Panel-data analysis of food prices and regional economic activity in Kenya
WFP food-price data | VIIRS nighttime lights | STATA panel econometrics
| CLIENT Seema Ahad | SOFTWARE STATA | OUTCOME Analysis chapter strengthened through academic review and guidance |
βThe data-layer issues identified during the earlier review have now been addressed, the complete Stata log reconciles with the reported tables, and the discussion now gives appropriate prominence to the key result. The chapter is much more methodologically defensible following these revisions.β
π§ 24Γ7 Support
Project at a glance
The assignment involved a STATA-based panel-data analysis combining World Food Programme food-price data with VIIRS nighttime-lights information to examine how changes in food prices relate to regional economic activity. Seema had already developed the core analytical work and required targeted academic and methodological support to strengthen it. Our role focused on reviewing the analytical workflow, identifying data and consistency issues, checking the alignment between STATA outputs and reported tables, and providing guidance on the interpretation and presentation of results. The objective was to help Seema refine her chapter so that the analysis was internally consistent, reproducible, statistically defensible, and clearly communicated for academic review.
| 01 Brief & Data Review | 02 Method Review | 03 Diagnostic Checks | 04 Panel Model Review | 05 Revision Guidance |
A Multi-Source STATA Assignment, Not Just a Regression
The client brief required a structured analytical pipeline using WFP food-price records and VIIRS nighttime-lights data within STATA. Seema had already progressed with the core analysis, but the workflow needed careful review to ensure that the datasets, transformations, statistical tests, and reported results were properly aligned. Our support focused on reviewing the data-integration process, checking the region-by-time panel structure, assessing the construction of analytical variables, and providing feedback on the use and interpretation of descriptive statistics, correlations, and panel-regression models. We also reviewed whether the reported outputs formed a coherent analytical narrative rather than appearing as isolated STATA results. The aim was to help Seema strengthen the consistency, transparency, and academic defensibility of her own analysis.
Core analytical requirements
β’ Import and harmonise World Food Programme food-price data.
β’ Merge food prices with VIIRS nighttime-lights observations.
β’ Structure the data as a regional panel across time.
β’ Generate food-price and economic-activity variables.
β’ Produce summary statistics, trends and correlation diagnostics.
β’ Estimate fixed-effects models and robustness specifications.
β’ Validate the model using statistical significance and fit diagnostics
Why the brief was analytically demanding
The methodological challenge extended well beyond selecting between fixed-effects and random-effects models. The analysis brought together datasets developed for different purposes, relied on nighttime lights as a proxy for regional economic activity, incorporated multiple food commodities, and required careful consideration of unobserved regional heterogeneity and within-panel dependence. Our review therefore focused on whether the studentβs analytical choices were appropriately justified and whether the evidence remained consistent across the cleaned dataset, STATA log, statistical tables, and written discussion. We highlighted areas where inconsistencies or unsupported interpretation could weaken the chapter, even when individual STATA commands were technically correct, and provided targeted guidance to strengthen methodological coherence and defensibility.
Method challenge
β’ Bivariate relationships did not necessarily align with the estimates produced by panel-data models.
β’ The choice between fixed-effects and random-effects specifications required clear diagnostic justification.
β’ The use of robust or clustered standard errors could materially affect the strength of statistical inference.
β’ Our review focused on identifying these methodological risks and guiding Seema on how to address and explain them clearly in the final chapter.
Data challenge
β’ The source datasets differed in structure, units, and time coverage, requiring careful review before interpretation.
β’ Panel identifiers and the time structure needed to be checked for consistency across merged data.
β’ Differences across food commodities could affect comparability and potentially distort simple aggregate patterns.
Building a Dataset the Econometric Model Could Trust
The first area of review was data integrity, because the reliability of the econometric results depended on the quality and consistency of the underlying panel dataset. The assignment specification required World Food Programme food-price data to be aligned with VIIRS nighttime-light observations at the regional level. Seemaβs analytical chapter also incorporated rainfall as a contextual control and retained several staple-food categories to reflect differences across the underlying food market rather than relying on a single commodity series.
Our support focused on reviewing how these different data sources had been structured, merged, and prepared for panel analysis, with particular attention to regional identifiers, time periods, variable consistency, and the treatment of multiple commodities. We highlighted areas requiring clarification or correction and provided guidance so that Seema could strengthen the dataset underpinning her subsequent STATA analysis.
The analytical dataset in the supplied chapter
As part of the review, we checked whether the descriptive evidence reported in Seemaβs chapter was consistent with the analytical dataset and whether each statistic was interpreted appropriately.
| Evidence from the supplied analysis | Why it mattered |
|---|---|
| 131 analytical observations | Indicated the number of observations retained for the panel analysis and provided an important basis for assessing the scope of subsequent estimation and diagnostics. |
| Mean nighttime-light value: 3.543 | Summarised the economic-activity proxy used as the principal outcome measure in the analysis. |
| Average food price: USD 0.822 | Captured the overall level of food prices within the analytical sample and provided context for interpreting variation across regions and periods. |
| White maize: 39.69% of observations | Showed that white maize represented a substantial share of the commodity observations, making commodity composition important when interpreting aggregate findings. |
| Dry beans: 21.37% of observations | Demonstrated that commodity representation was uneven, reinforcing the need for caution when generalising results across all staple-food categories. |
Our review highlighted the importance of connecting these descriptive statistics directly to the later econometric analysis rather than presenting them as standalone figures. This helped Seema strengthen the link between the structure of her dataset, the panel-model results, and the interpretation presented in the chapter.
