Applying similar practices across the nation would make significant strides towards alleviating poverty in Kenya. It was very helpful for parents to have trusted childcare providers every day so they could spend their time taking advantage of the available resources. Many adults and teenagers also found counseling sessions to be rewarding and beneficial for their mental health. According to the interviewee, the most popular services were job training and finance classes.
Several people taking these classes had entrepreneurial promise but lacked specific training and best practices. Courses, like those Grace Church offered, then helped members of the Huduma Church gain useful experience and insight regarding employment and finances.
Adults and children alike benefited from receiving medical assessments. First aid volunteers were able to give preliminary diagnoses of certain conditions and provided information for proper self-treatment as well as additional resources in the area for receiving medical assistance. In order to assess which long-term solutions are the most successful, several studies and past programs have offered valuable information.
First, expanding access to resources such as education, healthcare and housing for all citizens of a country regardless of income status is one of the most direct ways to improve living conditions.
Additionally, access to micro-financing options helps to stimulate the market. According to one comprehensive study evaluating poverty alleviation strategies in developing nations, establishing strong institutional resource systems is key to effective poverty reduction. Furthermore, following a human-based approach in these institutions by working in conjunction with people in poverty and empowering them to fully participate in their own socioeconomic improvements is vital for ensuring decreases in poverty are long-lasting.
Additionally, achieving steady economic growth has had an enormous impact on Kenyan poverty rates. Thanks to innovations in financial technology and telecommunications, Kenya experienced average annual growth rates of about 5.
The evolution of real wages is shown in Figure Real wages are measured as labour earnings, including allowances, of employees in the private sector divided by the GDP deflator. Real wages increased by about 25 per cent from the mids until the beginning of the s. This was followed by a slow decline until , when real wages started to increase rapidly. The growth of private-sector real earnings between and was 65 per cent. This may have been due to the lifting of labour market controls IMF Real returns to land have.
Indexes of real returns to factors in Kenya, — The GDP deflator was used to calculate the real values of earnings and land prices. The series for land prices is the moving average of the actual series. The main empirical regularity for the period — is that factor price movements were driven by changes in factor endowments, while in this regard the removal of trade restrictions had a limited effect.
Changes in income inequality have been strongly influenced by the long-term process of structural change. The rapid increase in real wages that started in the mids and lasted into the next decade could have been driven by labour policy reforms, though. One of the challenges encountered when trying to measure growth and income distribution in African economies is that the data are weak and inconsistent. The first challenge for our more detailed analysis of recent developments is to get measures of the growth of GDP.
To get income shares, the KNBS measures labour incomes and then deducts these from GDP to get operating surplus, which includes capital and rental incomes. There are two GDP time series with different base years that cover parts of the relevant time period. The first series is based on prices, while the other series is based on prices. There are considerable differences between the two series both in terms of levels and growth rates.
New rebased series should be better than the old ones, and they typically increase the level of GDP. We note that in the rebasing in GDP estimates increased very significantly at most 50 per cent for ; Muchiri and Audi n. For example, output of construction is considerably higher in the new series, which also means that fixed capital formation becomes larger.
Table Growth rates per cent for — using and prices. So why are the differences so large between the old and the new series? One reason is that the relative weights of the different sectors have changed. The share of manufacturing fell drastically with the new prices. For it fell from 13 per cent of GDP to 9. This kind of change can explain why the aggregate growth rates can be quite different when we have different sectorial developments.
It is harder to understand how the sectorial growth rates can vary as dramatically as they do. As noted, we have contradictory growth estimates depending on whether we use the old GDP series base or the newer series base. The columns for capital formation are included because differences in capital formation estimates underlie differences in the GDP estimates, but also because investment rates are of interest when we are trying to explain growth variations.
The new series is supposedly the better one, so we rely on this as much as possible. The last column gives our preferred estimates of per p. Our main concern is to understand what has happened to the incomes of individuals and households. We see that per capita income growth was weak and fluctuating between and Incomes fell in , which was a drought year, and in , which was an election year.
Then growth stabilized, but we note a drop in , because of the civil conflict following the December disputed election. We have compared the growth of GDP per capita from the national accounts, with the evolution of mean consumption spending according to household surveys. There was furthermore a 4 per cent increase in the share of GDP going to investment over the period.
So we think we can say that the aggregate GDP estimates square well with the aggregate household estimates. Factor quantity series are of interest to us, since we know Bigsten and Durevall that changes in factor abundance have been a crucial determinant of the pattern of specialization and factor prices and thus inequality and poverty.
The most noteworthy development is that labour force growth has outpaced the growth of capital from the s onwards. The country has not been able to pursue a strategy of capital deepening. This has had great implications for how the reallocation of labour in the economy has evolved. The labour pressure on land increases, continuously pushing labour off the land. This labour could have been absorbed by capital-intensive activities if capital had been accumulated at a sufficiently rapid rate.
But since this has not happened, labour has had to move into activities that use little capital, that is, the informal sector and various forms of low-productivity self-employment.
It is clear that it is mainly changes in factor endowments that drive structural change in the Kenyan economy. Since the investment rate continued until at about the same rate as —, we assume that the capital stock continued to grow at 1. We assume that the upward shift of the investment rate by 2 per cent of GDP from led to an increase of the capital stock by 2. The labour force growth. The majority of labour market entrants have moved into the informal sector, which consequently has exploded in numbers.
Part of the reported increase in informal-sector growth is certainly due to improved data coverage, but it is clear that the vast majority of labour outside agriculture is in the informal rather than the formal sector.
The government has had the ambition to try to attract more foreign direct investment FDI , but so far it has not been very successful less than 1 per cent of GDP.
