Rural Transformation in the Philippines and the Role of Institutions, Policies, and Investments

Abstract

This paper examines the status and rural transformation trends in the Philippines. Using secondary data covering 1988 to 2023, the levels of rural transformation across the country’s sixteen regions were analyzed based on three key indicators: the share of gross value added in high-value agriculture to the total GDP of agriculture, forestry and fishery, share of off-farm employment to the total rural labor force, and agricultural labor productivity. Through a panel regression analysis, this study also determined how selected institutional reforms, policies, and investments (IPIs) influenced the rural transformation that happened in the regions. The findings reveal that rural transformation in the Philippines has been generally slow because of the uneven progress across its local regions, with only three out of the sixteen regions reaching a high level of rural transformation from 1988 to 2023 and two regions remaining at low level of transformation, while the rest are still moving from a slow to moderate level of transformation. This study highlights the critical role of IPIs in driving rural transformation, demonstrating that their effects vary depending on the transformation stage of each region. These results underscore the need to properly identify, logically sequence, and efficiently integrate IPI interventions to address the transformation challenges effectively. Beyond its Philippine context, this study provides insights applicable to other developing countries facing similar rural transformation challenges, offering a replicable methodology for analyzing the transformation and guiding context-specific strategies.

1. Introduction

The economic performance of the Philippines has been on an expansionary path for the past three decades. Its robust growth has positioned itself among the fastest-growing economies in East Asia and the Pacific. This growth trajectory continued even after the COVID-19 pandemic, with the country recording a 5.2% growth in gross domestic product (GDP) in the third quarter of 2024 and a reduction in poverty incidence to 15.5% in 2023, a 2.6 percentage point decline from the 2018 level [1]. Key structural reforms that improved business climate and attracted more investment and government infrastructure programs, specifically those that improved logistics and connectivity, have been pivotal in sustaining the economic growth of the Philippines. Moreover, the continued enhancement of micro, small, and medium enterprises (MSMEs) strengthened the labor market, creating jobs and increasing household incomes [2,3,4]. The World Bank explained that more vibrant trade activities and the enhanced inflows of remittances have also been key drivers of the country’s sustained economic growth [5].

Despite the dynamic structural changes and strong economic growth in the Philippines, structural transformation—the transition from labor-intensive and low-productivity jobs to skill-intensive and high-productivity economic activities [6]—remains incomplete and non-inclusive. While the industrial sector has been instrumental in propelling national economic growth, it has not absorbed a significant portion of the labor force. Instead, the majority of surplus rural workers have been employed in the fast-growing yet low-paying service sector [7,8]. The productivity of the Philippines’ agriculture, fisheries, and forestry (AFF) sector has remained lethargic, growing at a rate of only 0.9% in the third quarter of 2024 compared to the 5.5% and 6.8% growth rates of its industry and service sectors, respectively [1]. Despite its limited growth, the AFF sector continues to employ approximately one-fifth of the country’s labor force [1]. Thus, AFF labor productivity remains significantly lower, estimated to be five times lower than that of the industry sector and three times lower than that of the service sector.

According to the World Bank [9], the Philippines has been experiencing slow progress in structural transformation because of the underperformance of its agriculture sector to drive growth in the countryside. The slow transformation of the rural sector has constrained the country’s ability to achieve broad-based and inclusive economic development. This is reflected in poverty statistics, wherein rural poverty incidence in the Philippines stood at 25.7%, compared to only 11.6% in urban areas [1]. Among farmers and fisherfolk, poverty rates were even higher, at 30.0% and 30.6% in 2021, respectively. A substantial proportion of Filipino households categorized as “poor” (83%) and “extremely poor” (89%) are concentrated in rural areas and depend heavily on agricultural activities for their livelihoods [10]. This economic disparity is further underscored by high income inequality, as reflected in the country’s Gini coefficient, which has shown minimal improvement over the past three decades, declining from 0.45 in 1988 to 0.39 in 2023 only [1]. Notably, the Gini coefficient of the Philippines remains the highest among ASEAN’s six largest economies—Malaysia, Indonesia, Viet Nam, Singapore, Thailand, and the Philippines—highlighting the ongoing challenges of achieving inclusive growth [11].

Agricultural growth, although not the sole driver of economic development, plays a critical role in stimulating growth in rural economies. Agricultural productivity triggers direct multiplier effects on rural incomes through the development and expansion of non-farm activities and the input, output, and expenditure linkages within the rural economy. In many Asian countries, improvements in agricultural productivity have historically driven rural transformation, laying the foundation for industrialization [12]. However, the growth path taken by the Philippines contrasts with that of other Asian countries like China, Japan, Vietnam, and Thailand, which demonstrated clear cases of agricultural growth driving rural transformation and, eventually, structural transformation.

The International Fund for Agricultural Development (IFAD) argues that no country in Asia or the Pacific has exhibited a fast reduction in rural poverty without undergoing rapid rural and structural transformation [13]. Rural transformations that drive structural transformation are essential developmental processes to propel rural development and eventually economic development, especially in developing countries [13,14,15]. They typically start with agricultural transformation, followed by the shift in the rural labor force from farm to non-farm activities, resulting in the growth of rural non-farm employment [16]. According to Zhong [17] and Wang et al. [18], the ultimate goal of rural transformation is to reach industrialization by strengthening agriculture to sustain the raw material needs of expanding industrialized cities and communities.

Existing studies highlight the need for a deeper understanding of why rural and structural transformation in the Philippines has remained generally slow and uneven across the local regions, despite the prioritization of broad-based agricultural modernization and rural development in successive Philippine Development Plans (PDPs). Since IFAD’s comprehensive study on rural transformation, there has been no subsequent research to reassess the status and dynamics of rural transformation in the country [13]. This paper seeks to address this gap by reassessing rural transformation trends at the regional level and examining how the selected institutional reforms, policies, and investments (IPIs) influenced the transformation that has happened. The findings of this study make a significant contribution to the growing body of literature on rural transformation both in the Philippine context and within a broader regional context. Furthermore, this study provides evidence-based and specific recommendations to overcome the challenges of promoting more inclusive and sustainable rural transformation in the Philippines, of which a number of these challenges are similarly faced by other developing countries in Asia.

The rest of this paper is organized as follows: Section 2 sets the context of the study by defining rural transformation and providing an overview of rural transformation in selected countries in Asia; Section 3 defines the rural transformation indicators used in the study; Section 4 presents the potential drivers of rural transformation; Section 5 discusses the research data and methods; Section 6 presents the results of the analysis; and the last two sections (Section 7 and Section 8) provide the theoretical and policy implications and the conclusion of the study, respectively.

2. Rural Transformation Overview—The Asian Context

Rural transformation is a critical component of economic development, especially in developing countries, where agriculture continues to play a central role in rural economies. IFAD describes rural transformation as an interrelated process of “rising agricultural productivity, increasing commercialization and marketable surpluses, and diversification of production patterns and livelihoods… expanded decent off-farm employment and entrepreneurial opportunities, better rural coverage and access to services and infrastructure, and greater access to, and capacity to influence, relevant policy processes” [13] (p. 23). Varying levels and paces of rural transformation can be observed across Asia, driven primarily by agricultural productivity growth.

