nep-eff New Economics Papers
on Efficiency and Productivity
Issue of 2023‒10‒23
nineteen papers chosen by

  1. Climatic Effects and Farming Performance: An Overview of Selected Studies By Neubauer, Florian; Wall, Alan; Njuki, Eric; Bravo-Ureta, Boris
  2. Allocative efficiency between and within the formal and informal manufacturing sector in Zimbabwe By Godfrey Kamutando; Lawrence Edwards
  3. Environmental Productivity Assessment: an Illustration with the Ecuadorian Oil Industry By Arnaud Abad; Michell Arias; Paola Ravelojaona
  4. GM technology over the agricultural productivity in Brazilian Cerrado By Guimaraes, Pablo Miranda; Braga, Marcelo Jose
  5. Productivity in the World Economy During and After the Pandemic By John G. Fernald; Huiyu Li
  6. A Comprehensive Regression Study on the Drivers of Labour Productivity By Shevelova, Anastasia; Machukha, Ielyzaveta; Motliuk, Mark; Kulinich, Volodymyr
  7. Technical efficiency and technological change of value chains in five Nigerian states By Paliwal, Neha; Songsermsawas, Tisorn; Azzarri, Carlo; Bravo-Ureta, Boris
  8. AI Adoption and Productivity of Japanese Firms: Spillover and innovation effects (Japanese) By IKEUCHI Kenta; INUI Tomohiko; KIM YoungGak
  9. The impact of pasture recovery in the agricultural GPV of Brazil’s Cerrado By Guimaraes, Pablo Miranda; Braga, Marcelo Jose
  10. The Effect of Bank Recapitalization Policy on Credit Allocation, Investment, and Productivity: Evidence from a Banking Crisis in Japan By Hiroyuki Kasahara; Yasuyuki Sawada; Michio Suzuki
  11. A Time-Series Examination of the Quality of Industry-Level U.S. Productivity Data By Lence, Sergio H.; Plastina, Alejandro
  12. Agglomeration and human capital: an extended spatial Mankiw-Romer-Weil model for European regions By Alicia Gómez-Tello; María-José Murgui-García; María-Teresa Sanchis-Llopis
  13. Insights into land size and productivity in Ethiopia: What do data and heterogenous analysis reveal? By Ashok Mishra; Kamel Louhichi; Giampiero Genovese; Sergio Gomez y Paloma
  14. Estimation and Determinants of Cost Efficiency: Evidence from Central Bank Operational Expenses By Mr. Romain M Veyrune; Solo Zerbo
  15. Trade-offs between economic, environmental and social sustainability on farms using a latent class frontier efficiency model: Evidence for Spanish crop farms By Amer Ait Sidhoum; H Dakpo; Laure Latruffe
  16. Estimating the Long-term Effects of a Fruit Fly Eradication Program Using Satellite Imagery By Salazar, Lina; Agurto Adrianzen, Marcos; Alvarez, Luis
  17. Farm Size and Income Distribution of Latin American Agriculture New Perspectives on an Old Issue By Gáfaro, Margarita; Ibáñez, Ana María; Sánchez-Ordoñez, Daniel; Ortiz, María Camila
  18. Reaching (Beyond) the Frontier: Energy Efficiency in Europe By Mr. Serhan Cevik; Kelly Gao
  19. Technical efficiency and technology adoption in beef By Aguirre, Emilio; Garcıa Suarez, Federico; Sicilia, Gabriela

  1. By: Neubauer, Florian; Wall, Alan; Njuki, Eric; Bravo-Ureta, Boris
    Abstract: The connection between farm productivity and climatic effects is of growing importance around the globe, as farmers are expected to satisfy a rising demand for food and agricultural products driven by an increasing population and income while contending with mounting uncertainty imposed by climate change. This presentation is a component of a larger project which seeks to establish the connection between the productivity performance of farming units and climatic effects. We seek to shed light on two specific issues: (i) what variables are most commonly-used to capture climatic effects; and (ii) to what extent does the choice of climatic indicators in production models affect the agricultural productivity measures obtained across different types of farming systems. An a priori requirement imposed when searching the literature and selecting the papers included in the analysis is that they apply stochastic production frontier (SPF) methods. The advantages and popularity of this methodology in agricultural productivity studies and beyond is well established (Fried et al., 2008, O’Donnell; 2018). The agricultural productivity literature has seen considerable growth in recent years, motivated by significant methodological developments and the increasing availability of microdata sets in some regions (e.g., LSMS-ISA data for Africa). An increasing body of productivity research is being devoted to the connection between farm output, food security and climatic effects, as well as to the role of different farming technologies or practices that can serve in strategies to promote adaptation. Two clear examples are the adoption of irrigation and improved seed varieties. The specific focus here is on three subsets of studies found in the received literature: (1) Dairy productivity studies published using data from different countries; 2) Water, irrigation, and precipitation studies again using data from different countries; and 3) Total Factor Productivity (TFP) studies that explicitly account for the climatic component in TFP in Latin America (LA) as well as in other geographical areas. Taken together, these studies provide a useful point of departure for our future work. Our choice of papers at this point is somewhat arbitrary but serves as an initial step towards undertaking a systematic search of the literature to cover a more comprehensive set of studies. We justify our current focus by noting the importance of dairy in farming systems in both the developed and developing world (Bravo-Ureta, Wall, and Neubauer 2022) and the critical role water plays in the adaptation of farming to climate change (Bopp et al. 2022). 1. Dairy Productivity Studies The measurement of TFP in dairy farming and its decomposition into different elements (e.g., technical efficiency, allocative efficiency, scale effects and technical change) has been the subject of several stochastic frontier studies going back at least to Ahmed and Bravo-Ureta (1995). Parametric output distance functions have been used to measure and decompose productivity in dairy farming by Brümmer et al. (2002) for Dutch, German and Polish farms, Newman and Matthews (2006) for Irish farms and Emvalomatis (2012) for German farms. All these studies report that TFP growth has been driven fundamentally by technological progress. Cechura et al. (2017) analyze the impact of technological progress in a study of 24 EU Member States. Aside from technical change (and efficiency gains) as drivers of TFP growth, Parikoglou et al. (2022) found that extension services contributed to the productivity growth-of Irish farms. Parametric input distance functions have been used to study dairy farm productivity by Sipiläinen