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The purpose of this paper is to estimate the impact of Internal Revenue Code cost recovery provisions – Section 179 and “bonus depreciation” – on farm capital investment.

The authors construct a synthetic panel of data consisting of cohorts of similar farms based on state and production specialization using the USDA’s Agricultural Resource Management Survey for years 1996-2012. Employing panel data methods, the authors are able to control for time-invariant fixed effects, as well as the effects of past investment on current investment.

The authors estimate statistically significant investment demand elasticities with respect to the Section 179 expensing deduction of between 0.28 and 0.50. A change in bonus depreciation, on average, had little impact on capital investment.

The estimates suggest there is a modest effect of the cost recovery provisions on investment overall, but a stronger effect on farms that have more than $10,000 in gross cash farm income. There are other implications for the agricultural sector: the provisions may encourage technology adoption with its associated benefits, such as reduced cost of production and improved conservation practices. On the other hand, the policy could contribute to the growing concentration in production as large commercial farms expand their operated acreage to take advantage of increasingly efficient physical capital.

To the authors’ knowledge, this is the first research to use a nationally representative dataset to estimate to impact of Section 179 and “bonus depreciation” on farm investment. The findings provide evidence of the provisions’ impact on farm capital purchases.

The purpose of this paper is to examine the impact of changes in farm economic conditions and macroeconomic trends on US farm capital expenditures between 1996 and 2013.

A synthetic panel is constructed from Agricultural Resource Management Survey (ARMS) data. A dynamic system GMM regression model is estimated for farms as a whole and separately within farm typology categories. The use of farm typologies allows for comparison of the relative magnitudes of these estimates across farms by farm sales level and the operator’s primary occupation.

Changes in gross farm income levels, tax depreciation rates, and interest rates have a significant impact on crop farm investment, while changes in output prices, net cash farm income levels, tax depreciation rates, and farm specialization levels have significant impacts on livestock farm capital investment. The relative significance and magnitudes of these impacts differ within farm typologies. Significant differences include a greater responsiveness to change in tax policy variables for residential crop farms, greater responsiveness to changes in output prices and debt to asset ratios for intermediate livestock farms, and larger changes in commercial crop and livestock farm investment given equivalent changes in farm sales or the returns to investment.

These findings are of interest to agricultural economists when constructing farm investment models and employing pseudo panel methods, to those in the agricultural equipment and manufacturing sector when constructing models to manage inventories and plan for production needs across regions and over time, to those involved in drafting tax policy and evaluating the potential impacts of tax changes on agricultural investment, and for those in the agricultural lending sector when designing and executing agricultural capital lending programs.

This study uniquely identifies differences in the level of investment and the magnitude of investment responsiveness to changes in farm economic conditions and macroeconomic trends given differences in income levels and primary operator occupation. In addition, this study is one of the few which utilizes ARMS data to study farm capital investment. Utilizing ARMS data provides a rich panel data set, covering producers across many different crop production types and regions. Finally, employing pseudo panel construction methods contributes to efforts to effectively employ cross-sectional data and dynamic models to study farm behavior across time.

The purpose of this paper is to examine the use of accelerated depreciation deductions, which includes Section 179 and bonus depreciation, taken in the first year of asset life by Michigan farms. The frequency, value and influence of accelerated depreciation on farm investment are also analyzed.

Accrual adjusted income statements, balance sheets, depreciation schedules, and income tax information for 66 Michigan farms from 2004 to 2014 provide data for the analysis. The present value of the accelerated deduction and change in the cost of capital were calculated. Finally, investment elasticities were used to arrive at the change in investment due to accelerated depreciation.

Accelerated depreciation was utilized across all applicable asset classes. Section 179 was used more often than bonus depreciation in part because it was available in all the examined years. Based on actual farm business use, accelerated depreciation lowered the cost of capital for the operations resulting in an estimated increase in investment of 0.27 to 11.6 percent depending on asset class.

The data utilized are of a detail not available in previous investigations which used either aggregate data or estimated rather than the observed use of accelerated depreciation. This analysis reveals that accelerated depreciation as used by commercial farms lowers the cost of capital and thus encourages investment particularly in machinery and equipment.

The purpose of this paper is to determine if there are positive microeconomic effects from a state‐funded loan participation program on farm productivity and investment behavior.

The authors take the approach that access to credit solves a liquidity problem. If a credit constraint exists it results in a suboptimal allocation of resources and a reduction in farm output and profitability. A two‐stage regression model approach is used to analyze farmer survey and loan application data. In the first stage, a probit regression model is used to identify the farmers who are likely to be credit rationed. In the second stage, switching regression models are used to observe the effect of credit rationing on farm productivity and on farm investment behavior.

It is found that there are liquidity effects of credit constraints for a significant share of the beginning and low‐resource farmers who participated in the state‐funded farm loan program. After controlling for various farm and farmer characteristics, the estimated productivity and investment demand equations imply that a 1 percent increase in credit received by credit constrained farmers under the state program increased their gross income by about 0.49 percent, and their investments in depreciable assets by about 0.33 percent.

This paper is the first to apply the switching regression model to a state‐funded farm loan program for the purpose of evaluating the financial impacts on farmer participants.

The Old Farmer Pension Program (OFPP) represents Taiwan’s long-standing efforts aiming at improving farm household income and well-being; however, how effective the pension program is in terms of achieving the policy agenda has remained unclear. The paper aims to discuss this issue.

Based on data drawn from the Survey of Family Income and Expenditure during 1999–2013, two identification strategies are used to examine the effect of OFPP. First the authors apply the Blinder-Oaxaca decomposition to address the concern if the program reaches the socially/economically disadvantaged farm households. The second identification strategy involves using the static and dynamic decomposition approaches to identify the major factors contributing to farm household income inequality and the redistribution role of the OFPP.

