Succeed or Secede: The Economic Benefits of
State Secession
Ajay Shenoy∗
February 11, 2015
First Version: 11 Feb 2015
Abstract
Although every theory of secession predicts that the new state benefits,
this basic prediction remains untested. I test whether secession raised output in three new states within India. I use a difference-in-discontinuities
design to measure the causal effect of the new state borders on output. Secession raised output by between 0.5 and 1 percent, with the effect varying
by state. I confirm the effect is not driven by damage to the old state rather
than benefits to the new, and derive a method to rescale the benefit at the
border into an average benefit.
PRELIMINARY AND INCOMPLETE
∗
University of California, Santa Cruz
2
1
AJAY SHENOY
Introduction
Many models—models of trade, models of taxes, models of democracy and
bureaucracy—have been built to explain why states split and merge. But across
all models the criterion is simple: the region secedes if it will do better on its
own (for example, Wittman, 1991). Until now, however, this most basic prediction has not been rigorously tested. Even ignoring the collapse of South Sudan
and the paralysis of East Timor, it is not obvious the prediction is right. An ambitious leader might march her nation to independence for personal glory rather
than general welfare. A nation might demand independence for the sake of
common culture, shared experience, or the dignity of self-rule. No declaration
of independence begins with a demand for efficient bureaucracy.
But in explaining why a region secedes, these theories also show why testing their most basic prediction is hard. A region that expects to benefit from
independence probably differs from the state it abandons. These differences
explain why the region broke away in the first place. Simply comparing the new
state to the regions left behind would give a biased impression of the benefits
of secession. The right counterfactual is a world in which the new border had
never been drawn—or rather, drawn differently. Given that the border is often
drawn around a region that is already poorer or angrier or more devastated by
strife, it is hard to separate the result of secession from its cause.
This paper gives the first rigorous evidence that regions that secede do benefit. In 2000 the borders within India were redrawn to form the new states of
Chhattisgarh, Jharkhand, and Uttarakhand. I run a series of spatial regression
discontinuities—a differences-in-discontinuities design—to assess whether output discontinuously rose at each of the the new borders in the years after secession. I measure economic activity using the “night lights” data that Henderson
et al. (2012) link to economic growth. These data let me measure output within
small cells on either side of each border for ten years before and after it forms.
According to theory, these three states would only have chosen to secede if they
expected to benefit.
By studying the formation of new states within India I implicitly control for
things that might confound my tests. India’s founders wrote into its constitution
a way to peacefully divide states. In the case of the three states I study there was
SUCCEED OR SECEDE
3
no “war for independence.” For several reasons, not least of which is the bureaucratic delay for which India is famous, the decision to split—and thus the
agitation that led to it—happened years before the actual split. To minimize disruption the new borders were drawn along the existing census districts, making
it less likely that the borders were drawn solely to enclose regions that are getting richer or poorer. I confirm there is no statistically significant trend at any
of the new borders before the new states are formed.
At the same time, the costs and benefits of forming a new Indian state are
similar to those of forming a new nation. Indian states may tax goods produced
locally but sold in other states, effectively imposing export tariffs. Together with
the wide variation in state laws and regulations, these taxes simulate the trade
costs that many theories assume make secession inefficient (Casella and Feinstein, 1990; Wei, 1992, 1991; Bolton and Roland, 1997; Friedman, 1977). Likewise, a new state must pay all the costs of forming a new capitol and building
a new government. On the other hand, the new state has full control over its
budget, including transfers from the Union government. As in many theories of
state formation, the chance to wrest funds from the center may drive the fringes
to secede. Finally, although these three secessions may seem minor, in fact they
affected millions of people. Before it split, Uttar Pradesh contained more people than Brazil. Had it been an independent country it would have been the
world’s fifth largest. In short, studying secession within India balances the need
for good identification with the desire for general results.
In all three states I find evidence that secession raised output. The evidence
is strongest in Uttarakhand, where secession raised output at the border by 1
percent, and Jharkhand, where secession raised output by 0.5 percent. Though
the estimate for Chhattisgarh has a similar size, it is noisier. For Uttarakhand I
also have data on measures of household welfare from the Indian census. According to these data, secession gave households in Uttarakhand better access
to electricity and latrines
Next I confirm that these estimates are not biased by two well-known shortcomings of a regression discontinuity design: a failure of the stable unit treatment value assumption, and a neighborhood effect that is unrepresentative of
the average effect. The first would occur if the secession of the new state damages the border regions that remain in the old state. I find no evidence of such
4
AJAY SHENOY
damage. I then derive a method to rescale the border effects into average effects. I assume more productive regions potentially benefit more and follow
Ciccone and Hall (1996) in assuming that productivity rises in population density. The rescaled estimates differ little from the original estimates, but they do
compress the estimates for each state towards each other. Despite their differences the three states seem to have reaped similar benefits from secession.
2
Research Design
2.1
Secession in a Spatial Model of Production
Suppose the unified state is spread across a geographic area, which for simplicity I represent as the interval from 0 to 1. (The argument applies just as easily to
the more realistic case of two dimensions.) At each point i ∈ [0, 1] on the interval
lies a local economy that combines land, labor, and Hicks-neutral productivity
to create output. At time t, local output is
Yt (i) = At (i)F [Tt (i), Lt (i)]
Productivity splits into two terms: an exogenous process ait and a public
good G. The level of the public good G is a function of exogenous productivity.
