Over the past few years Tanzania government has been struggling to solve the mismatch or disconnection between economic growth and poverty reduction. National Bureau of Statistics (NBS) of Tanzania provides annual GDP growth rate averaged 6.72% from 2002 until 2015, highest being 12.40% in the third quarter of 2007 and a lowest record of 2.60% in 2009. The growth on the GDP is an indication that the national cake is increasing hence the expectation of the citizens in Tanzania is that poverty will reduce by having equal distribution of the national cake. Tanzania’s Human Development Index increased from 0.445 to 0.530 from the period of 2005 to 2010. Distribution of the national income cake is reported uneven since most of the improvements in the poverty indicators occurred in Dar es Salaam region. Poverty in Dar es Salaam declined by over 70% but in the rural areas only by 15%, this uneven spatial decline of poverty is related to the pattern of economic growth, which is almost entirely cantered in Dar es Salaam region, where there is the most expanding and growing sectors.
Poverty has become more responsive to growth; the poverty headcount appears to have declined just as economic growth has continued to expand since 2007. In December 2014, Tanzania released revised gross domestic product (GDP) figures with a base year of 2007. GDP growth averaged 6.3 percent from 2008 to 2013, with a marked increase in volatility compared to the previous series of numbers. The new figures suggest a stronger impact of economic growth on poverty reduction than previously observed. The magnitude of the poverty reduction response to economic growth, however, depends on how economic growth is defined. When growth is measured by changes in GDP per capita, the growth elasticity of poverty is ‘1.02 during 2007’2011/12’in other words, a 10-percentage increase in GDP growth per capita can be expected to produce a 10.2 percentage decrease in the proportion of the poor. When economic growth is defined using changes in mean household consumption calculated from HBS, however, the growth elasticity of poverty is ‘4.0 during the same period, indicating an increase in household consumption.
Inequality is also increasing between urban and rural areas as well as between city centre and other regions. Household consumption grew faster in the metropolitan and urban zones than in rural areas, inducing an increase of inequality between geographic regions. In Tanzania, income inequality can also be explained by family background, that is family background seems to have a greater influence on the disparity of living standards than the characteristics of the local community, such as access to basic services and infrastructure, connection to markets and population centres. This indicates significant problems of intergenerational poverty and inequality persistence. Addressing the influence of parental education and background on children’s opportunities is a long-term mission that is often complex. But without additional policy actions, there are limited chances for the generations disadvantaged by circumstances to spring out of the poverty and inequality also endured by their parents.
Tanzania shows moderate levels of inequality in 2012. With the Gini coefficient estimated at less than 40, inequality in Tanzania is moderately high by international standards but lower than Sub-Saharan average inequality.
GINI COEFFICIENT IN SUB SAHARAN AFRICA
The Gini coefficient of real per capita monthly consumption indicates that the level of inequality for Tanzania is approximately 36, below the SSA average of 45.1 and the low income countries average of 40.13 Among East African countries, Tanzania’s Gini coefficient is below that of Burundi, Kenya, Uganda, and Rwanda and is only slightly higher than Ethiopia. It is on par with levels of inequality in South and East Asia, which range around 38.4, and significantly lower when compared to parts of South America, such as Mexico, Bolivia, and Brazil, where levels of inequality range from 47 to 55.
Inequality in Tanzania shows a slightly decreasing trend over time. The Gini coefficient decreased from 38.5 to 35.84 between 2007 and 2012 (see Table 2.1). The HBS and NPS datasets show slightly different levels and trends for inequality. This is possibly due to differences in measurement methods of consumption expenditures between the two datasets. The first uses the diary method and the second a seven-days-recall method for collection of food consumption data. Also, NPS data do not collect information on clothing expenditures, and there have been no changes in the survey design similar to those introduced in HBS. But although the inequality estimates from NPS did not confirm the declining trend of inequality, it still provides evidence of moderate and fairly stable inequality at a level below 40-as estimated by Gini index.
