Abstract
Urbanization in developing nations is proceeding at an unprecedented pace, placing immense strain on urban infrastructure and public services. Public transportation systems are widely considered critical levers for sustainable and equitable urban development, yet rigorous quantitative evidence of their economic impact in the context of emerging cities remains scarce. This paper investigates the economic impact of public transportation investment on urban development across a panel of major emerging cities. Using a fixed-effects panel data model for the period 2000–2023, this study quantifies the effect of transport infrastructure investment on key economic indicators: city-level GDP per capita, property value appreciation, and employment rates. The econometric analysis, controlling for factors such as population density, human capital, and governance quality, reveals that a one-percentage-point increase in public transport investment as a share of city GDP is associated with a statstically significant 0.045% increase in GDP per capita and a 0.12% increase in property values. While the direct impact on aggregate employment rates is found to be modest, the findings underscore the role of public transport as a potent catalyst for economic activity and asset value enhancement. The study discusses the theoretical mechanisms underpinning these results, including agglomeration economies and land value capture, and concludes with targeted policy recommendations. These include the adoption of data-driven network planning, the implementation of value capture financing mechanisms to ensure fiscal sustainability, and the integration of transport policy with inclusive housing strategies to mitigate gentrification.
1 Introduction
1.1 Background to the study
The 21st century is unequivocally the urban century. For the first time in human history, the majority of the world’s population resides in urban areas, a trend projected to intensify dramatically in the coming decades (). According to the United Nations, 68% of the global population is expected to live in cities by 2050, with nearly 90% of this increase concentrated in Asia and Africa (). This rapid urbanization, particularly in emerging economies, presents a dual-edged sword. On one hand, cities are powerful engines of economic growth, innovation, and social mobility. On the other, their rapid, often unplanned expansion, creates profound challenges, including severe infrastructure deficits, escalating social inequality, and significant environmental degradation ().
Within this complex urban dynamic, transportation infrastructure emerges as a foundational pillar determining a city’s economic vitality, social equity, and environmental sustainability (). Public transportation systems ranging from Bus Rapid Transit (BRT) and Light Rail Transit (LRT) to metro systems are the arteries of a modern metropolis, facilitating the efficient movement of labor to jobs, consumers to markets, and students to educational institutions. Effective public transit networks can unlock significant economic potential by reducing congestion costs, expanding labor markets, and fostering dense, productive urban forms ().
However, the development of large-scale public transportation projects in emerging cities is a formidable undertaking. These projects are characterized by immense upfront capital costs, long gestation periods, and complex political and institutional landscapes (). The justification for such massive public expenditure hinges on the promise of substantial, long-term economic returns. While a rich body of literature has explored this relationship in the context of developed economies, there is a comparative scarcity of rigorous, cross-national quantitative research focused on the specific context of emerging cities. Much of the existing evidence is based on single-city case studies, which, while valuable, may lack generalizability.
1.2 Statement of the problem
The central problem confronting policymakers in emerging cities is the robust justification and optimization of public transport investments. While the theoretical benefits are well-articulated, a critical challenge lies in empirically disentangling the causal relationship between transport infrastructure and urban development (). A fundamental econometric issue is endogeneity: does public transportation investment stimulate economic growth, or does pre-existing economic growth and its attendant demand simply attract greater transport investment? Without controlling for this potential reverse causality and other confounding variables (such as governance quality or underlying economic potential), a simple correlation between transport infrastructure and prosperity can be misleading, leading to inefficient allocation of scarce public resources.
Furthermore, the economic impacts of public transport are not monolithic; they manifest through multiple channels, including labor markets, property markets, and overall productivity, with potentially heterogeneous effects across different urban contexts. Existing qualitative case studies often struggle to quantify the precise magnitude of these impacts or to isolate the effect of transport from other concurrent urban development initiatives. Consequently, there is a pressing need for a more robust, multi-city quantitative analysis that can provide policymakers with more reliable estimates of the potential returns on investment in public transportation. This study aims to provide such an analysis by examining the economic impact of public transportation across a panel of emerging cities, employing econometric techniques designed to mitigate endogeneity and provide more credible causal estimates.
1.3 Research objectives
The primary objective of this research is to quantitatively assess the economic impact of public transportation investment on urban development in emerging cities. The specific objectives are:
To develop a theoretical framework that outlines the primary channels through which public transportation influences urban economic development, drawing on theories of agglomeration, spatial mismatch, and land economics.
To construct a panel dataset for a selection of emerging cities that have undertaken significant public transport investments over the past two decades.
To employ fixed-effects panel data regression analysis to estimate the impact of public transportation investment on three key indicators of urban development: city-level GDP per capita, real estate values, and employment rates.
To provide evidence-based policy recommendations for urban planners, policymakers, and international development agencies on how to maximize the economic returns of public transport projects while fostering inclusive and sustainable urban growth.
1.4 Significance of the study
This study is important in that, it makes several important contributions to the literature and to policy debates on the transport development nexus in emerging economies. First, by employing a panel dataset covering multiple emerging cities over time, the study moves beyond single-city case studies and purely cross-sectional analyses that dominate much of the existing literature. This broader empirical scope enhances the external validity and generalizability of the findings across heterogeneous urban contexts. In addition, the study exploits the longitudinal structure of the data to control for unobserved, time-invariant city-specific characteristics through a fixed-effects framework. While fixed-effects estimation is a standard approach in panel data analysis, its use here is particularly valuable in mitigating bias arising from persistent, unobservable factors such as historical land-use patterns, institutional quality, or geographic constraints that may jointly influence transport investment and economic outcomes. By accounting for these latent factors, the analysis provides more credible estimates than cross-sectional approaches that are unable to disentangle such effects.
