Classification, cognition and context: The case of the World Bank

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Poetics 38 (2010) 133–149 www.elsevier.com/locate/poetic

Classification, cognition and context: The case of the World Bank Hana Shepherd Department of Sociology, Princeton University, Wallace Hall, Princeton, NJ 08544, United States Available online 16 December 2009

Abstract A growing literature in sociology examines external classification systems, usually implemented in the context of industries. Classification systems are analogous to the structure of individual cognition, though the specific nature of the link between these external classification systems and individual cognition is usually unexplored. This paper uses the example of the World Bank’s lending classifications to examine the relationship between rules and classification and to argue that the organizational context of classification is central to assessing the cognitive impact of classification. The paper adds these results to existing work on external classification systems to provide a framework for understanding the range of possible relationships between external classification systems and the cognition of individuals. # 2009 Elsevier B.V. All rights reserved.

1. Classification systems and cognition Categorization has been a core topic of cognitive psychological research for decades as it lies at the heart of basic questions regarding the organization of knowledge and learning. Classification systems, such as those used to characterize types of businesses in industries, have received a good deal of scholarly attention in sociology of late. Because of their analogue with the cognitive process of categorization, classification systems are assumed to be important in structuring the cognition and action of those individuals who interact with them, but this assumption is rarely interrogated. This paper examines the relationship between formal rules of classification and classification outcomes using the World Bank’s classification of countries based on their lending status and argues that the organizational context in which classifications are used must be considered in order to understanding the relationship between classification systems and individual cognition. The paper reviews recent work on classification systems in organizations and industries in order to build a set of analytical factors which must be taken into account in order to understand the relationship between individual cognition and these external classification systems. E-mail address: [email protected]. 0304-422X/$ – see front matter # 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.poetic.2009.11.006

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A number of assumptions regarding the source of classifications underlie the illustrious sociological lineage of work on classification systems and boundaries (Durkheim and Mauss, 1963; Douglas, 1986). Classifications may arise from fundamental psychological processes creating boundaries between types of people that are then made manifest in the world (e.g. Massey, 2007), or from shared assumptions about value that are translated into formal classifications, much along traditional Durkheimian lines (e.g. Douglas, 1986; Mohr, 1994), or from professional and political contestation (e.g. Bowker and Star, 1999; Lounsbury and Rao, 2004). Classifications may represent the knowledge and learning of organization members and they shape cognition of the people using them by directing attention, understanding, and evaluations (see Aldrich and Ruef, 2006). Classifications are often treated functionally and as top-down (i.e. imposed by authorities) systems. Starr (1992) identifies a number of purposes of classification within the political realm where states represent the main classifying agents: to ‘‘achieve economy of memory,’’ to reduce complexity, to develop new information embodied in the category, to systematize procedures, to ‘‘meet ‘decision-making demands,’’’ to provide a framework for incentives, and to suggest action plans. 1.1. Existing work on classification systems Classification is typically thought to exert its effects on the cognition of individuals by way of two main, though not mutually exclusive mechanisms: through ‘‘direct importation’’ into the minds of individuals, and through shaping the behaviors and expectations of those in contact with the classification system. In the most straightforward articulations of the effects of classification, classifications ‘‘colonize minds’’ (e.g. Douglas, 1986) and provide cognitive representations of the classified entities (e.g. Lounsbury and Rao, 2004). Once absorbed, classifications increase the perception of homogeneity within and heterogeneity between categories (e.g. Zerubavel, 1996); allocate attention (Simon, 1997); reduce uncertainty; facilitate efficient processing of information (e.g. Rosch and Lloyd, 1978); represent differences as objective; create a social reality; and shape interpretations of the world. The mechanisms through which classifications become absorbed and the conditions under which these cognitive effects are realized are usually unstated. Other literature focuses on how classifications exert their effects through creating social identities that shape behaviors towards and of the classified entities themselves. Classifications create role entailments and expectations that audiences have for the behaviors of those classified entities (e.g. Carroll and Hannan’s ‘‘blueprints,’’ 2000; Zuckerman, 1999; Zhao, 2005), provide relevant reference groups and rivals (e.g. Porac and Rosa, 1996), or explicitly shape the behaviors of categorized entities in the service of their position in the classification (e.g. Espeland and Sauder, 2007). Actors develop schemas or exemplars for the content of categories and evaluative heuristics for those categories that shape cognition and action. In this manner, classifications create shared beliefs, instead of merely representing or reflecting beliefs. Many studies have demonstrated the effects of classification on how classified entities are treated, focusing mainly on penalties for being ambiguously or multiply categorized (e.g. Zuckerman, 1999, 2004; Hsu, 2006a,b; Ruef and Patterson, 2009). Across a variety of contexts, these studies clearly demonstrate that classifications carry category-based rules for attention and evaluation that produce behaviors favoring clear and unique categorization over ambiguous and hybrid categorization. But some observations raise additional questions regarding the cognitive consequences of classifications. For example, Hsu (2006a) finds that the strength of the evaluative schemas of

