The Impact on the St. Lucia Labor Market - World...
46
March, 2015 Lea Gimenez, Edwin St. Catherine, Jonathan Karver and Rei Odawara The Aftermath of the 2008 Global Financial Crisis in the Eastern Caribbean The Impact on the St. Lucia Labor Market Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized
Transcript of The Impact on the St. Lucia Labor Market - World...
March, 2015Lea Gimenez, Edwin St. Catherine, Jonathan Karver and Rei Odawara
The Aftermath of the 2008 Global Financial Crisis in the Eastern CaribbeanThe Impact on the St. Lucia Labor Market
Pub
lic D
iscl
osur
e A
utho
rized
Pub
lic D
iscl
osur
e A
utho
rized
Pub
lic D
iscl
osur
e A
utho
rized
Pub
lic D
iscl
osur
e A
utho
rized
Pub
lic D
iscl
osur
e A
utho
rized
Pub
lic D
iscl
osur
e A
utho
rized
Pub
lic D
iscl
osur
e A
utho
rized
Pub
lic D
iscl
osur
e A
utho
rized
WB406484
Typewritten Text
94766
The Aftermath of the 2008 Global Financial Crisis in the Eastern CaribbeanThe Impact on the St. Lucia Labor Market
March, 2015
Lea Gimenez, Edwin St. Catherine, Jonathan Karver and Rei Odawara
Acknowledgements
The authors of this brief are Lea Gimenez (Economist, World Bank - Poverty Global Practice), Edwin St. Catherine (Director of Statistics, St. Lucia Central Statistical Office), Jonathan Karver (Research Fellow, Inter-American De-velopment Bank) and Rei Odawara (Economist, World Bank - Macro and Fiscal Management Global Practice). The brief was produced under the general guidance of Louise J. Cord, (Practice Manager, World Bank - Poverty Global Practice) and Francisco Galrao Carneiro (Program Leader, World Bank - Caribbean Countries Country Manage-ment Unit). This report is the direct result of a collaboration between the St. Lucia’s National Statistical Office and both the Poverty and the Macro & Fiscal Management Global Practices of the World Bank. The report is also the result of an important partnership to support evidence based policy making in the OECS being led by the OECS Commission with the support of the United Nations Development Program (UNDP) and the World Bank.
The authors would like to thank Kathleen G. Beegle (Lead Economist, World Bank- Accra Western Africa), Dean M. Jolliffe (Senior Economist, World Bank – Development Research Group, Poverty and Inequality), David L. Newhouse (Senior Economist, World Bank - Poverty Global Practice), Lara Blanco (Deputy Resident Representa-tive, UNDP), McDonald Thomas (Operations Officer, Caribbean Development Bank), Sean C. Mathurin (Economic Affairs Officer, OECS Commission), and Wayne Mitchell (International Monetary Fund Resident Representative - Eastern Caribbean Currency Union region) for providing valuable feedback. Special thanks go to Tracy Polius (Permanent Secretary, Department of Economic Planning, Ministry of Finance, Economic Affairs, Economic Plan-ning and Social Security), Reginald Darius (Permanent Secretary, Department of Finance, Economic Affairs, and Social Security, Ministry of Finance, Economic Affairs, Economic Planning and Social Security) and the partici-pants in the Workshop on St. Lucia Labor Market Outcomes for their invaluable feedback on an earlier version of this report. The authors are grateful to St. Lucia’s Central Statistical Office for granting access to the Labor Force Survey data. The views expressed here are those of the authors alone and may not necessarily represent those of the World Bank, Inter-American Development Bank, or the Government of St. Lucia.
Executive Summary
This brief expands the scarce literature on the impact of the global financial crisis on labor market outcomes and welfare in the Organization of Eastern Caribbean States (OECS). The brief focuses on the economy of St. Lucia, one of the OECS member states. The statistical information assembled here should help decision makers and the public in the OECS to develop policy options that can sustain job creation and thereby enhance public welfare. It also can help gauge the effectiveness of policies over time.
The evidence presented in this brief shows how the recent financial crisis had significant and long-lasting neg-ative impacts on the welfare of St. Lucians. The government of St. Lucia attempted to use fiscal policy to boost growth and enhance labor market opportunities in the island. Still, unemployed and underemployed St. Lucians together accounted for over 40 percent of the working-age employable population. They suffered a significant decline in welfare in the aftermath of the crisis. They lost not only their income but also the collateral benefits that are often associated with being fully employed in good quality jobs in the “formal” sector of the economy.
These findings are not surprising given the nature of the economies of St. Lucia and other OECS economies. These island states rely heavily on industries such as tourism, construction, agriculture, and financial services. Those in turn depend greatly on external demand from wealthier economies that also were damaged by the financial crisis. Travel and tourism related activities alone are estimated to account for 30 percent of gross do-mestic product (GDP) and employment in the OECS. Moreover, like most of the OECS economies, St. Lucia has a high level of national debt. The debt burden limits the ability of the government to invest in social programs and human capital. Debt also compromises the government’s capacity to aid the poor and vulnerable in times of crisis.
Organization
The brief has five sections:
Section 1– provides a short introduction to the main findings from this report.
Section 2 – summarizes the St. Lucian macroeconomic context in the aftermath of the 2008 global financial crisis.
Section 3 – presents an in-depth description of labor market trends in St. Lucia between 2008 and 2013.
Section 4 – looks at the impact of the crisis on unemployment, wages and welfare.
Section 5 – highlights conclusions from the brief.
8
Although the improvement of economic conditions following the 2008 global financial crisis was signif-icant throughout the Latin America and Caribbean region, progress was sluggish and very limited in the OECS1 economies. Evidence from household survey data from St. Lucia indicates that the recent financial crisis did significant, long-lasting harm to St. Lucians’ wellbeing. The government of St. Lucia attempted to use fiscal policy to boost growth and enhance labor market opportunities in the island. Still, unemployed and underemployed St. Lucians together accounted for over 40 percent of the working-age employable population. They suffered a significant decline in wel-fare in the aftermath of the crisis. They lost not only their income but also the collateral benefits that are often associated with being fully employed in good quality jobs and under formal contracts.
Key Findings
A growing labor force can be an asset for income gen-eration and growth. From 2003 to 2013, St. Lucia’s la-bor force grew by 18 percent. But the impacts of the financial crisis on St. Lucia between 2008 and 2013
1 Throughout the text, OECS economies refers to the six indepen-dent member states of the Organization of the Eastern Caribbean States which includes Antigua and Barbuda, Grenada, Dominica, St. Kitts and Nevis, St. Lucia, and St. Vincent and the Grenadines.
impeded the full exploitation of its demographic div-idend. Since the crisis, the labor market has not been able to fully absorb the growing number of workers. The result has been an increase in unemployment and underemployment. The negative impact of the crisis on employment prospects was particularly severe among the young (those between 15 and 24 years of age) — by the end of 2013, nearly half of all young adults were unemployed.
The relationship of educational attainment to em-ployment was mixed. Among those who stayed in the country, workers with secondary education experi-enced the greatest increase in unemployment. Work-ers with less than primary education remained em-ployed, but received lower wages. On the other hand, workers who left the country over the last decade, representing about 5 percent of the current popula-tion, were significantly more educated than average.
Unemployment and asset ownership data tracked be-tween 2008 and 2013 indicate that the financial crisis affected the poorest 40 percent of households more harshly than the wealthiest 60 percent in St. Lucia. From early 2008 through late 2009, the unemploy-ment rate was around 15 percent for the population as a whole. Yet from 2011 to 2013 the unemployment rate for the two poorest quintiles was nearly double
CHAPTeR 1inTRoduCTion
9
that of the two wealthiest quintiles. In addition, pri-or to the crisis, the characteristics of the bottom forty percent and the top sixty percent were relatively sim-ilar in St Lucia, while since the crisis, there is a grow-ing rift between the two groups. For example, except for being more likely to be self-employed and less likely to work in the professional service sector, the bottom forty percent was virtually indistinguishable from the rest in 2008. By 2013, however, the bottom forty percent was significantly more likely to be un-employed, significantly less likely to be an employee or an employer, had significantly lower levels of edu-cation, higher probability of residing in urban areas, and higher probability of being headed by a female. In terms of sector of employment and relative to the top sixty percent, by 2013 they were twice as likely to work in the agricultural sector, more likely to work in the construction and manufacturing sector, and sig-nificantly less likely to work in the education, health, social and professional services sectors.
These findings are not surprising given the nature of the economies of St. Lucia and other OECS econ-omies. These island states rely heavily on industries such as tourism, construction, agriculture, and finan-cial services. Those in turn depend greatly on external demand from the wealthier economies that also were damaged by the financial crisis. Travel and tourism re-lated activities alone are estimated to account for 30 percent of gross domestic product (GDP) and employ-ment in the OECS. Moreover, like most of the OECS economies, St. Lucia has a high level of national debt. The debt burden limits the ability of the government to invest in social programs and human capital. Debt also compromises the government’s capacity to aid the poor and vulnerable in times of crisis.
10
The OECS economies were hit harder than the rest of the Latin American economies by the 2008 global financial crisis. Data from the World Devel-opment Indicators (WDI) show significant negative impact on growth in per-capita GDP in all of the OECS member countries (Figure 1). Antigua and Barbuda, the largest economy prior to the pre-crisis period, was the least resilient to the crisis. In 2009, per-capita real GDP declined for all of the OECS economies and fell the most in Antigua and Barbu-da (-13 percent), followed by St. Kitts and Nevis (-7.4 percent), and Grenada (-7 percent). Economic activi-ty remained sluggish in most of the OECS during the post-crisis years, more so than in the rest of Latin America and the Caribbean (LAC).
Progress in the aftermath of the crisis was gen-erally sluggish and very limited in the OECS. However, the impact of the global financial cri-sis on St. Lucia’s GDP growth was milder than in other OECS countries. St. Lucia’s economy grew an average of 1.9 percent during the pre-crisis period (2000-2007) and 1.3 percent during the crisis (2008-2010) The OECS countries’ economy grew by 3.5 per-cent on average prior to the crisis and declined 1.2 percent on average during the period from 2008 to 2010. Preliminary estimates from the International Monetary Fund (IMF) indicate that St. Lucia’s GDP
was expected to contract for a third consecutive year in 2014.2
Since the crisis, St. Lucia’s major sources of growth have suffered from declining competitiveness and weak external demand related to slow recovery in advanced economies. At the onset of the crisis, the contribution of construction, tourism, and agriculture to real GDP growth declined significantly and have not recovered since (Figure 2).3 The subdued growth performance of the OECS during the global financial crisis was worsened by weakening competitiveness of the tourism sector. The Eastern Caribbean Currency Union’s (ECCU) share of tourism receipts from its tradi-tional sources such as the United States, United King-dom, and Canada declined by 37 percent.. The region was not able to attract visitors from other potential markets with higher growth.4
2 The sources of real GDP figures referred in this paragraph are IMF’s Article IV and the World Economic Outlook (WEO) database.
