Development Workshop Emiko Nishi and Aleksandra Olszewska

Outline General Overview: Why is it hard to assess the inequality-growth relationship? Evolution of Empirical Studies on Inequality - From 1950’s to present Where do the contradictory results come from? Methods. So what exactly are the results? Interpretation of the results: the complex effects of inequality - Short- & Medium vs. Long-run effects Concluding Remarks

Overview Inequality seems to influence growth >> yet, mainly for the following reasons, assessing the impact of inequality on growth has been challenging: the lack of sufficient data available (e.g. inequality) (i.e. Inequality computed by gross income vs. expenditures Income concept applies to individual vs. household ) omitted variables and endogeneity problems

Evolution of Empirical Studies on Inequality 1950’s and 1960’s >> Nicholas Kaldor and Simon Kuztnet showed that there is a trade-off relationship between reducing inequality and promoting economic growth. In the post world war period >> Countries in Latin America were growing at a fraction of the avg. East Asian rate. What is the true relationship between inequality and growth?

Evolution of Empirical Studies on Inequality During the 1990’s >> many empirical studies suggested the negative- and statistically significant impact of inequality on growth. (e.g. Barro & Sala-i-Martin, 1995; Alesina & Perotti, 1994; Alesina & Rodrik, 1994; Persson & Tabellini, 1994). The late 1990’s >> some started questioning the robustness of these results by pointing out that the negative relationship is mainly driven by exogenous factors ( i.e. level of development, income, and political institutions)

Evolution of Empirical Studies on Inequality Forbes (2000) used more sophisticated/complex econometric methodology to overcome the risk of biased estimators resulting from: - country-specific characters - global shocks which may influence growth Used more comprehensive dataset for inequality developed by Deininger and Squire (1996). >> a positive & statistically significant relationship is suggested for the short- and medium-run.

Evolution of Empirical Studies on Inequality Is inequality really bad/good for growth? Where does the pattern of existing results come from? “The negative effect of inequality on growth shows up for poor countries, but the relationship for rich countries is positive” (Barro, 1999) >> the pattern of existing results may be driven by idiosyncratic differences

Evolution of Empirical Studies on Inequality Research Question Today: Are the differences in empirical results driven by countries included, time period used, or independent/control variables used in each model? Or are they simply driven by the choice of methods?

Halter, Oechslin and Zweimüller (2010) It’s not that any of the views was wrong but the results depended on the methods used. Two main methodologies: Estimations based on time-series variation only Estimations based also on cross-sectional variation

Time-series variation Regresses changes in the log output against changes in the lagged inequality (e.g. first-differences GMM estimator) Similar to the fixed effects estimator in that it uses only within-country variation Solves the problem of unobserved heterogeneity Criticised for ignoring cross-sectional variation

Time-series and cross-sectional variation Regresses the log output (level) against a lagged Gini coefficient. (e.g. system GMM estimator) Exploits cross-country variation >> more efficient

Results First difference GMM estimator (time-series variation only) gives a positive relationship. Figure 1: inequality and output (changes) System GMM estimator (time-series and cross-sectional variation) gives a negative relation. Figure 2: inequality and output (levels)

Results - cont. Apply the two estimation methods to a similar dataset to the one used by Forbes (2000) to check if the results systemaically differ. First-differences GMM estimator: Like Forbes, they find a significant, positive relationship of a similar magnitude (coefficient of inequality = against Forbes’s ) Robustness check: more countries, more time periods, divide countries by income and change the structure from 5-yr to 10-yr periods >> still get a positive, significant relationship

Results - cont. System GMM estimator: Full sample >> negative but not significant relationship between output growth and inequality *full sample=adding more countries and more periods to the one used by Forbes (2000) Divide countries according to income >> 1. positive association for high income countries but not significant, 2. negative and statistically significant impact for lower-middle income or low income countries Robustness check: switching to a 10-yr structure doesn’t change the results

Interpretation Positive channels: Savings Selection of physical investment projects Positive demand-side effects through R&D Negative channels: Fiscal policy (taxes & government spending) Human capital formation Political instability

Interpretation – cont. Positive – purely economic mechanisms – materialise relatively fast – short- and medium-run effects Negative – political-economy-social arguments – materialise slowly – long-run effects It is exactly this pattern (positive in the short, negative in the long-run) which is responsible for the different estimation results.

Interpretation – cont. First-difference GMM estimation regresses changes in output on moderately lagged changes in inequality >> the estimator picks up only the short- and medium-run effects >> positive relationship System GMM estimator finds a negative link if 2 conditions are satisfied: 1) long-run effects must dominate the short or medium- run effects 2) within-coutry inequality has to be persistent „high inequality, low income” or „low inequality, high income” – any points that do not fit into this pattern are treated as noise

Additional arguments High inequality >> the poor prefer direct transfers over investment in the public good - higher ineqality generates a short-run increase and a long-run decrease in output The poor are usually less productive than the rich (less educated, unable to use advanced technology) >> lower output & higher, persistent inequality

Conclusions How can the two, contradictory results of decades of scientific research be reconciled? Some are short-run while others are long-run So, is inequality good or bad? Rational economists answer: look at long-run >> inequality is bad Thank you