Dar es Salaam and secondary cities display more uneven distributions of consumption than rural areas. The Gini coefficients are respectively of 36, 38, and 30 for the capital city, rest of urban, and rural areas in 2011/12. The distribution of consumption is equalizing over time in all the regions, with the most substantial improvement occurring in the rural areas, Much of the reduction in inequality seems to be driven by an increase in the welfare share accruing to the poorest segment of the population, as the consumption share of the poorest quintile grew by more than 16 percent between 2007 and 2011/12 and by over 20 percent during the past five years, except in the secondary cities, where it grew by only 11 percent over the past decade (bottom part of Table 2.1). Even though part of the increase in the share of consumption going to the bottom quintile can be attributed to improvements in the survey design, the adjusted inequality estimates using the reweighting procedure, as well as the small area estimation techniques, reveals also positive changes over the past decade in the consumption shares of the lowest quintile groups.
1.2 Overview of Tanzania economy
Tanzania has a total population of 47.4 million as recorded in census 2014. It is one of the world’s poorest economies in terms of per capita income but the country achieved high growth rates due to gold production and tourism. The economy depends on agriculture that consists of more than one quarter of the GDP of the country. Tanzania has been receiving funds from World Bank, IMF and other donors to support Tanzania’s aging economic infrastructures. Recent banks reforms have helped increase private sector growth and investment also the government has increased spending on agriculture to 7% of its budget; however the financial sector in Tanzania has expanded in recent years that are having foreign owned banks for about 48% of the banking industry’s total assets. Emergency of foreign banks in the country has resulted to significant improvements in the efficiency and quality of financial services. A positive growth rate in Tanzania has been contributed by continued donor assistance and solid macroeconomic policies. Also high gold prices and increased production also led to growth of Gross Domestic Product (GDP). Growth in GDP in 2009-2013 was a respectable 6-7% per year.
Over the past few years the national strategy for growth and reduction of poverty (MKUKUTA) in Tanzania has given high priority to eradicating extreme poverty and promoting broad-based growth. Achieving pro-poor growth has also been widely recognized by the World Bank as a critical strategy for accelerating progress toward its twin goals of eliminating extreme poverty at the global level by 2030 and boosting shared prosperity by fostering income growth among the bottom 40 percent in every country. This national strategy of poverty reduction reported positive results in 2013, which shows that there is decrease in poverty. In early 2000s, Tanzania’s economic growth was seen to have strong resilience to external shocks. In 2007, the poverty rate in Tanzania remained stagnant around 34% despite of the robust growth at 7%.
In 2007, HBS revealed that the percentage age of Tanzania citizens living below the basic needs poverty line has slightly fallen from 35.7% in 2001 to 33.3% in 2007 this shows a decrease of 2.4%, this is the case for the urban area while in rural areas there is a decrease of only 1.3% on the people living below the basic needs poverty line. This implies that the statistics shows the growth has not manifested itself into poverty reduction in Tanzania.
A quarter of Tanzanian adults have no formal education, and 29 percent can neither read nor write. In rural areas, 30 percent of the population has no education. A significant rise has taken place in the proportion of households headed by a woman, and women are about twice as likely as men to have no education. Rural women are particularly disadvantaged; 41 percent are unable to read or write. Poverty levels are strongly related to the education of the head of household (Tanzania 2002a). Life expectancy is 44 years and falling (UNDP 2003), largely due to HIV/AIDS, leaving an orphan population estimated at more than 1.1 million (with 50,000’60,000 new orphans each year). HIV/AIDS has had and will continue to have a detrimental effect on Tanzania’s health, economy, and environment. Famine resulting from floods or droughts is not uncommon. Since the mid-1990s, adverse weather conditions have undermined food security. Social well-being, however, is good in Tanzania, a country with a long history of participatory planning and implementation of public programs. Aside from some instability in the late 1960s and early 1970s, Tanzania has enjoyed peace, stability, and unity since independence.
1.3 Statement of the Problem
Over the last 10 years, Tanzania’s economic growth has been fairly impressive. However, the growth does not seem to have translated into income inequality reduction. While the GDP grew at an average rate of 7.1 percent per year from 2000 to 2007, income inequality remained stagnant with little or negligible decrease as well as poverty, as measured by the head count index, barely declined during this time. Even more puzzling, while the poverty has remained constant and growth has been impressive, income inequality has also remained constant. One would expect that if the cake is increasing but the share of the cake to the poor remains constant, then the share of the rich would have gone up, and thus increasing inequality. But the Tanzanian cake seems to have increased, the poor remained with the same share of the care and surprisingly; the data suggests that inequality did not increase either. This puzzle needs to be explained if insight is to be gained on how the existing data can inform policy for pro-poor growth.
