Land Titling and Microcredit in Cambodia: Examining the Reality of Hernando de Soto’s ‘Three Steps to Heaven’
Bibliographic record
Abstract
Starting with the work of Hernando de Soto in the 1980s, the role of land tenure soon attracted the attention of the international development community as a neoliberal-oriented theory of change rooted in the possession and use of private individual land titles by the poor. One of the central mechanisms proposed by de Soto was a three-step process that involves the poor (1) “securing” their tenure with land titles, (2) using their newly acquired land titles as collateral to leverage large amounts of microcredit to be used to establish a functioning microenterprise, and then (3) escaping from their poverty due to the jobs and income associated with founding and running a microenterprise. This paper explores what I call de Soto’s “three steps to heaven” theory of change, a concept that was taken on board with gusto by leading Western governments and virtually all of the main international development institutions, particularly the World Bank. I argue that Cambodia provides the ideal setting for evaluating de Soto’s concept because, since around 2020, it has possessed the largest microcredit sector in the world (on a per capita basis), thanks largely to the obligatory use of land titles as the collateral required to obtain microcredit. While the first two of de Soto’s “three steps to heaven” have been realized, the evidence shows that the assumptions related to step three have proved to be extremely problematic: the ubiquity of microcredit that was achieved since the early 2010s via steps one and two has not, in general, improved the lives and communities of Cambodia’s poor through accelerated microenterprise development. It appears, instead, to have contributed to deeper poverty, insecurity, vulnerability and inequality. I conclude that de Soto’s “three steps to heaven” theory reflects a fundamental misunderstanding of the real institutional drivers of sustainable local economic development and poverty reduction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".