]).

Colombia’s economy is the fourth largest in Latin America in terms of GDP (World Bank, 2020[16]), after Brazil, Mexico and Argentina. It stands out in the international scene for the significant growth it has experienced in the last decade in the export of goods and for the attractiveness it offers to foreign investment and tourism. Its economy is heavily based on services at 64.2% (vs. 71.4% OECD average), followed by industry, including construction, at 28.8% (26.0% OECD average) and agriculture, at 7.1% (2.6% OECD average) (OECD, 2022[17]). Colombia’s main exports are mineral fuels, lubricants and related materials representing 54.1% of the total, followed by food and live animals at 13.2%. Its net trade balance is negative, as exports represent 15.9% of the Colombian economy while imports amount to 21.7%.

Agriculture is gradually reducing its weight in the national and rural economy. Despite still employing most rural workers (62%), the sector’s share of national GDP declined by more than half over the past decades, from 14% in 1995 to 6% in 2020. The diversity of the country’s climatic soils makes Colombia one of the world’s top 5 producers of coffee, avocado and palm oil and one of the world’s top 10 producers of sugar cane, banana, pineapple and cocoa.

Over the last two decades, Colombia enjoyed remarkable economic stability and growth (Figure 2.5). Indeed, Colombia’s GDP growth rate averaged 3.8% per year during that period, which was coupled by a declined in the unemployment rate (from 15.1% in 2001 to 13.4% in 2021). Even so, deep structural problems remain in the labour market, such as high informality, where women and youth were particularly affected not only during the pandemic but also since the 2008 crisis.

After the global financial crisis and until the health crisis that led to an economic crisis in the wake of the COVID-19 pandemic in 2020, Colombia’s economy grew solidly, above many Latin American countries such as Brazil, Chile or Mexico. Colombia experienced a 39.1% increase in its economy between 2012 and 2019 (the OECD average was 21.3%), which was then hit hard by the arrival of COVID-19 with a 6.8% drop in GDP (4.9% in the OECD). Colombia’s economy surpassed the pre-crisis level in the third quarter of 2021 and GDP is projected to grow by 5.5% in 2022 and 3.1% in 2023. Private consumption is the main driver of the recovery as employment picks up, although at a slower pace than economic activity (OECD, 2022[19]).

The economic growth after the 2008 crisis has come with a reduction of regional inequalities. In a country with an important historic income gap between the richest regions and poorest ones, reducing inequalities is an important objective to improving well-being and social cohesion in the country. During 2008 and 2018, Colombia was the sixth OECD country that registered the greatest drop in regional income inequality (measured as the ratio of the top 20% richest region over the bottom 20% poorest region) (Figure 2.6). This closing of the gap was associated with the greater growth of regions dependent on natural resources, which took advantage of the higher prices of commodities in the post-crisis period.

The demographic concentration has increased with the flow of regional migrations towards medium-sized cities. Whereas, the country’s economy has been deconcentrated through the convergence of its regions. This has important implications and is a relatively positive starting point for capitalising on pockets of growth in these medium-sized cities. Moreover, it has the potential for spreading to the surrounding rural regions and clustering some of the growth of these economic clusters, more delocalised from large cities than is common in OECD countries.

Despite the progress in closing the regional gap in the last decades, Colombian remains the OECD country with the highest income gap between the richest and poorest regions (Figure 2.7). This territorial inequality in Colombia translates into an urban-rural regional gap, where Bogotá has almost five times the GDP per capita of a rural region like Vaupés (Figure 2.8).

Thus, the last two decades of sustained growth have not been evenly spread across the territory and have involved rapid growth in urbanisation. This was the result of massive migration flows from rural to urban areas since the late 1930s (Camargo et al., 2020[20]), partly due to the effects of violence that affected rural areas more. As in most OECD countries, agglomeration economies tend to achieve larger economies of scale, with higher sectoral productivity that allows higher wages than those located in less densely populated regions (OECD, 2020[1]). The regions with the lowest GDP per capita in the country coincide with those that rank as the most rural, e.g. Vaupés and Vichada are the two regions with the lowest economic level (DANE, 2022[21]). As in other OECD countries, rural regions that register the highest GDP per capita are resource-rich regions; in Colombia, these regions specialise in fossil fuels (e.g. Casanare, Meta).

Inequalities are not solely monetary but affect a wide range of multidimensional factors that together dictate the degree of well-being of the population. Thus, such urban-rural regional disparities also reflect in well-being inequalities, including educational outcomes, access to digital connectivity labour and land informality and security, among others (see section on well-being enablers). Effective access to quality services also lags behind urban areas, with rural areas facing lower education attainment and healthcare coverage (Chapter 4), poorer quality and accessibility to transport and water infrastructure as well as lower broadband connectivity. Ultimately, the socio-economic disparities across the country become a more complex task where inequality affects access to enabling development factors, beyond just monetary inequality.

