Political Institutions and Productivity Growth in Africa s Renaissance

Transcription

1 Political Institutions and Productivity Growth in Africa s Renaissance Robert H. Bates Harvard University, Dept. of Government 1737 Cambridge Street, CGIS Knafel Building 213 Cambridge, MA robert.bates.harvard.edu@gmail.com Steven Block (corresponding author) Tufts University, Fletcher School 160 Packard Ave., Medford, MA steven.block@tufts.edu Abstract In the late 20 th and early 2st Century, many African states replaced authoritarian political regimes with competitive electoral politics; the economies of many also began to grow, some for the first time in decades. We argue that the two changes may have been causally related. We present evidence suggesting that changes in political institutions led to changes in political incentives that in turn led to changes in public policies. We also demonstrate that the new policies strengthened incentives for economic agents to make more efficient use of factors of production, resulting in growth of the economy. Keywords: Africa, Institutions, Productivity Growth JEL: O11, O43 1

2 Introduction Writing in the 1990 s, William Easterly and Ross Levine famously labeled Africa a growth tragedy. 1 Less than twenty years later, Alwyn Young pointed to an African growth miracle 2, while Stephen Radelet, less effusively, stated that Africa was emerging, noting its rising rate of economic growth, improving levels of education and health care, and increasing levels of investment in basic infrastructure: roads, ports, and transport 3. In this paper, we address Africa s economic revival. Viewing it from the point of view of political economy, we focus on the role of political institutions. In addition, we ask: Is this surge in Africa s fortunes likely to endure or will it be transitory, as has previously been the case? 4 The Pattern Figure 1 captures the change in Africa s economic fortunes. It highlights the collapse of the 1980s when, given the rate of growth of population in Africa, the rate of growth of output per capita turned negative in per capita terms. It also reveals that in the 1990s, the rate of growth of Africa s economies then turned positive, eclipsing even that of the golden years immediately following independence. Figure 2 reports the growth rates of average GDP per worker across Africa's five regions and, by so-doing, reveals that the decline and resurgence of Africa s economies assumed different forms in different portions of the continent. In West and Central Africa, countries experienced negative growth rates of GDP per worker (on average) for approximately 25 years, while the rate of growth of GDP per worker in Southern and North African countries remained positive (on average) from 1960 to Each region tended, however, to exhibit the U-shaped pattern that provides the motivation for this paper. Not only did country-level growth patterns vary by region; the trajectories varied depending on the countries economic base. Table 1 shows that growth rates among the oil exporting countries far exceeded the Africa average prior to the early 1990s and increased even while the continental 1 Easterly, W. and R. Levine (1997). "Africa's Growth Tragedy: Policies and Ethnic Divisions." Quarterly Journal of Economics 112(4): Young, A. (2012). The African Growth Miracle. NBER Working Paper No Cambridge MA, NBER. 3 Radelet, S. (2010). Emerging Africa: How 17 Countries Are Leading the Way. Washington DC, Center for Global Development. 4 For a review of previous periods of growth and decline, refer to the classic by Anthony Hopkins, A. G. (1973). An Economic History of West Africa. New York, Columbia University Press. 2

3 average began its decline in the 1970s. From the 1970s to the 1980s, output per worker grew more rapidly in the landlocked countries than in the coastal economies, while from the 1980s to the 2000s, the growth path of the mineral rich countries paralleled that of those less fortunately endowed. All economies exhibited a pattern of decline and recovery. In an effort to account for Africa s growth performance, we first decompose the aggregate growth rate of income per workers into its components: that resulting from growth in human capital, physical capital, and total factor productivity. As seen in Table 2, in the 1970s, the growth rate remained positive in the 1970s, largely, it would appear, because of the accumulation of physical capital. From the 1970s through the 1990s, the contribution of total factor productivity was negative, however, leading first to a decline and then to negative rates of growth. While the contribution of human and physical capital remained roughly the same from the 1980s to 2000, in the 2000 s total factor productivity turned sharply upwards. It is the growth of total factor growth that appears to account for Africa s recent economic revival. Figure 3 replicates the decomposition graphically and by decade. 5 Not only, then, does a common trajectory of decline and resurgence underlie the otherwise heterogeneous performance of Africa s economies, but also this trajectory may be attributed, by and large, to a common source: changes in total factor productivity. The question before us, then, is: What accounts for the changes in the efficiency with which producers in Africa employed the factors of production? Our Approach Taking our cue from the broader literature 6, we focus on government policies. And taking a cue from the new institutionalism, we focus on the role of institutions 7. We argue that changes in Africa s political institutions led to changes in the policies of its governments, which in turn generated changes in behavior of the owners of the factors of production. When authoritarian 5 We derive our estimates of TFP growth from the standard Solovian transformation of a Cobb-Douglas production function with constant returns to scale. The resulting growth accounting decomposition is based on data from Penn World Tables 8.0, from which we draw as well the Africa-specific mean output elasticity of capital equal to 0.45, applied to each country. This growth decomposition was initially conducted under the auspices of the Economic Commission for Africa, and appears in ECA (2014). 6 See the discussion and review in Perkins, D., S. C. Radelet, et al. (2012). Economics of Development. New York, W. W. Norton., Jones, C. I. and D. Vollrath (2014). Introduction to Economic Growth. New York, W.W. Norton. 7 North, D. C. (1990). Institutions, Institutional Change, and Economic Performance. New York, Cambridge University Press., Harriss, J., J. Hunter, et al., Eds. (1995). The New Institutional Economics and Third World Development. London and New York, Acemoglu, D. and J. A. Robinson (2012). Why Nations Fail: The Origins of Power, Prosperity, and Poverty. New York, Crown Publishers. 3

