94766 Lea Gimenez, Edwin St. Catherine, March, 2015 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 March, 2015 The Aftermath of the 2008 Global Financial Crisis in the Eastern Caribbean The Impact on the St. Lucia Labor Market 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. CHAPTER 1 Introduction Although the improvement of economic conditions impeded the full exploitation of its demographic div- following the 2008 global financial crisis was signif- idend. Since the crisis, the labor market has not been icant throughout the Latin America and Caribbean able to fully absorb the growing number of workers. region, progress was sluggish and very limited in the The result has been an increase in unemployment and OECS1 economies. Evidence from household survey underemployment. The negative impact of the crisis data from St. Lucia indicates that the recent financial on employment prospects was particularly severe crisis did significant, long-lasting harm to St. Lucians’ among the young (those between 15 and 24 years of wellbeing. The government of St. Lucia attempted to age) — by the end of 2013, nearly half of all young use fiscal policy to boost growth and enhance labor adults were unemployed. market opportunities in the island. Still, unemployed and underemployed St. Lucians together accounted The relationship of educational attainment to em- for over 40 percent of the working-age employable ployment was mixed. Among those who stayed in the population. They suffered a significant decline in wel- country, workers with secondary education experi- fare in the aftermath of the crisis. They lost not only enced the greatest increase in unemployment. Work- their income but also the collateral benefits that are ers with less than primary education remained em- often associated with being fully employed in good ployed, but received lower wages. On the other hand, quality jobs and under formal contracts. workers who left the country over the last decade, representing about 5 percent of the current popula- Key Findings tion, were significantly more educated than average. A growing labor force can be an asset for income gen- Unemployment and asset ownership data tracked be- eration and growth. From 2003 to 2013, St. Lucia’s la- tween 2008 and 2013 indicate that the financial crisis bor force grew by 18 percent. But the impacts of the affected the poorest 40 percent of households more financial crisis on St. Lucia between 2008 and 2013 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 1  Throughout the text, OECS economies refers to the six indepen- dent member states of the Organization of the Eastern Caribbean as a whole. Yet from 2011 to 2013 the unemployment States which includes Antigua and Barbuda, Grenada, Dominica, St. rate for the two poorest quintiles was nearly double Kitts and Nevis, St. Lucia, and St. Vincent and the Grenadines. 8 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. 9 CHAPTER 2 St. Lucia’s Macroeconomic Context in the Aftermath of the 2008 Crisis The OECS economies were hit harder than the was expected to contract for a third consecutive rest of the Latin American economies by the 2008 year in 2014.2 global financial crisis. Data from the World Devel- opment Indicators (WDI) show significant negative Since the crisis, St. Lucia’s major sources of growth impact on growth in per-capita GDP in all of the have suffered from declining competitiveness and OECS member countries (Figure 1). Antigua and weak external demand related to slow recovery in Barbuda, the largest economy prior to the pre-crisis advanced economies. At the onset of the crisis, the period, was the least resilient to the crisis. In 2009, contribution of construction, tourism, and agriculture per-capita real GDP declined for all of the OECS to real GDP growth declined significantly and have economies and fell the most in Antigua and Barbu- not recovered since (Figure 2).3 The subdued growth da (-13 percent), followed by St. Kitts and Nevis (-7.4 performance of the OECS during the global financial percent), and Grenada (-7 percent). Economic activi- crisis was worsened by weakening competitiveness ty remained sluggish in most of the OECS during the of the tourism sector. The Eastern Caribbean Currency post-crisis years, more so than in the rest of Latin Union’s (ECCU) share of tourism receipts from its tradi- America and the Caribbean (LAC). tional sources such as the United States, United King- dom, and Canada declined by 37 percent.. The region Progress in the aftermath of the crisis was gen- was not able to attract visitors from other potential erally sluggish and very limited in the OECS. markets with higher growth.4 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 2  The sources of real GDP figures referred in this paragraph are IMF’s Article IV and the World Economic Outlook (WEO) database. average of 1.9 percent during the pre-crisis period 3  Real GDP growth figures of the ECCB are different from those of (2000-2007) and 1.3 percent during the crisis (2008- the IMF’s Article IV and WEO. 2010) The OECS countries’ economy grew by 3.5 per- 4  This is not surprising, since travel and tourism related activities cent on average prior to the crisis and declined 1.2 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 percent on average during the period from 2008 to OECS (Country Reports, World Travel and Tourism Council, 2012). 2010. Preliminary estimates from the International In Antigua and Barbuda, the OECS economy that experienced the most negative impact on GDP, travel and tourism represents more Monetary Fund (IMF) indicate that St. Lucia’s GDP than 70 percent of GDP and employment. 