China exemplifies a successful rural transformation, which led to its rapid structural transformation. Its rural transformation has been closely tied to agricultural reforms implemented as early as the late 1970s and the subsequent agricultural market reforms that boosted the country’s agricultural productivity [19]. These reforms, along with increasing agricultural diversification, also facilitated the shift of surplus agricultural labor to non-agricultural industries, improving rural incomes in China [19,20]. As surplus agricultural labor was increasingly absorbed by non-farm sectors, targeted agricultural policies, infrastructure investments, and social programs for rural populations fostered rural–urban integration, reducing rural poverty and transforming the national economy [16,20].

Japan also provides valuable lessons on leveraging agriculture for broader rural transformation. Post-World War II land reforms in Japan redistributed land from landlords to tenant farmers, increasing productivity and rural incomes [12,21]. Investments in agricultural research and infrastructure, along with the promotion of high-value crops like fruits and vegetables, supported rural transformation [22]. By the 1980s, Japan had largely completed its rural transformation [23]. As of 2022, agriculture accounted for only 1% of GDP in 2022 and 3% of the total employment [24].

In Viet Nam, agricultural reforms involving granting land use rights and promoting agricultural diversification increased high-value crop production and boosted agricultural yields and incomes. Expansion of off-farm employment followed and, consequently, rural transformation of the country [25,26]. As of 2020, Viet Nam’s rural transformation was progressing steadily, with its non-farm activities accounting for approximately 30% of total rural labor [27] and the share of non-agricultural income of rural household incomes reaching 73% by 2016 [28]. Thailand’s experience likewise highlights the importance of agricultural productivity as the initial step in rural transformation. The country’s government policies prioritized high-value crop production, rural infrastructure development, and export-oriented agriculture, enabling rural households to diversify their incomes [29,30].

In contrast, countries such as Bangladesh, India, and Indonesia present a contrasting narrative of rural transformation, which mirrors patterns observed in the Philippines as discussed in the previous section. Agriculture’s underperformance remains a bottleneck to sustained rural transformation and poverty reduction in these countries [9]. For example, while incremental agricultural gains have been observed in Bangladesh, the focus of policies and incentives on low-value crops, specifically on promoting the achievement of rice self-sufficiency, has left the HVA sector and rural economy underdeveloped [31]. Unequal access to resources and limited diversification of agriculture have constrained rural transformation, with many households remaining reliant on low-productivity farming. Similarly, India continues to face persistent transformation challenges because of the uneven productivity growth in agriculture across the different states due to limited diversification into high-value crops since the Green Revolution and the simultaneous focus on industrialization, which created regional disparities and environmental challenges [32]. The agriculture sector remains the largest absorber of the country’s labor force, yet the services sector accounts for the largest share in the GDP. As of 2021, 43% of the India’s labor force were still engaged in agriculture, indicating an incomplete rural transformation [24].

In Indonesia, the lack of diversification and rural non-farm employment opportunities has slowed the rural transformation progress [33]. The rural transformation has been further impeded by inadequate investments in agricultural modernization and rural infrastructure, resulting in over 60% of the rural population remaining reliant on agriculture [34].

The trends and status of rural transformation present a mixed picture of progress in Asia, with some countries achieving significant progress, while others lag behind. The contrasting progress and varying strategies in rural transformation underscore the importance of conducting country-specific analyses to identify localized bottlenecks and context-specific approaches for informing policies that align with specific needs and fostering inclusive rural transformation. The phenomenon of the Philippines is an interesting case, mirroring the struggles faced by other developing countries in Asia in achieving full rural transformation.

3. Rural Transformation Indicators

According to Huang, rural transformation can be divided into four stages, namely: (1) agriculture focused on staple food production; (2) agricultural diversification and commercialization; (3) rising off-farm employment; and (4) integrated urban–rural development and sustainable agriculture (

Table 1. Indicators of rural transformation by stage of transformation.

Stage	Rural Transformation Indicator	Indicator Description
Agriculture primarily on staple food production	High-value agriculture (HVA) share in total AFF GDP (%)	The percentage contribution of HVA to the total agricultural output (AFF GVA). HVA is computed as AFF gross value added (AFF GVA) minus the combined value of production of low value crops including rice, corn, sugarcane and coconut.
2. Agricultural diversification and commercialization
3. Rising off-farm employment	Off-farm employment share in total rural labor force (%)	The proportion of non-agricultural employment to the total labor force in rural areas. The value is used to indicate the shift agricultural surplus labor to non-agriculture sectors.
4. Integrated urban-rural and sustainable development	Agricultural labor productivity (PHP, in 2000 constant prices)	A proxy indicator of rural–urban integration; computed as the amount of agricultural output produced by each employed person

) [35]. Timmer [36] and Huang [35] further argue that the speed and pathways of rural transformation vary across countries. Countries undergo the process of rural transformation at different paces and times due to various factors that either hinder or stimulate the transformation [37].

Various indicators have been used in previous literature to represent rural transformation. In IFAD’s study, rural transformation was measured by agricultural labor productivity [13]. In another study by Huang, the share of the gross value of non-cereal products was used as a rural transformation indicator and the average annual change in this share as a proxy indicator of the speed of rural transformation [35]. More recent studies considered two indicators to measure rural transformation. For instance, Shi and Huang used the share of high-value products in agriculture and the share of non-farm employment in rural labor as key indicators of the rural transformation path in China [20]. Al Abbasi et al. likewise used high-value agriculture share and non-farm employment to measure rural transformation in Bangladesh [38]. In a systematic review by Rola-Rubzen et al., agricultural labor productivity, proportion of high-value agriculture in the total agricultural output, and percentage of non-farm employment to total labor force were among the rural transformation indicators identified [39].

While the different stages and speeds of rural transformation are recognized by scholars, no study has yet attempted to measure rural transformation specifically by stage of transformation. Even the studies by Huang [35] and IFAD [13], from which this study primarily built its rural transformation definition and framework for analysis, only used a single measure of rural transformation. Thus, this study extended the existing rural transformation measurements in the literature by drawing from the previously used rural transformation indicators and combining selected indicators that would best represent each stage of the transformation. Using previous research as reference but also considering the availability of the data in the Philippines, this study offers a more encompassing analysis of rural transformation at the regional level by using three indicators that capture the progress from the early stages to the advanced stage of the transformation (Table 1).

In economic theory, the Lewis model characterizes the traditional agricultural sector as having low productivity, subsistence farming, and a large surplus labor force that reflects the availability of workers willing to accept low-paying jobs due to limited alternative employment opportunities [40,41]. Shifting to modern agriculture through agricultural diversification or the production shift from low-value crops to high-value crops or non-crops (e.g., livestock, poultry, and fisheries) and expanding non-farm employment in rural areas, represents the fundamental step towards achieving rural transformation within the agricultural landscape. Policymakers and experts have recognized that agricultural diversification stimulates agricultural growth and productivity and creates opportunities for achieving better and more stable rural incomes [42]. Hence, the share of high-value agriculture to total agriculture output was used as one of the indicators of rural transformation, as in Huang and Shi [16], to describe the extent to which shifts have been made from traditional crops to high-value crops.

