The Real Effects Of Debt

Intro:

This study looked at 18 OECD countries from 1980-2010. This study was different because it divided the debt up into 3 different classifications: household debt, non-financial corporate debt, and government debt. The authors also answer two questions- Is there a negative effect between debt and growth, and if so, is there a threshold effect. In previous economic models, debt had been neglected as a by-produce of the monetary system, namely central banks and monetary policy. “Borrowing allows individuals to smooth their consumption in the face of a variable income. It allows corporations to smooth investment and production in the face of variable sales. It allows governments to smooth taxes in the face of variable expenditures” (Cecchetti, Mohanty, Zampolli, 2012). We can see that debt, and more importantly good debt management is very important for robust economic growth. The amount of debt in advanced countries has sky rocketed since the 1980s, going overall from around 160% of GDP to nearly 350% of GDP. This is because of a more stable global environment (low interest rates, favorable tax policies, and favorable policies for private companies) that have encouraged more debt issuance (Cecchetti, Mohanty, Zampolli, 2012). When a government has high levels of debt and there’s a shock to the economy, it is limited with the amount of additional debt that it can issue. Debt is a very good way to help the economy stabilize and an economy will struggle if the government is limited in the amount of debt in can issue. “At low levels, debt is good. It’s a source of economic growth and stability. But at high levels, private and public debt are bad, increasing volatility and retarding growth” (Cecchetti, Mohanty, Zampolli, 2012). Theoretically, due to the nature of debt, people that can afford the most debt should bear the most risks for it. The impact of Debt on GrowthUsing OLS, private debt (-0. 0197), corporate debt (-0. 0204), household debt (-0. 0254), and total non-financial debt (-0. 0199) all have a statistically significant negative effect on GDP growth rates. To further their understanding, the authors first estimated a model, and then add debt features to the model. The model accounts for country and time-specific effects. They also use an overlapping 5 year average. “To minimize the potential for endogeneity bias, all regressors… are predetermined with respect to the 5 year forward average growth” (Cecchetti, Mohanty, Zampolli, 2012).

The following are the regressors that they included in the model:

• gross saving (public and private) as a share of GDP;
• population growth;
• the number of years spent in secondary education, a proxy for the level of human capital;
• the (total) dependency ratio as a measure of population structure and ageing;
• openness to trade, measured by the absolute sum of exports and imports over GDP;
• CPI inflation, a measure of macroeconomic stability;
• the ratio of liquid liabilities to GDP, as a measure of financial development;
• a control for banking crises taking the value of zero if in the subsequent five years (as identified by Reinhart and Rogoff (2008)) there is no banking crisis, and the value of 1/5, 2/5, and so forth, if a banking crisis occurs in one, two, etc, of the subsequent five years.

The authors used LSDV (least squares dummy variable) estimation to perform their analysis. (Cecchetti, Mohanty, Zampolli, 2012). The above are the results when the authors added the various kinds of debt. As we can see, public/government debt had the largest negative effect on GDP growth. The above results would seem to indicate that there’s a negative relationship between certain types of debt and GDP growth rates.

Threshold Effects:

The authors then moved on to a potential threshold effect. A graph they provided would suggest that there’s a non-linear effect of debt on GDP growth so the authors used an indicator variable to test for threshold limits. Similar to the above equation, the authors had a set of control variables and then added the various debt types to see if there was a threshold/ did the threshold change. “To examine the statistical significance of the estimated threshold, we can then use a likelihood ratio (LR) statistic…” (Cecchetti, Mohanty, Zampolli, 2012). The above result show that a threshold seems to be present. However, we see that very few of the results are statistically significant. Cecchetti, Mohanty, Zampolli (2012) come to the conclusion that high debt is harmful to growth. However, the authors don’t really offer a theoretical explanation (as is noted by themselves). They believe this is because that current models aren’t sophisticated enough to account for debt levels.