Estimation Of Production Function Research Articles

An adaptation of Random Forest to estimate convex non‐parametric production technologies: an empirical illustration of efficiency measurement in education

AbstractThis paper presents a novel approach to conduct non‐parametric estimations of production technologies that adhere to the basic assumptions of production theory axioms, including free disposability in inputs and outputs and convexity. The methodology is rooted in adapting the highly effective machine learning techniques associated with Random Forest and the use of splines. The new method features a piecewise linear estimator analogous to data envelopment analysis (DEA); however, it distinguishes itself by addressing DEA's overfitting and lack of robustness via randomization of data and input variables in the construction of the models. In this paper, the virtues of employing machine learning techniques for assessing the efficiency of public services, particularly in the realm of educational institutions, are underscored. The new approach has the capability to predict outputs based on inputs, even for units not included in the observed sample. Furthermore, it enables the identification of the most relevant inputs in relation to output production. To demonstrate the advantages of our method, an estimation of the educational production function is conducted for Spanish regions utilizing data sourced from the Program for International Student Assessment.

FARO-44 RICE PRODUCTION EFFICIENCY OF FADAMA III ADDITIONAL FINANCE II BENEFICIARIES IN TARABA STATE,

The study analysed the faro-44 rice production efficiency of fadama III additional finance II beneficiaries in Taraba State, Nigeria. A Multi-stage sampling procedure was used to select 250 respondents. Data were analyzed using descriptive and inferential statistics. The findings of the study revealed that The result of maximum likelihood estimates of the stochastic frontier production Function of dry season faro-44 rice farmers reveal that farm size (P<0.01), fertilizer (P<0.05), quantity of seed (kg) (P<0.01), herbicide (liter) (P<0.05), were positive and significant increasing output of the farmers. Similarly, the age (P<0.01) level of education (P<0.01), farming experience (P<0.05) and household size (P<0.05 were the socio-economic characteristics influencing the increase in technical efficiency of the farmers. Furthermore, the technical efficiency of the farmers is glaringly below 1, an indication that the farmers are operating below their production efficiency frontier. A reasonable proportion (41.20%) of the respondents were within 0.60-0.69 technical efficiency index range with highest value of 0.91 for the most efficient farmers and minimum of 0.50 for the least efficient or worst farmer. Consequently, farmers obtained mean technical efficiency of 0.76 which will improve their technical efficiency by 24%. The result further revealed that, the farmers attained an estimated mean allocative efficiency of 0.88. It was concluded that most of the farmers were unable to obtain technical efficiency level above 76% and farmers were 88% allocative efficient. The study recommended that farmers level of technical efficiency can be increased by 27% by building farmers’ capacity through training, workshops and seminars on postharvest loss management, processing techniques, farm record keeping in order to avoid wastages and sustain efficiency.

Pan-European Water Use Efficiency and Sustainability Evaluation Based on Stochastic Meta-frontier Analysis

In 2007, the European Union (EU) intended to become a water-efficient community. Yet, despite the EU’s commitment to the United Nations (UN) sustainable development goals (SDGs), relevant insights based on scientific research are still sparse. This study presents a pan-European water use performance evaluation, considering differences in production technologies and potential efficiency determinants. The empirical results are obtained by analysing country-level panel data from 2011 to 2020. Our paper provides more instructive and encompassing findings to inform holistic policy formulation and management practices than prior studies that have typically relied on partial-factor indicators with limited explanatory power. We find that European countries are subject to technological and efficiency heterogeneity, and our production function and inefficiency equation estimations attest to the existence of divergent cause-effect relationships, calling for decentralised, customised solutions. Arguably, our comparative benchmarking analysis constitutes the first comprehensive cross-country investigation for Europe of its kind, underscoring the importance of impactful science in fostering the preservation of high civilisation in line with the theme of this special issue.

