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8/22/2011 Bates_Block_IV

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Revisiting African Agriculture: Institutional Change and

Productivity Growth

Robert H. Bates

Harvard [email protected]

Steven A. BlockTufts University

[email protected]

AbstractAfrica is largely agrarian and the performance of its agricultural sector shapes theperformance of its economies. Building on a recent analysis of total factorproductivity growth in African agriculture, we explore the politics underlying theeconomics of this sector. In so doing, we explore the relationship betweenpolitical institutions and economic development. The introduction of competitivepresidential elections, we find, is significantly related to the resurgence ofproductivity growth in African agriculture. The emergence of electoralcompetition appears to have altered political incentives, resulting in both sectoraland macroeconomic policy reforms that strengthened productive incentives infarming.
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]

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Page 2

1. Introduction

In the later decades of the 20thCentury, political institutions in Africa changed. Prior to

the late 1980s, open competition for national office was rare: politicians became heads of

state either by launching military coups or by consolidating their political backing within

the ruling party. Subsequently, most heads of state were instead chosen in elections

contested by rival parties that competed to capture political support from a majority of the

national electorate.

Figure 1 documents the nature and magnitude of these changes. Classifying political

systems along a 7-point scale that captures the level of electoral competition (see below),

the figure depicts the striking shift towards competitive politics. In the 1970s, the mean

lay below 3; by the 21stcentury, it lay above 6.

In general, institutions change slowly and by few degrees and this lack of variation

renders it difficult to study their impact (Persson and Tabellini 2008). The speed and

scope of Africas political transformation thus renders the continent a potential source of

evidence concerning the relationship between political institutions and the performance

of economies.

On average, one third of Africas people work in farming and agriculture remains the

single largest industry in most of Africas economies. The decline of the rural sector in

the 1970s foreshadowed Africas economic collapse (World Bank 1981; Bates 1981);

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Page 3agricultures current revival lends impetus to the continents economic recovery. It is our

claim that changes in Africas political institutions have shaped this economic trajectory.

The Literature

More broadly, this study engages the so-called Lipset hypothesis (Lipset 1959).

Observing a relationship between economic development and democracy in a global

cross section of nations, Lipset (1959) posited that the former caused the latter. Using

panel data and applying a Markov model, Przeworski et al. (Przeworski, Alvarez et al.

2000) challenged Lipsets argument, arguing that richer countries were no more likely to

become democratic; rather, they were more stable and, if democratic, less likely than

poor countries to turn authoritarian (but see Epstein, Bates et al. 2006 and Boix and

Stokes 2003). Applying a dynamic OLS estimator with fixed effects on panels that ran

far deeper than those employed by either Lipset (1959) or Prezeworski et al. (2000),

Acemoglu, Johnson et al. (2008) too found no significant relationship between

democracy and development. Subsequently, Persson and Tabellini (2008) applied

matching techniques to a contemporary cross-section of nations and failed to detect

significant differences between levels of democracy and rates of economic development.

Particularly among authoritarian countries, they report, the within variance in growth

rates exceeds the between, thus accounting for their finding.

Undeterred by this legacy of negative findings, we return to Lipsets argument, but in a

manner that differs in key respects from those who have gone before. Rather than using

global samples, we restrict ourselves to a regional sample and make use of data from

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Page 4Africa. In contrast to other regions of the world, in Africa, institutions are unstable.

While disadvantageous in many respects, this instability offers advantages to those

seeking to explore whether institutional differences bear upon differences in

development. In addition, with the exception of Przeworski et al. (2000), investigators

have tended to employ the Polity measure of democracy; and all have employed the gross

domestic product per capita as a measure of economic development. The Polity score is

derived from data on several institutions, however; in addition, it is subjective, recording

such attributes as the quality of political competition and the degree of political openness.

The Polity score thus lacks sufficient micro- level precision to give rise to strong

expectations as to how a change in political institutions would alter the behavior of policy

makers, the policies they might choose, and thus the impact of institutions upon the

performance of the economy.

In response, while grounding our analysis on African data, we make use of alternative

measures of political institutions and economic development. Instead of the Polity

measure, we employ a measure first developed for the analysis of political transitions in

Africa (Bates, Ferree et al. 1996) and subsequently collected for a global set of nations by

the World Bank (Beck, Clarke et al. 2001). Focusing on the manner in which chief

executives secure political office, the measure can be thought of as a Schumpeterian

(Schumpeter 1950) index of political competition: the higher the number, the greater the

degree of party competition.1

1 Note that the measure has been shown to yield a Guttman scale when applied to African data Bates, R.H., K. Ferree, et al. (1996). Toward the Systematic Study of Transitions. Development Discussion PaperNo. 256. Cambridge MA, Harvard Institute for International Development.

Because the measure does not take into account the degree

to which this competition takes place in an environment in which there are safeguards to

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Page 5civil and political rights or to which elections, though competitive, are free and fair, it

offers little insight into the quality of democratic government, however (Dahl 1971).

Despite this deficiency, when applied to the political systems of Africa, the measure

suggests important insights into the nature of the political incentives confronting those

aspiring to national office and the nature of the political strategies that they are likely to

adopt. Africa is poor and its urban population and industrial centers are small, while its

rural population and agrarian sectors are large. In the absence of party competition i.e.

when the value of our measure is low we should therefore expect economic interests to

seek to influence public policies through lobbying by interest groups. Insofar as this is

the case, we expect economic policies to be urban biased. But when there is a change

to party competition i.e. when the value of our measure becomes high electoral

majorities acquire political weight and those seeking office will therefore begin to

advocate measures designed to attract rural voters. They should therefore begin to

support policies that favor agriculture. The change from interest group politics to

electoral politics should therefore induce a policy change; it should induce a shift in

relative prices in favor of agriculture, thereby rendering agriculture more profitable and

strengthening the incentives for farmers to make more productive use of factors of

production.

Moving from macro- level indices such as the Polity score and GDP per capita -- to

micro- level measures of political institutions and incentives thus suggests a line of

argument linking political institutions and economic performance.

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Page 6

Note a key assumption underlying this argument: that the African voter engages in

performance voting and that Africas politicians therefore need fear political punishment

should they fail to adopt policies desired by a majority of the electorate. One counter

argument would see ethnic identity trumping economic self-interest in the minds of

voters; a second would see distributive politics clientelism and vote buying as

dominating policy preferences. Taken together, the two would suggest that politicians in

Africa need not champion public policies when competing for electoral majorities and

that the expectations that underlie our argument are therefore unjustified.