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movie critics shapes the attention they pay to certain film genres. Hsu does not assume that categories have a standardized effect, but rather postulates that critics hold more or less welldeveloped schemata for different categories, which then direct their attention and lead them to review more movies in genres with better-developed schema. Here, classifications do not create uniformly strong mental representations, but rather the strength of the representation may vary by category, though the reason for this is left unexplained. Similarly, Zuckerman and Rao (2004) fail to find an effect of classification of Internet stocks by the largest investors, which are inductively derived, on their stock market outcomes, and thus argue that researchers should consider the possibility that categories carry different amounts of information for different users. The consistent application and use of categories and their associated cognitive consequences should be a matter of empirical inquiry. 1.2. Framework for understanding the effects of classification systems Understanding the cognitive tools provided by classifications requires contextualizing classification systems. This paper asks how organizational factors influence classification systems, in particular the relationship between the rules for classification and classification outcomes, using the case of the World Bank’s lending categories. The World Bank is a paradigmatic example of a technocratic institution and the technical aspect of the Bank is crucial to its choice as a case site. The Bank is primarily staffed by professional economists who have been trained within similar institutions using classical economic theories and who must develop ideas and theories given the resource constraints and instruments available (Woods, 2006). The influence of the information, ideas and theory developed by the Bank on individual countries, whether donors or borrowers, and on other organizations is paramount. The research division has a staff and administrative budget of over $100 million per year. The annual World Development Report is the most widely read development publication, and the over three hundred articles published in academic and professional journals each year by Bank staff are ‘‘cited 10–50 percent more than the average for economics articles’’ (Weaver, 2003, p. 116). Goldman (2005) cites the World Bank’s data and ‘‘knowledge’’ creation, accompanied by a worldview and development framework, as a central feature of the growing influence of the Bank over other organizations and states. In such an organization, one would expect the technical rules and procedures, like classification, to be well-developed and well-integrated into organizational life, and to have legitimacy and sovereignty based on the organizations size and commitment to rationalized decision-making. Most of the literature on classification systems assumes that individuals understand and utilize categories according to a logic of appropriateness—where rules or procedures are seen as natural, rightful and legitimate (Olsen and March, 2004). This process is also cognitive; legitimate entities or rules often entail cognitive beliefs about social reality (Johnson et al., 2006). In the case of the World Bank’s system for classifying its loan recipients, I find evidence of a discrepancy between rules for classification and some classification outcomes. This suggests that rules are used strategically and deliberately within the organization, perhaps according to a logic of consequences (March and Simon, 1993)—where interests and rational calculation drive behavior. This deliberative use of rules for classification may make categories less natural, less ‘‘attributes of the world,’’ and therefore less cognitively immediate. In summary, the organizational context of classification is central to understanding how rules for classification are applied. The way in which these rules are applied may have cognitive implications by influencing how individuals understand and use classifications.

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There are a number of other dimensions of classification systems and characteristics of the users of these systems that affect the conditions under which the cognitive and behavioral effects of classifications are realized,1 to which I add the dimension of organizational context. 1. The degree of institutionalization of the classification system Ruef and Patterson (2009) argue that the extent to which a classification system is institutionalized influences how hybrid category members are perceived and thus treated. They find that once a classification system is institutionalized – integrated into practice such that it seems natural (in this case indicated by the presence of detailed categories, individuals who were specifically trained to use the classification system, widespread organizational adoption of the classification system, and the absence of legal or moral sanctions against the classification system) – boundaries between categories become strengthened and boundary violations are consequently punished. 2. The strength of boundaries between categories and the degree of hierarchy and potency of categories (DiMaggio, 1987) Rao et al. (2005) discuss the implications of the strength of boundaries between categories in French cuisine and find that weaker boundaries allow for more borrowing from elements of other categories since the penalties are less. The authors also demonstrate that critics recognize changes in chef behavior and the content of categories and adjust their expectations accordingly. 3. Presence and degree of feedback between the classification system and the users of the classifications (where no feedback indicates a totally top-down imposition of a classification system) Kennedy (2008) finds that the media plays an active role in constructing cognitive associations between different elements of an emerging category, suggesting that categories can be constructed abductively (choosing an hypothesis (vs. an explanation) that best explains the available evidence) by audiences who are exposed to repeated references of items together. In this way, new products or firms become ‘‘cognitively embedded’’ as they are represented in connection to other products or firms, in this case facilitating the cognition of reporters regarding the new product vis-a`-vis a limited number of existing products and firms. Hsu (2006b) finds that films classified under multiple genres gain a larger audience of critics and consumers but are less favorably evaluated, perhaps because of a poor fit between expectations and tastes for a genre and the product itself. She argues that these findings suggest that the strength of a boundary as well as the ways audiences perceive the locations of boundaries change, indicating dynamism in the categories themselves based on audience consensus. Highly malleable classifications would lead to individuals being less accustomed to any particular arrangement of categories, and feedback mechanisms might lead individuals to buy into classifications—either through cognitive incorporation or through behavior—to a greater extent if they (collectively) play a role in shaping them. 4. Political context of classifications Lounsbury and Rao (2004) provide evidence that powerful actors influence the structure of categories by actively lobbying against category changes that disadvantage those actors. The 1

I do not mean to imply that these different dimensions necessarily entail unique mechanisms for influencing cognition, though they might.