4 regimes gave way to competitive political systems, we suggest, political incentives altered and governments were then less likely to endorse policies that weakened productive incentives. Before introducing the evidence with which to asses this argument, we address a preliminary question: Why might the introduction of competitive electoral systems alter policy preferences and, in particular, lead to a preference for policies that reward those who engage in production? The Political Roots of Policy Preferences Changes in political incentives, which would result from institutional reform, would yield a change in preferences over policies. To illustrate, consider Figure 4, which depicts a market for foreign exchange that equilibrates when exporters supply and importers demand equal amounts of dollars ; they do so when the suppliers of dollars charge 10 cedis to the dollar. Now let the government intervene; arguing that the cedi is stronger than private agents might realize, let it insist that an importer need pay but 5 cedis when purchasing a dollar and exchanges cedis for dollars accordingly. Receiving fewer cedis for each dollar, exporters now ship fewer goods abroad. At this rate of exchange, those who produce goods that can be imported from abroad now face greater competition; goods that might have cost 100 cedis now cost but 50. They too therefore produce less. Not explicitly captured in this diagram is another actor with interests that diverge from the producers of exports and import competing goods, and this is the public sector. As those who serve in the government produce an untraded service, they are paid in cedis. As a result of the governments over-valuing the local currency, they can now purchase dollars more cheaply and import goods at lower cost. In many countries in Africa, the exporters are likely to be the producers of coffee, cocoa, and other cash crops; the importers are likely to be a mix of farmers producing food crops and smalltime manufacturers who produce processed foods, clothing, and other consumer goods. And members of the governing elite tend to have a strong preference for goods imported from abroad. In the short run, at least, this intervention in the market for foreign exchange thus imperils the majority of those who inhabit the real economy while benefiting those who govern them. Now follow the story one more step and note that, as shown in Figure 4, at 5 cedis per dollar, the market is not in equilibrium: the quantity of dollars demanded exceeds the quantity supplied. Given that exporters lack the incentives to produce more for export, the supply of dollars is quantity constrained; the market can only equilibrate as a result of changes in price. The price of dollars therefore rises; it rises above its market price. By seeking to lower its price, the government has rendered foreign exchange scarce and therefore more costly. It has created a black market premium for foreign exchange. 4

5 Returning to the actors in this story, we have already noted that the government s management of the exchange rate redistributes purchasing power from those who produce goods to those who, like the government, produce services. Now we see that these measures have created an additional economic resource: were a minister or permanent secretary to secure foreign exchange at the official price 5 cedis to the dollar she can then sell them in the black market for 15 cedis to the dollar, tripling her earnings. 8 Alternatively, she can confer them upon an influential supporter, thereby fortifying her political base. Lying behind the existence of the black market premium, then, is a story that links the choice of public policies to the generation of privilege. Access to foreign exchange is regulated politically, enabling those who control it to accumulate wealth and, by doing favors, power as well. The very policies that concentrate wealth and power erode the incentives to make productive use of land, labor, and capital. Were we to look at the governments intervention in markets for credit or transport or the subsidized provision of fertilizers or pesticides, we would encounter similar stories. The reason we anticipate that institutional reform will alter policy preferences now becomes clear. While those at the top, politically, may have benefitted from these policies, the majority of the citizens did not. With the introduction of competitive elections, it is then not just the elite who determine the political future of politicians; it is also the broader electorate, many of whose members have suffered as a consequence of government policies. We therefore find it plausible that changes in Africa s political institutions might lead to changes in policy preferences and, indeed, to the favoring of policies that reward those who are better at combining land, labor and capital into the production of goods. Some may challenge this line of reasoning because it appears to presume an electorate that is well informed and capable of understanding how the policies chosen by governments affect their wellbeing. But being ambitious for office, politicians have incentives to bear the costs of information; and being better educated than many members of the electorate, they can do so more easily. Politicians have the incentives and capacity to inform the public. But what of the argument that elections in Africa are not issue based, but rather turn on the allocation of pork and on the supply of private rather than public goods. There can be no question but that politicians in Africa distribute money and projects with a keen eye for electoral advantage. But neither can there be any question but that with the re-introduction of electoral politics and the re-enfranchisement of Africa s people, such strategies become less attractive. Where but a few control the fate of a politician, a small number of bribes and favors might well secure his future. But with the enfranchisement of virtually the entire citizenry, the costs of this strategy rise. Better to devote time, effort, and funds to the creation of public goods, the benefits of which can be enjoyed by a numerous people. Public goods are likely to displace private 8 While producing nothing. 5