et al. (2014), who investigated the profitability and productivity dynamics of Finnish and Norwegian farms; Sauer and Latacz-Lohmann (2015), who analyzed TFP change for German farms (with a Luenberger index); and Singbo and Larue (2016) for farms from Quebec. The climatic effect has been clearly absent in much of this work. 2. Water, Irrigation and Precipitation Studies Water is critical in the adaptation of farming to climate change. Therefore, we review studies that consider precipitation as a climatic variable in their models. Bravo-Ureta et al. (2016) identify 110 water studies in a meta-analysis of technical efficiency in agriculture and find that most ignore climatic effects. Among the studies that use SPF methods, only five considered a precipitation variable. McGuckin et al. (1992) specify a continuous rainfall variable in a study analyzing maize farmers in the USA. Sherlund et al. (2002) use the number of rainy days and the quantity of rain in a study of rice producers in Ivory Coast. Mariano et al. (2010) define dummies for dry and wet seasons in a rice farming study in the Philippines. Hussain et al. (2012) employ a composite variable for the number of irrigations and rainfall in a sample of wheat farmers in Pakistan. Ndlovu et al. (2014) incorporated a location dummy for high rainfall areas of maize farming in Zimbabwe. We complement these studies with 11 papers that have been published more recently. 3. Total Factor Productivity (TFP) Studies with a Climatic Component. We focus explicitly on work that examines the climatic component in TFP in LA as well as in other geographical areas. Agriculture is a major sector in the economy of most LA countries. To implement effective policies addressing climate change and promoting the adaptation of farming to the rising climatic threat, it is critical to have a thorough understanding of what drives productivity change and the role climatic effects have in the region’s agricultural productivity growth. However, a recent review by Bravo-Ureta (2021) reveals that productivity research for LA is limited and based primarily on aggregate county-level data with scant inclusion of climatic effects. We conjecture that the limited supply of studies for LA is likely due to data limitations and lack of funding to conduct the necessary work. We highlight around 10 recent articles that address the connection between climatic effects and TFP and explicitly quantify the effect of a climatic component in TFP growth.
    Keywords: Agribusiness, Demand and Price Analysis
    Date: 2023
  2. By: Godfrey Kamutando (Post-doctoral Research Fellow, School of Economics, University of Cape Town and Policy Research in International Services and Manufacturing (PRISM).); Lawrence Edwards (School of Economics, University of Cape Town and Policy Research in International Services and Manufacturing (PRISM).)
    Abstract: Resource misallocation has the potential to reduce aggregate total factor productivity and undermine industrial development. These effects can be particularly pronounced in emerging economies where large market frictions impede efficient resource allocation. This paper investigates the extent and nature of resource misallocation between and within the formal and informal manufacturing sector in Zimbabwe. Applying the approach developed by Hsieh & Klenow (2009) to firm-level microdata, the results reveal extensive resource misallocation in both the formal and informal manufacturing sector. Misallocation is more pronounced in informal sector firms and is associated with relatively large capital market distortions. Further, misallocation is more pronounced amongst relatively productive firms, thus exacerbating aggregate losses in total factor productivity (TFP). Estimates indicate that aggregated gains in TFP of 126.7% can be realized through efficient resource allocation.
    Keywords: Misallocation, total factor productivity, informal sector
    JEL: E24 D24 E29 L60
    Date: 2023
  3. By: Arnaud Abad (BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - Université de Haute-Alsace (UHA) - Université de Haute-Alsace (UHA) Mulhouse - Colmar - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Michell Arias (UPVD - Université de Perpignan Via Domitia); Paola Ravelojaona (ICN Business School, CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine)
    Abstract: In this paper, environmental productivity change is analysed through the production theoretic approach to index numbers. Specifically, pollution-adjusted Malmquist and Hicks-Moorsteen productivity indices are considered. These productivity indices are defined as combination of multiplicative distance functions. Non convex pollution-generating technology is assumed to estimate the pollution-adjusted Malmquist and Hicks-Moorsteen productivity measures. Moreover, the main sources of the environmental productivity change are displayed. An empirical illustration is provided by considering a sample of 20 Ecuadorian oil companies over the period 2014-2018. The results are estimated through a non parametric analytic framework.
    Keywords: Data Envelopment Analysis (DEA), Ecuadorian Oil industry, Environmental Efficiency, Productivity Indices, Non Convexity, Pollution-generating Technology
    Date: 2023–07–31
  4. By: Guimaraes, Pablo Miranda; Braga, Marcelo Jose
    Abstract: Occupying the central part of Brazil, Cerrado has about 204 million/ha distributed in 1390 municipalities, with 8.2% preserved areas, 24.4% of Brazilian GDP, and 36.5% of the Gross Production Value of Brazilian agriculture. The "Brazilian savanna", and its outstanding capacity for agricultural production and has been considered extremely relevant for the country in the last decades, taking Brazil a relevant player in the commodity market. The development and agricultural consolidation of the Cerrado can be characterized by three moments, occurring in different periods: the first, wide availability of land, migration, and public policies; advances in mechanization and technological contributions; and, research, improvements of handling techniques and genetic advances that allowed the expansion of productivity. Since legal permission, in 2005, the wide use of GM seeds, average yearly yields of the main GM crops in Brazil (maize and soybean) have increased by 4.93% and 2.63%, respectively. After the introduction of GM maize, many more farmers were able to plant two high-yield crops per year (CELERES, 2018) GM crops have become particularly important, but have not been studied extensively in Brazil. The economical literature is mainly based on Bt cotton crops analysis with groups of farms classified by GM adoption (QAIM; ZILBERMAN, 2003; CROST et al., 