Rising off-farm employment is the next step towards rural transformation. As agricultural surplus labor is generated from a more efficient and modern agriculture sector and employment shifts take place towards the fast expanding and higher paying non-farm economic sector in both rural and urban areas, rural incomes increase, thereby boosting the non-farm economy in rural areas [12]. Sustained labor movement to the non-farm sector stimulates the rural–urban (or agriculture and non-agriculture sector) integration.

In the integration process, we expect the productivity gaps between agriculture and non-agriculture sectors to narrow and the role of agriculture to shift from being an economy’s backbone to being a sector that promotes convergence of labor force and living standards [43]. We can expect the integration to happen as the agriculture sector copes with the growth of the industry and services sectors and becomes increasingly linked to the rest of the economy. In this study, agricultural labor productivity is used as a proxy indicator for the final stage of rural transformation, reflecting rural–urban integration and the convergence of agriculture with the non-agriculture sector. In Arslan et al. [37], IFAD [13], and Huang [35], agricultural labor productivity was also used as the key measure of rural transformation, where higher productivity indicates a higher level of transformation. Briones and Felipe [43] and Timmer [44] further explained that economic integration is driven by an increase in agricultural labor productivity, which creates a multiplier effect on the non-farm economy, making agriculture’s role just as important as that of numerous sectors of the economy.

This virtuous cycle of development that catalyzes rural transformation is further triggered by the modernization and commercialization of a highly diversified agriculture where agricultural output is enhanced and returns to labor are raised [14]. Farmers thereby become more financially capable to invest outside agriculture. The increase in off-farm and non-farm incomes allows for new investments in the farm sector, possible re-attraction of more workers, and further stimulation of diversification and, later, trade and specialized production [45]. Simultaneously, agricultural commercialization enhances farmers’ purchasing power for non-food items that can trigger diversification and specialization of rural economy [14].

4. Drivers of Rural Transformation

Previous literature has consistently identified institutions and institutional innovations, policy settings and reforms, and investments (IPIs) as among the key driving forces of rural transformation, agricultural growth, and structural change in developing Asia, including the Philippines. For instance, David et al. asserted that the poor agricultural performance of the Philippines was a result of weaknesses and distortions in the policy and institutional frameworks, specifically in price and trade policies, lack of market infrastructure, underinvestment in agricultural research, and land market distortions due to the agrarian reform program [46]. Vos likewise argued in his rural transformation study on selected Asian countries that where policies and institutional reforms are weak, agricultural and rural transformations are generally slow and fail to effect more rapid poverty reduction [47].

Additionally, the Asian Development Bank (ADB) cited that inadequate infrastructure, institutional weaknesses, and lack of specific policies were among the reasons for the reduced interest of investors in improving agricultural productivity in the country [48]. The World Bank also identified persistent policy and institutional distortions as reasons for the slow growth of agriculture and manufacturing in the past decades [49]. It further attributed the failure of Philippine agriculture to modernize and diversify to the low public and private investments, protectionist policies (e.g., rice self-sufficiency policy), and institutional distortions (e.g., land reform) in the country. In its more recent development report for Mindanao, the World Bank identified poor policies, underinvestment, and institutional deficiencies as key constraints to maximizing the transformative potential of agriculture [50].

Moreover, IFAD did a comprehensive analysis of the decisive roles of IPIs in achieving a faster and higher level of inclusive rural transformation and structural transformation in different countries, including the Philippines [13]. Its Philippine case study confirmed that IPI, especially land reform, rural investments, and sectoral policies, influenced the speed and path of rural transformation of the country. Similarly, Huang’s comparative trend analysis of rural transformation in developing Asia identified IPIs like land and market reforms, trade policy, and investment in technology, irrigation, and roads as key drivers of transformation in fast-transforming countries like China and Vietnam [35]. Another study by Huang and Shi examined and confirmed the effectiveness of IPIs in fostering rural transformation in China [16].

While extant studies demonstrate the positive effect of IPIs on rural transformation, Wang et al. argued that there remains a need to adequately understand the IPIs as mechanisms of rural transformation [18]. They also recognized the need to examine the impact of IPIs on the different stages of rural transformation as well as on the speed of the transformation. Prior studies have established the link between IPIs and rural transformation. However, they commonly investigated this domain of inquiry at the cross-country level and offered evidence mostly based on unidimensional descriptive analysis only.

This study aims to fill in the gaps in the rural transformation literature. A quantitative investigation of the IPIs driving rural transformation at different transformation stages and across regions in the Philippines was conducted. Such a context-based analysis is especially important for the Philippines, given its archipelagic and geographic nature that affects the regions’ proximity and integration to the major cities of the NCR and other urban cities in the country and, consequently, their speed of economic development. The specific IPI factors investigated in this paper are discussed below.

4.1. Institution: Access to Land (Under Land Reform Program)

Access to land is a critical institutional factor of agricultural and rural development. The World Bank articulated that an effective rural development agenda should address this basic constraint of smallholders [51]. The Philippines has long been dealing with an inequitable distribution of landholdings, with the landless farmers largely representing the poorest in the rural areas [52]. To address the skewness of distribution, land reform had been operationalized under the Comprehensive Agrarian Reform Program (CARP), which was instituted in 1988 under Republic Act No. 6657, otherwise known as the Comprehensive Agrarian Reform Law (CARL) [53]. This was extended after 10 years through the Comprehensive Agrarian Reform Program Extension with Reforms (CARPER) under Republic Act No. 9700 in 2009.

The land distribution of about 2.1 million hectares, representing 71% of the distributed land titles under land acquisition and distribution (LAD), is in collective titles instead of individual land ownership titles. Securing individual land titles enables farmers to focus on productivity-enhancing investments like new technologies, which in turn can raise their productivity and income [13,49]. Previous studies on CARP found positive welfare effects, especially in areas where covered lands are reported to have higher productivity [54]. However, such effects were muted due to the slow pace of implementation of the agrarian reform program. Because a huge portion of the distributed farms remain covered under collective land ownership awards (CLOAs) and therefore could not be used as collaterals to avail credit, agrarian reform beneficiaries have been discouraged to invest in improving and modernizing their farm production processes and activities despite owning land. This situation has led to negative consequences on the overall investments in the modernization of the agriculture sector in the Philippines [55].

4.2. Investments: Irrigation and Road Infrastructure Development

Investments in rural infrastructure, such as roads and irrigation facilities, are also critical to agricultural growth because such infrastructure increases production and facilitates market access and integration as well as the achievement of economies of scale in marketing [48]. Increasing investment in public goods and services, such as irrigation, farm-to-market roads, and market access, among others, has been identified as an impetus for more diversified and productive agriculture, which in turn reduces the dependence on low-value or traditional crops [49]. Llanto explained that rural infrastructure boosts agricultural productivity in the Philippines and, consequently, the growth in the rural sector through increased agricultural wages and off-farm employment opportunities [56]. Balisacan likewise emphasized that investing in infrastructure projects is critical in sustaining gains and enabling transformation in the country [57].