It is also instructive to note that over this period, from 2000 to 2007, the Tanzania’s Human Development Index increased from 0.445 to 0.530, overtaking some few countries in the Sub-Saharan Africa. Since development has to be evaluated in terms of its multidimensional extent, it is significant that while income poverty seems to have remained stagnant from 2000 to 2007, a more multidimensional evaluation records some progress in Tanzania, albeit a modest one.
Still, the big question remains: why is it that evidence largely suggests that growth has not been pro-poor in Tanzania? This is a pertinent question because MKUKUTA seeks to promote broad-based growth, and poverty reduction remains an overriding policy objective. Specifically the following questions are pertinent in reviewing pro-poor growth strategy for Tanzania Why economic growth does not seem to be translated into poverty reduction? How come the impressive growth from 2000 to 2014 did not lead to the reduction in the poverty nor did it lead to an increase in income inequality? What policy would generate pro-poor growth in Tanzania?
1.4 Objectives of the study
1.4.1 General Objectives
This study has a general objective of identifying the correlation of economic growth (GDP), poverty reduction and reduction in income inequality.
1.4.2 Specific Objectives
i. To find out how economic growth (GDP) has been effective in reducing poverty rate.
ii. To find out how economic growth (GDP) has been effective in stabilizing Gini-index
iii. To identify government policies and strategies in place for reducing poverty, income inequality and enhancing increasing GDP.
1.5 Research Questions
The following are the specific questions that are to be researched in this study
i. What is the correlation of increasing GDP and reducing poverty rate?
ii. What is the correlation of increasing GDP and reducing income inequality (stabilizing Gini-index)?
iii. What are the government policies and strategies in place for reducing poverty, income inequality and enhancing increasing GDP?
1.6 Significance of the study
The study will broaden knowledge on the income inequality, poverty reduction and economic growth to different stakeholders. Also will provide insight on the usefulness of Gini coefficient measure in measuring income inequality
The study will also provide some useful materials that could be used in teaching at colleges, high school as well as University.
The study will also provide useful suggestion to the government on the part of ensuring there is a better distribution of country’s national cake.
This study will also provide basis for reference in further researches. Such as checking the relationship between economic growth, poverty reduction and income inequality
2. LITERATURE REVIEW
2.0 Definition of Key terms
2.0.1 Measuring Inequality
Cowell, (2015) explained that the rich just get richer and the poor get poorer, the answer might seem easy. But what if the income distribution changes in a complicated way? Can we use mathematical or statistical techniques to simplify the comparison problem in a way that has economic meaning? What does it mean to measure inequality? Is it similar to National Income? Is it enough just to work out the Gini coefficient? Measuring Inequality tackles these questions and examines the underlying principles of inequality measurement and its relation to welfare economics, distributional analysis, and information theory. Measuring Inequality is designed to appeal to both undergraduate and post-graduate students, and academic economists. Its emphasis on practical application means that it will also be useful to policy analysts and advisors.
(Reardon & Bischoff, 2011), investigated how the growth in income inequality from 1970 to 2000 affected patterns of income segregation along three dimensions: the spatial segregation of poverty and affluence, race-specific patterns of income segregation, and the geographic scale of income segregation. The evidence reveals a robust relationship between income inequality and income segregation, an effect that is larger for black families than for white families. In addition, income inequality affects income segregation primarily through its effect on the large-scale spatial segregation of affluence rather than by affecting the spatial segregation of poverty or by altering small-scale patterns of income segregation.
2.0.2 Economic Growth
(Rostow, 1959) summarizes a way of generalizing the sweep of modern economic history. The form of this generalization is a set of stages of growth, which can be designated as follows: the traditional society; the preconditions for take-off; the take-off; the drive to maturity; the age of high mass consumption. Beyond the age of high mass consumption lie the problems which are beginning to arise in a few societies, and which may arise generally when diminishing relative marginal utility sets in for real income itself. These descriptive categories are rooted in certain dynamic propositions about supply, demand, and the pattern of production; and before indicating the historical content of the categories I shall briefly state the underlying pro- positions.
(Becker, Glaeser, & Murphy, 1999) There is substantial evidence that contradicts the conclusion that population growth is adverse to economic growth. Most empirical analyses of the relationship between population and economic growth do not find that there is an adverse effect. The history of the world has been that periods of low population growth have been periods of low economic growth and that high rates of economic growth have occurred when population growth is also high. Most of human history has had low population and low economic growth. Only recently has there been both rapid population and economic growth.
