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Who’s to Blame? The Distribution of Responsibility in Developing Democracies

Published online by Cambridge University Press:  01 November 2010

Isabella Alcañiz  and
Timothy Hellwig
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Abstract

International structures tie the hands of policy makers in the developing world. Dependency on the world economy is blamed for low growth, high volatility and less redistribution of income than average, but the effect of international constraints on mass politics is relatively unknown. This study examines how citizens of developing democracies assign responsibility for policy outcomes. A theory of the distribution of responsibility, combining insights from the political economy of development and the study of mass behaviour, is presented. Evidence from seventeen Latin American countries shows that citizens often blame policy outcomes on international and private-sector actors, to which they, as voters, have no direct recourse. Ties to world markets and the International Monetary Fund, especially foreign debt, shift responsibility towards international actors and tend to exonerate national politicians.

Type
Research Article
Information
British Journal of Political Science , Volume 41 , Issue 2 , April 2011 , pp. 389 - 411
Copyright
Copyright © Cambridge University Press 2010

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References

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29 This approach is similar to several recent papers that use the Latin American experience to shed light on broader issues in international development (e.g., Brooks, ‘Explaining Capital Account Liberalization’; Fornos, Carolina A., Power, Timothy J. and Garand, James C., ‘Explaining Voter Turnout in Latin America, 1980 to 2000’, Comparative Political Studies, 37 (2004), 909–940CrossRefGoogle Scholar; Wibbels, Erik and Arce, Moisés, ‘Globalization, Taxation, and Burden-Shifting in Latin America’, International Organization, 57 (2003), 111–136CrossRefGoogle Scholar; Wibbels, ‘Dependency Revisited’.

30 For point of comparison, annual growth rates over this period averaged 7.2 per cent for East Asia, 3.8 per cent for Middle East and North Africa, 5.2 per cent for South Asia, 3.4 per cent for Sub-Saharan Africa, and 4.2 per cent for Europe and Central Asia (World Bank’s World Development Indicators CD-Rom).

31 For evidence of these intra-regional variations, see Appendix Table A1.

32 Measures on the dependent variable were not asked in earlier or more recent Latinobarómetro surveys. Study design and data are available at www.latinobarometro.org. Latinobarómetro bears no responsibility for the findings reported in this study or for inferences based on them.

33 A negatively-worded question is appropriate insomuch as governments are far more likely to be blamed for poor outcomes than rewarded for good ones ( Lewis-Beck, Michael and Stegmaier, Mary, ‘Economic Models of Voting’, in Russell Dalton and Hans-Dieter Klingemann, eds, The Oxford Handbook of Political Behavior (Oxford: Oxford University Press, 2007), pp. 518–537Google Scholar). Few would disagree that there were at least some ‘economic problems’ in these countries at the time. The question imparts no judgement as to the severity of these problems but leaves it up to the respondent.

34 US survey data used in studies by Gomez and Wilson, ‘Causal Attribution’, and Rudolph, ‘Who’s Responsible for the Economy?’ ask respondents to identify the actor they believe to be most responsible and thus do not distinguish between those respondents who distribute responsibility across several targets.

35 The Economist, 24 October 2002.

36 We do not distinguish public-international from private-international in our statistical analyses since we are mainly interested in pairing private against public and international against domestic (see Table 1). The residual ‘No one is directly responsible’ option is not used to contribute to any of our dependent variables.

37 It might be argued that the response of ‘the banks’ does not clearly fit into any one of these categories. The results of exploratory data analyses, however, strongly suggest that it be placed in the International Actors set of targets. There also is a substantive rationale for this grouping as reforms since the 1980s increased dramatically the share of foreign banks. In the majority of countries in our sample, foreign ownership accounts for well over 50 per cent of the sector. For example, from 1990 to 2001, foreign banks’ share of assets went up from 10 per cent to 61 per cent in Argentina, 0 per cent to 90 per cent in Mexico, and 4 per cent to 61 per cent in Peru ( Moguillansky, Graciela, Studart, Rogerio and Vergara, Sebastian, ‘Foreign Banks in Latin America: A Paradoxical Result’, CEPAL Review, 82 (2004), 21–37Google Scholar. In addition, privatization of the banking sector has increased greatly in recent decades, from 65 per cent under state control in 1970 to only 40 per cent in 1995 ( Micco, Alejandro and Panizza, Ugo, ‘Bank Ownership and Lending Behavior’, Economics Letters, 93 (2005), 248–254CrossRefGoogle Scholar.