At (i) = at (i)GX [at (i)]
The idea is that the local economy may be better (or worse) at getting the
public good from the government if it is more productive. For example, places
with more educated citizens might be both more productive better able to lobby
for public funds. Whatever the reason, allowing the public good to depend on
productivity implies that places that receive fewer public goods, and thus might
benefit from secession, would also suffer slower growth even if public goods
were distributed evenly.1
1
Adding inflexible capital to the model is trivial, as it behaves like land. The effect of fully
mobile capital depends on whether areas near the border b differ greatly in their land, labor,
and productivity. In equilibrium the level of capital rises with these three factors: Kt∗ (i) =
K[At (i), Tt (i), Lt (i)]. Define F̃ [log At (i), log Tt (i), log Lt (i)] = F [Kt∗ (i), Tt (i), Lt (i)]. Take a firstorder approximation of log F̃ around the border b. Then log output is (1 + αA ) log At (i) +
SUCCEED OR SECEDE
5
Now suppose that one side of the interval—the fringe—has a chance to secede from the other side—the core. If the fringe secedes a new border is drawn
at b ∈ (0, 1). Assume for now that b is arbitrary. The level of public goods spending for the fringe is
GX (at (i)) =

GU (a (i))
if X = U for union
GI (a (i), ξ)
if X = I for independent
t
t
(1)
where ξ is a shock that represents the unpredictable costs of secession. Here,
G is the rule that prevails across the entire state while united whereas GI is a
new rule that takes effect if the fringe secedes.
Translating the best known theories of secession to fit this framework is straightforward:
U
• Suppose that the fringe is more ethnically homogeneous than the union,
perhaps because people of certain castes cluster in the fringe. Then the
models of Friedman (1977) and Wittman (1991) predict that GI > GU in
the fringe because the fringe can more efficiently tax its people. People of
a homogeneous state are less likely to move away to avoid taxes, letting
the independent fringe collect higher taxes at lower cost. Alternatively the
cost of collecting taxes might be convex in the size of the state, making it
efficient for large states to split.2
• Suppose both a and G are decreasing in the distance from the capitol,
which is in the core. Then the most lavish public goods—the nicest schools,
the biggest power plants—are built in places closer to the capitol. In the
model of Alesina and Spolaore (1997) the economies of the fringe want
a closer capitol. This dispute over how the government allocates public
goods drive the fringe to secede.
• Suppose G is financed with taxes. It is larger for smaller values of a, but the
entire function is lower for higher levels of taxation. Then G represents
f˜[Tt (i), Lt (i)] where αA is the elasticity of F̃ with respect to At (i) and f˜ is not a function of
productivity. Define G̃X = (G̃X )1+αA , and the argument passes through as in the main text. If
land, labor, and productivity differ at the border versus away from it, the analysis of Section 6,
which rescales border effects into average effects, may apply.
2
Barankay and Lockwood (2007) find evidence of efficient devolution among Swiss cantons.
6
AJAY SHENOY
Figure 1
Productivity Along a Line
Border
a(i)
𝑖
1
0
𝑖−
𝑖+
distortionary transfers. If a is distributed more unequally in the fringe
than in the core, the fringe will want more redistribution than the core.
According to Bolton and Roland (1997) the resulting difference in preferences will make the fringe want to secede.3
Alternatively, fringe will declare independence if in expectation aggregate
output is higher under GI .
2.2
Identification
Figure 1 illustrates the dilemma for identifying whether secession does raise
output in the fringe. Productivity is not distributed evenly across the state.
Suppose the fringe is the region to the right of the proposed border. Average
productivity is higher in the core, confounding any estimate of the gains from
secession.
To see this, compute the true change in average log output in the fringe af3
Casella and Feinstein (1990); Wei (1992, 1991) also build models in which different preferences drive states to split.
SUCCEED OR SECEDE
7
ter secession—that is, the difference between output if the fringe secedes and
counterfactual output if the fringe does not secede.
Z
Zi∈f
−
[log at (i) + log GI (at (i), ξ) + log F [Tt (i), Lt (i)]]
[log at (i) + log GU (at (i)) + log F [Tt (i), Lt (i)]]
i∈f
Suppose GI (at (i), ξ) = exp βtI + ξ mt [at (i)] and GU (at (i), ξ) = mt [at (i)]. This
might best match a model in which the fringe believes it can more efficiently
raise revenue if independent. After secession both core and fringe keep the
same rule for allocating resources, but the fringe can raise them more efficiently.
(In Section 6 I consider different rules.)
Assuming population and land are unaffected by secession, the true gain is
(1 + γ)(βtI + ξ) = β̂tI + ξˆt
Economic theories of secession predict that the fringe will secede if
Et [e−ρt β̂tI ] + Eξ,t [ξˆt ] > 0
(2)
In short, economic theories predict that expected output should increase
after secession.
A good test of this prediction must address two challenges. First, for any
given secession the unanticipated effect ξˆt differs from its mean. If the variance
of ξˆt is high the realized benefit β̂tI + ξˆt could come out negative even though
the expected benefit is positive. I deal with this problem by studying the division of three states within India. India’s constitution allows the Union to create
new states and dictates many of their institutions. Moreover, the three states
I study seceded peacefully, ruling out the risks created by wars for independence. These conditions make the outcome of secesson more predictable and
thus minimize the variance of ξˆt , making it more likely that the outcome is as
expected.
The second challenge is selection bias. A common approach in cross-country
studies is difference-in-differences, which in this case means comparing the average change in output before and after secession across the fringe and core.
8
AJAY SHENOY
If the distribution of a differs between the core and fringe the difference-indifference estimate will be biased. To see this let gta (i) = ∆{log at (i)+log m[at (i)]}
and gtF (i) = ∆ log F [Tt (i), Lt (i)]. The difference-in-differences estimate is
β̂tI + ξˆ + (E[gta (i) | i ∈ f ] − E[gta (i) | i ∈ c]) + (E[gtF (i) | i ∈ f ] − E[gtF (i) | i ∈ c])
This estimate conflates the gains from secession with differences in productivity that would have existed regardless of whether the fringe seceded.
But suppose instead I exploit the discontinuity created by the border drawn
at b. I make two crucial assumptions
1. The functions (at (i), Tit , Lit ) are continuous in i, and the function F is continuous in all its arguments
2. The allocation rule under union GU (at (i)) remains unchanged after secession. That is, mt (·) is not affected by secession.
