Photo courtesy IPS
Prof. N.Balakrishnan Memorial Oration delivered in Colombo on 21st June 2014
It is five years since the decisive end of the civil war in May 2009. The government’s approach to resolving the long festering ethnic conflict in the country has been reconciliation and peace through economic development. Although I do have reservations about the validity of the government’s mantra of reconciliation and peace through economic development, I do want to accept that approach as fait accompli and would like to investigate whether such an approach has resulted in real economic peace dividends in the two provinces most affected by the civil war, viz. Eastern and Northern provinces, and the Southern province of Sri Lanka.
At the national level the economy has recorded an average 7.5% growth between the four year period 2010-2013, inflation has remained at single-digit level for about five years now, official unemployment rate has steadily declined to 4% in 2013, and the headcount poverty ratio has declined to 6.7% in 2013. However, economic data at the provincial level depicts a different picture of Sri Lanka’s post civil war economic development experience.
The three provinces that received the largest quantum of public capital investment in the post civil war period are the Eastern, Northern, and Southern provinces; the former two being conflict affected provinces and the latter being the home province of the President and the self proclaimed “people’s dynasty”. It is these three provinces that are subjected to investigation in this paper and compared and contrasted with the most prosperous Western province to assess the extent to which economic peace dividends have been realised. Thus there are four provinces targeted for investigation in this paper (out of the nine provinces in the country). This is a novel approach to investigating post civil war economic development and assessing economic peace dividends as opposed to analysing the national data.
The armed conflict ended in the Eastern province in July 2007 and in the Northern province in May 2009. The Southern province was almost directly untouched by the armed conflict. Disaggregated provincial level economic data is available until 2011 to date; that is, for four years since the end of the armed conflict in the East and for two years since the end of the armed conflict in the North. The Household Income and Expenditure Survey (HIES) undertaken by the Department of Census and Statistics (DCS) during 2012-2013 was the first one that covered the entire districts of the country since the mid-1980s. Similarly, the Labour Force Survey undertaken in 2012 was the first one to cover the entire country after the mid-1980s. Therefore, we have critical mass of district and provincial economic data to investigate the post war economic development in the targeted provinces.
We investigate the composition of the provincial economies and economic growth rates by sub-sectors to find the sources of economic growth in respective provinces, per capita income and income inequality based on HIES at district and provincial levels, employment generation (or lack thereof) by district and province and by economic sector and type of employment, and headcount poverty ratio by district in the targeted four provinces. Provincial macro economic data in Sri Lanka has serious methodological problems which have been highlighted by this author previously. Therefore, the provincial macro economic data used herein (Tables 8-10) are conjectural and therefore should be considered cautiously.
In democratic countries economic development has political implications. A value addition of this paper is that it investigates the electoral dividends (or lack thereof) of economic peace dividends (or lack thereof) to the ruling coalition government. In other words we investigate whether the massive development drive throughout the country (particularly in Eastern, Northern, and Southern provinces) in the post civil war period has paid political dividends to the ruling coalition government. For this purpose the share of votes secured by the ruling coalition party, United People’s Freedom Party (UPFA), at the provincial elections in the immediate aftermath of the civil war is compared with the most recent provincial elections in the targeted provinces, viz. Eastern, Northern, Southern, and Western.
The sources of economic growth is equally, if not more, important as growth itself. In order to investigate the outcomes of economic growth, at national or provincial levels, it is imperative to identify the sources of economic growth and evaluate whether those sub-sectors are sustainable in the long run and whether those sub-sectors do substantially contribute to the improvement of livelihoods in the province. In most post-war development scenarios there is a sudden spurt of economic activities that do not last long.
