Federal Budget Allocation in an Emergent Democracy. Evidence from Argentina.
José J. Bercoff Osvaldo Meloni [email protected] [email protected] Universidad Nacional de Tucumán, Argentina
Semimario Desarrollo Financiero y Economía Regionales Banco Central de la República Argentina 13 y 14 de Marzo de 2008 Feeding the National Budget
Federal Tax- Other Taxes Sharing (example: import and Agreement export duties) (Coparticipación) 43% 57% National Provinces Budget Public Expenditure in Argentina, 2004
PE, FF & other entities National Administration Budget
100% Law: $ 59,712 6%
48% 100% 8% National 90% 11% Administration 80% Expenditures 13% 70% Other
60% Debt Service Payroll 50% 50% 34% Pensions Provincial 40% Transfers Expenditures 30%
20% 34% 37% 10%
0%
Municipal Expenditures 9%
0%
Source: Abuelafia et al. (2005) Federal Budget Allocation. Average 1996-2004
Figures expressed in pesos of 2004 Provinces Real Per Capita Total Real Per Capita Real Per Capita Direct Budget Capital Expenditure Investment Underrepresented Advanced 977.8 83.7 10.7 Overrepresented 1264.5 155.3 45.5 Intermediate 1178.2 106.8 25.4 Poor 1154.3 153.0 47.4 Low density 1749.3 269.5 85.8
29% 85% 325%
Low density provinces receive more Underrepresented districts receive increasingly more money than Poor provinces resources as we move to less rigid components of the budget
Note: Underrepresented provinces: Buenos Aires, Córdoba, Mendoza, Santa Fe; Overrepresented provinces: Tucumán, Entre Ríos, Salta San Juan, San Luis; Formosa, Chaco, Santiago del Estero, Jujuy, Catamarca, La Rioja Misiones; Santa Cruz, Chubut, Río Negro, Neuquén, La Pampa, Tierra del Fuego. Advanced provinces: Buenos Aires, Córdoba, Mendoza, Santa Fe; Intermediate provinces: Tucumán, Entre Ríos, Salta San Juan, San Luis; Poor provinces: Formosa, Chaco, Santiago del Estero, Jujuy, Catamarca, La Rioja and Misiones; Low-Density provinces: Santa Cruz, Chubut, Río Negro, Neuquén, La Pampa, Tierra del Fuego. Real per capita Capital Expenditures. 1997 - 2004
Province Year Classification 1997 1998 1999 2000 2001 2002 2003 2004
Advanced 109.8 101.4 91.7 86.2 87.3 57.7 60.3 75.5
Intermediate 154.77 133.53 139.89 109.84 111.44 67.30 59.94 77.92
Low density 351.60 351.57 299.65 260.71 273.27 165.01 194.42 260.04
Poor 205.99 200.37 186.43 165.82 160.89 96.06 85.65 123.11
Per capita Capital Expenditures are The impact more affected than per capita Total of the Crisis Budget by the business cycle Pesos of 2004 Real per capita Capital Expenditure Average 1997-2005
References More than $500 From $301 to $400 Pesos of From $ 201 to $300 2004 From 101 to 200 Less than $100
Buenos Aires 98.8 Tucumán 79.9 Mendoza 77.9 Santa Fe 70.3 Córdoba 61.2 Province Standard Coefficient of Average Maximum Minimum deviation variation Santa Cruz 522.5 347.2 0.66 1446.4 188.7 La Rioja 385.0 167.5 0.44 771.5 220.5 Real per Córdoba 61.2 24.9 0.41 121.3 29.3 capita San Juan 163.1 65.5 0.40 252.5 71.0 Capital Corrientes 135.5 53.9 0.40 215.2 68.3 Expenditures Santiago del Estero 128.0 49.0 0.38 185.1 54.6 Santa Fe 70.3 25.6 0.36 124.5 35.3 Formosa 201.7 72.9 0.36 340.8 100.5 San Luis 205.8 72.5 0.35 312.4 93.9 Pesos of 1999 Tucumán 79.9 27.3 0.34 127.2 39.6 Tierra del Fuego 583.1 197.3 0.34 931.9 251.1 Entre Ríos 100.9 32.8 0.32 153.0 52.2 Salta 104.7 32.3 0.31 145.1 56.8 Catamarca 193.0 59.2 0.31 262.2 100.2 Chaco 115.1 33.8 0.29 154.4 64.3 Neuquén 215.0 62.6 0.29 302.5 122.2 Chubut 223.9 64.0 0.29 300.3 132.8 Mendoza 77.9 21.4 0.28 112.3 50.0 Country 114.0 27.7 0.24 151.9 71.8 Misiones 113.2 27.3 0.24 145.5 66.8 Jujuy 181.8 43.4 0.24 239.5 125.2 Río Negro 223.9 51.1 0.23 305.0 155.3 Buenos Aires 98.8 18.9 0.19 122.9 69.7 La Pampa 338.5 61.0 0.18 409.7 209.4 Models of Distribute Politics
• Models stressing the role of Congress.