3 Real GDP growth figures of the ECCB are different from those of the IMF’s Article IV and WEO.
4 This is not surprising, since travel and tourism related activities alone are estimated to account for about 40 percent of GDP and employment in St. Lucia and an average of 30 percent of GDP in the OECS (Country Reports, World Travel and Tourism Council, 2012). In Antigua and Barbuda, the OECS economy that experienced the most negative impact on GDP, travel and tourism represents more than 70 percent of GDP and employment.
CHAPTeR 2sT. LuCiA’s MACRoeConoMiC ConTexT in THe AFTeRMATH oF THe 2008 CRisis
11
Foreign direct investment (FDI), particularly in tour-ism-related construction, has historically played an important role in St. Lucia. FDI contracted sharply as a result of the crisis. FDI steadily increased from 7 percent of GDP in 2000 to 24 percent in 2007; it was 12.5 percent of GDP on average. From 2008 on-wards, however, the flow of FDI declined to an av-erage of 9.2 percent of GDP during 2008–2013. FDI remains much below the pre-crisis level, although it increased slightly in 2013 from the previous year, to 6.3 percent of GDP. With FDI declining since the crisis, construction activity — in particular, private sector construction activity — has slowed. In the post-crisis period, it has been difficult for St. Lucia to finance tourism-related investment projects. Lack of
investment makes it hard to upgrade the country’s tourism assets.
The decline of tourism and of construction activi-ties severely affected the banking sector in St. Lu-cia and led to a credit crunch. Traditionally, the bank-ing sector in St. Lucia faces high delinquency rates and has a large exposure to tourism related real es-tate. Non Performing Loans (NPLs) in the banking sys-tem have been rapidly rising since 2008. On average, NPLs rose from 15.5 percent in 2012 to approximately 22 percent in November 2013. Banks have increased loan loss provisions, and have become very selective about lending. High numbers of delinquencies and weak domestic conditions have prevented recovery
Source: World Bank sta� calculations based on data from the Eastern Caribbean Central Bank (ECCB).
Figure 2. St. Lucia: Sector Contribution to Real GDP Growth, 2001-2013
10
8
6
4
2
0
-2
-4
-6
Agriculture Construction Banking Real Estate, Renting and Business AdministrationReal GDP growth Manufacturing Tourism Public Administration Other
201320122011201020082006200420022001 2009200720052003
Source: World Development Indicators.
Figure 1. Real Per-capita GDP Change (annual %) in OECS Countries and the LAC Average
-15-12
-9-6-30369
1215
LAC Average(all income levels) St. Vincent and the Grenadines St. Lucia St. Kitts and NevisGrenada Dominica Antigua and Barbuda
201320122011201020092008200720062005
12
of liquidity. In addition, with high lending rates and stricter lending requirements by banks, credit condi-tions have not improved. Private credit remained flat over the past three years — further hindering recov-ery of major domestic industiries.
A series of natural disasters between 2010 and 2012 compounded the negative impact of the cri-sis. Hurricane Tomas damaged roads, bridges, elec-tricity and other infrastructure in St. Lucia in 2010. Tropical Storm Ernesto did further damage in 2012. A major outbreak of a banana leaf disease5 in 2011 further set back St. Lucia’s already declining banana exports.
The negative impacts of the 2008 crisis and nat-ural disasters combined with St. Lucia’s lax fiscal policies led to persistent fiscal deficits. The fis-cal balance in St. Lucia deteriorated in 2009, when the drag of the global crisis began to be felt in the island’s economy. In 2012, the overall deficit rose to 9.3 percent of GDP, and the primary deficit to 5.8 per-cent, as the newly-elected government undertook expansionary fiscal policy to support growth and boost employment.6 As a result of the government’s
5 Black sigatoka.
6 This was mainly in the form of active labour market programs to increase employment opportunities. Among these programs: Single Mothers in Life Empowerment Project (SMILES), which pro-
fiscal consolidation effort, the overall deficit was re-duced by over 36 percent in 2013/14 — down to 5.7 percent of GDP from 9.2 percent the previous year.7 The reduction was driven mainly by cuts in capital expenditures. Flat FDI growth, further borrowing in the Regional Government Securities Market, and continuing fiscal deficits caused the country’s public debt to grow. St. Lucia’s public debt to GDP ratio rose to around 80 percent of GDP in 2013 from the pre-cri-sis level of 57 percent of GDP (International Monetary Fund, 2014). The reduction in foreign and public in-vestment has diminished the country’s medium- and long-term growth prospects.
The important tourism sector continued strong performance through the crisis and post-crisis pe-riod — better than in other OECS countries. Tour-ism has been a major driver of economic growth in St. Lucia over the last decade. The number of tourist arrivals to St. Lucia increased since the early 2000s. Arrivals grew for more than a decade at an average
vides beneficiaries with life skills training, technical and vocational training and job placement support; The National Initiative to Cre-ate Employment (NICE), with the goal of assisting in the creation of sustainable employment opportunities for an estimated 4500 peo-ple over a three year period; and Holistic Opportunities for Personal Empowerment (HOPE), a 5 million dollar short term employment programme designed to provide short term employment, training, personal development, and health care to unemployed St. Lucians.
7 Prime Minister’s 2014 Budget Statement, delivered March 31, 2015.
20
30
40
50
Source: World Bank; WDI and Bank Sta� calculations.Note: OECS includes Antigua and Barbuda, Dominica, Grenada, St. Kitts and Nevis, and St. Vincent and the Grenadines.
Source: National Authorities ad Bank Sta� Calculations.
Figure 3. International Tourism in St. Lucia and the OECS
Number of arrivals (stay-over visitors) Arrival decomposition and visitor expenditure
Mill
ares
Mill
ares
Rest of OECS 1/Total Visitor Expenditure (in percent of GDP, right axis)
100
150
200
250
300
350
2012201120102009200820072006200520042003200220012000 0
200
400
600
800
1000
1200
Yacht Passengers Cruise Ship Passengers Stay-Over Visitors
2012201020082006200420022000
St. Lucia
13
rate of more than 2 percent for stay-over visitors and 2.6 percent for total visitors8 (Figure 3). Tourism ac-tivity slowed after hitting its peak in 2005. Stay-over arrivals and total arrivals dropped by 12 percent and 7 percent respectively after 2006. However, St. Lucia tourism has shown signs of recovery since 2010, while the arrivals in the rest of the OECS countries have de-clined from their peak in 2006.
St. Lucia’s resilience to the global crisis relative to the rest of the OECS may be related to its lev-el of economic diversification and type of tourism products. Although tourism is a primary growth driv-er of the St. Lucian economy — accounting for about 40 percent of GDP9, as mentioned before — other industries such as agriculture, agribusiness and con-struction also play key roles in the economy. More-over, St. Lucia is globally recognized for its high-end luxury tourism which tends to be less sensitive to economic shocks and swings compared to mass and budget tourism. Antigua and Barbuda is also recog-nized for high-end tourism; however, unlike St. Lucia, its tourism sector is the largest sector in the economy representing about 63 percent of GDP.
8 The total visitors consist of stay-over visitors, excursionists, and cruise ship and yacht passengers.
9 This estimate is taken from the World Travel and Tourism Coun-cil (2014).
14
Anecdotal evidence and GDP figures for St. Lucia indicate that the economic crisis had a significant adverse impact on the nation’s unemployment rate. GDP growth in St. Lucia has been more resilient than that of most Eastern Caribbean states. Never-theless, the effect of the crisis on national income was significant. GDP declined in the years following the crisis (Figure 1). The negative impact of the crisis was also evident at the micro level. Table 1 provides a snapshot of labor market characteristics covering the period from 2008 to 2013. The data show a sub-stantial increase in the unemployment rate. While the proportion of the unemployed10 stood at just over 15 percent of working age (15 to 64 years of age) indi-viduals in 2008,11 it increased gradually to a high of just below 24 percent in 2013 (an increase of nearly 9 percentage points).
10 Part of the analysis considers both standard (the proportion of economically active individuals without work and actively seek-ing employment) and broad (the proportion of economically active individuals who are without work) national unemployment rates Most of the analysis focuses on the latter.
11 Although working age in St. Lucia is officially defined as 15 to 64, there is no upper limit in practice. Therefore, the analysis be-yond ADePT considers those 65 and over as part of the working age population.
MAin LAboR MARKeT indiCAToRs
The crisis led to a decrease in the availability and quality of jobs. The working age proportion of the population increased by 3.5 percentage points from 2008 to 2013. At the same time, unemployment in-creased by 8.6 points. So workforce growth was not accompanied by an increase in the demand for labor. The proportion of working age individuals with more than one job fluctuated between 1 and 6 percent be-tween 2008 and 2013, reaching a high of 5.9 percent in 2012. The share of workers with access to insurance through their employment (the National Insurance Corporation or privately provided coverage) has re-mained relatively stable at around 82 percent since 2011.12 However, the share of workers with a formal (written) contract has dropped somewhat: from 53.8 percent with a contract in 2011 to just above 49 per-cent in 2013 (see also Box 1).
Demographic trends and macroeconomic chal-lenges compounded the rise in unemployment. Population growth slowed down over the last thirty years, declining from an average of 1.6 percent per year between 1984 and 1993 to an average of 1.2
12 The questions on insurance and contract in current employ-ment were not asked prior to 2011.
CHAPTeR 3LAboR MARKeT TRends in sT. LuCiA
15
percent per year between 2003 and 2013 (World De-velopment Indicators, 2014). Despite the slowdown, St. Lucia added nearly 18,000 people to its total pop-ulation, an 11.5 percent increase between 2003 and 2013. The working age population (15–64) expand-ed more rapidly than the total population, at an av-erage rate of 1.9 percent between 2003 and 2013. The result was an 18 percent increase in the size of St. Lucia’s workforce over this period. The bulge in the 10–19 age group in the 2011 population pyramid in-dicates that the workforce will continue to grow over the next decade (Central Statistics Office of St. Lucia, 2011). While a growing labor force can be an asset for income generation and growth, absorbing the wave of new entrants every year poses a major challenge for the labor market. 13
Since 2008, the labor market has not been able to fully absorb the growth of the labor force, leading to both an increase in unemployment and under-
13 ADePT (Automated DEC Poverty Tables) labour module, a computational tool created by Computational Tools Team (DECCT) and Poverty Reduction and Equity Unit (PRMPR) of the World Bank to evaluate labour and poverty, among other trends from house-hold survey data. The ADePT computational tool contains useful templates for the creation of straightforward tables and figures detailing key characteristics of the labour market given the survey data used. For more information about ADePT and the labour mod-ule, see the ADePT Labour Module User Guide (Lara-Ibarra, G.) Ver-sion: January 7, 2014.
employment. Figure 4 shows quarterly national level estimates of the proportion of the labor force, em-ployment, unemployment, and underemployment (defined as working less than 35 hours per week) for the 2008–2013 period. The workforce, by either broad or standard measures,14 increased steadily over the time period considered, rising on average 0.8 per-cent per year, from 74 percent in 2008 to 78 percent in 2013. At the same time, the unemployment rate in-creased by nearly 9 points, or an average of 1.8 points per year. In other words, 2 of every 3 individuals who entered the workforce between 2009 and 2013 were unable to find jobs.
