The Model. While the historical record shows that the Federal Reserve attempted to use the nonrate terms of accessto control discount window borrowing in the 1920s and early 1930s it is not clear the attempts were successful. Assessing whether changes incredit policy had a material effect on bank borrowing requires empirical tests using a model that can measure the administrative pressure applied at the discount window. A useful starting point in developing such a model is in specifying the costs to banks in meeting a reserve need. Cost-minimizing banks would weigh the cost of borrowing from the discount window against the cost of liquidating assets or borrowing from an alternative source. The cost of borrowing from the Federal Reserve is the interest paid at the discount rate, Rd, plus the implicit costs of supervisory surveillance. These costs are those resulting from the administrative pressure supervisors apply to discourage banks from engaging in arbitrage when discount rates were below market rates of interest, as they were for most of the 1920s and many months in the early 1930s. In this model the implicit costs are represented bya borrowing function in which the marginal surveillance cost of access to the discount window, c(B/K), rises with the amount borrowed, B, relative to bank capital, K. Capital is the appropriate scale variable given thatFederal Reserve banks had from the start focused on borrowing relative to capital when seeking to restrain bank borrowing (Xxxxxxxxxxx, 1932, p. 44). The implicit costs may be thought of as the opportunity costs of providing the collateral for the borrowings or the capital adjustmentsrequired by supervisors whose attention is drawn to the bank by the borrowing. The cost of liquidating the assets is the foregone interest on the assets, RA, plus the transactions costs incurred in selling the assets, tc, measured as a portion of the value of the assets. Although a small market for federal funds developed in several cities in the 1920s in which banks could borrow or lend reserves, most banks met reserve drains by liquidating assets — particularly their holdings of call loans and short- term marketable securities (Xxxxxx, 1938, 93-97 and Xxxxxx, 1964) 3-13). The total cost, C, to banks of meeting their reserve need, RN, can be expressed as: where

The Model. 7.1.1 RCC and the MOD will operate within a Public / Public partnership. The overriding objective is to ensure that the site is developed in such a way as to deliver the agreed vision for the site. Hence whatever vehicle is adopted it must ensure that RCC and MOD retain control over exactly what gets delivered.

The Model. As mentioned in the Introduction, we follow Xxxxxxxx and Xxxxxx (2000) and estimate the following two specifications of an equation designed to account for changes in employment in 3-digit ISIC (Rev. 2) manufacturing industries: LDEMPLit = β0 + β1 LDCONSit + β2 LDPRODit + x0 XXXXXxx + x0 XXXxx + uit (5) and LDEMPLit = β0 + β1 LDCONSit + β2 LDPRODit + x0 XXXXXxx + x0 XXXxx (6) + x0 (XXXxXXXXX)xx + uit where uit = μi + xxx xxx xxx ~xxx(0, x0). We assume the cross-section component μi to be fixed since the 3- digit industries that make-up the panel have not been chosen at random. Hence, both specifications are estimated using a fixed effects estimator that is, basically, OLS with cross-section dummies. The variables used may be defined as follows: LDEMPL = The natural log of the absolute value of the change in employment (L) between t and t-n. LDCONS = The natural log of the absolute value of the change in aparent consumption (C = Q + M - X) between t, t-n, Q being output. LDPROD = The natural log of the absolute value of the change in labour productivity, measured as output per worker, between t and t-n. LTREX = The natural log of trade exposure [(X+M)/Q]. IIT = May be GL, ΔGL or A. IITxLTREX = The interaction between IIT and trade exposure. LDEMPL is a proxy for the costs of adjustment in the labour market. The assumption is that the costs of moving labour across industries is proportional to the size of net changes in wage payments and, furthermore, that this proportion is the same for all industries and over time. The expected sign for the coefficient of LDCONS is positive. One would expect the coefficient of LDPROD to be negative since increases in productivity would tend to reduce the labour requirement to produce the same level of output. This expectation is supported by evidence from the accounting measure of employment change found in, e.g., Xxxxxxxx and Xxxxxx (1999) for Belgium, Xxxxxx et al. (1999) for Greece and Erlat (2000) for Turkey. The prior expectation for the coefficient of LTREX is that it should be positive since trade exposure is expected to increase inter-industry specialization pressures (Xxxxxxxx and Xxxxxx, 2000: 730). Finally, the coefficients of both IIT and IITxLTREX are expected to be negative given the smooth adjustment hypothesis. The reason for the introduction of IITxLTREX in the second specification is the expectation that IIT should matter more in sectors where the level of trade is high.