The oil and water discoveries in northern Kenya might change this. So far, there has been FDI in relation to telecom privatization and investments in railways, whereas not much has happened in manufacturing. Over the same period, its share of world GDP fell from 0.
There has been some diversification in terms of export destinations, but there has not been any significant diversification of Kenyan exports in terms of products. One important determinant of overall inequality is the factorial income distribution. We have noted that the KNBS constructs estimates of labour income and GDP, where the income to capital and land is computed as the difference between the two.
What we can check about factor distribution is thus whether the reported labour share has changed over time. In Table We see that there have been some changes over time, but there is no trend. The series starts at a 40 per cent share and ends there as well. This is a lower share than in richer countries, but if smallholder agriculture incomes are excluded the share should be at least 50 per cent.
We can conclude that there is no long-term trend in the distribution of income between capital and land and labour. The stability of the factorial distribution should contribute to keeping the overall distribution stable, but inequality can of course still increase if the distribution within the categories becomes more uneven. We have no information about the distribution of capital incomes, but we have some data on labour incomes. The Kenyan labour force has changed structure over recent decades.
There has been a strong decline in the proportion of employees with p. Growth of formal and informal employment and real earnings of formal labour, — We see that very many of the new jobs were in the informal p. Source : Republic of Kenya a , a. What we see here is a remarkable growth of earnings during the period that is the focus of our study, followed by dramatic declines from onwards. There are no systematic income data for the informal sector. Looking at the evolution of formal and informal median earnings it seems as if the gap between the two sectors has been fairly constant Table We do not have any clear picture as to why real wages in the formal sector suddenly started to fall from onwards.
In this section, we analyse the evolution of inequality and poverty over the past two decades. The aim is to provide evidence on changes in level and distribution of household welfare over a period for which nationally representative datasets are available Republic of Kenya , a , Tables The region with the lowest poverty rates is Central, which got an early start in development as we pointed out in our historical review.
We may also note that the capital city, Nairobi, has the lowest poverty rate in the country despite its large in-migration. The national Gini coefficient increased from 0. This was driven by three forces. First, the urban share of households increased, which meant that a larger share of households were in the high-inequality region. Second, urban income inequality rose between and And finally, the urban—rural gap in inequality increased between the two periods.
In the historical section we saw that the national Gini was driven by changes in the urban—rural gap. We compare the average consumption expenditure of rural and urban residents using our household surveys. These show that the average rural consumption expenditure was 46 per cent of the average urban consumption expenditure in , while it had declined to 33 per cent by Thus, at least during this period the increase in the overall Gini is correlated with the increase in the rural—urban gap.
The increase in urban inequality also contributed to the overall increase. Due to data limitations, the situation for the recent period could not be assessed, but it is possible that the redistribution and devolution initiatives started during the Kibaki government —13 , especially the cash transfer programmes and the constitutional reform, have reduced the rural—urban consumption disparity.
Absolute poverty measures per cent for Kenya, — Regional welfare inequality Gini coefficient in Kenya, — Average annual expenditure of rural and urban residents in Kenya, — The most common metric of poverty is some threshold level of income or consumption expenditure. However, poverty is multidimensional and goes beyond the conventional single-index metrics, which ignore many wider aspects of poverty. Empirical studies have shown that using the monetary approach alone may be deceptive and needs to be complemented with non-monetary measures.
Poverty is associated not only with insufficient income or consumption but also with insufficient health, nutrition, and literacy, and p. Source : Republic of Kenya , , b , a , b. The indicators for health poverty presented in Table Health status in Kenya has on the whole been improving since In particular, most forms of mortality have declined.
Access to medical services as proxied by the vaccination coverage improved from 44 per cent in to 79 per cent in then declined to 57 per cent in Between and , it improved by twenty percentage points, indicating that Kenya is on a good track to universal health care coverage.
In general, the year saw bad performance in many of the indicators, such as infant mortality rates and life expectancy, due to extreme weather conditions, including drought, landslides, and floods that were widespread in that year, leading to food deficits in most parts of the country Republic of Kenya The general trend in access to water is not clear.
However, the emerging picture is that there is a slight improvement in access to safe drinking water in the country. As can be seen from Tables Percentage of people without access to improved drinking water. Percentage of children under five years classified as malnourished, — The main nutritional indicators in Kenya and elsewhere are child anthropometrics. They include height-for-age stunting percentage of children under five years classified as malnourished ; weight-for-height wasting percentage of children under five years classified as malnourished ; weight-for-age underweight percentage of children under five years classified as malnourished ; children stunted percentage overall ; and children underweight percentage overall.
These are standard indices of physical growth that describe the nutritional status of children. The nutritional status of children under five has improved somewhat since the early s. The height-for-age stunting declined from The weight-for-height stunting has remained more or less constant during this period, while weight-for-age underweight declined from Education level is a key measure of non-income poverty.
School enrolment rates, dropout rates, and literacy rates are good indicators of non-income poverty. School enrolment rates tend to be higher in rich households and lower in income-poor households. However, this enrolment-income gradient p. The school enrolment rates for both primary and secondary schools improved between and Primary gross enrolment has been on the rise since the year However, between and , the gross enrolment rate increased due to the introduction of free primary education.
Net primary enrolment has also increased steadily in the last decade, with the lowest being recorded in , while the peak was in Both gross and net secondary enrolments have increased since the year While reforms in the s started to address these issues , it remains difficult for the average citizen of Kenya to pull him or herself out of poverty. Jobs outside the agriculture industry are rare, and the education required for such jobs is even rarer, especially for poor families. The lack of economic diversity, opportunity, and education along with rapid population growth are crippling for the average citizen.
On the other hand, the flower industry in Kenya is flourishing especially with exports to European countries, along with coffee and tea farming. Another industry currently on the rise in Kenya is the tourism industry.
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