The conventional measure, total factor productivity or TFP, is the workhorse of agricultural policy analysis. It summarises the ratio of marketed outputs to marketed inputs: cereals, livestock, milk and other products on one side; fertiliser, fuel, machinery, labour and capital on the other. The UK’s Department for Environment, Food and Rural Affairs publishes an official TFP series built with chained Fisher index numbers, a transparent method consistent with national statistical practice. What this accounting deliberately excludes, however, are the non-market consequences of farming. Emissions can fall or nutrient pollution can shrink, and the official productivity index registers nothing at all.

The new study, published in Environmental and Sustainability Indicators by Jorge Campos-González, James Lowenberg-DeBoer and Dimitrios Papadas, borrows the Environmentally Sustainable Productivity Index framework to plug that gap. The idea is mathematically simple but consequential: alongside the good-output index and the market-input index, the researchers construct an environmental-pressure index combining three variables — agricultural greenhouse-gas emissions, nitrogen surplus and phosphorus surplus. A tuning parameter, alpha, controls how strongly the environmental component weighs on the final figure. When alpha equals zero, the new index collapses into conventional productivity; as alpha rises, environmental performance matters more.

The raw ingredients come entirely from public data. Market outputs and inputs are drawn from DEFRA’s agricultural productivity datasets, which the team carefully reconstructed to match the published series. The environmental pressures come from the UK territorial greenhouse-gas inventory and DEFRA’s soil nutrient balance statistics, which measure the difference between nutrients applied to farmland and those removed in crops and livestock. Because annual nutrient reporting only begins in 2000, the researchers interpolated the missing years of the 1990s and then tested whether that interpolation mattered — it turned out to shift the final result by less than half a percent.

The thorny problem is weighting. Emissions are measured in millions of tonnes of carbon dioxide equivalent, nutrient surpluses in thousands of tonnes; how do you combine them fairly when none has a market price? The researchers use data envelopment analysis, a non-parametric technique, as a weighting device. Each year is treated as a decision-making unit, and the method derives relative weights from the data itself. In the principal specification, greenhouse-gas emissions receive a weight of 0.550, nitrogen surplus 0.299 and phosphorus surplus 0.151. Crucially, the authors insist these are operational aggregation weights, not welfare-based estimates of environmental damage or implicit policy rankings.

The headline finding is striking. By 2023, market output was 4.8 percent above its 1990 level while aggregate market-input use had fallen 11.8 percent, pushing conventional productivity 18.9 percent higher. But the weighted environmental-pressure index had fallen 37.2 percent over the same period. When that decline is credited, the environmentally adjusted index climbs well beyond the conventional one: 24.0 percent above baseline for a mild environmental weighting, 34.8 percent for a moderate one, and 46.6 percent for the strongest weighting examined. In other words, British agriculture’s sustainability story looks considerably better than the standard statistics suggest — driven less by output growth than by falling input use and reduced environmental pressures.

The team did not stop at one number. They ran an unusually thorough battery of robustness checks: re-referencing the indices to different base years, rescaling variables, dropping each environmental pressure in turn, using equal weights instead of data-driven ones, perturbing every environmental observation by up to one percent a hundred times, and testing alternative solutions to the weighting model. The direction of the result proved remarkably persistent — the environmentally adjusted index stayed above conventional productivity at the 2023 endpoint in every deterministic specification — but its magnitude was genuinely sensitive to which variables were included. Excluding greenhouse-gas emissions pushed the moderate-weighting endpoint up more than ten percent, while excluding phosphorus pulled it down nearly five.

The study is equally candid about what the indicator cannot do. Chained Fisher indices provide a chronological record but not a globally path-independent multilateral index, so the measure is a national monitoring tool rather than an instrument for comparing arbitrary countries or periods. The alpha parameter is a sensitivity knob, not an estimated economic quantity. A directional distance-function analysis showed that frontier classifications depend heavily on how environmental pressures are represented, and the assembled data could not support a more structurally complete by-production model of pollution generation. Nor is the time series long enough to evaluate agricultural research investments, whose effects are known to materialise over decades. And the environmental block covers only three pressures; biodiversity, soil health, water quality and animal welfare remain outside the frame.

There is also policy context. The researchers descriptively compared three institutional periods marked by the 2016 EU referendum and the end of the transition period in 2020. Between 1990 and 2015, conventional productivity rose 19.5 percent while the environmentally adjusted measure rose 29.9 percent. Both dipped slightly between 2015 and 2020, and between 2020 and 2023 the environmentally adjusted index climbed 6.7 percent against just 2.1 percent for conventional productivity, driven by a 14.9 percent fall in the environmental-pressure index. The authors caution that with only three post-transition annual observations and substantial volatility, these patterns cannot be read as Brexit effects — but they illustrate exactly the kind of monitoring the indicator was designed to support.

