This short note provides some supplementary analysis to the regressions in Section 5 of Ghate and Wright (forthcoming), that was carried out after the refereeing process for that paper was completed, and hence could not be included in the published version. It is not a free-standing paper, but is intended to be read in conjunction with the published paper

Ghate, C.

Wright, Stephen

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Correlates of statewise participation in the great Indian growth turnaround: some preliminary robustness results

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ISSN 1745-8587 Birkbeck Working Papers in Economics & Finance   School of Economics, Mathematics and Statistics    BWPEF 1104    Correlates of statewise participation in the great Indian growth turnaround: some preliminary robustness results     Chetan Ghate Indian Statistical Institute   Stephen Wright Birkbeck, University of London        September 2011 ▪ Birkbeck, University of London ▪ Malet Street ▪ London ▪ WC1E 7HX ▪ Correlates of statewise participation in the greatIndian growth turnaround: some preliminaryrobustness resultsChetan Ghate and Stephen WrightySeptember 2, 20111 IntroductionThis short note provides some supplementary analysis to the regressions inSection 5 of Ghate and Wright (forthcoming), that was carried out afterthe refereeing process for that paper was completed, and hence could notbe included in the published version. It is not a free-standing paper, but isintended to be read in conjunction with the published paper.2 Participation in the turnaround: some prelimi-nary robustness resultsIn Ghate and Wright (forthcoming), henceforth GW we showed that whilethe common nature of the growth turnaround, as identied by the V-Factor,appears to correspond fairly well to observable shifts in India-wide economicpolicy, the quite disparate impact of the turnaround across the states (asillustrated in GW Figure 2) was striking. In GW Section 5 we used our paneldataset to attempt to provide some regression-based evidence that sheds atleast some light on this issue. However while we found some evidence ofCorresponding Author. Address: Planning Unit, Indian Statistical Institute, 7 SJSSansanwal Marg, New Delhi 110016, India. Tel:91-11-4149-3938; Fax:91-11-41493981. E-mail:cghate@isid.ac.inyDepartment of Economics, Birkbeck College, University of London, Malet Street,London W1E 7HX, UK. s.wright@bbk.ac.uk1collective explanatory power, there was a limit to how much we could sayabout individual indicators using conventional regression analysis.In Table 1 (the rst four columns of which are identical to GW Table 3)we present some evidence on the correlates of the state-wise distribution ofthe turnaround in growth after our best estimate of a breakpoint, in 1987,across both states and sectors. The table summarizes cross-sectional regres-sions in which the dependent variable is the change in average log growthacross these two sub-samples, for each of the 207 series in our largest panel(running from 1970 to 2004). The rst three columns report regressionswhere the only regressors are dummy variables for each sector and state.Consistent with the evidence of GW Figures 1 and 2, there is strong evi-dence for signicant di¤erences across both sectors and states, whether bothare included (as in regression (1)) or just state dummies (in regression (2))or just sector dummies (in regression (3)).In the remaining columns of Table 3 we investigate whether identi-able state characteristics can account for the disparate performance acrossthe states. We retain the sectoral dummies, but include 11 di¤erent statecharacteristics (all either time-invariant, or measured just before the turn-around), in place of the state dummies. In regression (4), which includesall 11 indicators, the overall goodness of t barely di¤ers from the bench-mark regression (1) and the implied restrictions are easily accepted: ie, thestate-level regressors jointly span all signicant variation across states.1However, most individual regressors in regression (4) are statisticallyinsignicant. This is unsurprising since we have nearly as many regressorsas states, and the regressors are mostly quite strongly mutually correlated.To provide some assessment of the robustness of the relationship be-tween individual indicators and participation in the turnaround, we followthe