WHY PEOPLE CHOOSE TO PARTICIPATE IN THE INFORMAL SECTOR IN SRI LANKA

Transcription

1 The Indian Journal of Labour Economics, Vol. 53, No. 2, 2010 WHY PEOPLE CHOOSE TO PARTICIPATE IN THE INFORMAL SECTOR IN SRI LANKA Nisha Arunatilake and Priyanka Jayawardena* More than 60 per cent of Sri Lanka s labour force is employed in the informal sector, which comprises various economic activities in agriculture, fishing, livestock rearing, micro and small-scale enterprises, petty trade, and other small commercial activities. The informal sector is characterised by low social security and changing incomes. Given this, it is interesting to determine why such a large proportion of the labour force chooses to participate in the informal sector. This paper attempts to answer this question by using household survey data from two time periods. The paper lays special focus on assessing how access to services and infrastructure affects participation in the informal sector. The results show that a multitude of factors relating to individual preferences, location characteristics, and labour market conditions affect the occupational choice of individuals. Moreover, participation in the informal sector is due to a mix of market considerations and skill mismatches, moderated by household and societal factors. The paper argues for improving economic development in a more equitable manner across geographical regions, on the one hand, and improving the quality and relevance of educational opportunities, on the other. I. INTRODUCTION In the recent past, the informal sector has received renewed interest from a social security stance due to the fact that most workers in the informal sector are not safeguarded by protection available to workers in the formal sector. According to Canagarajah and Sethuraman (2001), the insecurity faced by workers in all countries arises mainly from income inadequacy and income variability, though it is discernible through various forms. Earlier schemes that intended to provide social protection to the informal sector operated to protect the workers from falling into poverty through inadequacy and variability of incomes. These most often took the form of health insurance schemes, micro-finance schemes, old age pensions schemes, etc. One main shortcoming of these schemes was that though they protected workers who were affected by various risks, they did not prevent them from being affected by the same risks in future. The new thinking on social protection aims to treat social security in a more comprehensive manner, wherein emphasis is laid on both risk prevention as well as protection from risks. As per this thinking, risk prevention is achieved through policy reforms aimed * The authors are Research Fellow and Research Officer, respectively in Institute of Policy Studies of Sri Lanka. priyanka@ips.lk. An earlier version of this a paper appeared in Nisha Arunatilake (2004), Why People Choose to Participate in the Informal Sector in Sri Lanka, Global Development Network, Labour Markets in Lowincome Countries, Workshop, Dakar, Senegal, 23 January 2005.

2 226 The Indian Journal of Labour Economics at improving the income-earning potential of workers while minimising the variability of incomes that cuts across all sectors of the economy (Canagarajah and Sethuraman, 2001). Sources of insecurities and vulnerabilities of workers in the informal sector have been well documented in the literature. The nature and magnitude of these insecurities vary across socio-economic groups and across geographical regions. Taking the World Development Report (WDR) and the Social Risk Management (SRM) framework as a base, Canagarajah and Sethuraman (2001) summarise the issues that needs to be addressed in order to ensure that workers in the informal sector have the potential to earn adequate incomes, which are consistent over time. According to them, the policy reforms in the informal sector should be viewed from three perspectives, namely, expanding opportunities, ensuring empowerment, and providing security. The reason as to why individuals participate in the informal sector is widely debated in the literature (Das, 2003). A better understanding of the determinants of participation in the informal sector participation is the first step towards achieving a more comprehensive policy for identifying the areas that need attention in terms of preventing the risks faced by the informal sector. This paper analyses data from two surveys for different periods with the aim of identifying the factors affecting work choice and changes occurring in them over time. For this purpose, the study makes use of the Sri Lanka Integrated Survey (SLIS ) and the Consumer Finance and Socio-economic Survey (CFS ) in order to analyse how individual, household, and location-specific characteristics determine employment in different sectors. An occupation choice model is used to explore the determinants of sector participation. Canagarajah and Sethuraman (2001) point out that providing opportunities to those in the informal sector implies an expansion of social and physical infrastructure facilities that are available to the rest of the economy to those in the informal sector, in order to increase the returns from their economic activities. Some specific areas which have gained a lot of attention in this regard in the literature include provision of access to credit, to roads and markets, to electricity, water and sanitation, and to education and healthcare. While providing opportunities, the government policies should also accord special attention to the opening up of opportunities for those who have been marginalised due to gender and socio-cultural differences, and to the special needs of those living in remote rural areas. Given the availability of data, special attention is given to determining how the availability of opportunities influences sector participation. The results of the study are used to identify areas wherein policy reforms are needed in terms of promoting opportunities. II. BACKGROUND This section provides background information on the labour and employment situation in Sri Lanka, while covering the formal sector, the informal sector, and the unemployed segments of the labour force.