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degree to which categories are assumed to be ‘‘natural’’ depends on whether they are perceived to be amenable to political influence. Taken together, these factors offer a framework for assessing possible cognitive and behavioral consequences of classifications. 2. The case of the World Bank’s lending classifications How might organizational context influence the practice of classification? A central problem that both the World Bank and the IMF had to grapple with as they moved from financing postWWII reconstruction in Europe to financing aid in so-called developing countries was the basic issue of ‘‘defining to whom they should lend or under what conditions’’ (Woods, 2006, p. 39). Neither institution had a substantial history or a specific economic theory to draw on in order to solve that problem. Goldman (2005) argues that Robert McNamara, president of the World Bank from 1968 to 1981, thought existing academic theories to be inadequate for the Bank’s purposes and ‘‘his strategy was therefore to create a new paradigm in development thinking: to measure, analyze and overcome’’ (77). The Bank had to balance between a set of countervailing forces including political pressure from powerful donors, the development of economic theories and fashions, and the bureaucratic requirements of the organization (Woods, 2006) in order to work out a tenable set of rules governing which countries to lend to and how much money to give. The structural and organizational constraints on the World Bank, Woods (2006) argues, are crucial to understanding the goals and actions of the Bank as it defined development since ‘‘technical ideas are shaped by political and bureaucratic imperatives’’ (39). The Bank adopted a highly standardized classification system that specified what types of loans different countries were eligible to receive. This was presumably intended to address the uncertainty around the problem of determining to whom they would lend and in part to convey the legitimacy of lending based on an appeal to commensuration, ‘‘the transformation of different qualities into a common metric’’ (Espeland and Stevens, 1998, p. 314). 2.1. World Bank lending categories The World Bank classifies countries by income, by geographical region, and, for the countries that are borrowers but not donors to World Bank (though this distinction is often fluid as borrowers are often also donors) by operational lending status, which is the object of inquiry here. The operational lending categories specify the types of loans and loan conditions for which countries are eligible. The records for these categories begin in World Bank Fiscal Year 1980, which corresponds to the calendar year 1978. There are five classes for the operational lending classification. The differences between them include whether they are IDA or IBRD loans2 and the length of the maturity of the loan: 2

The World Bank is composed of the International Bank for Reconstruction and Development (IBRD) and the International Development Agency (IDA). The two are organizationally equivalent but the pools of money for lending are different. The IDA was formulated explicitly to allow poor countries who could not meet the terms required for a loan from the IBRD to borrow money and it is funded mainly through donations from middle- and high-income member countries while the IBRD resources are raised through the investment of funds, including the money made from the interest on loans to countries, in world financial markets. But primarily, the loans made through the IBRD factor into the credit rating of the Bank, which is a major source of its prestige and subsequent wealth (Woods, 2006). As such, the high volume of IBRD loans is central to the maintenance of the World Bank.

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1. Category 1 countries are eligible for Civil Works Preference. These grants and loans are geared towards basic infrastructural development, and are available to countries that possess the lowest per capita income and the lowest credit rating. 2. Category 2 countries are eligible for IDA (International Development Agency) loans, which are intended for countries with low per capita income that cannot borrow from IBRD because they do not meet World Bank standards for creditworthiness. These are interest-free loans and grants for ‘‘boosting economic growth and improving living conditions’’ (McClure, 2003). Category 2 can also comprise those countries that are eligible for IBRD (International Bank for Reconstruction and Development) 20-year loans, which indicates that they are creditworthy but have GNI levels that qualify them for IDA loans. 3. Category 3 countries are eligible for IBRD loans with a 17-year lending period. 4. Category 4 countries are eligible for IBRD loans with a 15-year lending period (which translates into less money repaid to the Bank in interest than the 17- or 20-year period). 5. Category 5 countries are considered graduated from the IBRD and no longer in need of these resources, though countries can move into the other categories if their situation changes.

2.2. World Bank criteria for lending classification The operational lending classification is based on two explicit criteria set by the Bank: first, the thresholds from the analytical income classes and second, the ‘‘long-term economic prospects’’ for a particular country. According to an employee, the ‘‘World Bank focuses on longer term trends of GNI per capita (instead of making decisions based on 1 year’s GNI per capita) and takes into account the creditworthiness of each country assessed by the long-term economic, political, fiscal, and financial sector situation’’ (Batjargal, Oct. 20, 2006). Bank employees set the thresholds for the initial analytical classification, described above, subjectively. After determining these thresholds in the early 1980s using data from the 1970s, the thresholds have been changed only by adjusting the values for international inflation rates (measured by the average of inflation in Japan, the UK, the US and the Euro Zone). Thus, from an initially rather subjective set of thresholds, there have been no substantive changes. As a point of reference, 25% of all countries that experience movement between categories move in a direction that counteracts the country’s initial movement within 3 years (i.e. if a country is moved upward, 25% of the time it is moved down again within 3 years and vice versa). This indicates a substantial amount of volatility in movement on a relatively short time scale. One piece of evidence suggests that the relationship between the rules and classification does not correspond to the ideal typical relationship involving one-to-one correspondence between the two; despite the explicit criteria, classification does not always strictly dictate what types of loans are received. Some countries can receive ‘‘blend’’ loans, when they are ‘‘eligible for IDA loans because of their low per capita incomes but are also eligible for IBRD loans because they are financially creditworthy’’ (World Bank, n.d.). Small island countries are excused from some creditworthiness obligations in order to allow them to borrow from the IBRD.3 These exceptions indicate a degree of slippage between lending status and category membership, which suggests that the classification is not purely functional in the sense of grouping together countries that have similar lending status for the sake of consolidating information. Instead, there is some degree of 3

For a more complete list of exceptions, see the footnotes of http://siteresources.worldbank.org/OPSMANUAL/ Attachments/21186907/OP3.10.AnnexD.updated.Jan.17.2007.pdf.