• Theories emphasizing the importance of Political Parties.
• Models relying on the democratic system rules. Models stressing the role of Congress
House and Senate leaders, committees chair, caucus leaders and certain representatives with a high profile and acceptance among voters are able to obtain a substantial share of the available federal founds for their jurisdictions.
Federal outlays allocation should be driven by a pork-barreling scheme. Models relying on the democratic system rules
The number of districts and the number of delegates per district for the Congress established by the law and the Constitution are key elements to explain policy. Senators from small states have voting power equal to that of senators from large states Small states outnumber large states. Differences in states sizes mean that all states are not equally costly to program budgets, but each senator’s vote is of the same value to the coalition. A small-state senator’s vote may be influenced at a lesser cost to the program budget than that of a larger-state senator. Theories emphasizing the importance of Political Parties
View of Political Parties as actors maximizing some mix of policy and reelection goals, Î controlling the budget process is crucial to their objectives.
Same-party politicians cooperation could be limited, because conflicting preferences. However, they may be able to direct resources to their constituency. National Budget Process
• Stages
1. Proposal (Executive)
2. Discussion and Enactment (Congress)
3. Implementation (Executive)
4. Control The Budget Game in Argentina Key players in the national budget negotiation scheme: 1. The President 2. The Governors and/or Local Party Bosses. 3. The Congress. 3.1. Committee Chairs and specially, the Appropriation Committee chair. 3.2. Senior Congressmen and Senators.
The Congress is the only branch of government entitled by Law to modify total expenditure and the total authorized debt level. Only the Congress can modify the economic classification of expenditures and its objective and function. Argentinean Congress Characteristics
Legislators obtain a relevant position in the ballot list appointed by the provincial leader. Therefore, they have little incentives to specialize in committees like their U.S. colleagues.
Amateur legislator, professional politicians (Jones, Saiegh, Sanguinetti &Tommasi, 2002) Implementation Stage
De facto practices carried out by the Executive at the implementation stage erode the prerogatives of the Congress in the budget process thus inhibiting its participation in the distribution of budgets expenditures.
Two main ways:
by changing the approved budget by means of decrees.
by using the administration of the quota system in a discretional manner. Model
FFi, j = f (PIi, j , Eij )
• FFi,j are the real per capita federal expenditures contained in the National Budget Law for province i in year t.
• PIi,t are the political and institutional variables for province i in year t.
• Ei,t are the economic variables for province i in year t. Dependent Variables
PCTE it: is the real per capita Total Expenditure contained in the National Budget Law for province i in year t. (in 1999 constant pesos)
PCCE it: is the real per capita Capital Expenditure for province i in year t. (in 1999 constant pesos)
PCDIit: is the real per capita Direct Investment for province i in year t. (in 1999 constant pesos) Democratic System Rules & Congress Overrepresentation
OVERREPSEN it: number of senators per capita in province i in year t. OVERREPHOUit: number of House Representatives per capita in province i in year t. Seniority
SENATE it: Share of senators with at least 7 years in Congress for province i in year t, in percentage terms.
HOUSE it: Share of congressmen with at least 5 years in Congress for province i in year t, in percentage term. Committees
COMMITTEESEN it: Share of senators holding the presidency of a committee for province i in year t in percentage term.
COMMITTEEHOU it: Share of deputies president of a committee for province i in year t in percentage term.
APPROPRIATIONSEN it: Dummy variable that takes the value 1 for the president of the Appropriations Committee in the Senate and zero, otherwise, for province i in year t.
