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Inside the Black Box: Uncovering Dynamics and Characteristics of the Chilean Central Government Bureaucracy with a Novel Dataset

Published online by Cambridge University Press:  09 January 2024

Daniel Brieba
Affiliation:
Daniel Brieba is an LSE Fellow in political science and public policy at the School of Public Policy, London School of Economics and Political Science (LSE), London, UK, and an assistant professor at the School of Government, Universidad Adolfo Ibáñez, Santiago, Chile. d.r.brieba@lse.ac.uk.
Mauricio-René Herrera-Marín
Affiliation:
Mauricio-René Herrera-Marín is an associate professor and director of basic sciences at the Faculty of Engineering, Universidad del Desarrollo, Santiago, Chile. mherrera@udd.cl.
Marcelo Riffo
Affiliation:
Marcelo Riffo is a researcher at the School of Government, Universidad Adolfo Ibáñez, Santiago, Chile. mariffo@alumnos.uai.cl.
Danilo Garrido
Affiliation:
Danilo Garrido is an adjunct lecturer at the Faculty of Engineering, Universidad del Desarrollo, Santiago, Chile. dggarridom@gmail.com.
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Abstract

This article examines bureaucracies using a novel dataset of Chilean central government employees from 2006 to 2020. Unlike perception-based sources, this dataset provides objective, disaggregated, and longitudinal insights into bureaucrats’ characteristics and careers. The authors validate it against official employment statistics and conduct an exploratory and descriptive analysis, presenting six descriptive findings about the Chilean bureaucracy that cannot be discovered using available aggregate data. The analysis reveals significant degrees of personnel stability and professionalization in the civil service, but with considerable rigidity in careers and substantial interagency heterogeneity in turnover, wages, and exposure to political cycles. These findings suggest that the Chilean national bureaucracy is mostly well developed along Weberian lines, though not uniformly so. These measurements also serve as a benchmark for comparing other Latin American bureaucracies in the future.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of University of Miami
Figure 0

Figure 1. Difference in Total Employees Between DIPRES and Our Data, by Year (percent)

Figure 1

Figure 2. Total of Employees by Agency/Year, DIPRES vs. Our DataNote: Scatterplots of the Budget Office’s estimates of employees in each agency/year against the same estimates derived from our data. Only agency/years with up to five thousand employees in the x axis (which are nearly all) are shown. For a plot covering all agencies, see figure A7 in online annex 2.

Figure 2

Figure 3. Distribution of Employment Regime by Year (percent)Note: Data include all agencies that have full data (i.e., beginning no later than February 2006 and ending on or after March 2020), but exclude JUNJI (which has missing data for 2011). Data are for the month of March in each year. See online annex 6 for the full list of included agencies.

Figure 3

Figure 4. Turnover Rates by Contract Type and Year (with 95 percent confidence intervals)Note: Data include all agencies that have full data (see online annex 6), excluding the statistics agency (see figure A4 in annex 1 for an explanation of this exclusion). “Presidential year” is defined in the text. Managerial positions are excluded.

Figure 4

Figure 5. Turnover Rates by Year, Without JUNJI (with 95 percent confidence intervals)Note: Data include all ranks and all agencies with full data (see online annex 6), except JUNJI. Permanent and yearly contracts only.

Figure 5

Figure 6. Kaplan Meier Survival Estimates by Rank (2006–2020)Note: Data include all agencies that have full data (see online annex 6).

Figure 6

Table 1. Initial and Final Contract Type, by Job Spell (totals and row percentages)

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Figure 7. Percentage of Individuals with Only One Job Spell (Discounting All Fully Temporary Job Spells), by RankNote: Includes all agencies.

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Figure 8. Grade Progression by Length of Job Spell (in years)Note: Calculation includes all nontemporary job spells in agencies with full data (see online annex 6). For ease of visualization, 0.12 percent of observations were excluded.

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Figure 9. Word Clouds of Most Frequent Professions, for Top Grades Only and for All IndividualsNote: Includes all agencies.

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Table 2. Most Frequent Professions in Top Grades

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Table 3. Most Frequent Professions in Top Grades, Selected Agencies

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Figure 10. Kaplan-Meier Survival Estimates, Selected Agencies, 2006–2020Note: Shorter lines correspond to agencies with fewer than 172 months of data.

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Figure 11. Turnover by Agency, Year 1 of New Government vs Other YearsNote: Data include all agencies with full data (see online annex 6). Two agencies have two years of missing data. Presidential years (as defined in the text) are used.

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Figure 12. Average Wage and Average Turnover by Agency, Top 8 Grades OnlyNote: Data include all agencies that have data beginning on or before January 2010 and ending on or after January 2020. Wages in nominal Chilean pesos. The y axis begins at 1,500,000 for better visualization. N = 86; four agencies have one or two years of missing data.

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