R&D investment: A dynamic efficiency approach in the EU countries

This paper aims to analyze the role of research and development (R&D) in the production efficiency of European Union Member States. Utilizing Bayesian methods within a dynamic framework, the study jointly estimates production functions and efficiency for a sample of 27 countries over the 2000–2021 period. The findings reveal that human capital investment exhibits a higher output elasticity compared to physical capital investment. Additionally, the results indicate that inefficiencies persist due to escalating costs—both monetary and temporal—as inputs expand. Across the studied countries, an upward trend in R&D expenditure is associated with increasing technical efficiency levels, establishing a positive relationship between R&D spending and technical efficiency scores. Geographically, eastern and southern European regions exhibit lower average efficiency levels. These insights are crucial for policymakers seeking to foster innovation-driven policies, highlighting the importance of maintaining or increasing R&D spending to achieve the economic and social objectives of the European Union. Through appropriate R&D policies, policymakers can enhance technical efficiency, ensure the EU's global competitiveness, and promote more equitable development across the Union.

Land investment regulation and allocative efficiency: evidence from the Chinese manufacturing sector

Abstract This article analyzes the role of production factor land in the production process. Using a novel dataset of 20,205 newly established firms in the Chinese manufacturing sector from 2007 to 2014, our production function estimates suggest the existence of a sizable gap (averaging 50 USD/m2) between the marginal productivity of land and its user cost. Basic economic theory suggests a possible relationship with China’s minimum investment intensity (MII) regulation. An analysis using changes in MII limits over time and the resulting discontinuities at county borders confirms that larger gaps are significantly associated with increases in MII limits. Thus, the results of this study suggest that MII regulation leads to allocative inefficiency of land.

Dissecting performance gains from export-induced marketing and technological investments: Revisiting learning by exporting in Indian manufacturing

The impact of export decisions on firm performance has been extensively studied empirically, yet little attention has been given to the investments resulting from these decisions and their subsequent returns. Moreover, existing research predominantly examines export choices from a technological perspective, with minimal emphasis on the marketing aspects of exporting. Our study offers new insights by examining whether exporting induces firms to invest in marketing and research and development (R&D), and how these investments affect their performance before and after entering export markets. Using a panel of Indian firms from 2002 to 2019, our two-step methodology employs propensity score matching (PSM) to extract export-induced expenditures and production function estimation to assess their impacts on firm performance. The findings reveal that both export-induced marketing and R&D expenditures positively influence firm performance, with marketing investments exhibiting a stronger impact. The combined effect becomes significantly evident post-export entry. We also utilized the instrumental variable (IV) method to validate our findings. Heterogeneous IV analysis highlights that mature firms and those facing financial constraints particularly ramp up these investments post-export entry. Our results hold implications for managers and policymakers, emphasizing the importance of carefully designing export investment policies alongside other export support programs.

Determinants of meat and milk production of Awassi sheep in Syria: A Cobb-Douglas production function estimation approach

Awassi is a fat-tailed sheep breed, and the best breed in Syria is famous. Awassi sheep are Syria's main source of red meat and milk production. In this study, we estimated the influence of various factors on sheep meat and milk production using time-series data from 1961 to 2020. This study employed the Cobb-Douglas production function to analyze the data. The results obtained indicate that Awassi meat production in Syria was positively and significantly influenced by carcass weight (p < 0.001) and quantity of slaughtered sheep (p < 0.001), with elasticity coefficients of 0.994 and 1.000, respectively. The model results show that raw milk yield and milk animals have a positive and significant influence on milk production at the 0.1 % significance level, with an elasticity coefficient of 0.998. However, climatic factors (precipitation and temperature) have a significant effect and negative influence on milk production at the 1 % and 5 % significance levels, respectively. This result sheds light on the policies and procedures the government should implement to develop sheep production in Syria. Therefore, it can be stated that policymakers should enhance the quality and productivity characterization of sheep through the implementation of breeding programs, improve grazing potential, and protect natural pastures. Furthermore, feed rations should be supported to help breeders overcome the effects of drought and high feed prices.