Therefore, firstly we identify the sub-sectors that make the highest contribution (>10%) to the respective provincial economies under investigation (Tables 8, 9 and 10) and then work out the composite growth rate of those identified sub-sectors during the three-year period 2009 to 2011. (Table 1)
Accordingly, in the Eastern province, construction, factory industry, government services (public administration, defence, and other government services), and wholesale and retail trade are the highest contributing sub-sectors to the provincial economy (or the economic drivers). In the North it is the construction, government services, other food crops (chilies, onions, etc), and transport are the economic drivers; construction, factory industry, transport, and wholesale and retail trade are the economic drivers in the South; while finance (banking, insurance, real estate, etc), factory industry, transport, and wholesale and retail trade are the economic drivers in the Western province. (Table 1)
There are differences and similarities between the targeted provinces as evident in Table 1; while the Southern and Western provinces have three (out of four) economic drivers that are common to both the provinces, Eastern and Northern provinces have two (out of four) economic drivers that are common to both the provinces. While the construction sub-sector is a common economic driver in Eastern, Northern, and Southern provinces, factory industry is a common economic driver in Eastern, Southern, and Western provinces. Similarly, while the transport sub-sector is a common economic driver in Northern, Southern, and Western provinces, wholesale and retail trade is a common economic driver in Eastern, Southern, and Western provinces.
On a different note, while all the economic drivers in the Western province (factory industry, finance, transport, and wholesale & retail trade) and three (out of four) economic drivers in the Southern province (factory industry, transport, and wholesale & retail trade) are private sector driven, just two economic drivers in the Eastern (factory industry and wholesale and retail trade) and Northern (other food crops and wholesale and retail trade) provinces are private sector driven. While the construction sub-sector is largely driven by public capital investments in major infrastructure developments in the Eastern, Northern, and Southern provinces, government services are entirely public money driven. Public investments in Sri Lanka are riddled with corruption, wastages, and cost overruns due to persistent delays in completion of public works.
The foregoing highlights the strengths of the Western and Southern economies vis-à-vis the Northern and Eastern economies because the public investment/expenditure driven economic growth riddled with corruption, wastages, and cost overruns stifle direct and indirect benefits to the people of the respective provinces. However, public money spent on providing essential education, health, and other social services (by and large free-of-charge) do immensely benefit the local people. Moreover, these gigantic infrastructure projects sponsored by the state are contracted to big foreign companies (mostly Chinese companies, which even employ substantial number of imported Chinese labour) or mega Sri Lankan companies based in the Western province and therefore may not significantly benefit the local people in terms of providing employment to local youths or by way of plough-back of profits within the respective provinces.
Even more important is to investigate whether the gigantic infrastructure projects undertaken by the government in the three provinces, viz. Eastern, Northern, and Southern, are necessary for the provincial and/or national economy, or whether they are financially and economically viable, or whether the government has ensured maximisation of benefits accruing from those projects to the respective local populations during the course of the project implementation as well as in the aftermath. Although these are critical questions that need to be answered, the scope of this paper does not allow in-depth investigation of these issues.
However, anecdotal evidence suggests that many, if not all, gigantic infrastructure projects in all three provinces appear to be white elephants. The second international airport built in Hambantota district in the Southern province (Mattala Rajapaksa International Airport – MRIA) at a cost of around USD 200 million hardly has any departures or arrivals from/to that airport thus far though it is over a year since its official opening in March 2013. The first phase of the new harbour built in Hambantota at a cost of around USD 400 million (Magam Ruhunupura Mahinda Rajapaksa Port – MRMRP) has so far very little business in terms of arrivals or departures of commercial ships though its first phase was officially opened in late 2010. The second phase is expected to cost over a billion dollars. The international cricket stadium built in Hambantota several years ago has hosted just a handful of international cricket matches there. A state-of-the-art international convention centre built in the same district is patiently waiting for its national and international patrons yet. An ultra-modern massive hospital being currently built with tax-payers’ money in the same district could profit from treating animals rather than humans because Hambantota has a huge cattle and wildlife population.
Six to eight lane (3-4 lanes on each direction) highways built in Hambantota (Southern province) and Mullaithivu (Northern province) districts are extravagances because bulk of the people in Hambantota do not own a vehicle and therefore those highways are deserted most of the time and cattle and wildlife population outnumbers human population (91,947) in the Mullaithivu district. Yet there is not a single dairy development project worthy of mention in the Mullaithivu district to date. On the contrary, New Zealand’s cattle population outnumbers its human population (4.4 million as of mid 2013) and it has one of the largest and best dairy industries in the world.