Moreover, Colombia ranks as one of the OECD and Latin American countries where a low-income citizen requires more time to reach the national median income, 11 generations, which is 2 more generations than in Brazil (9 generations), 3 more than Chile (6) and 5 more than Argentina (6), and far above OECD average (4.5) (OECD, 2018[22]). Nevertheless, Colombia’s reform of the General System of Royalties (2011-12) has helped reduced regional inequalities (OECD, 2017[23]) and its most recent reform in 2020 should help accelerate the investment in the poorest regions and reduce regional economic disparities over time.

Despite the economic performance of the last decades, the growing risks of a global economic slowdown in the short term, due to tighter financial conditions and inflationary pressures, would impact Colombia's economic growth. Therefore, policy must ensure that the economic slowdown does not translate into greater territorial inequality in the country.

Colombia has experienced a territorial concentration of its population in the last decade and ranks as one of the most demographically concentrated countries in the OECD (Figure 2.9). In addition to the rapid urbanisation trend in Colombia, the country’s geography, based on the presence of extensive forests like the Amazon, desert land and mountainous terrain, has led to a concentration of settlement patterns in a few geographic areas. In 2019, the country registers the fifth greatest territorial concentration after Canada, Australia, Israel and Chile.

Unlike many Latin American countries, such concentration has not occurred in one single large metropolitan region (above half a million inhabitants) but across different medium/small FUAs, which reveals the polycentric structure of the country (OECD, 2022[12]). This distribution of settlement patterns highly concentrated around specific urban zones (Colombia has a classified system of 18 cities) could be mobilised as an opportunity to unlock urban-rural synergies around pockets of growth.

While a large structure of small cities could in principle bring challenges in terms of achieving economies of scale across all of them, it can also bring benefits to unlock development poles based on synergies with surrounding rural areas. Mobilising these opportunities implies particular strong co-ordination, locally across the municipalities but also with national policies (this would be discussed in the next chapters of this review) (OECD, 2021[24]).

Moreover, the analyses on the evolution of geographical concentration reveal a relatively lower concentration of economic activities than people (Figure 2.10). It might be indicative of the relevant role of extractive industries in the economy, which concentrates high value in a few regions that have a low share of the population (e.g. Casanare). Such areas of economic activity can be further mobilised with particular policies to create more diversified economies with greater linkages to urban regions.

The Colombian economy has made a remarkable recovery from the COVID-19 crisis, leaving an opportunity to redirect these efforts and a positive trend for the country’s territorial cohesion. In fact, GDP is estimated to grow by 5.5% in 2022 (OECD, 2022[17]). However, while some regions have coped relatively well with the dual health and economic crisis, others have lagged, with significant effects on the well-being of their populations. Overall, COVID-19 has exacerbated territorial inequalities that were already present before the health crisis. Medium-term growth prospects depend on reforms to expand social protection and boost productivity. Continuing efforts to make the rural economies converge is an important process that brings not only economic prosperity but also social stability and peace.

During COVID-19, the unequal access to broadband and its quality further boosted territorial inequalities in well-being in Colombia. As in most OECD countries, in Colombia, rural regions are the ones with lower shares of the population with broadband connectivity and digital skills (see the section on well-being enablers), which undermined the capacity of rural inhabitants to access work opportunities, government services, education and health though virtual means. The post-pandemic context provides an opportunity for the government of Colombia to close territorial inequalities in access to services by improving broadband connectivity and digital skills as well as implementing strategies to attract remote workers to rural areas (see Chapters 3 and 4).

Overall, Colombia’s rural regions have a number of assets to be mobilised, including an outstanding diversity in terms of demography and environment: they are relatively young and have emerging economic activities outside agriculture.

However, the country faces a number of medium- to long-term challenges that are bottlenecks for sustainable rural development.

Colombia’s rural economy has evolved from an agricultural economy to a more diversified one. Until the beginning of the 1990s, agriculture was the main productive sector of Colombia and the economy as a whole was closely linked to its performance. Since the trade openness in the 1990s and the process of structural transformation, the participation of agriculture in the national economy has reduced significantly (from around 14% of the GDP in 1995 to 7.6% in 2020) (Figure 2.11).

This trend is associated with the development process of the country that has transitioned to a more urbanised territory, where the service economy has gained participation. For instance, during 2010-20, the sectors that grew the most in the economy were the public administration sector (+3.2 percentages points [pp]), as well as financial and insurance activities (+1.9 pp). All in all, this transition has indeed brought the participation of the agricultural sector closer to Latin American and closed the gap with levels of OECD countries.

Greater labour productivity levels in other sectors have driven new economic activities to overtake agriculture and contribute to larger share of the national GDP. The labour productivity of Colombian agriculture has remained about 4 times below the average of agricultural sector in OECD during 2005-18 (Figure 2.12).

Part of the reason behind the low productivity in agriculture relates to the atomisation of landholdings in Colombia: 71% of all farms in Colombia are less than 5 hectares in size, with only 5% larger than 50 hectares. As Chapter 3 will develop, this situation poses a challenge to the scalability of the agricultural sector. Associativity among producers is an ongoing policy that leads to economies of scale to invest and manage machinery as well as to apply best-applied techniques, all aspects that improve competitiveness. Likewise, that chapter will highlight that rural information systems have room for improvement, as the suitability of crops and their management is greatly hampered by the lack of information available to experts and producers.