Estimando el efecto del clima en la agricultura con el valor productivo de la tierra agrícola

Objective: the Ricardian model, in which market land values are modeled as a function of climate, has been estimated extensively in the context of developing countries where its core assumptions are likely to fail due to missing or incomplete markets. This article proposes a new measure of land valuation that better reflects agricultural productivity in such contexts: the Productive Value of Agricultural Land (PVAL). Methodology: a three-step empirical strategy is applied to data from a survey of Mexican rural households. The first step is the estimation of an agricultural production function. In the second step, parameter estimates are used to calculate PVAL. In the third step, PVAL is used as the dependent variable in a Ricardian regression. Results: suggests that PVAL increases with more precipitation and decreases with by extreme heat. When the Ricardian regression is estimated using market land values, the positive effect of precipitation is underestimated and the effect of extreme heat on land productivity is null. Limitations: omitted variables bias could still influence the Ricardian estimates obtained using PVAL. Originality: a novel version of the Ricardian model is estimated, one that does not rely on market values of land. Conclusions: failing to account for the market setting of agricultural producers, particularly in developing countries, may lead to an underestimation of the effects of climate change in agricultural productivity.

Debt Puzzle: A Comparative Analysis of Public Debt’s Impact on Production Efficiency across OECD Countries

Debt is a fundamental component of modern economic systems. It serves as a source of financing for government, business, and individual projects. Many earlier studies concentrated on the direct relationship between debt and economic performance using different econometric methodologies. This work investigates the effect of debt on production efficiency, extracted from the estimated production function. Unlike previous econometric approaches, we employ a production stochastic frontier analysis (SFA) on data for 18 OECD countries spanning from Quarter 1, 2015, to Quarter 3, 2021, to capture the short-run effect of debt on the production efficiency and, thus, output growth. The results show that, in the short run, as debt increases by $1 billion, efficiency increases by 0.04%. Additionally, we found that the most indebted countries are the most efficient countries. In our sample, those were the UK and France. Furthermore, the average efficiency for the 18 OECD countries was 70.07.

To analyze the technical and allocative efficiency of drill paddy cultivation in Bhandara district

The present study on paddy was undertaken in Bhandara district of Vidarbha region. The district was selected purposively. Three tehsils were selected. Out of the seven tehsil, Sakoli, Lakhandur and Pauni tehsil was selected for the study based on drill paddy cultivars. Three villages were selected purposively from each tahsil and from each village 10 farmers were selected overall 90 farmers were selected. The study revealed that how different cost components affect the profitability of production paddy crop and also proposes that at how much level of efficiency, the farmer is producing the paddy crop and how much level of efficiency is yet to be obtained. The maximum livelihood estimates of the stochastic production function revealed that fertilizer showed a negative and significant effect on paddy production. The co-efficient of the frontier profit function revealed that machine hours, seed and fertilizer shows a negative and significant impact on paddy farmer profitability. The mean technical, economic and allocative efficiency of paddy cultivation was found to be 97.49 per cent, 87.97 per cent and 89.86 per cent indicated that there is still scope for paddy farmers to increase the economical and allocative efficiency by operating at a full efficient level by proper utilization and allocation of existing resources and technology.

Measuring Efficiency Performance in Economic Productivity Function: Parametric and Non-Parametric Frontier Approach

The parametric and non-parametric frontier approach is the famous frontier measurement efficiency in economic productivity. Many researches on economics-based has focused on efficiency. Basically, efficiency comprises two important components. First is technical efficiency and second is allocative efficiency. Combination of technical and allocative efficiency called as economic efficiency. In order to measure both components of efficiency, parametric and non-parametric approaches have been utilized to either estimate or calculate the efficiency frontier. Many studies have adopted non-parametric, deterministic approach such as Data Envelopment Analysis (DEA) that impose no functional form on the cost or production function, but parametric estimation of cost, production, or profit functions are more relevant approach in the economics-based literature. Stochastic Frontier Approach (SFA) is the most popular among parametric model approach. Deterministic frontier functions can be solved using either mathematical programming or econometric techniques, while stochastic specifications are estimated solely through econometric methods. This study’s main contribution is a review of efficiency concepts, measurement methods, and the strengths, limitations, and data requirements of parametric and non-parametric models.