Of course one could argue that gigantic infrastructure projects undertaken in East, North, and South will take several years to yield positive returns on such public investments. However, this author is not convinced about such claims because we have not seen any financial or economic feasibility studies undertaken for any of these gigantic projects, whether in the East, North or South. Even some of the infrastructure projects (such as Southern Highway and Colombo-Katunayake Expressway; both of which are toll highways) that have the potential to be successful are stifled by low speed limits (maximum 100 kilometres per hour – whereas it is up to 200 kilometres per hour in many other countries). The rationale for building toll highways is to reduce the time taken for the transport of goods and passengers and thereby reduce the economic transaction costs. However, low speed limits imposed in the pretext of safety undermines the very purpose of toll highways, which is reflected in very low traffic flows in these toll highways. I am told that most times and most days one can literally count the number of vehicles using these highways.
Public investments per se are not necessarily bad for the economy or its people. It is the types of public investments that matter. Public investments in economic infrastructure by restoring electricity supply and linking it to the national grid, restoring and upgrading telecommunications, and reconstruction of social infrastructure such as schools and hospitals in the North do immensely benefit the people in that region. Whilst the most prosperous Western province and the relatively prosperous Southern province are largely private sector driven and therefore relatively higher productivity economies, the Eastern and Northern provinces are largely driven by the public sector and low value agriculture producers and therefore they are lower productivity economies. The foregoing characteristics or composition of respective provincial economies is reflected in the outcomes of economic growth to the people of the respective provinces as shown below.
Three leading sub-sectors in the Northern province had recorded three-digit growth during the period 2009-2011 (Table 1) due to the pent-up demand as a result of protracted civil war. It is heartening to note that there was deceleration of government services in monetary value (especially defence services) in the North in 2011, but the government services in the East has disappointingly accelerated in the same year. However, government services continued to make the single largest contribution to the Eastern (17.8%) and Northern (24.8%) provincial economies in 2011.
Per Capita Income
The per capita income is the average annual income of every single person in the country/province/district/household. The growth of per capita income in the four provinces under investigation between 2009 and 2012 is outlined in Table 2. Per capita income is measured by two methods: one by simply dividing the Provincial Gross Domestic Product (PGDP) in current prices by the total population of that province (Columns 1&2); the other is per capita income measured by the Household Income and Expenditure Survey (HIES). (Columns 4&5)
The per capita income measured in terms of PGDP is deceptive because it includes the incomes of households, government, and businesses/industries in the province and the incomes of the government and businesses/industries may not necessarily filter down to the household incomes. In contrast, the HIES accounts for solely the incomes and expenditures of the households, which is the real disposable income of households and by extension individuals. Hence, a significant part of the Per Capita Income derived from the PGDP is phantom income as far as the households and individuals are concerned; which is reflected in the huge discrepancy between the Per Capita Income derived by the two methods, viz. the HIES and PGDP. Furthermore, while PGDP covers only the formal economy of the province the HIES covers both the formal and informal economies. Therefore, the latter is a relatively more accurate measure of household and individual income.
Although we have outlined the results of the two measures in Table 2 for comparative purpose and clarification of methodologies, we take only the HIES data for analysis here. In 2009-10, among the provinces under investigation, the Western province had the highest per capita income followed by Southern, Eastern, and Northern provinces. The per capita incomes of Eastern (Rs.67,956) and Northern (Rs.66,180) provinces were almost the same in 2009-10; the East having a slight edge over the North. The per capita income in the Southern province (Rs.96,420) was 46% greater than that of the Northern province in 2009/10. The per capita income in the Western province (Rs.138,732) was 110% greater (more than double) than that of the Northern province in 2009-10. (Table 2)
There are mostly small variations in the per capita incomes of the districts within each province in 2009-10, except in the North where the per capita income in the Jaffna district (Rs.53,208) was half that of Vavuniya district (Rs.107,856). It is also important to note that the per capita income of the Hambantota district in 2009 (Rs.90,396) was the lowest in the Southern province in spite of receiving the highest quantum of public capital investments. This result supports our contention earlier that bulk of the public works undertaken by Chinese and Western province based mega companies may not have improved the incomes or livelihoods of the local people to any significant extent.