However, it is worth noting that agriculture has increased its productivity in recent years, accompanied by progress in agricultural extension in the country. Despite the productivity gap with other sectors, labour productivity of agriculture (sectorial GVA per worker) has increased (26.9%) from 2005 to 2018, above the labour productivity of the whole economy (22.4%) (Figure 2.12). Increasing efforts to improve enabling development factors in the rural regions (e.g. infrastructure, land formalisation, etc.) need to be maintained to boost agricultural output, improve food security and overall economic performance in the country.

Agriculture continues to be the main employer for rural workers, but non-traditional sectors are increasingly becoming a relevant source of employment for the rural population. The national population employed in agriculture in Colombia dropped from 22% in 2000 to 16.6% in 2019. In rural economies, agriculture still employs most rural workers (60.3% in 2019), according to DANE’s territorial classification (DANE, 2022[31]).Yet, the structural transformation in the country has led to a rise of the role of service sectors in rural employment. Between 2015 and 2021, the sectors that increased the most participation in rural employment were professional and scientific activities, transport and distributive trade (Figure 2.13).

By 2019, tertiary activities2 accounted for the largest value-added of Colombia’s municipalities in rural regions (Figure 2.14). The more urbanised the municipality, the greater the presence of secondary activities, leaving the remote regions with a strong focus on primary activities, mainly agriculture, fishing, forestry and hunting. If the primary sector accounts for 20% of the value-added in rural regions close to a large city, the figure more than doubles for remote regions (41%).

OECD regions that have undertaken a catching-up process in their stages of economic development have been characterised by a high level of diversification and greater share of concentration in tradable activities (OECD, 2016[32]). Likewise, a diversified economic structure increases opportunities for innovation and economic resilience (Warf and Storper, 2000[33]). However, in Colombian rural regions, non-tradable sectors such as public administration and distributive trade are the main sources of employment (Figure 2.15).

Rural regions show higher levels of specialisation (employees in a region devoted to a single sector) than urban regions. For example, an average specialisation of 37.6% for rural regions, over 24.9% for urban regions in their more predominant sector (Annex Table 2.A.1). In the top 5 regions with a high degree of rurality, most of the employment is concentrated in public administration – e.g. Chocó (41% in 2020), Vichada (38.2%) and Caquetá (35.2%) – with the exception of those rural regions highly specialised in extractive industries- e.g. Casanare (46% in industry and mining) (Figure 2.15). In the top 5 regions with the highest share of urban population, the distributive trade sector stands out as the relevant sector in most regions- e.g. Atlántico (20.8%), Risaralda (20%), and Bogotá (19.6%). In the other high-urbanised regions, Quindío and Valle del Cauca, the predominant sectors are public administration (20.8%) and manufacturing (18.2%), respectively.

Diversification can also be associated to competiveness and resilience across regions. Figure 2.16 explores regional competitiveness though the Colombian Private Competitiveness Council’s regional competitiveness index, which reveals that the most competitive regions – e.g. Antioquia, Valle del Cauca or Santander tend to record the highest levels of diversification, measured through the Herfindahl-Hirschman index (HHI).3 This contrasts with the three regions with the lowest level of competitiveness: Vichada, Guainía, which also record among the lowest levels of diversification. While the association of diversification and competiveness does not hold evenly across regions (as in Casanare or Meta), it sheds lights on drivers and goals to boost regional growth.

More urban regions have greater diversity, allowing them to diversify their activities and generate sectoral interlinkages that allow for marginal increases in productivity. As productivity is one of the major bottlenecks in rural regions – as well as labour informality –, increasing diversification requires a series of important incentives and enablers, from infrastructure to innovation programmes among others as mentioned above.

Labour informality in Colombia is geographical more prevalent in rural economies (Figure 2.17). Colombia has long faced high levels of labour informality (63% in 2019), relative to the average of selected Latin American countries (45.9%), below Peru (78%) but above countries like Argentina, Brazil and Chile (OECD, 2022[17]). Informal employment is highly segmented by socio-economic characteristics. Most migrants, young workers, self-employed and part-time workers hold informal jobs. Labour informality in rural areas (85.6% in 2019) is almost 23% higher than the above the national average and this gap has slightly increased in the last decade (20% in 2008), although the national informality level has decreased, supported by increased development and contribution of the 2012 tax reform that reduced payroll taxes and employer’s health contributions, facilitating the supply of formal employment (OECD, 2022[17]).

Various factors help explain such geographic concentration of labour informality. First, the relevance of the agricultural sector as the main source of employment for rural economies has been accompanied by the high use of informal contracts and a low level of insertion into the social security system. The largest share of agricultural workers is classified as self-employed, followed by private sector employees, day labourers (or farm workers) and unpaid family workers. In fact, 66% of those employed in agriculture are self-employed or day labourers and contribute less to pensions than in the urban sector (Otero-Cortés, 2019[35]). Also, the protection system is based on the individual’s ability to pay and their employment status (based on contributions), it leaves little room for inhabitants of rural areas, who have lower and more unstable incomes and often lack formal contracts. This issue is still present as pension coverage is less than 15% across rural workers (Otero-Cortés, 2019[35]). Lastly, the historic internal conflict that produced forced displacement also led to informal labour arrangements and job uncertainties (Calderón-Mejía and Ibánez, 2016[36]; Fernández, Ibánez and Peña, 2014[37]).