2.03 Effects of income inequality on economic growth
(Qin et al., 2009) A pilot empirical study is carried out on how income inequality affects growth through incorporating panel data information into a quarterly macro-econometric model of China. Provincial urban and rural household data are used to construct income inequality measures, which are then used to augment household consumption equations in the model. Model simulations test the inequality effect on GDP growth and its components. Results show that income inequality forms robust explanatory variables of consumption and that the way inequality develops carries negative consequences on GDP and sectoral growth.
(Shin, 2012), Despite the extensive existing literature on income inequality and economic growth, there remains considerable disagreement on the effect of inequality on economic growth. Existing literatures find either a positive or a negative relationship. Shin attempted to theoretically examine that relationship with a stochastic optimal growth model. He made the disagreement clear within a single model and concluded that both are possible – that is, higher inequality can retard growth in the early stage of economic development, and can encourage growth in a near steady state, also that income redistribution by high income tax does not always reduce income inequality. Income inequality can be reduced by higher income tax in a near steady state, but it cannot be reduced in the early stage of economic development, and that two government polices – rapid economic growth and low income inequality – can be achieved by low income tax in the early stage of economic development, but both cannot be achieved simultaneously in a near steady state.
2.0.4 Pro poor growth
(Martin Ravallion & Chen, 2003), the rate of pro-poor growth can be measured by the mean growth rate of the poor, which equals the rate of change in the Watts index of poverty normalized by the headcount index.
(Son & Kakwani, 2008) express pro poor growth in this way that captures gains and losses of growth rates due to changes in the distribution of consumption. The gains imply pro-poor growth, while the losses imply anti-poor growth. The statistical test carried out in the paper shows that regional location of countries has a significant association with the pro-poorness of growth. The paper also attempts to test for the association between growth patterns and certain variables that the literature has identified as significant determinants of growth and inequality. Out of many variables, the paper focuses on four, namely, inflation, the share of agriculture in GDP, openness to trade, and the rule of law.
2.2 Empirical Review
2.2.1 Gini coefficient
The Gini coefficient is a measure of inequality of a distribution. It is defined as a ratio with values between 0 and 1: the numerator is the area between the Lorenz curve of the distribution and the uniform distribution line; the denominator is the area under the uniform distribution line. It was developed by the Italian statistician Corrado Gini and published in his 1912 paper “Variabilit” e mutabilit”” (“Variability and Mutability”). The Gini index is the Gini coefficient expressed as a percentage, and is to the Gini coefficient multiplied by 100. (The Gini coefficient is equal to half of the relative mean difference.)
The Gini coefficient is often used to measure income inequality. Here, 0 corresponds to perfect income equality (i.e. everyone has the same income) and 1 corresponds to perfect income inequality (i.e. one person has all the income, while everyone else has zero income).
The Gini coefficient can also be used to measure wealth inequality. This use requires that no one has a negative net wealth. It is also commonly used for the measurement of discriminatory power of rating systems in the credit risk management.
Gini, which measures the degree of income inequality, is also defined as the log of the Gini index in 1970 or its closest neighbouring period but cannot exceed 1975. Gini is taken from Deininger and Squire (1996), who make the necessary efforts to compile high-quality income distribution data. In particular, they impose three stringent quality criteria before the data can be accepted. First, data must be based on household surveys (not from national accounts that make some assumptions about patterns of income inequality). Second, data must be based on comprehensive coverage of population (not based on some segments of population only). Third, data must be based on comprehensive coverage of income sources (not based on wage incomes only but also nonwage incomes)
Apart from Deininger and Squire (1996), there are other authors who used gini coefficient as a measure of income inequality; these include Lecaillio et al (1984), Pearson & Tabellini (1994), Paukert (1973) as well as Alesina & Rodrik (1994).