Recent research suggests that while there is an element of truth to both arguments,

politicians nonetheless remain accountable for their policy choices. In a recent paper,

Philip Keefer (2010) marshals data from 16 Afribarometer surveys and finds little

evidence that ethnic groups systematically align with political parties. In a study of the

2008 election in Ghana, Ferree et al. (2009) find little evidence of bloc voting by ethnic

groups; and while politicians did make ethnic appeals, the authors find that voters

focused on the policies and the competence of the political parties rather than on the

ethnic identity of the candidates. In a study of the 1999 elections in South Africa, Mattes

and Piombo (1999) present evidence suggesting that voter decisions result from a

comparative evaluation of the incumbent and opposition party, in which the incumbent is

evaluated retrospectively and the opposition prospectively. In making these evaluations,

they argue, voters focus on the direction of the economy and the direction of the

country (p. 110). Race, which is akin to ethnicity in South African context, may affect

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Page 7the manner in which economic conditions are evaluated, they find, but not their

importance to the voter.2 In their study of the Zambian election of 2002, Posner and

Simon (2002) find that while ethnicity affects voter preference, so too does the voters

satisfaction with the condition of the economy; those who expressed dissatisfaction

were 10 to 15 percentage points less likely to vote for the incumbent (p. 319) i.e. to

hold the government politically accountable. The magnitude of this effect was greater

than that associated with ethnic differences.3

As argued by Vicente and Wantchekon (2009), when political parties fail to act as agents

of ethnic interests, then political organizers must find other means for delivering votes.

Incumbents use government agencies and patronage to mobilize votes; being unable to do

so, challengers seek to buy them. Insofar as these strategies work and Vicente and

Wantchekon (2009) argue that they do they undercut incentives for politicians to

advance policies capable of benefitting electoral majorities. As noted by Stokes (2005),

however, vote buying through either cash or clientelism requires perfect information;

resources would be wasted if targeted on people with strong partisan attachments and

politicians must be able to verify that a vote, once paid for, stays bought. Judging by

Stokes (2005) results, even in Argentina, where the Peronists have long been held to

have mastered the art of clientelist politics, perfect information is difficult to achieve.

Parties appear to garner less than 10% of their votes in this fashion.

2Economic conditions that may be beneficial to the bond holder may be regarded unfavorably by themarginally or un-employed.3That is: being a Bemba-speaker (as was the incumbent) or not.

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Page 8While clientelism, like ethnicity, may be productive electorally, its overall impact may

therefore not be large. Expected policy benefits i.e. the content of policies and the

competence with which they are implemented appear to play the dominant role in voter

decisions. The assumption we make about the African voter thus appears to be

justified, judging by recent research.

Our paper relates to the work of Stasavage (2005) and Kudamatsu (2007). Working with

data from 44 African countries, 1980-1996, Stasavage (2005) found that governments

chosen in elections openly contested by rival political parties spend more on primary

education. Political reform led to higher levels and more geographically dispersed

service delivery, he contends. Working with household-level data from 28 African

countries, Kudamatsu (2007) found lower levels of infant and neo-natal mortality for

children born following the introduction of competitive elections. As did Stasavage

(2005), he attributes the change to improvements in service delivery, as politicians

respond to the need to secure votes from an enfranchised citizenry. By focusing not only

on policy change but also upon its impact, we extend the agenda set by Stasavage (2005)

and Kudamatsu (2007). We do so by advancing evidence that institutional change

appears to have promoted the growth of total factor productivity in farming.

Our research also contributes to the literature on democracy and development (Persson

and Tabellini 2008). In contrast with Barro (1996), when estimating the relationship

between democracy and growth, we allow for the possibility of reciprocal causation

(Lipset 1959). In doing so, we dissent from Glaeser, La Porta et al. (2004): we find that

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Page 9democracy is related to growth, not because growth induces a demand for political

reform, as they contend, but rather because, in the political and economic context which

we address, democratization induced changes in policy, thereby altering economic

incentives. Like Giavazzi and Tabelinni (2005), we acknowledge that the devil is in the

details; because our investigation is narrowly targeted, we are better able than they to

isolate and trace the channels linking institutional change to variation in economic

performance.

Section 2 presents our argument. Section 3 provides political and economic background,

portions of which provide justification for our estimation strategy. Section 4 describes

our data, while Section 5 presents our estimates of the relationship between institutional

change, policy reform, and the economic performance of agriculture. Section 6

concludes.

2. Engels Law, Structural Transformation, and the Logic

of Political Representation

The relationship between political reform and economic change in Africa can be derived

from well-established insights into the consumption behavior of poor persons and the

structure of their economies on the one hand and from the logic of collective action and

party competition on the other.

Engels law holds that as income rises, the proportion of income spent on food declines;

the income elasticity of food consumption is less than unity. From this micro-level

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Page 10regularity a macro-level implication follows: that economic development implies

structural change (Kuznets 1966; Chenery and Taylor 1968). When people are poor, a

large percentage of their total expenditure will be devoted to food; absent foreign trade

and significant economies of scale in farming, the rural sector will therefore be large.

But when people earn higher incomes, the percentage spent on food will be less and the

rural sector will then comprise a smaller portion of the economy. The process of

economic development is thus marked by the marginalization of agriculture.

Given commonly observed characteristics of farms and firms, poor countries therefore

exhibit a characteristic political-economic geography. The majority of the population

works in farming; it lies widely scattered, each member producing but an infinitesimal

percentage of the total output of agricultural goods. A small portion of the population

often less than 10% -- works in manufacturing and service provision and dwells in towns.

Because economies of scale exist in manufacturing, a significant percentage of the urban

dwellers derive their incomes from a small number of employers (Little, Scitovsky et al.

1970; Little 1982). While those who farm are thus dispersed, economically and

geographically, those who earn their incomes in other portions of the economy are not.

Spatially, they are concentrated in a few settlements and economically they often labor in

enterprises sufficiently large to dominate their markets.

The political implications are immediate and ironic: In countries with large agricultural

populations, farmers form a weak political lobby. Being small, farmers in poor

countries rationally refrain from expending effort in attempts to influence agricultural

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Page 11prices; not so urban interests, which stand large in their markets. Being widely

scattered, farmers face high costs of organizing; concentrated in towns, urban interests

find it less expensive to do so. Urban interests therefore hold a relative advantage as

lobbyists in less developed economies. In so far as government policy is influenced by

organized groups, in countries with large agricultural sectors, it tends to be adverse

toward the interests of farmers (Olson 1971 and 1985; Bates 1981).

