Public Investments and Inclusive Growth in Uganda Stephen D. - - PowerPoint PPT Presentation

Public Investments and Inclusive Growth in Uganda

Stephen D. Younger, Sarah Ssewanyana, Ibrahim Kasirye and Elaine Hill Cornell University and Economic Policy Research Centre, Uganda

Overview of presentation

• Why the focus on inclusive growth in Uganda
• Infrastructure
• Data and methods
• Decompositions spatial differences in
• utcomes
• Simulation of poverty impacts of

infrastructure

• Conclusions

Background and Motivation

• Drivers of economic growth and poverty

reduction are a matter of public concern in Uganda

– Positive macroeconomic growth in past 20 years – Poverty reduction from 56% in 1992/93 to 25% by 2009/10 – worsening inequality; Gini increased from 0.33 to 0.42 – Pace of poverty reduction has slowed down in the recent past.

Large geographical variation in poverty over time

Uganda: Trends in income poverty 1992/3-2009/10

Headcount poverty (%)

1992/93 1999/00 2002/3 2005/6 2009/10

All Uganda

54.9 33.4 38.8 31.1 24.5

Rural

58.5 37.4 42.7 34.2 27.2

Urban

27 9.6 14.3 13.7 9.1 By geographic location

Central

45.6 19.3 22.5 16.4 10.7

Eastern

58.8 34.2 45.9 35.9 24.3

Northern

72.2 63.4 62.9 60.7 46.2

Western

53.1 25.9 32.9 20.5 21.8

Background and Motivation

• Growth is necessary but not sufficient for

sustainable poverty reduction in Uganda

– Some authors suggest that inequality is driven by unequal access to public goods (Fox et al., 2009)

• E.g. education, basic infrastructure and transport services

– Both education and returns to education are lower in rural areas and in urban area, outside the centre – How can public investment programs be used to reduce such discrepancies?

General Approach of the Paper

• We estimate regression decompositions

(Oaxaca-Blinder, 1973) using a nationally representative LSMS type survey.

– Extent to which spatial differences in outcomes are due differences in endowments or returns to endowments

• Skilled labour, infrastructure etc

– Dependent variable: household consumption expenditure per adult equivalent – Regressions based on 4 geographical regions of Uganda.

Approach (contd)

i i i i

X W ε β + = ) ln(

Approach (contd)

[

]

N N C C N C

X X W W E β β − = − ) ln( ) ln(

[

] (

) ( )

( ) (

)

N N C N C C N N N C N C C C N C

X X X X X X X W W E β β β β β β β − + − = − + − = − ) ln( ) ln(

Data

• Uganda National Household Survey (UNHS)

2009/10

– Conducted during May 2009 and April 2010 by Uganda Bureau of Statistics – Covered all areas of Uganda – At least 6,800 households surveyed – Similar to Living Standards Measurement Surveys (LSMS) by the World Bank – Based on two stage stratified random sampling procedure – Enumeration areas (EAs) are principal sampling units (PSU) and 10 households randomly selected from each EA.

Regression Decompositions

• Dependent variable

– Household consumption expenditure per adult equivalent

• Explanatory variables

– Physical infrastructure i.e. Roads, Agricultural markets, health facilities, electricity, phone service, factory – Household head’s characteristics i.e. education attainment, age, spatial location

• Infrastructure regressors measured at

community level

– E.g. electricity means household is resident in community/LC1 with electricity and not household itself has access

Issues with infrastructure variables

• Presence of infrastructure in community/LC1 is co-linear
• Risky to interpret the individual infrastructure coefficients

(“returns”)

• Hence we focus on sum of coefficients e.g. “all infrastructure”

– weighted average of all coefficients on each infrastructure

service for the endowments criteria – Weights are actual frequencies observed in the region

• Interpretation: % change in welfare resulting from the presence
• f all of the infrastructure in the region

– E.g. Impact of the infrastructure package observed in central Uganda is to increase welfare by 15%

Table: Average Endowments and Returns to Those Endowments, by Region, UNHS 2009/10

Returns Endowments

Central Eastern Northern Western Central Eastern Northern Western Education Level of HHH < primary 7 11 12 13 0.38 0.45 0.46 0.41 primary 31 31 28 30 0.34 0.30 0.28 0.28 O-level 50 45 54 57 0.08 0.06 0.04 0.04 A-level 93 81 73 70 0.07 0.04 0.04 0.04 Post-secondary 129 137 125 107 0.02 0.01 0.00 0.01 All education: 26 21 18 19 Age of HHH 1 44 44 42 44 urban residence 19 23 22 18 0.29 0.08 0.14 0.08 Presence in LC1 of: Public health centre

• 6
• 1
• 9

13 0.06 0.08 0.06 0.11 Public hospital

• 9

8 24 0.00 0.02 0.00 0.00 All health infrastructure:

• 15

7

• 9

37 All season feeder road

• 4
• 7

12 5 0.81 0.65 0.39 0.69 Tarmac trunk road 3 14 8 13 0.23 0.13 0.11 0.07 Factory within 10km 15

• 8

17

• 1

0.16 0.03 0.01 0.05 Telephone service 6 13 7 3 0.71 0.41 0.19 0.64 Agricultural input market 6

