Benevolent Policies: The Politics of Welfare State Expansion in Southeast Asia
Bibliographic record
Abstract
Why do governments expand social policies? This question is central to our understanding of the welfare state. Conventional scholarship suggests that governments expand policies to placate groups with high political power or garner political support. Left unknown, however, is why governments expand social policies if clear, short-term incentives are absent. I draw attention to a class of social policies which serve groups with low political power and address issues with low visibility. I call these benevolent policies. Governments do not have strong incentives to prioritize, then formulate benevolent policies – yet some have sprung into action, introducing rapid policy change. What explains why governments expand benevolent policies? And why do some governments expand benevolent policies, while others do not? In contrast to the existing literature, which conceptualizes welfare state expansion as a product of conflict amongst societal groups, organized to protect or advance their interests, or of political manipulation and bargaining, I draw attention to the role of policymakers within the bureaucracy. I argue that policymakers are the primary source of policy reform. They mobilize in some governments, but not others, because of variation in bureaucratic capacity. This dissertation highlights the role of international socialization in triggering policymaker mobilization when bureaucratic capacity is high. Policymakers use the leverage provided by international pressure to deploy bureaucratic capacity and push through policy change. When bureaucratic capacity is moderate or low, however, other actors, such as civil society organizations or development partners, mobilize but are not as well positioned to facilitate reform. I develop this argument through a study of nutrition policies in Indonesia and the Philippines, and Laos and Cambodia. Nutrition policies, I contend, are emblematic of benevolent policies. Drawing upon nine months of fieldwork – including observations at a UN agency, 71 in-depth, semi-structured interviews, and observations at three closed-door regional health meetings – this project challenges influential theories of interest group strength, clientelism, and civil society mobilization. Instead, I show that policymakers are the defining agents of change. This dissertation therefore lays the foundation for future research on benevolent policies and invites further exploration of the conditions under which they expand.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".