The proportion of the working age population clas-sified as underemployed moved in tandem with un-employment from late 2008 to late 2012, reaching a peak of approximately 18 percent in the third quarter of 2012. By the fourth quarter of 2013, this measure had dropped to its earlier levels (around 11 percent).15
14 Broad unemployment captures the proportion of economi-cally active individuals who are without work and who would like to work (regardless of whether they are actively searching) where-as standard unemployment considers as unemployed only those without work and actively seeking employment. That is, those not actively seeking work are considered economically inactive.
15 The average number of hours worked per week cannot be de-fined in the fourth quarter of 2011 or the first quarter of 2012, so these values are interpolated from the average across time.
Table 1: Main Labor Market Indicators 2008 2009 2011 2012 2013 Change
Unemployment rate 15.2 18.5 21.2 21.4 23.3 8.1
(0.53) (0.70) (1.02) (0.86) (0.79) (0.95)
Employment-to-working-age-population ratio 62.6 60.5 58.3 60.9 59.7 -2.9
(0.63) (0.69) (1.13) (0.84) (0.76) (0.99)
Working age population as a fraction of total population 65.4 66.6 68.5 68.5 68.9 3.5
(0.49) (0.49) (0.72) (0.59) (0.57) (0.75)
Share of workers with two or more jobs 1.4 2.0 3.6 5.9 2.5 1.1
(0.18) (0.23) (0.64) (0.55) (0.31) (0.36)
Share of workers with social security 82.0 82.4 82.8
(1.31) (1.01) (0.98)
Share of workers with formal contract 53.8 48.1 49.3
(2.12) (1.75) (2.95)Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013. Note: Estimates computed using ADePT version 5.5. Proportions are with respect to individuals of working age (15-64). Changes shown between years 2008 and 2013.13 Standard error in parenthesis.
16
These findings are not surprising. St. Lucia, like all OECS economies, relies heavily on industries such as tourism, construction, agriculture and financial ser-vices. Those in turn depend greatly on external de-mand from wealthier economies. When those fell into a deep recession, they reduced spending.
Travel and tourism related activities alone are esti-mated to account for over 30 percent of GDP and em-ployment in St. Lucia. Moreover, as discussed in Sec-tion II of this report, St. Lucia also suffers from a high level of national debt. The debt burden limits the government’s ability to invest in social protection or in human capital, and generally constrains its capac-ity to respond effectively to macroeconomic shocks.
Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013.
Figure 4. Trends in Labor Market Indicators in St. Lucia, 2008-2013
0.4
0.5
0.6
2013
Q420
13Q3
2013
Q220
13Q1
2012
Q420
12Q3
2012
Q220
12Q1
2011
Q420
11Q3
2009
Q420
09Q3
2009
Q220
09Q1
2008
Q420
08Q3
2008
Q220
08Q1
2013
Q420
13Q3
2013
Q220
13Q1
2012
Q420
12Q3
2012
Q220
12Q1
2011
Q420
11Q3
2009
Q420
09Q3
2009
Q220
09Q1
2008
Q420
08Q3
2008
Q220
08Q1
0.6
0.8
1.0
0.05
0.10
0.15
0.20
0.25
2013Q42013Q32013Q22013Q12012Q42012Q32012Q22012Q12011Q42011Q32009Q42009Q32009Q22009Q12008Q42008Q32008Q22008Q1
Labor force (broad)Labor force (standard)
Unemployed (standard) Working < 35hrs (week)Unemployed (broad)
Prop
ortio
n of t
otal
popu
lation
Prop
ortio
n of E
AP/w
orkin
g pop
ulatio
n
Prop
ortio
n of E
AP (b
road
)
Employed
17
Box 1: Formal Contracts in the St. Lucian Labor Market (2011–2013)
The rate of insurance coverage of workers has remained high at around 80 percent since 2011 (not reported in the figure). But there has been an important drop in the proportion of workers with a formal (written) contract through their primary employment (figure below). In late 2011 through the second quarter of 2012, around 55 percent of employees had a written contract through their employer. However, at the end of 2013 only 49 percent of employees did.
The proportion of males with a contract was slightly lower than of females. Similarly, the proportion of adult workers with a contract went from 56 percent in 2011 to 49 percent in 2013. At the same time, the proportions of both youth and elderly employees with a contract increased 4 and 15 percentage points, respectively.
Finally, more salaried public workers had a contract than private sector employees did — the difference was about 20 percent. However, the decline in contracted workers was somewhat greater in the public sector than in the private sector.
Not surprisingly, employees in the “education, health and social services” and “professional services” sectors are more likely to be covered by formal contracts. The most substantial drop in the proportion of workers with a contract, however, was for those employed in the social services sector (where coverage through a contract dropped from approximately 70 percent to 50 percent).
The goal of the amendment to the St. Lucia Labor Code (2006) was to provide protection to workers. However some of its provisions — such as changes to the definition of continuity of employment that affect workers’ benefits and severance payments in favor of workers — re-sulted in disincentives for employers to issue contracts. This disincentive is likely to have been exacerbated by the context of the aftermath of the crisis.
Source: Labor Force Survey 2011, 2012, and 2013.
Proportion of Working Population with Formal Contract
Proportion of Working Population with Formal Contract (cont.)
0.4
0.5
0.6
2013q12012q42012q32012q22012q12011q42011q3 0.2
0.3
0.4
0.5
0.6
201320122011
0.2
0.3
0.4
0.5
0.6
0.7
0.8
201320122011 0.2
0.3
0.4
0.5
0.6
0.7
0.8
201320122011
0.3
0.4
0.5
0.6
0.7
0.8
201320122011 0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
201320122011
Primary or less
Formal contract
Prop
ortio
n of w
orkin
g po
pulat
ion/e
mplo
yees
Prop
ortio
n of e
mplo
yees
with
cont
ract
Prop
ortio
n of e
mplo
yees
with
cont
ract
Prop
ortio
n of e
mplo
yees
with
cont
ract
Prop
ortio
n of e
mplo
yees
with
cont
ract
Prop
ortio
n of e
mplo
yees
with
cont
ract
No formal school
Salaried - privateSalaried - public
SecondaryTertiaryPost-secondary
2nd quintile1st quintile 3rd quintile5th quintile4th quintile
MaleFemale Youth (15-24)Retirement age (65+)Adults (25-64)
ManufacturingAgriculture ConstructionEducation, health, social servicesMining/EnergyTransportation/communicationsProfessional servicesOther/not de�nedServices and trade
18
LAboR MARKeT TRends
The following discussion highlights trends in em-ployment (categories, distribution, education) and in unemployment (characteristics of the unemployed, workforce adaptations).
employment
employment categories — shares in total employment
The distribution of employment by employment status (salaried, self-employed, or family worker) remained largely unchanged between 2008 and 2013 in the non-agriculture sector. (Table A2.1.) At the national level, the only group that experienced a marginal decline in its share of employment was “unpaid family worker or apprentice,” which declined from 4.6 percent in 2008 to 3 percent in 2013. Be-tween 2008 and 2013, the distribution of employment among salaried, self-employed, and family workers remained virtually unchanged in the non-agricultural sector. During the same period, in the agriculture sec-tor, there was a large increase in the share of self-em-ployed (18.1 percentage points) and proportional de-
clines in the remaining employment categories — the most significant being the decline in “unpaid family worker or apprentice.”
distribution of employment by economic sector
The 2008 global financial crisis led to notable shifts in sectoral participation in St. Lucia — from profes-sional services,16 agriculture, and construction to basic services, trade, and education, health, and social services. Table A2.2 shows the distribution of the working population by sector of economic activ-ity and over time. In 2008 just over 20 percent of the working population was employed in professional ser-vices (public or private); by 2009 the proportion had dropped to 18.5 percent. The decline in professional services jobs continued gradually through 2012 to 16 percent; it rose to just above 18 percent in 2013. Mean-while, employment in the services and trade sector increased from only 15 percent of the working popula-tion in 2008, to over 18 percent of the working popula-
16 Professional services include financial and insurance activities, real estate activities, professional, scientific and technical activities, administration and administrative support (public or private), and defence. Basic services and trade include wholesale and retail trade, and other basic services such as repairs and other low-skill activities.
Box 1: Formal Contracts in the St. Lucian Labor Market (2011–2013) (cont.)
Source: Labor Force Survey 2011, 2012, and 2013.
Proportion of Working Population with Formal Contract
Proportion of Working Population with Formal Contract (cont.)
0.4
0.5
0.6
2013q12012q42012q32012q22012q12011q42011q3 0.2
0.3
0.4
0.5
0.6
201320122011
0.2
0.3
0.4
0.5
0.6
0.7
0.8
201320122011 0.2
0.3
0.4
0.5
0.6
0.7
0.8
201320122011
0.3
0.4
0.5
0.6
0.7
0.8
201320122011 0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
201320122011
Primary or less
Formal contract
Prop
ortio
n of w
orkin
g po
pulat
ion/e
mplo
yees
Prop
ortio
n of e
mplo
yees
with
cont
ract
Prop
ortio
n of e
mplo
yees
with
cont
ract
Prop
ortio
n of e
mplo
yees
with
cont
ract
Prop
ortio
n of e
mplo
yees
with
cont
ract
Prop
ortio
n of e
mplo
yees
with
cont
ract
No formal school
Salaried - privateSalaried - public
SecondaryTertiaryPost-secondary
2nd quintile1st quintile 3rd quintile5th quintile4th quintile
MaleFemale Youth (15-24)Retirement age (65+)Adults (25-64)
ManufacturingAgriculture ConstructionEducation, health, social servicesMining/EnergyTransportation/communicationsProfessional servicesOther/not de�nedServices and trade
19
tion a year later. The two sectors continued to employ about 18 percent of the working population up to 2013.
