The study, conducted by Huong Thi Thu Pham of Hung Vuong University, Nga Thi Pham of Thai Nguyen University of Economics and Business Administration, Anh The Khuc and Anh Bao Nguyen of the National Economics University in Hanoi, set out to resolve a stubborn weakness in the banking literature. Most previous analyses of how net interest margins affect profitability assumed a linear relationship: every additional basis point of margin was presumed to add roughly the same amount of profit regardless of the bank’s size. Others divided banks into small, medium, and large categories using arbitrary cutoffs chosen by the researchers rather than by the data. Both approaches, the authors argue, risk missing the scale-dependent structural breakpoints that actually govern how banking business models perform.

To find where that breakpoint lies, the team turned to an endogenous panel threshold regression framework, a statistical technique that lets the data itself reveal the critical value at which the relationship between two variables changes. Rather than imposing a threshold chosen in advance, the model searches across possible asset levels and identifies the point that best splits the sample into two distinct regimes. Applied to a panel of 58 commercial banks across Southeast Asia observed from 2013 to 2025, the procedure converged on a specific total asset threshold of USD 1.3 billion, expressed in the model’s logarithmic scale as a size value of 14.0794. The precision of the estimate matters because it transforms an abstract question about economies of scale into an actionable benchmark that regulators and bank executives can actually use.

The coefficients on either side of that line tell a striking story. Below the threshold, each unit of net interest margin is associated with an estimated profitability coefficient of 0.2608. Above the threshold, the same coefficient rises to 0.4014—an increase of more than half. In practical terms, this means that once a bank crosses the critical asset scale, every increment of core intermediation income it generates contributes substantially more to its bottom line than the identical increment would at a smaller institution. Crucially, the study finds that crossing the threshold amplifies rather than reverses the profitability benefits of margins, countering any suggestion that oversized banks suffer diminishing returns on their core lending business in emerging markets.

The mechanism behind this asymmetry, the authors contend, lies in economies of scale. Larger banks can spread fixed operating costs—branch networks, core banking systems, compliance infrastructure, risk management platforms—across a much wider asset base. They typically enjoy stronger bargaining power in funding markets, allowing them to gather deposits and wholesale funding more cheaply. They can also invest in the technology and analytical capacity needed to price loans more accurately and manage credit risk more efficiently. Together, these advantages mean that interest income is generated with less waste, so a greater share of each unit of margin flows through to net profits. The threshold result captures the point at which these scale economies become decisive enough to visibly change the margin-to-profit relationship.

The timing of the study gives its findings particular weight. The 2013 to 2025 window spans a period of profound stress and transformation in Southeast Asian banking, including the disruptions of the pandemic era, shifting interest rate cycles, and accelerating digitalization. Sustainable profitability—the ability of banks to remain consistently profitable across such turbulence rather than enjoying isolated windfall years—is critical for emerging economies, where commercial banks supply the overwhelming majority of external financing for firms and households. When bank margins translate efficiently into profits, banks can absorb losses, maintain lending through downturns, and support the robust flow of economic credit that development strategies depend on.

For policymakers in emerging economies, the implications are direct. The authors argue that government support programs and banking consolidation strategies should explicitly target scale optimization rather than treating bank size as an afterthought. In many Southeast Asian markets, the banking sector remains fragmented, with dozens of small institutions competing in the same territories and duplicating infrastructure. The threshold evidence suggests that policies encouraging mergers, acquisitions, or organic growth up to and beyond the USD 1.3 billion mark could unlock measurable efficiency gains across the system. At the same time, the finding invites careful calibration: consolidation policy must balance the efficiency benefits of scale against the competition and systemic risk considerations that have long dominated the regulatory conversation.

Bank managers receive an equally concrete prescription. The study advises executives to prioritize asset growth and cost reduction in order to leverage the asymmetric benefits of net interest margin. For institutions still below the threshold, the message is that margin expansion alone will deliver only limited profit gains; the strategic payoff arrives when margin management is paired with a credible path across the critical scale. For banks already above the line, the results justify continued investment in scale-sensitive capabilities, from digital delivery channels to centralized credit underwriting, that deepen the economies of scale the threshold analysis reveals. Cost discipline, in this framing, is not merely housekeeping but a precondition for converting intermediation income into durable profitability.