6 benefits in the political production function, then, when the institutional environment changes from authoritarian to electoral regimes. 9 We now turn to exploring the evidence for this argument. We begin by defining our variables and describing our data. We then turn to our efforts to evaluate our argument. Data and Indicators To explore the links between institutional change, policy change, and economic growth in Africa, we draw upon data from a sample of 35 African economies, We focus our attention on measures of political institutions, total factor productivity, and public policies. We present the definitions and document the sources of our control variables in Appendix A. Political Institutions To characterize Africa s political institutions, we make use of two measures. The first was generated by the Harvard Africa Research Program (Bates, Ferree et al. 1996) and subsequently incorporated into the World Bank s Database of Political Institutions as the Executive Index of Electoral Competition. (Beck, Clarke et al. 2001). The measure maps institutions to numbers in the following manner: Level 1 -- No executive exists. Level 2 -- Executive exists but was not elected. Level 3 -- Executive was elected, but was the sole candidate. Level 4 -- Executive was elected, and multiple candidates competed for the office; opposition parties not allowed. Level 5 Executive was elected and multiple parties were legally permitted by law to compete for the office. Level 6 -- Candidates from more than one party actually compete in the election, but the President wins more than 75% of the vote. Level 7 -- Candidates from more than one party competed in the election, and the President won less than 75% of the vote. 9 This line of reasoning derives from Adam, C. and S. A. O'Connell (1999). "Aid, Taxation and Development in Sub-Saharan Africa." Economics and Politics 11(3): , Bueno de Mesquita, B., A. Smith, et al. (2003). The Logic of Political Survival. Cambridge MA, The MIT Press. 6

7 In much of the analysis that follows, we employ a dummy variable called elecomp6 -- that takes the value 1 when the government is rated 6 or above and 0 otherwise. We judge the first set to be subject to the force of political competition; those that score 5 or below we deem noncompetitive or, more loosely, authoritarian. Many discussion of institutions tend to characterize them broadly. Using elecomp6, we seek to be more precise. We count as competitive only those polities whose head of state was selected in an election in which opposition parties were legal, put forward a challenger, and gained at least one quarter of the votes case in the election. 10 We also employ a measure devised by Polity, called Polcomp. 11 On the basis of information gleaned from the polity codebook, we combined all observations rated 9 or 10 into one category. According to the codebook, the countries thus classified allowed the formation of organizations opposed to the government in power and political participation by groups that had formerly been excluded on the basis of their ideology, color or religion. We then created a dummy variable called polcomp which took the value 1 when Polity assigned a country 9 or 10 on the Polcomp variable. 12 As can be seen in Figure 5, in the late 1990s, Africa s political institutions changed. In the case of elecomp6, this change was abrupt. The proportion of observations with electoral competition increased from just over 25 percent in the late 1980s to nearly 90 percent by Polcomp910 followed a similar but less radical course, but nonetheless still more than doubled over that same period). 13 The rising value of these indices underscores the introduction of greater levels of political competition in the selection of national executives. Increasingly, those who led Africa s states were those who had emerged victorious from national elections in which rival political parties advanced competing candidates for office. 14 Public Policies In the era marked by the debt crisis and the efforts at structural change, many governments in Africa 15 strove to restore macro-economic balances. Some argue that premium paid for the local 10 See Bates, Ferree, et. al. (1996) for details. 11 Marshall, et. al. (2013) 12 The correlation between the 5-year period averages of EIEC and Polcomp is 0.757; the correlation between their dummy variable constructions described in the text is Thus, while measuring related institutional characteristics, these indicators are clearly distinct from one another. 13 See Bratton, M. and N. van de Walle (1997). Democratic Experiments in Africa. Cambridge, Cambridge University Press. And Bates, R. (2009). Political Reform. The Political Economy of Economic Growth in Africa, B. J. Ndulu, S. O'Connell, R. Bates, P. Collier and C. Saludo. Cambridge, Cambridge University Press. 14 Bates, R. (2009). Political Reform. The Political Economy of Economic Growth in Africa, B. J. Ndulu, S. O'Connell, R. Bates, P. Collier and C. Saludo. Cambridge, Cambridge University Press. 15 Bank, W. (1986). Sub-Saharan Africa: From Crisis to Sustainable Development. Washington DC, The World Bank., Mosley, P., J. Harrington, et al., Eds. (1991). Aid and Power. New York and London, Routledge., Bank, W. (1997). Adjustment Lending in Sub-Saharan Africa: An Update. Report No O. E. Department. Washington DC, The World Bank. 7