Construction employment declined between 2008 and 2013, falling from about 13 percent to 8 percent. Education, health and social services, on the other hand, rose from 4 percent in 2008 to 10 percent in 2013. This reflected an increase in public sector em-ployment as well as the implementation of several govermnent programs meant to support growth and boost employment opportunities (see footnote 6). This might also reflect a higher demand for re-training post crisis to improve labour market conditions. Over-all, there was a marked transition from professional services (public and private) and construction to ser-vices and trade and to education, health and social services. However, the tourism industry (accommo-dation and food services) continued to employ about 13 percent of the working population through the entire period — consistent with government policies to support employment in the tourism sector with air-line subsidies and tax reductions.
educational attainment of the employed by sector of employment
Most people employed in the non-agricultural sec-tor in St. Lucia have at least upper secondary ed-
ucation; a higher level of education is becoming more common. Most employees in the agricultural sector have primary schooling or less but the trend is toward lower secondary education. Table A2.3 presents the distribution of the employed by educa-tional attainment. The proportion of the employed with primary schooling or less has increased gradually since 2008, from 5.8 to 7.7 percent. At the same time, the proportion of the working population with low-er or junior secondary education dropped nearly 10 percentage points and the proportion with upper sec-ondary schooling rose by 2.3 points. The proportion of the working population with more than secondary studies more than doubled from 2008 to 2013.17
The trend toward higher levels in educational attain-ment started from a lower level in the agricultural sec-tor than in non-agricultural employment.18
High-skilled workers shifted to the education, health and social services sector; low-skilled work-ers have shifted to accommodation and food ser-
17 There was a significant change in the coding of the education variables between 2009 and 2011. As a result, individuals formerly included under the post-secondary, non-tertiary education group are now included under the tertiary education group.
18 The 2006 Country Poverty Assessment for St. Lucia of the Ca-ribbean Development Bank shows that those employed in the agri-cultural sector have the lowest levels of education.
Source: Labor Force Survey 2008 and 2013.
Figure 5. Level of Education of Workers by Sector of Employment: 2008 vs. 2013
0 20 40 60 80 100Other/ not de�ned
Accommodation & Food serviceServices and Trade
Transportation/ communicationsProfessional services
Education, health, social servicesMining/Energy
ConstructionManufacturing
Agriculture
0 20 40 60 80 100Other/ not de�ned
Accommodation & Food serviceServices and Trade
Transportation/ communicationsProfessional services
Education, health, social servicesMining/Energy
ConstructionManufacturing
Agriculture
2008 2013
None Pre-primary (Infant) or Primary Lower/ Junior SecondaryUpper Secondary (Forms 1-3) / Senior Primary Post Secondary, non-tertiary (diploma or associate degree) Tertiary (University)
20
vice and trade. All economic sectors saw an increase in the share of workers with tertiary education from 2008 to 2013 (Figure 5). The increase was particularly pronounced in education, health, and social services sectors both in absolute and relative terms. The share of employees with only primary education declined in most sectors.
educational attainment of the emigrant population
The 2010 census data showed an increase in emi-gration of more-educated workers from St. Lucia during the previous decade. The number of emi-grants over the previous decade is about 5 percent of the current St. Lucia population. The emigrants’ aver-age educational attainment was significantly higher than that of the average population (Figure 6). Nearly 55 percent of the emigrants cited either employment (28 percent) or education (27 percent) as the number one reason behind their decision to leave St. Lucia. During the same decade, remittances grew.
unemployment
unemployment by individual and household char-acteristics19
The crisis has been particularly hard on St. Lu-cia’s young adults — by the end of 2013, nearly half were unemployed. Figure 7 and Table A2.4 report unemployment estimates by gender and age group. Since 2008, unemployment has been the highest and has increased the most rapidly (17 points) among those 15 to 24 years old. Youth un-employment reached a high of 47 percent in the last two quarters of 2013. The unemployment rate of both adults (25–64) and the elderly (65+) increased by over 6 points. From the second quarter to the fourth quarter of 2013, the adult unemployment rate declined from 20 percent to 17 percent. Despite an increase in late 2012, unemployment of the el-derly has been comparatively moderate at around 15 percent.
19 This section focuses on broad unemployment which is the of-ficial measure of unemployment in St. Lucia.
The unemployment rate of females has been slightly higher than that of males, but the rate for both groups increased more than 8 points. The crisis particularly led to increased female unemploy-ment in late 2008, but rates for men and women have been similar since. By 2013, the unemployment gap between males and females narrowed from 5 down to 4.3 points as male unemployment rose.
Unemployment increased most for workers with secondary education and less for workers with either more or fewer years of schooling. Throughout most of the period since the crisis, un-employment has been highest for those with lower secondary education, though unemployment has fluctuated quite rapidly quarter by quarter, espe-cially for this education cohort. Notably, since 2008 the unemployment rate of those with an upper sec-ondary education grew steadily, even surpassing the rate for those with only lower secondary school-ing in the third quarter of 2013. The unemployment rate for those without schooling, while very volatile, has actually dropped over time, whereas the unem-ployment rate for those with tertiary studies (who claim the lowest level of unemployment of all edu-cation cohorts) has mostly remained below 10 per-cent since 2008.
Source: Saint Lucia Population & Housing Census 2010.
Figure 6. Resident and Emigrant PopulationBy Highest Level of Formal Education
% of Census 2010 Population % of Census Emigrants (2001 - 2010)
0%
5%
10%
15%
20%
25%
30%
35%
40%
Tertiary and higherSecondaryPrimary
21
Workforce adaptations
The impact of the crisis on St. Lucians varied with gender and education. Between 2008 and 2013, female employment shifted from profession-al services to the education, health, and social ser-vices sector, particularly after 2010. The distribution of male employment moved from the construc-tion sector to the service and trade sector (Figure 8, top panel). These trends are consistent with the implementation of government programs, such as SMILES, which targets single women, and NICE, for which 70 percent of beneficiaries are women (NICE Administrative Data).
In the post-crisis period, there also was a shift of more-educated workers from professional services to the education, health and social services sector. At the same time, less-educated workers were em-ployed relatively less in the agriculture and construc-tion sectors and more in the services and trade sec-tor. The sectoral participation of those with mid-level education (that is, more than primary but no more than secondary) remained relatively unchaged. (Fig-ure 8, bottom panel.)
Men faced greater volatility in the total number of hours worked; women and the elderly were more likely to be underemployed. The median number of hours worked by males and females was 40; the
average number of hours worked followed a similar temporal trend (Table A2.5). Qualitative results from a mixed, qualitative-quantitative study (United Nations Development Programme, 2010) reveal that St. Lu-cian respondents agreed with the premise that men in St. Lucia are generally less willing than women to take just “any” job. Participants in the study claimed that a “man would rather starve than do menial la-bor.” In fact, men on average worked longer hours than women, and there was greater volatility in the number of hours men worked. St. Lucians age 65 and over tend to work fewer hours weekly than young-er cohorts. However, in early 2013 the average hours worked for those between 15 and 24 years of age was not much higher than the average for those of retire-ment age.
Between 2008 and 2013 underemployment in-creased significantly in the agricultural sector (Figure 9).20 In all other sectors, a substantial jump in the proportion of underemployed workers in the immediate aftermath of the crisis was followed by a gradual drop in the underemployed workforce seg-ment. Because the agricultural sector traditionally employs older farmers and laborers, this trend aligns with the increased underemployment of the elderly in general.
20 The agricultural sector traditionally employs older farmers and labourer.
Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013.
Figure 7. Unemployment Rate (broad), by Gender and Age
0.0
0.1
0.2
0.3
0.4
0.5
2013q42013q32013q22013q12012q42012q32012q22012q12011q42011q32009q42009q32009q22009q12008q42008q32008q22008q1
Female Male St. Youth (15-24) Retirement age (65+)Adults (25-64)
22
2122
21 The job gap measures the number of unemployed persons to each vacancy and is computed by dividing the total number of unem-ployed by the number of vacancies. This measures the relative difficulty of finding a job at the sector, occupation, administrative district or industry level.
22 Similarly, data from the Enterprise Survey (World Bank 2010) reveals that large firms (more than 100 employees) in St. Lucia perceive that an inadequately educated workforce is a major obstacle to business development.
Box 2: What Are Employers Looking For?
Data from the Labor Market Needs Assessment Survey of 2012 shows that the national average job gap in St. Lucia was 15.21 However, the job gap varied significantly by region. Rural areas and Vieux-Fort presented much higher job gaps, 28 and 24 respectively, relative to Gros-Islet islet which had a job gap of 5.
Employers are looking for workers with higher education levels than the unemployed have. Between September and Novem-ber of 2012, 44 percent of employers reported wanting workers with tertiary education to fill job openings; 31 percent required at least secondary education certificates; and only one of every four employers claimed to need employees with less than secondary education. On the supply side, 60 percent of job seekers and the unemployed reported having less than secondary education; 33 percent had attained secondary education; and only 7 percent had tertiary education. 22
To compete in the job market, job seekers not only need higher education — they also must develop “soft” skills that em-ployers value. The same survey asked employers to evaluate the importance of various skills to their business as well as the skills of current employees. Employers deemed soft skills, such a strong work ethic, customer service, interpersonal skills, and adaptability, as the four most important skills. The evaluation of skills of current employees by employers mirrored the importance which employers placed on these skills.
Source: St Lucia Labor Market Needs Assessment Survey 2012; Central Statistics Office.
Qualification requirements for job openings, Sep - Nov 2012
Degree of importance of various skills to firms in St. Lucia
Qualification of job seekers/unemployed, Sep - Nov 2012
Below secondary level
44% 33%
31% 60%
25%7%
Secondary Tertiaty education
Project ManagementComputer literacy
General businessDecision making
Emotional intelligenceProblem solving
Ability to plan Profound technical knowledge
Numeracy and quantitative Literacy
Adaptability Interpersonal
CommunicationCustomer service Strong work ethic
Not Important Somewhat Important Important Very Important
0 20 40 60 80 100
23
Source: Labor Force Survey 2008, 2009, 2011, 2012 and 2013.
Figure 8. Sectoral Shifts in Employment by Gender and Education
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Other/not de�ned
Accommodation & food service
Services and trade
Transportation/communications
Professional services
Education, health,
social services
Mining/Energy
ConstructionManufacturingAgriculture
0.00
0.05
0.10
0.15
0.20
0.25
2013 2012 2011 2009 2008
Other/not de�ned
Accommodation & food service
Services and trade
Transportation/communications
Professional services
Education, health,
social services
Mining/Energy
ConstructionManufacturingAgriculture
0.0
0.2
0.4
0.6
0.8
1.0
Other/not de�nedAccommodation & food serviceServices and tradeTransportation/communications
Professional servicesEducation, health, social servicesMining/EnergyConstructionManufacturingAgriculture
TertiaryPost secondary
Secondary or less
Primary or less
No formal school
0.0
0.2
0.4
0.6
0.8
1.0
TertiaryPost secondary
Secondary or less
Primary or less
No formal school
2008 2013
24
Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013.