Methodologically, the paper’s contribution lies in demonstrating what endogenous threshold modeling can uncover where linear panels cannot. By letting the breakpoint emerge from the data, the approach avoids the arbitrary size classifications that have fragmented earlier findings and provides a replicable template for studying scale effects in other regions and other financial systems. The framework could readily be applied to banking markets in other emerging economies, to different profitability measures such as return on average assets, or to other candidate thresholds defined by capitalization, deposit base, or loan book size. If the Southeast Asian pattern holds elsewhere, the notion of a universal, size-blind relationship between margins and profits may need to be retired altogether.

The study also carries a broader warning for the field of sustainable finance research. As attention shifts toward environmental and governance dimensions of banking stability, the new results are a reminder that the humble mechanics of intermediation—how efficiently a bank turns its interest spread into retained earnings—remain foundational. A banking sector whose small institutions struggle to convert margins into profits is a sector with thinner loss-absorbing buffers, weaker capacity to fund green transitions, and greater vulnerability to shocks. By pinpointing the exact asset scale at which that conversion becomes efficient, the research offers emerging economies a quantified target in the pursuit of resilient, sustainable financial systems, and gives the global debate over bank size a number worth arguing about.

Subject of Research: Threshold effects of bank size on the relationship between net interest margin and sustainable profitability in Southeast Asian emerging economies

Article Title: Threshold effects of net interest margin on bank sustainable profitability in emerging economies

Article References: Pham, H. T. T., Pham, N. T., Khuc, A. T., & Nguyen, A. B. (2026). Threshold effects of net interest margin on bank sustainable profitability in emerging economies. Discover Sustainability. https://doi.org/10.1007/s43621-026-04921-z

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04921-z

Keywords: net interest margin, bank profitability, threshold regression, economies of scale, Southeast Asia, emerging economies, commercial banks, banking consolidation, ROAA, sustainable finance, panel data, financial economics

The research, published in the journal Discover Agriculture, examined the sources of total factor productivity (TFP) and profit growth in Beninese cotton production between 2000 and 2018. A team led by Idelphonse O. Saliou of the University of Abomey-Calavi assembled an impressive dataset: 482 cotton producers surveyed across three major agroecological zones—the central region around Savalou, the northern district of Banikoara, and the western Atacora region around Cobly. To qualify for the study, producers had to be at least 40 years old with a minimum of 19 consecutive years of farming experience, ensuring that each participant could reconstruct nearly two decades of production history.

Reconstructing nineteen years of farm records in a setting where most smallholders keep no formal accounts is a formidable challenge. The researchers used retrospective recall techniques, dividing the study period into four sub-periods aligned with successive presidential regimes—a practical aid to memory in a country where agricultural policy shifts with political transitions. Producers identified years of high and low performance within each sub-period, then supplied production details for the remaining years. Where available, farmers supplemented their recollections with accounting books, input purchase invoices, and labor contracts. The final estimation sample included 5,577 observations, reduced from a theoretical maximum of 9,158 by recall limitations, missing variables, and strict data-cleaning procedures that the authors argue should not introduce systematic bias under standard missing-data assumptions.

Methodologically, the study employed a parametric translog cost frontier approach, a flexible second-order approximation of the true cost function that imposes few prior restrictions on the underlying technology. The framework, following the decomposition methods of Kumbhakar and colleagues, separates productivity growth into three distinct components: technical efficiency change, which measures how close farmers operate to the best-practice frontier; technological change, which captures shifts in that frontier itself; and scale effects, which reflect economies or diseconomies of expanding production. Input prices for fertilizer, labor, and a Laspeyres index of other inputs—including seeds, insecticides, herbicides, and animal or mechanical traction—were normalized by land prices to satisfy linear homogeneity constraints. Crucially, the model accounted for unobserved heterogeneity among farms, distinguishing persistent inefficiency rooted in structural conditions from time-varying inefficiency that fluctuates year to year.