8 currency in black markets reflects the degree to which government policies have distorted the macro-economy. We use a measure of that premium as an indicator of these changes in macroeconomic policy. Economic reform involved more than economic stabilization, however; it also required the withdrawal of governments from industries, the de-regulation of markets, the privatization of firms, and the reduction of trade barriers, both internally and at the borders. Using data from the International Monetary Fund (IMF), the World Bank, central banks, and other sources (Giuliano, Mishra et al. 2013) constructed a composite index of deregulation, focusing on the removal of restrictions and controls in product markets, agriculture, trade, and finance. We use changes in the black-market premium and the index of de-regulation as measures of changes in public policy. We list the variables, define them, and note their sources in Appendix A. The questions we address are: Do the changes in Africa s political institutions bear a significant and plausibly causal relationship with changes in these policies? And do the changes in political institutions and public policies appear causally related to changes in total factor productivity? 16 Descriptive Evidence The initial evidence suggests the possibility of affirmative answers to these questions. The evidence consists of data concerning policy choices and political institutions with and without and before and after the movement to competitive politics. As a first cut at a with/without comparison, we performed simple t-tests (for both of our treatment indicators of political competition) for differences in levels of income, TFP, black market premium and regulatory reform. With only one exception among the resulting eight tests, we find statistically different outcomes such that the existence of political competition is associated with higher levels of income, TFP, and regulatory reform, and a lower level of black market premium. 17 Figure 6 repeats the growth decomposition of Figure 3 but with the sample split between countries with and without electoral competition (defined by elecomp6). It is clear that the negative rates of TFP and GDP growth in the continental average are driven largely by the performance of uncompetitive systems. In Figure 7 we examine the temporal patterns in our outcome variables in the years preceding and following countries transition into electoral competition. 18 Panels A and B report the 16 For further details regarding these measures and the sources from which they are taken, consult Appendix A which lists as well the other variables introduced in our estimations. 17 Only in the case of the t-test for difference in level of TFP using elecomp6 as our treatment do we fail to reject the null hypothesis of equality across sample means. 18 We estimate these patterns by replacing X in equation (1) with several lags and leads of the time dummies (for 3- year periods) relative to the period of transition into electoral competition. Figure 7 graphs the point estimates for these lags and leads from the resulting difference-in-difference specification. 8

9 average levels of GDP before and after this transition, while panels C and D trace the levels of TFP; in both instances, we employ both indicators of electoral competition. While the confidence intervals are wide, the point estimates generally support our argument. Although the increase in GDP following the transition into elecomp6 is more moderate and short-lived, the broad message of Figure 7 is that both GDP and TFP increased following countries transitions into electoral competition. It is important to note that the effect (economic performance) did not precede the treatment. Changes in economic performance are not related to changes in electoral competition because those who dwelt in countries with rising TFP or per-capita incomes preferred to be governed by leaders chosen in competitive elections. Multivariate Analysis It is, of course, possible that other variables are at play. 19 To purge our estimates of the potential bias arising from the exclusion of such variables, we turn to multivariate models and introduce a series of controls. While we have focused on the significance of institutional change, a second political factor could be significant: conflict, and specifically civil wars, whose number, intensity and geographic spread waxed and waned over the sample period. 20 We therefore include a dummy that takes on the value 1 for each country year in which there is was civil war. We control as well for whether a country was participating in a program under the direction of the International Monetary Fund. Given the growing importance of conditionality which over time came to include institutional measures the possibility arises that policy choices and economic performance on the one hand and institutional changes on the other could be related because of being joint products of IMF interventions. We address the role of commodity price shocks by including a dummy variable for whether a country is mineral rich, along with interactions between those dummies and dummy variables for each time period. 19 For recent discussions, see Ross, M. L. (2013). The Oil Curse: How Petroleum Wealth Shapes the Development of Nations. Princeton N. J., Princeton University Press. And Haber, S. and V. Menaldo (2011). "Do Natural Resources Fuel Authoritarianism? A Reappraisal of the Resource Curse." American Politcal Science Review 105(1): For a survey, see Bates, R. H. (2008). When Things Fell Apart: State Failure in Late Century Africa. New York, Cambridge. 9