APPROPRIATIONHOU it: Dummy variable that takes the value 1 for the president of the Appropriations Committee in the House and zero, otherwise, for province i in year t. Political Parties & President-Governors influence
SAMEPARTY it: Dummy variable that takes the value 1 when the governor has the same political affiliation as the president or it is political allied and zero otherwise for province i in year t.
PRESIDENT it: Dummy variable that takes the value 1 for the president home province and zero otherwise for province i in year t.
VOTES it: difference (in percentage terms) between the votes obtained by the president’s incumbent party and the main rival party in presidential and representatives elections in province i in year t.
ALIGNEMENT it: Alignment between the senator and the governor. This is a dummy variable equal to one when the party affiliation of the governor and two of the senators (representing the majority) of the district are the same and zero otherwise for province i in year t. Control Variables
GDP it: Gross domestic product for province i in year t.
POP it: Population of province i in year t.
UNEMP it: Average unemployment rate in the year when the budget is discussed, for province i in year t.
POVERTY it: Percentage of the population under the poverty line in province i in year t (the year the budget is discussed).
CYCLE it: Rate of growth of the Gross Domestic Product of the country in year t. Estimation Technique and Data
Dynamic panel technique developed by Arellano and Bond .
Panel data includes all 23 provinces and 9 years (from 1996 to 2004).
The Federal District (Capital Federal) is excluded. Variable I II III PCCE (t-1) 0.45** 0.64* 0.68** (1.53) (1.95) (2.21) OVERREPSEN -139.46 -141.60 (-0.95) (-1.01) Results OVERREPHOU -276.58** -259.52*** (-2.52) (-2.85) COMMITTEESEN 4.85 (1.01) COMMITTEEHOU 11.09** (2.06) Explaining APPROPRIATIONSEN -2.49 (-0.07) Real Per APPROPRIATIONHOU -31.52 (-1.07) Capita Capital SENATE 1.69 (0.69) HOUSE -6.27 Expenditures (-1.11) ALIGNMENT 25.66* 40.26* 53.12** across (1.48) (1.65) (1.98) PRESIDENT 254.04** 232.70** 233.06** (2.35) (2.12) (2.45) provinces. SAMEPARTY 29.44** 27.21*** 33.13* (1.99) (1.79) (1.94) 1996- 2004 VOTES 0.81 0.40 0.35 (1.01) (0.56) (0.51) ELECYEAR 8.84 19.94* 16.23 (0.79) (1.70) (1.51) VFI -0.10 0.89 -0.14 (-0.60) (0.50) (-0.12) GDPPC 20.60*** 46.81** 42.84** (2.64) (2.38) (2.10) CYCLE 5.04*** 4.99*** 4.90*** (4.62) (4.47) (4.58) UNEMP 3.17** 3.93** 4.07** (2.44) (2.48) (2.54) Constant 10.07* 8.67* 9.08*** (1.93) (1.88) (1.86) Observations 161 161 161 F Statistic F(10, 150)= F(12, 148)= F(18, 142)= 50.71 321.74 531.54
Variable I II III PCCI (t-1) -0.19*** -0.18*** -0.22** (-3.71) (-2.97) (-2.48) OVERREPSEN -163.65 -105.05 (-1.07) (-1.17) Results OVERREPHOU -175.25*** -180.20*** (-2.68) (-2.59) COMMITTEESEN 8.69 (0.95) COMMITTEEHOU 5.78** Explaining (2.43) APPROPRIATIONSEN -26.61 Real Per (-1.00) APPROPRIATIONHOU -14.40 Capita Direct (-0.61) SENATE -0.95 (-0.66) Investment HOUSE -7.03 (-1.11) across ALIGNMENT 27.66 39.22* 40.04** (1.58) (1.82) (2.13) provinces. PRESIDENT 120.86*** 115.20*** 100.39*** (3.46) (3.85) (3.01) SAMEPARTY 2.55 -5.18 -2.46 1996- 2004 (0.25) (-0.80) (-0.27) VOTES 1.67** 1.46*** 1.38*** (2.91) (2.84) (2.64) ELECYEAR -7.39 1.08 3.16 (-0.79) (0.16) (0.32) VFI 0.51 1.04 -0.05 (0.24) (0.50) (-0.04) GDPPC -12.39 18.14 24.31 (-0.65) (1.19) (1.10) CYCLE 4.43*** 4.38*** 4.53*** (4.71) (4.17) (4.17) UNEMP 2.35*** 2.98*** 3.32*** (2.61) (3.72) (3.99) Constant -5.10 -6.20*** -14.64*** (-1.53) (-1.82) (-2.90) Observations 161 161 161 F Statistic F(10, 150)= F(12, 148)= F(18, 142)= 25.09 22.44 230.54 Conclusions
1. The disparity observed in the allocation of the federal funds among districts cannot be analyzed using the same factors observed in studies examining the U.S case.