3 WHY PEOPLE CHOOSE TO PARTICIPATE IN THE INFORMAL SECTOR IN SRI LANKA Labour and Employment Situation of Sri Lanka Sri Lanka is a small economy with a population of about 20 million people. In 2007, its per capita GDP was US$ 1,617 (CBSL, 2007). In 2006, the total labour force amounted to 7.6 million, which represented a labour force participation rate of 57 per cent. By sectors, services contribute most to employment (41 per cent), followed by industry (27 per cent), and agriculture (32 per cent). The labor force grew at an annual average rate of 2.3 per cent from 1992 to 2006 in line with the working age population, thereby keeping the labour force participation rate constant. The male participation rate was more than twice that of the female rate. Unemployment rates have declined by almost half in recent years but remain high among women and youth. 1 Sri Lanka introduced free market policies in 1977, including liberal trade policies, relatively low tariff levels, dismantling of price controls, removal of government subsidies, privatisation of State-owned enterprises, encouragement of foreign direct investment (FDI), liberalisation of the current account, and advanced intellectual property protection laws. These policy measures have been intensified over the last two decades. With liberalisation of the economy in Sri Lanka in 1977, many private sector entrepreneurs ventured into various sectors of economy, which led to an increase in the number of workers in the informal economy. This trend opened up avenues for employment in transport, security, export and import, and other service sectors as well as in the manufacturing sector, thereby increasing the number of workers in the informal sector substantially (ICFTU, 2004). Defining the concept of the informal sector is not easy because the border between the formal and informal sector is unclear. As discussed earlier, the formal sector is characterised by employment that offers better income security and better legislative protection, which provide other non-income benefits such as pensions, paid holidays, and job security. The informal sector is the remaining sector with relatively less income security and informal terms and conditions of work that are not protected by law. As in many other countries, Sri Lanka does not have an official definition to distinguish between the formal and informal sector economic activities. Different institutions use different definitions. The Central Bank of Sri Lanka (CBSL) classifies organisations that do not contribute to the general Employees Provident Fund (EPF) or do not maintain their own provident funds with the approval of the Commissioner of Labour as informal private sector organisations (CBSL, 2005). For the Department of Census and Statistics, a formal organisation is one that has accounting systems or registered organisation or has 10 or more total number of regular employees in the organisation (DCS, 2006). Recognising the importance of statistics on the informal sector, the Department of Census and Statistics revised its Labour Force Survey (LFS) to include questions that facilitate a clear identification of those in the informal sector since These questions are particularly aimed at obtaining information on the registration of organisations, the account-keeping practices of an organisation, and the size (according to the number of employees) of an organisation. Since as per the law, only firms with more than 15 workers are required to pay EPF, the definition of informal sector by

4 228 The Indian Journal of Labour Economics DCS (which considers firms with more than 10 workers to be formal) is less stringent than that by the Central Bank. 2. The Formal Sector Typically, employment in the formal sector comprises employment in the government sector, including all levels of the government: central, provincial, and local the corporate sector, and employment in large establishments in the private sector, including foreignowned enterprises and employment in the estate sector. The Sri Lanka Labour Force Survey (LFS), DCS, 2006, estimates the formal sector workforce to be 2.8 million, which accounts for more than one-third of the total employment in the country. Of these, around 1 million workers are estimated to be in the public sector. The formal sector employees are covered with basic protection of core labour standards. It should be noted that Sri Lanka has ratified eight ILO conventions on core labour standards including the right to collective bargaining and freedom of associations 2. Tripartite mechanisms for social dialogue have been in place for a long time and trade unions, employers, and the government interact collectively to resolve critical issues in industrial relations (ICFTU, 2004). According to most labour legislation, workers in firms with more than 15 workers are required to abide by these laws. Wage setting in the formal sector in Sri Lanka takes place through collective bargaining, or through deliberations by tripartite wage boards or remuneration boards. There seems to be no objective criteria for setting wages, and they are generally based on the existing wage rates. Wages in Sri Lanka are not indexed, that is, they do not automatically adjust for inflation. Instead, they are periodically revised (Anant, Sundaram, and Tendulkar, 1999). 3. The Informal Sector Due to lack of data, little is known about the informal sector, though its importance is often highlighted, given its size and its links to poverty. It is a heterogeneous sector consisting of economic activities in agriculture, fishing, and livestock-rearing, small and medium scale enterprises, petty traders, and other small commerce, industry and service occupations (Rodrigo, 2001). Market forces, for the large part, determine wages in the informal sector. According to the Labour Force Survey for 2006, 61 per cent of the total employment is the informal sector. The category of skilled agriculture and fishery workers category has the largest proportion of workers in the informal sector (98 per cent), followed by craft and related workers (66 per cent). A majority of those in the occupation categories at the lower end of the occupation ladder are in the informal sector, as well as a majority of those in the senior officials and managers category (see Table 2). The high percentage of informal sector workers in the category of senior officials and managers can be explained by the large number of workers who are self-employed. Apart from the social security schemes for formal sector workers, there are special social security benefits for informal sector workers (Farmers Pension Act of 1987; Fishermen Pension Scheme Act of 1990, and Self-employed Pension Scheme of 1996) Although there are social security programmes in the informal sector, the coverage of these programmes are limited. Also, the returns on these schemes are very low (Gaminiratne, 2007).