10 In addition, by including both country and time period dummies, we address the possibility of selection into treatment based on unobservables. The former account for selection into treatment based on time-invariant country-specific unobservables; the latter control for the impact of unobservable period-specific cross-country shocks, such as those resulting from sharp rises in the prices of traded commodities. 21 Given these control variables, we argue, selection into electoral competition is rendered conditionally independent. Our interpretation of Africa s renaissance emphasizes politics. The more fashionable alternative would be to emphasize the external demand for petroleum and minerals. By way of rejoinder, note that Figure 1 demonstrates that the turnaround in African growth began around 1990, while international oil prices declined throughout the 1990s. Further, during the 2000s, when oil prices rose rapidly, Table 1 indicates that the growth rate of Africa s oil exporters was slightly slower than that of its importers. Note as well that we control for the effects of commodity exports in by including a dummy variable for resource rich countries and by interacting that dummy with dummy variables for each time period. 22 Endogeneity bias constitutes a third threat. Consider, for example, our attempt to control for the impact of the International Monetary Fund (IMF). Given its importance in Africa, we dare not omit consideration of this agency. But the problem remains: participation in an IMF program might signal a country s determination to introduce both political and economic reforms. A similar problem arises with respect to oil or mineral deposits. As discussed in the literature on the resource curse, countries thus endowed tend both to falter economically and to remain authoritarian in their politics. To address the potential endogeneity of such time-varying variables, we employ a System-GMM dynamic panel estimator. In addition, to smooth the noise inherent in our data, we create a panel of 5-year averages for all variables. Specification To test our arguments, we employ a difference-in-difference specification. Given that institutional change occurred in different years in different countries, our initial specification takes the form: (1) llllll iiii = γγllllll iiii 1 + αα ii + λλ tt + δδdd iiii + XX iiii ββ + εε iiii where YY iiii is the log level of GDP or total factor productivity in country i in year t, αα ii are timeinvariant unobservable country effects, λλ tt are dummy variables for each 5-year time period, X is the vector of observed covariates specified above, DD iiii is a dummy equal to one for each country- 21 Including explicit controls for fuel and mineral exports proved impractical, as we lose over one-third of our observations. 22 Beny and Smith (2009) find evidence for the roles of both higher commodity prices and improved economic management in explaining Africa s growth acceleration, but suggest that the latter is essential for preserving growth. 10

11 period observation in which there is electoral competition, δδ provides a measure of the relationship between electoral competition and the level of GDP or TFP (which we assume to be a constant), 23 and XX iiii is the vector of control variables. Our main dependent variables often appear quite stable over time. In such cases, where γγ is not statistically different from 1, we constrain it to equal 1 by subtracting llllyy iiii 1 from each side of equation (1). In the resulting specification, the dependent variable becomes the growth rate of Y. 24 To address problems of both potential endogeneity and of bias arising from the inclusion of a lagged dependent variable with fixed country effects, we employ a System-GMM estimator (Blundell and Bond, 1998). The validity of the estimates generated by the difference-in-difference specification depends upon an identifying assumption: that the treated and untreated observations follow a common trend. Revisiting Figure 5, note that the institutional reforms cluster in the mid-to-late 1990s. By estimating equation (1) with the sample limited to pre-1990 data, we used this temporal clustering to search for common pre-treatment trends in TFP growth. We failed to detect any difference in trends prior to treatment. Analysis Our analysis proceeds in three steps. We first estimate the relationship between the nature of a country s political institutions and of the growth of its GDP (Table 3) and total factor productivity (Table 4). We then explore the relationship between electoral competition and policy reform, first as measured by changes in the black market premium (Table 5) and then by regulatory reform (Table 6). Table 7 closes the argument by regressing productivity growth on both political reform and policy reform. To the extent that political reform affects productivity growth through its impact on policy reform, these final specifications will exhibit a reduced effect of political reform and a significant impact from policy reform. In Table 3, columns 1 and 5 relate the growth of GDP to the presence or absence of political competition without added time-varying controls. In column 1, the measure of competition is elecomp6; in column 3, it is polcomp910. Columns 2 and 6 repeat the exercise while controlling for the impact of civil war and of the IMF. In all four models, countries that allowed for 23 Since the data are 5-year averages, this amounts to an indicator of the proportion of the period in which a country had electoral competition. 24 In terms of equation (1), δ is the short-run effect of D on Y. With partial adjustment towards a long-run equilibrium relationship given by γγ, the long-run effect of D on Y is given by δδ (1 γγ ). In the case where γγ 1, adjustment becomes instantaneous and the long-run and short-run effects coincide. In presenting our results, we report specifications with γ constrained to equal unity where statistically indicated, and specifications with the lagged dependent variable (and the implied long-run coefficient) when γγ is statistically different from unity. 11

12 competition for national political office exhibited higher rates of growth. These results are robust as well to controls for the impact of mineral wealth. Columns 3 and 7 introduce a dummy variable indicating mineral rich countries, and columns 4 and 8 include in addition interactions between that dummy and the dummy variables for each time period. In each case we continue to find a statistically significant positive impact of electoral competition on GDP. 25 Table 4 reports estimates for the relationship between political institutions and a second measure of economic performance: the level of total factor productivity. 26 Columns 1-2 and 4-5 repeat the steps taken in Table 3. To address potential confounders in the measure of TFP, columns 3 and 6 include the growth rates of aggregate factor inputs (physical and human capital). 27 In five of the six models reported in Table 4, countries that allowed for political competition exhibited higher rates of TFP growth; in four, the coefficients were significant at the.05 level or above. When estimating models 4 and 5, we cannot discount the possibility of second-order serial correlation, thus casting doubt on the exogeneity of the instruments in those specifications. This problem disappears, however, in column 6. Here, as in Table 4, the point estimates for polcomp910 tend to be smaller than those for elecomp6 a result to be expected from its slower rate of increase (Figure 5). In Table 5, we address the possibility that the impact of institutions on growth might run directly through the accumulation of physical and human capital rather than through total factor productivity. Recently Stasavage (2005) has noted the changes in educational funding introduced by democratic governments in Africa. Viewed more broadly, Stasavage s finding suggests that governments that tolerate political opposition also tend to favor investing in human and physical capital as a way to improve the welfare of their citizens. While we acknowledge Stasavage s contribution, we are less inclined to attribute Africa s resurgence to capital formation. Nor do we find persuasive the claim that governments competing for votes would favor investment; it seems more plausible that they would favor consumption, and indeed the literature on political business cycles lends credibility to this claim (Block, Ferree, and Singh, 2003; Drazen, 2001). Testing for the potential effect of electoral competition on stocks of human and physical capital in Table 5, the relationships that emerge (see columns 1 and 3) are weak and significant at only the.10 level, and only for 25 Note that these point estimates indicate changes across 5-year periods. 26 The growth rates of TFP in Figures 3 and 6 are drawn from Block s work for the Economic Commission for Africa (see Economic Commission for Africa 2014) and are based on data from PWT8.0. The regressions in Table 4 use the TFP ( ctfp ) data presented in PWT 8.0, in which each level of TFP for a given country is expressed as a percentage of the level of TFP in the United States. As discussed above, in Table 4, the TFP data appear as growth rates. We have chosen so to specify them because when we estimated a dynamic panel model (equation 1) we found the coefficient on the lagged dependent variable statistically indistinguishable from unity.. 27 These results are also robust to the inclusion of indicators for mineral wealth and their interaction with period dummies. 12

