Second Annual Conference, Learning and Teaching Forum, The Sino-British College, USST

1. Second Annual Conference, Learning and Teaching Forum, The Sino-British College, USST TOPIC: Political support and Infrastructure resource allocation in growing democracies: The carrot and stick mechanics. Research paper Presenter: Kagimu Ivan Graduate Teaching Assistant Fudan University School of Economics

2. Introduction • Political Support was introduced by David Easton in his seminal work “A Systems Analysis of Political Life” (1965:159) • David Easton gave a hypothetical view about the political life, he argued that politics was a matter of linkage, which involves the way in which a citizen isconnected to his government in a relationship which allows for the fulfillment of those citizen’s societal needs that cannot be met by other societal means. • Literatures:Boynton, Patterson and Hedlund 1968: 163-80, Patterson, Wahlke and Boynton 1973: 282-313, Boynton and Loewenberg 1973: 169-89; Patterson and Boynton 1974: 56 and Abramson, Kurt Weyland 2000, 44 - 502 and Inglehart 1970: 419-42; Dennis 1975: 187:230 e.t.c • Question: How a leader can maintain political support amidst low levels of economic growth, high rates of unemployment, staggering inflation rates and poor infrastructure. • The Gap. Literature points out political support increase when incumbents do well but its not the case in many African growing democracies.

3. Research Motivation.

4. Research Motivation.

5. Purpose • This study attempts to fill the gap in the previous slide by making use of a dataset that contains data for an entire country’s national budget that shows clearly how infrastructure is allocated to different provinces or administrative regions through budget allocations according to political support. I go one step further by examining carefully the cost of not supporting the incumbent by analyzing how budget allocations change in regions that do not support the incumbent. • 𝑙𝑛polsup = 𝛼 + 𝛽1𝑙𝑛HLTH + 𝛽2 𝑙𝑛WRKS + 𝛽3 𝑙𝑛EDUC + 𝛽4 𝑙𝑛LD + 𝛽5𝑙𝑛PROD + 𝛽5𝑙𝑛WAT+ 𝜀𝑡 𝜀𝑡 • VARIBALES • POLSUP is political support • HLTH is health • WRKS is works • EDUC is education • LD is local development • PROD is production • WAT is water • 𝜀𝑡 is the error term.

6. Methods • Data: Political Support = Data about political support was obtained from the general elections results of Uganda. The elections data used covers a period of more than 20 years and take into account four general elections. Infrastructure = health, works (transportation), water, local development, education and production Data about these particular infrastructures was got from National budgets of Uganda. • Why Uganda ?. • Inclusion & Exclusion criteria: To test for the correlation between political support and infrastructure resource allocation I exclude districts that were dissolved and merged, I include new districts that have been created with in time. I further remove missing and unusable data. • Instrumentation: After all data was entered into spreadsheets, STATA software was used to run regressions between the independent variable and the dependent variables. • Cost of Rebelling: I use political support of time t as the independent variable and use infrastructure resource allocation at time t+1 as the dependent variable in order to be able to catch the change in resource allocation due to low support in the previous election (45% and below Districts were chosen out).

7. Statistical Analysis • The model: 𝑙𝑛polsup = 𝛼 + 𝛽1𝑙𝑛HLTH + 𝛽2 𝑙𝑛WRKS + 𝛽3 𝑙𝑛EDUC + 𝛽4 𝑙𝑛LD + 𝛽5𝑙𝑛PROD + 𝛽5𝑙𝑛WAT+ 𝜀𝑡 𝜀𝑡 • Why Use log or ln:linear models seldom indicate the reality, hence non-linearity logarithmic functions are known to suppress heteroscedasticity (i.e. not constant variance in errors) and make the error distribution more normal (Gujarati, 2004). • Auto Correlation testing: I used the Durbin-Watson statistic which is a number that can capture autocorrelation. It is always between 0 and 4. • Does the Model Fit ?: The low p-values, the fact that DW statistic (1.58) which is approaching to 2, the value of adjusted R^2 (70%) as well as the presence of homoscedasticity verify that the model that I employed for my research fits well with the chosen variables. • Analysis: For the analysis I used OLS regressions to test for the relationship between political support and infrastructure variables, still while testing for then cost of rebelling OLS regression were used.

8. Results • Relationship between political support and Infrastructures:

9. Effects of a 1% increase in Central government transfers to local governments for each of the variables.

10. Results • The cost of rebelling:

11. Effects of a 1% decrease in political support.

12. Discussion • Interpretation: The relationship between political support and infrastructure resource allocation is positive in developing democracies, just as I have proved it statistically we can confidently reckon that voters respond positively to incentives (Dinos Stasinopoulos 1995). • Infrastructure variable water (wat) has the strongest effect on political support, this is because a one percent increase in transfers for the infrastructure water leads to a surprisingly high increase of around 10% in political support. [(David Easton, "A Reassessment of the Concept of Political Support," British Journal of Political Science 5 (October 1975), pp. 435-57)] and [Hibbing and Elizabeth Teiss Morse. Cambridge University, Cambridge University Press. Anderson, C.J. and CA. Guillory. 1997. "Political Institutions and Satisfaction with Democracy: A Cross National Analysis of Consensus and Majoritarian Systems." American Political Science Review 91(1): 66-82.] • Secondly, Infrastructure variable works (wrks) also according to the results has a gigantic effect on political support one percent increase in transfers to local governments for infrastructure development works (wrks) will cause a significant 6.2% increase in political support. [Seymour M. Lipset, eds., Democracy in Developing Countries: Asia (Boulder, Colo.: Lynne Rienner, 1989), pp. 267-303]

13. Water

14. Roads

15. Conclusion • Voters respond mostly to increments in finances or transfers for infrastructures that affect their lives directly this is because a one percent increase in transfers for the infrastructure water leads to a surprisingly high increase of around 10% in political support • Infrastructure variable works (wrks) also according to the results has a gigantic effect on political support. 3. Government investment in infrastructure education health and production do not at all affect political support. This can be intuitively explained by the dominant private sector in Education and health sector. (Mulago Hospital privatisation.)

16. Conclusion • Government investment in infrastructure education health and production do not at all affect political support. This can be intuitively explained by the dominant private sector in Education and health sector and easy mobility for better services. (Mulago Hospital privatization.) 5. Money allocated for infrastructure development of each district rises significantly in 2010 (before the 2011 presidential election) then drops significantly and remains low through to 2014 and rises significantly in 2015 (before the 2016 presidential election) then remains high in 2016 after the elections

17. Conclusion 6. The secret unveiled: My research results conclude with an possible way how many leaders in developing countries and young democracies mange to keep themselves into power for many years after they are successful to do away with presidential term limits, then they can be life presidents.

18. NO HOPE “If a leader is successful to eliminate term limits, it is only external shocks that can save the country from political dictatorship ”. Political Order and Political Decay by Francis Fukuyama.