5 WHY PEOPLE CHOOSE TO PARTICIPATE IN THE INFORMAL SECTOR IN SRI LANKA 229 Table 1 Distribution of Employed Population by Their Main Occupation and Sector of Employment, 2006 Occupation Category Formal Informal No. % No. % Senior Officials and Managers 273, , Professionals 322, , Technical and Associate Professionals 313, , Clerks 255, , Sales and Service Workers 232, , Skilled Agricultural and Fishery Workers 40, ,549, Craft and Related Workers 410, , Plant and Machine Operators and Assemblers 212, , Elementary Occupations 670, , Total 2,767, ,339, Source: Author s calculations using the Department of Census and Statistics, Quarterly Labour Force Survey, Unemployment The available information on the Sri Lankan labour market shows that unemployment rates have come down since the early 1990s. Although this is a promising development, the nature of unemployment is still of grave concern for several reasons in the country. Table 2 Structure of the Unemployed by Level of Education 2006 Level of Education Male Female Number Unemployment Rate Composition (%) Number Unemployment Rate Composition (%) Below Primary Level 13, , Below Secondary Level General Certificate of Education Ordinary Level [GCE (O/L)] General Certificate of Education Advanced Level [GCE (A/L) and above] 110, , , , , , Total 225, , Source: Author s calculations based on the Department of Census and Statistics, Quarterly Labour Force Survey, First, the unemployment rates are still rather high in the country, with the highest rates being reported among youth, the educated, and females. A little more than half the unemployed persons have obtained the equivalent of the GCE (O/L) or above (see Table 2). Moreover,

6 230 The Indian Journal of Labour Economics unemployment amongst degree-holders has been a pressing problem in the recent past. Second, the duration of the unemployment period has been more than a year for almost a half (48 per cent in 2006) of the unemployed. Third, anecdotal evidence suggests large-scale under-employment in the country, wherein individuals work in jobs below their expectations, given their educational backgrounds. 3 The dynamics of the informal economy are more complex than is commonly understood. In particular, studies have not given proper consideration to the role of culture and the driving force in the entire dynamics of the informal sector economy. Linking institutional and contextual factors to cultural characteristics leads to a more comprehensive understanding of the informal sector. This study uses survey-based inferences to explore the determinants of sector participation. As explained earlier, the analysis is based on two different household and community level surveys SLIS , and CFS Both surveys include a wide variety of socio-economic variables at both the individual level as well as the community level. The variables selected for the analysis were the same for the most part of the analysis in order to facilitate a consistent comparison with the time lag. III. DATA 1. Sri Lanka Integrated Survey (SLIS ) The Sri Lanka Integrated Survey (SLIS) was carried out across all provinces of the country between October 1999 and the third quarter of A total of 7,500 households were surveyed in 500 urban, rural, and estate communities. In addition to providing information on the economic activities of the individuals, the survey includes information on the sociodemographic variables of families, educational attainment of individuals, a household s access to social and physical infrastructure, and family assets. 2. Consumer Finance and Socio-economic Survey Data (CFS ) The Consumer Finance and Socio-economic Survey (CFS) was carried out by the Central Bank of Sri Lanka during the period A sample consisting of around 12,000 households was surveyed covering most districts, except some districts affected by the prevalent conflict in Sri Lanka. The CFS data consists of comprehensive household data on demographic features, household level income and expenditure, labour force, housing conditions as well as household amenities and land ownership. These two surveys together signify a unique opportunity to examine the work choice behaviour of individuals controlling not only for individual and household level variables, but also for community level variables. 3. Models Theoretical Model The underline theoretical model for the analysis is a modified version of the model used by Verme (2000). In his model, Verme (2000) assumes that two factors affect sector participation:

