THE MISSING MIDDLE –
OR IS THERE AN OBVIOUS RESOURCE GAP FOR LMICS?
Stefan Dercon*, # and Nick Lea#
*
Oxford University and #Department for International Development
s-dercon@dfid.gov.uk, n-lea@dfid.gov.uk
May 2015
Much has been made recently of a ‘resource gap’ for LMICs as ODA declines. In this note, we
show that some of the alleged patterns are the result of the selection of specific sample
countries. Using data description with similar techniques that others have used, we find no
evidence of a systematic decline in total available resources (taxes, natural resource rents
and ODA) across countries in the LMIC range. Focusing on social sector spending (health and
education) we also cannot find any evidence of social spending being crowded out when
countries get richer as they progress into middle income. The lack of a systematic pattern
does not mean that specific LMICs do not face specific resource gaps or risks of crowding out
of social spending, but that generalisations towards all LMICs or all countries below $3000
per capita would be misleading.
1.
There is little doubt that most of the world’s extreme poor live in MICs, about 70
percent to be more precise, but most of these in countries that are just above the
threshold for LMICs. MICs are typically countries with a GNI per capita of more than
$1,045 but less than $12,746. This changing geography of poverty leads many to argue
that more financial flows need to be directed towards MICs, and that the focus of
development assistance on LICs is misplaced. Less widely known is that about 90% of
the world’s poor live in countries with a GNI per capita below $1700. In fact, the
majority of the world’s poor live in countries with GNI per capita between about $1000
and $1500 – there are a lot of poor people in countries like Pakistan, India, and
Nigeria… These are countries that relatively recently became MICs – and took a lot of
poor people with them into that status.
2.
The case for higher flows to MICs is also made in other ways, by pointing to sudden
changes in expenditure, or resource cliffs, related to reaching MIC status. A simple
argument against MICs getting more resources because they still have need is that they
are also likely to have more access to other resources: domestic tax and international
non-aid flows. Recent work by Kharas, Prizzon and Rogerson1 has argued that this is
indeed the case. Figure 4 and 5 from their paper show that total resources (tax plus
ODA) tend to decline when countries get richer, and tax revenue is only able to
compensate once a country is well into HIC status. In particular, near the threshold of
$1000, tax resources (as a percentage of GDP) remains relatively insensitive to growth
and only increase slowly as incomes rise towards $4,000. In short, there is a problem. In
1
Kharas, H., Prizzon, A., Rogerson, A., (2014), ‘Financing the post-2015 sustainable development goals – a
rough roadmap’, Overseas Development Institute.
1
this range, as figure 5 shows, total external resources (including remittances and FDI)
are nose-diving, showing that tax is unable to compensate for decreasing external
resources.
Figure 1 - (Figure 4 from Kharas, Prizzon and Rogerson)
Figure 2 - (Figure 5 from Kharas, Prizzon and Rogerson)
3.
Multilateral agences such as GAVI, GFTAM and others are arguing that this should
mean that graduation has to be slower. For example, a background paper for the
Equitable Access Initiative has the figure below2, which apparently is widely circulating
amongst health multilaterals, as amunition to make the case for more support in these
middle income countries. Besides the fact that the graph is always wrongly labelled (the
X-axis, despite what it say in all the published versions should be labelled GDP per
capita (logarithms) and not levels (linear scale), it is used to suggest an area between
2
Global Fund, (2015), “Evolving the Global Fund for Greater Impact in a Changing Global Landscape”, Report
of the Development Continuum Working Group, signed by 27 authors, p.12. The figure is sourced as based on
Brookings Analysis. The common author of this and the earlier quoted paper is Homi Kharas, of Brookings.
Correspondence with him and another co-author of the Kharas et al. (2014) suggests that they are not
necessarily agreeing with the implication for which these papers are used, i.e. that these middle income
countries need more aid resources and graduation from aid must be delayed.
2
around $500 and $3500 (the pink area) see declining ODA, still low tax revenue and a
clear potential ‘gap’ in financing. $3500 is higher than their usual graduation cliffs. So it
is argued that these countries are facing a financing problem over a range of GDP per
capita with tax resources available only picking up when they get considably richer and
that this means that grant and concessional financing should be offered to these
countries corresponding to their need.3
Figure 3 - From background note on EAI
4.