Figure 9. Underemployment for Selected Sectors
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
20132012201120092008
Agriculture
Unde
rem
ploym
ent
Manufacturing Construction Mining/Energy Education, health, social servicesProfessional services Transportation/communications Services and trade Accommodation & food service
Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013.
Figure 10. Unemployment Rate (broad), by Level of Household Wealth
Distribution of employed and unemployed by Quintiles of Wealth Index
0.05
0.10
0.15
0.20
0.25
0.30
0.35
2013q42013q32013q22013q12012q42012q32012q22012q12011q42011q32009q42009q32009q22009q12008q42008q32008q22008q1
22%
18%
21%
22%
17%
Unemployed − 2008
26%
25%22%
14%
12%
Unemployed − 2013
22%
18%
20%
20%
20%
Employed − 2008
17%
18%
20%
21%
23%
Employed − 2013
1st quintile 2nd quintile 3rd quintile 4th quintile 5th quintile
1st quintile 2nd quintile 3rd quintile 4th quintile 5th quintile
25
Households with more wealth fared better than those with less wealth; wealth disparity increased after the crisis. A study by the United Nations Devel-opment Programme (2010) found that the diverse im-pacts of the crisis in St. Lucia included: “Sale of house-hold assets to make ends meet.”23 Data presented here show that higher unemployment was associat-ed with diminished household wealth. Figure 10 (top panel) shows the trend in unemployment by level of asset-based household wealth — the 5th quintile is the richest and the 1st quintile the poorest.24 Through late 2009, the unemployment rate did not differ sub-stantially by levels of asset-based household wealth. However, after 2010 unemployment for the two poor-est quintiles (1st and 2nd) rose to nearly 30 percent by mid-2013 while unemployment in the two richest quintiles remained around 15 percent. The bottom panel of Figure 10 compares the distribution of as-set-based household wealth of the unemployed and the employed in 2008 and 2013. In 2008, 40 percent of the unemployed belonged to the first and second wealth quintiles — by 2013 the proportion increased to 51 percent. In contrast, the distribution of the em-ployed improved somewhat in 2013 relative to 2008. By 2013 the proportion of the employed in the two lowest household wealth quintiles decreased to 35 percent and the proportion in the two highest quin-tiles increased to 43 percent. The changes in the dis-tribution of welfare by employment status and the in-creasingly positive relationship between the welfare index and both education and reported income (see Figure T.A.1b) suggest that the sale or non-replace-ment of assets, reflective of a tightening credit envi-ronment, were used as a coping mechanism to en-dure the negative impact of becoming unemployed.
23 Study data were derived primarily from interviews and focus group discussions. Secondary information came from published and unpublished reports.
24 The assets index was constructed using principal component analysis and household assets. For details, see Technical Annex.
26
uneMPLoyMenT
Unemployment in St. Lucia increased after 2008; not only was there a temporal effect of the 2008 cri-sis on unemployment, but the increase in unemploy-ment was persistent from 2009-2012. The year-to-year increase of the unemployment rate grew after the cri-sis, from 3 points initially up to 9 points. The results are almost identical when considering broad unemploy-ment rather than standard unemployment.
Female unemployment was stable through 2010, after which it grew steadily. This result is consis-tent with qualitative evidence (UNDP 2010) which suggest that, rather than falling into unemploy-ment, women are more likely than men to accept less desirable jobs whereas man are more likely to opt out of the labor market when better opportuni-ties are not available.
The increase in unemployment was most severe for young adults (15–24). By 2013, the unemploy-ment rate of young workers grew by 15 points. The increase in unemployment for older age groups was smaller and slower to develop.
The increase in unemployment was greater for workers with only secondary education than for
workers with either more or less schooling. Work-ers with no formal education were relatively unaffect-ed by the crisis. But even when they are employed, unschooled workers have significantly lower wages and higher rates of underemployment.
WAges And WAge CoRReLATes
Real wages stagnated. The coefficient estimates cor-responding to the survey years are mostly insignifi-cant; there is no sign of wage growth.25
Between 2008 and 2013, education is the most significant factor in predicting inequality in in-come; higher education is associated with sig-nificantly higher levels of income. Table A2.7 pres-ents the results from pseudo-wage regressions.26 The results show that, on average, males make 317 XCD per month more than females, and heads of household make just under 80 XCD more per month
25 The average inflation rate in St. Lucia for the 2008 to 2013 pe-riod was 2.1 percent. Because the pseudo-wage regressions have nominal income brackets (or intervals) as dependent variables, if wages remained constant the coefficients corresponding to the year indicators should have a positive gradient and should be sta-tistically significant.
26 puted using OLS and gender, age, education, asset based household wealth, and year and district indicators as predictors. The OLS estimation predicts around 30% of the variation in the in-come variable. The results under both cases are very similar.
CHAPTeR 4THe iMPACT oF THe 2008 gLobAL FinAnCiAL CRisis on uneMPLoyMenT, WAges, And WeLFARe
27
than non-heads of household, both of which we would expect. The relationship between education and income is approximately linear and positive: compared to those with no schooling, those with primary, secondary, post-secondary, and tertia-ry schooling make 145, 293, 1,275, and 2,262 XCD more per month from employment, suggesting that returns to education in St. Lucia remain high. The latter estimate implies that, on average, employees with tertiary education earned over XCD 27,000 per year more than workers with no formal education. A similar (though much smaller in magnitude) pattern can be observed when considering wealthier house-holds. Analyzing the results by age groups (where those in retirement age are the reference group) reveals that youth (15-24) make substantially less (nearly 230 XCD), while adults make between 154 and 244 XCD more than those in retirement age. In terms of district, income from employment is much lower in Castries than in most districts, when hold-ing all else constant.
WeLFARe
Disparities in household wealth increased af-ter 2008. Disparities between the bottom forty percent and the top sixty percent of households on the asset-based wealth index grew significant-ly between 2008 and 2013. 27 The poorest workers are more likely to be self-employed and less likely to work in the professional service sector. Still, in 2008 overall employment of workers in the least wealthy forty percent of households was on a par with other cohorts (Table 2). By 2013, however, the poorest forty percent were significantly less edu-cated; more likely to be unemployed (by 11 points); and less likely to be an employee or an employer. The least wealthy also were more likely to reside in urban areas; in smaller households; and in house-holds headed by a female. By 2013 the poorest 40 percent were twice as likely to work in the agricul-
27 This index is constructed by year using the principal com-ponents analysis (PCA) method. Households are classified as be-longing to the bottom forty percent (top sixty percent) when their household level asset-based wealth score derived from this asset index falls in the bottom two (top three) quintiles of the distribution of this index. For more details, please refer to the Technical Annex.
tural sector; more likely to work in the construction and manufacturing sector; and significantly less likely to work in the education, health, social and professional services sectors.
28
Table 2: Characterizing The Bottom Forty Percent Relative to the Top Sixty percent in 2008 and 2013 2008 2013
Top sixty Bottom forty P-value of diff. Top sixty Bottom forty
P-value of diff.
Employed 84.96% 85.11% 0.88 80.80% 69.82% 0.00
Unemployed 15.08% 14.89% 0.85 19.20% 30.18% 0.00
Employee 26.89% 25.92% 0.31 30.87% 25.11% 0.01
Employer 6.83% 6.87% 0.93 2.92% 1.35% 0.01
Self-employed 1.31% 1.86% 0.04 7.25% 7.37% 0.92
Unpaid worker 1.56% 1.81% 0.35 0.95% 1.33% 0.52
Multiple jobs 1.21% 1.66% 0.22 2.51% 2.42% 0.90
Education level 3.39 3.37 0.65 3.80 3.17 0.00
Urban residence 58.75% 61.58% 0.17 52.82% 62.56% 0.00
Female headed household 14.95% 14.16% 0.21 13.90% 17.49% 0.00
Agriculture 11.06% 12.67% 0.14 7.22% 14.53% 0.00
Manufacturing 5.20% 4.73% 0.50 4.35% 6.07% 0.04
Construction 12.13% 12.85% 0.48 6.05% 10.70% 0.00
Mining/Energy 0.72% 0.91% 0.49 1.77% 1.33% 0.37
Education, health, social services 5.03% 5.41% 0.58 11.02% 6.88% 0.00
Professional services 21.47% 18.62% 0.02 22.22% 12.81% 0.00
Transportation/communications 6.10% 6.95% 0.29 7.48% 6.62% 0.39
Services and trade 15.60% 14.95% 0.56 18.09% 17.41% 0.64
Accommodation and food service 12.92% 13.20% 0.79 12.84% 12.85% 1.00
Other/not defined 9.77% 9.73% 0.97 8.95% 10.80% 0.14
Other/not defined 9.77% 9.73% 0.97 8.95% 10.80% 0.14Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013.
29
The recent financial crisis had significant and long-lasting microeconomic impacts on St. Lucia. Unemployed and underemployed St. Lucians joint-ly accounted for over 40 percent of the working-age employable population by the third quarter of 2012. Both suffered significant welfare reductions in the af-termath of the crisis. They lost not only their income but also the collateral benefits that are often associat-ed with being fully employed in good quality jobs and under formal contracts.
Workers with only secondary education were hardest hit by increased unemployment. Workers with less than primary education remained employed but received significantly lower wages. Workers with more than secondary education were relatively less affected — they experienced both lower levels of un-employment and higher wages. The negative impact of the crisis was particularly severe among St. Lucia’s youth (those between 15 and 24 years of age) — by the end of 2013, nearly half of young adults were un-employed.
Employment shifted away from the construction and professional services sector to basic services and trade, education, health, and social services. About 13 percent of the working population re-mained employed in the tourism industry through
the post-crisis period. Sectoral shifts in employment varied by gender. Female employment shifted from professional services to the education, health, and social services sector, particularly between 2011 and 2013. The distribution of male employment moved from the construction sector to the service and trade sector. These labour market trends are generally in line with the observed macroeconomic trends (FDI, construction, tourism and both growth and sectoral composition of GDP). They are also consistent with the policies enacted by the government in response to the crisis, which focused on maintaining employ-ment in the tourism sector, improving the education and skills of the labor force, and aiding the most vul-nerable populations (youth and single mothers). 28
Social disparities in St. Lucia increased after the 2008 global financial crisis. The poorest forty per-cent of households were nearly indistinguishable from the top sixty percent in 2008. By 2013 individuals
28 To support the tourist industry, the government subsidized airlines and lowered initial VAT rates for hotels. Since 2011, gov-ernment programs to support the labor market included: Holistic Opportunities for Personal Empowerment (HOPE), the National Skill Development Center (NSDC), the National Initiative to Create Employment (NICE), Single Mothers In Life Enhancement Skills (SMILES), the Short Term Employment Program (STEP), and the Constituency Development Program. These initiatives aimed to im-prove job opportunities and promote vocational training and skill development.
