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This paper aims to evaluate the potential impact of the 2008-2009 financial crisis on the economic inequality among US farm households. To accomplish this objective, a multivariate regression procedure and a decade-long pooled state-level cross sections of data from the Agricultural Resource Management Survey (ARMS) were used. Among the major findings is the inverse relationship between both income and wealth inequality and factors such as when the famer has a college education or beyond, when the farm is owned partly or fully, and when the farm is located in the Midwestern region. These findings underscore the importance, in terms of policy perspectives aimed at lowering economic inequality, of investing in human capital and in maintaining a stable agricultural economy.

The literature on the disparity in the distributions of income and wealth and how to measure it, because of relevance to social welfare, continues to grow, not just in the US, but in many countries around the world. Aristei and Perugini [

the late eighties and in Canada between 1993 and 2005. Examples of yet other research with a broader economic and statistical implementation of the concept of inequality as it relates to underlying theoretical and welfare-based assumptions (e.g., savings, inheritance policies, labor heterogeneity, effects of population and economic growth, human and nonhuman capital; quality of life, long run development of income and wealth distributions, etc.) with references to the population at large are those by [

Reinsel [

The main data source for this research is the 2003-2012 ARMS. The ARMS, which has a complex stratified, multi-frame design, is a national survey conducted annually by the National Agricultural Statistics Service (NASS) and the Economic Research Service^{2}. Each observation in the ARMS represents a number of similar farms (e.g., based on land use, size of farm, etc.), the particular number being the survey expansion factor (or the inverse of the probability of the surveyed farm being selected for surveying), and is referred to henceforth as survey weight, or w_{i} (i = 1, ∙∙∙, n, where n denotes sample size). In using these weights, the expanded population of farm households in any of the years considered stands at around 2.1 million.

Because some of the states in some of the years were too small to allow for statistical precision of estimates (i.e., 30 observations or less), eight states out of the forty-eight lower states were combined into one geographic category; “other”^{3}. Summary statistics of indicators of income and wealth inequality and of factors perceived to impact these separate indicators are then computed over the 410 state/year observations.

This method of measuring inequality was developed originally by Chen, Tsaur, and Rhai [^{*}) corrects the problems associated with the presence of negative observations, which are prevalent in the data that were used, by normalizing the distribution of Yin a manner so that the upper bound on the Gini coefficient is unity^{4}. The “adjusted” Gini coefficient is computed on a national (except for Alaska and Hawaii) and on a state-level basis over the 2003-2012 periods as:

In this equation, and for each of the time periods, w_{i} is the survey weight of the i^{th} household in the state, n and N are, respectively, the sample size and the expanded number of farm operator households in the state, s_{i} is the corresponding weighted income share of the i^{th} household in the state, Y_{i} is the household’s total income (or total wealth) in the state where _{i}< 0, and m is the size of the subset of the households whose combined weighted income is zero with

Prior to implementing the measurement of inequality, Y_{i} is divided by the square root of household size in order to allow, without differentiation between adults and children, for certain level of economies of scale [^{5}. The implication of this equivalised notion of Y is that a household’s economic requirements increase less than proportionally with its size; e.g., the needs for a family of four persons are twice as great as those of a single person household [

The choice of the variables in the regression models used to attend to this question was based primarily on the human capital and the life-cycle theories due to their relevance to the size distribution of both income and wealth^{6}. The central thesis of the human capital-based theory is that investment in skill through formal education and/or through experience acquired through on-the-job training is rewarded in the labor market, because of enhanced productivity of workers, through higher earnings. The most consistent literature on the relationship between human capital development and the potential for higher earnings dates back to the early work of Friedman and Kuznets [

The need for incorporating human capital and life-cycle effects when examining the size distribution of farm income and wealth is rooted in their potential impact on the income generation and wealth accumulation capacity by farmers. Studies by Schultz [^{7}. Similarly, the importance of the stage in the life cycle of farmers to production agriculture has been documented in many studies. For example, because older farmers have a shorter planning horizon and are more averse to risk than young farmers, they tend to be less inclined to adopt new technology or to purchase newer equipment [

The factors that may explain state-level inequality in income or wealth of farm households over the time period considered (state = 1, ∙∙∙, 41; t = 1, ∙∙∙, 10) are identified by estimating a weighted least squares (WLS) regression model. Since the values of ^{8}:

where Y (1 = income, 2 = wealth) is the pertinent economic measure, log is the natural logarithm operator, x is socio-economic explanatory variable, ^{th} coefficient in (2) signifies a positive association between inequality and the k^{th} explanatory variable.