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decoupling of classification outcomes and the rules for classifying. The main finding of this analysis is that the explicit economic rules for classifying countries account for most of the movement between categories for upward transitions (e.g. from category 2 to category 3) but that those economic rules do not sufficiently account for downward transitions (e.g. from category 2 to category 1). I discuss how organizational context might explain this finding and discuss the implications of this for understanding the influence of classification on cognition. 3. Data and methods The analysis concerns the movement of countries between the categories within the World Bank’s lending category system in order to examine the relationship between the rules for classification and classification outcomes. I used event history analysis to address questions regarding what factors change the risk of being moved from one category to another category over the 28-year period for which the World Bank recorded their lending categories. 3.1. Dependent variables The World Bank’s record of the changes in operational lending status of countries constitutes the dependent variables for the analysis. There are 271 movements of countries between categories during the time period. See Table 1 for counts of countries in the analysis and movements by year. For the analysis, the events of interest are any movement of a country upward within the classification system or any movement of a country downward within the classification system (instead of separating by type of movement). Table 2 presents the sum of the types of movements (between which categories) across the full time period. 3.2. Independent variables A number of different sources provided the independent and covariate variables. The independent variables include those variables that are an explicit part of the Bank’s classification criteria (crossing the GNI thresholds and creditworthiness) and others that are meant to capture aspects of what the Bank might be taking into account when it assesses the long-term economic outlook of a country. Political variables were included because, though the Bank explicitly cannot take political factors into account in their disbursing of loans as per their Articles of Agreement, most theories of development require interaction with the state. The neoliberal inclinations of the World Bank and its conditional lending classifications are much publicized and politicized (see Goldman, 2005). Neoliberal development agendas entail, according to Portes (1997), requiring adjustment measures such as unilateral opening to foreign trade, extensive privatization of state enterprises, deregulation of goods, services, and labor markets, liberalization of the capital market, fiscal adjustment, restructuring and downscaling of state-supported social programs, and an end to forms of state capitalism. Regardless of the specific nature of the relationship between the economic factors considered important to development and political factors, they are closely intertwined, even if implicitly, in most theories of development. Demographic and regional characteristics of countries were included in the analysis to provide proxies for some of the more implicit associations that might contribute to how Bank staff

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Table 1 Number of countries in analysis and number of movements by year, 1978–2005.a Year

Total countries

New countries

Total moves

Upward moves

Downward moves

No moves

1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

101 101 106 108 111 112 114 115 116 116 116 117 119 130 137 144 145 145 145 146 146 146 146 146 147 147 147 147

2 0 5 2 3 1 2 1 1 0 0 1 2 11 7 7 1 0 0 0 1 0 0 0 1 0 0 0

6 7 6 11 7 12 1 8 7 15 5 11 5 7 21 14 11 6 12 13 12 12 7 12 10 13 11 9

3 5 6 6 5 5 0 1 0 2 3 4 2 2 8 5 2 5 9 9 8 6 7 6 6 11 11 9

3 2 0 5 2 7 1 7 7 13 2 7 3 5 13 9 9 1 3 4 4 6 0 6 4 2 0 0

95 94 100 97 104 100 113 107 109 101 111 106 114 123 116 130 134 139 133 133 134 134 139 134 137 134 136 138

a

Mean number of moves per country: 1.84. Mean number of moves per country if the number of moves > 0: 2.85.

thought about assigning countries to categories. These assumptions may factor into the Bank staff’s assessments of creditworthiness or of risk rather than as more simple assumptions about relationships between the types of appropriate loans and appropriate countries. 3.2.1. Explicit economic variables To address the explicit economic criteria used by the World Bank for classification of countries, a dummy variable was created for whether a country crossed the threshold between categories based Table 2 Sum of type of movement of countries between categories across all years, 1978–2005 (N = 271). From category

I II III IV V

To category I

II

. 48 0 0 0

38 . 40 1 0

III

IV

V

0 47 . 30 0

1 0 42 . 6

0 0 0 18 .

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on GNI per capita in any given year. The other quantifiable criteria, the creditworthiness ratings, are not released by the Finance-Credit Risk Department of the World Bank, which constructs the ratings, as they are considered highly confidential.4 As a proxy, a measure of the countries’ creditworthiness ratings was drawn from Euromoney’s country risk rating. This rating was chosen as a proxy for the Bank’s country risk assessments on the basis that its criteria most closely matched the World Bank’s stated criteria for determining country risk of the available measures.5 This rating and the country risk rating from the International Investor are widely used by banks, mutual funds, and other financial organizations as legitimate and authoritative risk measures which are constructed by polling the economics departments of world-wide financial institutions. Many aspects of the country risk rating are covered in other variables as well. 3.2.2. Other economic criteria A variety of other measures associated with neoliberal conceptions of development were included to more fully explain how countries were moved between lending categories. Measures of aid as a percentage of GNI, inflow of foreign direct investment, and long-term debt (scaled by GNI and by 100,000) were taken from the World Bank’s World Development Indicators. (A measure of debt not scaled by GNI was also run in the models and the results were not substantively different.) A measure of openness of the economy – measured by imports plus exports divided by the GDP per capita – and a crucial part of neoliberal theories of development, was drawn from Penn World Table data (Heston et al., 2006) and indicates total trade as a percentage of GDP. 3.2.3. Political variables Political risk, though ill-defined, is included in the measures of country credit ratings. The relationship between particular types of economic development and political forms, whether or not explicitly incorporated into lending practices, is central to common theories of development; most of these neoliberal theories, whether empirically correct or not, associate democratization with neoliberal economic reforms (see Centeno, 1994). The large body of work by Meyer and colleagues (e.g. Meyer et al., 1997) draws on neoinstitutional organizational theory assumptions to suggest that there is a convergence of governmental types and policies as a result of the interaction and the spread of ideology and shared frameworks between, and the coercive powers of, countries and inter-governmental organizations. They suggest that particular structures of government and particular forms of international participation have become normative. This suggests that the World Bank may be rewarding countries, either explicitly or through their evaluation of the ‘‘creditworthiness’’ of countries, for particular forms of government (for example, their degree of democratization) and for their degree of participation in the world system. 4 The broad criteria the department uses, which includes both quantitative and qualitative analysis, for constructing the ratings are as follows: political risk, external debt and liquidity, fiscal policy and public debt burden, balance of payments risks, economic structure and growth prospects, monetary and exchange rate policy, financial sector risks, corporate sector debt and vulnerabilities (World Bank, 2005). 5 According to Euromoney, the rating takes into account the following factors with the following weights: economic data (25%), political risk (25%) from a poll of risk analysts, risk insurance brokers and bank credit officers, debt indicators (10%) calculated from the World Bank World Debt Tables, debt in default or rescheduled (10%), credit ratings (10%) which are an average of the ratings from Moody’s, Standard & Poor’s, and International Bank Credit Analysis Ltd., access to bank finance (5%), access to short-term finance (5%), access to international bond and syndicated loan markets (5%) which is designed to measure how easily a country might ‘‘tap the markets now’’, and access to and discount on forfaiting (also known as medium-term capital goods financing) (5%) (Country Risk, 2004).