It is well documented in the literature that development and access to irrigation significantly increase agricultural productivity [51,58,59] and would then help facilitate rural transformation [13,35,60]. Further, the modernization of irrigation systems is found crucial not only to improve crop productivity but also to promote crop diversification and commercialization [9]. However, there is also evidence of the non-significant effect of irrigation on agricultural development from the previous literature [56,61,62]. A common reason for the non-significant impact of irrigation despite the expansion made has been the inadequate investment on complementary structures to improve the quality and sustainability of irrigation service [63].

Investing in rural roads is recognized to increase agricultural productivity and improve physical connectivity and access to urban markets and other economic activities like off-farm employment [35,56,59]. The World Bank [9] and Llanto [56] both emphasized the importance of improving road quality and conditions to enable the movement of produce between farms and markets. Prior literature [56,61,64] has consistently found road density, or the ratio of the length of the total road network to the land area of a country or region, to be a significant driver in the country’s agriculture sector, especially in terms of increasing agricultural income and productivity.

Despite the evidence of a positive link between (investment in) rural infrastructure and agricultural and rural transformation, underinvestment has been persistent in the country [35,49]. While the government committed to spend 5% to 6% of the GDP to develop a number of key infrastructure flagship projects in the agriculture, water resources, and physical connectivity from the previous to the current Marcos Administration [57], this targeted expenditure commitment is nonetheless still much lower than the 30% share of investment in physical infrastructure to the GDP in the 1970s [49].

According to ADB, the worst infrastructure can be found in rural and remote areas, more specifically in poor municipalities [48]. The World Bank also highlighted that while road conditions have been improving nationwide through various ongoing programs and projects, some areas, specifically in Mindanao, still need more investments to improve the quality and overall density of their roads [9]. This issue has been recognized by the current national government, thereby prioritizing infrastructure development in the Mindanao region in its 2023–2028 PDP [57].

4.3. Policy Reforms: Agricultural Wages and Intra-Regional Price Integration

Policy reforms related to price and trade have been identified to play an important role in rural transformation [13,35]. In this study, we focused on the policies related to wages and market or price integration as proxied by intra-regional price differentials.

Under R.A. 6727, otherwise known as the Wage Rationalization Act, regional minimum wages for agricultural (as well as non-agricultural) employees and workers in every region are prescribed by the Regional Tripartite Wages and Productivity Boards [65]. Wages are adjusted based on the consumer price index and influenced by several factors, including the demand for living wages, cost of living, needs of workers and their families, need to induce industries to invest in the countryside, improvements in standards of living, prevailing wage levels, fair return of the capital invested and capacity to pay of employers, effects on employment generation and family income, and equitable distribution of income and wealth along the imperatives of economic and social development.

In 2012, a major wage policy measure called the two-tiered wage system (2TWS) was implemented by the Department of Labor and Employment (DOLE) through the National Wages and Productivity Commission to minimize the unintended outcomes of mandated minimum wage, improve the coverage of the vulnerable sectors, and promote productivity improvement and gain-sharing. The 2TWS maintains the mandatory minimum wage under R.A. 6727 as the first tier, which is complemented by a voluntary productivity-based pay scheme as the second tier [66].

Given that wages reflect a wide range of economic and social conditions that predict labor supply and productivity in a region based on minimum wage criteria, we consider agricultural wage as a potential antecedent of rural transformation. Agricultural wage workers are considered among the poorest in rural areas [67,68]. A mandate for increasing the minimum wage has been considered a necessary condition to help the poor achieve the earning level needed to live a healthy and dignified life [69].

Briones argued that inclusive growth requires boosting the incomes of agricultural workers by either raising their wages or shifting them to better-paying jobs in the non-agriculture sector 68]. He further explained, through the lens of the Lewis model, that growth in rural and agricultural wages is often a reflection of surplus rural labor absorption and integration of rural and urban labor markets [40,70]. In Llanto’s study, wages were also confirmed to raise (through its incentive effect) the productivity of agricultural workers but at a diminishing rate of returns once the level of productivity can no longer absorb the cost impact of rising wages [56].

With regard to the intra-regional price integration as a driver of rural transformation, we measured the extent of integration using the difference between the provincial retail prices and the average retail price of cereals (i.e., rice special rice premium, well-milled rice, regular milled rice, yellow corn, white corn, and yellow corn grits) in each region in the Philippines. Narrowing price differentials, which denote price convergence, is an output of increasing or deepening market integration [71].

Drawing from the related theory of the Law of One Price (LOP), a well-integrated market is described as a situation wherein the prices of identical commodities traded in spatially separated markets are the same [72,73,74]. It implies an efficient flow of goods and services from surplus production areas to deficit areas without barriers to trade and spatial price differentials other than the associated transport and other market costs (spatial arbitrage conditions). Barrett suggested that the lack of spatial market integration is a reflection of inadequate physical infrastructure and storage facilities, imperfect competition, institutional weakness in credit and risk management, and inefficient flow of information [75]. Market distortion policies, such as price support mechanisms and protective policy instruments, were also identified as reasons for weaker co-integration between spatially separated markets [76].

Applying the LOP concept at the local level, we argue that market integration could be a critical factor in rural transformation, particularly in terms of promoting commercialization and increasing marketable surpluses, which are both parts of rural transformation.

4.4. Other Factors: Non-Agricultural GDP Share and Amount of Rainfall

Since the main interest of this study is the effect of IPI variables, we controlled the possible effects of other factors that may influence rural transformation, namely the structural transformation and climate impact. The share of non-agricultural GDP relative to its agricultural output is a common indicator of the status of structural transformation of an economy, wherein a higher non-agricultural GDP share (i.e., the combined total of the GVAs of industry and services sectors) generally denotes a higher structural transformation level and vice versa [13]. We postulate a negative influence of a region’s increasing non-agricultural GDP share on its rural transformation because following the ideal sequence of rural development, rural transformation ideally should happen first before structural transformation. A premature structural transformation may have a negative effect on the early stages of rural transformation. Moreover, if the increase in GVA in the non-agriculture sector is primarily driven by the low-paying services sector, then the increasing non-agricultural GDP share in the region may not significantly influence rural transformation in the region.

The other factor is the amount of rainfall, a variable that can characterize climate impact. IFAD identified climate change as a key external factor in the rural transformation process [13]. We expect a negative relationship between the amount of rainfall and rural transformation, given the negative impact on agricultural production of high precipitation or above-average rainfall [77,78,79].