Consider the difference in output between local economies i+ = b + ε just
on the fringe-side of the border and i− = b − ε just on the core-side. At any time
t this difference is
β̂tI + ξˆ + {mt [a(i+ )] − mt [a(i− )]}
+ {a(i+ ) − a(i− )}
+ {F [T (i+ ), L(i+ )] − F [T (i+ ), L(i+ )]}
≈ β̂tI + ξˆ
which is an unbiased estimate of the true gains from secession.
Until now I have assumed the border b is arbitrary. In truth the border might
be set to contain a region that differs from the rest of the country. For example,
Uttarakhand was formed from several Himalayan districts. Economies within
such a district might have a different production function F f [T (i), L(i)] = χb (i)F [T (i), L(i)].
Then a regression discontinuity design will estimate β̂tI + ξˆt + χb (i) instead of the
true effect β̂tI + ξˆt . But one more assumption can solve this last source of bias:
3. χb (i) is fixed across time
9
SUCCEED OR SECEDE
Then instead of comparing the change in levels at the border, I can compare
the change in changes at the border: a difference-in-discontinuities estimator.
This estimate is
β̂tI + ξˆ + (gta (i+ ) − gta (i− )) + (gtF (i+ ) − gtF (i− )) ≈ β̂tI + ξˆ
(3)
Of the three assumptions I have made, two of them—continuity and an edogenous border whose effect is fixed—can be tested directly. If they fail the differencein-discontinuities estimator will show statistically significant changes at the border before secession. I consider a relaxation of the other assumption—that the
allocation rule remains unchanged—in Section 6.
2.3
Estimating the Effect at the Border
Extending the method to a two-dimensional country is easy. Each local economy occupies a cell centered on (i, j), an element of a two-dimensional grid.
The proposed border between the core and fringe is a curve. To compute the
effect at the border I use a control function in the latitude and longitude i and
j.
Abbreviate Ft (i, j) = F [Tt (i, j), Lt (i, j)]. Output is
log Yt (i, j) =

log a (i, j) + log F (i, j)
if core
log a (i, j) + log F (i, j) + χb (i, j) + (β I + ξ )
t
t
t
t
if fringe
t
t
Since a and F are smooth, their sum can be approximated by a polynomial. Following Dell (2010) I use a third-order polynomial in (i, j) (call it P 3 ).
This polynomial approximation works as a control function for productivity
and other sources of bias. Since the approximation is only accurate over a small
range I restrict the sample to cells within 75 kilometers of the border. To estimate the difference in discontinuities I add a cell fixed-effect. My main measure
of output is the fraction of the cell that emits light at night. The positions i and
j are the latitude and longitude of the centroid of the cell.
10
AJAY SHENOY
I estimate
[Light](i,j),t = [F ixed Ef f ect](i,j) +
2012
X
κt [Y ear Dummy]t + [P ost-Split]t × P 3 ([Lat], [Lon])
t=1993
+ β[P ost-Split]t × [N ew State](i,j) + [Error](i,j),t
(4)
taking 2001 as the year of secession. (The states officially seceded in November
of 2000, which is so late in the year that 2001 better describes the first year of
independence.) To account for correlation in the error term across time and
space I cluster standard errors by sub-district. There is no direct term for the
polynomial P 3 (·) or the dummy [N ew State] because they are absorbed into the
fixed-effect. The coefficient β measures the average effect of secession.
To confirm that there are no pre-existing trends, I estimate a more flexible
version of 4 that estimates separate control functions and border effects for every year:
[Light](i,j),t = [F ixed Ef f ect](i,j) +
2012
X
κt [Y ear Dummy]t
t=1993
+
+
2012
X
[Y ear Dummy]t × Pt3 ([Lat], [Lon])
t=1993
2012
X
(5)
βt [Y ear Dummy]t × [N ew State](i,j) + [Error](i,j),t
t=1993
By plotting the coefficients {βt } I measure the effect of the new border in each
year, even in years before secession. Any pre-existing trend would appear in
the coefficients for the years before 2001. The absence of a trend is evidence in
favor of the identification assumptions of Section 2.2.
SUCCEED OR SECEDE
3
11
The Secession of Uttarakhand, Jharkhand, and
Chhattisgarh
By 1997, the legislative assemblies of Madhya Pradesh, Uttar Pradesh, and Bihar had all passed resolutions calling for the formation of the three new states.
India’s national parliament, which holds sole authority to create new states,
passed laws to create all three states within weeks of each other in late 2000:
Chhattisgarh on 1 November, Uttarakhand on 8 November, and Jharkhand on
15 November. But the paths that led each region to secede differ drastically.
Uttarkhand’s path may be the most contorted (Tillin, 2013). Unrest began
in Uttar Pradesh’s Himalayan region when environmentalists protested timber
concessions. These quickly turned to anti-environmentalist protests after the
Union government under Indira Gandhi banned the felling of tall trees, stifling
economic development. But these protests only became serious calls for a new
state when the state government enforced new quotas on places in local universities in 1994. The government reserved a constant fraction of seats for people of disadvantaged castes across the state. Since the Himalayan regions have
fewer people of disadvantaged castes, this made it disproportionately difficult
for high caste students in Uttarakhand to get into university. The opposition
Bharatiya Janata Party capitalized on the discontent, arguing that the state government in Lucknow could never represent the interests of the Himalayan region.
Figure 2 uses data from the Demographic and Health the relative condition
in 1993 of the districts that would become Uttarakhand. For example, the first
point shows the gap in wealth between Uttarakhand and Uttar Pradesh. (The
smaller dots mark the endpoints of 95 percent confidence intervals.) Consistent
with the narrative above, Uttarakhand has fewer people from Scheduled Castes.