This analysis and its conclusion are misleading in four ways. First, all these graphs and
especially the last picture are somewhat misleading, with the shape of the regression
line driven by the selection of the sample and cut-off points. Second, it assumes that
the prevailing tax policy is “optimal”, the best for the country involved, and should
therefore be taken as given. Thirdly, it suggests a causality that declining ODA is the
main cause for resource gaps in these countries. Fourthly, analysis using shares of GDP
rather than levels add further confusion. To illustrate these points, we repeated the
analysis on total tax plus ODA relative to GNI per capita. Figure 4 shows first ODA plus
tax as a percentage of GDP plotted against (log) GNI per capita. We use the same data
sources for tax data (ICTD data to account for anomalies in some of the IMF statistics; it
includes all tax, duties and natural resource rents the state collects). We include the
‘cloud’ (scatterplot) as well as the smoothed curve (as in figure 1 above); and contrary
to figure 3, we do not restrict the sample. Although we could not fully replicate the
figure quoted in the Global Fund document, it was suggested to us that small
economies (with population below 5m) and high income countries were excluded to
3
See for example, M.Suzman, “Crunch meeting in July to decide finance for world’s poor”, Financial Times
Blog, April 8, 2015.
http://blogs.ft.com/beyond-brics/2015/04/08/crunch-meeting-in-july-to-decide-finance-for-worlds-poor/ To
quote “..[T]he economic success of many developing countries is making them ineligible for financing that
provides life-saving benefits to the millions people within their borders who still live in extreme poverty.”
“Their own domestic finance is unable in the short-term to make up the shortfall, particularly in the so-called
“social sectors” such as health and education, which are critical to their continued economic progress.”
3
draw the pattern.4 Dropping ‘small’ countries with populations below 5m may seem
innocuous, but this means not that they just drop ‘island economies’ as Salvado and
Walz claim, but dropping up to 80 countries such as Costa Rica, Central African
Republic, Congo, Liberia, Mauritania, Panama, Uruguay, Bhutan, Equitorial Guinea,
Gabon, Gambia, Botswana, Lesotho, Jamaica and Namibia.5
Figure 4 - Tax and Natural Resource Revenue plus ODA (% GDP)
60%
LSO
COG WSM
AFG
50%
MWI
40%
BDI
ERI
STP
MOZ
DJI
AZE
AGO
CPV
SAU
RUS
TON
SYC
ZWE
GNQ
DZA ZAF
ZAR
PLW
MRT
DMA
BTNBOL VUT
HUN
TTO PRT
TGO
GAB
MDA
MKD
MNE
BRA
GMB SLE
CYP
SRB
KGZ VNM PNG
GEOUKR BIHNAM
RWA
KNA
BLR
MLTGRC
MLIBEN
BWA
MDV
BLZ
TZA
HTI
EGY
MAR
TCD
VCT SUR
POLATG
TUR
FJI
NER
LCA
SWZ
JAM
NIC
BFA
BRB
HRV
COM
SENKEN
TJK
JOR
EST
KOR
UZB YEM
SVN
ARG
GNB
GRD LBN
COL
BGR
ALB
CZE
ETH CAFUGA
ZMB HND
ARM TUN IRN
LVA
ECU
KAZ
LAO
ROM
MUS
CIV
URY
VEN
MDG
THA
NGA
MYS
LTU SVK
GINNPL
CHN
INDGHA
PAN
CMR
SDN
PRY
TKM
CHL
KHM
MEX
BHS
LKAIDN SLVPER
PAK
DOM
CRI
PHL
BGD
GTM
30%
20%
10%
MNG
GUY
MMR
0%
5
6
7
8
9
10
11
Log GDP per capita
Note: lnGDP per capita=6.2 implies about $500; 6.9 is about $1000 and 7.3 is about $1500
Sample restricted to all countries below $30,000 GDP per capita.
Curve smoothed using a polynomial up to the fifth power (and not an imposed quadratic as in
figure 3)
5.
We do not find the trends implied by the previous graphs: there does not seem to be
obvious systematic declining pattern during early MIC status. There are some
differences in the data, apparently, but they are relatively small – although our figure 4
uses far more data points and does not limit the sample. Looking at this figure, one may
be tempted to say that the impression of declining total resources as a share of GDP is
there. Actually, if there is declining pattern then it is in the range of GDP per capita
between $500 and about $800, after which the curve is essentially flat.6 The quadratic
trend in figure 3 – that argues for the declining resources – is not there, once all
countries for which we have data are used, and no quadratic function is enforced. In
short, there appears to be no systematic trend of declining resources at middle income
status ‘when tax is still low and ODA is declining’. The pattern in the ODI study is also
not quite borne out either.
4
Figure 4 has some restriction – countries above $30,000 per capita were excluded but in Appendix Figure A.1, all
countries were included and the pattern is the same.