• 4
• 4

2 0.32 0.38 0.27 0.43 Agricultural output market

• 7

3 11

• 2

0.57 0.70 0.44 0.71 Community has electricity 25 9 5 16 0.54 0.17 0.14 0.12 All physical infrastructure: 15 4 11 9 All phys. infra. except electricity: 2 3 10 7

Endowments

• Central region has better infrastructure

– Interpretation: share clusters in the survey reporting having a service in its LC1/community – Exceptions are health centres and agricultural markets

• Northern region is worse off compared to rest
• Education attainment levels better in central

– Difference mainly due to higher secondary and post-secondary levels – May reflect migration of the highly educated from

• ther regions

Returns to endowments

• Returns to education are higher in Central Region but

difference not very large e.g. 26% vs.20%

• Returns to health irregular

– Negative in central and Northern Uganda probably due to collinearity – Possible that health infrastructure benefits children more than others – No contemporaneous variable to measure impact of previous health investments for today’s adults.

• Returns to physical infrastructure higher in central and

lowest in Eastern

– Regional differences influenced by electricity

• Overall: No equity-efficiency trade off for non

electricity infrastructure

– Only for electricity are returns higher in Central Region

Oaxaca-Blinder Decompositions

• We decompose the means differences

between central and each of the other regions

– Central richest in incomes and infrastructure

• Sources of welfare differences across regions

– Decompose Eq3 further to report the returns and endowment effects for household characteristics

• Separate the intercept coefficients from the

“return effects” to determine unexplained effect.

Oaxaca-Blinder Decomposition of Average Welfare Differences, Central Region vs. Others (%)

Eastern Northern Western

Return effects

• n household characteristics

14 33 5

• n infrastructure
• 9
• 10
• 17

sub-total, returns:

6 23

• 12

Endowment effects household characteristics

10 11 11

infrastructure

10 14 11

sub-total, endowments:

20 25 22

unexplained

43 78 36

Total difference in welfare

69 126 47

Notes: "Unexplained" is the difference between the constant coefficient in Central region minus the listed region The return effects show the change in the listed region's average welfare if it had Central region's regression coefficients (returns to assets) The endowment effects show the change in the listed region's average welfare if that region had Central region's endowments Reported values are averages of the estimated effects using Central region and the listed region as the reference region Total differences in welfare are for the regression samples only

Oaxaca-Blinder decompositions

• If other regions had Central’s endowments, average

welfare levels would be in range of 20%-25% higher

– 10%-11% due to better education endowments – 10%-14% due to better infrastructure in Central

• Return to effects are more varied

– Northern region would increase welfare by 23% – Eastern region only 6% – Western region worse off

• Overall no equity-efficiency trade off for infrastructure

investments – Returns are better in poorer regions including Northern

Poverty impacts of social and infrastructure investments

Simulated Poverty Impacts of Social and Infrastructure Services, by Region

Variable All Uganda Central Eastern Northern Western HH head's educational attainment < primary 0.03 0.01 0.05 0.03 0.02 primary 0.06 0.05 0.07 0.05 0.03 O-level 0.02 0.02 0.02 0.01 0.01 A-level 0.03 0.03 0.03 0.01 0.01 post-secondary 0.01 0.02 0.01 0.00 0.00 All education 0.15 0.13 0.17 0.10 0.07 HH head's age Urban 0.03 0.03 0.01 0.02 0.01 Infrastructure in LC1 Health centre 0.01 0.00 0.00 0.00 0.01 All season feeder road 0.01

• 0.01
• 0.03

0.03 0.01 Tarmac trunk road 0.01 0.01 0.02 0.00 0.01 Factory w/ > 10 people 0.01 0.01 0.00 0.00 0.00 Telephone service 0.03 0.02 0.05 0.01 0.01 Agricultural input market 0.01 0.01

• 0.01

0.00 0.00 Agricultural output market 0.00

• 0.01

0.02 0.03

• 0.01

Electricity 0.03 0.07 0.01 0.00 0.01 All infrastructure 0.09 0.09 0.06 0.06 0.03

Notes: simulations based on the results in Table 5. Reported value is the increase in the poverty headcount that would obtain if that variable were zero instead of its observed value.

Simulated poverty impacts

• Poverty impacts of existing education level is

greater than poverty impacts of physical infrastructure

– Difference in Eastern region especially large – For entire country, education attainment contributes 15 % point reduction in poverty – Existing infrastructure contributes a 10% point reduction

• Central regions returns to education and

infrastructure not greater than other regions

– Less than Northern and Western regions for infrastructure – Poverty impact is greater in Central because of larger average endowments of these services than other regions

Conclusions

• Our results suggest that there is not too severe a

trade-off between equity and efficiency in public infrastructure investments

– returns are similar in the less well-endowed regions (especially the North, but also Eastern and to a lesser extent Western) to those in Central region. – any attempt to distribute social and physical infrastructure more equitably will not be very costly in terms of sacrificed rates of return – Caution about interpreting “rate of return” as we do not have information on the cost of providing infrastructure

Conclusion

• Returns to physical infrastructure are not

very high

– Bringing other regions physical infrastructure up to the level of Central Uganda will have a modest effect on poverty

• Returns to education are much bigger

– Investments in education may do better at equalizing poverty rates than investments in infrastructure

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