Moreover, high levels of business informality also led to low-quality labour contracts. Around 76% of microenterprises were not registered with the tax administration and 89% were not registered in the chamber of commerce in 2020 (OECD, 2022[17]). This is highly correlated with low compliance for hiring formal workers and following sanitary standards, e.g. 88% of microenterprises do not contribute to healthcare or to pension and 95% do not contribute to professional risks (Eslava, Haltiwanger and Pinzón, 2019[38]).

As identified in previous studies by the OECD, high mandatory social contributions and costly and complex business regulations hamper the formalisation of firms and jobs in Colombia. Some of the OECD recommendations to reduce informality include establishing a comprehensive strategy to foster formalisation, including lower non-wage costs, stronger enforcement and improvements in tax administration and a reduced tax burden on formal labour income, by gradually shifting the financing burden of social protection towards general taxation (OECD, 2022[17]).

The formal employment rate is highly correlated with the educational attainment of workers (Figure 2.18). The highest employment rates are found among workers with tertiary education (47%). Moreover, the degree of informality is also lower at this level of education, accounting for only 24% as opposed to 59% for those with secondary education, 81% for primary education and 91% for those with no education. Providing training to adults and access to higher education also proves to be essential to enable formalising older workers who would find it difficult to re-enter the education system.

Although high levels of informality could affect statistics in comparing unemployment rates, the absorption of employment in rural areas is relatively better than in urban (Figure 2.19). In 2021, the unemployment rate in rural areas (8.1%) was lower than in urban areas (14.8%) and the national average (13.4%). The trend over the last 2 decades has been one of declining unemployment; accompanying the good performance of the economy at the national level, from 15.1% in 2001 to a level of 10.5% in 2019, before the COVID-19 pandemic (2022 would also reach a level below 11%) (DANE, 2022[31]).

Particularly positive has been the employment performance in rural areas, which has gone reached a level before the pandemic of 6.5% (2019). The COVID-19 pandemic also revealed the role of rural economies to maintain levels of employment relative to urban areas, which was associated with the relevance of primary sectors as essential activities that cushion economic shocks. Primary economic sectors, such agriculture, helped cushion the impact of the external shock and proved to be essential activities composed of indispensable workers.

Urban regions have a larger population size, yet the majority of the territory is rural. Half of the regions are rural and the other half urban (16 of 32 excluding Bogotá Capital District, see Figure 2.4), although the rural regions are home to 42.1% of the country’s total population. Moreover, when looking at the administrative areas, 88% of municipalities are classified as rural, with an important share of municipalities in remote rural regions (51%).

Most urban regions, especially the capital Bogotá, have populations ranging from 7.8 million inhabitants to 562 000 in Quindío. On the rural side, Boyacá has a population of 1.2 million inhabitants and Vichada has 114 000 inhabitants. The same fortune has been experienced by the rural regions, with Vichada leading the way in population growth (128%), followed by Casanare (121%). The urban regions concentrate the highest population rates but, due to their size, their relative growth is more contained, although all the regions have experienced positive growth in the last decade.

This recent positive trend is coupled with a regional displacement within the territory as a result of the armed conflict: 774 494 households have been expelled from 1 115 municipalities and departmental districts, which means that 7.3% of the Colombian population lives or has lived in a condition of forced displacement (RUPD, 2017[40]). All in all, this phenomenon has resulted in large internal migration flows, especially from rural to urban areas of all sizes. For instance, the most affected were Antioquia with more than 100 000 displaced persons, followed by Chocó, Magdalena and Bolívar with more than 40 000. The most critical years of mass displacement were 2000, 2001 and 2002, coinciding with the increase in confrontations between guerrillas, the state and paramilitaries. This situation has improved significantly with the peace treaty, having reduced by 90% the number of displaced persons in recent years.

Colombia’s population has grown in all regions, especially in large metros, but aggregated increases in the rest of the regions show a rather balanced demographic increase throughout the territory (Figure 2.20). Large metro regions have grown by 57% since 2000, above the 22% of remote rural regions. For small and medium metro regions, the values are close (39% both in 2021), with urban metros maintaining slightly higher growth until the pandemic, having equalised after the COVID-19 pandemic. Rural regions close to a small city have maintained a constant gap from rural regions close to a large city over the same period (31% and 29%).

In recent years there has been an important international migratory flow in Colombia, especially from Venezuela. In fact, according to figures from the Interagency Group on Mixed Migration Flows, of the 1.7 million Venezuelans who have immigrated to Colombia, around 760 000 have regular status. Of these, around 95% have the intention of staying in Colombia. In particular, Bogotá (393 716) and Antioquia (264 148) are the departments that host the most Venezuelans (Ministry of External Relations, 2021[41])

Contrary to the trend of ageing populations across OECD countries, Colombian rural regions benefit from a high share of young people (0-14 years old) (26% in 2021), far above the OECD average (17%) and other OECD Latin American countries, like Chile (20%) (Figure 2.21). This demographic structure is a bonus for rural regions as young people are a source of innovation and vitality for the community, and an important asset for future economic growth and regional attractiveness. Across rural regions, regions close to small cities have the highest share of young people in Colombia (27%), above the average for the same type of regions in Latin America (25%) and the OECD (16%).