But now, consider: What if the process of representation should follow the logic of

electoral competition rather than collective action? Then the very factors size and

dispersal that render farmers weak lobbyists would render them powerful (Varshney

1995; Bates 2007). Where representation is achieved through electoral channels and

where rural dwellers constitute a large segment of the voting population, then politicians

have an incentive to bear the costs of political organization and to cater to the interests of

farmers. The search for political majorities should lead them to resist the political

pressures emanating from urban consumers and to champion policies that cater to the

interests of the rural majority.

The mean income in Africa is less than $1,000 per annum (constant $US 2,000).

Consistent with Engels law, agriculture is the largest single industry: on average, one

third of its nations workers derive their incomes from farming and three quarters of its

population resides in the rural areas. On the basis of the reasoning above, then, we would

expect that the implications of this argument would follow and that changes in Africas

institutions would generate concomitant changes in the behavior of its governments.

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Page 12

3. Background

By providing political and economic background, this section informs the subsequent

quantitative analysis and, in particular, the identification strategies that we employ.

3.A: Political Background

Responding to the decline of Africas economies in the post-independence period, many

policy makers and political activists attributed it to politics. Africas governments, they

stressed, were authoritarian (Figure 1); not having to win the support of their people, they

were free to impose policies that served their private interests4

rather than the needs of

the public. The implication was clear: by changing Africas political institutions and

rendering their governments accountable, reformers could alter their choice of policies,

thereby promoting economic development.

The reformers consisted of political dissidents within Africa and persons abroad: political

activists, of course, but, of more immediate relevance, technocrats in financial

institutions. The technocrats focused on the governments management of the macro-

economy. Fiscal imbalances and lax monetary policies led, it was argued, to high rates of

inflation and adverse trade balances, necessitating a search for loans from abroad. They

also criticized the manner in which governments intervened in markets, using trade

policies to protect domestic industries from foreign competition and licensing

4Ideological as well as personal, as eloquently argued by Ndulu, B. (2007). Chapter 9: The Evolution ofGlobal Development Paradigms and their Influence on African Economic Development. The PoliticalEconomy of Economic Growth in Africa, 1960-2000. B. Ndulu, P. Collier, R. H. Bates and S. O'Connell.Cambridge, University of Cambridge Press.

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Page 13requirements to protect them from domestic competition, while leaving farmers

unsheltered.

As noted and stressed by Bates (1981), Krueger, Schiff et al. (1992) and others (Bates

2007; Ndulu and O'Connell 2007, and Anderson and Masters 2009), both the macro-

economic policies and the interventionist measures adopted by Africas post-

independence regimes resulted in urban bias. When coupled with rigid exchange rates,

the macroeconomic measures generated an appreciation of the domestic currency, which

undermined the competitiveness of agricultural exports and lowered the price of

foodstuffs imported from abroad. While urban industries were sheltered from foreign

competition, rural producers were not; and while firms could legally maintain monopolies

in domestic markets, farms confronted legal monopsones, as governments restricted

competitition for the crop as a way of reducing prices for consumers. The mix of policies

shifted relative prices against farmers.

Given that agriculture production constituted a major portion of Africas exports, as they

weakened incentives for farming, Africas governments also undermined incentives to

generate earnings abroad. To secure foreign exchange, they therefore increasingly turned

to the World Bank and International Monetary Fund. After repeated applications for

funding from Africas debtor nations, these institutions demanded that they alter their

policies and governance structures.

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Page 15arises: Did this political change bear a systematic relationship to these changes in the

performance of Africas rural economy? We argue that it did, both directly and through

its impact on government policies.

In the section that follows, we describe our data. We then employ it to estimate the

relationship between institutional reform, policy change, and the performance of Africas

agrarian economies.

4. The Data

As our measure of economic performance, we employ Blocks estimates of TFP growth

in agriculture. Using aggregate crop output figures for each country, and Africa-specific

prices and PPP exchange rates,6

(1) () = + () +=2 () + =1 () +kj=1 ()+

1=1 + ()

Block derives his estimates from a semi-parametric

specification of a constant returns to scale Cobb-Douglass production function:

whereyi(t)is aggregate crop output for country iin year t,xij(t)is a vector ofj

conventional agricultural inputs (land, chemical fertilizer, tractors, and livestock),zij(t)

are quality shifters associated with these inputs (average years of schooling to adjust

labor quality, as well as rainfall and irrigated land share to adjust for the quality of land),

pij(t)are other potential explanations for TFP growth (to include political competition),

6Block (2010) constructs these aggregates from crop-specific output data published by the Food andAgricultural Organization of the UN. Other studies simply employ the FAOs pre-constructed outputaggregates, which are based on global prices and exchange rates. Blocks estimates thus more closelyreflect the circumstances actually faced by Africas farmers.

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Page 16TDare annual time dummies, and CDare country dummies. All variables are in logs,

normalized by the size of the labor force in agriculture.

This semi-parametric specification effectively partials out the linear effects of the

conventional inputs and country dummies, measuring TFP growth with a non-parametric

kernel regression of output on the annual time dummies, (()).7

For arbitrarily

small changes in time, the rate of TFP growth can be derived by differentiating

(()):

(2) =(())

The baseline estimates (shown in the cross-country aggregates in Figure 2) exclude the

adjustments for input quality contained in the vectorz. We then re-estimate the function

while adjusting for land quality (by controlling for the effect of annual rainfall and

irrigated land share), and then re-estimate it once again while adjusting as well for labor

quality (by controlling for average years of schooling). While the estimate of TFP

growth is reduced by the extent to which those additional variables explain the initial

baseline estimate, the adjustments help to differentiate between productivity increases

resulting from the use of improved inputs from those that result from increases in the

efficiency with inputs are employed.

To derive the country-specific rates of agricultural TFP growth the principal dependent

variable in this paper -- we estimate equation (1) country-by-country.

7Yatchew (2003) provides comprehensive detail on semi-parametric regression.

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Page 17

As our measure of institutions, we make use of a scale that provides a measure of the

degree of political competition that the incumbent chief executive faced when coming to

office.8

1 -- No executive exists.

For each country in each year, the scale assigns a number that indicates whether:

2 An executive exists but was not elected.

3 The executive was elected, but was the sole candidate.

4 The executive was elected, with multiple candidates competing for the office.

5 -- Multiple parties were also able to contest the executive elections.