In 2012-13, among the provinces under investigation, the Western province had the highest per capita income (Rs.196,320) followed by Southern (Rs.132,780), Northern (Rs.114,396), and Eastern (Rs.82,452) provinces. (Table 2) In 2012-13, the Northern province had significantly overtaken the Eastern province in terms of per capita income. That is, Northern per capita income was 39% greater than that of the Eastern per capita income. This is largely because of data adjustment in the North because the 2009-10 HIES did not cover the three Vanni districts (Kilinochchi, Mannar, and Mullaithivu). Besides, households in Northern province receive significant amount of remittances from kith and kin abroad. In 2012-13, the per capita income of Western province was 138% greater than that of the Eastern province; 72% greater than the per capita income of Northern province; and 48% greater than the per capita income of Southern province. (Table 2) District breakdown of per capita income in 2012 is not available to date.
The per capita income of Northern province experienced the highest growth of 73%, among the provinces under consideration, between 2009 and 2012. The Western province experienced the second highest growth of 41.5% followed by 38% growth of per capita income in the Southern province, and 21% growth of per capita income in the Eastern province. (Table 2)
The per capita income is average income that hides variations in income between different income groups within a household, district, province, or country. Therefore, the inequality in per capita income should also be taken into consideration in order to understand the welfare implications of per capita income to individuals in different income groups. Income inequality is measured by Gini Coefficient on a scale of 0 to 1, where 0 denotes complete equality of income distribution and 1 denotes complete unequal distribution of income (which means the entire income is concentrated in a single person).
Accordingly, the prosperous Western province had the highest inequality in the distribution of per capita income (0.48) and all other three provinces had almost the same inequality in the distribution of per capita income (0.41) in 2009-10. However, in 2012-13, inequality in terms of per capita income has widened dramatically in the North to 0.52 and considerably in the South to 0.45. On the other hand, inequality in terms of per capita income dropped negligibly in the Western province to 0.47 and in the Eastern province to 0.40. (Table 2) What the foregoing data implies is that as the per capita income rises income inequality also tends to rise.
The number of employments newly created in the districts and provinces during the post civil war period would be an indication of the benefits that had accrued to the respective local people, especially youths. The annual Labour Force Survey (LFS) data of 2010, 2011, and 2012 are used for this purpose. (2013 annual data is not released to date) For the Northern province data is available only for 2011 and 2012 so far.
A total of 422,111 net employments were created in the country between 2010 and 2012. Among the four provinces under consideration in this paper, the highest number of 61,133 was created in the Western province; 30,742 were created in the Southern province; 24,303 were created in the Northern province (2011-12); and unfortunately there was a drop of (-) 3,671 jobs in the Eastern province between 2010-2012. (Table 3)
In terms of percentage change in employed population during the reference period, the Island as a whole experienced 5.5% increase; 2.6% rise in Western province, 3.3% rise in Southern province; 8.0% rise in Northern province; and 0.8% drop in Eastern province. Hence, in terms of percentage change, North experienced the highest rise (significantly above the national average) followed by South and West (both well below the national average). Although the East is of concern, the drop in the employed population there could have been temporary due to severe floods in eastern districts during late 2011-early 2012.
There are district variations in the change in employed population within each province. For example, in the East, the overall drop in the employed population between 2010 and 2012 was entirely in one district, namely Ampara; i.e. Ampara lost 33,293 jobs between 2010 and 2012 (-14.6%). On the other hand, 26,241 net employments were created in Trincomalee (+25.7%) and 3,381 net employments were created in Batticaloa (+2.4) during the same period. (Table 3)
Similarly, in the North, between 2011 and 2012 Mullaithivu experienced the largest rise of 20.6% in employment (+4,031), 12.2% rise in Jaffna (+20,932), 6.0% rise in Mannar (+1,745), and 2.4% rise in Vavuniya (+1,281). However, there was 12.8% drop in employed population in Kilinochchi (-3,686) between 2011 and 2012. (Table 3)
In the South while the employed population increased in Hambantota and Matara districts, it declined in Galle district between 2010 and 2012. Hambantota had the highest rise of 13.5% in employed population (+32,465) and in Matara the rise was 9.0% or 27,323 in numbers. However, in Galle there was 7.3% drop in employment (-29,046) during the reference period. (Table 3)
In the Western province all three districts have generated net employments but modest. In percentage terms Kalutara had the highest rise of 3.1% followed by Gampaha 2.9% and Colombo 2.1%. In terms of numbers, Gampaha had the highest rise (25,750) followed by Colombo (18,756) and Kalutara (16,627). (Table 3)
The foregoing data on employment generation among provinces in the post civil war period reveals that in spite of massive infusion of public capital into Eastern, Northern, and Southern provinces only 7.3% of the total employment created in the country (422,111) accrued to the Southern province (30,742) and 5.8% accrued to the Northern province (24,303). On the other hand, Western province accounted for 14.5% of the net employments created in the country during 2010-2012 (61,133) where private capital investments predominate.