The broader lesson is about measurement. A higher environmentally adjusted productivity figure does not mean every aspect of farming’s environmental footprint improved at once, and it says nothing about how performance varies between individual farms. What the framework delivers instead is an architecture: market output, input use and environmental pressures remain separately visible within a common index, so policymakers can see where progress comes from and where it stalls. As UK agricultural policy increasingly ties payments to climate mitigation, nutrient management and public goods, a measure that credits genuine environmental improvement — while being honest about the assumptions behind it — could become an essential companion to the productivity statistics that have long dominated the debate.

Subject of Research: Environmentally adjusted productivity measurement in UK agriculture combining TFP with greenhouse-gas emissions and nutrient surpluses, 1990–2023

Article Title: Integrating productivity and environmental pressures in UK agriculture: an ESPI-based sustainability indicator

Article References: Campos-González, J., Lowenberg-DeBoer, J., & Papadas, D. (2026). Integrating productivity and environmental pressures in UK agriculture: an ESPI-based sustainability indicator. Environmental and Sustainability Indicators, 32, Article 101559. https://doi.org/10.1016/j.indic.2026.101559

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101559

Keywords: UK agriculture, total factor productivity, sustainability indicators, ESPI, greenhouse-gas emissions, nitrogen surplus, phosphorus surplus, data envelopment analysis, Fisher index, DEFRA, environmental policy, nutrient management

The research team, led by Minting Huang of Aerospace Center Hospital in Beijing and Lau Nga Wun of Macao Polytechnic University, assembled a provincial panel covering all 31 of China’s province-level jurisdictions across the full twenty-year window. Rather than relying on a single analytical lens, they deployed a multi-method framework in which each tool answered a distinct question. A data envelopment analysis combined with the Malmquist index, known as DEA-Malmquist, measured how efficiently provinces converted healthcare inputs into services over time and whether changes came from technological improvement or from catching up to best practice. The Dagum Gini coefficient decomposed inequality in allocation outcomes to reveal where spatial disparities actually originated. Finally, a GM(1,1) grey prediction model, a technique designed for forecasting with limited data, projected future trajectories of supply and demand. Treating efficiency and equity as related but independent dimensions, the design allowed the authors to track each separately across a period that included both deepening healthcare system reforms and the shocks of public health emergencies.

The efficiency findings carry the sharpest edge. Total factor productivity in healthcare allocation is a ratio-style measure: when it exceeds 1, the system is producing more health services per unit of beds, personnel and expenditure than it did before; when it falls below 1, growth in inputs is outpacing growth in outputs. The study found the index fluctuating downward across the two decades, and critically, it identified the technological progress component, abbreviated Techch, as the weak link, with a value of 0.929 indicating an average annual shrinkage of the technology frontier of more than 7 percent in the periods where it declined. In efficiency analysis, this frontier represents the best achievable performance given current knowledge and practice. When the frontier itself contracts rather than expands, no amount of managerial effort by individual provinces can fully compensate, because the ceiling of what is possible is dropping.

Geography mattered enormously. The analysis revealed a regional heterogeneity best summarized as high in the East, low in the West, with the technological change gap between the eastern provinces and the rest of the country statistically distinguishable under province-level bootstrap testing, a resampling technique that guards against the possibility that observed differences are artifacts of small sample sizes. Perhaps more telling was where decreasing returns to scale concentrated: in the richer regions. In production theory, decreasing returns to scale means that adding inputs yields progressively smaller output gains, a signal that simply building more capacity no longer pays off. The fact that this pattern clustered in China’s wealthiest, most resource-dense provinces suggests that scale expansion there has reached a point of diminishing returns, and that further growth in those areas requires new technology and better organization rather than more beds.

The equity analysis told a more nuanced story. Using the Dagum Gini coefficient, a decomposition method that splits overall inequality into within-region, between-region, and trans-variation density components, the researchers examined inequality in the provincial productivity change measure itself. Trans-variation density, which captures the degree to which regional distributions overlap rather than sit cleanly apart, has long dominated the decomposition, contributing 52.96 percent of overall inequality in 2022. The authors caution that this overlap reflects one of several possible sources of disparity, but they identify the gradient gap between the Eastern and Western provinces as the root cause of overall inequality. In other words, the deepest fault line in Chinese healthcare productivity runs between the wealthy coastal belt and the interior, even as the distributions of individual provinces blur across regional boundaries.

Interestingly, the study’s conclusions note a divergence between two kinds of equity. Inequality in physical resources per head, such as beds and staff relative to population, declined over the study period, indicating genuine progress in distributing the hardware of healthcare more evenly across the country. Yet inequality in productivity change did not decline, meaning that even as provinces converged in what they possess, they diverged in how effectively they converted those resources into services. This distinction matters for policy because it implies that the remaining inequity is not primarily about where the buildings and machines are, but about the human capital, management practices, and technological adoption that determine what those assets actually deliver to patients.