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The polity score, a scale of degree of democracy minus degree of autocracy of the government, was taken from the Polity IV dataset which constructed this measure based on a series of measures regarding governmental characteristics (the adjusted Polity 2 measure was used). I compiled information from the CIA Factbook regarding civil wars, revolutions, coups and coup attempts and these events were summed due to a small overall number, and a dummy variable of presence or absence of political instability was created for any one year. Both lagged and nonlagged versions of this measure were tested in the models, and the results were not substantively different. Counts of membership in inter-governmental organizations serve as a measure of the degree of integration into the world system. A raw count of memberships was taken from the dataset constructed by Schofer (2003) and Schofer and Meyer (2005). The greater the number of memberships in inter-governmental organizations, the greater a country’s integration into the world system, this body of work argues. Greater integration into the world system should be associated with more normative political and economic behavior. 3.2.4. Demographic and regional variables Demographic and regional variables were included to assess non-economic and non-political associations that the Bank might have with countries that affected the types of loans for which they were eligible. A measure of population density was taken from the World Bank’s World Development Indicators and was included based on the observation that many of the countries with a large number of moves between categories also had high population density and because countries with high population density might be associated with particular associations, risks, and needs that are not covered by economic and political data. The region of countries and whether there is a Muslim majority in that country (whether over 50% of the population is Muslim) were also included.6 The Muslim majority dummy was based on 2006 population data from the CIA Factbook due to a lack of adequate data over the entire time period. These variables were meant to capture more implicit associations between countries and development or lending that were not captured in the other variables. 3.2.5. Category membership Dummy variables for category membership were included in the analyses to assess the effect of categorization itself on movement between categories, or whether the hazard ratio was shifted depending on the category involved. These variables examined the hypothesis suggested by previous work that the practice of classification itself has an effect on how countries are categorized.7 3.3. Analysis A series of event history models evaluated the predictors of countries’ movement between the categories over the complete time period. Two separate analyses were run for the two dependent 6 All of the reported models were also run using percentage Muslim as a continuous variable and the substantive results were identical. 7 Additional methodological notes: multiple imputation was used for missing values of independent variables using the Amelia II program (Honaker et al., 2008) for time-series data. All models presented below were also run without the multiply-imputed data and list-wise deletion was instead employed. The pattern of results obtained using list-wide deletion is substantively identical to the results reported below. The variance inflation factors and the condition index in the multicollinearity analysis indicated no problem of multicollinearity in the data.

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variables: movement up and movement down.8 For these analyses, a semi-parametric model was fit using a piecewise exponential hazard function with repeated events and time-varying covariates. The piecewise exponential function was chosen to take into account hazard rate changes between different time periods, corresponding to World Bank organizational time periods.9 A series of nested models were run first including the explicit economic criteria, then political variables, then demographic/regional variables and finally categorical variables. All variables except regional membership variables and Muslim majority dichotomous variables were time-varying by year. Models with interaction terms for the effect of credit rating by category, polity score by category, and crossing the GNI threshold by category, as well as region by time period were run. None of the interaction terms significantly improved the fit of the models and thus were excluded from the analyses results reported below.10 4. Results The models addressed predictors of movement between World Bank lending categories. Odds ratios and standard errors for the nested models are presented in Table 3. Based on the model including all variables, upward movement is very strongly driven by the explicit economic criterion used by the Bank: the odds ratio for crossing the GNI threshold, the first criterion for movement between categories, is very large. Generally, all of the economic variables included in the model have a positive effect on the hazard of a country moving upward: a higher credit rating leads to greater odds of movement upward, as do higher rates of foreign direct investment and greater levels of trade openness. Higher levels of aid decrease the odds of moving upward, as would be expected. Non-economic factors such as region and category membership were also significant: countries in East Asian, South Asia and Africa had lower odds of upward movement (the reference category was Europe/Central Asia). Countries in categories three and four also had lower odds of upward movement (the reference category was category 1). 8 Countries entered into the analysis and became ‘‘at risk’’ when they were IBRD and IDA members, if they were not at the beginning of the time period. Because of the possibility of repeated events, countries remained ‘‘at risk’’ until the end of the time period, 2005. Countries in category 1 for any year were excluded from the downward movement analysis, as it was not possible to move downward once in that category and countries in category 5 for any year were excluded from the upward movement analysis, as it was not possible to move upward once in that category. (This results in a different number of countries and time periods at risk between the two analyses.) 9 The existing literature suggests three organizationally meaningful periods within the span of time of this data: from 1977 to 1982, from 1983 to 1996, and from 1997 to 2005. First, there was a significant change in the approach of the Bank given the world-wide economic downturn of the 1970s and the debt crisis of the early 1980s; in 1982, the Bank instituted procedures, including going to U.S. bond markets instead of European capital markets and instituting a 1.5% fee on all loans, which shifted the burden of payment for the Bank onto borrowers. Additionally, in 1982 the Bank began using variable lending rates for IBRD loans, which is important in this case because it gave additional meaning to the different lending categories. Another significant shift in the orientation of the Bank occurred in 1996 with the initiation of the Heavily-Indebted Country Program initiative. The HIPC initiative was oriented towards alleviating the debt burden on the poorest countries but this required that heavily indebted countries ‘‘undertake deep economic restructuring and long-term improvements in performance’’ (Woods, 2006, p. 166). Woods (2006) suggests that for this initiative, the Bank had to undertake ‘‘a revision of conditionality’’ to accompany new approaches to managing debt. Rethinking debt relief presumably impacted how the Bank was thinking about lending. These periods roughly match the inductively derived periods based on the slope of the hazard function for risk of movement of countries between categories. I used the ‘‘stpiece’’ Stata routine developed by Sorensen (1999) for the analysis. 10 Simplified models were run including only the explicit predictors designated by the Bank (crossing the GNI threshold and credit rating). The complete models presented here had a significantly improved fit over the basic models, as measured by the log likelihood ratio.