It is also wealthier and more unequal. These conditions are consistent with theories in which the fringe secedes because its people have different preferences
from the core (Casella and Feinstein, 1990; Wei, 1992, 1991). It is harder to tell if
Uttarakhand’s secession is consistent with a literal interpretation of Bolton and
Roland (1997), as Uttarakhand has fewer people of disadvantaged caste but also
greater inequality.
Jharkhand’s path to independence had much in common with Uttarakhand’s
12
AJAY SHENOY
(Tillin, 2013). Though older—the idea of a separate Jharkhand was mooted even
before India became independent—it too gained momentum through protests
against forestry policies. But unlike Uttarakhand, Jharkhand has a large population of Scheduled Tribes, indigenous people who were marginalized throughout India’s history. But though the movement began with calls for a state for
Scheduled Tribes, these calls only grew strong in the late 1980s when couched
in economic rather than social terms by the Bharatiya Janata Party. Its local
leader Singh Namdari claimed that ”Jharkhand’s money was spent in North Bihar” leaving little for Jharkhand’s own development (Tillin, 2013, p. 87).
Figure 2 shows that Jharkhand’s large Scheduled Tribe population is obvious
in the data. But aside from that, Jharkhand is not drastically different from Bihar. In particular, it does not seem any wealthier despite hosting many large
industries (most notably Tata). This may be why Jharkhandis claim North Bihar
robs them of their resources. If so, Jharkhand’s secession seems most consistent
with Collier and Hoeffler (2004), albeit without conflict, or the predictions made
by the model of Arzaghi and Henderson (2005) in the case where the fringe has
higher income.
By contrast, Chhattisgarh seceded with no clear secession movement. Tillin
(2013) argues that although Chhattisgarh has much in common with Jharkhand—
natural resources and a large population of Scheduled Tribes—its secession was
more a political stratagem than a struggle for independence. Since the 1960s,
the Praja Socialist Party used the threat of secession to extract concessions for
the landowners it represented from the ruling Congress party. These threats
only became serious when a faction within Congress used the issue of statehood
to sideline traditional party elites. The new elites called for statehood to prove
themselves “asli (real) Chattisgarhis.” Meanwhile, local leaders of the Bharatiya
Janata Party blamed a string of election defeats on the perception that they represented only the high caste landowners. By calling for a new state they could
appear to represent the Scheduled Tribes whose votes they needed.
Figure 2 shows that, like Jharkhand, Chhattisgarh has an unusually large
Scheduled Tribe population. Unlike Jharkhand, Chhattisgarh is clearly poorer
and more disadvantaged. Given its poverty, it is harder to find an economic justification for Chhattisgarh’s secession; for example, the model of Arzaghi and
Henderson (2005) suggests this is precisely a case where the fringe should not
13
SUCCEED OR SECEDE
Figure 2
How did the Seceeders Differ from the States They Left?
Uttarakhand
Jharkhand
Average Wealth (DHS Index)
Average Wealth (DHS Index)
Ever Had Polio?
Ever Had Polio?
Has Electricity?
Has Electricity?
Head of Household Literate?
Head of Household Literate?
Scheduled Caste?
Scheduled Caste?
Scheduled Tribe?
Scheduled Tribe?
Urban?
Urban?
Wealth Inequality (DHS Index)
Wealth Inequality (DHS Index)
-.75
-.25
.25
.75
.25
.75
Chhattisgarh
Average Wealth (DHS Index)
Ever Had Polio?
Has Electricity?
Head of Household Literate?
Scheduled Caste?
Scheduled Tribe?
Urban?
Wealth Inequality (DHS Index)
-.75
-.25
-.75
-.25
.25
.75
14
AJAY SHENOY
secede. It may be that Chhattisgarhis felt their share of public spending was
lower than what they could get when independent even with a lower average
income. Or it could be that, as Tillan claims, Chhattisgarh’s secession was a
matter of politics rather than policy. If so, it might explain why the evidence for
a benefit from secession is weakest for Chhattisgarh.
4
Data
I measure economic output using the Nighttime Lights Time Series from 1992 to
2012. The National Geophysical Data Center created this series using data from
Defense Meteorological Satellite Program. The series divides the earth into a
grid with cells of 30 arc seconds on each side. For each year the series gives an
index of the average intensity of light emitted from each cell after correcting for
cloud cover and natural sources of light (e.g. forest fires).
To measure the effect of secession I link the night lights to administrative
boundaries created by the Global Administrative Areas project. The data give
the boundaries of all six states, which have not changed since the three new
states were formed. For each pair of states, the new state and the rump state
left behind, there is a boundary drawn in late 2000 that split the original state.
Figure 3 shows the example of Uttarakhand, which split from Uttar Pradesh.
The blue lines mark the boundaries that have not changed and the purple line
marks the new boundary formed between Uttarakhand (northeast of the boundary) and Uttar Pradesh (southwest of the boundary). The left-hand panel shows
1992, the first year of night lights data, and the right-hand side shows the last
year 2012.
In 1992 the new boundary did not yet exist, and there is no clear evidence
of a difference in the intensity of light on either side. Several clusters of light,
which roughly mark urban agglomerations, are split by the future boundary.
But 11 years after the boundary was drawn it is clear that the parts of these agglomerations on the northeast side of the boundary have grown brighter than
the southwest. Though the econometrics outlined in Section 2 quantify the difference, this plot of the raw data already shows the main result.
I split each state into a grid of 0.1x0.1 degree cells. For each cell and for
SUCCEED OR SECEDE
15
each year I compute the average light intensity, making the cell-year my unit of
observation. Henderson et al. (2012) measure light intensity with log of the average “digital value,” meaning the value of the index created for the Nighttime
Lights series. Though this measure works well for entire countries or states, it
creates problems for the small cells I use because many are completely dark. Instead I take as my main measure the fraction of the cell that emits any light. This
measure is defined for all cells and has an easy interpretation. Nevertheless, I
show in Sections 5 that the qualitative results hold when I instead measure intensity with log(1 + Digital V alue).
