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 Article A Note on Preliminary Tests of a Public Choice Framework for Understanding Welfare Effects of IMF Lending Brandon Roddey and Philip A. Cartwright *The authors thank Professor Foued Ayari, The American Graduate School in Paris, Professor. Douglas Yates, Professor of Political Science, The American University in Paris and the American Graduate School in Paris for helpful insights and comments. ABSTRACT This note presents preliminary results addressing the relationship between the use of International Monetary Fund credit, conditionality and welfare for both the IMF and recipient countries in the context of a public choice model proposed by Bird (1995). While the model is familiar, this paper uses a data set constructed from the Monitoring of Fund Arrangements Database for 2002 – 2009 covering 85 countries (of which 78 are used in the analysis), preliminary tests bases on correlation analyses are reported. Overall, preliminary results from aggregate data support the hypothesis that IMF utility (pass rate) increases as the amount of lending to a recipient country increases. Increasing conditionality is associated with lower Pass Rates. Based on bivariate tests, we reject the hypothesis that IMF utility decreases when lending increase relative to conditionality. For the borrowing or recipient country, the hypothesis that higher conditionality is associated with lower welfare (Real GDP, Purchasing Power Parity per capita) is rejected at any reasonable level of significance. The findings do support the hypothesis that recipient country utility and fund credit are positively correlated. 1. Introduction This work represents a further step in the direction of gaining insight into the empirical evidence relating to the action of the IMF and borrowing countries. The paper analyses the relationship between the use of International Monetary Fund credit, conditionality and welfare for both the IMF and recipient countries in the context of a public choice model proposed by Bird (1995). Using the Monitoring of Fund Arrangements Database for 2002 – 2009 covering 85 countries (of which 78 are used in the analysis), preliminary tests bases on correlation analyses are reported. Overall, results from aggregate data supports the hypothesis that IMF utility (pass rate) increases as the amount of lending to a recipient country increases. Increasing conditionality is associated with lower Pass Rates. We reject the hypothesis that IMF utility decreases when lending increases relative to conditionality. For the borrowing or recipient country, the hypothesis that more conditionality is associated with lower welfare (Real GDP, Purchasing Power Parity per capita) is rejected at any reasonable level of significance. The findings do support the hypothesis that recipient country utility and fund credit are positively correlated. This work builds upon previous research using bargaining models, which have in part provided the intellectual basis for concepts developed under neoliberal institutional thought – asymmetrical interdependence, cooperation and hegemony – as well as public choice. The research takes as its point of departure the model posited by Bird (1995). The public choice framework is key, stating the empirically testable hypotheses of interest. In the empirical section of the work we use data from the Monitoring of Fund Arrangements (MONA) database to test the direction and strength of relationships between lending, conditionality and welfare for both the IMF and recipient countries. 1. The Theoretical Model Some scholars have already considered the effects on IMF lending as operating under the influence of a principal agent who controls the institution but has a different objective set than the one publically stated by the IMF (Bird 1995, Stone 2004, Dreher and Jensen 2007). Others, such as Vaubel (1991), have used the public choice model to argue that the IMF’s objective set is a function of individual utility maximizing agents who look primarily to increase or protect their personal income. The model used here also uses the utility-­‐maximization framework, but does not analyze individual bureaucratic incentives. In line with the institutional analysis framework, this thesis utilizes an extension of the public-­‐choice model proposed by Bird (1995) based upon public choice considerations, whereby “the Fund seeks to maximize its power and influence” (94). The actors under question are organizational-­‐institutional entities. Bird (1995) proposes a “public-­‐choice approach to Fund lending,” the Fund “seeks to maximize a utility function which incorporates conditionality and lending” (60). For the borrower, utility will increase as the amount of lending increases, but will decrease with increased conditionality. For the IMF, its utility will increase as lending increases, but also as conditionality rises. This model reflects the IMF’s objective to maximize its power, in addition to a bargaining model in which it discourages increased lending through increased conditionality. Thus, conditionality should also increase as lending increases in order to prevent moral hazard, a term given to describe slacking by borrowing countries who do not take repayment seriously since they bet that the IMF would not allow sovereign debt insolvency (Vaubel 1983). Following the above stated convention, this study aims to build upon the general equilibrium model applied to IMF lending. Bird provides a formalization of the lending-­‐conditionality utility maximization functions as follows: UF = f (C, L) where C = conditionality, L = lending, and and (dUF/ dC) > 0 (dUF/dL) > 0 Thus, increases in conditionality increase the utility of the IMF, and the same for the amount of lending. For potential borrowers: UB = f (C, L) Where (dUB/ dC) < 0 and (dUB/ dL) > 0 The expression states that when the number of conditions increases, borrower utility decreases, whereas additional amounts of lending correspond to greater borrower utility. As for the IMF, the model states that increases in conditionality result in increases in IMF welfare, and increases in the amount of lending also contribute positively to IMF welfare.1 The borrowing country will prefer to accept loans when the number of conditions attached is low relative to the size of the loan (decreasing price of lending in terms of conditionality, L/C). The IMF will be happier when overall conditionality and lending are increased (increase in area of rectangle given by LC). The borrowing country will be better off when the amount of lending per unit of conditionality increases. Keeping conditionality constant, both IMF and borrowing country utility will be increased whenever the lending constraint increases, but the two actors differ with respect to conditionality. The borrowing country’s welfare increases when equation L1/C1< L2/C2 is true. IMF’s welfare increases when equation L2C2> L1C1 1
Adopted from Bird (IMF Lending to Developing Countries 1995).