TECHNICAL EFFICIENCY AND ITS DETERMINANTS IN DAIRY FARMERS IN KURUNEGALA DISTRICT: STOCHASTIC FRONTIER PRODUCTION FUNCTION

The objectives of this study are to estimate the technical efficiency and examine the impact of demographic and farming characteristics on technical efficiency among smallholder dairy farmers in Maho Divisional Secretariat in Kurunegala district during 2022. A structured questionnaire was used to collect information about milk production inputs and outputs, as well as other pertinent data, from 150 dairy farmers through convenience sampling. Maximum likelihood estimates of a stochastic production function were employed in the study and its results revealed that milk output was positively influenced by the number of milking cows and negatively influenced by the expenditure on veterinary at a 1% significant level. The average technical efficiency of the selected milk production farmers given by the Cobb – Douglas production function was 91% with minimum and maximum efficiency levels of about 73% and 96% respectively. This indicates that there is a scope and exists room for improving the existing level of milk production through enhancing the level of technical efficiency by 9% without increasing the level of input. In order to identify the impact of demographic and farming characteristics on technical efficiency, the Tobit model was applied. The results showed that age, sex, family size, and the number of milking times positively influenced the technical efficiency of milk production while access to credit marginally and positively influenced the efficiency of milk production in the study.

Intangible capital and productivity: Firm-level evidence from German manufacturing

Abstract This article analyses the importance of intangible capital for firm productivity using comprehensive official firm-level data from German manufacturing. Both aggregate intangible investment and the share of investing firms increase over time. However, the distribution of intangible investment is heavily right-skewed, with many firms investing nothing or little and few investing a lot. This holds both for manufacturing overall and within narrowly defined industries. The group of top investors is highly persistent from year to year, and persistence increases over time. Production function estimations reveal a positive output elasticity of intangible capital which is small on average but increases substantially with firm-level intangible capital intensity. There are also effect heterogeneities by firm size and enterprise group membership, especially among high-intangible-intensive firms. Finally, considering intangibles as production factors reduces the measured dispersion of Total Factor Productivity (TFP), mainly by tightening the upper part of the TFP distribution.

Trajectories of Early Childhood Skill Development and Maternal Mental Health.

We investigate the impacts of a perinatal psychosocial intervention on trajectories of maternal mental health and child skills, from birth to age 3. We find improved maternal mental health and functioning (0.17 to 0.29 SD), modest but imprecisely estimated improvements in parenting (0.07 to 0.11 SD), and transitory improvements in child socioemotional development (0.06 to 0.39 SD). The intervention had negligible influence on physical health and cognition. Estimates of a skill production function reveal the intervention attenuated the negative association between maternal depression and child outcomes, and narrowed outcome gaps between mothers who were and were not depressed in pregnancy.

Motherhood and the Gender Productivity Gap

Abstract Using Danish matched employer–employee data, I compare the relative pay of men and women to their relative productivity as measured by production function estimation. I find that the gender “productivity gap” is 8%, implying that almost two thirds of the residual gender wage gap is due to productivity differences between men and women. Motherhood plays an important role, yet it also reveals a puzzle: The pay gap for mothers is entirely explained by productivity, whereas the gap for non-mothers is not. In addition, the decoupling of pay and productivity for women without children happens during their prime-child bearing years. These estimates are robust to a variety of specifications for the impact of observables on productivity, and robust to accounting for endogenous sorting of women into less productive firms using a control-function approach. This paper also provides estimates of the productivity gap across industries and occupations, finding the same general patterns for mothers compared to women without children within these subgroups.

Distributional effects of parental time investments on children's socioemotional skills and nutritional health.

Parental behavior is paramount to child health and skill formation, explaining a significant portion of differences in developmental outcomes. However, little is known regarding the distributional effects of parental time allocation at different levels of children's outcomes. I use a national administrative dataset of Chilean pre-school students to the estimate production functions for socioemotional development and body mass index z-scores at every decile of the distribution at baseline. Modest average effects conceal significant heterogeneity on the returns to parental time investments. Children in the bottom of the socioemotional development distribution could gain up to 0.4 standard deviations for a one standard deviation increase in time investments. A similar increase can lead to a reduction of 0.8 standard deviations in body mass index among severely obese students. Evidence reveals that children with high developmental scores are unlikely to benefit from additional parenting time.