13 elecomp6. The AR(2) calls even these results into question; they suggest the presence of serial correlation, thus casting doubt on the exogeneity of instruments. Given the weakness of the evidence of alternative channels, we find that TFP appears to be the primary channel through which political competition contributed to Africa s post-2000 growth. We turn next to an exploration of the mechanisms at play. Mechanisms The data thus far suggest that changes in Africa s political institutions played a significant role in the revival of its economies, and that the revival of economic growth appear to have been ignited by the impact of institutional change on the manner in which people made use of the factors of production. But just why would institutional change affect economic incentives? The reason, we have suggested, is that institutional change led to changes in public policy. Table 6 explores the relationship between the nature of political institutions and the nature of public policies. Columns 1-4 address changes in the black market premium; columns 5-8, changes in the index of regulatory reform. Turning first to models 1-4, we find a negative and significant long-run effect of elecomp6 on the black market premium, both with and without controls for civil wars and IMF agreements. 28 The long-run effect of polcomp910 on the black market premium is also negative, but statistically significant only when we include those timevarying controls. Switching our attention to models 5-8, we find that only one measure of institutions, polcomp910, is significantly related to the index of regulatory reform. By including both the treatment (measures of institutions) and mediating (measures of policy) variables, the models in Table 7 enable us to discern whether the effect of institutional change runs through changes in policy. Should the coefficient on the treatment variable decline in magnitude or significance when the mediating variables the size of the black market premium or the index of regulatory reform are incorporated into the model, that would suggest that some portion of the effect of institutions on economic outcomes is transmitted via the effect of institutions on policy choices. 29 Columns 1-4 report the evidence regarding differences in TFP; columns 5-8 report the evidence for differences in the growth of GDP. The first two specifications in both halves of the table report estimates of the relationship while employing pocomp910 as the treatment variable; the 28 In these specifications, the point estimates for the lagged dependent variable is statistically different from unity, permitting us to distinguish between long-run and short-run effects. In columns 1 4, these short-run effects are significant only when including out time-varying controls. 29 See Imai, K., L. Keele, et al. (2010). Unpacking the Black Box: Learning about Causal Mechanisms From Experimental Observational Studies. Working Paper. Princeton, Department of Government. 13

14 second pair of specifications in the two halves of the table report estimates based upon the use of elecomp6. For each of these sets of treatments and outcomes, we present pairs of specifications to determine the extent (if any) to which the effect of political competition is transmitted through its impact on policy. The even numbered columns report the relationship between institutions and economic performance; the odd numbered columns report estimates of that relationship while introducing policy measures among the controls. With regard to TFP, the evidence in Table 7 is clear. For both indicators of political competition, including our mediating policy variables reduces the point estimates for political competition by nearly one-half, and renders both statistically insignificant. Political competition thus appears to have contributed to TFP, and a substantial portion of its impact appears to have been channeled through its effect on policy making. Similar findings characterize the estimates of the relationship between institutionalized changes and economic growth (columns 5-8), although in one model there is reason to doubt the exogeneity of the instruments. Discussion We, like others, have been cheered by Africa s recent economic resurgence, and in this article we have sought to account for it. Throughout much of Africa, self-selected governments ones that that gained power as a result of military coups or were installed by a party that could not legally be opposed -- have given way to governments that rule because they have received a majority of the votes in a competitive election. In addition, governments less frequently distort macroeconomic prices or intervene in markets, with the result that economic incentives can better elicit the efficient use of labor, and capital. We have argued that these changes are related: changes in the nature of Africa s polities produced changed political incentives, such that governments changed their policies, with the result that producers now make more efficient use of factors of production. While our analysis leaves much of the growth of Africa s economies unexplained, the relationship between the reform of political institutions and the revival of economic growth emerges as a robust finding. If confirmed, our analysis is of obvious importance to policy makers, to humanitarians, and, above all, to those who live in Africa. Our findings are also significant to scholars, for they cut to the core of contemporary theorizing in the study of development. North (North 1990), Acemoglu and Robinson (Acemoglu and Robinson 2012), and others (Harriss, Hunter et al. 1995) argue that differences in institutions correlate with differences in economic performance, both historically and in the contemporary world. When politicians must compete for approval from the citizens in order to secure political power, they argue, then they face incentives to adopt policies that encourage the production of wealth rather than predation. Insofar as we have identified a plausibly causal relationship between the changes in Africa s political institutions 14