2. Our results and estimations confirm the presumption of previous descriptive analysis of the Argentine Legislative Power. The distribution of the national budget is mainly dominated by the action of the Executive and the governors.
3. Presidents favor their home provinces, the districts where they get the higher difference with the main rival parties and provinces administered by governors affiliated to their same party. Conclusions
4. The importance of the lagged dependent variable supports the choice of the Arellano-Bond method of estimation.
5. Unlike some findings for the U.S., neither the overrepresentation variables nor the congressional theories find support in our sample. The only congressional indicator with usual levels of significance is the Chair of the Appropriations Committee of the Senate
6. Our findings reinforce the idea that a different framework is needed for analyzing the case for an emergent democracy. The understanding of these mechanisms are central in a Federal Republic where the distribution issue is a complex process. Federal budget allocation in an emergent democracy. Evidence from Argentina
José J. Bercoff Osvaldo Meloni Universidad Nacional de Tucumán Universidad Nacional de Tucumán [email protected] [email protected]
ATINER, Athens August, 2007 Real per capita Total Budget. 1997 - 2004
Province Year Classification 1997 1998 1999 2000 2001 2002 2003 2004
Advanced 1096.6 1024.0 1009.9 988.1 999.3 861.0 918.8 924.5
Intermediate 952.08 1399.33 1368.01 1132.45 1669.16 977.16 937.77 990.02
Low density 1941.62 1944.30 1828.62 2077.62 2007.96 1335.76 1367.66 1491.18
Poor 1102.39 1244.80 1196.47 1245.05 1504.90 967.98 927.91 1044.60
Pesos of 2004 Argentina
Federal Republic Presidential System (as the U.S.) 24 districts: 23 provinces + federal district (city of Buenos Aires) Regained democracy in 1983 after several coup d’etat (1930, 1943, 1955, 1962, 1966, 1976) Authors Country Period Main Findings Atlas, Gilligan U.S.A. 1972 - - Over representation, especially in the Senate, play an and 1990 important role in the allocation of federal net spending Hendershott among U.S. states. Some (1995) previous Levitt and U.S.A. 1984- - The federal domestic outlays are skewed in favor of empirical Snyder (1995) 1990 Democratic districts. results on - A substantial role for political parties is found although the capabilities of parties are limited. distributive Alvarez, and U.S.A. 1989- - Strong and systematic evidence of pork-barrel activities politics Saving, (1997) 1990 by committee members. Lee (1998) U.S.A. 1984- - Federal distributive programs are typically constructed 1990 so that a majority of states benefit. - Over-represented states, in the Senate, tend to receive higher federal allocations of federal funds per capita, especially in programs characterized as non-discretionary distributive. Knight (2004) U.S.A. 1995- - Positive relationship between per capita spending and 2003 per capita delegates. Larcinese, U.S.A. 1982- - Positive relationship between overrepresentation and Rizzo and Testa 2000 the dependent variables. (2005) - States with Governors affiliated to the same political party as the President receive more federal funds. - Evidence of substantial budgetary power of the President. - Senior members have disproportionate impact on grant allocation
Arellano- Bond
Dynamic panel-data models allow past realizations of the dependent variable to affect its current level.