10 Figure 1. Real Per-capita GDP Change (annual %) in OECS Countries and the LAC Average 15 12 9 6 3 0 -3 -6 -9 -12 -15 2005 2006 2007 2008 2009 2010 2011 2012 2013 LAC Average(all income levels) St. Vincent and the Grenadines St. Lucia St. Kitts and Nevis Grenada Dominica Antigua and Barbuda Source: World Development Indicators. Figure 2. St. Lucia: Sector Contribution to Real GDP Growth, 2001-2013 10 8 6 4 2 0 -2 -4 -6 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Agriculture Construction Banking Real Estate, Renting and Business Administration Real GDP growth Manufacturing Tourism Public Administration Other Source: World Bank sta calculations based on data from the Eastern Caribbean Central Bank (ECCB). Foreign direct investment (FDI), particularly in tour- investment makes it hard to upgrade the country’s ism-related construction, has historically played an tourism assets. important role in St. Lucia. FDI contracted sharply as a result of the crisis. FDI steadily increased from The decline of tourism and of construction activi- 7  percent of GDP in 2000 to 24 percent in 2007; it ties severely affected the banking sector in St. Lu- was 12.5 percent of GDP on average. From 2008 on- cia and led to a credit crunch. Traditionally, the bank- wards, however, the flow of FDI declined to an av- ing sector in St. Lucia faces high delinquency rates erage of 9.2 percent of GDP during 2008–2013. FDI and has a large exposure to tourism related real es- remains much below the pre-crisis level, although tate. Non Performing Loans (NPLs) in the banking sys- it increased slightly in 2013 from the previous year, tem have been rapidly rising since 2008. On average, to 6.3 percent of GDP. With FDI declining since the NPLs rose from 15.5 percent in 2012 to approximately crisis, construction activity — in particular, private 22 percent in November 2013. Banks have increased sector construction activity — has slowed. In the loan loss provisions, and have become very selective post-crisis period, it has been difficult for St. Lucia to about lending. High numbers of delinquencies and finance tourism-related investment projects. Lack of weak domestic conditions have prevented recovery 11 Figure 3. International Tourism in St. Lucia and the OECS Number of arrivals (stay-over visitors) Arrival decomposition and visitor expenditure 350 1200 50 300 1000 800 40 250 Millares Millares 600 200 400 30 150 200 100 0 20 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2000 2002 2004 2006 2008 2010 2012 St. Lucia Rest of OECS 1/ Yacht Passengers Cruise Ship Passengers Stay-Over Visitors Total Visitor Expenditure (in percent of GDP, right axis) Source: World Bank; WDI and Bank Sta calculations. Source: National Authorities ad Bank Sta Calculations. Note: OECS includes Antigua and Barbuda, Dominica, Grenada, St. Kitts and Nevis, and St. Vincent and the Grenadines. of liquidity. In addition, with high lending rates and fiscal consolidation effort, the overall deficit was re- stricter lending requirements by banks, credit condi- duced by over 36 percent in 2013/14 — down to 5.7 tions have not improved. Private credit remained flat percent of GDP from 9.2 percent the previous year.7 over the past three years — further hindering recov- The reduction was driven mainly by cuts in capital ery of major domestic industiries. expenditures. Flat FDI growth, further borrowing in the Regional Government Securities Market, and A series of natural disasters between 2010 and continuing fiscal deficits caused the country’s public 2012 compounded the negative impact of the cri- debt to grow. St. Lucia’s public debt to GDP ratio rose sis. Hurricane Tomas damaged roads, bridges, elec- to around 80 percent of GDP in 2013 from the pre-cri- tricity and other infrastructure in St. Lucia in 2010. sis level of 57 percent of GDP (International Monetary Tropical Storm Ernesto did further damage in 2012. Fund, 2014). The reduction in foreign and public in- A major outbreak of a banana leaf disease5 in 2011 vestment has diminished the country’s medium- and further set back St.  Lucia’s already declining banana long-term growth prospects. exports. The important tourism sector continued strong The negative impacts of the 2008 crisis and nat- performance through the crisis and post-crisis pe- ural disasters combined with St. Lucia’s lax fiscal riod — better than in other OECS countries. Tour- policies led to persistent fiscal deficits. The fis- ism has been a major driver of economic growth in cal balance in St. Lucia deteriorated in 2009, when St. Lucia over the last decade. The number of tourist the drag of the global crisis began to be felt in the arrivals to St. Lucia increased since the early 2000s. island’s economy. In 2012, the overall deficit rose to Arrivals grew for more than a decade at an average 9.3 percent of GDP, and the primary deficit to 5.8 per- cent, as the newly-elected government undertook vides beneficiaries with life skills training, technical and vocational expansionary fiscal policy to support growth and training and job placement support; The National Initiative to Cre- ate Employment (NICE), with the goal of assisting in the creation of boost employment.6 As a result of the government’s 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 5  Black sigatoka. programme designed to provide short term employment, training, personal development, and health care to unemployed St. Lucians. 6  This was mainly in the form of active labour market programs to increase employment opportunities. Among these programs: 7  Prime Minister’s 2014 Budget Statement, delivered March Single Mothers in Life Empowerment Project (SMILES), which pro- 31, 2015. 12 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). 13 CHAPTER 3 Labor Market Trends in St. Lucia Anecdotal evidence and GDP figures for St. Lucia Main labor market indicators indicate that the economic crisis had a significant adverse impact on the nation’s unemployment The crisis led to a decrease in the availability and rate. GDP growth in St. Lucia has been more resilient quality of jobs. The working age proportion of the than that of most Eastern Caribbean states. Never- population increased by 3.5 percentage points from theless, the effect of the crisis on national income 2008 to 2013. At the same time, unemployment in- was significant. GDP declined in the years following creased by 8.6 points. So workforce growth was not the crisis (Figure 1). The negative impact of the crisis accompanied by an increase in the demand for labor. was also evident at the micro level. Table 1 provides The proportion of working age individuals with more a snapshot of labor market characteristics covering than one job fluctuated between 1 and 6 percent be- the period from 2008 to 2013. The data show a sub- tween 2008 and 2013, reaching a high of 5.9 percent stantial increase in the unemployment rate. While the in 2012. The share of workers with access to insurance proportion of the unemployed10 stood at just over 15 through their employment (the National Insurance percent of working age (15 to 64 years of age) indi- Corporation or privately provided coverage) has re- viduals in 2008,11 it increased gradually to a high of mained relatively stable at around 82  percent since just below 24 percent in 2013 (an increase of nearly 9 2011.12 However, the share of workers with a formal percentage points). (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. 