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Credit is central in labour allocation decisions in smallholder agriculture in developing countries. The purpose of this paper is to analyse the effect of credit constraints on farm households’ labour allocation decisions in rural Burkina Faso.

The study used a direct elicitation approach of credit constraints and applied a farm household model to categorize households into four labour market participation regimes. A joint estimation of both the multinomial logit model and probit model was applied on survey data from Burkina Faso to assess the effect of credit constraint on the probability of choosing one of the four alternatives.

The results of the probit model showed that households’ endowment of livestock, access to news, and membership to an farmer-based organization were factors lowering the probability of being credit constrained in rural Burkina Faso. The multinomial logit model results showed that credit constraints negatively influenced the likelihood of a farm household to use hired labour in agricultural production and perhaps more importantly it induces farm households to hire out labour off farm. The results also showed that the other components of household characteristics and farm attributes are important factors determining the relative probability of selecting a particular labour market participation regime.

Facilitating access to credit in rural Burkina Faso can encourage farm households to use hired labour in agricultural production and thereby positively impacting farm productivity and relieving unemployment pressures.

In order to identify the effect of credit constraints on farm households’ labour decisions, this study examined farm households’ decisions of hiring on-farm labour, supplying labour off-farm or simultaneously hiring on-farm labour and supplying family labour off-farm under credit constraints using the direct elicitation approach of credit constraints. To the best of the authors’ knowledge, this study is the first to examine this problem in Burkina Faso.

The purpose of this study is to assess the impact of credit constraints on agricultural labour productivity as well as the impact of credit constraints and agricultural labour productivity on rural households' consumption in Ghana.

This study uses the Ghana Living Standard Survey round six (GLSS 6) as the main source of data, which happens to be one of the most comprehensive household datasets in Ghana. Quantitative estimation techniques (namely: Endogenous Switching Regression and Two Stage Least Squares) are used to address possible endogeneity and selection into credit markets.

First, large households are prone to credit constraints while age (experience) and compliance with extension advice reduce credit constraints. Second, the determinants of agricultural labour productivity for both constrained and unconstrained households are age, sex, farm equipment, herbicide and farm size. Third, household size, education and livestock rearing influence agricultural labour productivity of constrained households. Fourth, credit constraints, irrespective of how they are measured, impede agricultural labour productivity while access to credit fosters labour productivity. Lastly, credit constraints robustly reduce consumption while agricultural labour productivity strongly enhances rural households' consumption.

The first contribution is that, unlike most previous studies, we do not focus on the widely used measure of productivity – output per unit land, but on agriculture labour productivity in particular. Secondly, unlike most previous studies which examine the effect of credit constraints either on productivity alone or consumption alone, our study examines the impact of credit constraints on both. Thirdly, unlike the existing literature which uses one or two measures of credit constraints, we use a wide range of measures of credit constraints – seven different measures of credit constraints. Lastly, our empirical strategy solves at least two critical econometric problems – sample selection bias and endogeneity.

The purpose of this paper is to evaluate the effects of access to formal credit on rural household technical efficiency in China.

Based on the rural household survey data in Weifang city, Shandong province in northern China, the authors apply recent developed bootstrapped DEA approach to investigate rural technical efficiency at the household level under the consideration of off-farm activities. Rural households are then identified as credit constrained and classified as supply-side and demand-side credit constraints by applying direct elicitation method. Finally, the authors apply a tobit regression to examine the effects of credit constraints on household technical efficiency.

Rural households in China not only suffer supply-side credit constraints, but also demand-side credit constraints resulted from the transaction costs and risk rationing. The tobit regression discloses that demand-side credit constraints impose significant negative impacts on household technical efficiency.

The authors clarify the definition of credit constraints and classify the credit constraints into supply-side and demand-side credit constraints. The results of this paper have significant policy implications for rural finance policies in China.

The design of effective policies that increase access to agricultural credit should consider understanding credit constraint farmers’ groups and their response to changes in the credit conditions. To contribute to this understanding, this study surveyed farmers from Chile and classified them into five credit constraint categories discussed in credit literature. In addition, these farmers indicated how they would react to a series of hypothetical conditions related to changing interest rates, loan maturity and grace periods. Their responses were employed to measure credit demand scores (i.e. relative elasticities). Regression tests evaluated how different types of farmers reacted to changing credit conditions.

Farmers from Chile were surveyed using a mix of random and convenience sampling. Surveyed farmers were classified into five credit constraint categories proposed by previous research. Farmers rated their demand for credit on a five-point Likert-type scale for hypothetical changes in interest rates, loan maturities and grace periods. Their responses were employed to measure credit demand scores or relative credit elasticities. The study evaluated credit elasticity as a function of farmers’ credit constraint and some control variables using several regressions, including OLS, ordered probit and hierarchical regression.