5. Data and Methods

5.1. Data

Secondary data from PSA and other concerned government agencies were used in this study. Specifically, time-series data from 1988 to 2023 (or the latest year with available data) for the sixteen regions of the Philippines were utilized in the analysis and the estimation of the effects of IPIs on the regional rural transformation. The National Capital Region (NCR) was excluded from the analysis because of its lack of agricultural economic activities, exceptionally high level of economic performance, and advanced stage of urbanization and industrialization relative to the rest of the regions. Prior to the analysis, the regional data were transformed into panel data, to generate a larger number of observations, allow for higher degrees of freedom as well as lower standard errors, and thus provide greater estimation power than a single-dimension time series or cross-sectional data [64,80]

As mentioned in the previous section, the outcome variables of rural transformation are as follows: (1) the share of HVA output to the total agricultural output; (2) the share of off-farm employment to the total rural labor force; and (3) the agricultural labor productivity. On the other hand, the explanatory or independent variables include land distributed under the agrarian reform program (institutional factor); irrigation ratio and road density (investment factors); and agricultural daily wage and cereal retail price differential (policy factors). The control variables are the non-agricultural GDP share and the amount of rainfall.

Table 2. Definitions and measurement of variables.

Variable Definition	Variable Name	Measurement	Period 1	Data Source
Outcome Variables (Rural transformation)
HVA share in AFF GVA (%)	HVA	Annual average HVA share to the total AFF GDP of each region, where HVA = [(AFF GVA − total value of production of low value crops including rice, corn, sugarcane and coconut)/total AFF GDP] × 100.	1988–2023	PSA
Off-farm employment share (%)	OFF	Annual percentage share of non-agricultural employment to the total rural labor force in each region, where OFF = [(total rural employment − total employed in agriculture in the rural areas)/total rural employment] × 100, and where rural labor force = (rural–urban labor force ratio × total labor force)	1988–2023	PSA
Agricultural labor productivity (PHP)	AGLPlog	Log-transformed agricultural labor productivity per year in each region, calculated by PSA as the amount of output produced by each person employed in agriculture	1988–2023	PSA
Explanatory Variables (IPI factors)
Land distributed (hectare)	Land	Total area of land distributed in the region under the agrarian reform program per year	1988–2022	PSA
Agricultural wage (PHP)	Wageag	Average daily basic pay of agricultural workers in the region (at 2000 constant prices)	1988–2023	PSA
Cereal price differential (PHP)	Price	Deviation of the average retail price of cereals 2 in each province from the average of cereal retail price in the region to which the province belongs, where Price = average regional retail price − average provincial price in absolute terms, at 2000 constant prices	1988–2020	PSA
Irrigation ratio	Irrig	Ratio of total area of irrigated land to the total arable land area in the region	1988–2022	PSA
Road density (km/sqm)	Road	Ratio of the length of total road network per square kilometer of land area of the region	1988–2023	DPWH 3
Control Variables
Non-agricultural GDP (%)	GDPnon-ag	Share of the combined annual GVA in the industry and the services sectors to the total GDP of the region	1988–2023	PSA
Amount of rainfall (mm)	Rain	Average amount of rainfall per month in the region	1988–2019	PAGASA 4

¹ Data for CALABARZON and MIMAROPA prior to 2002 were derived using average percentage distribution between the two regions from 2002 to 2023. Available data for Caraga and ARMM cover years starting 1997 and 1995, respectively, following the year they were formed as a region. ² Cereals include rice special rice premium, well-milled rice, regular milled rice, yellow corn, white corn, and yellow corn grits. ³ DPWH—Department of Public Works and Highways. ⁴ PAGASA—Philippine Atmospheric, Geophysical and Astronomical Services Administration.

details the definition and measurement of each variable. The variables were selected based on prior literature and the availability of regional data. Additionally, the correlation and variance inflation factor of the pre-selected variables were also checked to avoid multicollinearity problems before estimation.

5.2. Estimation Method

This study employs panel regression techniques to estimate the effects of IPI variables on the rural transformation indicators. Panel data are often susceptible to heterogeneity bias, necessitating the estimation of three panel regression models: the common effects model (pooled ordinary least squares (OLS), the fixed-effects model, and the random effects model. For each model, the choice of specification was informed by the distributional properties of the dependent variables. Histograms were utilized to assess the normality of the variables’ distributions. For the variables exhibiting a normal distribution, HVA and OFF, a linear regression model was applied, while a log-linear regression model was employed for AGLP. The log transformation of AGLP ensured adherence to the assumption of normality, a key requirement for regression. The panel regression models were estimated using Stata 16.1, a widely used statistical software for econometric analysis.

Model selection was undertaken systematically to identify the most appropriate specification for each dependent variable. Figure 1 describes the panel regression model selection process conducted in this study to determine the most robust models for the analysis. The panel regression modeling started with the estimation of the common effects or pooled OLS model and the fixed-effects model. Following Fauziyyah and Duasa [81], the likelihood ratio (LR) test was employed to select between the two models. A significant test statistic (p < 0.05) indicated a rejection of the null hypothesis that the pooled OLS is better than the fixed-effects model, thereby favoring the latter model. If the fixed-effects model was selected, the Hausman test was subsequently applied to compare the fixed-effects and the random effects models. A significant result (p < 0.05) supported the fixed-effects model, while a non-significant result indicated the random effects model was more appropriate. For cases where the random effects model was selected, a Lagrange Multiplier (LM) test was further conducted to assess its suitability compared to the pooled OLS model. The random effects model is chosen if the null hypothesis of pooled OLS model in the LM test is rejected.

To validate the reliability of the selected models, post-estimation diagnostic tests were also conducted to ensure the robustness of the results. For the random effects model, heteroscedasticity was tested using a Breusch–Pagan test, whereas a Modified Wald test was applied to detect heteroscedasticity in the fixed-effects model. Where heteroscedasticity was identified, robust standard errors were utilized to address the issue. Serial correlation, which can bias standard errors in panel data models, was evaluated using a Durbin–Watson test and a Wooldridge test.

To enhance the analysis of the relationships between IPI and rural transformation variables, interaction terms were incorporated into the panel regression models. It is a stylized fact that IPIs do not work in isolation but are rather interrelated and complementary. To understand whether their joint effects or interactions support rural transformation or result in suboptimal or increased impact, we added interaction terms between variables in each rural transformation model. The addition of these variables is also expected to increase the explanatory power and the robustness of the panel regression models, which are estimated using the common effects, fixed-effects, and random effects methods.

As with the models excluding interaction terms, those including interaction terms underwent the same rigorous model selection and diagnostic procedures. The comprehensive application of these tests ensured the validity and reliability of the findings. By systematically exploring both individual and joint effects, this study provides robust evidence on the role of IPIs in driving rural transformation.

6. Results and Discussion

6.1. Rural Transformation Trends

Regional data trends on the proportion of HVA output to the total AFF GVA suggest that there has been a slow shift away from traditional agriculture towards higher-value agricultural commodities in the Philippines. Since the past three decades, the country has exhibited sluggish and uneven growth in the HVA share to total GVA in AFF (Figure 2).

The average annual growth in the HVA share was also consistently low, averaging below 1% per decade (

Table 3. Average annual growth of the share of HVA to the total GVA in AFF, Philippines, 1988–2023.

Period	Average Year-on-Year Growth Rate in HVA Share (%) *
1988–1998	−0.46
1999–2008	0.56
2009–2019	0.88
2020–2023 (pandemic)	−1.45

* Average growth of the sixteen regions (excluding NCR). Source of basic data: PSA [1].