Table 2.1: Shows income inequality measurement through Gini coefficient for 2007 and 2011/12
Income Shares Income Shares
Gini P90/p10 Low quintile Top quintile Gini P90/p10 Low quintile Top quintile
National 38.50 5.18 6.62 45.72 35.84 4.39 7.73 44.07
Rural 35.54 4.66 7.26 43.33 29.86 3.53 8.98 39.06
Other Urban 39.96 5.96 5.98 46.58 38.14 4.92 6.96 45.65
Dar es Salaam 40.12 5.60 6.44 47.76 36.04 4.36 7.74 44.40
Source: HBS 2007 and 2011/12
2.2.2 Galor-Zeira Model
The model was developed by Oded Galor and Joseph Zeira in 1988 and published in macroeconomic paper in 1993. The model shows that income distribution has a significant impact on the growth process. The model also demonstrated that in the presence of credit market imperfections, income distribution has a long lasting effect on investment in human capital, aggregate income and economic development. According to Kaldor (1957), income inequality is good for growth because concentrated wealth in the hands of few a permits greater savings, which are conducive for investment. Three alternative theoretical models have explained this phenomenon in early 1990s. The first model (political-economy model) expressed that income inequality is bad for growth because average citizens would push the government for more extensive redistributive policies, which are detrimental for investment growth. Second model known as socio-political instability model, income inequality is bad for growth because it might create social tension that is harmful for investment. The third model explained about the credit constraint model that is inequality is bad for growth because it restricts the number of people who have access to costly education.
Deininger and Squire (1998) revisited the analysis with an improved measure of income inequality that they carefully compiled earlier (Deininger and Squire (1996)). They found that the basic result is not robust with their improved inequality data. Then, Li and Zou (1998) generalized the Alesina-Rodrik specification to a panel data of countries. They found that the basic negative inequality-growth result is overturned. That is, income inequality has a positive impact on economic growth. This reversed result was also found by Forbes (2000), who generalized Perotti’s reduced-form specification to the panel data. However, this new result collapsed in Barro (2000), who deviated from the practice of adopting a parsimonious speci_cation by including the inequality data that Deininger and Squire (1996) regard as low quality. In this instance, Barro showed that income inequality has no effect on growth. Finally, Sylwester (2000) restored the basic result when he tested the inequality-education-growth link with cross-country data.
Levine (1997) conducted a study on income inequality distribution through using Galor-Zeira model, on this work her baseline analysis that was based on 46 countries yield the results that income inequality exerts a negative impact on long-run per capita income, and it does so through education channel. The same findings tend to hold on even though she tried to change several variables to the model.
2.2.3 Kremer-Chen Model
The model examines the impact of income inequality on economic growth through a fertility differential. There are different literatures also explained on this linkage apart from Kremer & Chen (2002), these are Maov (2005) and La Croix & Doepke (2003). Also Baro (2000) and Peretto (1996) investigated the inequality-fertility-growth link. According to the model households make a conscious decision on the optimal number of children that they wish to have. The trade off rises between the number (quantity) of children to be raised and the quality, this results to rich people tending to have few yet educated children while poor people have more yet educated children. The higher the fertility and educational differentials, the smaller the stock of human capital and the lower the level of per capita income in the future.
According to Kremer-Chen Model, the impact of income inequality on the long run income per capita is unlikely to be applicable to poor and rich countries. That is for poor countries, the case is this because the income effect dominates and for the rich countries, the case is this because the substitution effect becomes weaker.
2.2.4 Other Global views
Notwithstanding these studies, there is not much global comparative evidence on the relationship between economic growth, income inequality and poverty levels. One of the few studies is Fosu (2010) who provided global evidence on how economic growth translated into poverty reduction among developing countries. He examined the impact of growth on poverty among Eastern Europe and Central Asia (EECA), South Asia (SAS), sub-Saharan Africa (SSA), Latin American Countries (LAC) and Middle East and North Africa (MENA) for the period 1981-2005. With the exception of EECA, he found that, poverty levels for all regions decreased for both the $1.25 and $2 a day poverty lines. He also found that with the exception of MENA, all regions exhibited greater poverty declines in the mid-1990s to 2005 sub-periods. Growth since the early 1990s has been substantial, mainly because of the various structural reforms implemented by most developing economies since the early 1980s. He explained further that while growth is a major factor behind changes in poverty levels, income inequality nevertheless is very important because of its effects on the poverty pattern in most countries. This is because economic growth drives down poverty drastically under a favourable income distribution. He therefore proposed that special attention should be paid to reducing income inequality particularly in countries with highly unfavourable income distribution.