approach suggested by Sala-i-Martin (1997) in relation to cross-sectionalgrowth regressions, where it is well-known that the number of potential re-gressors far exceeds the number of regressors that can feasibly be includedin any given growth regression. We examine the distribution of coe¢ cientson individual state-level regressors, when included in all possible regressionsalongside a subset of other regressors. If a large part (or all) of the distri-bution of the resulting coe¢ cients lies to the right or left of zero, and the1For a more detailed discussion of regression diagnostics, etc, see Ghate & Wright(forthcoming).2coe¢ cient is on average statistically signicant, we follow Sala-i-Martin intaking this as evidence that the indicator has a robust relationship with thegrowth turnaround in individual states.Each of the regressions carried out in this exercise includes 5 regressors:the rst is the variable of interest; the second and third are always the sharesof agriculture and registered manufacturing (both of which are individuallysignicant in regression (4)); while the remaining two regressors are pickedfrom the set of remaining 8 regressors. We carry out a regression for everycombination of two out of eight possible regressors: thus we run 28 regres-sions per indicator. (This is a rather modest number compared to the 30,856regressions per indicator - 2 million in total - run by Sala-i-Martin). Giventhe strong mutual correlations between our regressors, 5 regressors virtuallyalways captures the great majority of the state-wise variation: the impliedF-test of the restrictions against equation (1) is not rejected at the 5% levelfor more than 9 out of 10 such regressions.As a summary indicator of robustness we use the unweighted averageCDF(0) proposed by Sala-i-Martin, where a number close to unity implies ro-bustness irrespective of sign, while a number close to 0.5 indicates a roughlyequal number of positive and negative coe¢ cients. We use the unweightedaverage of individual CDF(0) values for each equation, which does not re-quire us to assume normality. It is also more appropriate when, as here, mostof the indicators are likely to be endogenous. The pitfalls of likelihood-basedmodel averaging, as in Doppelhofer, Miller and Sala-i-Martin (2004), whichcan lead to a very high weight being placed on a very small number of re-gressions, have been analysed by Ciccone and Jarocinski (2010); but someat least of these problems had indeed already been alluded to in Sala-i-Martins (1997) original paper, leading him to give more prominence to theunweighted average CDF(0) statistic.The last two columns of Table 1 show the results, which help to shedsome light on the disparate impact of the turnaround: One strongly signicant individual e¤ect, both in regression (4) and interms of overall robustness is a negative impact of the sectoral shareof agriculture in any given state. Note that this impact does not re-ect any direct e¤ect of the resulting high weight of agriculture indampening growth of state NDP (given the relatively low growth rateof agriculture), since the regression results give each sector an equal3weight. Rather it suggests that the mere fact that a state was predom-inantly agricultural was itself an obstacle to that states participationin the turnaround in growth across all sectors. The only other individually signicant coe¢ cient in regression (4),which also appears to be extremely robust, is a negative impact ofthe share of registered manufacturing. This result directly contradictsthose of Rodrik & Subramanian (2005). They posited that the impetusfor the turnaround (which, it will be recalled, they dated signicantlyearlier), was a shift to a pro-business orientation, which they instru-mented in their regressions by the share of registered manufacturingin aggregate state level data. Our results suggest that, far from hav-ing a positive e¤ect on subsequent growth, a high share of registeredmanufacturing in any state just before our later estimated turnarounddate actually appears to have had a signicantly negative e¤ect ongrowth in that state. Furthermore, GW Figure 5 showed that regis-tered manufacturing was one of the very few sectors that actually grewless rapidly on average after 1987: this di¤erence, as measured by anegative coe¢ cient on the sector dummy in regression (3), is stronglysignicant. The