is true. Thus, no defined Pareto optimal solution exists, because any increase in C makes the IMF better off and the borrowing country worse off. The contrary statement regarding a decrease in C also applies in this case. The IMF’s indifference curves in this model behave the same way as Consumer A’s indifference curves did when the Consumer tried to maximize her welfare subject to a budget constraint. The constraint to the IMF lending in this case would be the resources available to it, either in physical quantity or some limitation as defined by quotas. In the model above, this constraint is defined by the straight vertical line Q. But now to address the rational-­‐choice question posed earlier: why do states, if rational actors, choose to enter into agreements with the IMF? It is easy to look at this model and make the mistake of thinking that it expresses that states are worse off with IMF conditional loans than they are without it. Yet, this conclusion is incorrect. Rather, the model expresses that states are worse off as the cost of borrowing increases in terms of economic sovereignty. To put this proposition in terms more familiar to the reader, think of an increase in resident’s rent: the renter is worse off than he was before, but he still benefits from having a place to live. Likewise, imagine what would happen if the amount of traded conditionality stayed constant while borrowing country’s use of funds increased. According to the model, both parties would be happier: the IMF because the amount it lends had increased, while the borrowing country would be happier to receive more loans at a better rate than before. In effect, this growth in utility would be equivalent to the size of one’s apartment magically increasing, leaving both the renter and the property owner happier than they were before. Alternatively, the IMF may prefer to solve structural economic problems by giving less loans and having more control. Additional lending would increase the debt burden of the borrowing country, and the IMF has expertise on monetary and financial issues. Thus, for the borrowing country, less loans to more conditions would be a “cheaper” and – for the IMF— less risky, way to fix a problem. It is left to the tests to determine which version of the story is corroborated by the evidence. According to Bird’s model, the IMF assigns more extensive conditionality in response to a weaker bargaining position represented by the borrowing country, or if conditions rather meet some minimum necessary to repaying debts and restoring financial balance, as few studies have been effected on the matter. It could just be that conditionality is determined without regard to the amount lent— for instance, to meet IMF goals or as a limit established by the borrowing country who agrees to undergo the effort of implementation. Another line of reasoning holds that countries receiving elevated levels of conditionality should demonstrate more embedded long-­‐term structural macroeconomic problems such that the nature of their balance of payments deficits is beyond “temporary,” thus requring augmentation of economic reform policies in order to “correct” problems. The theory of asymmetrical interdependence and public choice both would seem to suggest that bargaining power determines the process, as cited by Ariel Buira in a G24 discussion paper published by UNCTAD.2 The IMF may raise the price of borrowing in terms of conditionality when it wants to discourage borrowers from taking a loan (e.g. represented by movement of intersection C1 at UB3 to UB2), or when it gauges that it would benefit more from insisting upon additional conditionality. Yet in response, borrowing countries may only agree to accept a certain amount of conditionality in exchange for additional lending as a part of the bargaining process (e.g. refusal to agree to C1 at UB2, but acceptance at UB3). Again, one may wish to view the failure of countries to implement reform as an instance of on-­‐the-­‐fly renegotiation of credit conditions. 2. The Data and Methodology The Monitoring of Fund Arrangements Database reflects negotiated IMF lending programs spanning the years 2002 – present and involving 85 different countries. The entry points contained within the MONA database represents what was originally an internal source of information used by IMF staff until an internal review conducted by the Independent Evaluation Office (IEO) of the IMF concluded that the organization should release its available data to the 2
“An Analysis of IMF Conditionality,” Challenges to the World Bank and the IMF: developing country
perspectives, 2003