The relationship between economic growth, income inequality and poverty among Latin American countries was investigated by Sadoulet and Janvry (2000). They asserted that, Latin American countries have exceptionally higher levels of income inequality than other regions at similar levels of average income per-capita. They investigated the effects of economic growth on rural and urban poverty levels in Latin America from 1970-1994 taking into account the differences in income distributions. They found that, growth significantly reduced poverty levels when there were low levels of income inequality. There is therefore a high cost of income inequality. They recommended that income inequality in the region needs to be addressed through government policies since improving the distribution of income is unlikely to be achieved with economic growth alone. They recommended that, in order for growth to significantly reduce absolute poverty in the region, income inequality must be sufficiently low and countries should have higher levels of education.
Adam (2004) used data on 60 developing countries to analyse the relationship between economic growth and poverty. He argued that while economic growth leads to reductions in poverty among developing countries, the magnitude of the effect depends more on how economic growth is defined. He defined two measures of economic growth; the survey mean income and changes in GDP per-capita. He found that economic growth leads to poverty reduction irrespective of how growth is defined. However, poverty is reduced more when mean income is used than when GDP per-capita is used.
(M. Ravallion, 2001), The available evidence suggests that the poor in developing countries typically do share in the gains from rising aggregate affluence, and in the losses from aggregate contraction. But there are large differences between countries in how much poor people share in growth, and there are diverse impacts among the poor in a given country. Cross-country correlations are clouded in data problems, and undoubtedly hide welfare impacts; they can be deceptive for development policy. There is a need for deeper micro empirical work on growth and distributional change. Only then will we have a firm basis for identifying the specific policies and programs that are needed to complement growth-oriented policies.
2.3 Hypothesis Testing
The study has developed the following null hypothesis
H1: There is no correlation between economic growth (GDP) and reduction in poverty rate
H2: There is no correlation between economic growth (GDP) and reducing income inequality (stabilizing Gini-index)
3.0 RESEARCH METHODOLOGY
This chapter presents the methodology that was used to conduct the study. The nature of this study is both explanatory and quantitative, hence qualitative research methodology were used as the leading method but assisted with quantitative method in some cases. Qualitative research methodology enabled in obtaining data on income inequality and economic growth. The two methods enabled the researcher to do interviews with the experts in the field as well as conducting some analysis from the literature. This chapter covered the following parts research design, sample selection and sample techniques, data collection, data analysis and interpretation.
3.2 Research Design
This study is designed to employ quantitative and qualitative research methodologies in data collection. Both structured and un-structured questioners will be employed so as to achieve the stated objective. Due to time limitation of time and budget constraint the researchers will be able to do the study in five regions of Tanzania that is Dar es Salaam, Dodoma, Arusha, Tanga and Mwanza.
3.3 Sample selection and sampling techniques
In order to achieve the stated objective, the study will involve the following participants
Category Number of Individuals Data collection mode
Individuals from Dar es Salaam 70 Structured Questioners and unstructured interviews
Individuals from Arusha 50 Structured questioners
Individuals from Dodoma 50 Structured questioners
Individuals from Tanga 50 Structured questioners
Individuals from Mwanza 50 Structured questioners
Bank of Tanzania officers 5 Structured interview and structured questioners
National Bureau of Statistics (NBS) 5 Structured interview and structured questioners
Sampling techniques that will be employed in this study is non probability sampling specifically a non random sampling. This method will enable the researcher to gather in enough relevant data in detail concerning the matter; Irby & Lunenburg (2008) also describes the use of non probability sampling that is purposive sampling which focuses on small sample and enhances the researcher to obtain the required or relevant information in detail.
Due to the nature of the study, few personnel purposively will be selected and interviewed. This will make the study to use small sample size. So purposive sampling will be as shown above from the table. In total the study will take a sample of 190 individuals.
3.4 Data collection
Data will be collected by using two methods that is primary data collection method and secondary data collection method. Primary data method will involve the use of questioners that will be issued to the selected sample and the schedule and non schedule interviews. The sample will consists of people who can read and write hence it will be easy for the individuals in filling the questioners.
Also the study will use secondary data collection method; this method will involve internet search, journals, books, articles and different related materials on the subject matter of the concern. This method will cover the data collected from Bank of Tanzania as well as National Bureau of Statistics.
3.5 Data analysis and presentation
The study uses both qualitative and quantitative research methodologies, hence some data will be presented in tabular forms, and charts and graphs while other data will be analysed fro the models presented in chapter two of this study. On qualitative method will focus on what is observed from the models while on quantitative methodologies will involve the use of numbers and percentages in presenting the results of the respondents.