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Table 3 Predictors of movement between categories.a Odds ratio Movement up

Period 1 (1977–1982) Period 2 (1983–1996) Period 3 (1997–2005) Cross GNI threshold Up Down Credit rating Aid as % of GNI Foreign direct invest. Debt scaled by GNId Economic openness IGO memberships Polity score Political instability Pop. density Muslim East Asia b Latin America Middle East South Asia Africa Category 2c Category 3 Category 4

Movement down

Model 1

Model 2

Model 3

Model 4

Model 1

Model 2

Model 3

***

***

***

***

***

**

**

Model 4

.02 0.01 .01*** 0.004 .01*** 0.003

.02 0.01 .01 *** 0.01 .01 *** 0.01

.02 0.01 .02 *** 0.01 .02 *** 0.01

.02 0.01 .01*** 0.01 0.01*** 0.01

.24 0.08 .23 *** 0.07 .17 *** 0.07

.31 0.15 .31** 0.16 0.24** 0.14

.35 0.17 .42 * 0.22 .37 * 0.22

.28 *** 0.13 .32 ** 0.17 .27 ** 0.16

5.70*** 1.17 –

5.55*** 1.14 –

5.23*** 1.08 –

5.16*** 1.09 –

–

–

–

–

2.03** 0.65

2.06** 0.66

1.55 0.5

1.56 0.51

1.02*** 0.01 .97** 0.01 1.02** 0.01 1.00* 0.0001 1.01*** 0.001

1.01** 0.01 .97 ** 0.01 1.02*** 0.01 1.00* 0.0001 1.01*** 0.002

1.01** 0.01 .98 ** 0.01 1.02** 0.01 1.00* 0.0001 1.01*** 0.002

1.03*** 0.01 .96*** 0.01 1.02** 0.01 1.00** 0.0001 1.01*** 0.002

0.97*** 0.01 0.99 0.01 0.95 0.03 0.99 0.0001 0.99 0.002

.97*** 0.01 0.98 0.01 0.95 0.03 0.99 0.0001 0.99 0.003

0.97*** 0.07 0.99 0.01 0.96 0.03 0.99 0.0001 0.99 0.002

.97 *** 0.01 1 0.01 0.96 0.03 0.99 0.0001 0.99 0.003

0.99 0.01 1.04** 0.02 1.28 0.34

1 0.01 1.02 0.02 1.26 0.33

1 0.01 1.03* 0.02 0.64 0.02

0.99 0.02 0.99 0.01 1.31 0.35

0.99 0.01 1.01 0.02 1.53 0.42

0.99 0.01 1 0.02 1.8 ** 0.5

1 0.001 0.82 0.21 0.62* 0.18 0.72 0.17 0.66 0.28 .31 ** 0.18 .54 ** 0.15

1.002** 0.001 0.72 0.19 .40*** 0.13 0.77 0.19 0.7 0.32 .13*** 0.08 .44*** 0.13

0.99*** 0.001 2.53*** 0.64 0.79 0.29 0.73 0.21 .23 *** 0.09 2.99 3.53 1.24 0.35

.99 *** 0.001 2.97*** 0.77 0.82 0.31 0.66 0.19 .19 *** 0.08 4.47 5.33 1.24 0.34

0.94 0.12 .44*** 0.13 .17***

– 1.55* 0.37 2.32***

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Table 3 (Continued ) Odds ratio Movement up Model 1

Model 2

Movement down Model 3

Model 1

Model 2

Model 3

0.06 –

Category 5 Number of countries Number of failures Time at risk Wald chi-square Log likelihood Likelihood ratio test Chi-square probability

Model 4

147 147 3417 1264.4 195.45 – –

147 147 3417 1249.2 191.54 7.81 0.05

147 147 3417 1232.1 187.06 8.96 0.26

147 147 3417 1171.9 169.98 34.16 0

Model 4 0.64 2.03 0.95

147 124 2257 974.91 201.09 – –

147 124 2257 971.24 199.94 2.3 0.51

147 124 2257 911.05 181.11 37.65 0

147 124 2257 895.25 176.33 9.57 0.02

Parametric event history analysis using piecewise exponential distribution. a Standard errors are below coefficients. b Europe and Central Asia is the reference group for region. c Category 1 is the reference group for upward movement analysis; category 2 is the reference group for downward movement analysis. d Debt/GNI is scaled by 100,000,000. * p < .10. ** p < .05. *** p < .01.