7 WHY PEOPLE CHOOSE TO PARTICIPATE IN THE INFORMAL SECTOR IN SRI LANKA 231 rationing and individual preference. Rationing occurs because of demand shortages in the market. It is affected by labour market conditions in a given location as well as selection criteria used by potential employers based on education, experience, and other work-related skills. Preferences are influenced by a worker s expected income and other individual and household characteristics. Together, rationing and preference determine how individuals are slotted into various labour market sectors. In this framework, it is assumed that the choices available to the individuals are governed by opportunities in the labour market as well as the capability of individuals to make use of them. The individual capabilities are governed by the access and the quality of services available to individuals (that is, education and training) and the access to infrastructure (for example, roads, transport, etc.). Empirical Model The employment status of individuals is examined by estimating a multinomial logit model, as delineated in Maddala (1983). We assume that the individual faces a utility function, which is dependent on the individual s preferences determined by his personal and household characteristics, and labour market characteristics that affect labour demand and the expected wage rate he or she can receive on the job: (1) U ij = U(X i,w ij,z j ) where i indexes individuals and j indexes occupations. The wage of an individual is the function of the same (or a sub- set of) individual (X i ) and job (Z j ) characteristics as (2) W ij = f(x i, Z j ) Substituting (2) into (1) and assuming a linear functional form yields: (3) U ij = βx i + αz j + ε ij The individual i then faces the problem of maximisation of the expected utility, E (U ij ), by choosing the sector j. The observed selection by the individual would be an outcome of both rationing by the labour market and potential employers as well as individual preference. This study examines the factors that determine the sector placement of individuals by way of estimating a multinomial logit model as described above. Individual characteristics, household characteristics as well as community characteristics were employed in the explanatory analysis as suggested by anecdotal evidence. Individual characteristics such as age and gender as well as household characteristics such as marital status, head of the household, etc., were employed to capture the individual preferences. As proxy measures for human capital, education attainment and training were included to capture the employer s preferences for skills, education, and experience. Further, community characteristics and location variables were employed to capture the available labour market conditions such as credit, markets, and infrastructure. Explanatory variables were selected on the basis of the relevance to the study s theoretical model as well as data availability. Some of the variables employed in occupation choice

8 232 The Indian Journal of Labour Economics may have reverse causality. This leads to bias in multinomial estimates. Although the instrumental variables technique is recommended to overcome this problem in practice, such instruments are often hard to find. 4 Therefore, it is assumed that these biases are so small as to be virtually negligible. 4. Sample Selection and Classification of Employment Only working age individuals that is, individuals between the ages of 16 and 60 years were considered for this study. From this sample, the economically inactive people were also deleted. For the purpose of this study, the formal sector and informal sector considered in the two surveys were defined as follows. Classification of Economic Activities In the SLIS , the definition of the informal sector is not straightforward. Therefore, the following procedure was adopted to define the informal sector. First, the economic activities of the individuals were classified by the type of economic activity: salaried employment in the public sector, salaried employment in the private sector, wage employment, self-employment, and unemployment. Although the definition of the first two categories of employment and unemployment was straightforward, the definitions of wage employment and self-employment were somewhat obscure. For the purposes of this analysis, individuals who were engaged in petty or major businesses, or those who were engaged in their own farm activities and fishing were considered to be self-employed. The remaining, who were engaged in farm/non-farm casual labour and those providing personal services, were classified as wage workers. Further in the SLIS , information on the economic activities of the individuals was obtained for the 12 months prior to the survey. Some individuals were engaged in more than one type of economic activity over the year. This was especially true for individuals in self-employment or wage employment. Some individuals in salaried employment were also engaged in other types of economic activities. 5 For the purposes of the empirical model, employment was defined according to the following hierarchy: public salaried, private salaried, wage- or self- employment, unemployment, that is, if an individual who is in public salaried employment was also engaged in self-employment, he was considered to be in public salaried employment. For the purposes of this paper, in analysis of the SLIS data, the public and private salaried employees are considered to be in the formal sector, while the wage employed and self-employed are considered to be in the informal sector. According to the definitions discussed above, the economic activities of the individuals are as follows: Sector/Economic Activity Sample Size (persons) Public sector 1,808 Private sector 2,048 Informal sector 6,361 Unemployed 1,733 Total 11,950