The model specification for both the income and wealth inequality models as described in (2) is generally the same; the only exception pertains to the attempt by the paper to examine the potential impact of income inequality on the disparity in the distribution of wealth. Such a potential linkage was noted by Huggett [

The goodness-of-fit measure R^{2} that captures the explained variation in the regression models described in (2) is decomposed into contributing components based on the Shapley [^{2 }is thought of as the total value of the game and the series of contributing components X_{j}, with^{th} contributing component, the Shapley value (S_{j}) describes the average marginal contribution of this explanatory variable over all possible orderings towards R^{2} as described in [^{9}:

where ^{2} of a model q that includes the j^{th} variable, h is the number of variables in model q, ^{2}of the same model with the j^{th} variable excluded. To the extent that there are several explanatory variables in any particular regression model, the marginal effects would depend on the elimination order of these predictors, which explains the arithmetic averaging of all possible elimination sequences (k!) as shown in (3).

Despite the general pattern of an increase in inequality in farm household income and wealth across the lower 48 states over the 2003 to 2012 period, such an increase is not uniform across all of the states. ^{10}.

^{11}. Farms were most likely to be organized as sole proprietorships and to be partly-owned.

The estimated coefficients reported in ^{12}. By including these dummy variables in the regression models, the average differences across regions and time due to unobserved heterogeneity (e.g., entrepreneurial ability and/or level of sophistication of farmers; land quality, etc.) are thus purged away, and what is left over are the within-region impacts. The amounts of explained variation in the income and wealth inequality regression models were at 35.5 percent and 42.6 percent, respectively.

Regression results from the income inequality model, as depicted in (2), show a positive linkage between a state’s inequality and factors such as a state’s share of larger sized-farms, increasing rates of unemployment, and the years 2007, 2008, and 2012. The finding that an increase in the state’s average share of larger sized-farms, those 8 percent of all farms with at least $250,000 in annual sales and that accounted in 2005 for about 63 percent of the total value of production [

employment earning, thereby increasing income inequality. Such an adverse impact on states’ income inequality due to higher unemployment rates with its accompanying economic contraction is noted in the results where inequality in 2007, when the collapse in the US housing market occurred, and in 2008, the year of the Great Recession, exceeded its level in 2003.

The regression results identified a set of factors that are negatively associated with states’ income inequality. Of the farm operator characteristics, such an inverse relationship occurs when a greater share of farm operators in a state are white or have a college education. The results pertaining to the inverse linkage between education and inequality is supported by Becker and Chiswick [^{13}.

Findings point to a similar potential negative association with income inequality when there is an increase in the state’s share of farm households having a livelihood strategy that involved working off the farm only, or a strategy that involved combining off-farm work with participation in farm programs. The potential for a reduction in income inequality due to working off the farm or receiving farm program payments concurs with findings by Ahearn et al. [

Of the farm characteristics, accelerating rise in per-acre capital expenditures, higher shares within a state of sole proprietorships and of farms that are either partly-owned or fully-owned, and a farm location in either the Midwestern or the Southern regions of the US are all factors that are inversely related with income inequality.