4.0 ESTIMATION AND ECONOMETRIC RESULTS
4.1 Income inequality and poverty reduction in the economic growth-relationship
The relationship between the three variables was determined by using correlation tool of regression. The researcher set 5% as being level of significance. The results are being depicted below in the correlation table
The input data to test the correlation were as follows
Years Poverty rate Gini-index GDP (US $ Billion)
2007 34% 40.28% 21.5
2008 32.8% 39.78% 27.39
2009 31.7% 39.29% 28.57
2010 30.6% 38.80% 31.4
2011 29.6% 38.32% 33.88
2012 28.2% 37.78% 39.1
Source: National Board of Statistics
poverty rate gini index GDP growth rate
poverty rate Pearson Correlation 1 .999** -.987**
Sig. (2-tailed) .000 .000
N 6 6 6
gini index Pearson Correlation .999** 1 -.982**
Sig. (2-tailed) .000 .001
N 6 6 6
GDP growth rate Pearson Correlation -.987** -.982** 1
Sig. (2-tailed) .000 .001
N 6 6 6
**. Correlation is significant at the 0.01 level (2-tailed).
Correlation is significant but in the negative direction, there is very weak relationship between poverty rate and GDP growth rate of -0.987 and also there is very weak relationship between Gini index and GDP growth of -0.982. Hence fail to reject both H1 and H2, therefore there is no correlation on GDP growth with stabilizing Gini index and reducing poverty rate.
4.2 Implications of Galor-Zeira Model based on five regions
Galor Zeira model communicates the relationship between households income and the investment in education. According to the model investment in education depends on rate of return to investment in education this including looking on perceived benefits, direct costs (when they are high discourages investment in education) as well as opportunity costs ( if household is already educated and earn high income and continues to study then opportunity cost to invest in education will be high).
The final outputs on the education system are qualifications and learning outcome (skills). For instance, learning skills make people more productive (human capital). If qualifications and learning outcome are better hence level of income will improve and this will lead to economic growth.
Dependent variables of the model and assumptions
For easy understanding of the model and establishing the linkage between education and the level of the income, the following variables are of importance wages of unskilled labour (WU), wages of skilled labour (WS), Human capital (H), amount of quest (Bjt1) and income of the household (IJT1).
The model also has the following assumptions that will be used throughout the analysis
i. Return to invest in education is greater than the return to the amount needed to invest in education
WS ‘ WU > HR
ii. When theta is sufficiently large so that return to invest in education is less to the amount needed to invest in education
WS ‘ WU < H(”R)
Table 4.1: Investing in education if the amount of quest to parents is greater than the cost of schooling
No 137 48.9
Yes 143 51.1
Total 280 100.0
Source: Survey Field
Table 4.2: Investing in education if the amount of quest is less than the cost of schooling
Yes 172 61.4
No 100 35.7
Total 272 97.1
Missing 8 2.9
Total 280 100.0
Source: Survey Field
Implications of the two tables above
The tables above show households’ decision on investing in education at two scenarios. On table 4.1, if the amount quest to the parent is greater than the cost of schooling, respondents of the majority agreed on investing on the education which means
BTJ >= H
Hence, the income to these households will be equal to the wages of the skilled labour
IJT1 = WS + (BJT ‘ H)R
Basing on the second table (table 4.2) if the amount of quest is less than the cost of schooling, majority of the respondents replied that they will invest in education. The income of 61.4% of the respondents will be equal to wages of skilled labour minus how much they need to borrow times the borrowing rate. This can be expressed as follows
IJT1 = WS ‘ (H ‘ BJT) ”R
The income of the remaining 35.7% of the respondents will be equal to wages of unskilled labour plus return from lending out the amount that will be quest at interest R. This is illustrated as under
IJT1 = WU + BJTR
4.3 Government policies
Tanzania government has policies in place that enables to reduce poverty in the country but as well as reducing income inequality so as to ensure effective economic growth of the country. Tanzania has developed several policies and strategies to ensure effective economic growth. The current strategy that the country is working upon is National strategy for growth and reduction of poverty (NSGRP) which is termed as second national organizing framework for putting the focus on poverty reduction high on the country’s development agenda. The strategy is committed to millennium development goals (MDGs). The strategy requires commitment and resources from domestic stakeholders and development patterns in the medium term.
The government has reported positive results since the establishment of the strategy in the country, these positive results include improved economic performance at the macro level in the period of the past six years, increasing investment in infrastructure such as roads, telecommunications, mining and tourism.
