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Labour Supply in a High Unemployment Economy: Data

Labour Supply in a High Unemployment Economy: DataIn this section, we briefly discuss the Riinvest HLFS data and explain our estimation strategy. The Riinvest HLFS was conducted in December 2002. The unit of observation was the household and the sample was representative of urban and rural areas and the 7 main regions of Kosova. The sample consisted of 1,252 households (randomly selected after sample stratifications) with 8,552 members that is 0.45 percent of the population in Kosova. The survey questionnaire included questions regarding the demographic labour market information of each household member, household’s incomes and expenditures, household members abroad, the household land etc.
Figure A1 in Appendix shows how an individual’s labour force status is determined according to this survey, which complies with international labour standards. The LFP rate estimated at 58 percent is especially low for females. Following our discussion above, although we have data from the Riinvest HLFS survey, the model presented by Equation for the hours of work seems inappropriate in our case as only 30 percent of the sample of working age individuals in Kosova are employed (i.e. have h>0). From the practical point of view, it seems inappropriate to consider 70 percent of the sample as described by the corner solution of h=0.
We estimate a binary choice model of labour supply as described by Equation. Unlike those studies that consider participation to be equivalent to employment, we define participation as the sum of employment and unemployment. Note that for the employed in Kosova the desired hours of work are positive (hdi>0) by definition. Following our discussion above regarding the desired versus actual hours of work, we assume that hdi>0 for the unemployed as well since they engage in job search indicating that they would like to work. The inactive persons are considered as non-participants with hdi=0. Following this approach, we account for the demand-side restrictions in the labour supply decisions.
The three categories of explanatory variables are identified in Table 1. Conventionally, the educational dummies proxy for the potential wage as well as the probability of getting employment (because when unemployment is high the bargaining position of employees is expected to be weaker and the unemployed are likely to trade-off lower wages for a more secure employment). Age and age squared proxy the changing price of leisure over the life cycle as explained by the intertemporal substitution hypothesis. The dummy for marital status controls for the effect of family obligations as well as the effect of culture and attitudes toward work. A dummy for females of age 25-40 controls for the effect of females engaging in childbearing activities and can be thought of as accounting for some of the opportunity costs of employment.

Table 1: The explanatory variables used in the estimations of the determinants of labour force participation

Explanatory variables Definition of variables
Personal characteristics
Age Age of the respondent
Age squared Age squared
Education Dummies: less than upper-secondary (omitted), upper-secondary and higher
Marital status Dummy=1 if married, 0 otherwise
Female of age 25-40 Dummy=1 if true, 0 otherwise
Household characteristics
A household member is abroad Dummy=1 if true, 0 otherwise
Household labour incomes In per capita terms (€/month) net of own wage if employed: (incomes from salary, from working on the family farm and incomes from family business
Household non-labour incomes In per capita terms (€/month): other household incomes not included above
Contextual characteristics
Residence Dummy=1 if from urban areas, 0 otherwise
Regional dummies or regional 7 regions (omitted dummy for the region of
unemployment rate Prishtina)