Variables | Mean | Standard deviation |
---|---|---|

0.44 | 0.53 | |

0.10 | 0.29 | |

Explanatory variables: | ||

Operator’s age: 35-49 | 0.22 | 0.07 |

Operator’s age: older than 50 | 0.73 | 0.08 |

Operator is white | 0.92 | 0.08 |

Operator has a high school or some college education | 0.66 | 0.07 |

Operator has college education or beyond | 0.24 | 0.09 |

Household participates in farm programs only | 0.12 | 0.08 |

Household participates in off-farm work only | 0.48 | 0.17 |

Household participates in both farm programs and off-farm work | 0.24 | 0.16 |

Income inequality^{1} | 0.60 | 0.10 |

Farm is organized as a sole proprietorship | 0.92 | 0.05 |

Farm is partly-owned | 0.64 | 0.11 |

Farm is fully-owned | 0.30 | 0.10 |

Entropy index of farm commodity diversification | 0.11 | 0.05 |

Farm has crop insurance coverage | 0.15 | 0.14 |

Economic size of farm: at least $250,000 in annual farm sales | 0.10 | 0.06 |

Capital expenditures ($1000) per operated acres | 0.27 | 0.40 |

Farm is located in the Midwestern region | 0.29 | 0.46 |

Farm is located in the South region | 0.37 | 0.48 |

County unemployment rate (%) | 6.59 | 2.41 |

Sample size: 410 |

Source: Author’s calcualation using 2003-2012 ARMS (^{1}The income inequality variable

The positive yet insignificant coefficient of the linear term of the per-acre capital investment variable (e.g., investment in real estate, building new grain bins, expand use of pivot irrigation, etc.) and the highly significant and negative coefficient of its squared term indicate an inverse relationship with income inequality and improved income distribution but only at high levels of capital utilization.

Findings from the wealth inequality regression model reveal a positive association between wealth inequality as described in (2) and an increase in the state’s share of farm households having livelihood strategies of solely participating in farm programs or in an off-farm employment. Such a direct association between higher shares of farm program participation and wealth inequality can be explained by the fact that farms with a livelihood strategy of sole participation in farm programs are more likely to fall among those fewer yet the larger-sized with more farm assets and that tend to produce most of the farm output and that tend to be also more reliant on farm program payments. For example, data from the 2012 ARMS show that the 9 percent of farm households that

Variables | Income | Wealth | ||
---|---|---|---|---|

t-statistic | t-statistic | |||

Constant | 7.9451^{***} | 4.56 | 3.0216^{***} | 4.83 |

Operator’s age: 35-49 | −1.2773 | −1.12 | −0.7748^{**} | −1.98 |

Operator’s age: older than 50 | −1.6947 | −1.62 | −0.9670^{***} | −2.86 |

Operator is white | −0.8864^{**} | −2.42 | −0.4903^{***} | −2.67 |

Operator has a high school or some college education | −0.6060 | −1.21 | −0.3796 | −1.35 |

Operator has college education or beyond | −0.9168^{*} | −1.79 | −0.4997^{*} | −1.69 |

Household participates in farm programs only | −0.5085 | −0.98 | 0.5438^{*} | 1.88 |

Household participates in off-farm work only | −1.5253^{***} | −3.90 | 0.6104^{***} | 2.74 |

Household participates in both farm programs and off-farm work | −1.6431^{***} | −3.75 | 0.1030 | 0.49 |

Income inequality, G^{*} | -- | -- | 0.7355^{***} | 4.71 |

Farm is organized as a sole proprietorship | −2.1170^{***} | −2.74 | −0.6854^{**} | −2.49 |

Farm is partly-owned | −1.3689^{**} | −2.26 | −1.0139^{***} | −3.28 |

Farm is fully-owned | −1.3475^{*} | −1.94 | −1.1086^{***} | −3.43 |

Entropy index of farm commodity diversification | −0.9193 | −0.90 | −2.0660^{***} | −3.73 |

Farm has crop insurance coverage | 0.1783 | 0.34 | −0.0125 | −0.06 |

Economic size of farm: at least $250,000 in annual farm sales | 2.1651^{***} | 3.57 | −0.1759 | −0.53 |

Capital expenditures ($1000) per operated acres | 0.0941 | 1.11 | 0.0160 | 0.55 |

Capital expenditures per operated acres, squared | −0.0011^{***} | −2.74 | −0.0001 | −0.27 |

Farm is located in the Midwestern region | −0.2592^{**} | −2.50 | 0.1083^{**} | 2.11 |