Similarly, Colombian rural regions have a higher share of working-age population (15-64 years old) (65% in 2021) than rural regions in comparable Latin American countries (66%) and OECD countries (64%). Nevertheless, within Colombia, urban regions have a higher share of working-age population (69%), particularly large metropolitan regions.

Despite the demographic bonus of Colombia’s rural regions, the share of young people is shrinking in line with international trends (Figure 2.22). Between 2009 and 2021, the share of young people over the working-age population (youth dependency ratio) in Colombia’s rural regions decreased from 49% to 41%, a more drastic drop than the OECD average (from 30% to 29% in 2019). Likewise, this ratio in Colombian urban regions felt from 39% in 2009 to 31% in 2021.

Nevertheless, the share of elderly relative to the working-age population in Colombian rural regions (14% in 2020) has remained about half of the OECD average (29%) and has increased below the trend of OECD and the Colombian urban regions (Figure 2.23). Between 2009 and 2020, this share of the elderly population in Colombian rural regions increased by 2.5 pp, below the growth in Colombian urban regions (3.7 pp) and OECD rural regions (5.6 pp). The low elderly dependency ratio benefits from a still-growing working-age population, which can offer, with the right skills, a dynamic labour market to unlock new business opportunities.

As in most OECD countries, there is an increasing ageing trend that calls for short-term actions to capitalise on demographic bonuses in Colombian rural regions. To this end, OECD countries are increasing their focus on the empowerment of young people and their retention in rural economies thorough better matching of education opportunities and labour offers (Chapter 4) or support to social innovation and entrepreneurship (Chapter 3).

Women represent 50.04% of the population living in rural regions of the country (48.2% according to DANE’s classification). While Colombia has a slightly greater proportion of women than men (95 men per 100 women), this proportion is smaller in rural (99) than in urban regions (92). Large metropolitan cities concentrate the greatest number of women (approximately 9.7 million), followed by small- and medium-sized cities (5.7 million). Instead, in rural areas, remote rural regions have the greatest number of women (4.2 million), ahead of rural regions close to small cities (3.6 million) and those close to large cities (2.8 million).

However, while young women outnumber young men in all types of Colombia regions when looking at working age, remote rural regions are the only type of regions that experience a lower share of women. This shapes population growth, with remote rural regions experiencing the highest growth of the number of men relative to women across all types of regions (Figure 2.24). This phenomenon can be associated with lower opportunities for formal work in rural economies.

Women’s role in the rural and family economy and, thus, their social and economic conditions, have historically been invisibilised. Partly because their role in the economy has been mainly oriented towards tasks traditionally considered to be of low productivity (e.g. care roles), but indispensable for the good functioning of rural communities (Figure 2.25). In rural Colombia, only 29.2% of the employed population is female by 2020, although they represent 46.8% of the rural population in working age (DANE, 2021[42]). Consequently, unemployment rate of rural women (16.2% in 2020) is about three times higher than men (5.7%), and within the working-age population outside the labour force in rural areas (neither working nor looking for job), women represented twice the level of men (67.9% vs 32.9%). This contrasts with the higher educational attainment of rural women, where 7.9% have higher or postgraduate education, and 21.4% have secondary education, as opposed to 5.6% and 18.3% respectively for rural men (DANE, 2021[42])

All in all, women account for 82% of work in the home, as well as 59% of unpaid domestic work, compared to only 18% and 41% for men respectively. This imbalance in favour of men is also evident in other roles that provide better income in the labour market - e.g. private employees (72% men vs. 28% women) and employers (84% vs. 16%). Only the public sector in the rural context has more women (52%) than men (48%) (DANE, 2022[39]).

This gender specialisation has implications in poverty rates, with women more exposed to poverty as they are more engaged in lower-paid economic activities. By 2020, 46.9% of households headed by women were in a situation of monetary poverty, above the 41.5% of households headed by men (DANE, 2021[42]). Against this backdrop, focusing rural policies, whether productive or social, on the needs of rural women, with recognition of their care roles, would have an important multiplier effect in reducing rural poverty and labour informality, unlocking thus new business opportunities in these economies.

Quality of life is a cornerstone to build prosperous communities, improve social cohesion and regional attractiveness for people and businesses. Due to the lack of data available to compare well-being internationally at the provincial level (TL3 regions), the analysis presented in this section describes well-being at the department level (TL2 regions) by adopting the OECD Regional Well-Being framework (Box 2.4). The OECD regional well-being analysis provides a tool to assess the region’s strengths and weaknesses, monitor trends and compare their results with those of other regions, nationally and internationally (Box 2.4). As the available data on well-being come from various sources (e.g. DANE, OECD regional database), each using a different territorial typology, this section uses the typologies corresponding to the source data, mentioning when it differs from the OECD taxonomy.

