What Effects Could Global Value Chain and Digital Infrastructure Development Policies Have on Poverty and Inequality after COVID-19?
Bibliographic record
Abstract
It is clear that in the transition out of the COVID-19 crisis in Colombia there will be great need for formal job creation. One source that has been widely discussed in policy circles is strengthening linkages of Colombian firms with Global Value Chains (GVCs). Another source that has received recent attention, and deservedly so, is digital infrastructure development (DID)—which can boost telework and virtual human capital accumulation. Reduction in poverty and inequality through more and better formal employment is an important aspect of a jobs and economic transformation (JET) agenda. In this paper, we explore—through a computable general equilibrium model (CGE) and a microsimulation framework—to what extent reforms of the type envisioned in the JET agenda and which could generate GVC linkages, as well as through DID, for Colombia, and we project their impact on poverty and inequality up to 2030. Our findings show limited impact of the three types of policy changes considered for GVCs—namely (i) fall in barriers for seamless business logistics, (ii) reductions in tariffs, and (iii) lower barriers to foreign direct investment (FDI). The impact of DID on inequality is also moot. There is however a modest impact on poverty reduction in the combined policy of digital infrastructure with a boost in skilled labor. This finding can be linked to different factors. First, there are relatively few direct jobs created to benefit households with low levels of human capital. Second, there might be indirect job creation through backward linkages to local suppliers by firms linked to GVCs, but this effect would be a general equilibrium effect that our CGE model with a partial equilibrium microsimulation distributional module does not fully capture. Third, the positioning of Colombian firms to latch onto GVCs, and also generate demand for local intermediate inputs and services, is not optimal. Fourth, DID may generate more general labor market opportunities through telework and virtual learning expansions but could also induce larger wage gaps as the skill premium rises so that the net effect on inequality is ambiguous.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".