Farm is located in the South region | −0.2227^{***} | −3.09 | −0.0160 | −0.43 |

County unemployment rate (%) | −0.0705 | −1.45 | −0.0676^{**} | −2.53 |

County unemployment rate, squared | 0.0046^{*} | 1.68 | 0.0030^{*} | 1.96 |

Year: 2004 | 0.0373 | 0.37 | 0.1164^{**} | 2.09 |

Year: 2005 | −0.0651 | −0.69 | 0.1162^{**} | 2.09 |

Year: 2006 | 0.0527 | 0.59 | 0.0210 | 0.38 |

Year: 2007 | 0.2385^{**} | 2.12 | 0.0536 | 1.04 |

Year: 2008 (financial crisis) | 0.2531^{**} | 2.27 | 0.0094 | 0.18 |

Year: 2009 | 0.1301 | 1.12 | 0.1103^{*} | 1.66 |

Year: 2010 | −0.0602 | −0.57 | 0.1507^{**} | 2.54 |

Year: 2011 | 0.0398 | 0.41 | 0.0794 | 1.35 |

Year: 2012 | 0.2217^{*} | 1.67 | 0.0557 | 0.79 |

R-squared | 0.3551 | 0.4261 | ||

F-statistic: model | 8.53^{***} | 10.00^{*** } |

Source: Authors’ calculations. Notes: ^{***}, ^{**} and ^{*} are 1, 5 and 10 percent significance levels, respectively.

participated only in farm programs disproportionately produced 24 percent of the total farm output with an average farm sales of $412,000, and received about one-third of all the farm program payments. The average equivalised (i.e., after adjusting for family size) net worth of these larger-sized farms in 2012 was two times their smaller-sized non-participating counterparts in the base-category with average farm sales of $138,000; at $1.5 million compared to $0.7 million, respectively, with 80 percent of the total wealth farm related while the remainder was non-farm related. The distribution of the farm component of total farm household equivalised net worth was less concentrated than the distribution of the non-farm component as indicated by the respective values of “adjusted” Gini coefficients of 0.5697 and 0.773. Accordingly, the finding of an increase in the wealth inequality due to an increase in the state’s share of farm households having a livelihood strategy of solely participating in farm programs is likely to originate from these households’ highly concentrated distribution of nonfarm wealth.

In light of the fact that non-farm net worth comprises nearly 30 percent of total farm household net worth, the finding of a likely increase in wealth inequality due to a an increase in the proportion of farm households who only participate in off-farm work may be the result of this group being more likely to invest off the farm than all other groups of farm households, including their non-participating counterparts, as was found by [^{14}. In addition, evidence from published research [^{15}. Among the factors with a positive association with wealth inequality, the dominant factor, based on both the estimated coefficient and its accompanying t-statistic, is income inequality. Similar findings were also obtained in an earlier study by Wolff [

The regression results in

Of the farm characteristics, and as in the case of the income inequality regression model, increase in the state’s share of farms organized as sole proprietorships and of farms that have either part- or full-ownership of their farmland are all factors that are inversely related with wealth inequality. Findings also show higher levels of enterprise diversification as measured by the entropy index―which is done by farmers as a way to manage financial risk and reduce income variability [^{16}.

^{2} using the Shapley value approach as described in Equation (3)^{17}. The first and third columns in the

With regard to life-cycle and human-capital factors that provide the principal theoretical foundation of the analysis, their combined contribution to the variation in income and wealth inequality was unexpectedly modest; at about 8 percent and 7 percent, respectively. Of the age and education categories, the ones denoting an age of “50 or older” and an education of a “college degree or beyond” appear to have the largest contribution to income and wealth inequality. The race factor denoting the proportion of white operators explained about 5 percent of the variation in income inequality and nearly twice this amount of the variation in wealth inequality.