5 R.Salvado and J.Walz (2015), “Poor people or poor countries? Aid transition and the “missing middle” for LMICs” BMGF
Development Policy and Finance Discussion Paper. In the appendix Figure A.2 we can confirm that dropping these 80
countries can produce a U-Shape…
6 So these are larger numbers than just tax revenue as percent of GDP as in WDI or IMF data – natural resources rents and
any missed duties are included. If we use the standard ‘tax revenue’ data (without resource rents), then there is a slight
difference in the pattern compared to figure 4. Fitting an up to a fourth power polynomial to these data does not become
essentially a flat line from GDP per capita of about $800 but the percentage of resources in GDP decline from about $500
per capita until about $60,000 per capita! But the latter result is then largely due to the increasing share of these other
contributions to government resources when countries get richer.
4
6.
It is nevertheless striking that there is large variation in available resources between
countries at similar income levels – which is likely to reflect specific policy choices.
Countries with particularly low levels of total resources as a percentage of GDP shed
some insight on possible explanations for this wide dispersion. For countries above
$1000 GDP per capita but with percentages below 20%, some striking patterns emerge:
in the range near $1000 and $3000, many of the larger countries in this group with low
resources from tax and similar sources are Asian: such as Myanmar (MMR), Cambodia
(KHM), Bangladesh (BGD), Pakistan (PAK), India (IND), Philippines (PHL), Indonesia
(IDN). Those with higher GDP per capita but low resources are often Central or Latin
American economies. These low rates reflect policy choices: these economies are
known to have low tax collection efforts or success. India for example still has a
percentage of households paying income taxation less than a few percent. Hence, to
suggest that international resources should compensate for lower resources ‘because
tax collection hasn’t picked up’ seems an inappropriate policy conclusion!
7.
There is no obvious evidence that an emphasis on domestic tax effort (rather than
aid) should raise concerns that, on average, this will be at the cost of the social sector
spending. Figure 5 shows how ODA as a percentage of GDP steadily and sharply
declines as income grows – unsurprisingly. While this implies that domestic resources
become increasingly essential for social sector funding, figure 6 shows that as a
percentage of GDP, public expenditure on health and education increases steadily –
peaking at GDP per capita of around $3000 per capita. In short, LMICs, even if they may
have less resources from ODA, are able to raise spending to these sectors steadily.
While it is hard to judge the quality or equity of these expenditures, the removal of
ODA and increased reliance on taxes does not of itself seem to crowd out social
spending in a systematic way. There are countries which choose to spend relatively
little but from the data pattern it is not possible to argue that this is because aid fell.
5
Figure 5 – Net ODA received (% GDP)
40
LBR
35
AFG
30
25
KIR
20
STP
ZAR
15
10
BDI
5
0
5
6
WSM
GMBMOZ
CPV
SLE
NER
TJK
RWA
VUT
ETHCAF BFA
CIV
BTN
TZA
MLI
UGA
SEN
KGZ PNG
GINTGOBENLSO
KHM KENYEM
ZMB
NICMDA COGGEO
GHA
JOR DMA
MDG NPL
CMRLAO
BOL ALBMNG
SYC
BIHNAM
HND
KNA
SRB
ARM TUN
MDV
SDN
ISR
LCA
FJIBLZ
LBN
MAR
MKD VCT
MUS
BGD PAK
SLV
GRD
ECU
LKA
SUR
EGY
KAZ
GTM
AZE
BWA
BHR
PRY
NGA
UKR
DOM
TUR
BRB
GNQ
ZAF
HRV
MLT
CYP
AGO
PAN
SVN
PER
COL
IDN
ATG
BLR
JAM
BHS
DZA
IND
CRI
ARG
BRAHUN
CHL
VEN
URY
IRN
TTO
SGP
KWT
PHL
MYS
HITA
KG
POL
RUS
LVA
LTU
SVK
EST
CZE
PRT
GRC
ESP
JPN
GBR
NZL
ARE
FRA
ISL
DEU
BEL
AUT
FIN
IRL
NLD
CAN
USA
SWE
DNK
AUSCHEQAT
NOR
LUX
7
8
9
10
11
12
Log GDP per capita
Figure 6 – Health plus education public expenditure (% GDP)
30
25
MHL
LSO
20
TUV
KIR
DNK
FSM
SLB
NZL NLD
ISL
SWE
BEL
FRA
SWZ
PLW
NOR
AUT
GBR
CRI
USA
DEU
DJI MDA
FIN
NAM
CAN
MLT
TMP BWA
MWI
CHE
JPN
ARG
SVN
ESP
STP
WSM
PRT
ITA
IRL
AUS
SRB
RWA
JOR
GHA
KGZ
BOL
CZE
ZAF
BDI
URYEST GRC
CYP ISR
BLZTHAMDV
TUN
UKR
COM
HRV
LTU
POL
BRB
COL BRAHUN
SVK KOR
LUX
MNG
VCT
DMA
PRY JAM
VNM
BLR
TGO
TZA SENKEN NIC
LCA MYS
