POLITICAL INSTITUTIONSAND AGRICULTURAL TRADE INTERVENTIONS IN AFRICA

POLITICAL INSTITUTIONSAND AGRICULTURAL TRADE INTERVENTIONS IN AFRICA
ROBERT H. BATES AND STEVEN BLOCK
Political Representation
In the absence of electoral competition to affect policy, agents must lobby. Policy is then the result of elite choice, made in response to interest group competition.
Given the distribution of population and economic activity in poor nations—such as those in Africa—farmers should be relatively disadvantaged in efforts to in?uence
public policies,by comparison with city dwellers and urbanindustries.Thatthisissohelpstoaccount foranimportantirony:thatincountrieswhere farmers are numerous and
agriculture represents the single largest industry, governments choosepoliciesthatconstituteataxonfarmers. The reasoning that resolves this paradox
arisesfromthelogicofcollectiveaction(Olson 1971;Bates 1981).When rural dwellers constitutealargepercentageofthenationalpopulation,agricultural production tends to lie
in the hands of a large number of small producers dispersed throughout the countryside. As no singleproducercanin?uencegovernmentpolicy, and as organizing so large and
diverse a population is costly, the individuals’ incentive to lobby is weak. In countries with large agricultural populations,agriculture should therefore constitute
an ineffective interest group. In addition, in the early stages of structural change, in most countries, a few large ?rms dominate in each industry, typically as the
result of government protection,the tax treatment of capital, and the size of the market. Compared with rural producers, then, urban
Robert H. Bates, Eaton Professor of the Science of Government, Department of Government, Harvard University, 1737 Cambridge St., Cambridge, MA 02138. Phone +1 617 496
0919 robert.bates.harvard.edu@gmail.com.Thisarticlewaspresentedin an invited-paper session at the 2010 annual meeting of theAllied Social Science Associations in
Atlanta, GA. The articles in these sessions are not subjected to the journal’s standard refereeing process.
interestswouldexperiencestrongincentivesto lobby and low costs when doing so. Consumers should therefore hold a relative advantage as lobbyists in countries with large
agricultural populations. And we therefore expect governments in countries with large agricultural sectors to adopt relatively adversepoliciestowardfarming.However,the
very factors—size and dispersal—that render farmers weak lobbyists can render them powerful in electoral settings (Bates 2007).Where representation is achieved through
electoral channels and rural dwellers constitute a large segment of the voting population, politicians encounter powerful incentives to cater to the interests of
farmers. In environments with electoral competition, politicians encounter electoral incentives that would impel them to resist the political pressures emanating from
urban consumers.
Explaining Policy Choices inAfrica: Regression Results
This section draws upon these insights and, using both parametric and nonparametric methods, tests the implications that ?ow from them. Of central interest are the
correlates of the relative rates of assistance (RRAs) for agriculture versus non-agriculture and the nominal rates of assistance for agricultural importables and
exportables (tables 1–3). Anderson et al. (2008) construct RRA as the ratio of nominal trade protection accorded agriculturetothenominalprotectionaccorded non-
agriculture (minus 1). Thus, an RRA >0 indicates a policy regime that favors agriculturerelativetonon-agriculture(andviceversa for RRA <0). Each table reports four
sets of estimates, two (in columns 1 and 2) drawn from ordinary least squares (OLS) models (withandwithoutaninteractionbetweenrural
Amer. J.Agr. Econ. 93(2):317–323;doi:10.1093/ajae/aaq080 Published online December 9,2010 ©TheAuthor (2011). Published by Oxford University Press on behalf of
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318 January 2011 Amer. J.Agr. Econ.
Table 1. Determinants of Relative Rate ofAssistance (RRA),1975 to 2004 1 2 3 4 OLS OLS REa Syst GMMb Rural pop. share -0.0002 -0.003 -0.003 -0.002 (0.006)( 0.006)(
0.006)( 0.003) Elecomp dummy 0.072 -0.414 -0.547 -0.475 (0.052)( 0.298)( 0.268)** (0.162)** Log real GDP per cap 0.068 0.075 0.075 0.041 (0.070)( 0.073)( 0.065)(
0.040) Landlocked dummy -0.263 -0.278 -0.285 -0.163 (0.118)** (0.121)** (0.120)** (0.067)** Resource-rich dummy 0.130 0.142 0.156 0.094 (0.098)( 0.102)( 0.105)( 0.062)
Arable land share of total 0.017 0.017 0.017 0.008 (0.003)*** (0.003)*** (0.003)*** (0.002)*** Elecomp×rural pop shr 0.007 0.009 0.007 (0.005)( 0.004)** (0.003)** RRA
(t-1) 0.467 (0.107)*** Constant -0.934 -0.781 -0.737 -0.297 (0.861)( 0.864)( 0.799)( 0.443) Observations 375 375 375 373 R-squared 0.52 0.53 0.53 Total effect of:
Rural pop. share w/ comp. elections 0.004 0.006 0.005 (0.006)( 0.005)( 0.003)† Comp. election,w/ rural pop shr=50% -0.063 -0.100 -0.142 (0.086)( 0.069)( 0.041)***
Comp. election,w/ rural pop shr=85% 0.182 0.213 0.090 (0.105)* (0.103)** (0.063) Robuststandarderrors(clusteredbycountry)inparentheses.*signi?cantat10%;**signi?