Capital intensive heavy machinery based gigantic infrastructure projects, whether in the North or South, have not created much employment in either of the provinces; and in the East it is even worse because there was negative growth of employment. Alas these gigantic projects seem to have not much benefitted respective local populations in terms of providing employment opportunities and thereby improving their livelihoods.
The methodology by which unemployment is measured in this paper is different from the official measure by the Department of Census and Statistics (DCS). This author does not agree with the definition of ‘working age population’ used in the Labour Force Survey in Sri Lanka. According to the DCS, the total number of people who are ten years old or over (no upper limit) are considered to be the ‘working age population’. In fact, we would argue that it will be more appropriate to consider 15-59 years old population as the working age population. Further, the labour force survey in Sri Lanka considers anyone working at least one hour during the week in which the survey is carried-out to be employed. This is a very low threshold for the status of employment. Such a low threshold gives an artificially higher employment rate which is deceptive. Moreover, unpaid family labour (“contributing family workers”) is considered employed which is unrealistic.
According to the latest Census of Population undertaken in March 2012, the total working-age (15-59 years) population in the Eastern province was 948,548. Considerable number of the working age population would be still at schools or further or higher education institutions (including universities), or they are unable to work due to legitimate reasons such as disability. If we make an allowance for working-age people opting not to work because of legitimate reasons including schooling and higher education, we could assume that only 700,000 persons in the working-age group in the East would be able to work. Out of that only 467,281 were employed in 2012. (Table 3) Therefore, the total number of unemployed persons in the East was 232,719 in 2012 and the unemployment rate was 33.2%.
Similarly, the total working-age (15-59 years) population in the North was 647,271 in 2012. If we make an allowance for people not able to work due to legitimate reasons, we could assume that only 475,000 persons in the working-age group in the North would be able to work. As noted in Table 3 only 326,791 persons were employed in the North in 2012; which means there were 148,209 unemployed persons in the North in 2012. Hence, the unemployment rate in the Northern province was 31.2% in 2012.
The total working-age population in the Southern province was 1,491,905 in 2012. We assume that only 1,200,000 persons in the working-age group in the South would be able to work. However, there were only 974,661 employed persons in the South in 2012 (Table 3), which means 291,905 were unemployed. The foregoing works out an unemployment rate of 24.3% in the South in 2012.
The total working-age population in the Western province was 3,696,417 in 2012. We assume that only 3,000,000 persons in the working-age group in the West would be able to work. However, there were only 2,369,799 employed persons in the West in 2012 (Table 3), which means 630,201 were unemployed. The foregoing works out an unemployment rate of 21.0% in the Western province in 2012.
Nexus between Economic Growth and Employment Growth
Decomposition of the employment growth between 2010 and 2012 by sectors/sub-sectors (fourteen categories) in the provinces under investigation is catalogued in Table 4. Employment growth is also compared with the economic growth of the corresponding sectors/sub-sectors where data is available.