Though the night lights are my main measure of development, I also measure household welfare using town-level data from the 2001 and 2012 Census
of India. For each “town”—the Census defines a settlement as a town if it meets
a population threshold, the density of that population meets a threshold, and
most of that population is employed outside agriculture—I measure the fraction of households who have electricity, latrines, and solid homes (those with
a roof made of tile, slate, metal sheet, brick, stone, or concrete). I geocode the
towns in 2001 using data from the India Place Finder. I fuzzy match the 2001
towns to the 2011 towns by name to make a panel of towns. Finally, to rescale
my estimates of the effect of secession on light to an effect of secession on output I use data on gross state product from the Reserve Bank of India.
5
5.1
Results
The Bias of the Difference-in-Differences Estimator
Before reporting the difference-in-discontinuity results I show that, as argued
in Section 2, the difference-in-differences estimator is indeed biased. I first estimate
[Light](i,j),t = σD [N ew State](i,j) + σS [P ost-Split]t
+ σDS [N ew State](i,j) × [P ost-Split]t + [Error](i,j),t
(6)
The coefficient σDS gives the difference-in-differences estimate of the gains
from secession. I estimate this regression separately for each pair of formerly
16
AJAY SHENOY
Figure 3
Light Emissions from the Uttar Pradesh-Uttarakhand Border Before and After
Secession
1992 (9 Years Before Secession)
2012 (11 Years After Secession)
united states (Uttar Pradesh and Uttarakhand, Madhya Pradesh and Chattisgarh, Bihar and Jharkhand). The top panel of Table 1 shows that this estimate
is positive and significant for Uttarakhand and Chhattisgarh, but negative and
insignificant for Jharkhand.
Are these estimates trustworthy? The middle panel Table 1 re-estimates
Equation 7 with only data from before the true secession. I look for the “effect”
of a fake secession, taken to be in 1996 (five years before the true secession).
The imaginary secession has statistically significant effects in two of the three
states. The failure of this placebo test suggests output follows a different trend
in the new states even before they secede.
The bottom panel of Table 1 directly tests for a pre-existing trend. For each
pair I estimate
[Light](i,j),t = σD [N ew State](i,j) + σT t + σDT [N ew State](i,j) × t + [Error](i,j),t (7)
using only the data from before secession. The coefficient σDT gives the difference in the slope of the trend in the new state as compared to the regions of
17
SUCCEED OR SECEDE
Table 1
Difference-in-Differences Estimates are Biased by Pre-Trends
Secession
Cells
Cell-Years
Placebo Secession
Cells
Cell-Years
Pre-Trend
Cells
Cell-Years
Difference-in-Differences
Uttarakhand Chattisgarh Jharkhand
0.144***
0.149***
-0.001
(0.011)
(0.009)
(0.006)
860
18060
1088
1080
22848
22680
Fake 1996 Secession
Uttarakhand Chattisgarh Jharkhand
0.099***
-0.004
-0.042***
(0.008)
(0.005)
(0.005)
860
7740
1088
1080
9792
9720
Differential Trends
Uttarakhand Chattisgarh Jharkhand
0.021***
-0.003**
-0.008***
(0.002)
(0.001)
(0.001)
860
7740
1088
9792
1080
9720
18
AJAY SHENOY
the rump state. Table 1 shows that there is a pre-existing trend in all regions that
seceded. This trend confounds the difference-in-differences estimates, making
a better design necessary.
5.2
Secession and Light
Since the difference-in-differences estimator is biased, I turn to the differencein-discontinuities estimator. I first estimate Equation 5 for each pair of formerly
united states. The equation estimates a set of coefficients {β̂t } that measure the
effect of each new border in the years 1993 to 2012 relative to its effect in 1992.
Figure 4 plots these effects. In the years after 2001, which is marked with a
red line, these coefficients measure the benefit of secession—that is, how much
more light was emitted by the new state relative to the state from which it seceded. If the research design is valid the effect at the border should be zero in
the years before secession; if they are not, the research design may be invalid.
In all three cases there are no statistically significant effects before the border forms. Unlike the difference-in-differences estimates, these estimates are
not confounded by pre-existing trends. But after secession all three states start
emitting more light. Uttarakhand shows the sharpest increase; emissions rise
sharply after 2001 before stabilizing. Jharkhand enjoys more gradual and smaller
increases, but the increases are precisely estimated. By contrast, though Chattisgarh shows average increases of similar magnitude, they are noisier.
Figure 5 maps the predicted values of Equation 5 for Uttarakhand. (It is
essentially a smoothed version of Figure 3.) In 1992, light (and thus output)
radiates out from the southwest (the metropolitan area around Delhi). The
bands, which put predicted light intensity into bins, pass through the border
unchanged. In other words, there is no effect at the border before secession.
Twenty years later, after Uttarakhand has been independent for 11 years, the
effect of the border is sharp. Theseborder regions of Uttarakhand are brighter
than the adjacent regions just southwest of the border.
Table 2 estimates Equation 4 and reports β̂, the average change in light emissions after secession. According to the table, the fraction of regions lit is roughly
11 percentage points higher on Uttarkhand’s side of its border with Uttar Pradesh.
In Jharkhand the increase is roughly 5 percentage points, and in Chhattisgarh
19
SUCCEED OR SECEDE
Figure 4
The Effect on Light Emissions at the Border Rises after Secession
.25
Effect at Border
0
-.25
-.5
-.5
-.25
Effect at Border
0
.25
.5
Jharkhand
.5
Uttarakhand
1992
2002
Year
2012
-.5
-.25
Effect at Border
0
.25
.5
Chhattisgarh
1992
2002
Year
2012
1992
2002
Year
2012
20
AJAY SHENOY
Figure 5
Predicted Values for Light Emissions: Uttarakhand
1992 (9 Years Before Secession)
2012 (11 Years After Secession)
Table 2
Secesson Causes More Light Emissions
Secession
Cell-Years
Cells
Sub-districts
(1)
Uttarakhand
0.109∗∗
(0.04)
14364
689
65
(2)
Chhattisgarh
0.061∗
(0.03)
18081
864
42
(3)
Jharkhand
0.048∗∗
(0.02)
18963
907
44
roughly 6 percentage points (though this effect is only marginally significant).
