In Iran, Agricultural Bank is one of the institutions who have played an influential role in allocating loans in rural communities of Iran. In other words, in terms of number and amount of loans, micro loans account for 95 percent of total loans and 50 percent of the loans that were paid in Agricultural Bank of Iran in 2013. As a result, applicants for micro loans constitute a significant portion of the bank’s customers.
There are two main sources for financing these lending to small-scale farmers: increase in bank monetary resources and recovery of overdue awarded credits. Meanwhile, the specific approach of Agricultural Bank as a professional institution has resulted in less customer savings in comparison with other banks. Consequently, collection of overdue payments is the most important resource to finance future applications for loans. Unfortunately, Agricultural bank challenges a high rate of over due repayment and the percentage of default on loans is calculated around 20 percent. These non-payments have initiated major problems such as increased credit risk, waste of resources and time, increase in bank costs, and a blockage in the portion of the Bank’s lending resources and disruption in the plans of the Bank. Thus, the Agricultural Bank as a provider of financial services to farmers is entirely dependent on repayment of loans to provide necessary credit. However, Due to the high incidence of default in Agricultural Bank of Meshkinshahr, this bank faces serious problems in its potential in allocating loans to farmers in rural areas. Accordingly, the main objective of this study was to investigate the factors affecting the repayment behavior of loans by farmers in Agricultural bank of Meshkinshahr.
The results from the formal decomposition of total variance into between- and within-plant components based upon equations (1) and (2) are reported in Table 1 and Figure 1. Table 1 includes just selected years while Figure 1 depicts the patterns of the components for all years from 1975-92. While the formal decomposition is in terms of levels of hourly wages we are concerned about the possible effects of changes in scale. Therefore, the components in Figure 1 are depicted in terms of coefficients of variation. Continue reading
III. Between-Plant and Within-Plant Components of Wage Dispersion
In this section, we combine data from household and establishment surveys to decompose the variance of hourly manufacturing wages into between-plant and within-plant components. The decomposition methodology is from Davis and Haltiwanger (1991, 1996). The analysis in this section extends their results over a longer time period and incorporates nonproduction workers who work in auxiliary establishments such as central administrative offices, research facilities, and warehouses. The variance of hourly wages across hours worked in the manufacturing sector can be written as:
where a denotes production workers’ share of hours worked, Vp denotes the variance of wages across hours worked by production workers, V” denotes the variance of wages across hours worked by nonproduction workers, Wp is the hours-weighted mean of the production worker wage, and W” is the hours-weighted mean of the nonproduction worker wage. Equation (1) expresses the total variance of hourly wages as the hours-weighted sum of the variances of production and nonproduction workers along with a term reflecting the contribution of differences in the mean wages across production and nonproduction workers. For each worker type, the variance can be further decomposed as:
where V^p represents the between plant component and V^p the within plant component for worker type j.
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To estimate the components of the decompositions in (1) and (2) for the manufacturing sector we proceed as follows. We utilize household data from the 1975 through 1993 March Current Population Survey (CPS) and establishment data from the Longitudinal Research Database (LRD). From the individual-level wage observations in the CPS files, we calculate α, V, Vp, Vn, Wp, W” for all workers employed in manufacturing in each of the years under consideration (1975-1992). We also generate the production and nonproduction variances at the two-digit SIC industry level. From the plant-level observations in the LRD, we calculate the between-plant component for each worker type for each of the corresponding years at the two-digit level. For each worker type, we generate the within-plant component in equation (2) by taking the difference between the total variance calculated from the CPS and the between-plant variance calculated from the LRD at the two-digit level. Appropriately aggregating the between and within plant components across industries yields the decomposition at the total manufacturing level. As part of this aggregation, we decompose the overall between-plant component for each worker type into a between-plant, within-industry component and a between-industry component.