public. In January 2009, the database was published to the IMF’s website, where it has been available to the public. Drawing from the public-­‐choice model found in the literature, seven primary hypotheses are put forth concerning the relationships between use of Fund credit, conditionality, price of lending, and welfare. Also tested are the moral hazard argument proposed by Bird that the IMF will increase conditionality as lending increases, with the understanding that this relationship would exist because the IMF wants to discourage borrowers from abusing use of Fund credit. Implicit within the model given above are the following test sets of hypotheses: H1: Positive correlation between Use of Fund Credit (L) and Real GDP PPP PC. H2: Negative correlation between Real Conditionality (C) and Real GDP PPP PC. H3: Positive correlation between Use of Fund Credit (L) and Pass Rate. H4: Positive correlation between Real Conditionality (C) and Pass Rate. H5: Positive correlation between Use of Fund Credit/Conditionality (rate) and Real GDP PPP per capita. H6: Negative correlation between Use of Fund Credit/Conditionality (rate) and Pass Rate. H7: Positive correlation between Conditionality (C) and Use of Fund Credit (L). While the correlations between other variables are also tested, the above hypotheses should be understood as the most crucial elements for the purposes of confirming or rejecting the model proposed by Bird. Definition of Variables Lending Lending represents the amount of IMF credit used by the borrowing country. For the purposes of the research, the total amount of credit used by a borrowing country, listed in SDRs (Special Drawing Rights, IMF designated currency), is measured and converted to U.S. dollars by multiplying the amount of SDRs and the exchange rate on the day of disbursement. By doing so, it is hoped to give readers a better sense of the value of loans, since dollars are a more familiar unit of currency than SDRs. Conditionality Three different counts of Conditionality are taken: 1) Real Conditionality and 2) Failed Conditionality, and 3) Gross Conditionality. “Real conditionality” represents the number of conditions which the borrower has either already met, or is still expected to meet. It counts delayed, modified, and continuous conditions only once. A condition met (M) represents a successful modification of the country’s laws or institutions at the initiative of the IMF. Delayed (DL) and not met (NM) are counted since the country faces an upcoming audit or test of the condition. Waived and modified conditions are not included in the count. In other words, if the condition has been listed more than one time, then the redundant entries are removed. The measure is intended to give an indication of the actual number of conditions that borrowers face by discounting conditions which have been (at times repeatedly) failed, waived, delayed, modified, or cancelled. “Failed conditionality” incorporates every IMF data entry that measured NM, DL, CAN, W, and MOD. These measurements indicated that the measured variable was not present; the distinction between the coding reveals the IMF’s management decision, and does not signify a difference of degree of presence. “Gross conditionality” counts each test of conditionality, and thus every time the borrower was audited for the condition, in other words including every entry in the conditionality data field. The count of gross conditionality is useful as a comparison unit for measuring the extent of cancellations, waivers, modifications,etc. While for the sake of convention and simplicity many scholars (Gould 2003, 2006; Dreher and Jensen, 2007) have treated each unit of conditionality as equal, in reality one must understand that the situation is much more complicated. Take for example the issue of units of conditionality (Copelovitch, 2010). Depending on borrowing country and IMF relations amongst other things, the severity of enforcement, the scope of the demanded change, or the consequence of failure varies widely between cases. The decision to count each condition as equal simply ignores that variation. Nonetheless, the assumption is that even by disregarding that level of complexity, there are still meaningful and useful relationships which can be inferred regarding conditionality across programs and dates. Utility Utility is a measure used by economists to describe welfare. As shown before, it is generally understood to be an ordinal-­‐level measure of data, i.e. a “more than, less than” concept as demonstrated by the preference maps for the pay-­‐off matrix and for Bird’s IMF-­‐borrowing country utility maps.3 For the purposes of the tests, use two proxy measures in order to capture the effects on welfare of lending and conditionality. Taking from the public choice model that posits agents as rational calculating actors, utility is measured in terms of an objective function, meaning that utility is maximized as the person obtains his or her goal. For the IMF, “Pass Rate” is used, which measures the percentage of non-­‐failed conditions to total conditions. This reasoning comes from the understanding that what distinguishes the institution from a private lender is its public orientation as defined in the IMF’s Articles of Agreement. If its strategic and operational goals are based on the mandate which the organization has been given by member countries in its founding document, then the IMF’s utility should be measured according to its capacity to accomplish those ends. While the IMF’s objectives may suggest a variety of ways to capture IMF utility, conditional lending provides the best-­‐suited quantitative measure of the IMF’s overall objectives. Not only do the other objectives show up in the design of conditionality, but mandated reform and lending have been the primary mechanisms through which the institution has tried to achieve its 3
See , for example, Dennis C. Mueller (2003) and R.S. Pindyck and Rubinfeld, L. (1995).