In the Eastern province, out of the fourteen sectors/sub-sectors only four sub sectors experienced employment growth between 2010 and 2012; namely (i) construction, mining & quarrying, and utilities (+4.9%) (ii) wholesale and retail trade, repair of motor vehicles, motorcycles, and personal & household goods (+20.1%) (iii) transport, storage, and communication (+44.3%) (iv) financial intermediation, real estate, renting, and business activities (+11.1%). All other sub-sectors experienced negative growth including, agriculture (-1.4%), hotels and restaurants (-33.3%), and public administration, defence, and compulsory social security (-12.4%). (Table 4) Despite significant investments in the hotels and restaurants in the post-civil war East there have been job losses in that sector. In spite of massive investments in construction works, employment generation has been too small in that sub-sector.
In the North, eight sub sectors (out of fourteen) have experienced employment growth in just one year 2011-2012; manufacturing (+49.4%), construction, etc (+5.3%), wholesale and retail trade and services (+9.8%), transport, etc (+27.8%), finance (+133.8%), government services (+10.4%), education (+13.7%), and health (+23.9%). (Table 4) Again in the North, in spite of massive construction works employment creation has been pittance in that sub-sector. Although some of the foregoing rates of growth of employment have been very high, in terms of absolute numbers they are very small as noted in the previous section.
In the South, only four sectors/sub-sectors have created jobs between 2010 and 2012; agriculture (+5.5%), construction (+37.1%), public administration, defence, and compulsory social security (+16.1), and education (+31.4). Most of the foregoing employment generation is in the public sector. All other sectors experienced job losses including notably hotels and restaurants (-25.5%); hotels are major part of the Southern economy and significant investments have been made in recent years.
Although the Eastern, Northern, and Southern provinces have experienced construction boom because of massive infusion of public capital, only the Southern province has created significant jobs (37.1% rise in the employed population in the construction sector between 2010 and 2012); while in the East employed population in the construction sector increased by only 4.9% during the same period and in the North by only 5.5% (2011-2012).
There are sharp variations between economic growth of sectors/sub-sectors (that is, growth in monetary value of sectors/sub-sectors) and employment growth in corresponding sectors/sub-sectors. One striking comparison is worth highlighting here; whilst the construction sub-sector in the Eastern province expanded by 27.1% in monetary value between 2010 and 2012, employment in the same sub-sector expanded by only 4.9% during the same time period. Similarly, whilst the construction sub-sector expanded by 63.3% in monetary value between 2011 and 2012 in the North, employment expanded by only 5.3%. On the contrary, whilst the monetary value of the construction sub-sector expanded by 26.0% between 2010 and 2012 in the Southern province, employed population in the same sub-sector in the same time period grew by 37.1% (which is anomalous).
From a welfare perspective it is not necessary for employment to grow in tandem with economic growth. Due to modern technologies and machineries significant efficiencies could be gained by employing less labour that could improve overall welfare of people. However, given the special circumstances of the eastern and northern provinces where unemployment rates clock over thirty percent under post-civil war environment, policy makers should give high priority to local employment generation and thereby improve livelihood opportunities.
Type of Employment Growth
Investigation of extent of employment growth by type of jobs will reveal which income groups benefit more or less by such growth in employment. There are ten International Standard Classification of Occupations (ISCO) as noted in Table 5.
In the East, technical and associate professionals (87.1%), sales and service workers (23.6%), skilled agricultural and fishery workers (38.0%), and plant and machinery operators and assemblers (33.1%) had the highest rise between 2010 and 2012. (Table 5) The foregoing categories of employees, except sales and service workers, are paid above average salaries.
In the North, professionals (25.2%), clerks (31.0%), sales and service workers (22.1%), craft and related workers (20.5%), and plant and machinery operators and assemblers (34.4%) experienced the largest rise in employment. (Table 5) Out of the foregoing categories only professionals and plant and machinery operators and assemblers receive relatively high income.
Professionals (27.1%), clerks (55.8%), and proprietors and managers of enterprises (106.5%) were the categories experienced highest expansion of jobs in the Southern province between 2010 and 2012. (Table 5) However, only the professionals and proprietors and managers of enterprises are high income earners.