Kremer and Maskin (1996) also provide a theoretical structure for our empirical analysis. Their model can account for the simultaneous existence of increased wage inequality and increased segregation of workers of different skill levels into different plants. These forces are set in motion by changes in the skill distribution, which can be due to a skill-biased technical change, but need not be. The main features of their model are imperfect substitution among workers of different skills, complementary tasks within a plant, differences in worker skill effects which vary by task, and an exogenous distribution of worker skills. Intuitively, there are two competing forces at work in determining the equilibrium matching patterns at plants. The asymmetry of tasks in the production function favors cross-matching (less segregation) but the complementarity between tasks favors self-matching (more segregation). Unequally skilled workers will be cross-matched up to the point in which the differences in skills is so great that the second effect overwhelms the first and the plant moves to self-matching. With a diffuse skill distribution, an increase in the mean skill-level exacerbates wage inequality across plants. Continue reading
II Review of Theoretical Literature
The two theoretical papers that form the basis for our analysis are the papers by Caselli (1999) and Kremer and Maskin (1996). In this section we briefly outline the two models and present the most relevant predictions of the models. Caselli (1999) models the effect of a technical revolution on the dispersion of wages and productivity. In the Caselli model a technology is a matching of workers of type i who have the appropriate set of skills to operate machines of type I. An important feature of this technology assumption for our purposes is that workers are completely segregated by skill across plants. Continue reading
The paper proceeds as follows. In Section II, we briefly outline both the Caselli (1999) and Kremer-Maskin (1996) models as well as present the implications of these models that are most relevant for our analysis. In Section III we decompose the total dispersion in hourly wages into within and between components over the 1975-92 period. We find that virtually the entire increase in overall dispersion in hourly wages for U.S. manufacturing workers from 1975-92 is accounted for by the between-plant components. This result is quite important as it is at the core of the theories we are investigating. Continue reading
Our paper can also be viewed as helping to connect various strands of the literature studying wages, productivity, and computers. For example, many recent studies have sought to understand either the relationship between computers and wages (e.g., Krueger (1993), Doms, Dunne, and Troske (1997), Autor, Katz, and Krueger (1998)) or, alternatively, computers and productivity (e.g., Oliner and Sichel (1994), Greenan and Mairesse (1996), Siegel (1997) and Bresnahan, Brynjolfsson, and Hitt (1998)). One of our main objectives is to investigate these relationships simultaneously. Continue reading
THE ROLE OF COMPUTER INVESTMENT
Striking changes in the structure of production, wages, and employment have occurred over the last several decades. The introduction of computers and, more generally, advanced technologies into the workplace is widely viewed as one of the major factors underlying these changes. In particular, the role of advanced technology and computers has been closely linked to the rising inequality of worker wages. One hypothesis is that the introduction of advanced technologies and/or computers has led to a rising demand for skilled workers which, in turn, has led to a rise in the wages of skilled workers relative to unskilled workers. Competing hypotheses concerning the source of rising wage inequality include shifts in product demand and changes in institutional factors such as the decline of unions and changes in pay norms. Continue reading
5. Closing remarks
In summary, this paper finds self-insurance of property-casualty risks increases in state taxes. Tests are conducted assessing the relation between a state’s property-casualty insured losses and its tax levy on the insurance industry. As expected, a negative relation holds for nonautomobile coverage and automobile physical damage coverage. Similar relations are detected for workers’ compensation benefit payments. These findings are consistent with consumers opting to self-insure rather than bear the incidence of higher insurer taxes. Continue reading
North Dakota and Wyoming are excluded from the analysis because they prohibit selfinsurance, and Texas is excluded because 1993 was the first year that it permitted self-insurance. For the 47 remaining states, the percentage of workers’ compensation not covered through selfinsurance ranges from 54 percent to 92 percent with a mean (median) of 77 (79) percent and a standard deviation of 9 percent. A categorical variable (NOPRIVATE) is added to the explanatory variables to identify the four states (Nevada, Ohio, Washington, and West Virginia) that restrict coverage to self-insurance or state funds. Continue reading