Tables 3 and 4 test whether these results are robust. Table 3 re-estimates
Equation 4 using cells of varying distance from each new border. The second
column, which uses all cells within 75 kilometers of the border, simply repeats
Table 2. The first column uses cells within 100 kilometers of the border and the
third column uses cells within 50 kilometers. This is the two-dimensional analog of varying the window around the discontinuity. The estimates of the effect
on Uttarakhand remain significant and have similar size for all three cutoffs.
The effect on Jharkhand is smaller and insignificant when the window is wide,
but becomes larger and significant in tighter windows. Given that a smaller
21
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Table 3
Robustness: Changing the Window
Uttarakhand
Chattisgarh
Jharkhand
100km
0.138***
(0.043)
0.082**
(0.034)
0.031
(0.020)
Distance
75km
0.109**
(0.045)
0.061*
(0.032)
0.048**
(0.020)
50km
0.109**
(0.047)
0.030
(0.028)
0.041*
(0.021)
window typically suffers less bias, this suggests the true effect is positive. By
contrast, the effect on Chhattisgarh is large and significant in wider windows
but grows smaller and insignificant as the window narrows. This could imply
Chhattisgarh did not benefit from secession, though I explain in Section 6 that
the loss of significance may be driven by the lack of settlements near the new
border.
Finally, Table 4 estimates Equation 4 using a different measure of light emissions as the dependent variable. The ideal measure would be the log of the
average “digital value,” meaning the average of the index of light intensity. This
measure is what Henderson et al. (2012) use to show that changes in night lights
are a good proxy for changes in output. But since the average digital value is
zero in many cells, I instead use log(1 + [Digital V alue]). Though dealing with
zeros by adding 1 is common, it may cause some distortion, which is why I use
as my main measure the fraction of the cell that is lit. Nevertheless, Table 4
shows that the pattern of effects remains similar when using the alternate measure. Again, Uttar Pradesh enjoyed the biggest effect, Jharkhand a somewhat
smaller but still significant effect, and Chhattisgarh may not have benefitted at
all.
5.3
Secession and Output
Though an increase in night lights after secession suggests economic benefit, to
quantify the benefit I rescale my measures of light to predict changes in output.
I regress state-level output on my measures of light, then re-estimate the effect
22
AJAY SHENOY
Table 4
Robustness: Average Digital Value
Secession
Cell-Years
Cells
Sub-districts
(1)
Uttarakhand
0.349∗∗∗
(0.08)
10458
504
51
(2)
Chhattisgarh
0.059
(0.06)
13041
625
34
(3)
Jharkhand
0.094∗
(0.05)
14112
677
31
Table 5
Predicting Output with Light
Light Measure:
Light
State-Years
States
Base-year Dummies
Fraction Lit Log(1 + DV/Area)
0.073∗∗∗
0.692∗∗∗
(0.01)
(0.05)
639
639
31
31
Base-year × State Dummies
Fraction Lit Log(1 + DV/Area)
0.086∗∗∗
0.764∗∗∗
(0.01)
(0.05)
639
639
31
31
of secession on the predicted values.
Though Henderson et al. (2012) estimate the correlation between night lights
and output in a cross-country panel, I cannot simply use their estimates because by necessity I use a slightly different measure of night lights. Moreover,
given that the cells I study are small, it may improve the accuracy of my predictions to re-estimate the correlation using Indian states, which are smaller in
area than most countries.
I estimate correlation between gross state product for both my preferred
measure of light and the alternate measure. Unfortunately, the Indian Ministry
of Statistics and Programme Implementation periodically changes how it computes output and to my knowledge does not or cannot update earlier figures to
be consistent.4
4
The Ministry uses data from the Indian Census and the National Survey Sample to estimate both national accounts and gross state product. The different “base years” reflect not
only changes in the price index but changes in the Ministry’s estimates of the labor force and of
employment in each industry.
SUCCEED OR SECEDE
23
Rather than try to make the numbers consistent I include dummies to control for changes in the base years. To control the distortion caused by these
changes, for each measure of light I estimate two different regressions. The first
assumes distortions are constant across states, whereas the second allows the
effect to vary between states. Table 5 reports the results. In all four regressions
the dependent variable is real net state product—gross state product adjusted
to account for imports from other states. For either measure the estimate does
not change between specifications. A 1 percentage point increase in the fraction of a state lit predicts a .7 percent rise in output, and a 1 percent rise in the
average digital value predicts a .75 percent rise in output.
I use the coefficients from the second specification to scale each measure of
light like this:
[P redicted Output] = [Estimate] ∗ [Light]
I estimate Equation 4 using predicted output in place of light as the dependent variable. Table 6 reports the results. Using my preferred measure of light,
the estimates suggest secession raised output by roughly 1 percent in Uttarakhand, and by roughly half a percent in Jharkhand and Chhattisgarh, though
again the effect on Chattisgarh is only marginally significant.
The alternate measure suggests much larger effects—a 26 percent rise in
output in Uttarakhand and a 9 percent rise in Jharkhand. As in Table 4, the
effect on Chhattisgarh is statistically insignificant. Though the two measures
agree that at least two of the three states benefitted from secession, the alternate measure sizes the benefits larger by an order of magnitude. This could be
because unlike the fraction lit measure it is not bounded between zero and one,
leaving more room for an increase. But it could also be a sign that the measure
is noisy when applied to the small cells that make up my observations.