2007). Jointly with the advances of biotechnology over Cerrado, a new area is getting distinction, the MATOPIBA region. The region formed by 337 municipalities from four Brazilian states has been expanding its participation in Brazilian agriculture, especially grains. Modern agriculture and biotechnology in heterogeneous regions, such as Cerrado, have influenced the spatial and economic dynamics. These new agricultural processes, such as GM crops, have increased local income (MENDOLA, 2007; KASSIE, SHIFERAW, MURICHO, 2011) environmental benefits (BURACHIK, 2010; KOUSER), have positively affected other sectors, such as industry and services (BUSTOS; CAPRETTINI; PONTICELLI, 2016). Therefore, using a stochastic frontier, the present study explores and measures the effects on agricultural productivity of GM technology in Cerrado, Brazil's main agricultural biome. Different from the literature, we inserted GM technology in the frontier as a shifter. This study also examines elements that affect the technical efficiency of agricultural production in the biome. We also measure the productivity evolution between the MATOPIBA region and other regions of the Cerrado. All increments of production happened in the midst of the evolution of other factors that direct impact production and productivity in Cerrado, but one, in particular, contributed to the evolution of production, the adoption of genetically modified (GM) seed. For inputs and output levels, we used data from the Brazilian Agricultural Census of 2006 and 2017 for each of the 1390 municipalities from Cerrado to do a unique frontier for the whole area of the biome. The output is a real gross production value (price index IGP-DI used to adjust 2006 information). The inputs used are the number of employees (Labor), agricultural area (Land) and Capital measured as the number of tractors. The percentage of harvest land that used GMO seeds and a dummy for states were also used in the frontier. The inefficiency term was controlled by the percentage of establishment per size (Size); Schooling (EDU); Social Capital (CS) and Aridity Index (AI). The stochastic frontier defined in a polled data structure was estimated for all municipalities of Cerrado with data available. The sum of the input’s elasticities shows a constant return to scale. Helfand, Magalhães e Rada (2015) and Morais (2019) found similar results in analysis with Brazilian whole country information The model shows that a labor increase of 10% will expand production, on average, by 0.88%. A 10% expansion of capital produces almost eight times the expansion (7.74%), showing the relevance and impact of mechanization on Cerrado’s agricultural production. The expansion of capital and its relevance in the agricultural production process is demonstrated by Gasques et al. (2012). The Technical Change between 2006 and 2017 shows annual average productivity gains of 1.014%. Thus, the most efficient producers in 2017 were able to produce almost 17% more than in 2006 without increased inputs. In the local landscape, which is broad and flat, large-scale crops, such as cotton, maize, and soybean are particular crops well suited to modern agricultural technology. In the Cerrado, 74.49% of the total planted area in 2017 was devoted to these three crops (IBGE, 2017). The GMO coefficient is represented by a semi-elasticity. A one percentage point increase in the planting of GMO seeds increases the output by 0.364%. This positive result conforms to a priori expectations and correlates with several studies of the impact of GM technology on crops (QAIM; ZILBERMAN, 2003; ANDERSON; JACKSON; NIELSEN, 2005; BURACHIK, 2010; BAKHSH, 2017) When Technical Efficiency (TE), on average, is analyzed for all of the Cerrado, its value shows a small increase from 2006 to 2017, TE2006 = 0.7964 and TE2017 = 0.7967. Those coefficients show high technical efficiency in the Cerrado and a stable annual growth rate. Our results show positive rates while Rada (2013). Among all technical inefficiency controls estimated, only small farms are not supported by the data. Farms with more than 100 ha show greater inefficiency than those with 20 and 100 ha. The other variables, Schooling and Social Capital contribute positively to efficiency when compared with farms in other categories (lower level of education of the manager, for example). The Aridity Index significantly demonstrates that higher humidity increases technical efficiency. The relevant proposal of this study is to analyze the relevance of productivity of the MATOPIBA region when compared with the other regions of Cerrado. The MATOPIBA region had a gain of productivity 17.6% bigger than the rest of Cerrado. So, the main idea of the paper about the greater productive dynamics in the new frontier, as well as the contribution of the new technology, could be observed in the present work.
    Keywords: Crop Production/Industries, Productivity Analysis
    Date: 2023
  5. By: John G. Fernald; Huiyu Li
    Abstract: This paper reviews how productivity has evolved around the world since the pandemic began in 2020. Productivity in many countries has been volatile. We conclude that the broad contours of productivity growth during this period have been heavily shaped by predictable cyclical patterns. Looking at U.S. industry data, we find little evidence that the sharp rise in telework has had a notable impact, good or bad, on productivity. Stepping back, the data so far appear consistent with a continuation of the slow-productivity-growth trajectory that we faced before the pandemic.
    Keywords: growth accounting; productivity; remote work; pandemic; covid19
    JEL: E01 E23 E24 O47
    Date: 2023–09–28
  6. By: Shevelova, Anastasia; Machukha, Ielyzaveta; Motliuk, Mark; Kulinich, Volodymyr
    Abstract: Labour productivity is an essential economic indicator, offering insights into a nation's hourly economic output. Understanding a country's performance is pivotal for assessing policy effectiveness and shaping new strategies. This study aims to identify the primary determinants of labour productivity and analyze their impact. Employing data from the World Bank and ILOSTAT, the linear regression method was used for analysis to uncover significant insights. The findings reveal a positive correlation between urbanization and labour productivity, while employment in agriculture, as expected, exerts a negative influence. Furthermore, a direct relationship was observed between a country's income level and labour productivity, with higher incomes associated with increased productivity. Notably, the unemployment rate exhibits a positive association with labour productivity, and this effect intensifies as income levels decrease.
