In hindsight: the economic implications of the downsizing of the sugar industry

By: J.C. BHAGWANDIN, M.Sc.

The last labour force survey indicated that Guyana’s unemployment rate is 12 percent. However, at the time, the report did not consider the massive job losses and the ripple effect thereto. With those taken into consideration, Guyana’s real unemployment rate is anywhere in the region of 20-27 percent.

The closure of the sugar estates would have contributed to losses in foreign exchange; coupled with the extraction of about $40 billion from the economy, from the perspective of the multiplier effect and how an economy works – an economy effectively functions like a web of networks wherein every part is linked in an integrated manner — the consequence of this economic policy decision to right size the sugar industry, for example, would have taken out $10 billion dollars from the economy from those households in the form of their incomes.

Their incomes also help to sustain the village economy, the shops, supermarkets, the vendors in the market areas, to name a few. Moreover, these persons would be unable to pay their mortgages and other loans, which would have implications at the macro level in the banking sector in terms of non-performing loans.

To this end, an examination of some key economic indicators to further validate the above contentions is illustrated in the graphs and table below. The first graph shows total private consumption expenditure as a percentage of GDP for the past ten years, from 2009 to 2018.

The data shows that private consumption accounted for 82 percent of GDP in 2009, which reached a high of 93 percent of GDP in 2013, then went down back to 83 percent in 2014. Notably, private consumption remained above 80% of GDP for six years, through 2009 to 2014, before falling to 73.8 percent of GDP in 2015 and to the lowest of 56.33 percent of GDP as at the end of 2018.

Now, from looking at the level of non-performing loans (NPLs) in the banking sector for the same period, NPLs increased from $7.8 billion in 2009 to $11.3 billion in 2013, representing an increase of 45 percent within that five-year period; and then from $11.3 billion in 2013 to $29.7 billion at the end of 2018, representing an increase of 163 percent increase, which is more than 3 ½ times the rate of increase in NPLs over the preceding five-year period.

The sectorial concentration of the NPLs shows that the Business Enterprise accounted for 79 % of total NPLs in 2009, which then fell to 65 percent in 2013, before increasing to 70 percent in 2018. Agriculture accounted for 4 percent in 2009, 12 percent in 2013, and 7 percent in 2018. Mining and quarrying accounted for 2.5 percent in 2009, 2 percent in 2013, and went up to 4 percent in 2018. Manufacturing was 35 percent in 2009, went down to 16 percent in 2013, and increased to 17 percent in 2018.

The services sector, notably, accounted for 37 percent of total NPLs in 2009, 35 percent in 2013, and then increased to its highest level in ten years — to 43 percent of total NPLs. And finally, the households accounted for 21 percent in 2009, went up to 36 percent in 2013, and fell slightly to 30 percent in 2018.

Overall, for the year 2018, the business sector, manufacturing, services, and the households sector accounted for the highest levels of NPLs in the banking sector.

The evidence herein therefore suggests that these outturns are as a direct result of poor administration of economic policies; inter alia, the case of GuySuCo, for example, which accounted for massive job losses, significant reduction in consumer spending, owing to higher taxes, less disposable income, or spending power of households to spend in the economy, which helps to fuel economic growth. In terms of the high level of NPLs in the banking sector this would translate to more conservative lending by the banks, which would necessitate access to credit for commercial activities being curtailed.

When lending is curtailed, you have slower growth in the economy, less investment activities; and further, with loss of income and jobs by thousands of households, these people would become ineligible to borrow from the banks; and those who have mortgages stand to lose their properties which had been used as collateral for loans in the banking sector, simply because they would not be able to pay their mortgages.