fact that registered manufacturing appears to haveplayed a signicantly negative role in the turnaround is clearly morestriking than if it simply played no role at all. The remaining state characteristics are all statistically insignicant inregression (4), but their CDF(0) values suggest a quite disparate degreeof robustness when included in regressions with fewer regressors. On the positive side, literacy appears to be quite robustly correlatedwith participation in the growth turnaround; so to a lesser extent, doesthe degree of urbanisation. On the negative side, both landlocked andhighly populated states appear to have been less able to participate inthe turnaround. The former relationship is consistent with the well-documented problems with Indias transportation system (Panagariya,Chapter 18, 2008), and the apparent link between the timing of theturnaround and the time prole of trade liberalization discussed inGW Section 4. The robustness results also appear to o¤er some, albeit limited, sup-4port to Aghion et als (2008) rm-level analysis of the impact of thedismantling of the "Licence Raj". They found that in states whereemployment legislation was pro-worker (as proxied by a qualitativedummy variable), rms were less likely to be able to benet from thereforms. In our regressions the coe¢ cient on their dummy variable isalways negative, but on average not strongly signicant. It is also worth noting the state-wise regressors that do not appear tohave a robust relationship with participation in the turnaround (indi-cated by CDF(0) values close to 0.5). These include state level incomeper capita in 1987 (thus counteracting claims that have been madethat the turnaround has been restricted to a club of richer states),population growth and rainfall, as well as total development spendingas % of NDP.23 A cross-check: general-to-specic modellingIt is interesting to note that the results of our robustness exercise deliversimilar, though not identical results to a simpler econometric approach thathas tended to fall out of favour in recent years: namely, an iterative processof general-to-specic modelling. In this approach (often referred to as theLSE Approach(Hoover & Perez, 1999) insignicant regressors are progres-sively eliminated until all remaining regressors are signicant at some chosensignicance level. This approach can been subjected to a data-mining basedcritique on the grounds that p-values in the nal regression cannot be inter-preted in classical fashion, since they have arisen in a Darwinianprocessof directed search. On the other hand, as noted by Hoover and Perez, thisform of directed search should, in the limit, with su¢ ciently large datasets,converge on the true model (if such a true model exists). They reportsimulations (admittedly based on time series, rather than cross-sectionalregressions) that for a quite range of di¤erent data generating processes,suggest that the true size of t-statistics in equations that have arisen fromgeneral-to-specic modelling is actually quite close to the nominal size (ie ifa k% signicance level is chosen, roughly k% of regressors will be included2Wolcott and Clark (2003) also nd that several disaggregated, though measurable,dimensions of state development spending on physical and social insfrastructure havelittle connection with economic growth in Indian states.5erroneously).Regression (5) in Table 1 reports the results of an exercise of this typefor our dataset. We set the threshold signicance level for the marginalregressor relatively high, at 10%. Comparing the regression results withthe robustness results in the remaining two columns shows that all the re-gressors included also have reasonably high CDF(0) values, and indeed forincluded regressors there is at least a rough correspondence between p-valuesand 1 CDF(0) : Where the two approaches do di¤er is that the robustnessexercise gives fairly high CDF(0) values for two indicators - urbanisationand population (level) that were eliminated from regression (5) in the test-ing down exercise. But this turns out to be fairly readily explicable, andindeed casts some further light on the robustness results. Both of these vari-ables turn out to be highly collinear with other regressors (when regressedon the remaining regressors the equations have R2 values of 0.9 and 0.95respectively), thus when some of these are omitted in the