6 -- Candidates from more than one party competed in executive elections, and the

winner won more than 75% of the votes.

7 -- Candidates from more than one party competed in executive elections, and the

winner won less than 75% of the vote.

We employ as our measure a dummy variable, named electoral competition, that takes

the value 1 when the government is rated 6 or above and 0 otherwise.9

As our measure

of economic performance, we employ the growth of total factor productivity in farming,

as described above.

8The measure is taken from the World Banks Data Base of Political Institutions: Beck, T., G. Clarke, et al.(2001). "New Tools and New Tests in Comparative Political Economy: The Database of PoliticalInstitutions." World Bank Economic Review. The measure was devised by the Africa Research Program atHarvard, who established that it yields a cumulative (Guttman) scale. See Bates, R. H., K. Ferree, et al.(1996). Toward the Systematic Study of Transitions. Development Discussion Paper No. 256. CambridgeMA, Harvard Institute for International Development.9Others we term non-competitive or, more loosely, authoritarian.

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Page 18When discussing public policy, we employ two measures. One is the black market

premium (BMP) for foreign exchange. While the BMP is a direct measure of exchange

rate misalignment, we follow Rodriguez and Rodrik (2001) who view BMP as a proxy

for broader distortions in macroeconomic policy. Researchers point to the misalignment

of the exchange rate as a major disincentive to agricultural producers (e.g. Krueger,

Schiff et al. 1992): overvaluation reduces the local currency earnings of those who

produce cash crops for export and, in the absence of offsetting tariffs or quantitative

restrictions, confers a price advantage to imported agricultural products.

Our second policy indicator is the nominal rate of assistance to agricultural importables, a

sectoral indicator of trade policy intervention, which we take from the World Banks

database on Distortions to Agricultural Incentives (Anderson 2009). When an ad

valorem tariff is the sole policy intervention for good (x), the nominal rate of assistance

for commodityxis:

(3)m

m

x tPE

PEtPENRA =

+=

)1(

where tm is tariff rate,Eis the nominal exchange rate, and Pis the dollar-denominated

world price of the commodity. This basic formula can be modified to incorporate

additional distortions, such taxes and subsidies on domestic production of the relevant

commodities. The nominal rates of assistance for individual crops may be aggregated to

form the nominal rate of assistance for agricultural importables (NRA_totm), which are

typically foodgrains. Food imports compete with the product of Africas famers for the

domestic market. NRA_totm thus determines the prices at which local producers can sell

what they grow. It also influences the distribution of income, as the lower prices that

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Page 19lower the fortunes of rural producers enhance the purchasing power of urban consumers.

It follows from our central line of reasoning that the enfranchisement of the rural majority

should be associated with increased rates of nominal assistance for agricultural

importables.

Table 2 provides descriptive statistics for these data.

5. Analysis

5.A. Bivariate Relations

To motivate the analysis that follows, we introduce Figure 3, which distinguishes the

time path of TFP growth rates in observations with and without electoral competition10.

The TFP growth rate in settings characterized by electoral competition progressively

diverges from the TFP growth rate in settings that lacked it. On average, countries with

electoral competition experienced agricultural TFP growth of 1.04% per year, while the

average rate was only 0.48% per year in countries without.11

We have suggested that changes in political incentives led to changes in the policy

preferences of Africas governments. Figure 4 therefore compares government policies

in country-years with and without electoral competition. Each panel in Figure 4 contains

a box that depicts the portion of the observations of a variable that fall within the

interquartile range, i.e. those whose values place them between the lower 25% and the

10Net of adjustments for input quality.11These averages are statistically different in a two-sided t-test (P = 0.0014).

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Page 20upper 25% of the range of the values of the variable. The horizontal lines within the

boxes mark the variables median value. The upper and lower horizontal lines laying

outside the boxes mark the upper and lower values of the data.

The data suggest that governments headed by an executive chosen in a competitive

election not only exercise greater fiscal and monetary restraint than do their authoritarian

counterparts (as indicated by the virtual absence of black markets for their currencies)

and intervene in markets in ways less likely to shift relative prices against farmers (as

indicated by their relative rates of assistance); but also, that they spend more on

agricultural research, secure higher levels of educational attainment, and pave a larger

percentage of their roads. Calculating the means, we apply one-sided t-tests to the

differences and find each to be significant and in the expected direction. Governments in

competitive political systems act in ways that lower the costs, increase the earnings, and

strengthen the incentives for farmers.

In the estimates we develop below, we focus in particular on two of these policy

variables: the black market premium on foreign exchange and the nominal rate of

assistance to agricultural importables. Figure 5 demonstrates the effect of controlling for

the black market premium when estimating the time path of TFP growth. The difference

in the two time profiles can be viewed as the portion of TFP growth explained by the

black market premium and suggests that macroeconomic policy distortions have

accounted for a difference of nearly 30% in the quality-adjusted average rate of TFP

growth. Figure 6 applies this approach to the NRA_totm, which accounts for 16% of the

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Page 21estimated average growth rate of TFP. In both cases, differences in the mean growth

rates is significant at greater than the .10-level.

5.B. A Deeper Look: Difference-in-Differences

Probing deeper, we attempt to identify the causal effect of electoral competition on

agricultural productivity growth. In pursuit of that objective, we construct a difference-

in-difference model in which electoral competition constitutes the treatment. Given that

the treatment occurs at different times in different countries, our model takes the form of

a fixed effects regression with individual year dummies:

(4) = + + + + where is the growth rate of agricultural productivity in country iin year t, are time-invariant unobservable country effects, are year dummies,Xis a vector of observedcovariates, is a dummy equal to one for each country-year observation in which thereis electoral competition, and is the causal effect of electoral competition on agricultural

TFP growth (which we assume to be a constant). We adjust all standard errors for

clustering at the country level, in keeping with the cautions advocated by Bertrand,

Duflo, and Mullainathan (2004) regarding serial correlation in difference-in-difference

models.