cantat5%;***signi?cantat1%. †P=0.113.aRandom-effectsmodel; bone-step system GMM.Year dummies not reported. Source:Authors’ calculations.
population share and electoral competition), one drawn from a random effects model (column 3), and the last drawn from a system generalized method of moments (GMM)
model (column 4).The models include several control variables: per capita income (in logs), theextentofarableland,andthegeographical situation of the country, with
coastal location serving as the reference category. Before commenting on the tests of our hypotheses, we ?rst note the coef?cients on the control variables. Those in
tables 1 and 2 con?rm the absence of a relation between the measure of per capita income, RRAs, and nominal rates of assistance for importables (most of which are
food). In table 3, by contrast,the coef?cient on income is positive and signi?cant in all models,indicating that,as discussed below, the political logic in?uencing
governmentpolicytowardexportcropsdiffers from that for food crops. The regressions in table 1 indicate that landlocked countries substantially favor the interests of
other sectors over those of agriculture. In addition, we ?nd
(intable2)thatresource-richcountriestendto lower the domestic price of importables (i.e., food) by comparison with the policy stance assumed in coastal economies (the
category excluded from our speci?cation). The results in tables 1 and 2 also suggest that the policy orientationofgovernmentstowardagriculture does indeed vary
positively with the share of land that is arable, a proxy for the overall importance of farming.
Rural Population Share and Political Institutions
We expect government policies toward agriculture to be more adverse to the interests of producers the greater is the rural dwellers’ share of the population, with this
effect being conditional on the nature of the party system. Asanindicatorofthecountry’spartysystem, we employ the Executive Index of Electoral
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Table 2. Determinants of Nominal Rate of Assistance for Agricultural Importables, 1975 to 2004 1 2 3 4 OLS OLS REa Syst GMMb Rural pop. share -0.016 -0.019 -0.017 –
0.007 (0.006)** (0.007)** (0.013)( 0.003)** Elecomp dummy 0.198 -0.335 -0.438 -0.217 (0.058)*** (0.541)( 0.560)( 0.277) Log real GDP per cap -0.141 -0.133 -0.151 –
0.054 (0.100)( 0.105)( 0.121)( 0.038) Landlocked dummy -0.071 -0.086 -0.103 -0.032 (0.123)( 0.128)( 0.166)( 0.055) Resource-rich dummy -0.440 -0.426 -0.325 -0.120
(0.116)*** (0.116)*** (0.184)* (0.035)*** Arable land share of total 0.034 0.034 0.027 0.008 (0.003)*** (0.003)*** (0.004)*** (0.002)*** Elecomp×rural pop shr 0.008
0.009 0.004 (0.008)( 0.008)( 0.004) NRA_agimpt (t-1) 0.675 (0.087)*** Constant 2.102 2.269 2.285 0.834 (1.096)* (1.137)* (1.736)( 0.428)* Observations 375 375 375 374
R-squared 0.42 0.43 0.42 Total effect of: Rural pop. Share w/ comp. elections -0.011 -0.008 -0.003 (0.007)( 0.013)( 0.003) Comp. election,w/ rural pop shr=50% 0.049
0.035 -0.0001 (0.149)( 0.151)( 0.074 Comp. election,w/ rural pop shr=85% 0.319 0.367 0.152 (0.148)** (0.163)** (0.077)*
Robust standard errors (clustered by country) in parentheses.*Signi?cant at 10%;**signi?cant at 5%;***signi?cant at 1%. aRandom effects model;bone-step system GMM.Year
dummies not reported. Source:Authors’ calculations.