9 WHY PEOPLE CHOOSE TO PARTICIPATE IN THE INFORMAL SECTOR IN SRI LANKA 233 The incidence of the informal sector (wage and self-employed) are further categorised as follows: Sector/Economic Activity Sample Size (persons) Both wage and self-employment 280 Wage employment alone 3039 Self-employment alone 3,042 Sub Total 6,361 In contrast to the SLIS survey, in the CFS survey, the private formal sector is clearly defined. According to the CFS definition, the formal private sector is considered to include private sector institutions that contribute to the Employees Provident Fund (EPF) or maintain their own funds for contributory retirement benefit scheme with the approval of the Commission of Labour. According to that definition, the CFS sample is categorised as follows: Sector/Economic Activity Sample Size (persons) Public sector 2,283 Formal private sector 2,792 Informal private sector 11,435 Unemployed 1,707 Total 18,217 Then the informal private sector was further categorised as follows: Sector/Economic Activity Sample Size (persons) Wage employment 4,666 Self-employment/Employer 5,344 Unpaid family worker 1,425 Sub total 11, Independent Variables The independent variables consist of individual and household variables, including variables pertaining to both formal and job-related education, location variables, and community characteristics. The independent variables used in the analysis are described in Table 3. Apart from the usual demographic variables, several individual and household level variables were also used in the analysis that were deemed to affect the individuals decisions pertaining to labour force participation. As described above, the underline theoretical model assumes that individual participation in different employment sectors is affected by individual preferences and rationing. The variables affecting individual preferences are assumed to be the usual demographic variables, the marital status, and whether the individual is one of the primary earners of the household.

10 234 The Indian Journal of Labour Economics The household level variables considered include variables that capture the structure of the household, that is, whether the household includes dependents, and the wealth of the household. Both these factors could affect the employment decisions of individuals. When the children are very young, or when they are physically dependent on elders, the other members of the household will have less time for productive work, as they are called upon to care for children and the elderly. Several variables were included to capture the employer s preferences for skills and education and experience. Variable Individual Characteristics Male Age Head of household Marital Status Currently married Never married Other Education Less than primary level Secondary level Table 3 Description of the Independent Variables: SLIS Analysis More than secondary level Training Professional Technical Vocational Household Characteristics Number of kids Number of elders Household member working abroad Household assets (in Rs. millions) Location Western Central Southern North-east North-western North central Uva Sabaragamuwa Community Characteristics Time to the market Time to a main road Access to electricity Community unemployment rate Description = 1 if male Age = 1 if head of household = 1 if currently married = 1 if never married = 1 if previously married = 1 if either no education or less than primary level education = 1 if the individual has completed primary level of education but not secondary level = 1 if the individual has more than a secondary level education = 1 if obtained professional training = 1 if obtained technical training = 1 if obtained vocational training Number of 0 to 16 year old children in a household Number of older than 60 years adults in a household = 1 if the household has ever had a member working abroad household financial and physical assets in millions of rupees (does not include fixed assets) = 1 if in Western province = 1 if in Central province = 1 if in Southern province = 1 if in Northern or Eastern provinces = 1 if in North Western province = 1 if in the North Central province =1 if in Uva province =1 if in Sabaragamuwa province time taken to go to the closest market (minutes) time taken to go to a main road (minutes) =1 if the main source of lighting is electricity = No. unemployed/ total labour force * 100 (calculated for each community)

11 WHY PEOPLE CHOOSE TO PARTICIPATE IN THE INFORMAL SECTOR IN SRI LANKA 235 Apart from the variables described above, several variables were included to capture the opportunities that individuals have in accessing economic resources, such as credit, markets, and infrastructure. Access to credit was captured by two variables. The first describes whether any member of the household worked abroad, which for many rural households, would provide an exogenous avenue of income. The second indicates the amount of mobile assets, such as savings accounts, saving certificates, fixed deposits, held by the household. In addition to the variables described above, other variables were also included to capture an individual s ability to access markets and economic infrastructure, such as roads and electricity, and the local labour market conditions. Apart from the training skills and prior working experience, the same set of independent variables was used in both the analyses. In the CFS analysis, the training skills and working experience variables were defined as shown in see Table 4. Technical Skills No training Professional Other training Elementary skills Work Experience No experience Table 4 Description of the Additional Independent Variables CFS Analysis Description = 1 if individual has not obtained any professional or technical training = 1 if obtained professional training = 1 if obtained marketing, secretarial training = 1 if obtained training for skilled work = 1 if individual has no prior experience 1-5 years experience = 1 if individual has 1-5 year work experience Above 5 years experience IV. SAMPLE CHARACTERISTICS 1. Individual Characteristics = 1 if individual has more than 5 years work experience Amongst the four groups under consideration, in terms of individual characteristics, the unemployed distinguished themselves from the other three groups in several ways. The unemployed were, on an average, in their early twenties. Both the analyses reveal that in comparison to the unemployed, the public salaried and the informal sector workers were in their late thirties or forties, and the private salaried workers were in their early thirties. Perhaps partly due to their young age, only a very small proportion of the unemployed were heads of households. Most of them were not married 95 per cent in SLIS and 83 per cent in CFS The public salaried and the informal sector workers were predominantly male, but the number of private salaried workers and the unemployed was close to a balanced mix of males and females. 2. Education Although in terms of age, sex, and other individual characteristics, the public salaried and the informal sector workers were very similar, in terms of education, they were quite distinct from each other. The public salaried workers were significantly more educated than the