CHAPTeR 5ConCLusions
30
in the bottom forty percent were significantly less ed-ucated and more likely to be unemployed, residing in urban areas, and in a female-headed household. Sim-ilarly, by the end of 2013, individuals in the bottom forty percent were twice as likely to work in the agri-cultural sector, more likely to work in the construction and manufacturing sector, and significantly less likely to work in the education, health, social and profes-sional services sectors.
Debt and macroeconomic conditions limit the gov-ernment’s ability to invest in social protection or in human capital, and generally limit its capacity to respond effectively to economic distress. There is some evidence that government programs in re-sponse to the crisis have been successful at improving the job opportunities of the groups that they target. Nevertheless, unemployment, poverty, and social dis-parities in St. Lucia increased after the 2008 crisis. The difficult reality is that St. Lucia, like most of the OECS member states, faces a unique combination of micro- and macro-economic challenges that compromise the ability of the government to reach the poor and vulnerable. Among these challenges are high levels of national debt, insufficient economic diversification, and a lack of monitoring and evaluation systems in place to measure the impact and effectiveness of so-cial programs and public expenditure.29
29 Kouame and Reyes (2010) also report that because of the lack of fiscal space and the weaknesses of safety net programs in the re-gion, income shocks are unlikely to be cushioned by public transfers.
31
St. Catherine, Edwin. 2013. Analysis of the Saint Lucia Labor Market Needs Assessment Survey 2012. St. Lucia: Central Statistical Office.
Enterprise Surveys. 2010. Washington: The World Bank.
Government of Saint Lucia and the Caribbean Devel-opment Bank. 2008. Saint Lucia Country Poverty As-sessment Report. Prepared by Kairi Consultants, Ltd.
International Monetary Fund. 2010. IMF Country Re-port No. 10/14. Grenada: 2009 Article IV Consultation, Fourth Review Under the Three-Year Arrangement Under the Poverty Reduction and Growth Facility, Request for Modification of Performance Criterion, and Financing Assurances Review—Staff Report; Public Information Notice; and Press Release. Accessed August 15, 2014, at http://www.imf.org/external/pubs/ft/scr/2010/cr1014.pdf..
Kouame, Auguste and Maria Ivanova Reyes. 2011. “The Caribbean Region beyond the 2008-09 global fi-nancial crisis.” In documento presentado en la conferen-cia “Options for the Caribbean after the Global Financial Crisis”, Bridgetown, pp. 27-28.
Lokshin, Michael, Sergiy Radyakin, et al. 2014. AD-ePT Labor Module User Guide. The World Bank Group, Poverty Reduction and Equity Unit (PRMPR), Compu-tational Tools Team (DECCT), January 7. Accessed Au-gust 15, 2014 at: http://siteresources.worldbank.org/EXTADEPT/Resources/ADePT_Labor_Handout.pdf.
The Central Statistics Office of St. Lucia. 2011. Annual Statistical Digest.
United Nations Development Programme (2010). Social Implications of the Global Economic Crisis in Ca-ribbean Small Island Developing States: 2008-2009. St. Lucia Country Report, Feb 27.
World Development Indicators. Washington: The World Bank.
References
32
Annex 1TAbLes
Table A2.1: Employment Categories, Shares in Total Employment 2008 2009 2011 2012 2013 Change
Total
Salaried - private 55.4 57.9 57.6 58.1 54.5 -0.9
(0.93) (0.99) (1.54) (1.13) (2. 16) (2.36)
Salaried - public 18.8 18.8 19.4 19.0 19.9 1.1
(0. 73) (0.75) (1.15) (0.84) (1.76) (1.90)
Self-employed or member of productive cooperative 21.3 20.9 22.3 22.5 22.7 1.4
(0.76) (0.82) (1.37) (0.98) (1.74) (1.90)
Unpaid family worker or apprentice 4.6 2.4 0.7 0.4 3.0 -1.6
(0.38) (0.32) (0.25) (0.17) (0.89) (0.96)
Non-agriculture
Salaried - private 59.0 61.6 60.8 61.1 57.9 -1.1
(0.97) 0.99 (1.55) (1.17) (2.28) (2.48)
Salaried - public 21.1 20.6 21.1 20.5 21.8 0.6
(0.81) (0.83) (1.23) (0.90) (1.89) (2.06)
Self-employed or member of productive cooperative 16.3 16.1 17.6 18.0 17.3 1.0
(0. 70) (0.78) (1.26) (0.91) (1.64) (1.79)
Unpaid family worker or apprentice 3.6 1.7 0.5 0.3 3.0 -0.5
(0.35) (0.29) (0.21) (0.18) (0. 96) (1.02)
Agriculture
Salaried - private 27.1 23.0 23.1 27.6 19.3 -7.8
(2.51) (2.56) (4.20) (3.20) (4.90) (5.51)
Salaried - public 0.5 0.8 0.7 3.8 0.0 -0.5
(0.34) (0.56) (0.68) (2.21) 0.00 (0.34)
Self-employed or member of productive cooperative 60.1 67.2 73.1 67.5 78.2 18.1
(2.76) (2.76) (4. 36) (3.95) (5.03) (5.74)
Unpaid family worker or apprentice 12.3 9.0 3.1 1.0 2.5 -9.8
(1.83) (1.86) (2.09) (0.61) (1.78) (2.55)Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013. Note: Calculated using ADePT version 5.5. Changes shown between years 2008 and 2013.
33
Table A2.2: Distribution of the Employed by Economic SectorShare of total employment
2008 2009 2011 2012 2013 Change
Sector (industry) of primary employment
Agriculture 10.9 9.4 8.1 8.5 8.7 -2.2
(0.53) (0.56) (0.86) (0.69) (0.73) (0. 90)
Manufacturing 5.1 5.0 6.4 5.4 5.0 -0.1
(0.37) (0.36) (0.71) (0.48) (0.41) (0.56)
Construction 12.5 11.6 8.2 8.3 8.0 -4.5
(0.54) (0.57) (0.72) (0.56) (0.55) (0.77)
Mining/Energy 0.8 1.1 1.4 1.5 1.6 0.8
(0.14) (0.18) (0.30) (0.24) (0.24) (0.28)
Education, health, social services 5.2 4.2 8.7 9.6 9.9 4.7
(0.34) (0.36) (0.78) (0.57) (0.63) (0.72)
Professional services 20.9 19.0 18.9 16.7 19.4 -1.6
(0.68) (0.72) (1.24) (0.76) (0.88) (1.11)
Transportation/communications 6.6 7.3 7.5 6.6 7.1 0.5
(0.39) (0.46) (0. 75) (0.52) (0.49) (0.63)
Services and trade 15.2 18.8 16.3 16.4 18.1 2.9
(0.60) (0.63) (1.01) (0.77) (0.76) (0.97)
Accommodation and food service 13.4 13.1 13.5 13.4 13.3 -0.1
(0.53) (0.60) (0.91) (0.71) (0.70) (0.88)
Other/not defined 9.4 10.4 10.9 13.6 8.9 -0.5
(0.50) (0.58) (1. 06) (0.84) (0.57) (0.76)Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013. Note: Calculated using ADePT version 5.5. Changes shown between years 2008 and 2013.
34
Table A2.3: Distribution of the Employed along Selected Characteristics - Level of Education Share of total employment
Level of education 2008 2009 2011 2012 2013 Change
Total
None 2.0 1.9 3.9 2.7 1.7 -0.3
(0.24) (0.23) (0.65) (0.42) (0.32) (0.40)
Pre-primary (Infant) or Primary 3.8 4.4 6.3 5.3 6.0 2.2
(0.32) (0.38) (0.67) (0.47) (0.50) (0.60)
Lower / Junior Secondary (Forms 1-3) / Senior Primary 47.5 45.3 36.5 40.3 37.6 -9.9
(0.90) (0.92) (1.61) (1.15) (1.11) (1.43)
Upper Secondary (Forms 4 & 5) 31.3 31.8 33.9 32.6 33.5 2.3
(0.82) (0.88) (1.38) (1.02) (1.05) (1.33)
Post-secondary, non-tertiary (diploma or associate degree) 10.1 10.8 7.1 7.5 7.5 -2.6
(0.50) (0.58) (0.83) (0.57) (0.64) (0.76)
Tertiary (University) 5.3 5.8 12.3 11.7 13.7 8.4
(0.37) (0.50) (1.15) (0.87) (0.87) (0.95)
Non-agriculture
None 1.6 1.0 3.3 2.3 1.6 0.0
(0.23) (0.18) (0.61) (0.39) (0. 34) (0.41)
Pre-primary (Infant) or Primary 2.8 3.5 6.3 5.2 6.1 3.3
(0.30) (0.36) (0.72) (0.47) (0.53) (0.61)
Lower / Junior Secondary (Forms 1-3) / Senior Primary 44.5 43.1 33.3 37.2 33.4 -11.1
(0.95) (0.93) (1.62) (1.19) (1.10) (1.45)
Upper Secondary (Forms 4 & 5) 33.8 34.2 36.1 34.7 35.7 1.9
(0.87) (0.92) (1.47) (1.09) (1.11) (1.41)
Post-secondary, non-tertiary (diploma or associate degree) 11.3 11.8 7.7 8.1 8.1 -3.1
(0.56) (0.64) (0.89) (0.69) (0.62) (0.83)
Tertiary (University) 6.0 6.4 13.3 12.5 15.0 9.0
(0.42) (0.55) (1.25) (0.93) (0. 94) (1. 03)
Agriculture
None 5.2 10.4 10.3 6.6 2.6 -2.7
(1.15) (1.70) (2.98) (1.42) (0.93 (1.48)
Pre-primary (Infant) or Primary 12.1 13.5 6.2 6.0 5.0 -7.1
(1.69 (2.05) (2.59) (1.58) (1.47) (2.24)
Lower / Junior Secondary (Forms 1-3) / Senior Primary 71.7 66.8 72.5 72.5 80.6 8.9
(2.22) (2.74) (4.52) (3.58) (2. 80) (3.58)
Upper Secondary (Forms 4 & 5) 10.3 8.6 9.7 10.8 10.1 -0.2
(1.54) (1.55) (3.17) (2.23) (2.09) (2.59)
Post-secondary, non-tertiary (diploma or associate degree) 0.7 0.7 0.7 0.8 0.7 0.0
(0.40) (0.53) (0.68) (0.56) (0.46) (0.61)
Tertiary (University) 0.0 0.0 0.7 3.2 1.0 1.0
(0.00) (0.00) (0.67) (1.07) (0.61) (0.61)Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013. Note: Calculated using ADePT version 5.5. Changes shown between years 2008 and 2013. SE=Standard error.