38 We performed two exploratory analyses. First, we examined the relationships among our set of dichotomous indicators using tetrachoric correlations. Results support our categorization. For each indicator (‘government’s economic policy’ notwithstanding), the correlation was strongest for an indicator included in its response category grouping. Secondly, we regressed each target on the others using logistic regression. We found that each of the predictor variables exerts its greatest effect on other targets in a manner fully consistent with the grouping decision described above. The results of these analyses are available upon request.

39 These macro-measures are lagged one year from the date of the public opinion survey to account for information processing.

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41 Raymond Vreeland, James, The International Monetary Fund: Politics of Conditional Lending (New York: Routledge, 2007)Google Scholar.

42 The CLYPS dataset has been developed by Kevin Cowan, Eduardo Levy Yeyati, Ugo Panizza and Federico Sturzenegger and is available at www.iadb.org. We considered several alternative measures of country debt dependence, including total debt to GDP, total non-marketable loans issued by financial institutions (domestic or foreign) as proportion of GDP, and total debt owed to the IMF, World Bank, Inter-American Development Bank and other multilateral institutions as a proportion of GDP. Using these measures did not produce qualitatively different results from the statistical analyses we report below.

43 The index is composed using partisanship rankings from Victoria Murillo, Maria, Oliveros, Virginia and Vaishnav, Milan, ‘Voting for the Left or Governing on the Left?’ (paper prepared for the Conference on ‘Latin America’s Left Turn: Causes and Implications’, Harvard University, 2008)Google Scholar. We diverted from the Murillo et al. classification for two parties: the Partido da Social Democracia Brasileira (PSDB) and the Partido Demócrata Cristiano de Chile (PDC). We do not classify the PSDB as left for the years of interest to this study since the party had migrated to the political centre – or even centre-right, by some accounts, as the 2002 election approached. We code the Chilean PDC as centre-left since the party was a member of Chile’s governing centre-left coalition, the Concertación, since the country democratized in 1990. Also, the classification of parties in Ecuador proved challenging because of unstable electoral coalitions and policy switches. Here, however, we followed the classification advanced by Murillo et al. as well as Manuel Alcántara and Flavia Freidenberg, eds, Partidos Políticos de América Latina: Países Andinos (Ciudad de México: Fondo de Cultura Económica, 2003). Finally, for Guatemalan parties, we drew on Murillo et al. as well as Jacqueline West, South America, Central America and the Caribbean 2002 (London: Routledge, 2002).

44 Heston et al., Penn World Table Version 6.2.

45 Recall that the dataset merges the 2002 and 2003 Latinobarómetro files. We might expect the competency signal to be weaker in 2002, for example, given that this was a period of increased turmoil in several countries.

46 For survey respondents, we used a standard self-identification measure that reads, ‘In politics, people normally speak of “left” and “right”. On a scale where 0 is left and 10 is right, where would you place yourself?’

47 Party identification is also a reasonable control variable to include. Unfortunately, however, the Latinobarómetro survey includes measures of party support in their 2002 survey only.

48 Gelman, Andrew and Hill, Jennifer, Data Analysis Using Regression and Multilevel/Hierarchical Models (New York: Cambridge University Press, 2007)Google Scholar; Steenbergen, Marco R. and Jones, Bradford S., ‘Modeling Multilevel Data Structures’, American Journal of Political Science, 46 (2002), 218–237CrossRefGoogle Scholar.

49 Our discussion is limited to the effects of international structures on responsibility attributions. Briefly, however, model controls generally perform as expected. Table 3 results show that citizens are less likely to hold the government responsible when the left controls significant shares of power at the national level (coefficient on Left Power in Model 1 is negatively signed and precisely estimated) and more likely to blame private sector actors when government spending is high (see coefficient on Government Spending in Model 2). Self-Placed Ideology, Education, and Socio-Economic Status also have consistent effects: respondents sharing the chief executive’s ideology, having more years of formal education and possessing higher socio-economic status are more likely to say that international actors are responsible.

50 Note that each of the graphs in Figure 1 presents information from each of the three models in Table 3. Presenting information in this fashion allows us to make explicit comparisons of the effects of the three structural variables of interest across different categories of responsibility targets.

51 That is, Self-Placed Ideology = 0, Education = 4, Socio-Economic Status = 2, Female = 1 and the age category is 26–40 years.

52 Recall that the respondent may attribute responsibility to more than one actor such that these probabilities need not sum to one.

53 Not surprisingly, the role of the IMF is to alter the distribution of responsibility between domestic public and international actors and has no statistically significant effect on the probability of attributing responsibility to civil society (see Table 3, Model 1).