15 and the growth of total factor productivity, we have found evidence in support of these arguments. Given the results of our analysis, it is sobering to view our core finding in light of political trends in Africa. For several years, Freedom House has decried the decline in in the quality of political and civil rights on the continent. As stated in its report for 2010: 2009 marked the fourth consecutive year in which global freedom suffered a decline the longest consecutive period of setbacks for freedom in the nearly 40-year history of the report. These declines were most pronounced in Sub-Saharan Africa. As captured in Figure 8, in 2012, a higher percentage of the states in Africa moved to lower levels and a lower percentage ascended to higher levels of political and civil liberties, according to Freedom House, compared to the world as a whole. Worth mentioning too is the work of Daniel Posner, who has collected data on term limits in Africa. When competitive elections were introduced in the period of political reform, term limits were adopted in 33 African states. As of 2014, the limits had not been reached in 9 of these states; but in 11 of the remaining 24 states (46%), the chief executive and his supporters sought to abolish them; and in 8 (33%) they succeeded in doing so. Powerful forces are at play in Africa that seek to reverse the political reforms of the 1990s. If our analysis is correct, this political reversal will be economically costly. 15

16 References Acemoglu, D. and J. A. Robinson (2012). Why Nations Fail: The Origins of Power, Prosperity, and Poverty. New York, Crown Publishers. Adam, C. and S. A. O'Connell (1999). "Aid, Taxation and Development in Sub-Saharan Africa." Economics and Politics 11(3): Bank, W. (1986). Sub-Saharan Africa: From Crisis to Sustainable Development. Washington DC, The World Bank. Bank, W. (1991). Governance and Development. Washington DC, The World Bank. Bank, W. (1997). Adjustment Lending in Sub-Saharan Africa: An Update. Report No O. E. Department. Washington DC, The World Bank. Bates, R. (2009). Political Reform. The Political Economy of Economic Growth in Africa, B. J. Ndulu, S. O'Connell, R. Bates, P. Collier and C. Saludo. Cambridge, Cambridge University Press. Bates, R. H., K. Ferree, et al. (1996). Toward the Systematic Study of Transitions. Development Discussion Paper No Cambridge MA, Harvard Institute for International Development. Beck, T., G. Clarke, et al. (2001). "New Tools and New Tests in Comparative Political Economy: The Database of Political Institutions." World Bank Economic Review. Beny, L., and L. Cook (2009). Metals or Management? Explaining Africa s Recent Economic Growth Performance, American Economic Review: Papers & Proceedings 2009, 99:2, Block, S. A., K. Ferree, and S. Singh (2003). "Multiparty competition, founding elections and political business cycles in Africa." Journal of African Economies 12: Blundell, R. and S. Bond (1998), Initial Conditions and Moment Restrictions in Dynamic Panel Data Models, Journal of Econometrics87: Bratton, M. and N. van de Walle (1997). Democratic Experiments in Africa. Cambridge, Cambridge University Press. Bueno de Mesquita, B., A. Smith, et al. (2003). The Logic of Political Survival. Cambridge MA, The MIT Press. Dahl, R. (1971). Polyarchy. New Haven, Yale University Press. Drazen, A. (2001). The Political Business Cycle After 25 Years, NBER Macroeconomics Annual 2000,Cambridge MA: MIT Press. Easterly, W. and R. Levine (1997). "Africa's Growth Tragedy: Policies and Ethnic Divisions." Quarterly Journal of Economics 112(4):