The headline result is modest but meaningful: TFP in Beninese cotton production grew by an average of 1.26 percent per year between 2000 and 2018. Decomposition reveals that this growth was powered almost entirely by technological progress, which advanced at 2.81 percent annually, complemented by a modest gain in technical efficiency of 0.24 percent per year. The technological momentum reflects concrete changes in the field: continuous varietal improvement programs that guarantee quality seed to producers, the gradual replacement of hand tools with animal traction and mechanical power, and intensified use of mineral fertilizers and pesticides for weed control and plant health. The technical efficiency gains, meanwhile, are credited in part to Benin’s dense extension network—nearly all cotton farmers belong to Village Cotton Producers Cooperatives, and extension agents are evaluated on the basis of cotton production performance.

But there is a troubling counterweight. The scale component exerted a negative effect of 1.83 percent per year on TFP, indicating that Beninese cotton farms are operating under decreasing returns to scale. When farmers increased the use of all inputs, output rose proportionally less, driving up unit costs for each additional kilogram of cotton harvested. In plain terms, farms are growing beyond their most efficient size, and unexploited economies of scale represent a pool of unrealized productivity. The authors note similar findings in Chinese agriculture after reforms, and contrast them with European and Finnish dairy farms where scale effects contributed positively to productivity—evidence that lower average costs could be achieved by producing at more optimal scales.

The study also traced how productivity translated, or failed to translate, into the bottom line. Farm profits grew by an average of 1.65 percent per year over the period, but the sources of that growth were largely external rather than internal. Rising cotton prices, which climbed at 3.03 percent annually, were the dominant driver, aided by Benin’s price stabilization mechanism that guarantees a minimum income for producers and shields them from world market volatility. Output quantity growth of 0.89 percent per year and the TFP gains also helped. Working against these gains, input prices rose at 2.91 percent per year, steadily eroding profitability—a pattern the authors note mirrors Kumbhakar and Lien’s findings in Norwegian dairy farming. Profit growth was strongest between 2011 and 2015, driven by favorable price movements, while the earliest sub-period saw profits squeezed by input cost inflation.

One finding stands out for its starkness: the estimated overall cost efficiency of Beninese cotton farms is only about 23.5 percent, combining persistent efficiency of 28 percent with time-varying efficiency of 83.8 percent. Actual production costs remain substantially above the minimum attainable frontier. The authors caution that this does not simply reflect poor management. Rather, the persistent inefficiency component likely captures structural and systemic constraints beyond individual farmers’ control: poor rural infrastructure, high transportation and input transaction costs, imperfect access to mechanization services, climate variability, credit market imperfections, and institutional rigidities. Similar low-efficiency findings across West Africa support this interpretation, with prior research showing that institutional environments—particularly access to credit, inputs, and marketing channels—significantly shape producer performance.

The researchers confirmed both of their formal hypotheses: technical efficiency gains contributed positively to TFP growth, and rising cotton prices positively influenced farm profits. Yet they are candid about the study’s limitations. Retrospective data collection risks recall bias and measurement error; the sample of experienced, older producers may overestimate efficiency; the cost frontier framework does not address potential endogeneity, including the possibility that government-set cotton prices are not truly exogenous; and the assumption of full allocative efficiency—that farmers use optimal input combinations—may not hold in practice. Future work using profit frontier models could test how sensitive these conclusions are to those assumptions.

For policymakers, the recommendations are clear. The authors call for strengthening producer capacities through training, promoting technological innovations such as mechanization and pest-resistant varieties, and implementing incentive-compatible price policies that support optimal input use. The stagnation of TFP growth in the final sub-period of the study, driven by losses in technical efficiency and negative scale effects, serves as a warning that past gains are not guaranteed to persist. With nearly three million livelihoods tethered to the cotton plant, Benin’s experience offers a broader lesson for agricultural development across West Africa: productivity growth is possible even under structural constraints, but converting it into durable farmer prosperity requires tackling the institutions, infrastructure, and input markets that determine whether efficiency gains reach the farm gate.

Subject of Research: Total factor productivity and profit growth in cotton production in Benin, West Africa

Article Title: Total factor productivity and profit growth in cotton production in Benin, West Africa

Article References: Total factor productivity and profit growth in cotton production in Benin, West Africa. (n.d.). https://doi.org/10.1007/s44279-026-00749-3

Image Credits: AI Generated

DOI: 10.1007/s44279-026-00749-3

Keywords: Benin, cotton production, total factor productivity, technical efficiency, profit growth, cost frontier, agricultural economics, West Africa, technological change, economies of scale, farm profitability, agricultural policy