goals. Among the six objectives, only section (v), “making the general resources of the Fund temporarily available […] under adequate safeguards,” addresses the Fund’s lending activities. The five other articles define more or less the Fund’s public character and a general direction for its lending strategy. To test the seven set of hypotheses outlined above, a bivariate correlation function built into Microsoft Excel is used that enabled a quick and easy analysis of data sets compiled from the MONA database. As part of the correlation calculations, a data analysis program was used to measure the statistical significance of the correlation analyses, as well as the associated confidence levels. Although the source database contained information about 85 countries, the decision was made to not include certain countries in the count. For the counts, 78 countries were used to calculate the correlations between success rate and conditionality, lending, and the lending/conditionality ratio; 78 countries were also included in the tests concerning Real GDP PPP per capita and lending, conditionality, and the lending-­‐to-­‐
conditionality ratio.4 The results exclude Croatia, Costa Rica, El Salvador, Guatemala, Paraguay, and Peru on the basis that not one of these countries had used any of the credit for which they had been approved. Since the purpose of the tests was to examine the hypothesis that countries trade lending for reforms, it was judged that these countries were anomalies that did not fit the model and that they would best be accounted for by some other explanation. In an important way, the fact that countries do accept conditions without using credit in return challenges both the model presented here, as well as the preconceived notion that countries are averse to accepting conditions. PSI programs represent another method through which countries take on reform without asking for loans. Much to the chagrin of Fund critics who seek to delegitimize the organization’s conditional lending programs, the voluntary acceptance of conditions demonstrates that some countries do appreciate and proactively seek out the Fund’s advice. Yet without the lending “bargaining chip” being leveraged by the IMF, the country may 4
Serbia and Montenegro, which had appeared in the MONA database as a single entity prior to Serbia’s
independence, did not have a reported GDP for the reference year.
feel less pressure to actually perform these changes. These issues are returned to later in the conclusion. As previously mentioned, also tested are the correlations between some additional variables in order to investigate whether perhaps some other correlation between variables may provide a better link to understanding the IMF-­‐borrowing country behavioral mechanism. “C Total” to “Lending” (Gross L/C) is tested in order to evaluate the impact of adjusted figures against raw data. The correlation between Real GDP PPP per capita and the Pass Rate is examined in order to determine the statistical link between borrowing country utility and IMF utility. Other variables tested to see if they have an important influence on the Pass Rate are “Percentage of Funding Used” and “Total Approved.” On the one hand, “Total Approved” speaks to the IMF’s decision prior to implementation, while the Percentage of Funding Used demonstrates accounts for the progression of the IMF program. Overall, the purpose of the following section remains to test the hypotheses underlying the Bird model and to get a concrete sense of the concepts introduced in the introductory chapters. 3. Results and Concluding Comments As shown in the table below, it was estimated that higher uses of fund credit (L) and higher amounts of conditionality (C) would be correlated to a higher IMF utility (UIMF), captured as a measurement of a Pass Rate, which calculates the percentage of conditions passed to total amount of conditions enumerated. Concerning the use of fund credit, the aggregated data supports the hypothesis that IMF utility increases as the amount of lending used increases. Countries who borrowed more tended to have a higher pass rate. As for rising conditionality, countries tended to have lower pass rates as the amount of conditions increased. This finding contradicts the hypothesis that IMF utility would be aligned with increased conditionality. Table 1. Correlation Results Based on Aggregate Data Table 2. Expected and Observed Results, H1 – H7 Based on Sign and Significance (*significance at .05 level or better) Hypothesis/Variab
L, les C, Use Rea Rea Pas
Credit/Conditionali Credit/Conditionali , l l s s ty, GD
Real GDP PPP PC P P e e PP