regressions in therobustness exercise, the signicance of both indicators is boosted.4 ConclusionsThe results summarised in this note should only be viewed as preliminary.The contrast between the robustness exercise and the model selection exer-cise is also a reminder that all we are looking at is correlations with par-ticipation in the growth turnaround, not necessarily the true determinants.Thus when a given indicator appears as robustly signicant across a widerange of specications, this may simply mean that it is more reliably corre-lated with the true, unobservable, determinants. Nonetheless our results aresuggestive of future research avenues that might be pursued in investigatingthe ongoing - and very important - puzzle of why participation in the Indiangrowth turnaround has been so unevenly distributed across di¤erent states.5 BibliographyAghion, P, Burgess, R, Redding, S and Zilibotti, F (2008) "The UnequalE¤ects of Liberalization: Evidence from Dismantling the License Raj inIndia," American Economic Review, 98(4), pages 1397-1412.Ciccone, A and Jarocinski, M Determinants of Economic Growth: Will6Data Tell?American Economic Journal: Macroeconomics, 2(4), 222246Clark, Gregory and Susan Wolcott. (2003). "One Polity, Many Coun-tries: Economic Growth in India, 1873-2000." In In Search of Prosperity:Analytical Narratives on Economic Growth, (Ed.) Dani Rodrik, PrincetonUniversity Press, New Jersey.Doppelhofer, G , Miller R and Sala-i-Martin, X (2004) Determinants ofLong-Term Growth: A Bayesian Averaging of Classical Estimates (BACE)Approach.American Economic Review, 94(4): 81335.World Bank. 2004. World Development Indicators 2004. CD-ROM.Washington, DC: World Bank.Ghate, Chetan and Stephen Wright (forthcoming). "The "V-Factor":Distribution, Timing, and Correlates of the Great Indian Growth Turn-around", Journal of Development EconomicsHoover, K and Perez, S (1999) Data mining reconsidered: encompass-ing and the general-to-specic approach to specication searchEconomet-rics Journal 2,pp 167-191Rodrik, Dani and Arvind Subramanian. (2005). From Hindu Growthto Productivity Surge: The Mystery of the Indian Growth Transition.IMFSta¤ Papers, Volume 52(2), pages 193-228Panagariya, Arvind. (2008). India: The Emerging Giant. Oxford Uni-versity Press, New York.Sala-i-Martin, X (1997) "I just ran two million regressions", AmericanEconomic Review Volume 87(2) pp178-1837Regressors (1) (2) (3) (4) (5)state dummies all all none none nonesector dummies all none all all all CDF(0) Dominant Signshare of agriculture, 1987 -0.0014 (0.03) -0.0014 (0.00) 1.000 negativeshare of reg. manufacturing, 1987 -0.0036 (0.01) -0.030 (0.00) 0.999 negativereal state income per capita, 1987 0.02 (0.41) 0.555 negative% urban population, 1981 0.011 (0.62) 0.923 positiveliteracy rate, 1981 0.006 (0.84) 0.021 (0.09) 0.963 positiveaverage rainfall, 1983-1987 0.0060 (0.47) 0.593 positiveAghion et al's pro-worker dummy 0.0011 (0.82) -0.0037 (0.08) 0.840 negativelandlocked dummy -0.0145 (0.14) -0.0136 (0.05) 0.980 negativepopulation, 1981 -0.0198 (0.33) 0.980 negativepopulation growth, 1971-1981 0.542 (0.73) 0.758 negativedevelopment spending, % of NDP, 1981 0.071 (0.27) 0.786 positiveObservations 207 207 207 207 207R-bar-squared 0.307 0.115 0.170 0.310 0.322s.e. 0.036 0.041 0.039 0.036 0.036intraclass residual correlation (states) -0.035 -0.012 0.192 -0.034 -0.030intraclass residual correlation (sectors) -0.099 0.158 -0.118 -0.100 -0.100Likelihood Ratio (Chi-Squared) n/a 0.000 0.000 0.564 0.824Likelihood Ratio (F-Test) n/a 0.000 0.000 0.999 0.051Wald (F-Test) n/a 0.000 0.000 0.463 0.817* Unweighted average of individual CDF(0) (Sala-i-Martin, 1997) values in all possible regressions including variable, shares of agriculture and registered manufacturing, and two other regressors. A high value of CDF(0) indicates robustness, irrespective of sign.Tests of implied restrictions on Equation 1 (p -values)Table 1: State Characteristics and the Growth Turnaround: Cross-sectional Regression ResultsDependent variable:  Change in average log growth in state-sectoral real NDP per capita between 1970-87 and 1987-2004Regression DiagnosticsCoefficient estimates  (p -values in parentheses)Robustness Measures*

Correlates of statewise participation in the great Indian growth turnaround: some preliminary robustness results

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