5.4
Secession and Household Welfare
The tests derived in Section 2 focus on output, but output is not all that matters
for a citizen of a new state. Here I briefly study other outcomes using the 2001
and 2011 rounds of the Indian Census. The Census reports most outcomes by
sub-district or larger, but some outcomes—in particular, the number of house-
24
AJAY SHENOY
Table 6
Secession Causes a Rise in Output
Light Measure:
Secession
Cell-Years
Cells
Sub-districts
Uttarakhand
0.009∗∗
(0.00)
14364
689
65
Fraction Lit
Chhattisgarh
0.005∗
(0.00)
18081
864
42
Jharkhand
0.004∗∗
(0.00)
18963
907
44
Log(1 + DV/Area)
Uttarakhand Chhattisgarh Jharkhand
0.264∗∗∗
0.078
0.089∗∗
(0.06)
(0.05)
(0.04)
14364
18081
18963
689
864
907
65
42
44
holds with access to certain amenities—is reported by town.5 Since most of
these amenities—for example, access to a separate kitchen—take time to acquire, the 2001 data serve as a pre-period of sorts for the difference in discontinuities estimate.
I estimate Equation 4 using each town as an observation. Unfortunately only
one pair, Uttar Pradesh and Uttarakhand, have enough towns to draw any inference. Since Uttarakhand showed the biggest benefits in Section 5.2, the estimates below might serve as an upper-bound.
Table 7 reports the change from 2001 to 2011 in the fraction of households
who have a separate kitchen, whose homes are electrified, who have a latrine,
and who have a solid roof versus a thatched roof (a rough measure of the town’s
wealth).6 Given the increase in nighttime lights measured in Section 5.2, it is
not surprising that the fraction of homes with electricity rises by 16 percentage
points. But there is a 6 percentage point rise in the fraction of households with
a latrine and a 4 percentage point rise in the fraction with a solid roof. At least in
the towns of Uttarakhand, secession has improved not only income but welfare.
6
Other Assumptions about the Secession Rule
5
Some outcomes are reported by village, but the village data have not yet been released for
2011.
6
I define a roof as “solid” if it is made of tile, slate, metal sheet, brick, stone, or concrete. I
define it as “thatched” if it is made of grass, thatch, or “other.”
SUCCEED OR SECEDE
25
Table 7
Household Welfare in Uttarakhand Improves
11-Years After Split
Towns
Sub-districts
6.1
Kitchen
0.034
(0.03)
362
63
Fraction of Households that Have...
Electricity Latrine Thatch Roof Solid Roof
0.161∗∗∗
0.061∗∗∗
-0.037∗
0.037∗
(0.02)
(0.02)
(0.02)
(0.02)
362
362
362
362
63
63
63
63
Does the Allocation Rule in the Core Change After
Secession?
One of the assumptions made in Section 2 was that the rule used to allocate
public goods to local economies in the core does not change when the fringe
secedes. Unlike the other assumptions, whose failure would appear in Figure 4
as statistically significant effects in the years before secession, this assumption
is hard to verify.
To see why, suppose that the departure of the fringe created no benefit at all
for the fringe but made every economy in the core worse off. Since the change
is caused by secession it would show no effects before secession. After secession it would appear as a statistically significant “improvement” at the border
even though no one has benefitted. This is because areas near the border in
the core do not accurately reflect the counterfactual world in which the fringe
never seceded. In short, the Stable Unit Treatment Value Assumption fails.
To see the problem formally suppose the state allocates funds to local economies
by generalized Nash bargaining. The outside option for each economy is zero (a
single patch of land cannot credibly leave the union), and the bargining power
is productivity at (i).7 Suppose overall government revenue is Ḡt . The allocation
function Gt (i) solves
max
Gt (i)
7
Y
[Gt (i)]at (i)
i∈c∪f
This might arise if allocations are set by some legislative process. If at (i) is higher in more
densely populated areas (as I assume in Section 6.2), areas with higher at (i) have more representatives and can extract more concessions.
26
AJAY SHENOY
Table 8
No Evidence that Border Economies in the Rump States are Hurt by Secession
Trend
Post-Split
Cell-Years
Cells
(1)
Uttarakhand
0.005∗∗∗
(0.00)
0.030
(0.02)
2079
99
subject to a budget constraint
(2)
Chhattisgarh
0.003∗∗∗
(0.00)
-0.011
(0.01)
2730
130
R i∈c∪f
(3)
Jharkhand
0.003∗∗∗
(0.00)
-0.001
(0.00)
2730
130
Gt (i) = Ḡt . The solution is
at (i)
Ḡt
Gt (i) = R i∈c∪f
at (i)
If this is the rule for allocating funds, could spending in border regions in
the core change upon secession? It seems possible for two reasons. First, the
integral in the denominator will be over c instead of c ∪ f , meaning the border
regions in the core no longer face competition from the fringe over the state’s
resources. Second, Ḡt will fall because the government has lost tax payers. If
the fall in overall resources outweighs the rise in relative bargaining power, the
core’s border will be made worse off.
However, this analysis suggests a simple test. If secession makes the allocation less favorable to regions of the core near the border, output in these regions
should abruptly fall. This would appear as a statistically significant deviation in
output from the trend. I check for such a deviation by regressing output on
a dummy for the years after each united state split, controlling for the trend.
I restrict the sample to cells within 10 kilometers of the border in the “core”
from which Uttarakhand, Chhattisgarh, and Jharkhand seceded—that is, cells
just over the border in Uttar Pradesh, Madhya Pradesh, and Bihar. Table 8 reports the results. The coefficient on [P ost-Split] measures the deviation from
the trend. In all three cases it is statistically insignificant, meaning there is no
evidence that the border regions were much harmed by secession.
This is not the ideal test. Though output did not deviate from trend after
secession, it is impossible to know if output would have been even higher had
SUCCEED OR SECEDE
27
the states stayed united. However, it is hard to find a reasonable theory in which
output in the counterfactual undivided state would have permanently risen in
precisely the year that secession occurred.