The forecasting component adds a financial warning sign. The GM(1,1) grey prediction model projects that healthcare resource supply will continue expanding, but that the average hospitalization cost per visit would reach approximately 15,587 CNY in nominal terms by 2029, based on an illustrative extrapolation of the 2003 to 2022 trend. The authors frame this as pointing to a risk of rigid growth in government health expenditure, a scenario in which costs climb steadily regardless of efficiency gains, squeezing public budgets. For a system serving 1.4 billion people and aging rapidly, the gap between expanding supply and rising unit costs is precisely the combination that strains fiscal sustainability.

The authors are careful about what their study can and cannot claim. They describe the work as a descriptive assessment and explicitly state that it does not identify causal effects. The Malmquist framework measures changes in relative performance against a shifting frontier, not the impact of any specific reform or event. Still, the patterns they document point toward identifiable directions for policy. The study indicates frontier-expanding investment, meaning spending aimed at pushing out the technological frontier rather than merely adding capacity, alongside scale consolidation in provinces operating under decreasing returns to scale. It also points to cross-regional coordination to address the East-West gradient and to value-based payment reform, which ties reimbursement to outcomes rather than volume, as directions suggested by the observed patterns.

Beyond China, the findings speak to a dilemma facing many developing countries. The conventional development playbook is to build capacity fast: more hospitals, more beds, more staff. China executed that playbook at extraordinary scale over two decades, and by the measure of physical resources per person, it narrowed gaps between regions. But the study suggests that the playbook has a shelf life. Once resource stocks are broadly in place, further progress depends on the harder, less visible work of technological diffusion, institutional design and payment incentives, and those dimensions showed no such convergence. A country can equalize what it owns while still unequal in what it achieves.

The timing of the productivity slowdown, with the index below 1 from 2018 through 2022, spans both the later phase of China’s healthcare reforms and the COVID-19 pandemic years, though the descriptive design cannot attribute the decline to either. What the data do establish is a clear empirical baseline: a system whose input growth has outrun its output growth, whose technological frontier has been contracting, whose deepest inequality follows an East-West gradient rooted in productivity change rather than resource counts, and whose costs are projected to keep rising into the next decade. For policymakers in Beijing and for health economists watching from abroad, the study reframes the central question of Chinese healthcare from how much the system has, to how well it uses what it has. The answer, on current evidence, is not yet well enough, and the trajectory suggests that the next two decades will demand a fundamentally different kind of investment than the last two.

Subject of Research: Efficiency and equity of healthcare resource allocation across Chinese provinces from 2003 to 2022

Article Title: Efficiency, equity, and future trajectories of healthcare resource allocation in China: a multi-method provincial panel analysis, 2003–2022

Article References: Huang, M., Wun, L. N., Huang, S., Wu, J., Deng, T., Gong, H., Wang, Q., & Wu, Z. (2026). Efficiency, equity, and future trajectories of healthcare resource allocation in China: a multi-method provincial panel analysis, 2003–2022. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15751-0

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15751-0

Keywords: healthcare resource allocation, China, total factor productivity, DEA-Malmquist index, Dagum Gini coefficient, grey prediction model, regional disparity, health equity, health economics, health policy, provincial panel data, hospital costs

The study, conducted by Olimjon Gaybullaev and Henry Obaga Were of the University of Pécs in Hungary, is notable for how it dissects the timing of productivity effects. Using an augmented autoregressive distributed lag model combined with frequency-domain Granger causality analysis, the authors were able to separate the average long-run relationships between innovation, energy and productivity from the short-term dynamics that conventional methods tend to blur. The result is a nuanced picture in which green patents and R&D temporarily depress measured productivity before delivering substantial gains, while renewable energy works in precisely the opposite pattern.

The choice of Sweden as a test case is deliberate. The country ranks second in the 2024 Global Innovation Index, invests more than 3.6 percent of its GDP in research and development, and pursues a national target of net-zero carbon emissions by 2045. Its renewable energy share of final consumption stands at roughly 66 percent, among the highest of any advanced economy. Programmes such as Produktion2030 have woven Industry 4.0 technologies, circular economy principles and environmental innovation into the industrial fabric, making Sweden an unusually clean laboratory for asking whether green transformation and economic competitiveness can genuinely coexist.

The econometric strategy was tailored to the data. Because annual observations over 30 years produce a small sample, the researchers relied on the augmented ARDL bounds testing framework, which performs reliably with as few as 30 to 40 observations and accommodates variables with mixed integration orders. Unit root testing with the augmented Dickey-Fuller and Zivot-Andrews procedures, the latter allowing for structural breaks, revealed that productivity and green patent intensity behave as trending, cumulative processes, while R&D intensity and renewable energy shares show mean-reverting tendencies shaped by policy interventions. A significant structural break around 2008, corresponding to the global financial crisis, was captured with a dummy variable to prevent biased estimates.