    Keywords: Labour productivity, Country performance, Determinants of labour productivity, Linear regression analysis
    JEL: J0 J01 O4 O40
    Date: 2023–07–10
  7. By: Paliwal, Neha; Songsermsawas, Tisorn; Azzarri, Carlo; Bravo-Ureta, Boris
    Abstract: Value chain development projects focusing on agricultural commercialization have been shown to improve production, income, and assets (Reardon et al., 2009; Barrett et al., 2012). However, the extent to which these types of projects contribute to improved technical efficiency and technological change of small-scale producers participating in value chains is largely understudied. Our country study is Nigeria, where the limited evidence available suggests that productivity gaps in agriculture between men and women are wide, ranging from 17% in the South to 46% in the North (Oseni et al., 2015). Our study focuses on the first phase of the Value Chain Development Programme (VCDP), which was implemented between 2013 and 2019. VCDP worked with farmer organizations to increase productive capacity, productivity, and to foster market-linkages in two commodity-specific value chains: rice and cassava (IFAD, 2012). In our study, we analyse the impacts of VCDP on technical efficiency (TE), technological change (TC), and agricultural productivity of rice and cassava production in Nigeria. Further, we investigate whether the effects on the various performance indicators vary significantly between male and female farmers. Our dataset comes from a survey of 1, 784 (879 treated and 905 control) households conducted during February and March 2020 in five states -Anambra, Benue, Ebonyi, Niger, and Ogun- with distinct cultural and gender norms. We use a combination of propensity score matching with a stochastic production frontier model to correct for selection bias (SC-SPF) (Greene, 2010; Bravo-Ureta et al., 2012). First, we pre-process the dataset to ensure that treatment and control households are observationally and statistically comparable, with an adequate common support along their socio-demographic, economic and agricultural characteristics (Ho et al., 2007). Then, we explore the impact of VCDP on two key productivity components: TC -proxied by a shift in the production frontier-, and TE -which captures managerial performance-. These two productivity-related elements are the basis for testing performance differences between treatment and control households (Greene, 2010; Bravo-Ureta et al., 2012). We then analyse frontier output, TE, and technology gaps using a stochastic meta-frontier (SMF) framework, which provides the common benchmark required for valid comparisons (Huang et al., 2014; Amsler et al., 2017). Preliminary results indicate that treated and control households are statistically comparable based on a number of observable pre-intervention characteristics. Project impact estimates indicate that VCDP lead to higher rice productivity, while no significant impact is detected for cassava. Our study makes two key contributions to the literature. First, our focus is estimating the extent to which value chain projects could potentially alter underlying factors driving gender gaps in agricultural productivity by using a different methodological approach that would allow direct comparison of gender-specific TC and TE (Owusu and Bravo-Ureta, 2020). Second, our study complements the small -though increasing- number of studies that extend the SC-SPF methodology under the SMF framework to compare TE levels across treated and control groups using a common benchmark (Villano et al., 2015; Bravo-Ureta et al., 2020; Olagunju et al., 2021). In addition, findings from this study generate evidence potentially helpful for both future project design and agricultural policy in Nigeria. Since 2011, the Federal Government of Nigeria has prioritized investments in the development of the country’s agricultural sector with the aim of addressing low productivity, limited private sector investment, human capacity constraints, weak value chains, and untapped opportunities for value addition (Babu et al., 2014). We expect our findings to be directly relevant in the design of future gender-sensitive agricultural investments in value chains.
    Keywords: Agribusiness, Research and Development/Tech Change/Emerging Technologies
    Date: 2023
  8. By: IKEUCHI Kenta; INUI Tomohiko; KIM YoungGak
    Abstract: The use of Artificial Intelligence (AI) in business has been expanding in recent years, and there is growing interest in the mechanisms and extent to which AI affects firm performance. In this study, we analyze the impact of firms’ introduction of AI on their performance using the "Basic Survey of Japanese Business Structure and Activities" of the Ministry of Economy, Trade and Industry (METI), Japan, TSR's business-to-business transaction data, press release data from NIKKEI, and IIP patent database 2020. In addition to the introduction of AI-related patents generated by a company's own R&D activity, we also try to analyze the impact of the introduction of AI within their trading partners (suppliers and customers). In addition to the efficiency gains in production processes (process innovation), the study analyzes the creation of new products and improvements in existing products (product innovation). The main results from the analyses are as follows. (1) AI-related patents positively correlate with firm productivity and have a stronger relationship with productivity than non-AI patents. (2) The relationship between AI-related patents and firm productivity strengthened even after 2009 when the number of patent applications began to decline. (3) AI-related patents mainly contribute to the productivity of firms with productivity in middle or higher status within the industry, while AI-related patents have a negative impact on the productivity of firms with low productivity. (4) We cannot confirm that the introduction of AI by a firm's business partner has a positive or negative spillover effect on the productivity of that firm. (5) AI-related patents are strongly related to product innovation, process innovation, and technological innovation of the firms, and especially high-quality AI-related patents have a mid-term and important impact on innovation.
    Date: 2023–09
  9. By: Guimaraes, Pablo Miranda; Braga, Marcelo Jose
    Abstract: One of the main strategies to provide global food security is sustainable intensification, whereby technological improvements and specific management practices, increase agricultural yield without expanding agricultural area or causing significant negative environmental impacts. Brazil and its Cerrado biome have been prominent in these agricultural considerations, being the biome become known as “one of the world‘s great breadbaskets” (ECONOMIST, 2010). Agricultural production in the Cerrado has also posed relevant environmental issues and these have not gained as much attention as those in other Brazilian biomes, like the Amazon and Atlantic forest (NEPSTAD et al., 2014; SILVA; PERRIN; FULGINITI, 2019; SILVA et al., 2021). The Cerrado developments in modern agriculture have contributed to local development and have expanded food production but at the cost of a high conversion rate of native area (FILHO; COSTA, 2016; BOLFE; SANO; CAMPOS, 2020). Pasture accounts for 27% of 203.4 million hectares in the Brazilian Cerrado. In addition, the Cerrado has the highest potential for deforestation among Brazilian biomes, due to the absence of well-defined monitoring and surveillance programs (FILHO, 2018). Between 2006 and 2017, 11, 555, 342.43 ha was deforested in the Cerrado (PRODES/INPE, 2021). Sustainable intensification addresses this problem by recovering degraded pasture, allowing the continued increase in food and energy production without expanding into native areas, thus maintaining environmental equilibrium and reducing CO2 emission (FILHO; RIBERA; HORRIDGE, 2015). Therefore, the conversion of degraded pastures into productive agricultural areas is an important element in the intersection between agricultural expansion and environmental