P P PC PC H1 * Expected + Observed -­‐ H2 C, * Expected -­‐ Observed -­‐ H3 L, * Expected + Observed + of Fund Use of Fund C
ty, Pass Rate L H4 * Expected + Observed + H5 * Expected + Observed + H6 * Expected -­‐ Observed + H7 * Expected + Observed + It was predicted was that the IMF would be worse off when it increases lending (L) relatively to condionality (C), a ratio given as L/C. In other words, according to the premise that the fund is an hegemonic entity seeking to maximize its power, it would be better off receiving more conditionality and giving up less lending, although other reasons that such a relationship could exist were also proposed. Yet the evidence forces one to reject these hypotheses, for the Pass Rate tended to rise as the L/C ratio increased. Regarding the relationship between conditionality and lending, the amount of conditions required were estimated to increase with lending. Since the IMF loans at concessional rates, countries might prefer it to private lending, a problem known in the literature as the “moral hazard” issue.5 Additionally, if the IMF did not increase conditionality with lending, then it would be receiving a lower “price” for its good. Thus, whether to discourage irresponsible borrowing or simply to ensure that it is getting its fair share out of the lending-­‐conditionality trade, the IMF should enforce that higher levels of lending be matched by higher levels of conditionality. 5
See Marchesi and Sabani 2005, Vaubel 1983.
As for the borrowing country, higher conditionality was hypothesized to correspond to lower welfare, in this case measured by Real GDP PPP per capita. Higher lending was supposed to correspond to higher Real GDP PPP per capita. The rationale behind this thinking relied upon the idea that conditionality is unwanted by borrowers, and that it should thus be associated with lower levels of utility. Lending, desired by borrowers, should correspond to higher levels of utility. These findings support the relationship that both borrowing country utility and use of Fund credit move in the same direction. Given the decision to measure utility as real GDP PPP per capita, a measure of economic output per person, the these relationships may have reflected the capacity of the borrowing country to pay back lending rather than an increase in welfare associated with additional lending. Larger economies should get approved for larger loans. Thus, in terms of the causal connection between lending and real GDP PPP per capita, these findings indirectly confirm the public choice and neoliberal models in the following way. More wealthy countries have less need for lending, since at worse they can probably afford to reduce welfare and spending. Additionally, they would have increased bargaining power with the IMF, through their larger stakes in the organization, not to mention the influence gained through economic power measured in terms of GDP. Thus, if larger countries get more loans, it is because they ask for more loans, and they receive approval for use of credit exactly because they have more bargaining power. A significant negative correlation was found to exist between the pass rate and the percentage of credit used (in SDRs) measured against the total approved amount (in SDRs). Countries who borrow more against their ceilings are more likely to also fail their conditions. Those monitoring IMF programs should take note of this phenomenon, because not only may it provide an effective warning sign, but it may also speak to the way in which the IMF designs conditions and manages program In general, the aggregated data are marked by a large degree of heteroscedacity, thereby bringing into question the appropriateness of trying to understand lending through an aggregated analyses. In addition, extreme points that lie outside of clusters of data may be distorting the reported strength of a correlation in one direction or the other. Given the variation in the data, it is admitably difficult to make definitive inferences that apply to the entire group. Treatment of these issues will be considered as the research moves forward. For example, countries have been tentatively divided into different groups based on the size of their Real GDP PPP per capita, since the aggregate data does a poor job of showing underlying trends. Accordingly, four different ranges are used, less than $1000 PPP per capita, $1000-­‐2000 PPP per capita, $2000-­‐5000 PPP per capita, and $5000+ PPP per capita. Any significant differences between IMF programs accorded to large versus small countries will therefore be detected. One might expect on the basis of public choice theory, it would make sense that wealthier countries would receive preferential treatment because owing to their greater stake in the IMF. Thus far, the research conducted points to evidence in support of the claim that countries borrow out of self-­‐interest in order to avoid potential crises. The alternative to not participating in the system would be a loss for the country in crisis and other countries economically and politically networked to it, which might be hurt by uncoordinated macroeconomic policy (Webb 1991). IMF intervention in crisis situations was validated as a means of upholding international public goods, such as stability and policy coordination. On the other hand, the issue of post-­‐