Findings indicate significant differentials in the extent of explained variation attributed to the remaining control variables in the income and wealth inequality models. Of the variables denoting farm households’ livelihood strategies, the leading category in the income inequality model was a strategy of a household participating in “both farm programs and off-farm work”, while in the wealth inequality model it was when the household participates in “off-farm work only”, accounting for 4 percent and 6 percent of R^{2}, respectively. In terms of farm characteristics, the leading two variables in terms of their ability to explain the variation in income inequality were those denoting whether the farm was organized as a sole proprietorship and whether the farm was in the larger-sized farm group based on annual sales of at least $250,000, with shares of 16 percent and 14 percent of the overall R^{2}, respectively. Correspondingly, the two most important farm characteristic factors in the wealth inequality model were the extent of on-farm diversification as measured by the entropy variable and whether the farm was organized as a sole proprietorship, at 12 percent and 7 percent, respectively. The unemployment rate variable, which was used as a proxy for the health of the local labor market [^{2} compared to the share of 8 percent in the wealth inequality model. Similarly, the year- dummy variables contributed a share of 10 percent of the total variation in income inequality compared to a share of only 4 percent of the variation in wealth inequality. The “South” region and the year “2008” in which the financial crisis had occurred both were the most influential contributors to the variation in income inequality within their own groups; at about 11 percent and 3 percent, respectively. Perhaps the most notable finding in this ^{2}, which is the highest share among all others.

The paper has used a regression-based decomposition technique along with data from the 2003-2012 Agricultural Resource Management Survey to assess the extent that each of the factors considered in the analysis contributes towards explaining the state-level variation in income and wealth inequality.

Results from the estimation of the multivariate regression models and the ensuing decomposition of inequality show considerable heterogeneity in the drivers of income and wealth inequality and of the factors that contribute to their variation. With regard to attempts at capturing the drivers of income and wealth inequality, several points emerge. First, economic size of farm seems to matter the most to the disparity in income as an increase in the state’s proportion of farms with sales of $250,000 or more is found to have the largest single impact on the state’s indicator of income inequality. Other contributing factors to income inequality were those related to higher growth in a county’s unemployment rate and to the years 2007, 2008, and 2012. Second, a positive association is found between higher wealth-inequality and when farm households participate solely in farm programs or in combination with off-farm employment. A similar pattern of positive association is found between wealth inequality and factors such as income inequality, a higher growth rate in county’s unemployment, when the farm’s location was in a Midwestern region, and when farming was in the years 2004, 2005, 2009, and 2010.In terms of inequality decomposition, findings show the share of farms organized as sole proprietorships to be the

Variables | Income | Wealth | ||
---|---|---|---|---|

Shapley value | Percent | Shapley value | Percent | |

Constant | 0.0000 | 0.0000 | 0.0000 | 0.0000 |

Operator’s age: 35 - 49 | 0.0060 | 1.6946 | 0.0049 | 1.1880 |

Operator’s age: older than 50 | 0.0094 | 2.6450 | 0.0116 | 2.8182 |

Operator is white | 0.0186 | 5.2449 | 0.0419 | 9.7900 |

Operator has a high school or some college education | 0.0027 | 0.7664 | 0.0029 | 0.6440 |

Operator has college education or beyond | 0.0099 | 2.7814 | 0.0089 | 2.0494 |

Household participates in farm programs only | 0.0014 | 0.3981 | 0.0069 | 1.5996 |

Household participates in off-farm work only | 0.0109 | 3.0611 | 0.0268 | 6.1667 |

Household participates in both farm programs and off-farm work | 0.0155 | 4.3672 | 0.0097 | 2.1313 |

Income inequality, G^{*} | -- | -- | 0.0877 | 21.2616 |

Farm is organized as a sole proprietorship | 0.0580 | 16.3438 | 0.0306 | 7.1089 |

Farm is partly-owned | 0.0106 | 2.9923 | 0.0170 | 4.0421 |

Farm is fully-owned | 0.0114 | 3.2224 | 0.0270 | 6.2878 |

Entropy index of farm commodity diversification | 0.0112 | 3.1481 | 0.0522 | 12.0058 |

Farm has crop insurance coverage | 0.0113 | 3.1707 | 0.0035 | 1.0343 |

Economic size of farm: at least $250,000 in annual farm sales | 0.0510 | 14.3641 | 0.0024 | 0.8266 |