54 We ran several additional models to assess the robustness of these findings. First, we controlled for additional political factors, including divided government and central bank independence ( Polillo, Simone and Guillen, Mauro F., ‘Globalization Pressures and the State: The Worldwide Spread of Central Bank Independence’, American Journal of Sociology, 110 (2005), 1764–1802CrossRefGoogle Scholar. The results of these models do not differ from what we report above. Secondly, we examined different measures of country exposure to the international economy. Models which substituted exports and imports separately (both as percentage of GDP) for Trade Openness produce the same effects as reported in Tables 3. As far as mass responsibility attributions are concerned, growth of export markets has no distinguishable effect from increased access of foreign goods. We also included as contextual variables gross private capital flows and inflows of foreign direct investment from the World Development Indicators as well as a measure of restrictions on capital flows from Dreher, Axel, ‘Does Globalization Affect Growth? Evidence from a new Index of Globalization’, Applied Economics, 38 (2006), 1091–1110CrossRefGoogle Scholar. In none of the five models did any of these capital market variables have an effect on the distribution of responsibility. The asymmetric effect of trade and capital flows on responsibility attributions thus warrants further investigation.

55 Murillo et al. ‘Voting for the Left’.

56 Argentine President Néstor Kirchner’s (2003–07) rhetoric is typical of the new Latin American left. In 2003, during his first negotiation with the IMF, he declared on a popular television show that ‘The members of the Fund’s bureaucracy are employees of [economic] interests and are responsible for the Argentine crisis. They practically fleeced Argentina with their mistakes, and now that we are on the mend, they want to condition us’ (‘Kirchner apuntó al FMI, pero a la vez se pagaron US$ 268 millones’, 20 December 2003, at www.clarin.com). Even Brazilian President Lula (2002–11), one of the more pragmatic representatives of this new left, has expressed the opinion that the IMF ‘does not work’ (‘Lula: FMI e Banco Mundial, hoje, de nada servem’, 31 October 2008, at www.noticias.br.msn.com).

57 We choose a value of 0.75 rather than 1.0 to connote a high level of left power in office because the political left, in our dataset, rarely attains a value of 1.0 and, thus 0.75 is a more realistic (and more conservative) indicator of left power.

58 Under this same scenario, the citizen attributes responsibility to the government’s economic policy with probability of just 0.05.

59 It is possible that the connection between left-of-centre control of government and greater attribution of blame to non-governmental actors runs in the opposite direction; i.e., voters might put the left in office as a consequence of believing that non-governmental actors are responsible for poor economic performance. While we cannot completely discredit this possibility, we performed two additional sets of analyses to solidify our contention that the causal arrow runs from elite partisanship to public beliefs rather than vice versa. First, in order to assess whether the accumulated ‘stock’ of leftist influence affects the current distribution of responsibility, we re-estimated models in Table 4 with a measure of the historical strength of the left in each country. We develop a measure, Historical Left Power, which lies between 0 and 1 connoting the share of years between 1985 (the year associated with the (re)establishment of democracy for many countries) and the year of the survey (2002 or 2003) in which the left was in government. The correlation between Historical Left Power and the Left Power measure is 0.68. Results of the interactive models with Historical Left Power support our expectations as well as those reported in Table 4 – and in some cases, such as for International Actors Responsible, the effect of the left in office using the historical measure is considerably stronger. Secondly, we reverse the causal direction by using the Latinobarómetro data to estimate a model of individual party vote intentions (based on the hypothetical scenario that the election would be held ‘this Sunday’). The dependent variable is coded 1 if the respondent intended to vote for a left-of-centre party and 0 if s/he intended to vote for a non-left party. While we find that individuals who attribute responsibility to international actors are more likely to intend to vote for the left, this effect is no greater than the increased probability of voting left among those who assign responsibility to the government’s policy. The results of these additional analyses are available from http://mypage.iu.edu/~thellwig/.

60 Anderson, Christopher J. and Singer, Matthew M., ‘The Sensitive Left and the Impervious Right: Multilevel Models and the Politics of Inequality, Ideology, and Legitimacy in Europe’, Comparative Political Studies, 41 (2008), 564–599CrossRefGoogle Scholar, provide a recent overview of the growth of multi-level models of individual behaviour in comparative politics.

61 Duch, ‘A Developmental Model’, argues that tendencies to holding the incumbent accountable for the economy in new democracies increase as ambiguity regarding the link between government policy and economic outcomes declines. Duch focuses on individual information and trust levels as a source for this ambiguity. Our analysis is less optimistic in that ‘ambiguity’, for us, arises not only from individual capacities but mainly from the complexity of the international environment.

62 The Economist, 10 March 2007.