Regional well-being is measured through different areas, economic, environmental and social, which for Colombia are measured through eight dimensions (Figure 2.27). Out of these, some Colombian rural regions on two dimensions: environmental quality (on air pollution), where Vichada ranks at the top 20% of OECD regions, and community (social network support), where Arauca registers the highest performance. However, regional differences are stark in almost all well-being dimensions. In Environment, despite the good performance of Vichada, most regions are in the bottom 20% of OECD regions. This is similar in dimensions such as jobs and education. Yet, the high level of labour informality can distort the “jobs” dimension (employment and unemployment), and the high ranking of Guajira must be taken with caution. In terms of safety, all Colombian regions are part of the bottom OECD 25% regions (Figure 2.27).

Departments in Colombia, on average, experience a relatively good performance in the following dimensions:

  • Life satisfaction. Life satisfaction in Colombia is 6.2 on a scale between 0 and 10, broadly in line with the OECD average of 6.5. This indicator captures a reflective assessment of how things are going in one’s life and allows one to evaluate which life circumstances and conditions are important for subjective well-being. Caldas ranks as the region with the highest level of life satisfaction, close to the top 20% of the OECD, which scores 7.3.

  • Community perception of social network support. According to 2018 Gallup’s survey, the average of the Colombian regions registers a level of 88.6 percent, a relatively high figure given that the threshold of top 20% of the OECD is around 94 percent. As for the rural regions like Arauca, it manages to place itself among the top OECD regions with a greater perception of social networks. This dimension is relevant as it explains the nature of social interactions whose scope also has wider implication, including support for social innovation and resilience capacity.

In contrast, the four well-being dimensions where Colombian regions a markedly lower performance than OECD regions are:

  • Safety. Colombia concentrates almost all its regions in the bottom 20% of the OECD in terms of safety. In fact, the country average is 26.6, 10 times higher than the OECD average (2.6). Regions such as Valle del Cauca (47.5) are among the bottom 20% of the country. This is very important for the well-being of citizens, as it is part of the more structural perception of the daily living conditions. Indeed, the biggest impact of crime on people’s well-being appears to be through the feeling of vulnerability that it causes.

  • Civic engagement. The voter turnout in Colombia (53% in 2019) is far below the OECD average (69%). This is particularly relevant for well-being because participation in society, for example through the expression of political voice, is essential for individual well-being. Political voice is not only part of the basic freedoms and rights of human beings, but also enhances the accountability and effectiveness of public policy. Moreover, public trust in government is low (37% of the population in 2019) relative to OECD countries (52%) and has declined over time (from 51% of the population reporting trust in 2017) (OECD, 2022[17]). Low trust in public institutions also deter whistle-blowers from reporting corruption cases. In fact, 58% of Colombians consider it unlikely that complaints generate any consequences, while 78% indicate that they would suffer retaliation if they report (Pring and Vrushi, 2019[44]).

  • Jobs. Employment and unemployment show structural weaknesses in the country’s labour market, a fundamental building block on which to build a strong national economy. The average regional unemployment rate of Colombia regions (15.8% in 2020 last data available for international comparisons) is more than twice the OECD average (7.3%) (OECD, 2022[11]). The employment rate in Colombia is 58%, while the OECD average is 66%.

  • Environment. The environment in the regions of Colombia reaches very different values depending on the region, revealing a very high inequality in environmental outcomes. The average air pollution across Colombian regions (14.6 µg/m³ experienced by the population in 2019) is above the OECD average (12.0) and above European countries in the OECD (10.8). Yet, air pollution in Colombian regions is below the average across Latin-American countries in the OECD (Chile, Costa Rica, Mexico) (20.3). The average air pollution of Colombian regions classified with 100% rural degree (Figure 2.4) (14) is similar to the national average, which reveals that some regions (e.g. Arauca, Choco) face similar air pollution levels in their villages, which might be associated to methods to produce electricity (fossil fuels) or extractive activities that generate negative effects.

The Colombian road network spans 206 500 km2, of which 8% corresponds to the National Primary Network, 22% to the departmental network and 70% to the municipal network (also known as tertiary roads). Overall, Colombia ranks 102nd in road quality among 142 countries according to the World Economic Forum in 2019, resulting in high costs of transporting goods and people across the country and to ports (The Global Economy, 2020[45]).

Colombia ranks very similarly in quality of roads (3.0) and quality of overall infrastructure (3.1) to Brazil (3.2 and 3.1 respectively) and Argentina (3.3 and 3.2), but far behind other Latin American countries including Chile (5.2 and 4.7) and Mexico (4.4 and 4.1) (Figure 2.28). Rail infrastructure in Colombia is scarce and its quality ranks below the former four Latin American countries and the OECD.

The degree of isolation of Colombia’s territory has had an impact on the economic and social development of the regions. There is a high correlation pattern between the areas with the highest levels of violence, poverty and illicit crops in the country and those with developed road infrastructure (Figure 2.29). An adequate transport network would improve the accessibility of vulnerable communities to alternative sources of income and government services, including healthcare, education, security and justice.

Given the geographic challenges in Colombia and the historic lag in connectivity in some rural regions, advancing other types of transport infrastructure is relevant for rural development. As explained in Chapter 4, the lack of railways and other alternative modes in Colombia led to congestion of road traffic by both passenger and freight transport.