Consider the model: yit = y it-1 a1 + ... + y it-p ap + xit b1 + wit b2 + vi + eit i ={1,...,N{c )-}; t ={1,...,Ti},
Where: the a1,..., ap are p parameters to be estimated
xit is a (1 x k1) vector of strictly exogenous covariates b1 is a (k1 x 1) vector of parameters to be estimated wit is a (1 x k2) vector of predetermined covariates b2 is a (k2 x 1) vector of parameters to be estimated vi are the random effects that are independent and identically distributed (iid) over the individuals with variance sv*sv and eit are iid over the whole sample with variance se*se. The vi and the eit are assumed to be independent for each i over all t. First differencing the above equation removes the vi and produces an equation that can be estimated using instrumental variables.
Arellano and Bond derive a GMM estimator for a1,..., ap, b1, and b2 using lagged levels of the dependent variable and the predetermined variables and differences of the strictly exogenous variables. This method assumes that there is no second-order autocorrelation in the eit. Variable Obs Mean. Std. Dev Min Max PCTE 207 1553.646 1210.305 561.5599 11642.77 PCCE 207 196.6348 169.8028 29.33949 1446.378 Descriptive PCDI 207 60.56001 96.42177 1.143301 942.4429 Statistics OVERREPHOU 207 1.210506 .9744692 .4922691 5.971134 OVERREPSEN 207 .6094918 .6452734 .0210972 3.58268 COMMITTEEHOU 207 4.347826 7.490767 0 43.58974 COMMITTEESEN 207 4.347826 2.284571 0 12.5 APPROPRIATIONSEN 207 .0434783 .2044255 0 1 APPROPRIATIONHOU 207 .0386473 .1932203 0 1 HOUSE 207 4.35229 8.037814 0 45.45454 SENATE 207 4.371981 3.517866 0 15 VOTES 207 -.0628753 23.91162 -86.1087 71.59772 PRESIDENT 207 .0338164 .1811946 0 1 SAMEPARTY 207 .5652174 .4969302 0 1 ALIGNMENT 207 .763285 .4260963 0 1 GDP 207 8.072295 17.72242 .7038051 95.9373 POP 207 1.438913 2.728455 .0837362 14.21986 UNEMP 207 12.65741 4.394074 1.9 23 CYCLE 207 .5555556 .4981086 0 1 POVERTY 207 41.21132 14.77928 11.39998 73.5
Covariance Matrix
OVERREP OVERREP COMMITT COMMITT APPROPRI APPROPRIATI PRESID SAMEPAR ALIGNMEN SENAT PCTE PCCE PCDI VOTES UNEMP GDP POP HOUSE CYCLE POVERTY HOU SEN EEHOU EESEN ATIONSEN ONHOUN ENT TY T E PCTE 1.0000 PCCE 0.6933 1.0000 PCDI 0.3829 0.8062 1.0000 VOTES 0.2400 0.4577 0.4221 1.0000 UNEMP -0.1867 -0.4128 -0.3057 -0.2388 1.0000 GDP -0.1630 -0.2038 -0.1569 -0.0163 0.3253 1.0000 POP -0.2064 -0.2595 -0.1906 -0.0366 0.3561 0.9885 1.0000 OVERREPHOU 0.8398 0.7320 0.3686 0.2579 -0.2776 -0.2277 -0.2787 1.0000 OVERREPSEN 0.8087 0.7523 0.3990 0.2511 -0.3312 -0.2816 -0.3351 0.9840 1.0000 COMMITTEEHO -0.1928 -0.2106 -0.1427 -0.0681 0.2864 0.9142 0.9090 -0.2471 -0.2938 1.0000 U COMMITTEESE -0.0942 0.1034 0.2176 0.0670 -0.1352 -0.0332 -0.0271 -0.1097 -0.0525 -0.0154 1.0000 N APPROPRIATIO 0.0050 0.1403 0.0799 0.0140 -0.0138 -0.0488 -0.0648 0.0652 0.1032 -0.0558 0.1874 1.0000 NSEN APPROPRIATIO -0.0532 -0.0790 -0.0496 -0.0228 0.1225 0.0441 0.0308 -0.0748 -0.0909 0.0632 0.1105 0.0802 1.0000 NHOUN PRESIDENT 0.1991 0.4141 0.4508 0.3386 -0.1391 0.0472 0.0526 0.1220 0.1329 0.0079 0.2778 -0.0399 -0.0375 1.0000 SAMEPARTY -0.1070 0.0186 0.1115 0.0100 -0.1148 0.0832 0.0934 -0.1037 -0.0921 0.0737 0.1056 0.0914 0.1253 0.1641 1.0000 ALIGNMENT 0.0342 0.0859 0.0918 0.0485 -0.1045 0.1430 0.1169 0.0372 0.0951 0.0566 0.1968 0.1187 -0.0652 0.1042 0.1535 1.0000 HOUSE -0.1849 -0.2128 -0.1609 -0.0205 0.3145 0.9704 0.9713 -0.2386 -0.2867 0.8895 0.0051 -0.0235 0.0565 0.0409 0.0974 0.1642 1.0000 SENATE -0.0524 -0.0560 0.0127 -0.1090 0.0799 0.1860 0.2041 -0.1847 -0.1642 0.1435 0.0980 0.1433 -0.1343 0.2410 0.1004 0.0405 0.1410 1.0000 CYCLE 0.0045 0.2177 0.2636 0.2941 -0.2475 0.0103 -0.0027 0.0137 0.0146 -0.0000 -0.0000 0.0000 0.0280 0.0598 0.1373 0.0508 0.0005 0.0062 1.0000 POVERTY -0.4756 -0.5006 -0.2917 -0.3107 0.3318 -0.1439 -0.0524 -0.4929 -0.4639 -0.0412 0.0214 -0.1024 -0.0165 -0.1056 0.1117 -0.0972 -0.0934 0.0801 -0.0805 1.0000