The study identified 44% unconstrained nonborrowing farmers, 23% unconstrained borrowers, 14% quantity-constrained, 16% risk-constrained and 3% transaction cost-constrained farmers. Unconstrained borrowers and quantity-constrained farmers responded most to changing interest rates and loan maturity conditions. In addition, unconstrained nonborrowers and risk-constrained farmers were statistically less sensitive to changes in credit conditions than unconstrained borrowers. This finding is significant because, as discussed, unconstrained nonborrowers represent 44% of our sample. Furthermore, risk-constrained farmers were the least sensitive to changes in interest rates and loan maturity across all other credit categories.

This study gives insights that can guide agribusiness policies to enhance access to credit in developing countries such as Chile. Agricultural credit capital institutions can better target their clientele by identifying farmers’ possible reactions before implementing policy changes to increase access to credit. This study’s credit constraint categorization and the results discussed can guide that identification. For instance, policies directed toward unconstrained borrowing farmers may find positive responses. However, implementing policies targeting the other three groups (unconstrained nonborrowing, risk-constrained and transaction cost-constrained farmers) is more challenging because these farmers are less responsive to changing credit conditions.

This article correlates farmers’ propensity to borrow and credit constraints across five categories of farmers. Prior research using this categorization framework has not identified farmers into the five groups. Furthermore, in addition to interest rate and loan maturity credit demand relative elasticity, this study adds the grace period elasticity, which has not been included in previous studies on agricultural credit.

A correlation has been shown in the literature between credit constraints and the adoption of agricultural technologies, technical efficiencies and measures for adapting to climate change. The relationship between credit constraints, risk management strategy adoption and income, however, is not well understood. Consequently, the purpose of this study was to investigate how credit constraints affect the income and risk management practices adopted by Northern Nigerian maize farmers.

Cross-sectional data were collected from 300 maize farmers in Northern Nigeria using a multi-stage sampling technique. Descriptive statistics, seemingly unrelated regression and double hurdle regression models were the analysis methods.

The results showed that friends and relatives, banks, “Adashe”, cooperatives and farmer groups were the main sources of credit in the study area. The findings also revealed that the sources of risk in the study area included production risk, economic risk, financial risk, institutional risk, technological risk and human risk. In addition, the risk management strategies used to mitigate observed risks were fertilizer application, insecticides, planting of disease-resistant varieties, use of herbicides, practising mixed cropping, modern planning, use of management tools as well as making bunds and channels. Furthermore, we found that interest rate, farm size, level of education, gender and marital status were significant determinants of statuses of credit constraints while the age of the farmer, gender, household size, primary occupation, access to extension services and income from maize production affected the choice and intensity of adoption of risk management strategies among the farmers.

The study concluded that credit constrained status condition of farmers negatively affected the adoption of some risk management strategies and maize farmers’ income.

The study concluded that credit constrained status condition of farmers negatively affected the adoption of some risk management strategies and maize farmers’ income. It therefore recommends that financial service providers should be engaged to design financial products that are tailored to the needs of smallholder farmers in the study area.

This paper incorporates the role of constraints in influencing farmers’ decisions to uptake credits and subsequently their adoption behaviours on risk management strategies. The researcher approached the topic with a state-of-the-art method which allows for obtaining more reliable results and hence more specific contributions to research and practice.

This paper investigates constraints to yield enhancing technology adoptions, highlighting credit using data pooled from the first three waves of the Ethiopian socio-economic surveys.

Direct elicitation methodology is used to identify household's non-price credit rationing status. The panel selection model specified to examine causal effects of credit constraint on adoption variables allows us to tackle self-selection into adoptions and potential endogeneity of credit constraint while controlling for unobserved heterogeneity in both the selection and main equations.

Results show that about 54% of sample households face credit rationing, predominantly demand-side risk rationing. There is a negative association between measures of credit constraint status and adoption variables. The effect is stronger when the demand-side credit rationing is accounted for and when within household variation in credit constraint status overtime is considered as opposed to across constrained and unconstrained households.

Expanding physical access to institutional credit alone may not necessarily spur increased uptake of credit and instant investment by farm households. For a majority of them to take advantage of available credit and improved technology, interventions should also aim at minimizing downside risks.

This paper incorporates the role of downside risk in influencing farmer's decisions to uptake credits and subsequently his/her adoption behaviors. The researcher approached the topic by state-of-the-art method which allows obtaining more reliable results and hence more specific contributions to research and practice.

The purpose of this paper is to examine farmers’ access to credit, credit constraint, and productivity in the Northern Savannah ecological zone of Ghana.

Secondary data from the Ghana Feed the Future baseline survey involving a total sample of 2,968 farm households were used. The conditional mixed process (CMP) framework was applied to estimate access to credit, credit constraint, and productivity simultaneously. As a system estimator the CMP corrects for possible heterogeneity and sample selection bias.

The results from the estimations revealed that age, literacy, farm non-mechanized equipment, and group membership were the variables influencing farmers’ access to credit. Credit constraint conditions were determined by household size, locality, group membership, and household durable assets. Finally, the results showed that productivity of farmers was dependent on marital status, household size, locality, farm size, commercialization, farm mechanized equipment, group membership, and household durable assets.