intervention failure could represent misdirected IMF policy rather than failure to meet conditionality, and one shortcoming of the data used for this thesis is the lack of data on this point. It would be interesting to determine what implications, if any, these types of failures would have for neoliberal institutionalism and the collective-­‐choice dilemmas. Does the IMF pass rate accurately measure these cases? Further questions persist regarding the presence of outliers observed in the data. The data call attention to countries such as Tanzania’s (number of conditions), as well as and Pakistan’s use of Fund credit. Could it be that Liberia and Pakistan represent dreaded moral-­‐hazard client states?6 6
(Vaubel 1983, Marchesi and Sabani 2005)
Alternatively, these states may have special relations with powerful stakeholders in the IMF, such as the United States. Tanzania (124 conditions), along with Sierra Leone (114 conditions) and the Dominican Republic (119 conditions), present a particularly perplexing problem, as there has been little produced in the research to explain a disproportionate number of conditions. Is the number of conditions connected to a request by the country? Why do these countries deviate so remarkably from the aggregate average of 43 conditions? In sum, while the rational-­‐choice bargaining model presented in this research can accurately predict and explain lending agreements, the determining factors of IMF conditionality remain elusive, and attention to this controversial subject is in process. The results presented thus far are based on correlations and further research is underway. In the meantime, the paper does give preliminary empirical insights related to the model. Fortunately, the MONA database provides a valuable source for continuing research. BIBLIOGRAPHY Bird, Graham. 1995. IMF Lending to Developing Countries. New York, NY: Routledge. Buria, Ariel. 2003. An Analysis of IMF Conditionality. Discussion Paper, Port of Spain, Trinidad and Tobago: XVI Technical Group Meeting of the Ingergovernmental Group of 24, 2003. Copelovitch, Mark. 2010. “Master or Servant? Common Agency and the Political Economy of IMF Lending.” International Studies Quarterly 54, 49-­‐77. Dreher, Axel, and Nathan Jensen. 2007. "Independent Actor or Agent? An Empirical Analysis of the Impact of US Interests on IMF Conditions." Journal of Law and Economics 50, no. 1, 105-­‐24. Gould, Erica. 2003. "Money Talks: Supplementary Financiers and International Monetary Fund Conditionality." International Organization 57, no. 3, 551-­‐586. Gould, Erica. 2006. Money Talks: The International Monetary Fund, Conditionality, and Supplementary Financiers. Palo Alto: Stanford University Press. Marchesi, Silvia, and Laura Sabani. 2005. "IMF Concern for Reputation and Conditional Lending Failure: Theory and Empirics." November. Mueller, Dennis C. 2003. Public Choice III, 3rd Edition. Cambridge: Cambridge University Press. Pindyck, Robert S., and Daniel L. Rubinfeld. 1995. "General Equilibrium and Economic Efficiency." Chap. 16 in Microeconomics, Englewood Cliffs, New Jersey: Prentice Hall, 558-­‐570. Stone, Randall W. (2004). "The Political Economy of IMF Lending in Africa." American Political Science Review 98, no. 4, November, 577-­‐591. Vaubel, Roland. 1983. "The Moral Hazard of IMF Lending." World Economy 6, no. 3, 291-­‐
303. Vaubel, Roland. 1991. "The Political Economy of IMF Lending: A Public-­‐Choice Analysis." In Political Economy of International Organizations: A Public-­‐Choice Approach, by Roland Vaubel and Thomas A. Willet. Webb, Michael C. 1991. "International Economic Structures, Government Interests, and International Coordination of Macroeconomic Policy." International Organization 45, no. 3, 309-­‐342. By Brandon Roddey, M.A., American Graduate School in Paris Phillip A. Cartwright, Professor of Economics, Ecole Supérieur de Gestion MS, Paris 25, rue Saint Ambroise 75011 Paris, France [email protected] +33(0)6 22 69 96 65