10  Part of the analysis considers both standard (the proportion of economically active individuals without work and actively seek- Population growth slowed down over the last thirty ing employment) and broad (the proportion of economically active individuals who are without work) national unemployment rates years, declining from an average of 1.6 percent per Most of the analysis focuses on the latter. year between 1984 and 1993 to an average of 1.2 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 12  The questions on insurance and contract in current employ- population. ment were not asked prior to 2011. 14 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. percent per year between 2003 and 2013 (World De- employment. Figure 4 shows quarterly national level velopment Indicators, 2014). Despite the slowdown, estimates of the proportion of the labor force, em- St. Lucia added nearly 18,000 people to its total pop- ployment, unemployment, and underemployment ulation, an 11.5 percent increase between 2003 and (defined as working less than 35 hours per week) for 2013. The working age population (15–64) expand- the 2008–2013 period. The workforce, by either broad ed more rapidly than the total population, at an av- or standard measures,14 increased steadily over the erage rate of 1.9 percent between 2003 and 2013. time period considered, rising on average 0.8 per- The result was an 18 percent increase in the size of cent per year, from 74 percent in 2008 to 78 percent St. Lucia’s workforce over this period. The bulge in the in 2013. At the same time, the unemployment rate in- 10–19 age group in the 2011 population pyramid in- creased by nearly 9 points, or an average of 1.8 points dicates that the workforce will continue to grow over per year. In other words, 2 of every 3 individuals who the next decade (Central Statistics Office of St. Lucia, entered the workforce between 2009 and 2013 were 2011). While a growing labor force can be an asset for unable to find jobs. income generation and growth, absorbing the wave of new entrants every year poses a major challenge The proportion of the working age population clas- for the labor market. 13 sified as underemployed moved in tandem with un- employment from late 2008 to late 2012, reaching a Since 2008, the labor market has not been able to peak of approximately 18 percent in the third quarter fully absorb the growth of the labor force, leading of 2012. By the fourth quarter of 2013, this measure to both an increase in unemployment and under- had dropped to its earlier levels (around 11 percent).15 13  ADePT (Automated DEC Poverty Tables) labour module, a 14  Broad unemployment captures the proportion of economi- computational tool created by Computational Tools Team (DECCT) cally active individuals who are without work and who would like and Poverty Reduction and Equity Unit (PRMPR) of the World Bank to work (regardless of whether they are actively searching) where- to evaluate labour and poverty, among other trends from house- as standard unemployment considers as unemployed only those hold survey data. The ADePT computational tool contains useful without work and actively seeking employment. That is, those not templates for the creation of straightforward tables and figures actively seeking work are considered economically inactive. detailing key characteristics of the labour market given the survey data used. For more information about ADePT and the labour mod- 15  The average number of hours worked per week cannot be de- ule, see the ADePT Labour Module User Guide (Lara-Ibarra, G.) Ver- fined in the fourth quarter of 2011 or the first quarter of 2012, so sion: January 7, 2014. these values are interpolated from the average across time. 15 Figure 4. Trends in Labor Market Indicators in St. Lucia, 2008-2013 0.6 1.0 Proportion of total population Proportion of EAP (broad) 0.5 0.8 0.4 0.6 2008Q1 2008Q2 2008Q3 2008Q4 2009Q1 2009Q2 2009Q3 2009Q4 2011Q3 2011Q4 2012Q1 2012Q2 2012Q3 2012Q4 2013Q1 2013Q2 2013Q3 2013Q4 2008Q1 2008Q2 2008Q3 2008Q4 2009Q1 2009Q2 2009Q3 2009Q4 2011Q3 2011Q4 2012Q1 2012Q2 2012Q3 2012Q4 2013Q1 2013Q2 2013Q3 2013Q4 Labor force (standard) Labor force (broad) Employed 0.25 Proportion of EAP/working population 0.20 0.15 0.10 0.05 2008Q1 2008Q2 2008Q3 2008Q4 2009Q1 2009Q2 2009Q3 2009Q4 2011Q3 2011Q4 2012Q1 2012Q2 2012Q3 2012Q4 2013Q1 2013Q2 2013Q3 2013Q4 Unemployed (broad) Unemployed (standard) Working < 35hrs (week) Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013. These findings are not surprising. St. Lucia, like all Travel and tourism related activities alone are esti- OECS economies, relies heavily on industries such as mated to account for over 30 percent of GDP and em- tourism, construction, agriculture and financial ser- ployment in St. Lucia. Moreover, as discussed in Sec- vices. Those in turn depend greatly on external de- tion II of this report, St. Lucia also suffers from a high mand from wealthier economies. When those fell into level of national debt. The debt burden limits the a deep recession, they reduced spending. government’s ability to invest in social protection or in human capital, and generally constrains its capac- ity to respond effectively to macroeconomic shocks. 16 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. Proportion of Working Population with Formal Contract Proportion of working population/employees 0.6 0.6 Proportion of employees with contract 0.5 0.5 0.4 0.3 0.4 0.2 2011q3 2011q4 2012q1 2012q2 2012q3 2012q4 2013q1 2011 2012 2013 Female Male Youth (15-24) Formal contract Adults (25-64) Retirement age (65+) 0.8 0.8 Proportion of employees with contract Proportion of employees with contract 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 2011 2012 2013 2011 2012 2013 No formal school Primary or less Secondary 1st quintile 2nd quintile 3rd quintile Post-secondary Tertiary 4th quintile 5th quintile Proportion of Working Population with Formal Contract (cont.) 0.8 0.8 17 ith contract ith contract 0.7 0.7 0.6 0.7 0.7 Proportion of employees with co Proportion of employees with co 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 2011 2012 2013 2011 2012 2013 No formal school Primary or less Secondary 1st quintile 2nd quintile 3rd quintile Box 1: Formal the St. Lucian Labor Market (2011–2013) Contracts in Tertiary Post-secondary 4th quintile (cont.) 5th quintile Proportion of Working Population with Formal Contract (cont.) 0.8 0.8 Proportion of employees with contract Proportion of employees with contract 0.7 0.7 0.6 0.6 0.5 0.4 