Measuring State Self-Legibility in the Digital Age · 2014. 10. 22. · Measuring State...
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Measuring State Self-Legibility in the Digital Age Jonathan Phillips Motivation • Scott (1998) documents how states use their infrastructural and coercive power to make society more legible • Yet, in modern states the apparatus of the state itself is large, decentral- ized, and hard to control • The primary threat to political elites is often intra-state contestation, and the legibility challenge is reflexive – If all governments have an incentive to increase self-legibility, where do they achieve it? – If not, why does legibility vary across the political terrain? – What are the consequences of being rendered legible to the state? Measures of Self-Legibility • Central government databases seek to register government assets • Legibility is high where all government assets are accurately recorded • Three strategies to measure legibility – Comparing when assets enter government databases to when they’re produced – Measuring the amount of missingness in the data – Cross-validation of two sources When and Where do Assets Become Legible? • India’s annual DISE census records the year a school is founded • A school becomes legible where it is founded at least two years before it enters the data for the first time, and then remains in the data until at least 2011. • Since 2005, 335,402 schools have become legible • The central government has ‘discovered’ four times as many schools as have been built 2005 2007 2009 2011 Total 1,124,499 1,258,285 1,260,179 1,411,892 Continued from previous year 1,128,676 1,156,124 1,306,099 Dropped from previous year 66,554 72,796 50,740 Added Schools 129,609 104,055 105,793 —New-build 36,901 19,550 19,751 —Pre-existing 92,708 84,505 86,042 Legibility: % Of Schools Existing in 2005 and Known in 2011 that were also Known in 2005 0.000.250.500.751.00 known • The areas with the greatest increase in legibility are highly populated but more rugged areas • The infrastructure of schools that become legible is only marginally weaker than those already ’on the map’ • Government allocations fail to improve upon becoming legible • But inputs dependent on national government start off weaker and con- verge faster 2005 2006 2007 2008 2009 2010 2011 0.67 0.68 0.69 0.70 0.71 0.72 0.73 Year Inputs Index 2005 2006 2007 2008 2009 2010 2011 4000 6000 8000 10000 12000 Year Grants Received Do Governments know Less than INGOs about their own assets? • In 2011, the Nigeria Government conducted a census of health facilities in the North. In 2012, UNICEF and the WHO did the same. • 80% of observations in the UNICEF data appear in the government data, • 89% of observations in the Government data appear in the UNICEF data, • But the pattern of spatial overlap reflects local conflicts and governance capacity • Local partners and capacity are vital for central legibility 0.000.250.500.751.00 ehealth_in_MDGs % Clinics in UNICEF Database also in Gment database 0.000.250.500.751.00 MDGs_in_ehealth % Clinics in Gment Database also in UNICEF database Potential Applications • An objective measure of state capacity/legibility, since knowing about as- sets is often a requisite for managing and improving them • An explanatory variable for changes in public service performance and political engagement • A diagnostic tool for improving databases and measurement Prepared for Harvard Government Department Poster Session
Transcript of Measuring State Self-Legibility in the Digital Age · 2014. 10. 22. · Measuring State...
Measuring State Self-Legibility in the Digital AgeJonathan Phillips
Motivation
•Scott (1998) documents how states use their infrastructural and coercivepower to make society more legible
•Yet, in modern states the apparatus of the state itself is large, decentral-ized, and hard to control
•The primary threat to political elites is often intra-state contestation, andthe legibility challenge is reflexive
– If all governments have an incentive to increase self-legibility, where dothey achieve it?
– If not, why does legibility vary across the political terrain?– What are the consequences of being rendered legible to the state?
Measures of Self-Legibility
•Central government databases seek to register government assets
• Legibility is high where all government assets are accurately recorded
•Three strategies to measure legibility
– Comparing when assets enter government databases to when they’reproduced
– Measuring the amount of missingness in the data– Cross-validation of two sources
When and Where do Assets Become Legible?
• India’s annual DISE census records the year a school is founded
•A school becomes legible where it is founded at least two years beforeit enters the data for the first time, and then remains in the data until atleast 2011.
•Since 2005, 335,402 schools have become legible
•The central government has ‘discovered’ four times as many schools ashave been built
2005 2007 2009 2011Total 1,124,499 1,258,285 1,260,179 1,411,892Continued from previous year 1,128,676 1,156,124 1,306,099Dropped from previous year 66,554 72,796 50,740Added Schools 129,609 104,055 105,793—New-build 36,901 19,550 19,751—Pre-existing 92,708 84,505 86,042
Legibility: % Of Schools Existing in 2005 and Known in 2011 that were also Knownin 2005
0.000.250.500.751.00known
•The areas with the greatest increase in legibility are highly populated butmore rugged areas
•The infrastructure of schools that become legible is only marginallyweaker than those already ’on the map’
•Government allocations fail to improve upon becoming legible•But inputs dependent on national government start off weaker and con-
verge faster
2005 2006 2007 2008 2009 2010 2011
0.67
0.68
0.69
0.70
0.71
0.72
0.73
Year
Inpu
ts In
dex
2005 2006 2007 2008 2009 2010 2011
4000
6000
8000
1000
012
000
Year
Gra
nts
Rec
eive
d
Do Governments know Less than INGOs about theirown assets?
• In 2011, the Nigeria Government conducted a census of health facilitiesin the North. In 2012, UNICEF and the WHO did the same.
• 80% of observations in the UNICEF data appear in the government data,• 89% of observations in the Government data appear in the UNICEF data,•But the pattern of spatial overlap reflects local conflicts and governance
capacity• Local partners and capacity are vital for central legibility
0.000.250.500.751.00ehealth_in_MDGs
% Clinics in UNICEF Database also in Gment database
0.000.250.500.751.00MDGs_in_ehealth
% Clinics in Gment Database also in UNICEF database
Potential Applications
•An objective measure of state capacity/legibility, since knowing about as-sets is often a requisite for managing and improving them
•An explanatory variable for changes in public service performance andpolitical engagement
•A diagnostic tool for improving databases and measurement
Prepared for Harvard Government Department Poster Session