). In 2023, regional HVA shares ranged from as low as 24% (Cagayan Valley) to as high as 89% (Davao Region, refer to Figure 2). Among the sixteen regions, only half had an HVA share above the national average HVA share of 66% in 2023.

In terms of the off-farm employment share, all regions have exhibited an increasing trend in their respective off-farm employment shares since 1988, albeit at a slow and uneven rate. Only 50% of the regions exceeded the national average share of 68%, with CALABARZON achieving the highest off-farm employment share (92%) and BARMM reporting the lowest (48%) in 2023.

The rising off-farm employment or the reallocation of workers from the agriculture to non-agriculture sectors in rural areas is a general indication of progress towards rural transformation, particularly from the early stages to the more advanced stage of transformation. Yet, the magnitude of the progress of transformation would depend on the productivity of the sectors that have absorbed the agriculture surplus labor and the quality of jobs to which agricultural workers shifted. In the Philippines, the non-agricultural labor force is mainly absorbed by the services sector (Figure 3), which is relatively more vibrant than the agriculture sector. However, a large majority of the workers in the services sector rely on low-productivity and low-paying jobs, such as in wholesale and retail subsectors [82].

Finally, we looked into agricultural labor productivity performance to further assess rural transformation across regions. Agricultural labor productivity is the amount of AFF output produced by each employed person. A region with a relatively high level of agricultural labor productivity suggests that it has made significant progress in terms of rural transformation. In the Philippines, increasing levels of agricultural labor productivity are generally observed for the 1988–2023 period, suggesting that there has been a continuous progress in rural transformation across regions (Figure 4). However, the pace of the productivity growth differs across regions, with the highest agricultural productivity being recorded at PHP 469,684 (equivalent to approximately USD 7975) in Central Luzon and the lowest at PHP 110,386 (US$ 1874) in the region of Caraga. Only 31% of the sixteen regions achieved an agricultural labor productivity above the national average of PHP 81,185 (USd 1378) in 2023.

In terms of value, the labor productivity of AFF remains the smallest among the major sectors and has been growing at a very slow rate, if not declining, unlike the industry and services sectors. One of the reasons for its slow growth is the fact that while agriculture’s share in total employment has been declining, the number of people employed in agriculture continues to increase. Recent data show that the number of Filipinos employed in agriculture between 2021 and 2022 increased by 200,000 from 10.6 million to 10.8 million. The rising shares of non-agricultural and off-farm employment seem to be driven more by the faster urban population and the additional non-agricultural jobs created that employ the relatively faster urban labor force growth rather than the shift and absorption of agricultural or rural labor force into the non-agricultural or urban employment.

6.2. Rural Transformation Level of the Regions

Using the three rural transformation indicators (i.e., share of HVA value of production to the total AFF GVA, off-farm employment share, and agricultural labor productivity), a rural transformation (RT) index was developed to indicate the relative level of rural transformation of each region. Each region was categorized into low, medium, and high levels of rural transformation based on its RT index score or the summation of the scores for the three indicators. The cut-off scores for each indicator were rationally set based on the trend analysis, data means and medians and in consideration of the standard deviations across regions and years.

Table 4. Rural transformation (RT) index definition.

RT Level	RT Index Score (RTScore) Range	RT Indicator Point Scale (RTScore = HVA + OFF + AGLP)
		HVA Share to Total GVA in AFF (HVA)	Off-farm employment share (OFF)	Agricultural labor productivity (AGLP, in PHP)
Low	≤2	0 pt.: OFF < 50% 1 pt.: 50% ≤ HVA < 75% 2 pts.: HVA ≥ 75%	0 pt.: OFF < 60% 1 pt.: 60% ≤ OFF < 85% 2 pts.: OFF ≥ 85%	0 pt.: AGLP < 35,000 1 pt.: 35,000 ≤ AGLP ≤ 60,000 2 pts: AGLP ≥ 60,000
Medium	2 < RT ≤ 4
High	>4

details the RT index and scoring system developed for this study.

Figure 5 presents the progress of each region towards rural transformation in the last three decades. Overall, the progress has been sluggish in most of the regions, barely moving from the same stage after three decades of agricultural development. Only three out of the sixteen regions (19%), i.e., CALABARZON, Central Luzon, and Davao Region, have reached the high level of rural transformation by 2023. However, most regions have already graduated from the low rural transformation level, with only two regions—BARMM and Cagayan Valley—staying at the low level as of 2023 (Figure 6).

6.3. Effects of IPI Variables on Rural Transformation Indicators

Common effects, random effects, and fixed-effects panel regression models were run to determine the IPI factors that have had a significant effect on the rural transformation of the Philippine regions.

Table 5. Estimation results of selected rural transformation–IPI panel regression models (without interaction terms).

Variable	STAGE 1–2 (Shifting to HVA/Agricultural Diversification)	STAGE 3 (Rising Off-Farm Employment)	STAGE 4 (Increasing Urban-Rural Integration)
	HVA (Fixed Effects)	OFF (Random Effects)	AGLPlog (Fixed Effects)
Land	−9.58 × 10−8 ***	1.01 × 10−7 ***	5.40 × 10−7 ***
Wageag	−0.00034 **	0.0008	0.0027 ***
Price	−0.1254	−0.0713	−0.1724
Irrig	0.0270	0.0677	0.7094 ***
Road	0.0131 ***	0.0160 **	0.0339 **
GDPnon-ag	0.4559 ***	0.3564 **	−0.8311 ***
Rain	−0.00004	0.00003	0.0002

Note: *** and ** denote statistical significance at 5% and 1% level, respectively.

summarizes the estimation results of the best panel regression models (with and without interaction terms) that were chosen based on the relative estimates of their likelihood ratio, Hausman test, and LM test. The results of the fixed-effects model of the Stage 1–2 rural transformation variable, HVA, revealed that HVA was significantly affected by the IPI variables Land, Wage_ag, and Road. Estimation results suggest that under the agricultural diversification stage or the shift from low-value to high-value agriculture (Stage 1–2), the irrigation investments and the access to land through the land distribution program did not positively increase HVA share or agricultural diversification in the country.

The unexpected negative effect of the institution-related variable, land, or the increasing land distribution and access to land on HVA can be explained by the logic that the land under CARP is mainly used for traditional crops rather than for HVA. In the same way, irrigation facilities are focused on cereal production; thus, for regions where high-value agriculture is already dominating, an increase in the irrigation ratio may not have shown a significant positive impact on HVA. More productive use of land may rather be expected in regions with a higher level of rural transformation; thus, the positive significant effects on OFF (Stage 3) and AGLP_log (Stage 4). Policies to increase agricultural wages likewise did not have a positive effect at the early stage of rural transformation possibly because a large majority of the farm workers are still engaged in unpaid family labor at this stage of rural transformation. Positive effects can be more felt if the rural workforce is engaged in paid agricultural labor. On the other hand, the positive coefficient of Road implies that investments in road infrastructure are critical and effective at this stage of rural transformation.