conservation. Nevertheless, it is also important to know, what additional income accrues when degraded pasture is converted into well-managed pasture? The improvement of pastures efficiency implies an increase in production (FELTRAN-BARBIERI; FERES , 2021), the slowdown of deforestation (AZEVEDO; RODRIGUES; SILVA, 2021) and reduction of the GHG emission (SILVA et al., 2015). Therefore, this research offers economic parameters for the adoption of public and private actions to mitigate environmental issues and support livestock production. The sizing of additional marginal gain from the pasture restoration can support the design of rural advisory services, indicating a direct incentive based on GPV gains. Considering the difference in pasture qualities over the elasticity of pasture productivity, this article measure income gains in livestock production from the conversion of degraded pasture into the good-planted pasture. To do this, we estimated an Output Distance Function associated with a technology-changing variable. The measure established is directly and objectively based on local productivity and livestock GPV. The analysis used data at the municipal level from the last two Brazilian agricultural censuses, which provide a pooled database on all municipalities from Cerrado. The production information is represented by a local agricultural GPV per activity: livestock, agriculture, and other activities. The input variables for Labor are measured by the number of farm workers, Capital is the number of tractors. The productive agricultural areas are divided into four: crops land, good planted pasture, degraded pasture, and Natural pasture. To control the productivity considering the soil characteristics, the frontier is parameterized by the mean soil Suitability Index from each municipality. Three exogenous determinants are included: Schooling represents the share of farm managers who have, at least, a bachelor’s degree; Social Capital, represents the amount of agricultural land area in production by a member of cooperatives. The Aridity Index (AI) is based on the method proposed by Davis, Giuseppe e Zezza (2014). The degraded pasture variable is obtained by self-report, so the information is not necessarily based on technical parameters. The average share of degraded pasture among of pastures increased from 6.66% in 2006 to 8.11% in 2017. Even with the dynamics from Census, these scenarios are very optimistic when compared with analyses based on technical classifications, obtained from satellite and remote sensing technology. To demonstrate the effectiveness of the agricultural technology used in reclaiming degraded pasture, we use this Output Distance Function. Applying the implicit function theorem as in Rada, Buccola e Fuglie (2011), Rada e Valdes (2012) and Rada (2013) we can analyze the transformation of the region degraded pasture area into good planted pastures with the same yields average from the region, obtaining a semi-elasticity for good planted pastures to livestock production, . The technology change variable results show that a 10% change in the pasture area shifts the production function -0.2866. Complementary, the sum along with the other input coefficients shows the aggregated technology has constants return to scale. The effect of pasture degradation indicates a negative impact on the productivity of the land, while the increment of good pasture has a positive impact. Considering the huge stock of good and natural pasture, the direct marginal contribution of good pasture can be short. Although the impact of increment of pastures without management can result in a significant consequence, direct overproduction and also environmental. All the exogenous variables to control the inefficiency of the ODF behave working to increase local efficiency. The semi-elasticity of livestock GPV from good converted pastures where the livestock is a traditional and consolidated activity shows a short impact on the restoration of pastureland. In regions where the activity is consolidated the marginal conversion of one hectare has a small impact on production when compared with others. The economic impact to convert degraded pasture into a good planted pasture means a marginal increase of local GPV caused by the recovery of one hectare of degraded pasture. In regions, such as MATOPIBA, where the return of recovery is lower, the incentives to do deforestation are bigger. Considering the marginal GPV, to convert one hectare of degraded pasture into good planted pasture, in 2017 34% of municipalities from Cerrado showed average gains per hectare higher than R$ 652.46, while 61.8% showed marginal GPV higher than R$ 300.62. Therefore, the estimation of marginal gains to recovery pasture can support the development of actions, such as adjustments in rural credit lines focused on restoration, as developed by the ABC Plan, especially in regions with high levels of deforestation and a large area of degraded pasture.
    Keywords: Livestock Production/Industries, Land Economics/Use
    Date: 2023
  10. By: Hiroyuki Kasahara; Yasuyuki Sawada; Michio Suzuki
    Abstract: This paper examines the ramifcation of government capital injections into financially distressed banks during the 1997 Japanese banking crisis. By leveraging a unique dataset merging firm-levelfinancial statements and bank balance sheets, the study aims to examine whether the capital injections primarily benfited high-productivity firms or were misallocated to struggling "zombie" firms. The empirical results suggest that banks, post-injection, increased lending to both high-productivity non-zombie firms and low-productivity zombie firms. While the former is in line with conventional theories that prioritize high-productivityfirms for investment and productivity enhancement, the latter suggests credit misallocation towards struggling firms mainly for debt servicing. Intriguingly, the study finds no evidence that these injections promoted investments among firms, irrespective of their productivity orfinancial health status. In particular, we provide suggestive evidence that zombie firms even reduced investments, especially in infrastructure, while high-productivity non-zombie firms did not exhibit a signficant investment boost despite receiving more loans. However, these high-productivity firms displayed positive growth in labor productivity and total factor productivity, potentially driven by sales growth and increased advertisement expenses rather than employment and wage adjustments.
    Date: 2023–08
  11. By: Lence, Sergio H.; Plastina, Alejandro
    Abstract: A very large number of productivity analyses have focused on Total Factor Productivity (TFP), the volume of aggregate output produced per unit of aggregate input, as the measure of choice. For example, industry-level TFP data series have been widely used to investigate many important economic issues, including whether productivity gains have been concentrated in a few industries and whether such gains were linked to the use of information technology (Stiroh 2002), whether automation is labor-displacing (Autor and Salomons 2018), whether the recent rise in the capital share can be attributed to increasing automation (Aghion, Jones, and Jones 2019), how GDP growth has been impacted by sectoral trends in TFP and labor growth (Foerster et al. 2022), the contributions of individual industries to U.S. aggregate TFP growth (Jorgenson, Ho, and Samuels 2019), and the reasons for the productivity gap between Europe and the United States in the late 1990s and early 2000s (van Ark, O’Mahony and Timmer 2008). Recently, growing concerns about environmental degradation and climate change have spurred interest in “environmentally-adjusted” TFP indicators, which take into account the production of undesirable by-products and externalities, as well as how intensely natural resources are used (OECD 2020b). For the agricultural sector in particular, studies based on TFP have analyzed public investments (Fuglie, Wang, and Ball 2012; Fuglie 2018; Ortiz-Bobea et al. 2021), international trade (Garcia-Verdu