0.5 0.3 0.2 0.4 0.1 0.3 0.0 2011 2012 2013 2011 2012 2013 Agriculture Manufacturing Construction Salaried - public Salaried - private Mining/Energy Education, health, social services Professional services Transportation/communications Source: Labor Force Survey 2011, 2012, and 2013. Services and trade Other/not de ned Labor market trends clines in the remaining employment categories — the most significant being the decline in “unpaid family The following discussion highlights trends in em- worker or apprentice.” ployment (categories, distribution, education) and in unemployment (characteristics of the unemployed, Distribution of employment by economic sector workforce adaptations). The 2008 global financial crisis led to notable shifts Employment in sectoral participation in St. Lucia — from profes- sional services,16 agriculture, and construction to Employment categories — shares in total employment basic services, trade, and education, health, and social services. Table A2.2 shows the distribution of The distribution of employment by employment the working population by sector of economic activ- status (salaried, self-employed, or family worker) ity and over time. In 2008 just over 20 percent of the remained largely unchanged between 2008 and working population was employed in professional ser- 2013 in the non-agriculture sector. (Table A2.1.) At vices (public or private); by 2009 the proportion had the national level, the only group that experienced dropped to 18.5 percent. The decline in professional a marginal decline in its share of employment was services jobs continued gradually through 2012 to 16 “unpaid family worker or apprentice,” which declined percent; it rose to just above 18 percent in 2013. Mean- from 4.6 percent in 2008 to 3 percent in 2013. Be- while, employment in the services and trade sector tween 2008 and 2013, the distribution of employment increased from only 15 percent of the working popula- among salaried, self-employed, and family workers tion in 2008, to over 18 percent of the working popula- remained virtually unchanged in the non-agricultural sector. During the same period, in the agriculture sec- 16  Professional services include financial and insurance activities, real estate activities, professional, scientific and technical activities, tor, there was a large increase in the share of self-em- administration and administrative support (public or private), and ployed (18.1 percentage points) and proportional de- defence. Basic services and trade include wholesale and retail trade, and other basic services such as repairs and other low-skill activities. 18 Figure 5. Level of Education of Workers by Sector of Employment: 2008 vs. 2013 2008 2013 Agriculture Agriculture Manufacturing Manufacturing Construction Construction Mining/Energy Mining/Energy Education, health, social services Education, health, social services Professional services Professional services Transportation/ communications Transportation/ communications Services and Trade Services and Trade Accommodation & Food service Accommodation & Food service Other/ not de ned Other/ not de ned 0 20 40 60 80 100 0 20 40 60 80 100 None Pre-primary (Infant) or Primary Lower/ Junior Secondary Upper Secondary (Forms 1-3) / Senior Primary Post Secondary, non-tertiary (diploma or associate degree) Tertiary (University) Source: Labor Force Survey 2008 and 2013. tion a year later. The two sectors continued to employ ucation; a higher level of education is becoming about 18 percent of the working population up to 2013. more common. Most employees in the agricultural sector have primary schooling or less but the trend Construction employment declined between 2008 is toward lower secondary education. Table A2.3 and 2013, falling from about 13 percent to 8 percent. presents the distribution of the employed by educa- Education, health and social services, on the other tional attainment. The proportion of the employed hand, rose from 4 percent in 2008 to 10 percent in with primary schooling or less has increased gradually 2013. This reflected an increase in public sector em- since 2008, from 5.8 to 7.7 percent. At the same time, ployment as well as the implementation of several the proportion of the working population with low- govermnent programs meant to support growth and er or junior secondary education dropped nearly 10 boost employment opportunities (see footnote 6). percentage points and the proportion with upper sec- This might also reflect a higher demand for re-training ondary schooling rose by 2.3 points. The proportion post crisis to improve labour market conditions. Over- of the working population with more than secondary all, there was a marked transition from professional studies more than doubled from 2008 to 2013.17 services (public and private) and construction to ser- vices and trade and to education, health and social The trend toward higher levels in educational attain- services. However, the tourism industry (accommo- ment started from a lower level in the agricultural sec- dation and food services) continued to employ about tor than in non-agricultural employment.18 13 percent of the working population through the entire period — consistent with government policies High-skilled workers shifted to the education, to support employment in the tourism sector with air- health and social services sector; low-skilled work- line subsidies and tax reductions. ers have shifted to accommodation and food ser- Educational attainment of the employed by sector 17  There was a significant change in the coding of the education of employment 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. Most people employed in the non-agricultural sec- 18  The 2006 Country Poverty Assessment for St. Lucia of the Ca- tor in St. Lucia have at least upper secondary ed- ribbean Development Bank shows that those employed in the agri- cultural sector have the lowest levels of education. 19 vice and trade. All economic sectors saw an increase Figure 6. Resident and Emigrant Population in the share of workers with tertiary education from By Highest Level of Formal Education 2008 to 2013 (Figure 5). The increase was particularly pronounced in education, health, and social services 40% sectors both in absolute and relative terms. The share 35% of employees with only primary education declined in 30% most sectors. 