The strategy still working to further stimulating domestic saving and private investment response, human resource development, increased investment in quality education, deepen ownership and inclusion in policy making, paying greater attention to mainstreaming cross cutting issues as well as addressing discriminatory laws, customs and practices.
Apart from the main strategy or policy of the country towards poverty reduction and economic growth, Tanzania has also formulated several supporting strategies in improving education system so as to ensure citizens have necessary education to enable them to be productive in the economy and to increase growth domestic product of the country. These policies include the following
i. Enforced investment through compulsory schooling. The time or levels of compulsory education is always short to ensure effective economic growth. If it is long will affect economic growth in a way that people will be studying and have few for producing
ii. Public spending in financing education: providing free education at primary level of education
iii. Putting quality hurdles so as to prevent poor performance and avoid making bad investment. Quality hurdles let say qualification for a one to study medicine then should have background in biology
iv. Measures to combat external information asymmetries such as producing reports cards
4.4 Econometric results
Table 4.3: Shows the composition of gender of the respondents
Female 127 45.4
Male 153 54.6
Total 280 100
Source: Field Survey
The table above shows that out of 280 respondents, 54.6% were male and 45.4% were female. When conducted this research most of the rural areas women were refused to be asked questions hence reduce the number of women who were surveyed than men. Although the first target of the researcher was to have equality in the number of men and women to be interviewed, however the difference that occurred was taken by the researcher as negligible since with the same respondents will bring potential results.
Table 4.4: Shows the composition of the age of the respondents
18-35 years 122 43.6
36-55 years 104 37.1
Above 55 years 54 19.3
Total 280 100.0
Source: Field Survey
From the table above shows that the study concentrated much on the workforce generation since more than 80% of the respondents were coming from the workforce generation that is from the age of 18 to 55 years. The nature of the research itself support this, having a larger percentage of the respondents who are educated and also are the workforce of the community so as to easy the data collection and the whole process of filling in the questionnaires.
Table 4.5: Shows level of education
Secondary level 100 35.7
Bachelor Degree 144 51.4
Masters 24 8.6
Others 12 4.3
Total 280 100.0
Source: Field Survey
From the table above, shows that most of the respondents were coming from the students with bachelor degree which is 51.4% of the total respondents. Also the table above indicated that individuals with primary education and with no formal education were excluded, this was so in order to avoid complications that may arise in filling in the questionnaires and to reduce the number of mistakes encountered in filling the questionnaires. The category of others included 3 PhD holders and 9 diploma holders who were also interviewed.
The researcher believed that the selected sample will be able to fill in the questionnaires and be comfortable with the language that was used in the questionnaires.
Figure 4.1: occupation level
The figure below depicted that the highest number of people in the society are employed in different sectors that is depend on fixed income from their employer. Also 30 individuals out of 40 jobless individuals who were interviewed are having a bachelor degree and still are jobless, this call for the government to encourage about self employment and entrepreneurial activities to the people.
According to the figure below, 99 individuals (35.3%) out of 280 individuals being interviewed indicated that employment level in the society is high. However digging further on the data collected it is found that 30 individuals out of 40 jobless individuals interviewed had a bachelor degree but still are jobless, this is a clear indication that there still employment problem in the society.
Table 4.6: Amount of disposable income per month
Below Tshs 500,000 109 38.9
Tshs 500,001 to Tshs 1,000,000 70 25.0
Tshs 1,000,001 to Tshs 2,000,000 47 16.8
Tshs 2,000,001 to Tshs 3,000,001 29 10.4
Tshs 3,000,001 to Tshs 4,000,000 12 4.3
Tshs 4,000,001 to Tshs 5,000,000 8 2.9
Above Tshs 5,000,000 5 1.8
Total 280 100.0
Source: Field survey
From the table above, 80.7% of the total respondents have disposable income per month which is below Tshs 2,000,000 and only the remaining 19.3% have disposable income per month above Tshs 2,000,000. Out of those 80.7% interviewed, 38.9% which is 109 individuals out of 280 individuals have disposable income which is below Tshs 500,000. Only 13 individuals (4.7%) out of 184 individuals interviewed have disposable income above Tshs 4,000,000 per month. In which those 13 individuals are coming from Dar es Salaam (city centre). This is also an indication of uneven geographical distribution of the development and income.