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Hierarchical Logistic Regression Model for Multilevel Analysis: An Application on Use of Contraceptives Among Women in Reproductive Age in Kenya
Linda Vugutsa Luvai,
Fred Ongango
Issue:
Volume 4, Issue 5, October 2018
Pages:
58-78
Received:
19 July 2018
Accepted:
8 October 2018
Published:
16 November 2018
Abstract: Contraception allows women and couples to have the number of children they want, when they want them. This is everybody’s right according to the United Nations Declaration of Human Rights. Use of Contraceptive also reduces the need for abortion by preventing unwanted pregnancies. It therefore reduces cases of unsafe abortion, one of the leading causes of maternal death worldwide. According to Mohammed, in 2012 an estimated 464,000 induced abortions occurred in Kenya. This translates into an abortion rate of 48 per 1,000 women aged 15−49, and an abortion ratio of 30 per 100 live births. About 120,000 women received care for complications of induced abortion in health facilities. About half (49%) of all pregnancies in Kenya were unintended and 41% of unintended pregnancies ended in an abortion. The use of contraceptives in Kenya still remains a big challenge despite the presence of family planning programs through the government and other stake holders. In 2014 a household based cross-sectional study was conducted by Kenya National Bureau of Statistics on women of reproductive age to determine the country’s Contraceptive Prevalence Rate and Total Fertility Rate. This dataset is used to exemplify all aspects of working with multilevel logistic regression models, comparison between different estimates and investigation of the selected determinants of contraceptive usage using statistical software, since large surveys in demography and sociology often follow a hierarchical data structure. The appropriate approach to analyzing such survey data is therefore based on nested sources of variability which come from different levels of the hierarchy. When the variance of the residual errors is correlated between individual observations as a result of these nested structures, traditional logistic regression is inappropriate. These analysis showed that different regions have different effects that affect their contraception prevalence. The study also clearly revealed how single level modeling overestimates or underestimates the parameters in study and also helped to bring to understanding of the structure of required multilevel data and estimation of the model via the statistical package R 3.4.1.
Abstract: Contraception allows women and couples to have the number of children they want, when they want them. This is everybody’s right according to the United Nations Declaration of Human Rights. Use of Contraceptive also reduces the need for abortion by preventing unwanted pregnancies. It therefore reduces cases of unsafe abortion, one of the leading cau...
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Structural Vector Autoregressive (SVAR) Analysis of Maize Prices and Extreme Weather Shocks
Samuel Waiguru Muriuki,
Joseph Kyalo Mung’atu,
Antony Gichuhi Waititu
Issue:
Volume 4, Issue 5, October 2018
Pages:
79-88
Received:
18 September 2018
Accepted:
16 October 2018
Published:
16 November 2018
Abstract: Food prices have experienced enormous movements and volatility in the recent past which can be predominantly attributed to climate change. Extreme weather events such as drought, flooding and heat waves have adverse effects on agricultural production in areas where agriculture is weather reliant. Among the extreme weather events experienced in Kenya is a drought in 2008/09 which led to a record increase in food prices. It is against this backdrop that this study sought to investigate the dynamic relationship between maize prices and extreme agro-climatic indicators. The study uses structural vector autoregressive (SVAR) tools; Granger causality, Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) to examine the dynamic relationship between extreme weather indicators (minimum and maximum temperature and precipitation) and wholesale maize prices. Using different lag length determinant criterion, reduced-form VAR (2) is highlighted as the best model to fit the study data past weather and maize prices information over a data period spanning from January 2000 and December 2016. The study established that there exists granger causality between maize prices and weather variables. Agro-climatic indicators are therefore significant in predicting future maize prices. Principally, this significance can be inferred from the reliance of local agricultural production on phenological patterns. Maize price shocks exhibited inflationary effects on future maize prices, while a shock in weather variables has depreciating effects after three months. With regard to forecast variance, 30-39% of maize price variations resulted from its own shocks. The rest is attributed to precipitation (29-39%); maximum temperature (24-26%); and minimum temperature (7-8%).
Abstract: Food prices have experienced enormous movements and volatility in the recent past which can be predominantly attributed to climate change. Extreme weather events such as drought, flooding and heat waves have adverse effects on agricultural production in areas where agriculture is weather reliant. Among the extreme weather events experienced in Keny...