Overall, upward movement of categories is predominantly driven by the explicit classification criteria used by the Bank. The likelihood ratio tests of the nested models indicate that the addition of political variables to the economic variables significantly improves the fit of the model. The addition of demographic and regional variables do not improve the fit of the model, while the addition of category membership variables do improve the fit of the model. This suggests that, though economic variables best explain upward movement between categories, considering political variables and the effect of the categories themselves provides a more complete explanation for the upward movement of countries between categories. The predictors of movement upward are different from those of movement downward and this pattern holds across all the models run. No one type of factor strongly drives downward movement. Higher credit ratings, part of the explicit classification criteria of the Bank, significantly lowers the odds of downward movement, as do non-economic and non-political variables such as higher population density and being in the Middle East. Political instability and whether a country had a Muslim majority, a large coefficient, increase the odds of downward movement.11 Countries in categories three and four also had increased odds of downward movement. Likelihood ratio tests indicated a significantly improved model fit with the inclusion of demographic and regional variables, and a significant improvement of fit with the inclusion of 11

The finding about the increased odds of downward movement given a Muslim majority and the decreased odds of downward movement for countries in the Middle East (which has 12 Muslim majority countries out of 14) may seem counterintuitive though I remind the reader that Africa has ten countries with Muslim majorities (and six more with substantial Muslim populations), Europe/Central Asia has seven, South Asia has four and so on. Thus, Muslim majority countries occur across almost all regions, the exception being Latin America. Again, these results held when the measure for the Muslim population was measured as a percentage score.

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category membership variables. The inclusion of political variables did not improve the fit of the model. These results present a more complicated picture of the relationship between the stated classification rules and the lending classifications of the Bank. Explicit economic criteria drive upward movement while a variety of other non-economic and non-political factors, in addition to the explicit economic criteria, drive downward movement. More broadly, upward movement appears to be rule-governed while downward movement exhibits a decoupling between rules and classifications. Classification itself seems to have some effect on the movement of countries upward and downward, where membership in categories three and four affects the odds of being moved. There are three possible reasons for the asymmetry in upward/downward movement that point to the importance of the organizational context: first, upward movement might be subject to more transparency concerns which means that Bank staff may feel obligated to attend to the explicit rules to a greater extent than in the case of downward movement. Alternatively, Bank staff might be demonstrating risk-averse behavior in the sense that, because upward movement is associated with shorter loan repayment periods, Bank employees may want to guarantee that countries will be able to pay the Bank back within the given time period and thus they rely heavily on economic data in making that assessment (though this does not seem particularly plausible given that the differences in length of loans are not substantial). Finally, the classifications of the Bank may be considered not only to indicate the types of loans for which countries are eligible, but also may capture implicit assumptions of the Bank’s staff regarding levels of development, whereby membership in higher categories also signals a higher level of development. Bank employees might be drawing on a theory of development that associates development primarily with outcomes based on particular economic measures, and that cannot easily account for developmental ‘‘regression.’’ This finding highlights the importance of considering the organizational context of classifications in order to understand how individuals interact with and use classifications. Here, rules seem to be used strategically, which might challenge the degree to which the rules and the classifications are considered ‘‘natural,’’ with attendant cognitive consequences. The comparison between upward and downward movements indicating an uneven application of rules constitutes the main finding of interest. The World Bank may conceal some variable that employees use for classification, or the proxy for credit rating may be sufficiently different from the World Bank’s rating, which they do not publically disclose, thus rendering the specific coefficients incorrect, but these factors cannot account for the discrepancy in predictors between upward and downward movement. It may be that downward movements are governed by rules not fully captured in this analysis, but importantly, the criteria used for upward and downward movements differ from one other, indicating a degree of decoupling of rules from classification outcomes. 5. Discussion This case study and review illustrates that instead of assuming a straightforward cognitive absorption of systems of classifying, specific features of classification systems, characteristics of users, and the interaction between classification systems and users, need to be examined in order to understand the cognitive effects of classification systems. In particular, the following characteristics of classification systems need to be examined: the degree of institutionalization (e.g. whether they are considered appropriate, legitimate, and the amount of exposure people