Statistical estimation of potato production and cost functions in Qalyubia Governorate in the study sample

Statistical estimation of potato production and cost functions in Qalyubia Governorate in the study sample

Timing Assumptions and Efficiency: Empirical Evidence in a Production Function Context

Recent work estimating production functions has often used methodologies proposed in two literatures: (1) “proxy variable” estimation techniques (Olley, S. and Pakes, A., 1996, Econometrica, 64, pp. 1263–1295), and (2) “dynamic panel” estimation techniques. I illustrate how timing and information set assumptions are key to both, and how these assumptions can be strengthened (or weakened) almost continuously. I examinehow, in some common production datasets, strengthening or weakening these assumptions affects the precision of estimates—comparing these impacts to those achieved by imposing alternative assumptions sometimes utilized in these literatures. This illustrates efficiency tradeoffs between different possible assumptions, at least in the production function context.

CROP YIELD PREDICTION BASED ON MACHINE LEARNING

In order to find relevant information and draw recommendations, the data analysis process involves analyzing, cleaning, and modeling the data. India has a large population, hence it is important to protect the world's food supply despite climate fluctuations.Framers face serious problems in drought conditions.The agricultural output is significantly influenced by the kind of soil. Advising farmers to utilize fertilizers might assist them in making the best choice possible for their farming circumstances. There are several studies Information and Communication Technology (ICT) may be used for agricultural yield prediction. We can also anticipate the agricultural production by using data mining. We can advise the farmer to plant a better crop for a higher yield by thoroughly analyzing the prior data. Information from traditional farmers is transferred to educated farmers through smart agriculture. to calculate estimates of overall physical production functions for the yields of various crops in a given condition, taking into account a newly designed weather index as an input.To adequately compare our actual result, also known as the target, with the prediction model, which provides farmers with the analysis of rice production based on available data, regression and coefficient of determination analysis as well as average error rate were conducted. To increase crop production, several data mining approaches were employed to estimate the crop output. For operational projects, it is crucial to accurately and promptly evaluate the state of agricultural crops and predict possible crop yields. The goal of this study is to employ a variety of forecasting techniques to assess agricultural yield estimations in Ghana due to the significance of crop yield prediction. Crop yield prediction, which offers information to decision-makers.

The impact of climate change on economic growth: Evidence from a panel of Asian countries

Asia is recognized as one of the most vulnerable regions. This research aims to identify climate change effects on economic growth. It proposes a new and comprehensive perspective to examine the climate-economy nexus in this region and provides an underpinning for assessing the pathway of the growing Asian economy under the SSP climate scenario (CMIP6). We apply a macroeconomic-climate modeling approach with a climate damage function. The damage function, which is based on the temperature anomaly, was examined under three classes of damage severity. The climate change effect has been examined by including the direct effect on the output as the level effect and two more channels, including capital depreciation and productivity growth reduction. The augmented Solow growth model framework that considers different types of capital, including physical, human, social, and environmental capital, was applied. The labor productivity or technology is assumed to depend on trade-related variables, including trade openness, financial development, and foreign aid inflows. Also, in order to test the constant returns to scale assumption, the CES production function, which is the flexible production technology, was used. The study relied on the country-year panel of GDP per capita and other macroeconomic variables such as labor force, capital formation, financial development index, trade openness, and foreign aid gleaned for 1994–2017 to estimate production functions using the GMM estimation method. The climate data on the annual mean temperature and projections of future temperature are based on ICMP6 by 2100 for Asian countries. Based on the CES estimation results, constant returns to scale or Cobb-Douglas production function was adopted. The estimation results show that physical and environmental capital account for the highest contribution to the output per worker. However, the contribution of trade-related variables was found to be insignificant. The projected pathway by 2100 establishes that climate change would significantly reduce Asian output per worker, especially if severe damage function is examined, and climate change stays on the SSP scenario-anticipated trajectory. The output elasticity with respect to temperature under the most severe scenario was obtained −22.80, corresponding to the output per worker reduction of 46.7 percent in 2100. Among the three channels of the climate effect, productivity growth reduction showed the most adverse effect on output. The lower flexibility of the production function that restricts the environmental replacement with other inputs, may lead to a more binding role under growing scarcity conditions. It was also found that labor and technology could contribute dampening the capital damage induced by higher temperatures. From the research results, the findings suggest measures aiming at providing flexible production technology, development of capital-embodied technologies, and lower dependence on environmental capital.