In moving to the next phase of rural transformation (Stage 3), improving both the access to land and investment in road infrastructure are the key drivers in boosting non-agricultural employment in the rural sector. The random effects model estimation indicated that Land and Road were significant factors of OFF (Table 5). Consistent with the previous-stage findings, investment in roads (more than in irrigation) made a more significant contribution to rising off-farm employment. At this stage, access to land (as proxied by Land) was also found to be a significant driving factor of rural transformation. The result can be explained by the notion that land ownership is a key asset that can be used to generate financial resources that will enable the shift from agricultural to off-farm employment through investments in skills improvement and agribusiness development.

For the Stage 4 rural transformation, the fixed-effects model estimates indicated that Land, Wage_ag, Irrig, and Road were significantly associated with AGLP_log. All the significant IPI factors identified showed a positive relationship with the rural transformation variables. Institutional mechanisms to improve access to land are therefore needed in the advanced stage of the transformation to provide an asset for expanded and more productive high-value agricultural activities. The positive coefficient of Wage_ag implies that strengthening policies to improve the daily basic pay of the agricultural workers can improve agricultural labor productivity, which can in turn reduce the wage gaps and foster convergence between the agricultural and non-agricultural labor force. It is also noteworthy that investment in irrigation (as proxied by irrigation ratio) only turned out significant in the AGLP_log model. Besides the fact that irrigation is intended primarily for low-value crops, the estimation results also suggest that increasing the number of irrigated lands alone is not adequate to improve agricultural diversification and off-farm employment. Investments should not only focus on increasing the quantity of irrigation but also on ensuring the quality of the infrastructure and its services. Perhaps for regions already reaching the advanced stage of rural transformation (which happen to be mostly the high-income regions), there is a better capacity to upgrade the irrigation quality and services compared with regions that are less rurally transformed (which are mostly the low-income regions).

The three models were again estimated, controlling for the effects of structural transformation (as proxied by GDP_non-ag) and climate impact (Rain). In these estimations, only GDP_non-ag was statistically significant. The varying directions of the relationship of GDP_non-ag with the rural transformation variables suggest that a rapid structural transformation may not always have a positive effect on rural transformation.

Interaction terms represent the joint effects of the IPI variables on rural transformation variables rather than the unique or independent effect of each IPI variable. With the joint effects of IPIs considered in the models, some of the unique/independent effects of IPIs became insignificant and/or changed direction. However, we focus on reporting the interaction/joint effects in these models rather than the unique effects of individual IPIs.

Table 6. Estimation results of selected rural transformation–IPI panel regression models (with interaction terms).

Variable	STAGE 1–2 (Shifting to HVA and Agricultural Diversification)	STAGE 3 (Rising Off-Farm Employment)	STAGE 4 (Increasing Urban-Rural Integration)
	HVA (Fixed Effects)	OFF (Fixed Effects)	AGLPlog (Fixed Effects)
Land	−7.10 × 10−7 ***	−3.59 × 10−7	1.10 × 10−7
Wageag	0.0013 **	0.0011 ***	0.0009
Price	−0.0060	−0.1004	−0.3275
Irrig	0.1194	−0.0886	−1.1906 ***
Road	0.0311 ***	0.0101 **	0.0474 **
Land*Wageag	1.06 × 10−9	−1.10 × 10−9 **	−1.19 × 10−9
Land*Price	−4.30 × 10−7	1.49 × 10−7	−9.25 × 10−7
Land*Irrig	9.10 × 10−7 ***	−7.72 × 10−8	8.04 × 10−8 ***
Land*Road	2.55 × 10−8 ***	6.38 × 10−9	4.65 × 10−8 ***
Road*Wageag	−0.0002 **	−0.0001 ***	−0.0001
Road*Price	0.0030	−0.0020	0.0199
Irrig*Wageag	−0.0026 ***	0.0010 **	0.0103 ***
Irrig*Price	0.0681	0.1600	1.2273
GDPnon-ag	−0.4867 ***	0.1160 **	−0.8113 ***
Rain	0.0001	0.00002	0.0003 **

Note: *** and ** denote statistical significance at 5% and 1% level, respectively.

shows the results of the panel regression estimation with interaction terms. For all rural transformation variables, the fixed-effects model was selected as the best estimation model.

Among the interaction variables, Land*Irrig and Land*Road were significant factors of HVA and AGLP_log fixed-effects models, indicating that access to land and investments in irrigation development create significant positive effects on rural transformation, specifically on increasing the HVA share in AFF GVA and the agricultural labor productivity if accompanied by each other. The positive effect of land access on rural transformation also increases if accompanied by investment in road infrastructure (and vice versa). The variable Irrig*Wage_ag was also a significant driver of rural transformation. Specifically, in more advanced stages of rural transformation, a positive effect of investment in irrigation on rural transformation can be achieved if farmers using the irrigation facilities enjoy higher agricultural wages, as the latter will allow farmers to use irrigation beyond traditional crops. In contrast, the Irrig*Wage_ag coefficient turned out negative in the HVA model, which again could be due to the greater focus on traditional agriculture at this early stage of rural transformation than on HVA.

The structural transformation variable, GDP_non-ag, that was controlled for in the panel regression models was also statistically significant at all stages of rural transformation but negatively affected HVA and AGLP_log, possibly due to premature structural transformation. Ideally, agricultural diversification must occur first before rural-to-urban migration and structural transformation. Additionally, if the structural transformation has been primarily driven by low-paying jobs in the services sector, a positive relationship with HVA and AGLP_log may not be observed.

7. Theoretical and Policy Implications

The findings highlighted the varying phases of rural transformation across regions, revealing significant disparities. While a few regions, i.e., CALABARZON, Davao Region, and Central Luzon demonstrated exceptionally high performance across all the rural transformation indicators—HVA, OFF, and AGLP_log—the majority of the regions performed below the national average. Most of the regions exhibited slow to moderate levels of rural transformation after three decades of implementing programs and projects that promote agricultural and rural development. The uneven progress in agricultural diversification, the slow movement of surplus agricultural labor for absorption in the higher-paying jobs offered by the non-agricultural sectors, in particular manufacturing and processing industries, and consequently, the continued low returns to agricultural labor in the regions have prevented many regions from transitioning towards a relatively higher level of rural transformation.

This study further established the critical role of IPIs in driving rural transformation. However, their impacts varied across the different stages of transformation, emphasizing the importance of carefully identifying, sequencing, and integrating IPI interventions to address transformation challenges effectively. The findings also highlighted the necessity of implementing complementary IPI interventions to maximize their combined effects and expedite progress, particularly in higher stages of rural transformation.

Based on the findings, this study provides actionable insights for both theory and policy. It offers a robust framework for understanding rural transformation, with direct implications for policy and research in the Philippines and valuable lessons for other developing countries facing similar rural transformation challenges.

7.1. Theoretical Implications

The findings emphasize the need for context-specific theoretical frameworks that recognize the heterogeneity of rural transformation stages of regions within a country. This study suggests that the sequencing and integration of the IPIs must be tailored to the specific conditions of each local region. The findings also highlight the interplay between institutional reforms (e.g., land access), infrastructure investments (e.g., roads and irrigation), and policy mechanisms (e.g., agricultural wage policies). Theoretical models should incorporate the concept of IPI complementarity, where the combined effects of reforms exceed their isolated impacts.