The analytical dataset in the supplied chapter
The supplied chapter reported that the analytical dataset was examined using STATA 19.5, focusing on the relationship between average food prices, rainfall, and mean nighttime-light (NTL) values. The descriptive statistics covered 168 observations from 2014 to 2025. Within this sample, the average food price was approximately KES 102.76, average rainfall was 869.54 mm, and the mean NTL value was 1.94.
As part of our academic review, we checked whether these descriptive statistics were clearly reported and appropriately connected to the subsequent panel-data analysis. Particular attention was given to ensuring that the sample size, study period, variable definitions, STATA outputs, reported tables, and written interpretation remained consistent throughout the chapter. Our feedback helped Seema identify where additional clarification or stronger interpretation was required before refining the analysis chapter.
Diagnostics before model interpretation
Before interpreting the panel-model results, our review focused on whether the diagnostic evidence supported the reliability of Seemaβs modelling choices. The correlation matrix showed only modest pairwise relationships among the key variables, while the VIF values of 1.01 for both average food price and rainfall indicated that severe multicollinearity was unlikely to be affecting coefficient stability.
We also emphasised that these preliminary diagnostics should not be treated as substitutes for panel estimation. Simple correlations capture only bivariate associations and cannot adequately account for regional heterogeneity or time-specific effects. Our feedback therefore encouraged Seema to position the correlation and VIF results as supporting diagnostics, while relying on the subsequent panel models for the main econometric interpretation. This strengthened the methodological sequence of the chapter and made the transition from descriptive evidence to panel-based inference more defensible.
1. Describe
Review summary statistics and commodity coverage to confirm that the dataset was clearly represented and appropriately contextualised.
2. Diagnose
Check the correlation matrix and VIF results to identify potential multicollinearity or other issues affecting interpretation.
3. Estimate
Review the fixed-effects and random-effects model outputs, including the rationale for the selected panel-data specification.
4. Validate
Assess the Hausman test, robust inference, and interpretation of results to ensure that the conclusions were consistent with the diagnostic and econometric evidence.
Moving from Descriptive Patterns to Panel-Data Evidence
The supplied analysis applied both fixed-effects (FE) and random-effects (RE) regression models as part of a structured model-comparison process. During our review, we examined whether the purpose of each specification was clearly explained and whether the eventual model choice was supported by appropriate diagnostic evidence. The FE model accounted for time-invariant regional characteristics, while the RE model provided an alternative specification for comparison.
Particular attention was given to the Hausman test, which assessed whether the unobserved regional effects were correlated with the explanatory variables. In Seemaβs analysis, the test was strongly significant (ΟΒ² = 83.02, p < 0.001), supporting the fixed-effects specification under the reported model. Our feedback focused on helping Seema connect this diagnostic result directly to her model-selection rationale, ensuring that the preferred specification was methodologically justified rather than selected solely on the basis of coefficient significance.
REVISION & QUALITY CONTROL
The Revision That Turned the Chapter into Defensible Research
The clearest indication of progress emerged during the final review stage, when the revised chapter showed that the methodological concerns identified earlier had been successfully addressed. Rather than focusing on superficial presentation, our review checked whether the underlying data structure, STATA log, statistical tables, and written interpretation were fully aligned.
Following the feedback provided, Seema addressed the identified data-layer issues and reconciled the complete STATA log with the results reported in the chapter. The discussion was also revised so that the most methodologically important finding received appropriate prominence, rather than allowing secondary results to dominate the narrative.
Importantly, the subsequent review indicated that the core analytical workflow did not require rerunning. The remaining improvements centred primarily on interpretation, clarity, and academic polish. This demonstrated how targeted methodological review and revision guidance helped Seema strengthen her existing work into a more coherent, transparent, and defensible analysis chapter, while retaining ownership of the research and final revisions.
STATA Analysis and Key Outcomes
As part of our review, we examined whether the statistical relationships reported in Seemaβs STATA analysis were accurately interpreted and appropriately reflected in the written discussion. The supplied correlation results indicated a moderate negative relationship between average food prices and rainfall (r = β0.5302, p < 0.001), suggesting that periods with higher rainfall were generally associated with lower average food prices.
Mean nighttime light (NTL) also showed a negative correlation with food prices (r = β0.2059), while rainfall and mean NTL were positively correlated (r = 0.2494). Our guidance focused on helping Seema distinguish these preliminary associations from the subsequent panel-model evidence and avoid drawing causal conclusions from correlation coefficients alone. This helped strengthen the transition from descriptive relationships to the more rigorous econometric interpretation presented later in the chapter.
The supplied multiple regression model was statistically significant (F(2,165) = 6.18, p = 0.0026), although its explanatory power remained relatively modest (RΒ² = 0.0697; Adjusted RΒ² = 0.0585). Rainfall showed a positive and statistically significant association with mean NTL (Ξ² = 0.00151, p = 0.029), while average food price had a negative but statistically insignificant coefficient (Ξ² = β0.0193, p = 0.248).
During our review, we focused on ensuring that these results were interpreted in line with their statistical strength. In particular, we guided Seema to avoid overstating the food-price coefficient and to distinguish statistical significance from overall explanatory power. This helped strengthen the discussion by ensuring that the conclusions remained consistent with the reported STATA outputs and that the regression findings were presented as evidence of statistical associations rather than definitive causal effects.
Clients Review
Seema Ahad
“Thank you for the revision. This is a significant and successful improvement over the previous draft. Both of the data-layer issues I raised earlier have now been addressed, the complete STATA log is consistent with the reported tables, and the discussion now focuses on the correct result. Overall, the chapter is methodologically much stronger and more defensible”.