35
Tabl
e A2
.4: U
nem
ploy
men
t Rat
es A
mon
g Se
lect
ed G
roup
s Un
empl
oym
ent R
ate b
y Gro
ups
Grou
p Sh
are A
mon
g Un
empl
oyed
20
0820
0920
1120
1220
13Ch
ange
2008
2009
2011
2012
2013
Chan
geTo
tal
15.2
18.5
21.2
21.4
23.3
8.110
0.010
0.010
0.010
0.010
0.00.0
(0.53
)(0
.70)
(1.02
)(0
.86)
(0.79
)(0
.95)
(0.00
)(0
.00)
(0.00
)(0
.00)
(0.00
)(0
.00)
Gend
erM
ale12
.917
.319
.819
.521
.78.8
45.5
48.3
46.6
46.5
47.5
1.9(0
.67)
(0.86
)(1
.36)
(1.05
)(1
.01)
(1.21
)(1
.87)
(1.77
)(2
.63)
(1.90
)(1
.74)
(2.56
)Fe
male
17.9
19.9
23.7
24.1
26.0
8.154
.551
.753
.453
.552
.5-1
.9(0
.84)
(1.00
)(1
.50)
(1.21
)(1
.13)
(1.40
)(1
.87)
(1.77
)(2
.63)
(1.90
)(1
.74)
(2.56
)Ag
e gro
up15
-24
29.6
36.0
43.5
40.6
46.5
16.9
40.9
40.3
40.1
37.4
37.4
-3.5
(1.50
)(1
.69)
(2.57
)(2
. 03)
(1.92
)(2
.44)
(1.85
)(1
.62)
(2.49
)(1
.77)
(1.65
)(2
.48)
25-5
411
.414
.016
.317
.018
.47.0
59.1
59.7
59.9
62.6
62.6
3.5(0
.52)
(0.65
)(1
.01)
(0.83
)(0
.79)
(0.95
)(1
.85)
(1.62
)(2
.49)
(1.77
)(1
.65)
(2.48
)Ar
ea of
resid
ence
Urba
n15
.818
.921
.121
.023
.57.7
61.5
60.7
48.9
52.6
55.1
-6.4
(0.67
)(0
.90)
(1.30
)(1
.13)
(1.00
)(1
.21)
(2.58
)(2
.55)
(3.27
)(2
.54)
(2.17
)(3
.37)
Rura
l14
.418
.022
.322
.524
.19.8
38.5
39.3
51.1
47.4
44.9
6.4(0
.84)
(1.10
)(1
.57)
(1.31
)(1
.27)
(1.53
)(5
.58)
(2.55
)(3
.27)
(2.54
)(2
.17)
(3.37
)Hi
ghes
t lev
el of
educ
atio
nal a
ttai
nmen
tNo
ne15
.115
.810
.613
.88.9
-6.2
2.01.6
1.71.5
0.5-1
.5(3
.99)
(4.43
)(3
.78)
(4.14
)(3
.77)
(5.49
)(0
.58)
(0.47
)(0
.65)
(0.58
)(0
.23)
(0.62
)Pr
e-pr
imar
y (In
fant
) or P
rimar
y28
.122
.626
.328
.131
.73.6
8.45.6
8.17.4
8.90.6
(3.04
)(3
.10)
(3.66
)(3
. 20)
(2.89
)(4
.19)
(1.10
)(0
.86)
(1.43
)(0
.99)
(0.98
)(1
.47)
Lowe
r / Ju
nior
Seco
ndar
y (Fo
rms 1
-3) /
Seni
or Pr
imar
y14
.919
.723
.521
.522
.87.9
46.5
48.6
40.3
39.6
35.7
-10.7
(0.76
)(0
.98)
(1.69
)(1
.28)
(1.15
)(1
.38)
(1.83
)(1
.72)
(2.68
)(2
.07)
(1.64
)(2
.46)
Uppe
r Sec
onda
ry (F
orm
s 4 &
5)17
.521
.225
.126
.529
.812
.337
.137
.340
.842
.145
.78.7
(1.06
)(1
.24)
(1.82
)(1
.40)
(1.35
)(1
.72)
(1.89
)(1
.73)
(2.75
)(1
.92)
(1. 7
1)(2
.55)
Post-
seco
ndar
y, no
n-te
rtiar
y (di
plom
a or a
ssocia
te de
gree
)7.4
11.3
15.9
18.3
17.1
9.74.5
6.04.9
6.05.0
0.4(1
.27)
(1.66
)(3
.84)
(2.86
)(2
.62)
(2.91
)(0
.81)
(0.92
)(1
.33)
(0.99
)(0
.82)
(1.15
)Te
rtiar
y (Un
iversi
ty)
4.83.5
8.87.3
8.43.6
1.50.9
4.33.3
4.12.5
(1.37
)(1
.28)
(2.24
)(1
.38)
(1.43
)(1
.98)
(0.43
)(0
.34)
(1.10
)(0
.65)
(0.69
)(0
.81)
Sour
ce: L
abor
Force
Surv
ey 20
08, 2
009,
2011
, 201
2, an
d 201
3. No
te: Ca
lculat
ed us
ing AD
ePT v
ersio
n 5.5.
Chan
ges s
hown
betw
een y
ears
2008
and 2
013.
SE=
Stand
ard e
rror.
36
Table A2.5: Average and Median Hours Worked per week by respondent characteristicsGroup 2008 2009 2011 2012 2013
Avg. Med. Avg. Med. Avg. Med. Avg. Med. Avg. Med.
TOTAL 40.6 40 42.4 40 41.9 40 39.9 40 40.7 40
Gender
Female 39.8 40 40.8 40 39.4 40 38.4 40 39.6 40
Male 41.3 40 43.9 40 44.3 40 41.2 40 41.6 40
Age group
15-24 39.9 40 41.5 40 41.5 40 39.6 40 38.5 40
25-39 41.2 40 43.0 40 41.2 40 40.6 40 41.0 40
40-64 40.8 40 42.8 40 43.3 40 40.4 40 41.6 40
65 and over 36.6 40 37.4 39 36.7 40 32.4 40 35.4 40
Education level
No formal school 41.1 40 36.4 35 40.3 40 36 40 37.3 40
Primary or less 39.9 40 41.9 40 43.4 40 40.4 40 41.5 40
Secondary 40.5 40 42.5 40 42.1 40 39.7 40 40.2 40
Post-secondary 41.1 40 43.3 40 41.1 40 40.9 40 41.1 40
Tertiary 41.8 40 43 40 41.5 40 43.4 40 43.4 40
Income level (monthly labour)
Under 200 XCV 26.4 24 39.3 24 54.4 48 26.7 20 29.9 16
201-399 XCD 32.9 35 40.2 35 42.7 48 32.8 30 31.4 32
400-799 XCD 38.3 40 40.7 40 40.8 40 38.6 40 38.3 40
800-1199 XCD 40.3 40 41.6 40 43.9 40 41.6 40 41.8 40
1200-1999 XCD 42.3 40 42.7 40 43.2 40 44.1 40 42.1 40
2000-3999 XCD 44.4 40 46.2 40 42.5 40 44.2 40 42.3 40
4000-5999 XCD 42.1 40 46.9 40 43.4 40 46.6 40 47.0 40
over 6000 XCD 46.8 40 50.2 40 50.8 48 52.6 45 47.0 40
Assets index
1st quintile 40.6 40 43.4 40 41.2 40 36.2 40 39.0 40
2nd quintile 41.2 40 42.2 40 41.3 40 37.8 40 39.9 40
3rd quintile 40.6 40 41.7 40 43.1 40 38.2 40 41.0 40
4th quintile 40.0 40 43.0 40 43.3 40 43.7 40 41.5 40
5th quintile 40.9 40 42.2 40 41.0 40 42.4 40 41.6 40Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013.
37
Tabl
e A2
.6: U
nem
ploy
men
t (br
oad)
by
Grou
pVa
riabl
esAl
lFe
mal
eM
ale
Age 1
5-24
Age 2
5-39
Age 4
0-64
Age 6
5+No
form
al
educ
atio
nPr
imar
ySe
cond
ary
Post
-sec-
onda
ryTe
rtia
ry
2009
0.034
***
0.019
0.045
***
0.055
**0.0
25*
0.031
***
0.029
-0.03
2-0
.023
0.041
***
0.048
**-0
.015
[0.00
9][0
.013]
[0.01
1][0
.024]
[0.01
3][0
.011]
[0.03
6][0
.047]
[0.03
8][0
.010]
[0.02
2][0
.020]
2011
0.063
***
0.044
***
0.080
***
0.114
***
0.040
**0.0
62**
*0.0
65-0
.088
-0.04
10.0
71**
*0.0
77**
0.076
**
[0.01
1][0
.016]
[0.01
6][0
.030]
[0.02
0][0
.015]
[0.04
8][0
.071]
[0.05
4][0
.014]
[0.03
7][0
.032]
2012
0.072
***
0.068
***
0.077
***
0.110
***
0.070
***
0.054
***
0.091
**-0
.098*
-0.06
50.0
85**
*0.1
10**
*0.0
66**
[0.01
0][0
.015]
[0.01
3][0
.027]
[0.01
6][0
.012]
[0.03
9][0
.052]
[0.04
6][0
.012]
[0.03
0][0
.026]
2013
0.088
***
0.087
***
0.090
***
0.135
***
0.085
***
0.071
***
0.065
*-0
.105*
*-0
.018
0.101
***
0.104
***
0.082
***
[0.00
9][0
.014]
[0.01
1][0
.027]
[0.01
6][0
.011]
[0.03
7][0
.052]
[0.04
0][0
.011]
[0.02
6][0
.025]
Obse
rvat
ions
19,05
68,9
3410
,122
3,663
6,599
7,984
810
491
1,069
14,56
81,4
881,4
40
R-sq
uare
d0.0
960.1
080.0
880.0
570.0
510.0
380.0
440.0
590.0
930.0
890.1
050.0
90So
urce
: Lab
or Fo
rce Su
rvey
2008
, 200
9, 20
11, 2
012,
and 2
013.
Note:
Robu
st sta
ndar
d erro
rs in
brac
kets.
*** p
<0.0
1, **
p<0.0
5, * p
<0.1
.