Significant decline in the number of proprietors and managers of enterprises in Eastern (-66.1%), Northern (-74.2%), and Western (-69.7%) provinces is a serious blow to private enterprise development. Only the Southern province has doubled the number of proprietors and managers of enterprises (+106.5%) between 2010 and 2012. However, 42.8% drop in senior officials and managers in the Southern province is a negative development. On the contrary there was almost 30% rise in senior officials and managers in the Western province which is positive. (Table 5)
The foregoing data reveals that while in the East middle-income earners have increased their share between 2010 and 2012, most categories (seven out of ten) of income earners have grown in the North between 2011 and 2012. In the case of North just one year data cannot reveal much. In the South, out of the five categories of income earners that had grown during 2010 and 2012, all are middle or above middle income earners except clerks.
We compare and contrast poverty between the districts and over time between 2009-10 and 2012-13. (Table 6) Please note that provincial level poverty cannot be measured because the DCS does not compile provincial consumer price indices. For the first time after a long lapse poverty was estimated in Kilinochchi, Mannar, and Mullaithivu districts in 2012-13.
The headcount poverty ratio has dramatically declined between 2009 and 2012 in almost all the districts in Eastern, Northern, Southern, and Western provinces, except Vavuniya in the North. This is because in the 2009-10 HIES Vavuniya North divisional secretariat area (poorest out of all divisional secretariat areas in Vavuniya) was not covered and therefore the poverty figure of Vavuniya in 2009-10 was underestimation.
In 2009-10 the headcount poverty ratio in descending order was; Batticaloa 20.3% (highest in the country), Jaffna 16.1%, Ampara 11.8%, Trincomalee 11.7%, Matara 11.2%, Galle 10.3%, Hambantota 6.9%, Kalutara 6.0%, Gampaha 3.9%, Colombo 3.6%, and Vavuniya 2.3%. (Table 6) HIES could not be undertaken in three Vanni districts in 2009-10 for reasons well known.
In 2012-13 the headcount poverty ratio in descending order was; Mullaithivu 28.8%, Mannar 20.1%, Batticaloa 19.4%, Kilinochchi 12.7%, Galle 9.9%, Trincomalee 9.0%, Jaffna 8.3%, Matara 7.1%, Ampara 5.4%, Hambantota 4.9%, Vavuniya 3.4%, Kalutara 3.1%, Gampaha 2.1%, and Colombo 1.4%. (Table 6) The headcount poverty ratios in Mullaithivu (28.8%), Moneragala (20.8), Mannar (20.1), and Batticaloa (19.4%) in 2012-13 were the highest four in the country.
Poverty has declined dramatically between 2009 and 2012 in eight out of fourteen districts under consideration, namely Ampara, Trincomalee, Jaffna, Hambantota, Matara, Colombo, Gampaha, and Kalutara; in Batticaloa and Galle the decline was marginal; in Vavuniya poverty increased dramatically but still low in comparison to most other districts; Kilinochchi, Mannar, and Mullaithivu districts cannot be compared because no data are available for 2009-10.
In spite of massive infusion of pubic capital in the post civil war period, poverty levels in Batticaloa, Kilinochchi, Mannar, and Mullaithivu districts remains intolerably high.
The foregoing sections reveal that in spite of unprecedented infusion of public capital into Eastern, Northern, and Southern provinces in the aftermath of the civil war on infrastructure development, increase in per capita income or generation of employment in those provinces falls far short of the potential. Poverty still remains at double-digit level in most Eastern and Northern districts.
The lack of economic peace dividend is reflected in the electoral setback experienced by the ruling coalition government in recent times. We compare the votes received by the ruling coalition (United People’s Freedom Party – UPFA) in the provincial elections of 2008 (Eastern) and 2009 (Southern and Western) and parliamentary elections of 2010 (Northern) with that of the provincial elections of 2012 (Eastern), 2013 (Northern), and 2014 (Southern and Western). The total number of votes received by the ruling coalition party and the share of votes it represents in the province (in parentheses) is noted in Table 7. The results are as follows.