DZA
PAN
COG
VEN
TON
MEX RUS
LVA
BGR
VUT
MKD
CPV
CHL
SYC
HND
BTNMAR
MRT
BEN
SLV
GUY
TUR
LBR MOZ
GMB
NER
SAU
ECU
MLI
ROM KNA
NPL
GRD
FJI
BFA
ETHGNB
CIVYEM
OMN
ATG
IRN
BHS
ALB AGO
TTO BHR BRN
KWT
PER MUS
EGY
LBY
ZMB
KAZ
SLE TJK CMR
ZAR MDGUGA
GTM
ARM
DOM
CHN LBNGAB
SGP
IND
PHL IDN
QAT
LAO
GIN
AZE
GEO
BGDTCD
ERI AFG KHM
GNQ
PAK
CAF
ARE
LKA
HTI
MMR
15
10
5
0
5
6
7
8
9
10
11
12
Log GDP per capita
Note: lnGDP per capita=6.2 implies about $500; 6.9 is about $1000; 7.3 is about $1500; 8 approx $3,000
8.
Even if one wants to doubt some of the analysis above, the evidence points towards
generally increasing per capita spending in health and education. Some may still want
to argue that figure 3 or figure A.2 is the right way to look at the problem of resource
availability (i.e. excluding the set of countries excluded). But in any case drawing a
conclusion from figure 3 that this must mean that we should step in is very misleading
as well. This analysis is conducted in percentages, but what matters, say, for spending
on vaccines, is the size of the resource envelope, the total sum available and not the
percentage of GDP it represents. Drawing a curve in relationship to GDP per capita, is
easy enough. Figure 7 and 8 give this, and to drill home the point, first for all countries
with a population larger than 5m and also for the same group, but excluding high
income countries – hence replicating the Global Fund analysis but now in levels. We
find that there is a strong increase in resources per capita when GDP rises. At most,
there is decline between GDP per capita around $300 and $550, to pick up again
afterwards. Throughout the entire MIC range it increases persistently. So surely this is
not is there a cliff or a ditch, as total resources keep on increasing for these countries
rather fast, so there is more money for social services and development, even if there is
6
possibly some decline in the share of GDP.7 It definitely does not lead to the finding that
taking grants or concessional funding from development spending in lower income
countries to these lower MICs is the obvious equitable answer.
Figure 7 – Tax Revenue plus ODA per Capita in $ (only countries above 5m population)
Figure 8 – Tax Revenue plus ODA per Capita in $ (only countries above 5m population)
9.
The analysis in this note shows again that generalising about standard categories such
as LIC or LMIC is not helpful. There is not an obvious case for the existence of a
resource gap in LMICs (or countries between $1000 and $4000) where ODA has fallen
but taxation has not yet been able fill the gap. Similarly, LMICs, facing lower aid do not
appear to be systematically spending less on social sectors – used here as a possible
crude category of spending that may benefit the poor. If there are patterns, then they
7
It follows from figure 6 and 8 that repeating Figure 8 for education plus health expenditure also shows increasing
expenditure throughout the lower middle income range.
7
are likely to relate to specific countries with specific tax and spending policies. For DFID
this means that general policies ‘favouring’ LMICs or relaxation of ‘graduation’ criteria
for such groups of countries are not justified on the basis of income. It does not mean
that some of these countries should or could require specific attention, and of course, it
is true that there is still a lot of poverty in these places. But any attention to such
countries should be based on different or more nuanced information than a GNI per
capita cut-off or a current level of need, but also a clear assessment of ability to
contribute from domestic resources, overall access to financial resources well beyond
such ODA and an appropriate understanding why social sector spending is not
appropriate. Some of these questions are hard questions to ask our partners, but not
asking them would mean ignoring opportunities to achieve real progress for the poor
and misleadingly assume that the poor human development outcomes in some middle
income countries are always just an issue of lack of aid.
Appendix
Figure A.1 Unrestricted version (all countries in the world) of Figure 4
8
Figure A.2 Figure 4 but excluding countries below 5m population
9