Table 3 presents estimates of equation (4). Column (1) presents a baseline specification

that includes only country and year effects. This specification indicates a statistically

significant increase of 0.74 percentage points to agricultural TFP in settings with

electoral competition. The remaining columns of Table 3 test the robustness of this

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Page 22baseline finding. Columns 2 through 5 introduce a set of covariates that include,

respectively, the rural population share, a dummy for country-year observations with civil

wars, an indicator of the extent of electoral competition in each countries geographic

neighbors (averaged across neighbors and lagged by one year), and the combination of

these covariates. Rural population share can be interpreted as an indicator of

development. As expected, it enters negatively in column 2. Its inclusion increases the

point estimate for electoral competition, suggesting a relationship between the two that

we explore below. Civil wars were endemic in late century Africa, with 40% of countries

experiencing at least one year of war between 1960 and 2000. A dummy for each

country-year of warfare enters negatively in column 3, but the estimated effect is

statistically insignificant. If electoral competition does indeed lead to improved policies

and higher rates of agricultural productivity growth, its impact could conceivably spill

across national boundaries; it would then influence the relationship between political

reform and economic performance in neighboring states, introducing endogeneity bias.

Column 4 therefore controls for the lagged average of the degree of electoral competition

in each countrys neighbors; and indeed the coefficient is positive.12

A majorthreat to these estimates is that the effect may precede the treatment.13

12Bates estimates the magnitude of inter-country spillovers in his study of state failure (Bates, R. H.(2008). When Things Fell Apart: State Failure in Late Century Africa. New York, Cambridge).

As a

robustness test against this threat, we therefore add country-specific time trends to our set

of control variables, modifying equation (4) to read:

(5) =0 + 1 + + + +

13Note that one cannot judge this sequence from Figure 4, which is aggregated across all countries in thesample. Analyzing the sequence of treatment and effect requires country-level disaggregation.

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Page 23

where 0remains a country-specific intercept and 1is a country-specific trendcoefficient multiplying the time trend t. Table 3 implements this robustness test in

column 6, which includes all covariates. This result is a small reduction in the point

estimate for the effect of electoral competition on agricultural TFP growth, which

remains statistically significant at the .05-level.

As an additional robustness test we seek to ensure that past treatment causes the current

effects while future treatment does not, .In addressing this possibility, we follow Angrist

and Pischke (2009) who invoke a form of Granger causality. The relevant specification

then becomes:

(6) = + +,

=0++,+

=1+ +

which allows for mlags (post-treatment effects) and qleads (anticipatory effect). Figure

7 graphs the coefficient estimates of these post- and pre-treatment effects for m = q = 4

leads and lags surrounding the year in which each country transitioned into a system of

competitive elections. The results indicate no significant anticipatory effect on changes

in agricultural productivity. The difference between the mean coefficients before and

after political transition is 0.56 percentage points, or approximately the magnitude

reported in Table 3, column 6. Note that Figure 7 also suggests the possibility that the

impact of electoral competitiveness decays over time.

5.C. Mechanisms

Institutional change thus appears to bear a systematic relationship to economic

performance in rural Africa. The question remains: Through what mechanisms does this

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Page 24effect operate? In keeping with our previous argument, we focus on government policies,

starting with the nominal rate of assistance to agricultural importables (NRA_totm).

Table 4 presents our key results. Column 1 reports the estimates derived from a random

effects regression of NRA_totm on our dummy for electoral competition, controlling for

country and year effects and using the largest sample available.14

Electoral competition

has a substantial and statistically significant positive effect, supporting our hypothesis

that electoral competition leads to policies favorable to Africa's food producing majority.

Allowing for country-specific trends in NRA_totm fails to alter this result. The

magnitude of these point estimates is approximately one-half of the sample standard

deviation for NRA_totm, indicating a 17 percentage point increase in the rate of

protection accorded import-competing food producers (roughly the difference between

the 25th and 75th percentiles of the distribution). In anticipation of the smaller sample

available with added control variables (see below), column 3 repeats the estimation of

column 2 but with the smaller sample. The resulting loss of degrees of freedom slightly

reduces the point estimate and increases its standard error such as to push the resulting t-

statistic just beyond the range of statistical significance (P = 0.11).

14Our use of random rather than fixed effects models in Table 4 is largely pragmatic. Random effectsmodels are more efficient than fixed effects models, and exploit both "within" and "between" variation inpanel data. Yet, random effects specifications also require the assumption that the time-invariant countryeffect is uncorrelated with the other regressors. This latter assumption is not justified in our sample,introducing the potential for bias. However, most of the identifying variation in NRA_totm is crosssectional, and fixed effects models performed poorly in this application. An additional pragmaticconsideration favoring random effects is that it allows us to retain time-invariant regressors, the need forwhich is described below. Nonetheless, the poor performance of fixed effects models must count againstour argument.

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Page 25While we believe the risk of reverse causality (in the sense that NRA_totm would cause

electoral competitiveness)is minimal, we freely acknowledge the possible impact of

excluded variables. As suggested above, pressure from the donor community is a prime

candidate for such a variable: it credibly could account for the co-variation of electoral

competitiveness and policy support for domestic food producers. We therefore introduce

a dummy variable indicating whether a country in a given year was under any form of

agreement with the IMF. Adding this variable (column 4) while continuing to control for

country-specific trends leaves the point estimate for the coefficient on electoral

competition unchanged while slightly increasing its precision. The indicator for IMF

support itself is statistically insignificant in this specification.

There is an additional concern, however: that IMF agreements are not randomly

distributed across countries. In column 5 we therefore estimate a two-stage model in

which we, as do others (e.g. Easterly 2005), instrument for IMF agreements using each

country's level of US military assistance and previous colonial status.15

The resulting

coefficients (column 5) confirm the statistically significant relationship between electoral

competition and NRA_totm, albeit with a somewhat smaller point estimate.

If electoral competition in Africa increased the political influence of the rural majority,

then the magnitude of this effect could reasonably be a function of the size of that

population. Investigating this possibility, we expand our specification to include an

15That the latter of these two instrumental variables is time invariant reinforces our use of random effectsestimators, as such instruments would drop out of fixed effects models.

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Page 27country-specific time trends in black market premia (column 2) reduces the point

estimate, which nonetheless remains both economically and statistically significant.

As with our analysis of NRA_totm, we are concerned with potential of endogeneity

arising from the exclusion of variables that may influence both political and

macroeconomic policy reform. And, once again, we are drawn to the potential impact of

donor pressure. In anticipation of the loss of degrees of freedom when we control for

IMF agreements, we first repeat the estimation of column 2 in column 3, using the

smaller sample. The substantial reduction in available degrees of freedom results in a

statistically insignificant coefficient for electoral competition and one that is of the

wrong sign. Instrumenting as before for the IMF dummy causes electoral competition

to become statistically insignificant; and the re-introduction of country-specific trends

renders the electoral competition dummy marginally significant effect and of the "wrong"

sign. While the impact of institutional reform on macro-economic policy is robust to the

inclusion of country-specific time trends, it thus disappears when we attempt to purge the

relationship of endogeneity bias.

