What factors can help achieve SDGs?

Sustainable Development Goals (SDGs) are the set of seventeen Global Goals, initiated by the United Nations as the 2030 global agenda for sustainable development. Whilst the SDGs have come as the successor to the Millennium Development Goals (MDGs), the successful implementation of SDGs by the countries over the next 15 years will hinge on how they performed during the MDG period.
In this article, MDG Track Global Index of 140 countries, published by TAC Economics (www.mdgtrack.org), is used to assess the performance of countries in achieving MDGs during the period, 1990-2015. The MDG Track Global Index is the result of the mean average of each goal's percentage of completion, and the data are from World Bank MDGs dataset (http://data.worldbank.org/ data-catalog/millennium-development-indicators). Since, there are more than one country with same index value, 52 ranking positions are found for 140 countries. Top and bottom 10 countries in terms of MDG Track Global Index are shown in Table 1 and Table 2 respectively. In Table 1, Lithuania scores the highest as the country implemented the highest percentage of MDGs (77 per cent). The next other top performing countries include Egypt, Iran, Belarus, Maldives, Tunisia and China. In contrast, Table 2 depicts that, Somalia, Central African Republic and Korea Dem. Rep. are the worst performers with index values below 15 per cent. Five out of the eight South Asian countries are among the top 20 positions (Table 3). However, Afghanistan is the worst performer, and both Pakistan and India turn out to be poorer performers than Maldives, Nepal, Bangladesh and Sri Lanka.
To understand the potential performances of the countries over the next 15 years to implement SDGs successfully, we need to know what factors affected countries' performances of achieving MDGs during the MDG period (1990-2015). Taking into account the aforementioned MDG index of 140 countries as the dependent variable, we consider a set of explanatory variables for these countries which include initial per capita real gross domestic product (GDP), population growth rate, real GDP growth rate, trade-GDP ratio, public expenditure on education as percentage of GDP, public expenditure on health as percentage of GDP and different institutional variables. Except the initial per capita real GDP (for the year 1990), all other variables are averaged over the period 1990 to  2015 using weights derived from the time series data of real per capita GDP for these 26 years. The source of the data for these explanatory variables is the World Development Indicators (WDI). International Country Risk Guide (ICRG) variables have been used as the proxy of institutions for the countries. The OLS regression result suggests that, countries with higher average real GDP growth performed better in achieving MDGs. On average, one percentage point increase in the weighted average of real GDP growth is associated with 2.29 points increase in the MDG index. Similarly, on average, one percentage point increase in the weighted average of trade-GDP ratio is associated with 0.06 points increase in the index value. Furthermore, on average, one percentage point reduction in the average population growth rate is associated with 6.23 points increase in the index value. The initial per capita real GDP is found to have statistically significant positive association with the MDG index. On average, one hundred dollar increase in the initial per capita real GDP is associated with 0.1 points increase in the index value. Coefficients of both public expenditure on education as percentage of GDP and public expenditure on health as percentage of GDP are statistically significant with positive signs, which suggest that public expenditures on both education and health have critical positive association with MDG achievement. On average, one percentage point increase in weighted averages of public expenditure on education and health as percentages of GDP, increase the MDG index by 2.76 and 1.43 respectively. Major ICRG variables (reflecting institutional quality) are found to be the crucial determinants of MDG performance index. For instance, on average, one point increase in the average bureaucracy quality is associated with 7.80 points increase in the MDG index. Moreover, on average, one point increase in the average law and order, investment profile, government stability and socioeconomic condition are associated with 5.46, 2.77, 5.30 and 5.38 points increase in the MDG index respectively. It is also found that countries with lower internal and external conflicts and less ethnic tensions are associated with higher MDG index.  
Figure 1 depicts the goal wise performances of different countries. If a country has already achieved or will be achieving a particular goal by 2020, it is said to be 'on track', otherwise it is 'off track' in that particular goal. Worst performance is observed with respect to MDG5 (improve maternal health) and MDG4 (reduce child mortality rate), as out of the 140 countries, only 21 and 53 countries are on track in achieving desired level of MDG5 and MDG4 respectively.  
We have also employed a binary outcome model (Probit) to access the country wise MDG performances. Probit regression result suggests that, one percentage point increase in the weighted average of real GDP growth is associated with a rise in the predicted probability of eradicating extreme poverty and hunger (MDG1) by 0.18. Furthermore, one percentage point decrease in average population growth rate accounts for increase in the predicted probability of achieving MDG1 by 0.32. Countries with higher GDP growth and higher initial per capita real GDP have performed better in achieving the environmental sustainability. Also, reduction in child mortality was fueled by the reduction of population growth rate particularly in the developing countries. Public expenditure on education has a positive association with an increase in predicted probability of achieving MDG2 (achieve universal primary education), and public expenditure on health has a positive association with increase in predicted probabilities of achieving both MDG5 (improve maternal health) and MDG4 (reduce child mortality rate). Institutional variables such as, bureaucracy quality, investment profile and socioeconomic condition played vital role in achieving MDGs over the past 26 years. One point increase in average bureaucracy quality accounts for an increase in predicted probabilities of achieving MDG1, MDG2, MDG3 (promote gender equality and empower women) and MDG6 (combat HIV/AIDs malaria and other diseases) by 0.64, 0.82, 0.66 and 0.47 respectively. Similarly, one point increase in average investment profile of a country accounts for an increase in predicted probabilities of achieving MDG1, MDG2, MDG3 and MDG8 (develop a global partnership for development) by 0.35, 0.44, 0.34 and 0.25 respectively. Finally, one point increase in average socioeconomic condition of a country leads to an increase in predicted probability of achieving MDG1, MDG2, MDG3 and MDG7 (environmental sustainability) by 0.44, 0.43, 0.58 and 0.44 respectively.
The aforementioned analysis suggests that economic growth, together with reduction in population growth, enhanced trade-orientation, public expenditure on both education and health and better institutional quality helped countries do well during the MDG period. These lessons can be instrumental for a large number of countries in registering good performance with respect to achieving SDGs over the next 15 years. 
Dr. Selim Raihan is Professor, Department of Economics, University of Dhaka, Bangladesh, and Executive Director, South Asian Network on 
Economic Modeling (SANEM).  
[email protected] 
Wahid Ferdous Ibon, Research 
Associate, SANEM. 
[email protected]