This paper is the first, to the best of the authors’ knowledge, to use the CMP framework to jointly estimate access to credit, credit constraint, and productivity. The results indicate that estimating credit access and constraint models separately would have yielded biased estimates. Thus, this paper informs future research on farmers’ credit access, credit constraint, and productivity for informed policymaking.

The purpose of this paper is to identify factors affecting formal credit constraint status of rural farm households in Vietnam’s North Central Coast (NCC) region.

Using the direct elicitation method (DEM), the authors consider both internal and external credit rationing.

Empirical evidences confirm the importance of household head’s age, gender and education to household’s likelihood of being credit constrained. In addition, households who have advantages in farm land size, labour resources and non-farm income are less likely to be credit constrained. Poor households are observed to remain restricted by formal credit institutions. Results from the endogenous switching regression model suggest that credit constraints negatively impact household’s consumption per capita and informal credit can act as a substitute to mitigate the negative influence of formal credit constraints.

One limitation arises from the usage of the DEM to identify credit constrained households. The method cannot detect effective and ineffective constraints. Another limitation is the inability of cross-section data to capture long-term impacts of credit constraints on household welfare. Finally, causes of credit constraints from the lender’s view cannot be observed.

The results suggest that it is necessary to enhance the credit allocation regime to reduce the transaction cost and provide target households with sufficient credit. It should be emphasized that high transaction cost and the mismatch between credit demand and supply stemming from information asymmetry. The government can help formal financial institutions to reduce information cost by encouraging the active role of social organizations such as Women Unions, Youth Unions and Veteran Unions in bridging rural farm households with formal lenders.

There are limited studies focusing on determinants of credit constraints and their impacts on rural farm households. To the best of the knowledge, there is no study evaluating the impact of credit constraints on rural farm household welfare particularly in Vietnam. In addition, the studies related to credit constraints only considered full quantity rationing (households applied for the loan but were rejected), omitting the case of partly quantity rationing (loan obtained by the borrowers is less than their demand) and self-rationing.

The purpose of this paper is to examine the effect of credit constraints on agricultural productivity in China.

Using data from a rural financial survey, a switching regression model is used to account for endogeneity and heterogeneity. Carter presents three ways that credit might affect the production functions; a shift along a given production surface by allowing an optimal level of inputs, a shift the production surface out by allowing the purchase of more efficient inputs, and the third is to increase net revenue by more intensive use of fixed inputs and resources. Thus, the effects of factors on agricultural productivity may not be independent of credit status; therefore, separate functions for credit‐constrained and non‐constrained households are examined.

Empirical estimates of the impacts of credit constraints on agricultural productivity are provided for the Heilongjiang province, a major agricultural production area, in Northeast China. By removing credit constraints, average agricultural productivity was estimated to be increased by 75 percent. Under credit constraints, labor inputs, along with a farmers' education, cannot be fully employed because of an inappropriate mix of inputs.

Young farmers may not be able to leverage their comparative advantage for physically intensive farm work under credit constraints. Because of data limitations, the research does not include information on informal credit in the estimation, which may underestimate the effects of credit constraints.

This study provides an analysis of the impacts of credit constraints on rural household productivity for the Heilongjiang province, a major agricultural production region, in Northeast China.

The purpose of this paper is to analyse how farm production and input use (land, variable inputs, labour, and capital) is related to farm access to credit in the Central and Eastern Europe (CEE) transition countries.

Drawing on a unique farm level panel data set with 37,409 observations and employing a matching estimator, this paper analyses how farm access to credit affects farm input allocation and farm efficiency in the CEE transition countries. The large size of the FADN data set has an additional advantage. It allows the authors to employ a semi‐parametric estimator based on the propensity score matching. Using more than 37,409 observations assures that the loss in efficiency of semi‐parametric estimates, as compared to parametric ones, is not a problem. This is important for at least two reasons. First, applying a semi‐parametric propensity score matching (PSM) estimator allows to control for any heterogeneity in the relationship between farm performance and their observable characteristics (in particular access to credit). Second, matching estimators are robust in situations where farms having access to credit systematically differ from those that do not.

It is found that farms are asymmetrically credit constrained between inputs. The use of variable inputs and capital investment increases up to 2.3 percent and 29 percent, respectively, per 1,000 EUR of additional credit. The authors' estimates suggest also that farm access to credit increases the total factor productivity up to 1.9 percent per 1,000 EUR of additional credit, indicating that an improved access to credit results in adjusting the relative input intensities on farms. This finding is further supported by a negative effect of better access to credit on labour, suggesting that these two are substitutes. Interestingly, farms are found not to be credit constrained with respect to land.

To the best of the authors' knowledge, the present paper is the first to investigate the importance of access to credit for farm performance in the CEE region as a whole.