Moreover, this study validates stage-specific theories of rural transformation, such as those proposed by Timmer [12] and Huang [35]. By empirically demonstrating how different IPIs influence each of the stages, this study strengthens the theoretical basis for dynamic, multi-stage models of rural transformation. Lastly, it corroborates theories that warn against premature structural transformation, where non-agricultural sector growth outpaces agricultural transformation, adding empirical evidence to the theoretical discourse on the adverse effects of service-driven growth on rural economies.

Despite this study’s limitations in terms of the indicators used to analyze rural transformation trends and the scope of analysis, its findings align with broader literature, validating findings from other studies that highlighted the Philippines’ sluggish agricultural and rural economy performance. This study contributes new evidence supporting the role of regions to national rural transformation and the pivotal roles of institutional support and innovation, policy mechanisms, and infrastructure investments in accelerating regional rural transformation. Future research should expand on these findings by incorporating additional indicators and exploring the dynamic interplay of IPIs across different socio-economic and geographic contexts to guide more targeted and effective policymaking. Researchers can also replicate the methodology of this study in other countries to compare the effectiveness of IPIs in facilitating transformation. For example, a comparative analysis of rural transformation in Southeast Asian countries like the Philippines, Indonesia, Viet Nam, and Thailand could reveal region-specific challenges and shared success factors. Additionally, future research should look into the households’ perception of rural transformation, in particular, on how their reception to interventions delay or fast track the speed of such transformation.

7.2. Policy Implications

The results of this study recommend the following three critical policy actions and development programs to address the transformation issues and gaps more effectively, especially in the regions and, consequently, the country as a whole.

7.2.1. Effective Implementation of an Expanded Convergence Policy and Program

The demonstrated effects of IPIs and their complementarity imply the need to integrate the fragmented development programs in the Philippines. Currently, there are few convergence programs and projects being implemented by the government, a key project of which is the Department of Agriculture’s (DA) Philippine Rural Development Project (PRDP) funded by the World Bank. Under PRDP, national agencies like the Department of Agrarian Reforms (DAR), Department of Environment and Natural Resources (DENR), the Department of Public Works and Highways (DPWH), the Department of Trade and Industry (DTI), the Department of Science and Technology (DOST), together with local government units (LGUs), plan and implement interventions to raise agricultural productivity and uplift the welfare of farm communities. The National Irrigation Administration (NIA) and the DPWH have also had complementation activities in the repair and rehabilitation of irrigation facilities to prevent degradation of irrigation facilities and therefore improve the delivery of irrigation services. Another example is the DTI’s financing programs provided by RA 11901 or the Agriculture, Fisheries, and Rural Development Financing Enhancement Act for rural development initiatives such as the Rural Agro-Enterprise Partnership for Inclusive Development and Growth (RAPID Growth) Project. Under this project, DTI has reinforced its partnership with private sector organizations in the development of rural and agribusiness enterprises and empowering farmer organizations toward rural development.

Facilitating rural transformation is not the sole responsibility of the DA but of several agencies, as well as partners in the academe, people’s organizations, and the private sector. In this regard, the National Convergence Initiative for Sustainable Rural Development, which unites four government agencies, namely the DA, DAR, DENR, and the Department of Interior and Local Government, including LGUs, under Joint Administrative Order No. 01, series of 2015 to respond “to the fragmented delivery of rural development services towards improved governance and optimized use of resources…” [83], must not only be forcefully and efficiently implemented but expanded to involve other agencies, institutions, and entities for a more integrated delivery of interventions.

7.2.2. Stronger Alignment of National and Local Government Plans to Ensure Strategic and Efficient Use of Limited Public Resources

While most of the LGUs have socio-economic development plans and investment programs, the quality of these documents has to be assessed, especially in terms of how thoroughly the development gaps and needs are included and budgeted. Implementing agencies should consider these key intervention gaps to achieve a more integrated and harmonious development of the countryside and, consequently, the country as a whole. Another important aspect that has to be attended to is close monitoring and evaluation of the progress of these local plans.

At the national level, the National Economic and Development Authority (NEDA), through its Regional Development Councils (RDCs), should continue to align national and local plans in the formulation of the PDPs, highlighting the specific regional programs that would fast-track rural transformation, especially in regions that are lagging behind in the rural transformation. Together with the Department of Budget and Management and the Department of Finance, NEDA must ensure that such programs, projects, and activities of implementing agencies should be fully funded and streamlined to avoid budget duplication for the same activities, thereby maximizing the use of budgetary resources.

7.2.3. Greater Support to High-Value Agriculture and Strengthening Agri-Value Chain

Based on the findings of this study, and other similar studies, there is an urgent need to prioritize a shift from traditional crop production to high-value commodity production and, as such, to boost the growth performance of the latter sub-sector to further increase its contribution to the sector’s GVA, as well as the sector’s labor productivity. The policy bias towards rice in the allocation of budget resources has deprived other more lucrative crops from realizing their potential to contribute more to AFF GVA. For example, the National Rice Program had a budget allocation share of 57.3% (or PHP 15.8 billion) from the commodity program budget in 2023, while the National High Value Crops Development Program had a measly PHP 1.5 billion in 2023, down from the PHP 2.2 billion budget it had in 2009 [84].

A higher budgetary support to develop HVA should not only include interventions to increase yields but also the quality of produce. Research, training, and extension of improved and more resilient production inputs and technologies have to be enhanced and the support for the needed production and post production facilities be provided, including those for processing and value adding, marketing, and transport not only for domestic use but also international. In this regard, institutional support that encourages the establishment of strong linkages with the private sector, in particular the manufacturing and processing industries, is valuable, especially in the production of high-specialty products and regional brands. Finally, more affordable credit facilities should be made available for the sub-sector, beyond production activities, but also to include establishment of agro-enterprises.

One element to ensure the success of the abovementioned efforts is to organize farmers towards large-scale production and operation that would take advantage of economies of scale. Agricultural modernization, particularly through digitalization, is also critical in closing the productivity gap between the agriculture and non-agriculture sectors.

8. Conclusions

This study examined the status and rural transformation trends in the Philippines, analyzing secondary regional data covering sixteen regions (excluding NCR) from 1988 to 2023. The analysis decomposed the different stages in the rural transformation process, in which the factors affecting each stage of the transformation were investigated. It highlighted the need for nuanced approaches to rural transformation, underscoring the critical role of IPI interventions and their complementarity and proper targeting and sequencing. By addressing the unique challenges of uneven regional transformation, the Philippines can fast-track its journey towards inclusive and sustainable rural development. The findings and implications of this study also hold significant relevance for other developing Asian nations facing similar challenges, including those experiencing premature structural transformation, rural–urban divide, and spatial inequalities in rural transformation. They can guide researchers, policymakers, and development practitioners in planning strategies for accelerating rural transformation in other countries.