25% 20% Educational attainment of the emigrant population 15% 10% The 2010 census data showed an increase in emi- 5% gration of more-educated workers from St. Lucia 0% Primary Secondary Tertiary and higher during the previous decade. The number of emi- % of Census 2010 Population % of Census Emigrants (2001 - 2010) grants over the previous decade is about 5 percent of Source: Saint Lucia Population & Housing Census 2010. the current St. Lucia population. The emigrants’ aver- age educational attainment was significantly higher than that of the average population (Figure 6). Nearly The unemployment rate of females has been 55 percent of the emigrants cited either employment slightly higher than that of males, but the rate for (28 percent) or education (27 percent) as the number both groups increased more than 8 points. The one reason behind their decision to leave St. Lucia. crisis particularly led to increased female unemploy- During the same decade, remittances grew. ment in late 2008, but rates for men and women have been similar since. By 2013, the unemployment gap Unemployment between males and females narrowed from 5 down to 4.3 points as male unemployment rose. Unemployment by individual and household char- acteristics 19 Unemployment increased most for workers with secondary education and less for workers The crisis has been particularly hard on St. Lu- with either more or fewer years of schooling. cia’s young adults — by the end of 2013, nearly Throughout most of the period since the crisis, un- half were unemployed. Figure 7 and Table A2.4 employment has been highest for those with lower report unemployment estimates by gender and secondary education, though unemployment has age group. Since 2008, unemployment has been fluctuated quite rapidly quarter by quarter, espe- the highest and has increased the most rapidly (17 cially for this education cohort. Notably, since 2008 points) among those 15 to 24 years old. Youth un- the unemployment rate of those with an upper sec- employment reached a high of 47 percent in the last ondary education grew steadily, even surpassing two quarters of 2013. The unemployment rate of the rate for those with only lower secondary school- both adults (25–64) and the elderly (65+) increased ing in the third quarter of 2013. The unemployment by over 6 points. From the second quarter to the rate for those without schooling, while very volatile, fourth quarter of 2013, the adult unemployment has actually dropped over time, whereas the unem- rate declined from 20 percent to 17 percent. Despite ployment rate for those with tertiary studies (who an increase in late 2012, unemployment of the el- claim the lowest level of unemployment of all edu- derly has been comparatively moderate at around cation cohorts) has mostly remained below 10 per- 15 percent. cent since 2008. 19  This section focuses on broad unemployment which is the of- ficial measure of unemployment in St. Lucia. 20 Figure 7. Unemployment Rate (broad), by Gender and Age 0.5 0.4 0.3 0.2 0.1 0.0 2008q1 2008q2 2008q3 2008q4 2009q1 2009q2 2009q3 2009q4 2011q3 2011q4 2012q1 2012q2 2012q3 2012q4 2013q1 2013q2 2013q3 2013q4 Female Male St. Youth (15-24) Adults (25-64) Retirement age (65+) Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013. Workforce adaptations average number of hours worked followed a similar temporal trend (Table A2.5). Qualitative results from a The impact of the crisis on St. Lucians varied mixed, qualitative-quantitative study (United Nations with gender and education. Between 2008 and Development Programme, 2010) reveal that St. Lu- 2013, female employment shifted from profession- cian respondents agreed with the premise that men al services to the education, health, and social ser- in St. Lucia are generally less willing than women to vices sector, particularly after 2010. The distribution take just “any” job. Participants in the study claimed of male employment moved from the construc- that a “man would rather starve than do menial la- tion sector to the service and trade sector (Figure bor.” In fact, men on average worked longer hours 8, top panel). These trends are consistent with the than women, and there was greater volatility in the implementation of government programs, such as number of hours men worked. St. Lucians age 65 and SMILES, which targets single women, and NICE, for over tend to work fewer hours weekly than young- which 70 percent of beneficiaries are women (NICE er cohorts. However, in early 2013 the average hours Administrative Data). worked for those between 15 and 24 years of age was not much higher than the average for those of retire- In the post-crisis period, there also was a shift of ment age. more-educated workers from professional services to the education, health and social services sector. Between 2008 and 2013 underemployment in- At the same time, less-educated workers were em- creased significantly in the agricultural sector ployed relatively less in the agriculture and construc- (Figure 9).20 In all other sectors, a substantial jump tion sectors and more in the services and trade sec- in the proportion of underemployed workers in the tor. The sectoral participation of those with mid-level immediate aftermath of the crisis was followed by a education (that is, more than primary but no more gradual drop in the underemployed workforce seg- than secondary) remained relatively unchaged. (Fig- ment. Because the agricultural sector traditionally ure 8, bottom panel.) employs older farmers and laborers, this trend aligns with the increased underemployment of the elderly Men faced greater volatility in the total number of in general. hours worked; women and the elderly were more likely to be underemployed. The median number 20  The agricultural sector traditionally employs older farmers and of hours worked by males and females was 40; the labourer. 21 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. Qualification requirements for job Qualification of job seekers/unemployed, openings, Sep - Nov 2012 Sep - Nov 2012 7% 25% 44% 33% 31% 60% Below secondary level Secondary Tertiaty education Degree of importance of various skills to firms in St. Lucia Strong work ethic Customer service Communication Interpersonal Adaptability Literacy Numeracy and quantitative Profound technical knowledge Ability to plan Problem solving Emotional intelligence Decision making General business Computer literacy Project Management 0 20 40 60 80 100 Not Important Somewhat Important Important Very Important Source: St Lucia Labor Market Needs Assessment Survey 2012; Central Statistics Office. 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. 