Competitiveness (EIEC), which measures the level of competition that occurs during the executive selection process (Ferree and Singh 2002; Beck, Keefer, and Clarke
2008). The indicator consists of seven levels: level 1: no executive exists; level 2: executive exists but was not elected; level 3: executive is elected but was the
sole candidate; level 4: executive is elected, and multiple candidates competed for the of?ce; level 5: multiple parties were also able to contest the executive
elections; level 6: candidates from more than one party competed in executive elections,but the presidentwonmorethan75%ofthevote;andlevel 7: candidates from more than
one party competed in executive elections,but the president wonlessthan75%ofthevote.Wedeemaparty system competitive when the EIEC score is greater than 6; the dummy
variable Elecomp is then equal to one,and zero otherwise.1
1 Notethatweomitallconsiderationofthe“quality”ofelectoral competition,including whether elections have been deemed“free
Estimation Strategy Our generic speci?cation is: yit =a+?1Elecompit +?2Rurpopshare(1) +?3(Elecomp*Rurpopshare)it +Xitß +?i +eit whereyit
isoneofourkeypolicyindicatorsfor country i in year t,Rurpopshare is the share of a country’s population living in rural areas, X is a vector of the control variables
from our baseline speci?cation, and ?i captures unobserved time-invariant country-speci?c effects. The interaction term in equation (1) requires
thatweevaluatealinearcombinationofcoef?cients(?1 +?* 3 Rurpopshare)inordertoassess the impact of electoral competition (which we
and fair.”Note too that the mean share of the rural population in our sample is approximately 70%.The value of EIEC exceeded 6 in approximately 38% of country/year
observations.
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Table3. DeterminantsofNominalRateofAssistancetoAgriculturalExportables,1975to2004 1 2 3 4 OLS OLS REa Syst GMMb Rural pop. share 0.007 0.006 -0.002 0.007 (0.006)(
0.006)( 0.008)( 0.005) Elecomp dummy 0.091 -0.092 -0.414 -0.109 (0.059)( 0.411)( 0.392)( 0.334) Log real GDP per cap 0.270 0.273 0.227 0.268 (0.085)*** (0.089)***
(0.094)** (0.066)*** Landlocked dummy -0.175 -0.181 -0.159 -0.178 (0.087)* (0.090)* (0.094)* (0.076)** Resource-rich dummy 0.116 0.121 0.005 0.142 (0.113)( 0.117)(
0.151)( 0.118) Arable land share of total 0.004 0.004 0.011 0.002 (0.004)( 0.004)( 0.007)( 0.004) Elecomp×rural pop shr 0.003 0.007 0.003 (0.006)( 0.005)( 0.005)
NRA_agexpt (t-1) 0.115 (0.092) Constant -2.937 -2.879 -1.980 -2.714 (0.959)*** (0.939)*** (1.205)( 0.627)*** Observations 375 375 375 374 R-squared 0.48 0.48 0.44
Total effect of: Rural pop. Share w/ comp. elections 0.008 0.005 0.009 (0.006)( 0.008)( 0.005)† Comp. election,w/ rural pop shr=50% 0.040 -0.073 0.023 (0.135)( 0.129)(
0.103) Comp. election,w/ rural pop shr=85% 0.133 0.165 0.116 (0.098)( 0.099)* (0.090) Robuststandarderrors(clusteredbycountry)inparentheses.*signi?cantat10%;**Signi?
cantat5%;***signi?cantat1%.†P=0.113.aRandomeffectsmodel; bone-step system GMM.Year dummies not reported. Source:Authors’ calculations.
will evaluate at low and high levels of rural population share), and (?2 +?3) to assess the impactofruralpopulationsharewhentheelectoral system is competitive
(e.g.,when dummy variable Elecomp=1). In selected cases, we also present semi-parametric results for key explanatory variables. For each left-hand-side indicator, we
begin by excluding the interactiontermfromequation(1)whilestillallowing themeasuresofruralpopulationandelectoral competition to enter separately. In order to assess
the robustness of our estimates, we employ a series of estimators. We beginbyemployingOLS,initiallyconstraining ?3=0, then including the interaction term in
ourfullyspeci?edmodel.2 Wethenexploitthe panel structure of our data by employing two additional estimators. Most of the identifying variation lies in the cross-
sectional dimension of the data: the “within” standard deviation
2 All OLS estimates use robust standard errors, corrected for clustering at the country level.
in rural population share in our sample is only 3.6, compared with the “between” variation of 10.7, relative to the mean of 70.6. As the ?xed-effects estimator depends
solely on within-country variation, we therefore employ a random effects estimator, a choice supported by the Hausman test. Lastly, given the tendency for hysteresis
in policy choice, wealsoemployasystemGMMdynamicpanel estimator.UseoftheGMMestimatorhelpsto alleviateconcernswithendogeneitythatmight arise where rural population
shares and the adoptionofcompetitiveelectoralsystemsmay dependonfactorsthatin?uencethedependent variable as well and that had been excluded from the model.