6.2
An Allocation Rule that Implies Heterogenous Gains from
Secession
According to Equation 3 the regression discontinuity design estimates the average benefit of secession for all local economies in the new state. But the functional form imposed on the rule used to allocate public goods hides an important caveat about the regression discontinuity. Suppose instead that
GI (at (i), ξ) = exp (βtI + ξ)at (i) mt [at (i)]
meaning the cost or benefit of secession is amplified for more productive
economies. Then the average benefit of secession is
ˆ
(β̂tI + ξ)E[a
t (i) | i ∈ f ]
(8)
whereas the regression discontinuity estimate is
ˆ t (i+ )
(β̂tI + ξ)a
(9)
In other words, the local economy right next to the new border may benefit
more or less than might be expected of local economies away from the border.
Figure 6 shows one reason to expect border effects might differ from average effects: population density. For each state I map the population density as
measured from the 2011 LandScan dataset, coding darker (redder) regions as
more densely populated. The figure shows that, especially for Uttarakhand and
Chhattisgarh, the population density at the border differs from the average in
the rest of the state. Could this explain why I measure such large benefits for
Uttarakhand, whose new border splits relatively productive cities, and such inconsistent benefits for Chhattisgarh, whose new border is sparsely populated?
I can assess whether this issue matters by directly modeling productivity.
Suppose, as in Ciccone and Hall (1996), that productivity depends on popula-
28
AJAY SHENOY
Figure 6
Population Maps of the Three New States
Uttarakhand
Chhattisgarh
Jharkhand
29
SUCCEED OR SECEDE
tion density. If the benefits of secession are larger for a productive local economy, the effect of secession I estimate will be large for a state that has cities on
its new border (as does Uttarakhand) and small for a state that has a sparsely
populated border (as does Chattisgarh). From 8 and 9 the average gain from
secession is
[Avg. Gain]t = βtI ξE[at (i) | i ∈ f ]
E[at (i) | i ∈ f ]
at (i+ )
E[at (i) | i ∈ f ]
= [Border Gain]t ·
at (i+ )
= βtI ξat (i+ )
which is simply the border gain rescaled by the (inverse) productivity of the
border relative to the mean.
For simplicity, suppose production depends only on land T and labor L, as
in the simplest model of Ciccone and Hall (1996). Output in each cell is
Lt (i)
Yt (i) = (G )
T (i)
| t {z
X γ
γ−1
at (i)
Lt (i)
(10)
}
Assuming a constant labor force participation rate, Lt (i)/Tt (i) is proportional
to the population density [Den]t (i). Then the average effect is
E [Den]γ−1
(i)
|
i
∈
f
t
[Avg. Gain]t = [Border Gain]t · + ) | i+ ∈ f
E [Den]γ−1
(i
t
(11)
Likewise, the benefit to any single local economy is
[Den]γ−1
(i)
t
[Gain]t (i) = [Border Gain]t · + ) | i+ ∈ f
E [Den]γ−1
(i
t
(12)
Ciccone and Hall estimate γ ≈ 1.04 in the U.S. Using this estimate I plug
the population density at each point in the LandScan population raster into
Equation 6.2. I compute the denominator assuming i+ is every point in the new
state that lies within ten kilometers of the new border.
30
AJAY SHENOY
Table 9
Rescaling Border Effects to Average Effects
Rescale Factor
Border Effect
Average Effect
Uttarakhand
0.84
0.90
0.76
Chhattisgarh
1.04
0.50
0.52
Jharkhand
0.99
0.40
0.40
Note: Effects are in percentage points.
Figure 7 maps the results. Lighter areas mark areas that gained more from
secession. Not surprisingly, this map of the gains from secession resembles the
map of population density. Thus Dehradun, the densely populated capitol of
Uttarakhand (near the northwestern part of the new border), benefitted the
most from secession. By contrast, the largely unsettled regions near the border with China (in the northeast) got no benefit.8
But compared to the map of population, the map of gains has less variation. Only in the case of Uttarakhand, which has many uninhabited regions, is
there much difference in the gains from secession. Table 9, which applies Equation 6.2 by taking the average across all points in each state, confirms this. The
rescaling factor is close to 1 in Jharkhand and Chhattisgarh. Even in the case
of Uttarakhand the border effect falls only from 0.9 percentage points to 0.76
percentage points. Though average effects are more similar across states than
border effects, the gaps remain.
7
Conclusion
This paper tests what is arguably the most basic prediction of every economic
theory of secession: that new states benefit from secession. Given that economic theory assumes an agent acts only if it believes its action yields benefits,
in truth I test whether a prospective state can be modeled as an economic agent.
Doubts about this assumption come not only from non-economists, who argue
collective identity and national pride matter as much as material gain, but from
8
The pure white regions along the border with China are missing values, likely because LandScan could not make good estimates for the highest peaks of the Himalayas.
31
SUCCEED OR SECEDE
Figure 7
Map of Relative Gains from Secession
Uttarakhand
Chhattisgarh
Jharkhand
32
AJAY SHENOY
political economists as well. One can argue a nation is not a cohesive and optimizing whole, but a combination of competing individuals.
But in the three cases I study these doubts do not overturn the prediction.
I find evidence that Uttarakhand, Chhattisgarh, and Jharkhand benefited from
their secession. The difference-in-discontinuities design ensures the result is
not biased by existing trends, and I confirm the result is not driven by a failure of
the stable unit treatment value assumption or by unusual effects at the border.
Though non-economic forces and political competition might push a region to
secede, it seems that neither causes a secession that hurts the new state.
Showing merit in the economic approach to state secession is only a first
step. Future research must assess which of the many models of secession holds
most merit, and whether the amount of benefit reaped by a state after secession
depend on its reasons for seceding.
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