Capital expenditures ($1000) per operated acres | 0.0140 | 3.9459 | 0.0112 | 2.5640 |

Capital expenditures per operated acres, squared | 0.0024 | 0.6858 | 0.0008 | 0.1795 |

Farm is located in the Midwestern region | 0.0183 | 5.1630 | 0.0220 | 4.6856 |

Farm is located in the South region | 0.0376 | 10.5948 | 0.0159 | 3.7754 |

County unemployment rate (%) | 0.0100 | 2.8218 | 0.0126 | 3.0554 |

County unemployment rate, squared | 0.0100 | 2.8170 | 0.0111 | 2.6272 |

Year: 2004 | 0.0006 | 0.1734 | 0.0041 | 0.9523 |

Year: 2005 | 0.0053 | 1.4845 | 0.0039 | 0.8245 |

Year: 2006 | 0.0012 | 0.3480 | 0.0013 | 0.2968 |

Year: 2007 | 0.0061 | 1.7219 | 0.0017 | 0.3857 |

Year: 2008 (financial crisis) | 0.0098 | 2.7491 | 0.0022 | 0.5159 |

Year: 2009 | 0.0020 | 0.5720 | 0.0008 | 0.2066 |

Year: 2010 | 0.0043 | 1.2209 | 0.0027 | 0.6135 |

Year: 2011 | 0.0007 | 0.1971 | 0.0006 | 0.1536 |

Year: 2012 | 0.0046 | 1.3048 | 0.0012 | 0.2097 |

Total | 0.3551 | 100.00 | 0.4261 | 100.00 |

Source: Authors’ calculations using the REGO module in Stata.

most important contributor to the variation in income inequality. In comparison, income inequality is found to contribute the most to the variation in the disparity of wealth.

Findings point to a common yet inverse relationship between both income and wealth inequality and factors such as farm diversification, the share of farmers with college education or beyond, and the share of farms owned partly or fully. These findings underscore the value, in terms of policy perspectives aimed at lowering inequality in income and wealth, of investing in human capital and in maintaining a stable and well-diversified agricultural economy. These actions, in turn, would help in buttressing, respectively, the earning potential of farmers working on and off the farm and the value of farmland for landowners with their subsequent positive influence on households’ economic position. The fact that a similar inverse relationship was found between a livelihood strategy that involves participation by the farm household in off-farm work and income inequality, while at the same time a positive impact was found by this factor on wealth inequality, demonstrates the complexity of potential governmental policies that might be aimed at mitigating economic disparity. Specifically, while local, state, or Federal policies aimed at enhancing job opportunities for farmers in the local labor markets are shown to have an impact in reducing income inequality, these same policies can at the same time increase wealth inequality among farmers through their potential positive impact on the non-farm component of wealth by those farmers who tend to own a bigger share of these types of assets in their wealth portfolios [^{18}. In contrast, farm crop diversification, which is also used by farmers as one of the alternative tools to mitigate market and production risks in farming, was found to be inversely related to wealth inequality.

Like many of the standard income and wealth measures of inequality, the income measures presented in this paper represent a before-tax measure of income. At the same time, it seems clear that an after-tax measure of income may be a superior measure of well-being. The tax system, because of its progressive nature, tends to reduce overall income inequality. However, recent research has shown that changes in tax policy over the last decade, including lower marginal tax rates, especially for income from capital, has tended to reduce this effect, adding to the overall increase in income inequality during this period. Furthermore, farm operators tend to receive a larger share of their income in the form of capital gains than other taxpayers and farming also benefits from a variety of special tax provisions. Future research could explore the magnitude and trend over the last decade with regard to the effect of tax policy on income inequality for farm operator households and the effect of taxes on income inequality relative to all US households.

Hisham S. El-Osta,Ron L. Durst, (2016) Economic Inequality among US Farm Households: Assessment of the Role of the 2008-2009 Financial Crisis. Modern Economy,07,656-676. doi: 10.4236/me.2016.75071