et al. 2019; Yuan et al. 2021), and the design of policies aimed at decoupling productivity growth from environmental pressure (OECD 2020a), among other issues. In the United States, agricultural TFP measures have been extensively used to evaluate returns to public investments (Fuglie and Heisey 2007; Alston et al. 2011; Jin and Huffman 2016), identify the drivers of productivity growth (Capalbo 1988; Schimmelpfennig and Thirtle 1999; Huffman and Evenson 2006; Alston et al. 2010; Andersen, Alston and Pardey 2012; O’Donnell 2012, 2014; Plastina and Lence 2018), evaluate convergence in productivity across states (McCunn and Huffman 2000; Ball, Hallahan, and Nehring 2004; Poudel, Paudel, and Zilberman 2011), assess spillovers between agriculture and other sectors of the economy (Lence and Plastina 2020), and gauge the impact of weather and climate on aggregate productivity (Njuki, Bravo-Ureta, and O’Donnell 2018; Sabasi and Shumway 2018; Chambers and Pieralli 2020; Ortiz-Bobea, Knippenberg, and Chambers 2018; Plastina, Lence, and Ortiz-Bobea 2021; Ortiz-Bobea et al. 2021). Given the vast literature that has applied TFP to analyze issues concerning productivity, it is not surprising that significant efforts have been devoted to the development of proper measures of the individual components of TFP (OECD 2001; Fuglie, Wang, and Ball 2012; Fuglie 2015; Shumway et al. 2017; USDA-ERS 2021), as well as to the evaluation of the relative merits of alternative aggregation methods (Szulc 1964; Eltetö and Köves 1964; Jorgenson and Griliches 1967; Caves, Christensen, and Diewert 1982a, 1982b; Bjurek 1996; Balk and Althin 1996; O’Donnell 2012, 2016; Färe and Zelenyuk 2021). Contrastingly, there has been a dearth of studies exploring the quality of real-world TFP data series. Interestingly, studies analyzing productivity usually rely on a single source of TFP data, even in cases where more TFP sources are available. Typically, no robustness analyses are conducted to assess the extent to which inferences hold using alternative TFP data sources. Implicitly, such studies assume that the underlying TFP data being used is of sufficiently high quality to yield valid inferences. However, Alston (2018) and Andersen, Alston, and Pardey (2011) --among the few studies analyzing more than a single TFP source-- provide evidence that calls this assumption into question. The lack of studies concerning the quality of real-world TFP series provides the main motivation of the present investigation. We contribute to the literature by examining the industry-level TFP series for the United States obtained from three alternative sources, namely, (1) Jorgenson, Ho, and Samuels (JHS), (2) the U.S. Bureau of Labor Statistics (BLS), and (3) the U.S. Bureau of Economic Analysis (BEA). These three sources are of special interest because they are highly regarded and their series have been used extensively by researchers to analyze productivity (e.g., Stiroh 2002, Autor and Salomons 2018, Aghion, Jones, and Jones 2019, Foerster et al. 2022, Jorgenson, Ho, and Samuels 2019, van Ark, O’Mahony and Timmer 2008). Besides providing an empirical assessment of the relative quality of the aforementioned series, our study contributes to the literature by proposing a general method to examine the quality of alternative time series reportedly measuring the TFP of a particular entity or sector. The main goal of our study is to spur interest in the exploration of the quality of real-world TFP data series, with the aim of finding ways to enhance them and uncovering series whose quality may be deemed questionable. Our preliminary results show that, out of the 61 industry series for which TFP data from different sources are being compared, between 34 (for JHS vs. BEA) and 46 (for BEA vs. BLS) industries have inconsistent series across sources. In other words, only 31% to 64% of the industries have TFP data consistent between source pairs. These results strongly suggest that empirical analyses based on a single data source may not be sufficiently robust to draw strong inferences and implications. The results also demonstrate the need to devote greater attention to improving the reliability of TFP data.
    Keywords: Productivity Analysis, Research Methods/ Statistical Methods
    Date: 2023
  12. By: Alicia Gómez-Tello (University of Valencia); María-José Murgui-García; María-Teresa Sanchis-Llopis
    Abstract: Over the last two decades a handful of very rich European regions have increased the gap separating them from the European average in terms of labour productivity. In this paper we extend a spatial version of the Mankiw, Romer and Weil model (MRW, 1992) as developed by Fischer (2011) to accommodate human capital spillovers linked to agglomeration. After modelling this specific spillover, we go on to test empirically whether its effect has been to stimulate labour productivity growth in those European regions with the greatest potential to benefit from agglomeration economies. The theoretical model leads to a cross-sectional spatial Durbin model specification. The empirical analysis is carried out for 121 European regions for the period 1995-2014. We find significant conditional b-convergence, positive impacts of investment in physical and human capital, and a negative impact of population growth. Our most notable result involves the specific spillover effect that enhances the impact of investment in human capital in the most highly agglomerated regions. We find this externality significant in explaining labour productivity growth and therefore also in increasing labour productivity disparities across European regions.
    Keywords: Human capital, labour productivity, spatial externalities, European region
    JEL: R
    Date: 2023
  13. By: Ashok Mishra (Kemper and Ethel Marley Foundation Chair, Arizona State University, Tempe, AZ, United States); Kamel Louhichi (UMR PSAE - Paris-Saclay Applied Economics - AgroParisTech - Université Paris-Saclay - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Giampiero Genovese (JRC - European Commission - Joint Research Centre [Seville]); Sergio Gomez y Paloma (JRC - European Commission - Joint Research Centre [Seville])
    Abstract: This study investigates whether the historical inverse relationship (IR) between land (farm and plot) size and productivity holds for Ethiopia farms. The study uses plot level and household-level data from the three waves of the Ethiopia Socioeconomic Survey. The main finding, which confirms previous studies, is that the plot-size IR holds when productivity measurement is based on self-reported yields. However, the effects were reversed when we used crop-cut yields. Including labor inputs significantly reduces the magnitude of the coefficients on land size, but not the sign. Finally, the quantile regression reveals interesting findings. These are: (1) a strong positive effect of farm (and plot) size on productivity; (2) the magnitude of the effect decreases monotonically with quantile; (3) farm size displays a robust negative impact on gross revenue and the magnitude of the effect increases (in absolute terms) monotonically with quantiles; (4) the effect of farm (and plot) size on productivity decreases in magnitude when we control for labor input; (5) the IR between farm (and plot) size and total and family labor was negative and significant and the effect increases (in absolute terms) monotonically with quantiles.
    Abstract: Cette étude examine si la relation inverse (RI) historique entre la taille des terres (ferme et parcelle) et la productivité est confirmée pour les exploitations éthiopiennes. L'étude utilise des données au niveau des parcelles et des ménages des trois vagues de l'enquête socioéconomique en Éthiopie. La principale conclusion, qui confirme les études précédentes, est que la RI existe lorsque la mesure de la productivité est basée sur les rendements autodéclarés, alors qu'elle est directe quand les rendements sont mesurés avec la technique des coupes-témoins. L'inclusion de la main-d'œuvre réduit considérablement l'ampleur des coefficients, mais pas le signe.