Lagged dependent variable: statistically significant but surprisingly, the sign of the coefficient is negative indicating the existing of some compensating mechanism from one year to another.
Main determinants of the total budget allocation: overrepresentation variables. However, negative sign indicates underrepresentation instead of the expected overrepresentation. (could be interpreted as an indicator of the weakness of the Congress -the Senate is the chamber where overrepresentation is strongest)
Except for the chair of the Appropriations Committee of the Senate and the seniority of senators, the rest of the variables designed to capture the importance of Congress fail.
The variables representing the importance of the Executive Power, the Incumbent Party and the Governors also fail.
Control variables: only Population and Poverty performed well. Overrepresentation variables turn out to be not significant and the ones capturing the importance of the President, the Governors and the incumbent Party gain ground. The most important determinants of the federal funds distribution amongst provinces are related to the action of the Executive Power and the Governors. (PRESIDENT , SAMEPARTY, VOTES, (ALIGMENT) SAMEPARTY variable can also be interpreted as one belonging to the theories emphasizing the party strength in the budget process. The only congressional variables that show statistically significant behavior are APPROPRIATIONSEN and SENATE. Implying that quality matters. The lagged dependent variable in all four models shows that there is a great deal of inertia in the budget process. Except for the GDP and CYCLE, the control variables do not perform well. The sign of the business cycle variable denotes that per capita capital expenditures are sensitive to expansions and recessions. Authors Country Period Dependent Main Findings Variable
Atlas, Gilligan U.S.A. 1972 - Real per Over representation, specially in the Senate, play and 1990 Capita Federal an important role in the allocation of federal net Hendershott Net Outlays. spending among U.S. states. Some (1995) previous Levitt and U.S.A. 1984- Federal Outlay The federal domestic outlays is skewed in favor empirical Snyder (1995) 1990 per District. of Democratic districts. A substantial role for political parties is found results on although the capabilities of parties are limited. distributive Alvarez, and U.S.A. 1989- New outlays Strong and systematic evidence of pork-barrel politics Saving, (1997) 1990 for each activities by committee members. district.
Lee (1998) U.S.A. 1984- Per Capita Federal distributive programs are typically 1990 Outlay per constructed so that a majority of states benefit. District. Over-represented states, in the Senate, tend to receive higher federal allocations of federal funds per capita, specially in programs characterized as non discretionary distributive.
Knight (2004) U.S.A. 1995- Per Capita Positive relationship between per capita 2003 Spending. spending and per capita delegates.
Larcinese, U.S.A. 1982- 1. Real per Positive relationship between over Rizzo and 2000 Capita Federal representation and the dependent variables. Testa (2005) Expenditure. States whose Governor has the same political
affiliation than the President receive more 2. Real per federal funds. Capita Outlays by Program. Evidence of substantial budgetary power of the President. Senior members have disproportionate impact on grant allocation