38
Table A2.7: Pseudo-wage Regression, Predicted Monthly Labour Income in St. Lucia, 2008-2013 Variables Interval OLS
Missing cases Imputed cases
Wet season (low season) -9.212 -4.098
[26.171] [19.357]
Urban locality -91.087** -74.849**
[44.266] [32.013]
Household size (roster) -14.088** -10.551**
[6.216] [4.610]
Respondent is male 316.709*** 322.911***
[20.859] [15.402]
Respondent is head of household 78.548*** 69.367***
[21.704] [16.538]
Household owns dwelling -52.532** -36.330*
[26.598] [20.300]
15-24 -226.286*** -148.176***
[57.652] [38.491]
25-39 153.870*** 207.812***
[55.512] [37.080]
40-64 243.545*** 268.164***
[55.186] [37.019]
Primary or less 144.459** 107.652**
[69.590] [53.124]
Secondary 293.346*** 256.269***
[56.971] [44.567]
Post-secondary 1,274.858*** 1,236.494***
[74.707] [59.850]
Tertiary 2,262.044*** 2,282.125***
[100.378] [78.732]
2nd quintile 61.591** 96.803***
[28.970] [21.655]
3rd quintile 139.919*** 155.366***
[29.364] [21.736]
4th quintile 319.762*** 310.701***
[35.992] [27.095]
5th quintile 505.146*** 511.615***
[40.350] [29.634]
Anse la Raye/Canaries 33.905 23.920
[46.724] [31.869]
Soufrihre -222.412*** -208.160***
[52.119] [40.164]
39
Table A2.7: Pseudo-wage Regression, Predicted Monthly Labour Income in St. Lucia, 2008-2013 (cont.)Variables Interval OLS
Missing cases Imputed cases
Choiseul -12.196 19.394
[64.875] [50.786]
Labourie -161.208** -162.897***
[67.237] [48.406]
Vieux Fort -159.198** -159.086***
[69.848] [54.520]
Micoud 29.923 60.404
[53.982] [41.071]
Dennery -193.661*** -203.701***
[47.249] [35.585]
Gros Islet . .
[.] [.]
Year 2009 59.717* 66.705***
[32.842] [24.894]
Year 2011 10.611 22.192
[44.154] [33.144]
Year 2012 76.472* 60.754**
[39.211] [27.525]
Year 2013 46.188 43.784*
[32.549] [22.640]
Constant 699.583*** 593.171***
[85.295] [63.556]
Observations 11,423 15,290Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013. Note: Coefficients from interval regression (OLS) using the lower and upper bounds of the income ranges. Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1.
40
Table A2.8: Rates of Asset Based Poverty of the Working Age Population by Sector of Employment Household Head
2008 2009 2011 2012 2013 Change
Employed 40.6 36.8 33.8 28.7 34.8 -5.8
(1.25) (1.45) (2.03) (1.48) (1.40) (1.88)
Unemployed 43.4 42.9 60.5 47.9 53.3 10.0
(3.55) (3.75) (4.66) (3.79) (3.60) (5.05)
Agriculture 41.3 35.6 47.6 38.5 42.5 1.2
(3.07) (3.48) (6.64) (4.01) (4.77) (5.68)
Manufacturing 40.5 38.3 42.7 20.9 36.8 -3.7
(5.05) (5.37) (8.81) (4.72) (5.61) (7.55)
Construction 42.6 40.4 32.1 34.5 47.1 4.4
(3.23) (3.98) (5.50) (4.47) (4.42) (5.47)
Mining/Energy 42.9 46.7 32.5 31.2 17.2 -25.8
(11.52) (12.13) (13.65) (9.62) (6.35) (13.15)
Education, health, social services 42.4 27.9 27.9 28.3 32.9 -9.5
(5.03) (5.89) (6.12) (5.16) (4.58) (6.80)
Professional services 36.3 34.4 24.9 23.8 24.4 -11.9
(2.37) (3.37) (4.38) (3.17) (2.90) (3.74)
Transportation/communications 40.1 40.8 23.3 18.5 31.6 -8.5
(4.35) (5.21) (4.92) (3.58) (4.58) (6.32)
Services and trade 40.4 35.8 31.3 25.1 36.0 -4.4
(3.13) (3.16) (4.61) (2.79) (3.29) (4.54)
Accommodation and food service 43.0 34.6 33.8 31.9 33.0 -10.0
(3.24) (3.86) (5.69) (3.57) (3.80) (4.99)
Other/not defined 41.7 38.5 44.9 34.6 40.4 -1.4
(3.23) (4.03) (6.70) (4.09) (4.68) (5.69)Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013. Note: Estimates computed using ADePT version 5.5. Proportions are with respect to individuals of working age (15-64). Changes shown between years 2008 and 2013.27 Standard error in parenthesis.
41
Asset-based wealth index as a measure of welfare
The assets index is constructed using a short module in the LFS that captures household assets and charac-teristics of the household dwelling. This assets mod-ule was collected in all five survey years considered in the analysis (2008–2013). The index is constructed by year using the principal components analysis (PCA) method. PCA is typically used to construct wealth indices, and especially as a variable reduction tool in multivariate analysis. In other words, the index al-lows for summarizing multivariate information into a wealth score — a continuous measure of wealth which captures most of the variation in the individual variables considered.
The following 13 assets are considered in the con-structions of this index for St. Lucia: number of rooms in the dwelling; number of bedrooms in the dwelling; whether or not the household owns a television, re-frigerator, washer, landline phone, cellular phone, in-ternet access, computer, vehicle, VCR, or clothes iron. The predicted continuous assets index is divided into quintiles. The first (un-rotated) component of the PCA output is used to define the predicted wealth score.
Figure TA.1 displays the average assets index score by levels of labor income and education for the entire period considered. Overall, the relationship between the measure of asset-based household wealth, or wealth score, and both individual-level and education-level labor income appear positive and increasing.
However, the relationships between the assets index, education, and reported income brackets vary signifi-cantly across years. In particular, while in 2008 and 2009 the assets index varied little across socio-eco-nomic groups, in the 2011–2013 period the index had a positive correlation with both education and report-ed income brackets.
bottom forty percent
Households are classified as belonging to the “bot-tom forty percent” (“top sixty percent”) when their as-set-based household wealth score, derived from the asset index described above, falls in the bottom two (top three) quintiles of the distribution of this index. The table below presents the summary of bottom for-ty percent by employment status and sector of em-ployment.
Annex 2TeCHniCAL Annex
42
Pseudo-wage regression to predict levels of income — interval regression method
Since the measure of income available from the LFS is categorical (represents ranges, rather than values) a valid approach for establishing the factors associat-ed with levels of labor income is to estimate a pseu-do-wage regression using the interval regression method.30 To undertake this estimation, the categor-
30 It is important to note that a relatively high proportion of the working population (around 20 percent) does not report labour income data. In these cases, labour income is imputed using gen-der, age, education, asset-based household wealth, and year and district indicators as predictors. The pseudo-wage regressions are estimated both excluding individuals with missing labour income
ical income measure is first re-coded so that “6000 and over XCD” becomes “6000 to 7000 XCD.” Given the categorical nature of the income measure, changing the upper bound this way should not affect the re-sults. Then, the interval regression is estimated using Ordinary Least Squares (OLS) methods. As with a typ-ical wage regression, the coefficients resulting from this interval wage regression represent the monetary increase (or decrease) in income, given a one unit in-crease in each explanatory variable of interest (hold-ing all else constant).
information and including them with imputed income values. The results in both cases are very similar.
Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013.
Figure TA.1.a. Assets Based Wealth Index by Education and Income (2008-2013)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Terti
ary
Uppe
r Sec
onda
ry
Lowe
r sec
onda
ry
Prim
ary o
r les
s
No fo
rmal
schoo
l
Over
6000
XCD
4000
-599
9 XCD
2000
-399
9 XCD
1200
-199
9 XCD
800-
1199
XCD
400-
799 X
CD
201-
399 X
CD
Unde
r 200
XCD
No in
com
e
Asse
ts ind
ex
Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013.
Figure TA.1.b. Asset-Based Wealth Index by Education and Income (2008-2013)
Asset-Based Household Wealth and Education Asset-Based Household Wealth and Income
0
1
2
3
4
5
TertiaryPost secondaryUpper-secondaryLower-secondaryPrimary or lessNo formal school
4000-5999 XCD Over 6000 XCD2000-3999 XCD1200-1999 XCD800-1199 XCD400-799 XCDless than 399 XCD
20132012201120092008 0
1
2
3
4
5
20132012201120092008
43
definition of employment, unemployment, and inactivity
An individual is defined as employed if he/she sat-isfies one of the following three conditions:
• Hasworked for pay, profit, or family gain duringthe reference week (past 7 days) OR
• Has a job fromwhich he/shewas absent duringthe reference week because of vacation, materni-ty leave, sick leave, or a temporary lay-off (for the time being, we do not include “other reason” — these are included in inactive status)
• Nojoborabsencefromjobreported;whenaskedif looked for work, respond “have job”
An individual is classified as unemployed if he/she satisfied the following:
• Hasnotworkedforpay,profit,orfamilygain,norishe/she absent from work, in the past 7 days AND
• Hasbeenavailable (wanting) towork,within thepast 7 days
An individual can be classified as unemployed by the standard definition (actively seeking work) or the broad definition (wanting work, but not necessarily actively seeking).
An individual is classified as inactive (out of the labor force) if he/she satisfies neither of the two aforementioned situations. That is:
• Notworkingforprofitorpay,norabsentfromin-come earning activity in reference week
• Notwantingworkoractivelylookingforwork(thelatter depends on how we define unemployment, as standard or broad)
definitions considered by pre-defined employment characteristics
An individual is defined as employed if he/she sat-isfies the following:
• Reportsactivityforincomeinpast7daysOR
• In response tomultiple jobholdingquestion, re-port either “yes” or “no” to having more than one job
Annex 3dATA Annex
44
Individuals are classified as employed if they satisfy one of the two conditions above, but report that they are looking for work (wanted work in past 7 days). That is, when an inconsistency arises, the codifier favors employment to unemployment. This coding strategy is consistent with the International Labor Organiza-tion (ILO) Labor Force Framework.
An individual is defined as unemployed (standard) if he/she satisfies the following:
• Activelyseekingwork
However, as was mentioned before, employment al-ways takes precedence over unemployment. So if an individual reports both looking for work and another response consistent with having employment, this in-dividual is not considered unemployed.
An individual is defined as unemployed (broad) if he/she satisfies the following:
• ActivelyseekingworkOR
• Notseekingworkbecauseofthefollowing:
• Alreadyfoundworkandwillstartlater• Already made arrangements for self-employ-
ment activities• Awaitingrecalltoformerjob• Awaitingrepliesfromemployers• Believesnosuitableworkavailable• Believes no financial resources, land, equip-
ment, permits, etc. to start own business• Cannotmeetemployer’srequirements• Couldnotfindsuitablework• DoesnotknowhoworwheretoseekworkAND
when asked what would have prevented him/her from accepting an offer last week, responds “nothing”
Despite only using select variables, no conflicts arise in these definitions (that is, someone will not be de-fined as both employed and unemployed).
The World Bank
1818 H Street, NW,
Washington, DC 20433, USA.
www.worldbank.org
