Analyzing Effects of Migration Status of Labourer on Economic Conditions: Case Study Solwezi Mining Sector

This study investigates the effects of migration on labor markets, specifically focusing on the mining sector in Solwezi. The primary objectives include effects of migration status of household on wage inequality. effects of migration status on house hold poverty in Solwezi and effects of migration on labour force participation and housing condition in Solwezi mining industry within this context. Data were collected through a structured questionnaire, targeting Kansanshi mining sector, employing random sampling techniques with a sample size of 50 respondents. Data analysis was conducted using Stata software, supplemented by frequency tables and pie charts for clearer representation. The study employs a descriptive research design and triangulation to validate findings through cross-method analyses. The analysis of migration status effects on wage inequality, household poverty, and socioeconomic conditions in Solwezi presents compelling statistical evidence on labor market dynamics. A Pearson Chi-square statistic of 20.73 (p = 0.0021) reveals significant wage disparities among migration groups, with migrants earning higher wages, while economic migrants and undocumented migrants face lower earnings. Respondents identified language barriers (45%) as a primary factor contributing to wage gaps. Housing conditions also vary significantly, with a Pearson Chi-square statistic of 0.0019 indicating a strong link between migration status and living standards. Migration status 5 enjoys 100% good housing, while statuses 1 and 3 have 50% and 72.73% in average or poor housing, respectively. A Pearson Chi-square of 18.02 (p = 0.0062) underscores the variation in labor force participation, with migrants and refugees participating more actively than other groups. Consumption patterns also differ significantly (F-statistic = 5.08, p = 0.004), with 78% of respondents spending below 3500. Migrant participation in local businesses is notably higher for categories 1 and 2 (Chi-square = 20.05, p = 0.0027). These disparities highlight the need for targeted policies to address wage inequalities, housing shortages, and limited access to health services.