Introduction
Understanding the spatial distribution of employment-generating facilities in rural areas is crucial for economic development. Planning future initiatives based on this understanding can lead to the economic growth of villages and, ultimately, the country’s economy. Since economic activities, along with facilities and employment opportunities, are not evenly distributed across regions, each area requires tailored development programs that align with its unique characteristics. This study aims to evaluate spatial justice in the distribution of facilities and the trends in job creation across major economic sectors in the rural areas of Lorestan province, analyzed by county.
 
Methodology
In the present study, a descriptive and analytical research method was employed. The analysis focused on the area of Lorestan province, broken down by county. To gather information, data from the Deputy of Entrepreneurship and Employment Development at the Ministry of Cooperatives, Labor, and Social Welfare in Lorestan province from November 2021 were used, specifically for the registration and receipt of entrepreneurship credit loans. The study examined the pattern of dispersion and spatial distribution of rural employment-generating facilities and credits across various fields within the counties of Lorestan province in 2021. The spatial distribution of these employment-generating loans at the county level was mapped using ArcMap software. Finally, the hot spot analysis technique was applied to rank the distribution of loans received by the villagers.
 
Findings
According to data from the Deputy of Entrepreneurship and Employment Development of the Ministry of Cooperatives, Labor, and Social Welfare in November 2021, a total of 5,445 employment-generating projects across Lorestan province received financial assistance. The highest number of projects was in Khorramabad County, with 946 projects, while the lowest was in Rumeshkan, with 163 projects. The distribution of financial assistance and direct employment generated by these projects followed a similar pattern. Specifically, 24% of the financial assistance and 22% of the direct employment were in Khorramabad, while the lowest percentages, 2.3% and 2.7%, respectively, were in Rumeshkan. The findings show the distribution of project numbers, financial assistance, and direct employment across the counties. Regarding the correlation between direct employment generated by rural employment loans and the unemployment rates in rural areas of Lorestan counties, the data indicates a negative correlation of -0.13. In other words, counties with higher unemployment rates tended to have lower levels of employment generation, and vice versa.
To identify the spatial distribution pattern of rural employment loans across the counties of Lorestan province, the amount of financial assistance provided in each county was analyzed. Using the Hotspot technique in ArcMap, the spatial pattern was identified. The analysis revealed that counties in the central and southeastern parts of the province received the highest proportion of financial assistance, while peripheral and western counties received the least. This analysis indicates that the distribution of financial assistance across Lorestan’s counties has not been balanced or equitable.
Another major indicator for evaluating the distribution of employment loans in Lorestan province is the Hotspot analysis. Maps were created and analyzed at confidence levels of 90%, 95%, and 99%, focusing on both hot and cold spots. The Hotspot index in ArcMap shows significant spatial disparities in the distribution of loans across beekeeping, agriculture, horticulture, fisheries, and aquaculture sectors, indicating inequality among counties. However, in sectors like medicinal plants, tourism, services, and animal husbandry, these disparities are not significant, and no identifiable inequality was observed.
To evaluate the correlation between the production ratio of each county in the main rural activity sectors and the ratio of loans distributed across various sectors, a correlation analysis was conducted. The results indicate a positive correlation between production capacity and activity in the rural areas of Lorestan’s counties and the amount of loans distributed, except in the fisheries and fish farming sector, which showed a negative correlation. Specifically, the correlation in the animal husbandry sector is 94%, in tourism, handicrafts, and services 65%, in beekeeping 52%, in agriculture 42%, in the production and processing of medicinal plants 24%, in horticulture 13%, and in fisheries and fish farming -26%. Although the correlation is relatively low in the agriculture, medicinal plants, horticulture, and fisheries sectors, it is not negative. Providing more employment loans in these sectors in counties with potential can contribute positively to the sustainable development of rural areas. Overall, the distribution of loans has been aligned with the existing capacities in Lorestan’s rural areas, aiming to activate and strengthen these capacities. However, in the case of fisheries and fish farming, the distribution of loans has not been appropriate.
 
Discussion and Conclusion
The research results indicate that spatial justice has not been observed in the distribution of rural employment loans across the counties of Lorestan province. There is no direct correlation between the unemployment rates in rural areas and the allocation of these loans, with most of the funds concentrated in counties neighboring the provincial capital. However, the results also show a significant correlation between the environmental capacity of the counties and the job sectors that received employment loans, suggesting that, in this regard, spatial justice has been maintained across the counties.