12 236 The Indian Journal of Labour Economics Table 5 Sectoral Characteristics of Independent Variables SLIS Data (Means) Sample Public Private Wage or Self- Unemployed Total Salaried Salaried employment ,950 Individual Characteristics Male Age Head of household Marital Status Currently married Never married Other Education Less than primary level Secondary level More than upper secondary level Training Professional Technical Vocational Household Characteristics Number of kids Number of elders household members working abroad Household assets (Rs.) 94, ,786 39,451 47,558 60,000 Location Western Central Southern North-east North-western North central Uva Sabaragamuwa Community Characteristics Time taken to reach the market (tens of minutes) Time taken to reach a main road (tens of minutes) Electricity Community unemployment rate Source: Author s calculations using SLIS data.

13 WHY PEOPLE CHOOSE TO PARTICIPATE IN THE INFORMAL SECTOR IN SRI LANKA 237 informal sector workers. According to the SLIS survey in , on an average, 74 per cent of the public salaried workers had more than an upper secondary level education, as compared to only 19 per cent in the informal sector, while the corresponding figures in were 65 per cent and 18 per cent, respectively. The public salaried individuals were also more professionally, technically, and vocationally trained than individuals in all other sectors, particularly those in the informal sector. According to the analysis, almost 95 per cent of the informal sector workers do not have any professional or technical training. 3. Household Characteristics The four groups were not significantly distinct in terms of household characteristics, other than that the unemployed came from households with fewer young kids, the informal sector workers came from households with fewer elders, and the private salaried workers had a higher percentage of family members working abroad. 4. Location The private salaried workers predominantly came from the western province, followed by the public salaried workers. 5. Community Characteristics More of the informal sector workers and the unemployed seemed to come from the rural areas than the individuals in the other two sectors. They had to travel longer to reach a market or a main road, and had less access to electricity. The community unemployment rate not surprisingly was the highest for the unemployed sector, while it was similar for all the other sectors. The sample means of the SLIS data and the CFS data are given in Tables 5 and 6, respectively. V. ESTIMATED RESULTS OF THE EMPIRICAL MODELS 1. Results of the Sector Participation Model for the Full Sample Table 7 presents the results of the SLIS analysis, multinomial logit equation that compares the characteristics of the individuals in the private, informal, and unemployed sectors to those in the public sector, which is the base category. Several variables distinguish the individuals in different sectors from those in the public sector. Individual and Household Characteristics Results show that all the three groups the private salaried workers, informal sector, and the unemployed distinguish themselves from the public sector in several respects. Being a male reduces the likelihood of being in the private salaried category and the unemployed sectors, while it increases the likelihood of being in the informal sector. Being older decreases the likelihood of being in the private salaried category, and the informal and unemployed sectors. Being the head of the household increases the likelihood of being in the informal sector, while it decreases the likelihood of being unemployed, as compared to the public

14 238 The Indian Journal of Labour Economics Table 6 Sectoral Characteristics of Independent Variables CFS Data (Means) Public Formal Private Informal Private Unemployed Total LF Sample size Individual Characteristics Male Age Household head Marital Status Currently married Never married Other Household Characteristics Number of kids Number of elders household members working abroad household assets (in Rs. millions) Education Less than primary level Secondary level Upper secondary level AL and above Technical Skills No skills Professional Other training Elementary skills Work Experience No experience years experience Above 5 years experience Location Western Central Southern Northern Eastern North-western North central Uva Sabaragamuwa Community Characteristics Community Unemployment rate Electricity Source: Author s calculations using CFS data.