Results from the Blinder-Oaxaca decomposition indicate that about 60 percent of the income gap can be eliminated if the pension recipients’ socio-economic characteristics are the same as the non-recipient group, suggesting it is the disadvantaged group that receives the old farmer pension. Moreover, the results suggest the significant contributions of household investments in health and human capital as well as diversification toward nonfarm activities, to income inequality among Taiwan’s farm households. Results from the dynamic decomposition suggest that the first-wave adjustment of the OFPP enlarges farm household income inequality, the following two waves of adjustment, however, plays an equalizing role.

This study adds to the literature by providing a methodological refinement promoting the view that it calls for the use of the dynamic (change) decomposition framework to investigate the inequality-enlarging or inequality-equalizing role each income determinant plays.

Dairy farms, along with livestock and specialty crop farms, face a tight labor supply and increasing labor costs. To overcome the challenging labor market, farm managers can increase labor-use efficiency through both human resource and capital investments. However, little is known about the relationship between such investments and farm profitability. The purpose of this paper is to examine the relationship between dairy farm financial performance and labor-use efficiency, as measured by labor productivity (milk sold per worker equivalent); labor costs (hired labor cost per unit of milk sold and hired labor cost per worker); and investment in labor-saving equipment.

Cluster analysis is applied to partition dairy farms into three performance categories (high/middle/low), based on farms’ rate of return on equity, asset turnover ratios and net dairy income per hundredweight of milk. Next, the annual financial rank is fitted into both random- and farm-level fixed-effects ordered logit and linear models to estimate the relationship between dairy farms’ financial performance and labor-use efficiency. This study also investigates the implications of using a single financial indicator as a measure of financial performance, which is the dominant approach in literature.

The study finds that greater labor productivity and cost efficiency (as measured by hired labor cost per unit of milk sold) are associated with better farm financial performance. No statistically significant relationship is found between farm financial performance and both hired labor cost per worker and advance milking systems (a proxy of capital investment in labor-saving technology). Future studies would benefit from better measurements of labor-saving technology. This study also demonstrates inconsistency in regression results when individual financial variables are used as a measure of financial performance. The greater labor-use efficiency on high-performing farms may be a combination of hiring more-skilled workers and managerial strategies of reducing unnecessary labor activities. The results emphasize the importance of managerial strategies that improve overall labor-use efficiency, instead of simply minimizing total labor expenses or labor cost per worker.

This study examines the importance of labor productivity and labor cost efficiency for dairy farm management. It also develops a novel approach which brings a more comprehensive financial performance evaluation into regression models. Furthermore, this study explicitly demonstrates the potential for inconsistent results when using individual financial variable as a measure of financial performance, which is the dominant measurement of financial performance in farm management studies.

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.

The purpose of this paper is to explore smallholder agricultural financing in Ghana’s Northern region by identifying farmers’ preferred traditional and innovative financing methods and estimating the determinants of use of innovative financing methods.

This paper presented a list of documented traditional financing methods to farmers during in-depth interviews and employed descriptive statistics to summarize choice and amounts sourced from traditional methods. Two questions from the survey revealed a felt need for extra financing sources for credit-rationed farmers. Farmers with positive responses to either or both questions were classified as “users of innovative financing”. The authors then used a probit model to examine factors that influence decisions to use innovative financing method.

Farmers’ own savings, reinvesting past season’s profits and financing maize production with income from other commercial crops were the most popular traditional methods. The authors found complementary relations between formal and informal lending systems in the rural financial market. Smallholders also took farm and non-farm “by-day” jobs to raise income for farm investment and/or joined Village Savings and Loans Associations (VSLAs) specifically to take advantage of possible credit opportunities. These two latter methods were operationalized in this study as innovative agricultural financing. The results show that access to credit, social capital and market participation increased the likelihood of using innovative financing methods. Alternatively, farmer group membership, diversity in crop production and being a household head diminished the likelihood of innovative financing use.

The activities of VSLAs can be regulated and expanded to spread its benefits to more farmers. Also, creating avenues for dry season labour market participation in the region could enable farmers raise capital for farm investment.

This study explores existing practices and farmer innovations to agricultural financing and, by so doing, deviates from the vast literature focussing mainly on microcredit provisioning as the main model of smallholder agricultural financing in Africa.

A major policy issue facing leaders in the developing world is whether international migration, through remittances, contributes to the development process in migrant-sending communities or impedes the efficient allocation of labor and human capital at the origin countries. This study examines the impact of remittance inflows on out-farm migration of farm labor toward the nonfarm sector. Specifically, this study shows how international migrant remittances may alter the predictions of out-farm migration models by Harris–Todaro.

The authors use unbalanced panel time-series data on 77 developing countries between 1991 and 2010 within a dynamic panel time-series framework to estimate the impact of remittances on the out-farm migration rate.

The authors find two competing effects of remittances on out-farm migration of labor in developing countries. First, remittances decelerate the out-farm migration rates by supplementing farm income and consumption expenditures. Second, remittances provide a source of investment in nonfarm activities that increase the rate of migration out of agriculture over time. Combining these effects, on average, our elasticity estimates indicate that a 10% increase in remittances reduces the migration out of agriculture, on average, by 0.5% in developing countries over time.

The authors findings align with the “developmentalist” or “optimistic” views of international migration. International migration, through remittances, help make the inevitable transition out of the farm sector smoother for developing countries.

To the authors’ knowledge, this is the first study to extend the empirical literature on macro-level determinants of out-farm migration within the Harris–Todaro framework to explicitly account for the impacts of remittances inflows into developing countries that the new economics of labor migration (NELM) theory hypothesizes.