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Investigating the Existence of a Bubble in the Kenyan Real Estate Market
Paul Kiarie Njoroge,
Jane Akinyi Aduda,
Carol Mugo
Issue:
Volume 4, Issue 5, October 2018
Pages:
89-97
Received:
11 October 2018
Accepted:
23 October 2018
Published:
21 November 2018
Abstract: In the Kenyan real estate industry, laws of economics seem to be violated. The demand for houses has been increasing tremendously despite the oversupply. This violates the laws of economics indicating a possibility of a real estate bubble. The study aimed at estimating short term and long term real estate price dynamics in Kenya using co-integration tests. Secondly, the study aimed at identifying the presence of a Kenyan real estate bubble using the forward-recursive Generalized Augmented Dickey-Fuller test (GSADF) and finally measured the size of the bubble at a given time relative to other key macroeconomic variables. The study utilized quarterly data on house prices and rental prices in Kenya and macroeconomic determinants from the year 2004 to 2017 September. Stationarity test revealed that the variables were stationary in their first difference I (1). Cointegration test revealed that there was no long term and short term house price dynamics between house prices and the macroeconomic variables at a lag of 4 determined through AIC, SIC and HQ criterion. Again a Granger causality test was performed and the results revealed that the macroeconomic variables did not Granger-cause house prices and vice versa. To investigate the presence of a Kenyan real estate bubble, cointegration test, and GSADF were performed and the results indicated the existence of a bubble in the Kenyan real estate. Two time period bubbles were identified from September 2009 to January 2010 and the other from April 2011 to September 2011. Finally, the bubble sizes were measured and were found to be 15% each in the two periods.
Abstract: In the Kenyan real estate industry, laws of economics seem to be violated. The demand for houses has been increasing tremendously despite the oversupply. This violates the laws of economics indicating a possibility of a real estate bubble. The study aimed at estimating short term and long term real estate price dynamics in Kenya using co-integratio...
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Bayesian Modelling of Kenya Extreme Debt with Correction for Budgetary Leakage
Matabel Odin Odiaga,
Samuel Musili Mwalili,
Joseph Kyalo Mung’atu
Issue:
Volume 4, Issue 5, October 2018
Pages:
98-105
Received:
24 October 2018
Accepted:
8 November 2018
Published:
4 December 2018
Abstract: Total public debt levels in Kenya are exponentially increasing due to rising budget deficit, poor public fund management as well as movement of various macro-economic indicators such as balance of payments, inflation, Gross Domestic Product, exchange rates, and grants leading to worries on whether or not the high debt levels would be sustainable in future. The major concern is that a huge portion of the country’s revenue is committed to debt repayment and budgetary leakage strains the repayment efforts, thereby accelerating the country's debt unsustainability. This study sought to model extreme debt in Kenya with correction for budgetary leakage using a Bayesian approach to Extreme Value Theory (EVT) the main aim being to estimate the maximum debt tolerable for the country. A non-stationary Generalized Pareto Distribution (GPD) model is used for modeling the public debt extremes which depend on some covariates (macro-economic indicators) and Bayesian methods used to directly estimate the threshold and the GPD parameters. A major contribution of this study is the introduction of a compensator to allow for possible leakage due budgetary leakage through corruption, tax evasion, money laundering, and other forms of financial fraud, modelling it as a function of budget deficit. The established debt threshold is approximately KShs. 2 trillion which is the standard amount that should be borrowed, beyond which values are considered extremes. The results indicate that the movements in the macro-economic debt indicators significantly affect total public debt levels, and that budgetary leakage reduces Kenya's debt tolerance. The research concluded that the current debt level of around KShs. 5 trillion is still sustainable but high budgetary leakage may accelerate the country's long-run debt unsustainability. For further work, it is recommended to use a time-varying threshold to capture seasonality of the public debt series.
Abstract: Total public debt levels in Kenya are exponentially increasing due to rising budget deficit, poor public fund management as well as movement of various macro-economic indicators such as balance of payments, inflation, Gross Domestic Product, exchange rates, and grants leading to worries on whether or not the high debt levels would be sustainable in...
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