    Keywords: Agricultural productivity, Land-size, Farm-size, Inverse relationship, Quantile regression, Ethiopia, Sub-Saharan Africa, Productivité agricole, Taille des terres, Taille de l'exploitation, Relation inverse, Régression quantile, Éthiopie, Afrique subsaharienne
    Date: 2023–01–30
  14. By: Mr. Romain M Veyrune; Solo Zerbo
    Abstract: The finances of central banks is a topic of renewed interest: many central banks are posting significant losses due to the cost of monetary policy, over which central banks have no control. Conversely, operational expenses, over which the central banks have more control, is a subject of less attention. We use public income statement data from central banks to calculate a score for operational expense efficiency based on a stochastic frontier analysis. In addition, we offer potential explanations for the observed variations in efficiency levels across central banks. Our analysis reveals significant heterogeneity across countries and income groups. Central banks with a single objective demonstrate higher efficiency compared with those with multiple objectives. Regarding the output of price stability, central banks in low-income developing countries exhibit lower efficiency compared with central banks in emerging markets and advanced economies. Factors such as central bank independence, the depth of the financial system, and the degree of openness play a role in influencing efficiency levels. Our findings underscore the significance of well-defined objectives, the operating environment, and concentration on core activities in reducing inefficiency.
    Keywords: operational efficiency; stochastic frontier analysis; operating independence; trade; financial depth
    Date: 2023–09–15
  15. By: Amer Ait Sidhoum (TUM - Technische Universität München = Technical University of Munich); H Dakpo (UMR PSAE - Paris-Saclay Applied Economics - AgroParisTech - Université Paris-Saclay - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, D-MTEC - Department of Management, Technology, and Economics [ETH Zürich] - ETH Zürich - Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich]); Laure Latruffe (GREThA - Groupe de Recherche en Economie Théorique et Appliquée - UB - Université de Bordeaux - CNRS - Centre National de la Recherche Scientifique)
    Abstract: This article studies trade-offs of farms in terms of economic sustainability (proxied here by technical efficiency), environmental sustainability (proxied here by farmers' commitment towards the environment) and social sustainability (proxied here by farmers' contribution to on farm well-being and communities' well-being). We use the latent class stochastic frontier model and create classes based on three separating variables, representing farms' environmental sustainability and social sustainability. The application to a sample of Spanish crop farms shows that more environmentally sustainable farms are likely to have lower levels of technical efficiency. However, improvements in social concerns, both towards own farm and the larger community, may lead to improved technical efficiency levels. In general, our study provides evidence of trade-offs for farms between economic sustainability and environmental sustainability, but also between environmental sustainability and social sustainability.
    Abstract: Cet article étudie les compromis des exploitations agricoles en termes de durabilité économique (représentée ici par l'efficacité technique), de durabilité environnementale (représentée ici par l'engagement des agriculteurs envers l'environnement) et de durabilité sociale (représentée ici par la contribution des agriculteurs au bien-être de l'exploitation et des communautés). Nous utilisons le modèle de frontière stochastique à classes latentes et créons des classes basées sur trois variables séparatrices, représentant la durabilité environnementale et la durabilité sociale des exploitations. L'application à un échantillon d'exploitations agricoles espagnoles montre que les exploitations plus durables sur le plan environnemental sont susceptibles d'avoir des niveaux d'efficacité technique plus faibles. Toutefois, l'amélioration des préoccupations sociales, tant à l'égard de l'exploitation elle-même que de la communauté dans son ensemble, peut conduire à une amélioration des niveaux d'efficacité technique. En général, notre étude fournit des preuves de compromis pour les exploitations agricoles entre la durabilité économique et la durabilité environnementale, mais aussi entre la durabilité environnementale et la durabilité sociale.
    Date: 2022–01–10
  16. By: Salazar, Lina; Agurto Adrianzen, Marcos; Alvarez, Luis
    Abstract: This analysis applies a regression discontinuity approach combined with remote sensing data to measure the productivity impacts linked to a fruit-fly eradication program, implemented in Peru. For this purpose, satellite imagery was used to estimate a vegetation index over a 10-year span for a sample of 305 producers -155 treated and 150 controls-. The results confirmed that program participation increased agricultural productivity in the short and long terms, in a range from 12% to 49%. However, quantile regression methods suggest that most productive farmers were able to obtain greater impacts.
    Keywords: Agricultural productivity;Impact Evaluation;Remote Sensing;Satellite Images;Peru
    JEL: Q12 Q16 O13
    Date: 2023–08
  17. By: Gáfaro, Margarita; Ibáñez, Ana María; Sánchez-Ordoñez, Daniel; Ortiz, María Camila
    Abstract: Latin American and Caribbean countries have historically been known for their rates of land inequality, highest in the world. However, these countries also exhibit a high degree of heterogeneity in their patterns of land concentration and average farm sizes. These cross-country differences play a determining role in productivity of farms and the distribution of agricultural income. Constructing a new data-set matching agri- cultural census and household survey data, we provide suggestive evidence on the positive relationship between farm size and farm income and wages. We identify the prevalence of small farms and the resulting low agricultural incomes as an important mechanism contributing to high income inequality in agricultural regions. Low labor productivity in small farms appears as a key explanatory factor.
    Keywords: Land inequality;Productivity;Agricultural income
    JEL: O13 O15 O54 J43 Q12 Q15
    Date: 2023–08
  18. By: Mr. Serhan Cevik; Kelly Gao
    Abstract: The world is not decarbonizing fast enough, with global warming on track to reach as much as 4°C over the next century absent a global green transition. Policymakers in Europe—and beyond—still have an opportunity both to achieve net zero emissions by 2050 and to strengthen economic prospects by increasing energy efficiency, along with changing the energy mix from fossil fuels to renewables. In this paper, we assess energy efficiency (or intensity) in a panel of 38 European countries over the period 1980–2021 by using the stochastic frontier analysis and obtain statistically significant and intuitive results. We have two key findings. First, price signals, including through the introduction of a carbon tax and the removal of fossil fuel subsidies, are critical for energy efficiency, as consumers respond to changes in energy prices. Second, stronger environmental policies and institutions generate unambiguous improvements in energy efficiency by inducing investment in energy efficient equipment and buildings and nudging consumers for energy conservation. These results—robust to alternative specifications and methods—have important policy implications for green growth with higher energy efficiency.