Consistent with our central hypothesis, our difference-in-differences analysis thus

demonstrates that agricultural TFP growth is significantly greater in settings with

electoral competition. In investigating the extent to which government policies may act

as mechanisms through which electoral competition affects agricultural TFP growth, we

found strong evidence that countries with electoral competition adopt trade policies that

favor farming, but only suggestive evidence of an association between macroeconomic

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Page 28policy and electoral competition. Table 6 summarizes these findings by regressing

agricultural TFP growth rates against these mechanisms, with and without including

electoral competition. Should the inclusion of electoral competition eliminate what had

previously been a significant effect of policy on TFP growth, this would indicate that the

policy variable provides a mechanism linking electoral competition to economic

perfromance. Consistent with our difference-in-differences analysis, we find in the first

two columns of Table 6 that NRA_totm is significant only in the absence of electoral

competition, but that the point estimates for black market premium are not affected by the

inclusion of electoral competition. In both cases, electoral competition itself remains a

significant determinant of agricultural TFP growth.

6. Conclusions

As stressed by Persson and Tabellini (2008), efforts to estimate the relationship between

political institutions and economic performance run afoul of two major problems:

heterogeneity and invariance. By drawing our cases from a single region Africa we

address the first difficulty; Africas economies lodge among the ranks of the poor and

agrarian. Politically, they are notably unstable. It is precisely because of the frequency

with which institutions change in Africa, however, that the continent affords us variation

in political institutions that elude those wedded to the use of global samples. Focusing on

late century Africa, we take advantage of the data generated by efforts at political reform

to study the relationship between democracy and development.

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Page 29In doing so, we focus on what many regard as the core challenge to Africas economic

development: the performance of its rural economy. Building on a recent analysis of

agricultural productivity growth in Africa, we employ a difference-in difference approach

and conclude that the introduction of electoral competition was systematically related to

an increase of between 0.5 and 1.0 percentage points in the growth rate of total factor

productivity in African agriculture. We find that the transition to electoral competition

led to significant increases in the rate of protection offered Africas food-producers. The

magnitude of this effect appears to have been greater in settings with larger rural

majorities. Less persuasively, we also found evidence that electoral competition led to

improved macroeconomic policy, thus leading to higher domestic prices for food

producers. Taken together, the evidence suggests that the search for rural majorities led

to changes in government policies, which in turn strengthened the incentives for farming

in Africa.

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Page 30Table 1. The Diffusion of Political Reform

Country Date DurationElection Outcome: Incumbent

Month F&F? Ousted Retained

Benin Feb-90 1 week Feb-91 yes

Mar-96 yes

Congo Feb-91 3 months Aug-92 yes

Gabon Mar-90 3 weeks Dec-93 no

Mali Jul-91 2 weeks Apr-92 yes

Niger Jul-91 6 weeks Feb-93 yes

Burkina Faso Aug-91 2 months Dec-91 no

Ghana Aug-91 7 months Dec-92 yes

Togo Aug-91 1 month Aug-93 no

Zaire Aug-91 1 year -- --

CAR Oct-91 2 months Aug-92 yes

Chad Jan-93 3 months Jun-96 no

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Page 31Table 2. Variables and Descriptive Statistics

Variable Obs Mean Std. Dev. Min Max Source:

Agricultural TFP Growth 1494 0.614 2.117 -7.694 8.247 Block (2010)Dummy=1 if Exec. Index ofElectoral Competition >6 1460 0.427 0.495 0.000 1.000 Beck and Clarke (2009)Neighbors' Executive Index ofElectoral Competition 1230 4.289 1.586 1.500 7.000 Based on Beck & Clarke

Relative Rate of Assistance (RRA) 642 -0.279 0.299 -0.946 1.295 Anderson and ValenzuelaBlack Market Premium on ForeignExchange 1321 1.361 3.436 -6.908 6.122 World Devt Indicators (20

Civil War dummy 2162 0.166 0.372 0.000 1.000 Sambanis and Doyle (200

Rural Population Share 2064 71.713 16.410 12.700 97.960 World Devt Indicators (20

Countries for which we have estimates of agricultural TFP growth (boldface indicates the

existence of data for RRA for that country): Angola, Benin, Botswana, Burkina Faso,Burundi, Cameroon, Central African Republic,Democratic Republic of Congo, Cted'Ivoire, Equatorial Guinea, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya,Malawi, Mali, Mauritania, Mauritius, Mozambique,Niger, Nigeria, Rwanda, Senegal,Seychelles, Sierra Leone, Somalia, South Africa, Swaziland, Tanzania, Togo, Uganda,Zimbabwe.

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Table 3. The Effect of Electoral Competition on Agricultural Productivity Growth(1) (2) (3) (4) (5) (6)

VARIABLES Dependent Variable: Growth Rate of Agricultural TFPElectoral Comp dummy 0.744* 0.911** 0.780* 0.655 0.828** 0.548**

(0.432) (0.362) (0.433) (0.457) (0.362) (0.218)Rural Pop. Shr. -0.159*** -0.158*** -0.0552

(0.0373) (0.0354) (0.198)Civil War dummy -0.452 -0.163 -0.226

(0.493) (0.394) (0.150)Neighbors electoral comp (t-1)

0.294* 0.313* 0.193

(0.155) (0.155) (0.125)Constant 0.0377 10.48*** 0.0800 -1.598* 8.567*** 53.26

(0.552) (2.449) (0.593) (0.881) (2.402) (260.3)

Observations 635 635 635 635 635 635R-squared 0.126 0.308 0.139 0.150 0.336 0.677Number of countries 27 27 27 27 27 27Std Errors Clustered atCountry-level

YES YES YES YES YES YES

Country FE YES YES YES YES YES YESYear FE YES YES YES YES YES YESCountry-Specific Trends NO NO NO NO NO YES

Robust standard errors in parentheses*** p

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Table 4. Effect of Electoral Competition on Nominal Rate of Assistance to AgriculturalImportables

Dependent Variable: Nominal Rate of Assistance to Agricultural Importables(1) (2) (3) (4) (5) (6)

VARIABLES RE RE RE RE RE-2SLS RE-2SLSElectoral Comp. dummy 0.170*** 0.178*** 0.102 0.101* 0.0781* -0.216

