Other variables Clause Samples
The "Other variables" clause defines additional factors or conditions that may influence the interpretation or execution of the agreement. This clause typically addresses elements not covered elsewhere in the contract, such as external benchmarks, fluctuating market rates, or unforeseen circumstances that could impact obligations. By specifying these variables, the clause helps ensure that both parties are aware of and can account for potential changes or uncertainties, thereby reducing ambiguity and minimizing disputes related to unexpected developments.
Other variables. 1. Clause B17 (Transfer of and Discharge from Care Obligations): the parties have/have not agreed a Transfer of and Discharge from Care Protocol (delete as appropriate).
A6.2. Clause B19.1 (Service Review): The period for the Service Quality Report shall be every [month/quarter/6months] (delete as appropriate).
Other variables. Construct Item Scale
Other variables. The rest of the variables are less unique to our analysis, so we limit the write-up here.42 Consider the loan contract characteristics. The first is the dependent variable, the Interest Rate on the loan until the next revision. For fixed interest rate loans, this is the yield to maturity of the loan. For variable interest rate loans, this is the interest rate until the date at which the interest rate will be revised as stipulated in the contract.43 The average interest rate on a loan in our sample is 8.12% or 812 basis points (we employ basis points throughout the paper to facilitate the tabulation and interpretation of the results). The loan rate varies widely not only nationally (the standard deviation is 236 basis points), but also at the branch level (the average standard deviation at the branch level is still 217 basis points). Loan fees are not included in our dataset. Loan fees are rarely charged to single-person businesses and are set by the bank’s national headquarters. It is therefore unlikely that loan fees will be determined by any of the local variables of interest. The median loan size is BEF 300,000 (USD 7,500), but varies between BEF 5,000 (USD 125) and BEF 80,000,000 (USD 2,000,000). We will assume in our empirical analysis that loan rate and size are determined jointly. The variable Collateral indicates whether the loan is collateralized or not. We have no further information on the type of collateral provided. Approximately 26% of the loans are collateralized. We will assume, as in ▇▇▇▇▇▇ and ▇▇▇▇▇ (1995), ▇▇▇▇▇▇▇ and ▇▇▇▇▇▇▇ (1998), and ▇▇▇▇▇ and ▇▇▇▇▇▇▇ (1998), among others, that collateral and interest rate conditions are determined sequentially, with the collateral decision preceding the interest rate determination. Indeed, collateral is often pledged at the beginning of a relationship, is adjusted only infrequently or inconsistently, and may end up covering multiple loans. However, we will investigate alternative decision sequences with respect to loan size and collateral in various robustness checks. Another loan contract characteristic is the Repayment Duration of the Loan. For all loans to the firms, we know at what ‘speed’ the loans are repaid. This allows us to compute the exact repayment duration of a loan. We include the natural logarithm of (one plus) this variable in the regression analysis in order to proxy for the risk associated with the time until the loan is repaid. Four dummies capture the effect of the revisability o...
Other variables. Cultural similarity is operationalised as the sum of three different indicators: (1) the existence of a common border between a country pair,12 (2) the sharing of a common language and (3) the existence of common historical and religious tradition, expressed by the similar- ity of religion between two countries.13 Hence, this indicator can vary between 0 and 3 for each country pair (see table A4.3 in the annex). We conceive this variable as static, which means that cul- tural ties and geographic proximity are assumed to be highly stable factors that are not subject to far-reaching changes over time. Economic development is based on the income figures meas- ured as GDP per capita (see table A4.3 in the annex). In the pair approach the income figure is multiplied by the difference in income between the two countries. Domestic political demand for a comprehensive and stringent environmental policy could possibly be expressed in several ways, such as the number of environmental organisations or the public 12 Sea borders count, if located within a 200-mile zone. This results, for example, in a common border between the Netherlands and the UK or Denmark and Sweden.
Other variables. Whereas the main focus of the ENVIPOLCON project is on the influence of international institutions and international trade on environmental policy convergence, we also test a number of add- itional, mainly domestic variables which may be expected to be relevant. With one exception (see below), the set of other variables covered in the present chapter corresponds with that of chapter ł, i.e., environmental problem pressure, income, green party strength, cultural openness and time. Environmental problem pressure is operationalised with the help of two indicators: industrial CO2 emissions per square kilometre and population density. High environmental problem pressure is expected to lead to higher demand for environmental policies and thus to upward convergence.
(H 5.2.1): High environmental problem pressure at t1 (operational- ised as industrial CO2/km2 and population density) will lead to convergence towards the top during the following period. Following the theory of the ‘Environmental ▇▇▇▇▇▇▇ Curve’ a higher income (expressed as GDP per capita) is supposed to lead to upward convergence in two ways. First, higher income is generally associated with a higher degree of industrialisation, higher levels of environmental pollution and thus with higher demand for policy (see above). Second, political demand for the ‘luxury good’ of envi- ronmental policy may be assumed to be higher with increasing income (see further chapter 3).