22 Figure 8. Sectoral Shifts in Employment by Gender and Education 0.30 0.25 0.20 0.15 0.10 0.05 0.00 Agriculture Manufacturing Construction Mining/ Education, Professional Transportation/ Services Accommodation Other/ Energy health, services communications and trade & food service not de ned social services 0.25 0.20 0.15 0.10 0.05 0.00 Agriculture Manufacturing Construction Mining/ Education, Professional Transportation/ Services Accommodation Other/ Energy health, services communications and trade & food service not de ned social services 2013 2012 2011 2009 2008 2008 2013 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 No formal Primary Secondary Post Tertiary No formal Primary Secondary Post Tertiary school or less or less secondary school or less or less secondary Agriculture Manufacturing Construction Mining/Energy Education, health, social services Professional services Transportation/communications Services and trade Accommodation & food service Other/not de ned Source: Labor Force Survey 2008, 2009, 2011, 2012 and 2013. 23 Figure 9. Underemployment for Selected Sectors 0.35 0.30 0.25 Underemployment 0.20 0.15 0.10 0.05 0.00 2008 2009 2011 2012 2013 Agriculture Manufacturing Construction Mining/Energy Education, health, social services Professional 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 0.35 0.30 0.25 0.20 0.15 0.10 0.05 2008q1 2008q2 2008q3 2008q4 2009q1 2009q2 2009q3 2009q4 2011q3 2011q4 2012q1 2012q2 2012q3 2012q4 2013q1 2013q2 2013q3 2013q4 1st quintile 2nd quintile 3rd quintile 4th quintile 5th quintile Distribution of employed and unemployed by Quintiles of Wealth Index Unemployed − 2008 Unemployed − 2013 Employed − 2008 Employed − 2013 17% 12% 20% 17% 22% 26% 22% 23% 14% 22% 18% 18% 20% 18% 21% 22% 25% 21% 20% 20% 1st quintile 2nd quintile 3rd quintile 4th quintile 5th quintile Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013. 24 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. 25 CHAPTER 4 The Impact of The 2008 Global Financial Crisis on Unemployment, Wages, and Welfare Unemployment workers with either more or less schooling. Work- ers with no formal education were relatively unaffect- Unemployment in St. Lucia increased after 2008; ed by the crisis. But even when they are employed, not only was there a temporal effect of the 2008 cri- unschooled workers have significantly lower wages sis on unemployment, but the increase in unemploy- and higher rates of underemployment. ment was persistent from 2009-2012. The year-to-year increase of the unemployment rate grew after the cri- Wages and wage correlates sis, from 3 points initially up to 9 points. The results are almost identical when considering broad unemploy- Real wages stagnated. The coefficient estimates cor- ment rather than standard unemployment. responding to the survey years are mostly insignifi- cant; there is no sign of wage growth.25 Female unemployment was stable through 2010, after which it grew steadily. This result is consis- Between 2008 and 2013, education is the most tent with qualitative evidence (UNDP 2010) which significant factor in predicting inequality in in- suggest that, rather than falling into unemploy- come; higher education is associated with sig- ment, women are more likely than men to accept nificantly higher levels of income. Table A2.7 pres- less desirable jobs whereas man are more likely to ents the results from pseudo-wage regressions.26 opt out of the labor market when better opportuni- The results show that, on average, males make 317 ties are not available. XCD per month more than females, and heads of household make just under 80 XCD more per month The increase in unemployment was most severe for young adults (15–24). By 2013, the unemploy- 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 ment rate of young workers grew by 15 points. The nominal income brackets (or intervals) as dependent variables, if increase in unemployment for older age groups was wages remained constant the coefficients corresponding to the year indicators should have a positive gradient and should be sta- smaller and slower to develop. tistically significant. 26  puted using OLS and gender, age, education, asset based The increase in unemployment was greater for household wealth, and year and district indicators as predictors. The OLS estimation predicts around 30% of the variation in the in- workers with only secondary education than for come variable. The results under both cases are very similar. 26 than non-heads of household, both of which we tural sector; more likely to work in the construction would expect. The relationship between education and manufacturing sector; and significantly less and income is approximately linear and positive: likely to work in the education, health, social and compared to those with no schooling, those with professional services sectors. 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. 27 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 P-value of diff. forty 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% 0.14 Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013. 28 CHAPTER 5 Conclusions The recent financial crisis had significant and the post-crisis period. Sectoral shifts in employment long-lasting microeconomic impacts on St. Lucia. varied by gender. Female employment shifted from Unemployed and underemployed St. Lucians joint- professional services to the education, health, and ly accounted for over 40 percent of the working-age social services sector, particularly between 2011 and employable population by the third quarter of 2012. 2013. The distribution of male employment moved Both suffered significant welfare reductions in the af- from the construction sector to the service and trade termath of the crisis. They lost not only their income sector. These labour market trends are generally in but also the collateral benefits that are often associat- line with the observed macroeconomic trends (FDI, ed with being fully employed in good quality jobs and construction, tourism and both growth and sectoral under formal contracts. composition of GDP). They are also consistent with the policies enacted by the government in response Workers with only secondary education were to the crisis, which focused on maintaining employ- hardest hit by increased unemployment. Workers ment in the tourism sector, improving the education with less than primary education remained employed and skills of the labor force, and aiding the most vul- but received significantly lower wages. Workers with nerable populations (youth and single mothers). 