Twitter and Policy Outcomes- South and Central Asia

    If you read my previous post on Twitter and policy outcomes in Latin America, you will recognize the methodology of this look at Central and South Asia.  I plan to use these regional studies, comparing Twitter presence to policy metrics, to identify plausible case studies which may demonstrate the role of social media on policy outcomes.

     For more information on the data sets, please check out @Twiplomacy and @WorldBankData.

14 of 15 South and Central Asian countries have a Twitter presence.  Turkmenistan, the only country not on Twitter, is also the only one off Facebook, joins Pakistan and Uzbekistan without a YouTube channel, and is also not on Instagram, along with Bangladesh, Nepal, and Uzbekistan.

Social Media

For social media metrics, I ranked the countries by followers, tweets per day, and interaction rate.

There are 20 accounts representing 7 countries which were verified, active, and established prior to 2014 (the last year of available poverty and inequality data).  India alone has six accounts, evenly split between personal and institutional.  7 are personal accounts, 2 are Personal/Institutional, and the rest are institutional.  No South or Central Asian country has a leader among the best connected in the world, but India has three accounts among the most followed world leaders.

India dominates the charts for followers and tweets, with Indian accounts taking 6 of the top 10 spots.  The interaction top ten are more diverse: Bhutan, Afghanistan, Maldives, and Kazakhstan each have two accounts, and Pakistan and Sri Lanka foreign ministry accounts fill out the list at 5 and 6.

Policy

The poverty headcount ratio (less than $3.10 a day in 2011 PPP), which could indicate positive economic and policy decisions, and inequality, as measured by the Gini index (where 0 is perfectly equal and 100 is perfectly unequal) which could indicate democratizing decision making, were used as “policy” metrics.  While you may disagree with those rough characterizations, these metrics have a long history and good data across many different countries.

Nearly every nation witnessed a dramatic fall in poverty over the last two decades, regardless of their Twitter presence.  Their Gini index followed suit, albeit more slowly.  Countries with the best data, the best rates, or the biggest falls weren’t always those with a qualified Twitter account, though Bhutan’s 47% drop between 2003 and 2012 is the largest.  That drop brought its poverty rate to second lowest in the group.  Its first account was established in 2009.

None of the featured nations have data for every single year between 1997 and 2017.  The Kyrgyz Republic’s 16 years of data is the most complete, while Afghanistan has no data.  With the gaps, it is difficult to make connections between social media presence and outcomes in India, Sri Lanka, and Maldives.

Comparison

While Kazakhstan has a spot on each of the top 10 charts, the vast majority of its impressive poverty drop took place before 2005, 6 years before its first account was created, and the Gini index has been moving smoothly downwards in that time.

Bhutan, mentioned above for its falling poverty level, actually saw a slight rise in inequality after the creation of its first account, the personal account of the prime ministier.  The prime minister’s personal/institutional account is number one in the group for interaction rate, and his exclusively personal account makes the top 10 as well.

The creation of the first Pakistani account (institutional, foreign ministry) proceeded the one of its largest poverty drops, but its fluctuating inequality barely budged. That  account is 5th in interaction rate, but the nation isn’t represented on any other charts.

Based on these examples, if there is a relationship between Twitter presence and policy outcomes, it is minimal, or at least highly confounded.  However, because gappy data prohibited an effective analysis of chart toppers India and Afghanistan, further analysis there might prove fruitful.  Additionally, a similar analysis to the one here, for example, 10 years from now, will have more than a decade of data to compare, and may be able to better connect social media and policy outcomes.

Conclusions

Although the group has quite a few active accounts representing its people, overall there weren’t any impressive examples indicating their role in policy.

It may be that certain regions tend to use social media differently, or that the issues contributing to poverty in those areas are less likely to be affected by internal policymaking or democratic interaction.  If geography continues to be destiny, there may be little social media can do in the short term to shape outcomes in certain regions.

However, social media may be helping in ways not measured by the metrics used here.  More work will need to be done looking at how other measures may have been affected by the introduction of Twitter and other platforms.  It may be very interesting, for example, to look at how governments use the popular WhatsApp.  It may also be helpful for regional experts to examine the findings and unpack the sociological and historical conditions which shape the results.  As I’ve said, I invite any interested data scientists and researchers to contribute ideas.

If you are interested in the role of social media in politics and diplomacy, make sure you check out http://twiplomacy.com/blog/twiplomacy-study-2017/.  Additionally, the World Bank Data Bank has a massive amount of population, health, economic, and other data.

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