Between 2008 and 2012, there was a sharp drop in the votes received by the ruling coalition party in the eastern province. In the 2008 provincial elections the Tamil National Alliance (most popular political party of the Tamils) did not contest the elections in the East. Thus, ruling coalition party’s share of the votes dropped by 35.2% between 2008 (308,886) and 2012 (200,044). Moreover, while the total votes received by the ruling party in 2008 in the East accounted for 52.2% of the total valid votes in the same election, the total votes received by UPFA in 2012 accounted for just 31.6% of the total valid votes in the same election. (Table 7)
In the North, while UPFA received 85,144 votes in the parliamentary elections of 2010, it dropped by 2.7% to 82,838 votes in the provincial elections of 2013. In the 2010 parliamentary elections the total votes polled by the ruling coalition party accounted for 33.3% of the total valid votes in the North. However, the votes polled by the UPFA in the provincial elections of 2013 accounted for only 18.4% of the total valid votes in that election. (Table 7)
The drop in popularity of the UPFA is the same in the South as well, though to a lesser degree. In the 2009 provincial elections the ruling coalition party secured 804,071 votes (or 67.9% of the total valid votes), which dropped to 699,408 votes (or 58.1% of the total valid votes) in the elections of March 2014. That is, between 2009 and 2014 the ruling party’s votes dropped by 13.0% in the Southern province (Table 7), which is the province that received the single largest quantum of public capital investments (among all the provinces) in the same period.
The decline of the popularity of the ruling political dynasty is no different in the most prosperous Western province as well. In 2009 the UPFA received 1,506,115 votes (or 64.7% of the total valid votes) in the provincial elections which dropped to 1,363,675 (or 53.4% of the total valid votes) in the provincial elections of March 2014. Thus, between 2009 and 2014 the ruling party’s votes declined by 9.5% in the Western province. (Table 7)
In the aftermath of the civil war the government has been touting that economic development, which had been arrested during the civil war, is its primary goal. The government is strenuously trying to make this country a “Wonder of Asia” through a development strategy underpinned by the proposed Five Hubs – Aviation, Energy, Knowledge, Maritime, and Tourism. Under this grandiose strategy two specific goals are to attain a per capita income of USD 4,000 and to host 2.5 million tourists by year 2016.
The per capita income has reached USD 3,280 (according to provisional estimates) and tourist arrivals have reached 1.27 million by end of 2013. Among the proposed five hubs none seems attainable except the tourism hub. The Colombo city is being rapidly beautified in order to make it a “World Class City”. Glossy images of emerging Wonder of Asia occupy international business magazines at hefty cost to the tax payer.
Even the development of the tourism sector is recently being undermined by religious bigotry demonstrated by the deportation of a British national (but Buddhist) with a Buddha tattoo on her upper arm in April 2014 (in 2013 also a British male was deported for the same reason) and violence against the Muslim community in June 2014 in Aluthgama and Beruwala areas along the south-western coastal belt, which is a tourist hot spot. With the recent Anti-Muslim riots in Sri Lanka the spectre of Myanmar rather than “Wonder of Asia” hounds Sri Lanka.
Underneath all the foregoing glitter lies the bitter squalor of Sri Lanka, which is what this paper has demonstrated using official statistical data and more importantly the people of this country, whether in the North or South, East or West, have indicted in the provincial council elections held within the past nine months.
Muttukrishna Sarvananthan is Principal Researcher, Point Pedro Institute of Development, Point Pedro, Northern Province.
 Cabraal, Ajith Nivard, “Practical, not conventional, wisdom has driven Sri Lanka’s rebuilding and reconciliation”, Forbes Magazine, 28 March 2014. http://www.forbes.com/sites/realspin/2014/03/28/practical-not-conventional-wisdom-has-driven-sri-lankas-rebuilding-and-reconciliation/
 Sarvananthan, Muttukrishna, (2007), Economy of the Conflict Region in Sri Lanka: From Embargo to Repression, Policy Studies No.44, Washington, D.C.: East-West Center. http://www.eastwestcenter.org/fileadmin/stored/pdfs/ps044.pdf
 Auditor General, Department of, Annual Reports, Government of Sri Lanka. http://www.auditorgeneral.gov.lk/web/index.php?option=com_audititem&Itemid=89&lang=en
 Sarvananthan, Muttukrishna, (2013), “Real and Phantom Per Capita Income”, Daily Mirror, January 26, page A9. http://www.dailymirror.lk/opinion/172-opinion/25268-real-and-phantom-per-capita-income-.html