28 more than secondary education were relatively less affected — they experienced both lower levels of un- Social disparities in St. Lucia increased after the employment and higher wages. The negative impact 2008 global financial crisis. The poorest forty per- of the crisis was particularly severe among St. Lucia’s cent of households were nearly indistinguishable youth (those between 15 and 24 years of age) — by from the top sixty percent in 2008. By 2013 individuals the end of 2013, nearly half of young adults were un- employed. 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 Employment shifted away from the construction Opportunities for Personal Empowerment  (HOPE), the National and professional services sector to basic services Skill Development Center (NSDC), the National Initiative to Create Employment (NICE), Single Mothers In Life Enhancement Skills and trade, education, health, and social services. (SMILES), the Short Term Employment Program (STEP), and the About 13 percent of the working population re- Constituency Development Program. These initiatives aimed to im- prove job opportunities and promote vocational training and skill mained employed in the tourism industry through development. 29 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. 30 References St. Catherine, Edwin. 2013. Analysis of the Saint Lucia Lokshin, Michael, Sergiy Radyakin, et al. 2014. AD- Labor Market Needs Assessment Survey 2012. St. Lucia: ePT Labor Module User Guide. The World Bank Group, Central Statistical Office. Poverty Reduction and Equity Unit (PRMPR), Compu- tational Tools Team (DECCT), January 7. Accessed Au- Enterprise Surveys. 2010. Washington: The World Bank. gust 15, 2014 at: http://siteresources.worldbank.org/ EXTADEPT/Resources/ADePT_Labor_Handout.pdf. Government of Saint Lucia and the Caribbean Devel- opment Bank. 2008. Saint Lucia Country Poverty As- The Central Statistics Office of St. Lucia. 2011. Annual sessment Report. Prepared by Kairi Consultants, Ltd. Statistical Digest. International Monetary Fund. 2010. IMF Country Re- United Nations Development Programme (2010). port No. 10/14. Grenada: 2009 Article IV Consultation, Social Implications of the Global Economic Crisis in Ca- Fourth Review Under the Three-Year Arrangement Under ribbean Small Island Developing States: 2008-2009. St. the Poverty Reduction and Growth Facility, Request for Lucia Country Report, Feb 27. Modification of Performance Criterion, and Financing Assurances Review—Staff Report; Public Information World Development Indicators. Washington: The World Notice; and Press Release. Accessed August 15, 2014, Bank. 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. 31 Annex 1 Tables 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. 32 Table A2.2: Distribution of the Employed by Economic Sector Share 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. 33 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. 34 Table A2.4: Unemployment Rates Among Selected Groups Unemployment Rate by Groups Group Share Among Unemployed 2008 2009 2011 2012 2013 Change 2008 2009 2011 2012 2013 Change Total 15.2 18.5 21.2 21.4 23.3 8.1 100.0 100.0 100.0 100.0 100.0 0.0 (0.53) (0.70) (1.02) (0.86) (0.79) (0.95) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Gender Male 12.9 17.3 19.8 19.5 21.7 8.8 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) Female 17.9 19.9 23.7 24.1 26.0 8.1 54.5 51.7 53.4 53.5 52.5 -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) Age group 15-24 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) 25-54 11.4 14.0 16.3 17.0 18.4 7.0 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) Area of residence Urban 15.8 18.9 21.1 21.0 23.5 7.7 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) Rural 14.4 18.0 22.3 22.5 24.1 9.8 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) Highest level of educational attainment None 15.1 15.8 10.6 13.8 8.9 -6.2 2.0 1.6 1.7 1.5 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) Pre-primary (Infant) or Primary 28.1 22.6 26.3 28.1 31.7 3.6 8.4 5.6 8.1 7.4 8.9 0.6 (3.04) (3.10) (3.66) (3. 20) (2.89) (4.19) (1.10) (0.86) (1.43) (0.99) (0.98) (1.47) Lower / Junior Secondary (Forms 1-3) / Senior Primary 14.9 19.7 23.5 21.5 22.8 7.9 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) Upper Secondary (Forms 4 & 5) 17.5 21.2 25.1 26.5 29.8 12.3 37.1 37.3 40.8 42.1 45.7 8.7 (1.06) (1.24) (1.82) (1.40) (1.35) (1.72) (1.89) (1.73) (2.75) (1.92) (1. 71) (2.55) Post-secondary, non-tertiary (diploma or associate degree) 7.4 11.3 15.9 18.3 17.1 9.7 4.5 6.0 4.9 6.0 5.0 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) Tertiary (University) 4.8 3.5 8.8 7.3 8.4 3.6 1.5 0.9 4.3 3.3 4.1 2.5 (1.37) (1.28) (2.24) (1.38) (1.43) (1.98) (0.43) (0.34) (1.10) (0.65) (0.69) (0.81) 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 Table A2.5: Average and Median Hours Worked per week by respondent characteristics Group 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 40 Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013. 36 Table A2.6: Unemployment (broad) by Group Variables All Female Male Age 15-24 Age 25-39 Age 40-64 Age 65+ No formal Primary Secondary Post-sec- Tertiary education ondary 2009 0.034*** 0.019 0.045*** 0.055** 0.025* 0.031*** 0.029 -0.032 -0.023 0.041*** 0.048** -0.015 [0.009] [0.013] [0.011] [0.024] [0.013] [0.011] [0.036] [0.047] [0.038] [0.010] [0.022] [0.020] 2011 0.063*** 0.044*** 0.080*** 0.114*** 0.040** 0.062*** 0.065 -0.088 -0.041 0.071*** 0.077** 0.076** [0.011] [0.016] [0.016] [0.030] [0.020] [0.015] [0.048] [0.071] [0.054] [0.014] [0.037] [0.032] 2012 0.072*** 0.068*** 0.077*** 0.110*** 0.070*** 0.054*** 0.091** -0.098* -0.065 0.085*** 0.110*** 0.066** [0.010] [0.015] [0.013] [0.027] [0.016] [0.012] [0.039] [0.052] [0.046] [0.012] [0.030] [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*** [0.009] [0.014] [0.011] [0.027] [0.016] [0.011] [0.037] [0.052] [0.040] [0.011] [0.026] [0.025] Observations 19,056 8,934 10,122 3,663 6,599 7,984 810 491 1,069 14,568 1,488 1,440 R-squared 0.096 0.108 0.088 0.057 0.051 0.038 0.044 0.059 0.093 0.089 0.105 0.090 Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013. Note: Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1. 37 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] 38 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 15,290 Source: 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. 39 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. 