    Keywords: Energy consumption; energy efficiency; stochastis frontier analysis; Europe
    Date: 2023–09–22
  19. By: Aguirre, Emilio; Garcıa Suarez, Federico; Sicilia, Gabriela
    Abstract: Since the Second World War, the primary source of U.S. agricultural output growth has come from lifting productivity (Wang et al., 2015). Long-term investments in agricultural R&D appear as the predominate driver of those productivity gains (Alston and Pardey, 2021). Public research plays a critical role in the U.S. agricultural innovation process. From 1970 to the early 2000s, public research spending in the U.S. was nearly equal to private research spending, each amounting in 2002 to just under $6 billion (Wang et al., 2015, p. 41). However, Wang et al show that since 2002 when world commodity prices started climbing, a stark divergence between the two developed; by 2010, real public U.S. research spending fell to ~$5 billion and private research spending spiked to ~$9 billion. In the late 1990s and early 2000s, a new approach to funding U.S. innovation emerged: venture capital (VC) began to support newly-created firms to move promising inventions and business ideas from inception to commercialization (Kortum & Lerner, 2000; Arque-Castells, 2012). In agriculture, VC funding helps firms overcome high entry costs resulting from long-term research risk, spatial heterogeneity for applications, and economies of scale characteristic of many agricultural markets. In 2010, total VC investment in U.S. startups focused on farm production technologies was ~$400 million. By 2018, investment in VC-backed agricultural startups had grown to over $7 billion (Graff et al., 2020). In 2020, that investment was over $15 billion (AgFunder, 2021). Scholars hypothesize that VC investors became attracted to agriculture following the 2002 climb in commodity process, which increased farmers’ abilities to adopt new technologies and signaled to input suppliers that global demand may soon exceed supply (Fuglie, 2016). Others suggest a shift towards cleantech and biofuels in the 2000s introduced VC investors to agriculture amid an economy-wide surge in the financing of VC funds (Graff et al., 2020). It could be that the culmination of various general-purpose technologies (e.g., cloud computing, satellite imagery, vehicle automation, gene editing) opened technological opportunities in agriculture, as investors maximized economic benefits across multiple sectors of application (Olsson, 2005), including agriculture, given its historically high rates of return on research (Hurley et al, 2014). We explore the relationship between technological opportunity and the large exogenous shock in VC funding of agricultural startups. Specifically, we investigate the agricultural startup life-cycle. Within the cycle of firm birth, venture investment, and investor exit, what is the relationship between patents and firm financials? Do firms that patent have more successful financings and exits than those that patent little or not at all? In which industries/subsectors were technological opportunities pronounced? What are observed characteristics of the technological opportunity in agriculture? To investigate these issues, we began with a unique dataset of privately-held agricultural startups founded between 1977 and 2019. These unique startups were obtained from four commercial databases: Venture Source (now CB Insights), Crunchbase, Pitchbook, and CapitalIQ. Following a careful matching process, we identified 4, 681 firms from PitchBook (49.26% of the sample), 3, 399 from Capital IQ (35.77%), 1, 312 firms from Crunchbase (13.81%), and 111 from VentureSource (1.17%). From these 9, 503 firms, we narrowed to 7, 287 distinct startups founded in the United States on or after 1987. Of these agricultural startups, we matched 6, 084 to at least one establishment in the National Establishment Time Series (NETS) database, an 83.5% match rate. The NETS database is the most comprehensive source of establishment-level economic information for U.S. firms. Next, we matched the same set of agricultural startups to assignees listed in the USPTO’s pre-grant publication (PG Pub) and granted patent databases. Of those 6, 084 agricultural startups matched to economic information in NETS, we find 10% (634 startups) have one or more published patent application or grant, and 36% (2, 214 startups) have reported financing deals. Of the 634 startups with patent filing activity, 72% (458) report financing deals. We find a strong increase in the number of agricultural startups, both with and without VC investments, over the 1989-2019 period. Startups with VC grew, in terms of employment and sales, faster than startups without VC. We find substantial increases in patenting by the agricultural startups over time. Importantly, there has been great diversification of technology fields in which the startups patent, as well as of industry classifications in which startups operate, evidence of startups pursuing technological opportunity in agriculture. Among industries, we find the greatest increase of patenting by startups primarily classified in the manufacturing and professional, scientific, and technical services. Startups classified in these industries patented in Ag & Food, but also in biotech, chemicals, physics, electricity, and climate-change related new technologies. Next steps include detailing the timeline of firm birth, investment, and exit, and exploring causal and correlative relationships between patenting and VC-funded startups. REFERENCES AgFunder, 2021. AgFunder AgrifoodTech Investment Report. Available from: Alston, J., and P. Pardey, 2021. The Economics of Agricultural Innovation. In Handbook of Agricultural Economics, Eds., C. Barrett and D. Just. Vol. 5, Chapter 75, Elsevier Publishing. Arque-Castells, 2012. How Venture Capitalists Spur Invention in Spain: Evidence From Patent Trajectories. Research Policy (41): 897-912. Fuglie, 2016. The Growing Role of the Private Sector in Agricultural Research and Development World-wide. Global Food Security (10): 29-38. Graff, et al., 2020. Venture Capital and the Transformation of Private R&D for Agriculture. NBER Working Paper. Heisey and Fuglie, 2018. Public Agricultural R&D in High Income Countries: Old and New Roles in a New Funding Environment. Global Food Security (17): 92-102. Hurley, T., X. Rao, and P. Pardey, 2014. Re-Examining the Reported Rates of Return to Food and Agricultural Research and Development. American Journal of Agricultural Economics 96 (5): 1492-1504. Kortum, S., and J. Lerner, 2000. Assessing the Contribution of Venture Capital to Innovation. The RAND Journal of Economics (31): 674-692. Olsson, O., 2005. Technological opportunity and growth. Journal of Economic Growth 10: 35-57. Wang, S.L., P. Heisey, D. Schimmelpfennig, and E. Ball, 2015. Agricultural Productivity Growth in the United States: Measurement, Trends, and Drivers. Economic Research Report 189, Economic Research Service, U.S. Department of Agriculture. July.
    Keywords: Livestock Production/Industries, Research and Development/Tech Change/Emerging Technologies
    Date: 2023

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