In terms of the railway infrastructure, 49% of Colombia’s current railway network is inactive and 5% of the private network is dedicated to coal transport. the national government presented the Railway Master Plan in 2020, which is a strategy that seeks to reactivate the country’s railways and boost the economy after the pandemic caused by COVID-19 (DNP, 2020[48]). If the plan is successfully implemented, it is estimated to reduce costs by 26% by 2030, shorten logistics chain times, strengthen the attraction of foreign investment, strengthen services for transporting goods and raw materials, and increase the country’s competitiveness.

In fluvial transport, out of the 24 725 km of navigable rivers in Colombia, 74% (18 225 km) allow permanent year-round minor navigation. River transport is a passenger and freight transport alternative with great potential for use in Colombia, especially in riverine areas with a need to transport people, goods and supplies in regions that are difficult to access.

Given the geographic difficulties to connect rural communities with transport infrastructure, broadband access can be an important driver of rural development in Colombia. Yet, only 28.8% of households located in rural areas (according to DANE’s classification) have access to the Internet (fixed and/or mobile), below the 70% of households that have the same service in urban areas (DANE, 2022[39]). This gap is more striking when comparing accessibility to a fixed broadband connection, in which only 12.7% of rural households have this type of connection, below the 57.6% of urban households. As mentioned in Chapter 4 home connections are indispensable to drive connectivity in a sustainable way as they allow quality access to virtual services and allow the use of data-intensive activities, such as cloud computing, and demanding applications across sectors, for example industry automation or monitoring.

At the regional level, there is still a significant gap between remote rural regions and regions in the centre of the country. For instance, Amazonas has 2%, Guainía 3% and Vaupés 0.1% of its population covered by fixed broadband connections, far below the share of population covered in Bogotá (27%) of its or Antioquia (21%) (MinTIC, 2022[49]). Especially of concern is that seven of the eight regions with the lowest share of population covered by fixed broadband access have seen a decrease in this access in the last year (MinTIC, 2022[50]).

People do not only need to be connected but they also need a good connection. However, the speed of broadband services available in Colombian rural areas is considerably lower than in cities. In fact, Colombia is the fourth OECD country with the lowest fixed broadband speed in remote rural regions, relative to the OECD average (2022Q1) (Figure 2.31). Rural and remote areas have their unique “last mile” challenges due to their distance to core network facilities, making it more costly to deploy infrastructure in areas that, many times, are not commercially attractive (OECD, 2021[51]).

Education in Colombia, as in OECD, is one of the fundamental promoters of a country’s state of development, both present and future. Therefore, ensuring access to education has a number of multidimensional implications. On the one hand, it promotes the growth and integral development of the population, as well as increases social mobility and breaks cycles of poverty. On the other hand, access to education allows for the accumulation of human capital, e.g. a skilled labour force that can access better-paid jobs, as well as the formation of citizens who are aware of their role in society.

In Colombian rural areas, education outcomes are generally below those in urban areas. In 2018, illiteracy was 10 pp higher in rural areas (over 14%) than in urban areas (over 4%). This territorial difference is coupled with a slight asymmetry in gender illiteracy, whereas rural women reach a level of 10.6%, lower than that observed for rural men (12.1%) (Figure 2.32). Although women in rural areas record higher levels of illiteracy compared to urban areas, the urban-rural gap for women has been reduced by a 2-pp gap between 2010 and 2018 (MinAgricultura, 2018[52]). When compared to other Latin American countries, Colombia is among those where rural men have the highest illiteracy rates (12.1%), behind Brazil (19.9%) and Ecuador (15.9%).

Primary educational coverage in Colombia is wide. Based on the OECD regional typology, the metro regions (86.5% in 2020) record the highest coverage levels. Rural regions close to large cities (85.7%) and those close to small cities (85%) reach relatively higher values than large metropolitan regions (84.4%). However, those lagging far behind are the remote rural regions (81%), which have much room for improvement.

Secondary education in Colombia has greatly increased since 2015, from an average net coverage of 67.4% to 73.5% in 2020 (Figure 2.33). However, the secondary graduation rate4 of Colombia (72.3% for men and 82% for women) was slightly below the OECD average (77% and 82.3% respectively). This secondary coverage not only varies by gender but also by type of region. Rural close to small city regions have made the biggest jump in the last 5 years, from 66.7% in 2015 to 77.6% in 2020. Instead, remote rural regions area still lagging and register the highest average values in both coverage (71.1% in 2020) and dropout rates (3.44%) (Figure 2.33).

Greenhouse gas (GHG) emissions in Colombia (1.6 metric tonnes of CO2 eq. per capita in 2019) are relatively low in per capita terms (OECD average of 8.5) (Climate Watch/World Bank, 2020[53]). The country benefits from a relatively high green electricity generation, as over 70% of its electricity capacity is fed by renewable sources, especially hydroelectric plants (OECD, 2022[19]).