40 Annex 2 Technical Annex Asset-based wealth index as a measure Figure TA.1 displays the average assets index score of welfare by levels of labor income and education for the entire period considered. Overall, the relationship The assets index is constructed using a short module between the measure of asset-based household in the LFS that captures household assets and charac- wealth, or wealth score, and both individual-level teristics of the household dwelling. This assets mod- and education-level labor income appear positive ule was collected in all five survey years considered in and increasing. the analysis (2008–2013). The index is constructed by year using the principal components analysis (PCA) However, the relationships between the assets index, method. PCA is typically used to construct wealth education, and reported income brackets vary signifi- indices, and especially as a variable reduction tool cantly across years. In particular, while in 2008 and in multivariate analysis. In other words, the index al- 2009 the assets index varied little across socio-eco- lows for summarizing multivariate information into nomic groups, in the 2011–2013 period the index had a wealth score — a continuous measure of wealth a positive correlation with both education and report- which captures most of the variation in the individual ed income brackets. variables considered. Bottom forty percent The following 13 assets are considered in the con- structions of this index for St. Lucia: number of rooms Households are classified as belonging to the “bot- in the dwelling; number of bedrooms in the dwelling; tom forty percent” (“top sixty percent”) when their as- whether or not the household owns a television, re- set-based household wealth score, derived from the frigerator, washer, landline phone, cellular phone, in- asset index described above, falls in the bottom two ternet access, computer, vehicle, VCR, or clothes iron. (top three) quintiles of the distribution of this index. The predicted continuous assets index is divided into The table below presents the summary of bottom for- quintiles. The first (un-rotated) component of the PCA ty percent by employment status and sector of em- output is used to define the predicted wealth score. ployment. 41 Figure TA.1.a. Assets Based Wealth Index by Education and Income (2008-2013) 4.0 3.5 3.0 2.5 Assets index 2.0 1.5 1.0 0.5 0.0 No income Under 200 XCD 201-399 XCD 400-799 XCD 800-1199 XCD 1200-1999 XCD 2000-3999 XCD 4000-5999 XCD Over 6000 XCD Primary or less Lower secondary Upper Secondary Tertiary No formal school 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 5 5 4 4 3 3 2 2 1 1 0 0 2008 2009 2011 2012 2013 2008 2009 2011 2012 2013 No formal school Primary or less Lower-secondary less than 399 XCD 400-799 XCD 800-1199 XCD Upper-secondary Post secondary Tertiary 1200-1999 XCD 2000-3999 XCD 4000-5999 XCD Over 6000 XCD Source: Labor Force Survey 2008, 2009, 2011, 2012, and 2013. Pseudo-wage regression to predict levels ical income measure is first re-coded so that “6000 of income — interval regression method and over XCD” becomes “6000 to 7000 XCD.” Given the categorical nature of the income measure, changing Since the measure of income available from the LFS the upper bound this way should not affect the re- is categorical (represents ranges, rather than values) sults. Then, the interval regression is estimated using a valid approach for establishing the factors associat- Ordinary Least Squares (OLS) methods. As with a typ- ed with levels of labor income is to estimate a pseu- ical wage regression, the coefficients resulting from do-wage regression using the interval regression this interval wage regression represent the monetary method.30 To undertake this estimation, the categor- increase (or decrease) in income, given a one unit in- crease in each explanatory variable of interest (hold- 30  It is important to note that a relatively high proportion of the ing all else constant). 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 information and including them with imputed income values. The estimated both excluding individuals with missing labour income results in both cases are very similar. 42 Annex 3 Data Annex Definition of employment, An individual can be classified as unemployed by the unemployment, and inactivity standard definition (actively seeking work) or the broad definition (wanting work, but not necessarily An individual is defined as employed if he/she sat- actively seeking). isfies one of the following three conditions: An individual is classified as inactive (out of the • Has worked for pay, profit, or family gain during labor force) if he/she satisfies neither of the two the reference week (past 7 days) OR aforementioned situations. That is: • Has a job from which he/she was absent during • Not working for profit or pay, nor absent from in- the reference week because of vacation, materni- come earning activity in reference week ty leave, sick leave, or a temporary lay-off (for the time being, we do not include “other reason” — • Not wanting work or actively looking for work (the these are included in inactive status) latter depends on how we define unemployment, as standard or broad) • No job or absence from job reported; when asked if looked for work, respond “have job” Definitions considered by pre-defined employment characteristics An individual is classified as unemployed if he/she satisfied the following: An individual is defined as employed if he/she sat- isfies the following: • Has not worked for pay, profit, or family gain, nor is he/she absent from work, in the past 7 days AND • Reports activity for income in past 7 days OR • Has been available (wanting) to work, within the • In response to multiple job holding question, re - past 7 days port either “yes” or “no” to having more than one job 43 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: • Actively seeking work 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: • Actively seeking work OR • Not seeking work because of the following: • Already found work and will start later • Already made arrangements for self-employ- ment activities • Awaiting recall to former job • Awaiting replies from employers • Believes no suitable work available • Believes no financial resources, land, equip- ment, permits, etc. to start own business • Cannot meet employer’s requirements • Could not find suitable work • Does not know how or where to seek work AND 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). 44 The World Bank 1818 H Street, NW, Washington, DC 20433, USA. www.worldbank.org The original had problem with text extraction. pdftotext Unable to extract text.