(0.0570) (0.0606) (0.0637) (0.0615) (0.0428) (0.269)Under IMF Agreement -0.0033 -0.156* -0.224**

(0.0500) (0.0903) (0.103)Rural Pop. Share 0.0259**

(0.0125)Electoral CompxRural Pop. Shr. 0.00408

(0.00353)Constant 0.000218 1.822 -16.31** -16.49**

(0.101) (5.131) (7.617) (7.668)

Effect of ElectoralCompetitiveness evaluated withrural pop. share at:

25thpctl (61.5%) 0.035(0.065)

50thpctl (72%) 0.077*(0.054)

75thpctl (84.5%) 0.129**(0.054)

Observations 548 548 347 347 347 347

Number of ccode 18 18 18 18 18 18Std Errors Clustered at Country-level YES YES YES YES . .Country FE YES YES YES YES YES YESYear FE YES YES YES YES YES YESCountry-Specific Trends NO YES YES YES YES YES


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Table 5. Effect of Electoral Competition on Black Market Premium

Dependent Variable: log Black Market Premium on Foreign Exchange(1) (2) (3) (4) (5) (6)

VARIABLES RE RE RE RE RE-2SLS RE-2SLS

Electoral Competitiondummy

-1.054*** -0.842* 0.513 0.395 -0.0797 0.601*(0.353) (0.443) (0.434) (0.422) (0.317) (0.347)

Under IMF Agreement -0.721** -0.377 0.544(0.364) (1.082) (0.939)

Constant 3.532*** 620.4*** 696.4*** 649.6*** 3.837***(0.435) (50.83) (87.12) (85.15) (0.860)

Observations 686 686 390 390 390 390Number of Countries 33 33 31 31 31 31Std Errors Clustered at

Country-level

YES YES YES YES . .

Country FE YES YES YES YES YES YESYear FE YES YES YES YES YES YESCountry-Specific Trends NO YES YES YES NO YES


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Table 6. NRA and Black Market Premium as Mechanisms for Electoral Competition

Dependent Variable: Agricultural TFP Growth(1) (2) (3) (4)

VARIABLES RE RE RE RE

NRA_totm 0.943* 0.707(0.524) (0.542)

Electoral Comp. 0.855*** 0.918***(0.324) (0.325)

Log Black Mkt Premium -0.273* -0.275**(0.141) (0.122)

Constant 1.118 0.254 1.840** 1.174***(0.816) (1.011) (0.929) (0.414)

Observations 258 258 258 258

Number of countries 13 13 13 13Std Errors Clustered at Country-level YES YES YES YESCountry effects YES YES YES YESYear dummies YES YES YES YES

Robust standard errors (clustered at country level) in parentheses*** p

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Figure 1: Index of Political Competition

Source: Beck and Clarke (2009); Harvard University Africa Research Project

(http://africa.gov.harvard.edu/).

2.5

3

3.5

4

4.5

5

5.5

6

6.5

Aveage

Compe

tition

Score

1975 1980 1985 1990 1995 2000 2005year

Average Political Competition by Year
http://africa.gov.harvard.edu/http://africa.gov.harvard.edu/http://africa.gov.harvard.edu/http://africa.gov.harvard.edu/

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Figure 2. Agricultural TFP Growth Rates Adjusted for Input Quality

Source: Block (2010)

Baseline (no adjustment for input quality)

Controlling for adjustmentsin land quality

Controlling for adjustments in land and labor quality

0

.5

1

1.5

2

Grow

thRa

teo

fAgricu

ltura

lTFP

1960 1970 1980 1990 2000 2010year

semi-parametric regressions adjusting for input qualityAgricultural TFP Growth Rates, SSA Crops

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Figure 3. Agricultural TFP Growth Profile for Country-Years With and Without ElectoralCompetition

w/ EIEC>=6

w/ EIEC

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Figure 4

0

50

100

150

R&DExpen

diture

0 1Pr(T < t) = 0.0000

Agricultural Research

0

2

4

6

8

10

Averageyearso

fSc

hoo

ling

0 1Pr(T < t) = 0.0000

Education

0

20

40

60

Percen

tRoa

ds

Pave

d

0 1Pr(T < t) = 0.0037

Paved Roads

-100-5

0

0

501

00150

Indexo

fOvervalua

tion

0 1Pr(T > t) = 0.0001

Black Market Premium

-1

-.5

0

.5

Re

lative

Ra

teo

fAss

istance

0 1Pr(T < t) = 0.0000

Urban Bias

Policy Differences

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Figure 5. The Effect of Block Market Premium on Agricultural TFP Growth Rates

Source: Block (2010)

Baseline

including black market premium

0

.5

1

1.5

Grow

thRa

teo

fAgricu

ltura

lTFP

1960 1970 1980 1990 2000 2010year

semi-parametric regression

Agricultural TFP Growth, Controlling for Black Market Premium

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Figure 6. Effect of Nominal Rate of Assistance to Agricultural Importables on Agricultural TFPGrowth

Baseline

Including NRA_totm

0

.5

1

1.5

2

2.5

Grow

thRa

teo

fAgricu

ltura

lTFP

1960 1970 1980 1990 2000 2010year

semi-parametric regressionAgricultural TFP Growth, Controlling for NRA_totm

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Figure 7. Coefficients on Pre- and Post-Treatment Effects of Electoral Competition on

Agricultural TFP Growth

Mean of 4 yrs BEFORE transitionto electoral competition

Mean of 4 yrs AFTER transitionto electoral competition

Transition Yr

Difference in means = 0.56 (t = 3.35)***

-1

-.5

0

.5

1

Param

eteres

tima

te

0L.ele_trans_inL3.ele_trans_inF.ele_trans_inF3.ele_trans_in10

contolling for country trends, yr dummies, other covariates

Before & After Transition to Electoral Competition

Agricultural TFP Growth (conditional mean)

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References

Acemoglu, D., S. Johnson, et al. (2008). "Income and Democracy." American EcoonomicReview 98(3): 804-842.

Anderson, K., and E. Valenzuela (2008).Estimates of Distortions to Agricultural Incentives,1955 to 2007, core database athttp://www.worldbank.org/agdistortions

Angrist, J. D. and J.-S. Pischke (2008). Mostly Harmless Economics. Princeton, Princeton

University Press.

Barro, R. (1996). "Democracy and Growth." Journal of Economic Growth 1: 1-27.