However, GHG emissions have increased over the last decade. While in 2010, Colombia emitted 1.4 tonnes of GHG emissions per capita, the figure rose to 1.6 in 2019 (latest year available) as opposed to the decrease in OECD (from 9.8 in 2010 to 8.5 in 2019). The causes of these emissions are not homogeneous across the country (Figure 2.34). Rural regions are responsible for most of the total GHG emissions (73%) in the country, compared to urban regions (27%). This is associated with the greater specialisation of rural economies in agriculture, which is the sector that represents the greatest share of emissions in Colombia (32%). Agriculture represents on average 44% of rural region GHG emissions. In metropolitan regions, residential (9% of the country’s GHG emissions) and transport (6%) are the main sources of emissions.

It is worth noting that emissions from land use changes are not included in this disaggregation, which is a relevant issue in Colombia. Deforestation has risen in recent years, about 8 pp in 2020 compared to the total area devoted to the forest the year before. As part of the institutional efforts to tackle this issue, Colombia has committed to an ambitious target of zero net deforestation by 2030. Remarkable institutional efforts to curb deforestation have included a satellite-based early detection system (e.g. Operacion Artemisa)5, but limited resources for enforcement action only allows following up on a minor fraction of detections (OECD, 2022[17]). Moreover, the country has made efforts in climate adaptation by increasing to 18 000 hectares in the process of restoration, rehabilitation and/or ecological recovery in protected areas of the National Natural Parks System and its areas of influence.

Bogotá leads as the region with the largest increase in forest (+9 pp in 2020) (Figure 2.35). The increase in green spaces and the maintenance of existing ones followed by a lower forest base than in other regions due to its character as a capital city has led to this increase, which would be difficult to achieve in a rural area that already has large tracts of forest. In fact, rural regions are more clustered around values close to 0, with both negative and positive exceptions. The negative rates are most notable in Atlántico with a rate of more than 7 pp, followed by the rural regions of Arauca (-1.2 pp) and Guaviare (-0.2 pp), and the rural regions with positive rates are Córdoba (5.8 pp) and La Guajira (4.3 pp). Urban regions such as Meta (-.3 pp) or Norte de Santander (-1 pp) should also make efforts to curb deforestation and maintain healthy green spaces.

Colombia’s historic conflict has hampered quality of life and investment attraction in rural communities, which has also led an accelerated rural-urban migration. The National Registry of Displaced Population (RUPD) established that, in Colombia, 774 494 households have been expelled from 1 115 municipalities, which means that 7.3% of the Colombian population lives or has lived in a condition of forced displacement. According to the same registry, 87% of the victims of the conflict were displaced in 2014 (RUPD, 2016[54]).

Rural economic activities have historically been the most impacted by this conflict in terms of the destruction of infrastructure and the intervention of armed actors in economic activity in rural areas, which increases transaction costs for inputs and final products. In addition, it reduced workforce availability due to the recruitment of people by illegal groups, death and displacement. It also impacted investment decisions, making, for example, farmers adopt low-profit production strategies to minimise risks. Studies have estimated that during the 90s, its effects on the growth rate of total GDP were between 0.3 and 0.7 pp per year (Restrepo, Spagat and Vargas, 2003[55]).

Access to land, as well as its distribution and titling, has been one of the most important bottlenecks to rural development in Colombia. Land is also a key asset for unlocking economic opportunities associated with environmental preservation and climate change mitigation in rural areas. While traditional sectors such as agriculture and mining depend on access to land to develop activities, this is also true for sectors such as the bioeconomy, renewable energy and ecotourism and forest conservation. However, green economy sectors can benefit from different forms of land ownership, such as collective ownership, concessions and user rights, indicating that security of tenure can coexist with sustainable development. Land management analysis will receive a central focus in Chapter 5 of this review.


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← 1. In addition to this OECD regional classification, the OECD also uses a granular territorial methodology to classify local units into cities, towns/semi-dense areas and rural areas: the degree of urbanisation, which was endorsed at the 2020 United Nations Statistical Commission (OECD/European Commission, 2020[57]). The OECD Rural Review of Colombia uses the regional classification to determine the level of rurality to better recognise interactions and trends across different types of regions in Colombia

← 2. Primary activities include agriculture, livestock, forestry and fishing activities, mining and quarrying. Secondary activities include manufacturing and construction activities. Tertiary activities include electricity, gas and water activities; commerce; repair of motor vehicles; transport; accommodation and food service activities; information and communication; financial and insurance activities; real estate activities; professional, scientific and technical activities; administrative and support service activities; public administration; education; health; arts, entertainment and recreation; activities of individual households.

← 3. The Herfindahl-Hirschman index is adapted to measure employment concentration across sectors. The value of the index is the sum of the squares of the employment shares of sectors over the regional economy. Higher values indicate greater concentration. The formula of the Herfindahl index below represent the sum of the employment share (X) of the sector J in economy m for the time t.: HHI=k=0JX2j,m,t

← 4. The secondary graduation rate represents the estimated percentage of people who will graduate from secondary education over their lifetime. Data are broken down by gender and shown for upper secondary and post-secondary, non-tertiary levels of education.

← 5. Operación Artemisa is a military public policy strategy that aims to protect 200 000 hectares of forest every year. It has been recently renewed (January 2022) by the president of Colombia, Ivan Duque.

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