Term Paper Topic: Agricultural Subsidies and Corporate Farms

Federal subsidies are a contentious point in public economics.  They are supposed to benefit small businesses in theory but in reality large corporations are usually the ones that gain the most.  This paper seeks to determine if there is any advantage to granting federal subsidies.  An analysis of the impact federal subsidies (independent variable) have on the amount of corporate taxes paid (dependent variable) will determine the extent of the social benefits of subsidies.  Subsidies in effect lower the cost of production which implies that, other things equal, profits should increase for a given quantity of output.  Following this logic larger profits should result in an increase in the amount of taxes paid, ceteris paribus.  Higher tax revenues would allow Congress to lower inefficient taxes, such as those associated with labor, retire debt, or put this extra money towards any number of government projects.  Thus while large corporations stand to benefit from subsidies, which are themselves inherently inefficient, society may stand to benefit from them as well.  In particular, this paper will focus on federal agricultural subsidies because they are among the largest and most scrutinized subsidies, and also to narrow the scope of the research.  To address policy implications, I will create a regression to determine if subsidies increase output and worker efficiency.  Measuring output will help determine the source of corporate taxes paid; the more produce large farms sell, the more profit is generated and hence higher tax bill.  Measuring subsidies against worker efficiency will help infer whether corporate farms use the money from the subsidy to increase production efficiency.

There are several forces that may influence the regression.  Rainfall is important because crops cannot grow without water.  Years with higher average rainfall will likely lead to higher output and thus have a positive effect on the amount of taxes paid.  The corporate tax rate may also be problematic.  Changes in the tax rate will affect the amount corporate farms have to pay so years when taxes are low will lower the amount of taxes collected by the government.  There are also random shocks such as an insect outbreak which could adversely affect crop yield and thus taxes paid, as well as others that may have a positive effect.  Then there are variables such as consumer taste which may be hard to measure yet still impact the model.  We also cannot forget that federal tax revenues from other sources (i.e. income) play an important role in the size of the subsidy; the political climate may potentially affect the model.  When looking at worker efficiency, the role of technology is important.

Year Federal Agricultural Subsidies   (billions) Corporate Taxes Paid by   Agricultural Sector (millions) Real Farm Sector Output (index   numbers, 2005=100) Net Farm Value Added (index   numbers 2005=100) Total Factor Productivity (indexed to 2005)
1960 0.7 48 40.291 16.803 0.48
1961 1.5 62 41.29 17.008 0.49
1962 1.7 54 42.077 16.601 0.49
1963 1.7 65 43.628 17.169 0.5
1964 2.2 70 42.907 16.4 0.51
1965 2.5 81 45.159 17.542 0.52
1966 3.3 75 45.39 16.596 0.51
1967 3.1 54 47.915 18.043 0.53
1968 3.5 75 47.721 17.08 0.54
1969 3.8 86 49.244 17.747 0.55
1970 3.7 62 50.575 18.285 0.54
1971 3.1 91 52.537 19.413 0.58
1972 4 117 53.908 19.271 0.58
1973 2.6 271 54.857 18.818 0.6
1974 0.5 197 51.69 18.147 0.56
1975 0.8 266 55.019 22.295 0.61
1976 0.7 220 56.028 20.763 0.6
1977 1.8 232 58.525 21.98 0.63
1978 3 295 61.292 20.749 0.6
1979 1.4 319 64.96 22.991 0.62
1980 1.3 345 62.214 21.894 0.6
1981 1.9 254 66.901 32.834 0.67
1982 3.5 80 67.963 35.479 0.69
1983 9.3 118 57.747 18.58 0.6
1984 8.4 261 66.696 30.537 0.71
1985 7.7 228 71.203 41.231 0.75
1986 11.8 388 69.854 39.744 0.74
1987 16.7 368 72.647 41.081 0.75
1988 14.5 399 69.68 35.446 0.72
1989 10.9 497 73.907 42.956 0.78
1990 9.3 462 77.126 47.181 0.81
1991 8.2 389 78.195 48.483 0.82
1992 9.2 458 82.272 59.788 0.88
1993 13.4 515 80.352 50.339 0.83
1994 7.9 501 87.781 65.374 0.89
1995 7.3 444 84.146 48.244 0.82
1996 7.3 502 85.845 58.181 0.89
1997 7.5 506 91.838 66.149 0.91
1998 12.4 445 92.382 61.498 0.9
1999 21.5 470 94.494 65.249 0.9
2000 23.2 449 95.684 79.66 0.94
2001 22.4 317 94.308 71.209 0.95
2002 12.4 99 93.926 75.122 0.94
2003 16.5 516 97.879 88.338 0.97
2004 13 762 99.475 95.736 1.02
2005 24.4 853 100 100 1
2006 15.8 709 100.182 94.778 1.01
2007 11.9 761 101.571 81.636 0.99
2008 12.2 336 101.382 97.9 1.04
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