Relative Rate ofAssistance Table 1 presents our results for RRA. As expected, the point estimate for the impact of rural population share in the absence of electoral
competition is negative in all models
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Bates and Block Political Institutions andAgriculturalTrade Interventions inAfrica 321
–1
–0.75
–0.5
–0.25
0
0.25
0.5
RRA
60 65 70 75 80 85 90 95 Rural pop. share
RRA (elect comp)
Semi-Parametric Regression
RRA by Rural Pop. Share with & without electoral comp
RRA (no elect comp)
Figure 1. Relative rate of assistance as a function of rural population share
and positive in the presence of electoral competition, although in no case is it statistically different from zero. Adding the interaction term permits a more nuanced
analysis of the “shift”effect of party competition:at high levels of rural population share (85% compared with 50% compared with a sample mean of 75%), in the OLS and
random-effects models, electoral competition bears a positive and signi?cantrelationshipwithpolicychoicesthat favor the agricultural sector. While the coef?cient for
the GMM estimate does not signi?cantly differ from 0, it is greater than the effect of party competition on policy choice at low levels of rural population share by a
margin of 23% (P=0.024), based on the GMM estimate.3 Toprobetheserelationshipsmoredeeply,we relaxthatassumptionoflinearityandestimate semi-parametric (or“partially
linear”) models of the form yi=Xiß +g(Rurpopsharei)+ei where X includes all of the variables above except for the rural population share, and g(.)
3 The bottom rows of each table describe “total effects.” The total effect of rural population share with competitive elections (e.g., the partial derivative of the
regression with respect to rural population share) asks whether the slope coef?cient of rural population changes when there is party competition. Conversely, the total
effect of party competition (e.g.,the partial derivative of the regressionwithrespecttopartycompetition)askswhethertheshift effect of party competition varies with the
rural population share.
isanunknownfunctionrelatingthedependent variabletoruralpopulationshare.Weestimate this remaining nonparametric relationship for the subsamples with and without
electoral competitiveness. Figure 1 displays the semi-parametric relationship between RRA and rural population share while controlling for electoral competition. In
the absence of competitive elections,relativeassistancetoagriculturedeclines rapidly as the rural population share increases abovethesamplemean.Competitiveelectoral
systemsappeartocheckthenegativeimpactof larger rural populations.
Nominal Rate ofAssistance toAgricultural Importables and Exportables Consistent with our expectations, we ?nd in table 2 that trade policy support for agricultural
importables—largely consisting of food crops—declines as a function of rural population share but that this effect is ameliorated in the presence of party competition.
When the dummy variable for party competition enters without the interaction term (controlling for average rural population share in column 1), it increases the
nominal rate of assistance for agricultural importables by nearly 20%.When interacteddirectlywithruralpopulationshare, the results reveal that the effect of electoral
competition on nominal protection for agricultural importables depends critically on the
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322 January 2011 Amer. J.Agr. Econ.
–0.5
–0.25
0
0.25
0.5
NRA Agricultural Importables
60 65 70 75 80 85 90 95 Rural pop. share
nra_totm (elect comp)
Semi-Parametric Regression
NRA Ag Importables by Rural Pop. Share with & without electoral comp
nra_totm (no elect comp)
Figure2. Nominalrateofassistancetoagriculturalimportablesasafunctionofruralpopulation share level of rural population share. While not statistically different from
zero at relatively low levels of rural population share, we ?nd that electoralcompetitiontransformshighvaluesof ruralpopulationsharefromapoliticalliability into a
political asset. At a high level of rural populationshare(85%),theestimatesindicate asubstantialandstatisticallysigni?cantbene?t
fromelectoralcompetitioninallthreemodels. Figure 2 captures graphically the relationship while relaxing the assumption of linearity of the functional form. Table 3
suggests that rural population share bears no relation to the level of nominal protection of agricultural exportables. As seen at the bottom of that table, at high
levels of rural population share, producers of agricultural exportables do bene?t from electoral competition, but the impact is small and of little signi?cance. In
important respects, then, the ?ndings for importables and exportables differ, which suggests that the political forces that shape government policies toward them
differ as well. Elsewhere (Bates and Block 2009) we argue that the politics of cash crops is shaped by the forces of regionalism and
revenueextractiontoagreaterdegreethanare the politics of food crops.
Conclusion
We have explored patterns of variation in the content of agricultural policies in Africa. We
have looked at the incentives for farmers to lobby and their capacity to affect electoral outcomes. In addressing agricultural policy in developing nations, we should
focus on the nature of political institutions and their effect on the relative political in?uence of rural producers. In particular, we ?nd that the institution of
competitive elections has transformed rural producers in Africa from a disadvantaged lobby into a potent electoral in?uence.
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