15 WHY PEOPLE CHOOSE TO PARTICIPATE IN THE INFORMAL SECTOR IN SRI LANKA 239 Table 7 Results of the Multinomial Logit Equation for all in the Labour Force: SLIS Analysis Private Salaried Informal Unemployed Individual Characteristics Male -0.73*** 0.78*** -0.78*** Age -0.04*** -0.01** -0.18*** Household Characteristics Household head *** -0.24* Number of kids ** Number of elders ** Household members working abroad 1.27*** 0.46*** 0.54*** Household assets (in Rs. millions) 0.22* Marital Status Currently married b B Never married 0.79*** 0.36*** 2.03*** Other 0.66*** 1.01*** 1.78*** Education Less than primary level *** 0.54* Secondary level b B Upper secondary level -0.91*** -1.83*** -0.57*** Degree -3.13*** -4.23*** -1.9*** m_edu -1.76*** -1.7*** Training Professional -0.77*** -1.5*** -1.06*** Technical Vocational -0.89*** -2.06*** -1.42*** Location Western b B Central -0.4*** 0.36*** Southern -0.5*** 0.25** North-east -0.89*** *** North-western -0.6*** 0.41*** North central -1.32*** 0.46*** -0.38* Uva -0.95*** 0.24* -0.44** Sabaragamuwa -0.45*** 0.55*** Community Characteristics Time taken to reach the market (tens of minutes) Time taken to reach a main road (tens of minutes) Electricity -0.27*** -0.78*** -0.22** Community unemployment rate -0.01*** -0.01*** 0.08*** Constant 3.10*** 2.19*** 3.79*** Number of observations Pseudo R Note: The significance levels at 1, 5, and 10 per cent are indicated by ***, ** and * respectively. Source: Author s calculations using SLIS data. sector. Being in a household with young kids decreases the chance of being unemployed, and being in a household with the elderly increases the chance of being unemployed. Having a household member working abroad increases the chance of being in all the other sectors, as compared to the public sector. The possession of high household assets increases the chance of being in the private sector. Being never married or previously married increases the chance of being in all other sectors as compared to the public sector.

16 240 The Indian Journal of Labour Economics Educational Characteristics The more educated individuals are more likely to be in the public sector than individuals in all the other sectors. This is true for both formal education and other types of training. The greatest difference in education levels was seen for the individuals in the informal sector, as compared to those in the unemployed and the private sectors. Location Individuals residing in provinces other than the western province were less likely to be in private salaried employment as compared to those in public salaried employment. In contrast, individuals from most other provinces, with the one exception of the North-east, were more likely to be in the informal sector than in the public salaried sector. Individuals in the North-east, North central and Uva provinces were less likely to be unemployed than in public salaried employment. Community Characteristics Only two of the four community characteristics determined the sector participation of individuals. Individuals in communities using electricity as the primary source of lighting were less likely to be in all the other sectors as compared to the public sector. Not surprisingly, individuals coming from communities with high unemployment rates were more likely to be unemployed, and were less likely to be in private salaried and informal employment than in public salaried employment. Table 8 presents the results of the multinomial logit analysis of the CFS This analysis justifies the observations that were made in the SLIS analysis. Individual characteristics, household characteristics as well as community characteristics of the individuals in the private, informal and unemployed sectors were not varied with the time lag. Further, this analysis also highlights that the greatest difference in education levels can be seen in the informal private sector. Being an educated individual reduces the likelihood of being in the informal private sector. Not having any professional or technical skill also increased the chance of being in the informal sector. Individuals who have elementary skills are more likely to be in the informal private sector. Most of the individuals with work experience are in the public sector, as compared to all the other three sectors. Table 8 Results of the Multinomial Logit Equation for all in the labour force CFS Analysis Variable Description Formal Private Informal private sector Unemployed Individual Characteristics Male *** *** ** Age *** ** *** Household head ** *** Marital Status Currently married Never married *** *** *** Other ** *** ***

17 WHY PEOPLE CHOOSE TO PARTICIPATE IN THE INFORMAL SECTOR IN SRI LANKA 241 Contd... Variable Description Formal Private Informal private sector Unemployed Household Characteristics Number of kids * ** Number of elders ** *** Household members working abroad *** *** ** Household assets (in Rs. millions) ** Education Less than primary level *** *** * Secondary level Upper secondary level *** *** *** AL and above *** *** *** Technical Skills No skills *** *** Professional Other training * Elementary skills ** *** *** Work Experience No experience *** 1-5 years experience Above 5 years experience *** *** *** Location Western Central *** *** Southern *** *** ** Northern *** Eastern *** *** ** North-western *** ** North central *** Uva *** Sabaragamuwa *** ** Community Characteristics Community Unemployment rate ** *** Electricity *** *** *** Constant *** No. of observations Pseudo R Note: Significance levels at 1, 5, and 10 per cent are indicated by ***, ** and * respectively. Marginal effects of the regression are annexed. Source: Author s calculations using CFS data. Results of the Sector Participation Decision for Individuals in the Informal and the Unemployed Sectors From the earlier analysis, it was established that individuals in the informal sector were distinct from individuals in the public sector in several ways. In order to further analyse