The Model. In this section, we set up an analytical model with two symmetric countries, i and j. In each country, there is a national government which is assumed to be represented by a single policymaker. The policymaker is typically considered as a ruling party of the nation, and the policymaker is concerned with the social welfare of the general public of that country partly because it would aﬀect the prospect of his or her re-election. For simplicity, we suppose that the policymakers make their decisions concerning a particular international environmental issue we are focusing on, independently of the other political agendas. In each country, there exist producers and consumers of goods which emit this partic- ular pollutant in their production and/or consumption, and they respectively derive the benefits of the pollutant emissions in a country i, denoted by ei, during their production and consumption processes in terms ofcost savings, for example. We write the benefit of the pollutant emission, in the aggregate, as Bi(ei) for country i. As for a type of the pollution issue, we consider a case of global pollution, and suppose that the magnitude of environmental damages in country i from this pollution problem is determined simply by the sum of the amounts of the pollutant emitted by the two countries, i.e., ei + ej. We denote the damage cost of country i by Di(ei, ej). Thus, the social welfare of the citizens in country i, Wi(ei, ej), is given by

The Model. This chapter presents the model that is used to analyze the implications of the existence of staggered loan contract mechanism. The model builds on Xxxxxxxxx (2006) and Xxxxxxxx and Hercowitz (2005). The economy consists of two types of households, impatient (borrowers) and patient (savers) households; two production sectors - durable and non-durable goods sector - each populated by a large number of monopolistic competitive intermediate good producers and by perfectly competitive final good producers; private banks which operate under a monopolistically competitive environment and a central bank.

The Model. A A A A A P { } An asynchronous Broadcast protocol (also known as A-cast) [8] or an Asynchronous Byzantine Agree- ment (ABA) [9] protocol is carried out among a set of n parties, say = P1, . . . , Pn , where every two parties are directly connected by a secure channel and t out of the n parties can be under the influence of a computationally unbounded Byzantine (active) adversary, denoted as t. The adversary t, completely dictates the parties under its control and can force them to deviate from a protocol, in any arbitrary manner. The parties not under the influence of t are called honest or uncorrupted. The underlying network is asynchronous, where the communication channelsbetween the parties have arbitrary, yet finite delay (i.e the messages are guaranteed to reach eventually). To model this, t is given the power to schedule the delivery of all messages in the network. However, t can only schedule the messages sent by an honest party, without having any access to them. The inherent difficulty in designing a protocol in asynchronous network, comes from the fact that when a party does not receive an expected message then he cannot decide whether the sender is corrupted (and did not send the message at all) or the message is just delayed in the network. Therefore it is impossible to consider the values sent by all uncorrupted parties. So values of up to t (potentially honest) parties may get ignored because waiting for them could turn out to be endless. Moreover the tools that are applicable in synchronous settings cannot be deployed directly in asynchronous settings. Hence, designing asynchronous protocols require completely new set of primitives. For an excellent introduction to asynchronous protocols, see [9]. We now formally define A-cast and ABA.

The Model. The model presented in this section is a global emission game defined by a triple G = {I, A, Πi}. The set I = {1, 2, , n} is the set of the n players, each of them representing a country. This set I is split into two subset, denoted by I1 and I2, that contain the developed countries and the developing countries, respectively. Even if more asymmetry would be more realistic, the division in two homogeneous groups is suitable to take differences into account and largely used in literature (see e.g., Xxxxxxx and de Zeeuw, 2013). An environmental coalition is then a subset C = (C1 ∪ C2) ⊆ I, where C1 ⊆ I1 is the set of developed countries in coalition and C2 ⊆ I2 is the set of developing countries in coalition. The second element of the triple G is the set of strategies A. Also this set can be written by the union of two disjoint sets A = A1 ∪ A2, where A1 and A2 contain the strategies of developed and developing countries, respectively. The strategies contained in each set Ai are given by the emissions functions of player i, that are the functions of time ei(t) such that ei(t) ≥ 0 ∀t ∈ [0, +∞). The third element of the triple G is the payoff (or welfare) function Πi, i = {1, 2}, that is a map that, for every possible strategies profile, determines the gain of each player. The production of goods and services generates benefits to the citizens of a country and, as by-product, emissions of pollution too. Calling by xx(t) the total production of goods and services for country i at time t, is it possible to write the emission of the country i as function of its own production: ei(t) = 6 Altruistic behavior and International Environmental Agreements h(yi(t)), where h is an increasing function that satisfy h(0) = 0. If the function h is also smooth, than it is possible to express the relation between production and benefit in terms of emissions directly. A very well known form for this benefit in literature (see e.g., xx Xxxxxx and Xxxxxx-Xxxxxx, 2018), expressed by Xx(ei(t)) for player i, is the quadratic and concave function Bi(ei(t)) = αiei(t) − 2 ei (t), where αi is a strictly positive parameter. The assumption of two homoge- neous kinds of players means that there are only two different values for the parameter α; α1 for each i ∈ I1 and α2 for each i ∈ I2. Moreover, the usual presumption on these parameters is that α1 > α2. The simple idea is that developed countries are able to produce more good and services for unity of pollution respect to developing countri...