17 Economic Commission for Africa, Economic Report on Africa 2014: Dynamic Industrial Policy in Africa. Addis Ababa, UNECA. Giuliano, P., P. Mishra, et al. (2013). "Democracy and Reforms: Evidence from a New Dataset." American Economic Journal: Macroeconomics, American Economic Association 5(4): Haber, S. and V. Menaldo (2011). "Do Natural Resources Fuel Authoritarianism? A Reappraisal of the Resource Curse." American Politcal Science Review 105(1): Harriss, J., J. Hunter, et al., Eds. (1995). The New Institutional Economics and Third World Development. London and New York, Hopkins, A. G. (1973). An Economic History of West Africa. New York, Columbia University Press. Imai, K., L. Keele, et al. (2010). Unpacking the Black Box: Learning about Causal Mechanisms From Experimental Observational Studies. Working Paper. Princeton, Department of Government. Marshall, M., T Gurr, and K. Jaggers (2013). h Polity IV Project Dataset Users Manual, Center for Systemic Peace. Mosley, P., J. Harrington, et al., Eds. (1991). Aid and Power. New York and London, Routledge. Ndulu, B. J., S. A. O'Connell, et al. (2008). The Political Economy of Economic Growth in Africa, New York, Cambridge University Press. North, D. C. (1990). Institutions, Institutional Change, and Economic Performance. New York, Cambridge University Press. Perkins, D., S. Radelet, D. Lindauer, and S. Block (2013). The Economics of Development, 7 th edition. New York: W.W. Norton & Co. Radelet, S. (2010). Emerging Africa: How 17 Countries Are Leading the Way. Washington DC, Center for Global Development. Ross, M. L. (2013). The Oil Curse: How Petroleum Wealth Shapes the Development of Nations. Princeton N. J., Princeton University Press. Sambanis, N., and M. Doyle Making War and Building Peace: United Nations Peace Operations. 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 Political Science Review. 49(2): Vreeland, J. (2007), The International Monetary Fund: Politics of Conditional Lending. New York, NY, Routledge. 17

18 Young, A. (2012). The African Growth Miracle. NBER Working Paper No Cambridge MA, NBER. 18

19 Growth Rate of GDP Growth Rate (%/yr) -2% 0 2% 4% 6% Real GDP per Worker Real GDP Year constant 2005 national prices Figure 1. GDP Growth, (Source: Penn World Tables 8.0 and Economic Commission for Africa, 2014) 19

20 Growth Rate (%/yr) -4% -2% 0 2% 4% 6% Southern All North East Central West Year constant 2005 national currency Growth of GDP per Worker - by region - Figure 2. GDP Growth by Region (Source: PWT 8.0, and Economic Commission for Africa, 2014) 20

21 Growth Decomposition by Decade Avg. Annual Growth Rate -2% -1% 0 1% 2% 3% 1960s 1970s 1980s 1990s 2000s Growth of GDP per worker Contribution of Capital Stock per Worker Contribution of Human Capital Contribution of TFP GDP Growth = sum of contributions of physical capital per capita + human capital and TFP Figure 3. Sources of Growth Decomposition by Decade (Source: PWT 8.0 and Economic Commission for Africa, 2014) 21

22 Figure 4. The Market for Foreign Exchange S 15c/1$ 10c=1$ 5C/$1 D QS = QD 5C/$1 22

23 Indicators of Electoral Competition Proportion of Sample with Electoral Competition mean of polcomp910 mean of elecomp6 Figure 5. Indicators of Electoral Competitiveness (Source: Beck & Clarke, 2001 and Polity IV) 23

24 Growth Decomposition by Decade - With and Without Electoral Competition - Without Electoral Competition With Electoral Competition -3% -2% -1% 0 1% 2% n = 495 n = Growth of GDP per Worker Contribution of Capital Stock Contribution of Human Capital Contribution of TFP Electoral Competition = 1 if EIEC>=6 Figure 6. Sources of Growth, With/Without Electoral Competition (Source: PWT8.0 and Economic Commission for Africa, 2014) 24

25 Lags/Leads Relative to Political Transitions Period Dummy Point Estimates GDP Panel A: Transition Into Elecomp Yr Periods Period Dummy Point Estimates GDP Panel B: Transition Into Polcomp Yr Periods Period Dummy Point Estimates TFP Panel C: Transition Into Elecomp Yr Periods Period Dummy Point Estimates TFP Panel D: Transition Into Polcomp Yr Periods Figure 7. Temporal Patterns of GDP and TFP Before & After Transition to Political Competition (Source: PWT 8.0 and author calculations) 25

26 Figure 8. Political Rights and Civil Liberties Data from Freedom House, Freedom in the World 2014, freedomhouse.org Changes in Political Rights and Civil Liberties, 2012 (Percent of Nations Whose Scores Had Changed) World Africa World Africa Up Down Political Rights Civil Liberties 26

27 Table 1. Growth Rates of GDP per Worker by Endowment, Geography, & Oil Decade 1960s 1970s 1980s 1990s 2000s Endowment Mineral Poor Mineral Rich Geography Coastal Landlocked Oil Oil Importer Oil Exporter Africa (all) Source: PWT8.0 and World Bank 27

28 Table 2. Growth Decomposition by Region Decade 1960s 1970s 1980s 1990s 2000s Region Growth of GDP/Worker Contribution of: East Human Capital Physical Capital TFP Growth of GDP/Worker Contribution of: Central Human Capital Physical Capital TFP Growth of GDP/Worker Contribution of: Southern Human Capital Physical Capital TFP Growth of GDP/Worker Contribution of: West Human Capital Physical Capital TFP Growth of GDP/Worker Contribution of: North Human Capital Physical Capital TFP Africa (all) Growth of GDP/Worker Contribution of: Human Capital Physical Capital TFP Source: PWT8.0 & author calculations 28