18 242 The Indian Journal of Labour Economics the informal sector, a separate multinomial equation was estimated for the individuals in the informal sector for both the SLIS and CFS analyses. In the SLIS analysis, the dependent variable took the following values: both the selfemployed and those receiving wages for employment (hereafter referred to as the WSE), self-employed, wage employed, unemployed. The model was estimated by using the workers employed for wages as the base category. Individual and Household Characteristics Several individual and household characteristics distinguished the individuals in the four employment categories in the informal sector. Being male increased the likelihood of being in the WSE sector and it decreased the likelihood of being in the unemployed sector. Being old and being the head of the household increased the likelihood of being in the WSE and the self-employed sectors, while it decreased the likelihood of being in the unemployed sector. Being in a household with young kids increased the likelihood of being in the WSE sector, while it decreased the likelihood of being in self-employment and unemployment. Having a household member working abroad decreased the likelihood of being in self-employment. Having household assets increased the likelihood of being self-employed or unemployed. Being never married decreased the likelihood of being in the WSE and the self-employment sectors, while it increased the likelihood of being unemployed. Education Having less than a primary level of education decreased the likelihood of being in selfemployment and increased the likelihood of being unemployed. Having more than an upper secondary level education, on the other hand, had the exact opposite effect: it increased the likelihood of being self-employed and being unemployed. Being professionally trained decreased the likelihood of being self-employed, while having having undergone other types of vocational or technical training increased the likelihood of being unemployed. Location As compared to being in western province, being in all the other provinces increased the likelihood of being in the WSE and self-employment sectors. Being in the central and southern provinces decreased the likelihood of being unemployed, while being in all the other provinces did not affect the likelihood of being unemployed as compared to being in the western province. Community Characteristics Being away from the market increased the likelihood of being in the WSE and the self-employment sectors, while being away from the road decreased the likelihood of being unemployed. Having electricity decreased the likelihood of being in the WSE sector and the self-employment sector, while it increased the likelihood of being unemployed. Table 10 presents the results of the informal sector and unemployed analysis by using the CFS data. The multinomial logit equation compares the characteristics of the

19 WHY PEOPLE CHOOSE TO PARTICIPATE IN THE INFORMAL SECTOR IN SRI LANKA 243 Table 9 Results of the Multinomial Logit Equation SLIS Analysis: Informal and Unemployed Individual Characteristics Wage + Selfemployed Self-employed Unemployed Male 1.84 *** *** Age 0.02 ** 0.03 *** *** Household Characteristics Household head 1.37 *** 0.27 *** *** Number of kids 0.09 * ** -0.1 ** Number of elders Household member working abroad *** Household assets (in Rs. millions) *** 2.33 *** Location Western b B b Central 0.96 ** 0.34 **** *** Southern 1.96 *** 0.25 ** ** North-east 1.88 *** 0.7 *** North-western 1.61 *** 0.68 *** North central 2.12 *** 1.85 *** Uva 2.85 *** 1.72 *** Sabaragamuwa 1.39 *** 0.68 *** Marital Status Currently married b B b Never married *** ** 1.73 *** Other *** 0.61 * Education Less than primary level *** *** Secondary level b B b Upper secondary or higher level *** 1.45 *** m-edu Training Professional *** Other ** Community Characteristics Time taken to reach the market (tens 0.08 *** 0.09 *** 0.04 * of minutes) Time taken to reach a main road (tens * of minutes) Electricity *** 0.57 Community unemployment rate *** *** 0.08 *** Constant *** *** 1.87 *** No. of observations 8094 Pseudo R Note: Significance levels at 1, 5, and 10 per cent are indicated by ***, ** and *, respectively. Source: Author s calculations using SLIS data.

20 244 The Indian Journal of Labour Economics individuals who are self-employed/employers, unpaid family workers and the unemployed to employees in the informal sector, which is the base category. Individual and Household Characteristics Being a male reduces the likelihood of being self-employed, an unpaid family worker or unemployed. Being old and head of the household decreases the likelihood of being unemployed while it increases the likelihood of being self-employed or an employer. Education and Training As identified in the SLIS analysis, having more than an upper secondary level of education increases the likelihood of being self-employed, an unpaid worker or unemployed. Having acquired education below the primary level reduces the chances of being self-employed, an unpaid family worker or unemployed. Similarly, having no training also reduces the likelihood of being unemployed. Table 10 Results of the Multinomial Logit Equation CFS Analysis: Informal and Unemployed Variable Description Individual Characteristics Self-employed/ Employer Unpaid Family Worker Unemployed Male *** *** *** Age *** *** *** Household head *** *** *** Marital Status Currently married Never married *** *** Other ** ** ** Household Characteristics Number of kids ** *** *** Number of elders ** Household members working abroad ** * Household assets (in Rs. millions) Education Less than primary level *** *** *** Secondary level Upper secondary level *** *** *** AL and above *** *** *** Technical Skills No skills ** *** Professional skill Other training Elementary skills ** Work Experience No experience ***