The long-run results are striking. A 1 percent increase in green technology innovation, measured by the share of environment-related patents among all domestic inventions, is associated with an approximately 0.37 percent rise in total factor productivity. R&D expenditure carries an even larger elasticity of roughly 0.96 percent, underscoring how Sweden’s sustained research investment builds the absorptive capacity that allows new knowledge to be commercialised and diffused. Renewable energy consumption, by contrast, showed a negative and statistically insignificant long-run coefficient of about minus 0.24, a result the authors attribute to the fact that Sweden’s green transition is already mature, so the easy efficiency gains have largely been banked.

The short-run story inverts that picture. In the immediate term, both green innovation and R&D spending significantly reduce measured productivity, with coefficients of minus 0.278 and minus 1.844 respectively. The explanation lies in adjustment costs: retooling production lines, training workers, waiting out patenting delays and absorbing organisational restructuring all impose sunk costs before new technologies spread through the economy. In a mature, fiercely competitive market like Sweden, firms may also face saturation effects that slow diffusion further. Renewable energy adoption, however, delivered a positive short-run coefficient of 0.712, reflecting immediate efficiency gains from lower energy costs, cleaner production processes and reduced operational disruption.

The frequency-domain causality analysis adds a crucial layer that average elasticities cannot capture. Green technology innovation and total factor productivity Granger-cause each other across short, medium and long frequency bands, indicating a mutually reinforcing loop in which innovation drives productivity and productivity feeds back into further innovation. R&D shows a similarly bidirectional, long-run-dominated relationship. Renewable energy displays unidirectional causality flowing to productivity at long- and medium-term frequencies, even though the average long-run ARDL coefficient is insignificant. In other words, the productivity contribution of renewable energy is real but gradual, materialising through structural and technological adjustments in the energy system rather than through immediate output gains.

These patterns map closely onto two foundational economic theories. The Porter hypothesis holds that well-designed environmental regulation stimulates innovation that ultimately offsets compliance costs and enhances competitiveness, and the Swedish evidence supports both its weak and strong versions: regulation-driven green innovation exists, and it produces net productivity gains over time. Endogenous growth theory, which holds that purposeful investment in knowledge and technology generates spillovers and increasing returns, explains the delayed returns to R&D. The short-run productivity dip observed for innovation is exactly what these frameworks predict, as resources flow into knowledge creation before the returns arrive.

The policy implications are considerable. The authors argue that renewable energy policies should not be judged solely on environmental outcomes or short-run productivity metrics, since their economic value unfolds over extended horizons through infrastructure adaptation and capital-intensive adjustment. Investments in smart grids and energy storage could accelerate the integration of renewables with existing production systems. More broadly, the study makes the case for coordinated frameworks that jointly promote environmental technologies, R&D investment and renewable energy deployment, rather than treating each lever in isolation. Sweden could also deepen its position within the Nordic innovation ecosystem, where recent evidence shows that climate technologies and cross-border spillovers deliver the strongest green productivity gains precisely in Sweden.

The study’s authors acknowledge limitations. The single-country focus limits generalisability to economies at earlier stages of the green transition, and the 30-year sample may lack the statistical power to detect some long-run effects, which could partly explain the insignificant renewable energy coefficient despite the persistent causal signal in the frequency domain. The linear ARDL framework also cannot capture asymmetric or nonlinear dynamics, and aggregate national data mask sectoral heterogeneity. Future work using nonlinear models, alternative cointegration estimators or firm-level data could sharpen the picture. What the current evidence establishes, however, is that in one of the world’s most innovative economies, going green has not come at the expense of competitiveness. It has, on the evidence of three decades of Swedish data, helped pay for it.

Subject of Research: The effects of green technology innovation, R&D expenditure, and renewable energy consumption on Sweden's total factor productivity and economic competitiveness from 1993 to 2022.

Article Title: The role of green technology innovation, R&D, and renewable energy in Sweden’s economic competitiveness

Article References: Gaybullaev, O., & Were, H. O. (2026). The role of green technology innovation, R&D, and renewable energy in Sweden’s economic competitiveness. Clean Technologies and Environmental Policy, 28(10), Article 258. https://doi.org/10.1007/s10098-026-03605-6

Image Credits: AI Generated

DOI: 10.1007/s10098-026-03605-6

Keywords: green technology innovation, total factor productivity, research and development, renewable energy, Sweden, economic competitiveness, A-ARDL model, frequency-domain Granger causality, Porter hypothesis, endogenous growth theory, renewable energy transition, environmental patents

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