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have to them), the strength of boundaries between categories, whether classifications are imposed top-down or whether there is a feedback relationship between users and categories, the political context, and the organizational context of classifications. The analysis of the World Bank’s lending classifications illustrates the importance of understanding the relationship between rules and classifications. While I am not able to definitively assess the reasons for the asymmetry in rule application, the plausible explanations (transparency concerns, risk-aversion, or underlying assumptions about the process of development) suggest that organizational needs lie at the root of the discrepancy. Here, the discrepancy between rules and classification outcomes illustrates that classifications are used instrumentally, according to a logic of consequences. The decoupling of rules and classification likely challenges the taken for granted nature of the classifications such that they are less likely to be easily assimilated into cognitive schemas, though this was not specifically tested. For the employees of the Bank, the decoupling of rules and downward classifications may lead to the denaturalization of the categories themselves due to strategic implementation of classification. Outside observers (e.g. classified countries, international organizations) pay attention to the Bank’s lending classifications, which suggests that researchers should take into account how different types of actors use classifications in order to assess their effects. These outside actors may be more inclined to assume that classifications are natural and thus legitimate given their distance from the organizational context. They may be more likely to assimilate and draw on the classification in their perceptions of countries and their loan status. Thus, classifications that are used both inside of an organization and are transported and applied outside of an organization, may have differential cognitive and behavioral effects. Finally, there are cognitive implications based on the type of actor using the classification system (e.g. audience, gatekeeper, internal) and type of use (e.g. for evaluative purposes, for finding information and directing attention, for instrumental action, or incidentally), which should be accounted for when assessing the cognitive and behavioral effects of classifications. Tilly (1999) posits that classification systems in organizations play a central role in the maintenance of inequality through what he calls ‘‘category matching’’—the use of similar categories by organizations and by actors external to organizations. Organizations, as primary distributors of resources in society, create ‘‘interior categories’’ in order to facilitate action within an organization. Durable inequality occurs, he argues, when a correspondence arises between the categories associated with differential distributions of resources internal to an organization and categories that are utilized more broadly outside of any specific organization or population of organizations. If, as Tilly suggests, this process of category matching is a fundamental mechanism for entrenching social inequality, having a means by which to understand the process of how interior and exterior categories influence cognition, and perhaps differentially, will prove very useful. 6. Implications for studying culture The next step is to combine insight into how features of classification systems affect actor cognition with how they affect actor behavior; the existing literature provides a number of behavioral predictions. For example, Ruef and Patterson (2009) demonstrate that top-down classification systems that have been institutionalized lead instrumental users of the categories, in this case credit assessors, to depend on and enforce strong category boundaries. On the other hand, in classification systems where there is feedback between critics and classified entities, as in the case of French cuisine, critics are able to change their expectations and their evaluations in

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concert with changing behaviors of the chefs (Rao et al., 2005). A series of experiments by Boltanski and Thevenot (1983) explore how individuals who do not work for the government know and use official French occupational classifications (they argue the classifications have become like Schelling’s (1960) ‘‘salient points’’—taken for granted, socially shared rules), and ‘‘play with them, to play on them’’ (674). This approach suggests the utility of contextualizing studies of classification and developing studies of how people use and interpret classifications. These concerns bear on larger theoretical questions regarding how ‘‘culture works,’’ in particular through the interaction between external classification systems and how individuals think (e.g. DiMaggio, 1997; Hannerz, 1993). Acknowledgements I am very grateful to Martin Ruef, King-to Yeung, and Paul DiMaggio for their conversations and suggestions on earlier versions of this project. I would also like to thank Karen Cerulo, Michael Benediktsson, Matthew Ellis, and two anonymous reviewers for their useful comments on this draft. The author was supported by a National Science Foundation Graduate Research Fellowship while conducting this research. References Aldrich, H., Ruef, M., 2006. Organizations Evolving. Sage Publications, London. Batjargal, U., October 2006. Client Services Team, Development Data Group, World Bank. Personal Email Correspondence. Boltanski, L., Thevenot, L., 1983. Finding one’s way in social space: a study based on games. Social Science Information 22, 631–680. Bowker, G.C., Star, S.L., 1999. Sorting Things Out: Classification and its Consequences. MIT Press, Cambridge. Carroll, G.R., Hannan, M.T., 2000. The Demography of Corporations and Industries. Princeton University Press, Princeton, NJ. Centeno, M., 1994. Between rocky democracies and hard markets: dilemmas of the double transition. Annual Review of Sociology 20, 125–147. Country Risk, 2004. Editorial Review of Institutional Investor and Euromoney Country Risk Ratings. http://www.countryrisk.com/reviews/archives/000126.html. DiMaggio, P.J., 1987. Classification in art. American Sociological Review 52, 440–455. DiMaggio, P.J., 1997. Culture and cognition. Annual Review of Sociology 23, 263–287. Douglas, M., 1986. How Institutions Think. Syracuse University Press, Syracuse. Durkheim, E., Mauss, M., 1963 [1903]. Primitive Classification. University of Chicago Press, Chicago. Espeland, W.N., Sauder, M., 2007. Rankings and reactivity: how public measures recreate social worlds. American Journal of Sociology 113, 1–40. Espeland, W.N., Stevens, M.L., 1998. Commensuration as a social process. Annual Review of Sociology 24, 313–343. Goldman, M., 2005. Imperial Nature: The World Bank and Struggles for Social Justice in the Age of Globalization. Yale University Press, CT. Hannerz, U., 1993. Cultural Complexity: Studies in the Social Organization of Meaning. Columbia University Press, New York. Heston, A., Summers, R., Aten, B., 2006. Penn World Table Version 6.2. Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania. Honaker, J., King, G., Blackwell, M., 2008. Amelia II: A Program for Missing Data. Hsu, G., 2006a. Evaluative schemas and the attention of critics in the US film industry. Industrial and Corporate Change 15, 467–496. Hsu, G., 2006b. Jack of all trades and master of none: audiences’ reactions to spanning genres in feature film production. Administrative Science Quarterly 51, 420–450. Johnson, C., Dowd, T.J., Ridgeway, C.L., 2006. Legitimacy as a social process. Annual Review of Sociology 32, 53–78. Kennedy, M.T., 2008. Getting counted: markets, media and reality. American Sociological Review 73, 270–295.

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