Beck, T., G. Clarke, et al. (2001). "New Tools and New Tests in Comparative PoliticalEconomy: The Database of Political Institutions." World Bank Economic Review.(updated, 2009)

Bertrand, M., E. Duflo, et al. (2004). "How Much Should We Trust Differences-in-DifferencesEstimates?" Quarterly Journal of Economics 119(1): 241-275.

Blundell, R. and S. Bond (1998). "Initial conditions and moment restrictions in dynamic paneldata models." Journal of Econometrics 87(1): 115-143.

Boix, C. and S. C. Stokes (2003). "Endogenous Democratization." World Politics 55(4): 517-549.

Chenery, H. B. and L. J. Taylor (1968). "Development Patterns: Among Countries and OverTime." Review of Economics and Statistics(November): 391-416.

Collier, P. and J. W. Gunning (1999). "Why Has Africa Grown Slowly?" Journal of EconomicPerspectives 13(3): 3-22.

Dahl, R. (1971). Polyarchy. New Haven, Yale University Press.

Dunning, T. (2004). "Conditioning the Effects of Aid: Cold War Politics, Donor Credibility, andDemocracy in Africa." International Organization 58(2): 409-423.
http://www.worldbank.org/agdistortionshttp://www.worldbank.org/agdistortionshttp://www.worldbank.org/agdistortionshttp://www.worldbank.org/agdistortions

8/13/2019 Paper 075

44/46

8/22/2011Page 44

Easterly, W. (2005). "What did structural adjustment adjust?: The association of policies andgrowth with repeated IMF and World Bank adjustment loans,." Journal of DevelopmentEconomics 76(1): 1-22.

Easterly, W. and R. Levine (1997). "Africa's Growth Tragedy: Policies and Ethnic Divisions."Quarterly Journal of Economics 112(4): 1203-1250.

Ferree, K., C. Gibson, et al. (2009). Explaining the African Vote. San Diego, Department ofPolitical Science, University of California.

Giavazzi, F. and G. Tabelinni (2005). "Economic and Political Liberalizations." Journal ofMonetary Economics 52: 1297-1330.

Glaeser, E., R. La Porta, et al. (2004). "Do Institutions Cause Growth?" Joural of EconomicGrowth 9: 271-304.

Keefer, P. (2010). The Ethnicity Distraction? Political Credibility and Partisan Performance inAfrica. Policy Research Working Paper 5236. Washington DC, The World Bank.

Krueger, A. O., M. Schiff, et al., Eds. (1992). The Political Economy of Agricultural PricingPolicies, 5 vols. Baltimore, Published for the World Bank by Johns Hopkins UniversityPress.

Kudamatsu, M. (2007). Has Democratization Reduced Infant Mortality in Sub-Saharan Africa?Evidence from Micro Data. SSRN Discussion Paper No. 685. Stockholm, StockholmUniversity - Institute for International Economic Studies.

Kuznets, S. (1966). Modern Economic Growth. New Haven and London, Yale University Press.

Lipset, S. M. (1959). "Some Social Requisits of Democrcy: Economic Development andPolitical Legitimacy." American Political Science Review 53: 69-105.

Lipton, M. (1977). Urban Bias. London, Temple Smith.

Little, I. M. D., T. Scitovsky, et al. (1970). Industry and Trade in Some Developing Countries.Oxford, Oxford University Press.

Mattes, R. and J. Piombo (1999). "Opposition Parties and the Voters in South Africa's GeneralElection of 1999." Democratization 8(3): 101-128.

Ndulu, B. (2007). Chapter 9: The Evolution of Global Development Paradigms and theirInfluence on African Economic Development. The Political Economy of EconomicGrowth in Africa, 1960-2000. B. Ndulu, P. Collier,et al. Cambridge, University ofCambridge Press.

8/13/2019 Paper 075

45/46

8/22/2011Page 45

Ndulu, B. J. and S. A. O'Connell (2007). Chapter 1: Policy Plus: African Growth Performance1960-2000. The Political Economy of Economic Growth in Africa, 1960-2000. B. J.Ndulu, P. Collier, et al. Cambridge, Cambridge University Press.

Olson, M. (1971). The Logic of Collective Action. New York, Schoken Books.

Olson, M. (1985). "Space, Agriculture, and Organization." American Journal of AgriculturalEconomics 67: 928-937.

Persson, T. and G. Tabellini (2008). The Growth Effect of Democarcy. Institutions andEconomic Performance. E. Helpman. Princeton, Princeton Universty Press: 544-586.

Posner, D. N. and D. J. Simon (2002). "Economic Conditions and Incumbent Support in Africa'sNew Democracies." Comparative Political Studies 35(3): 313-336.

Przeworski, A., M. Alvarez, et al. (2000). Democracy and Development. New York, CambridgeUniversity Press.

Rodriguez, F. and D. Rodrik, 2001. "Trade Policy and Economic Growth: A Skeptic's Guide tothe Cross-National Evidence," NBER Chapters, in: NBER Macroeconomics Annual2000, Volume 15, pages 261-338 National Bureau of Economic Research, Inc.

Sambanis, N., and M. Doyle. 2006. Making War and Building Peace: United Nations PeaceOperations. Princeton, NJ: Princeton University Press.

Schumpeter, J. A. (1950). Capitalism, Socialism and Democracy. New York, Harpers and Row.

Stasavage, D. (2005). "Democracy and Education Spending in Africa." American PoliticalScience Review 49(2): 343-358.

Stokes, S. C. (2005). "Perverse Accountability: A Formal Model of Machine Politics andEvidence from Argentina." American Political Science Review 99(3): 315-325.

Varshney, A. (1995). Democracy, Development and the Countryside. Cambridge, CambridgeUniversity Press.

Varshney, A. (1995). Democracy, Development and the Countryside. Cambridge, CambridgeUniversity Press.

Vicente, P. C. and L. Wantchekon (2009). "Clientelism and Vote Buying: Lessons from FieldExperiments in Africa." Oxford Review of Economic Policy 25(2): 292-305.

8/13/2019 Paper 075

46/46

8/22/2011Page 46

World Bank. (1981). Accelerated Development in Sub-Saharan Africa: An Agenda for Action.Washington DC, The World Bank.

World Bank. (2008). World Development Indicators. Washington DC, The World Bank.

Yatchew, A. 2003. Semiparametric Regression for the Applied Econometrician. Cambridge:Cambridge University Press.

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