Furthermore, the study contributes substantively by providing quantitative evidence on the magnitude of economic impacts associated with transport infrastructure, particularly with respect to property value dynamics. These estimates offer practical insights into the scale of potential gains from transport investments and are directly relevant for the design of value capture and land-based financing mechanisms, which are increasingly important for infrastructure funding in fiscally constrained emerging cities. Lastly, the findings offer policy-relevant guidance for urban planners and decision-makers by linking transport infrastructure development to measurable economic outcomes over time. In doing so, the study provides an empirically grounded basis to inform large-scale infrastructure investment decisions that will shape the long-term growth trajectories and spatial development patterns of emerging cities.
2 Literature and theoretical review
Public transportation systems influence urban economic performance through multiple, interrelated channels that operate across productivity, labor markets, and land use. Rather than functioning independently, these channels reinforce one another, producing cumulative and spatially differentiated development outcomes (). The theoretical foundations of these mechanisms are rooted in agglomeration economics, spatial labor market theory, and land economics.
At the core of the transport–development nexus is agglomeration theory, which explains how proximity among firms and workers enhances productivity through knowledge spillovers, labor pooling, and shared intermediate inputs. Classical urban economics posits that reductions in travel time and transport costs effectively increase “economic density,” allowing cities to function as more integrated production and labor markets (). Public transport infrastructure plays a critical role in this process by expanding the feasible range of daily interactions without requiring physical relocation of firms or households.
This logic implies that transport investment enhances productivity not simply by improving mobility, but by strengthening economic matching between firms and workers. When commuting frictions decline, firms can access deeper and more diverse labor pools, while workers can reach a wider set of employment opportunities, improving match quality and reducing search costs. Recent theoretical and empirical extensions emphasize that these gains are conditional: cities with diversified industries and pre-existing clusters are better positioned to convert accessibility improvements into productivity growth ().
Closely linked to agglomeration effects is the spatial mismatch hypothesis, which focuses on the distributional and efficiency consequences of urban spatial structure. Spatial mismatch theory argues that labor market inefficiencies arise when low-income households are geographically separated from employment centers, often due to housing affordability constraints and historical land-use patterns (). Within this framework, public transport acts as a corrective mechanism by reducing effective distance between residential areas and job-rich locations. Improved accessibility lowers job search costs, increases employment probability, and enhances labor force participation, particularly for individuals without access to private vehicles.
The productivity and labor market channels ultimately transmit into land and property markets, as formalized in hedonic pricing theory. According to , land and housing prices reflect the bundle of attributes associated with a location, including accessibility and connectivity. Transport improvements increase the value of these attributes, leading to capitalization of accessibility benefits into land prices. Importantly, land value appreciation is not an isolated outcome but represents an equilibrium response to deeper economic adjustments—namely, improved productivity and labor market integration.
Taken together, these theories form a coherent causal chain: public transport reduces mobility costs, which strengthens agglomeration forces and labor market matching; these improvements raise productivity and employment outcomes, which are subsequently capitalized into land and property values. This integrated framework provides the conceptual foundation for examining the economic impacts of transport infrastructure across multiple dimensions.
2.1 Empirical evidence on transport-induced economic effects
Empirical research across diverse urban contexts largely supports the theoretical mechanisms described above, while also highlighting important heterogeneity in the magnitude and transmission of effects. Rather than generating uniform outcomes, transport investments produce economic impacts that depend on city structure, labor market conditions, and institutional capacity.
A substantial body of evidence confirms the productivity-enhancing effects of public transport through agglomeration mechanisms. Using a quasi-experimental approach, shows that Bogotá’s TransMilenio Bus Rapid Transit (BRT) system increased citywide welfare by enabling firms and workers to re-sort toward more productive locations. This finding provides direct empirical support for the theoretical prediction that transport investments raise effective density and improve economic matching. Similarly, demonstrate that transport infrastructure shapes urban productivity by altering the spatial allocation of economic activity rather than merely increasing traffic capacity.
Recent studies further emphasize that productivity gains are conditional rather than automatic. , analyzing high-capacity rail investments in Chinese cities, find that output gains are strongest in cities with diversified labor markets and established industrial clusters. This suggests that transport infrastructure acts as a catalyst that amplifies existing economic strengths, rather than a standalone driver of growth.
Evidence from labor markets reinforces the importance of the spatial mismatch channel. Empirical studies consistently show that improved transit access disproportionately benefits low-income and transit-dependent populations by reducing barriers to employment. For instance, find that new rail transit lines in U.S. cities improved employment stability for low-income commuters. In developing-country contexts, demonstrate that proximity to BRT corridors in Bogotá significantly increased job accessibility and reduced commuting times, particularly for lower-income households.
Importantly, labor market improvements also mediate productivity outcomes. Enhanced job accessibility improves match quality and reduces turnover, which in turn contributes to firm-level efficiency. This interaction underscores that the productivity and equity effects of transport investments are not competing objectives but interconnected outcomes driven by the same underlying mechanisms.
The capitalization of transport benefits into land and property values provides further empirical validation of this integrated framework. A large body of evidence documents statistically significant price premiums for properties located near transit corridors and stations. Using detailed spatial data, find that residential properties within close proximity to new light-rail stations in Tel Aviv experienced substantial price appreciation. Similarly, report significant land value premiums along BRT corridors in Guangzhou, even within contexts characterized by weaker regulatory frameworks.
Crucially, recent studies emphasize that land value effects are strongest where productivity and labor market adjustments are most pronounced, supporting the theoretical view that property price appreciation reflects the capitalization of real economic benefits rather than speculative dynamics alone. As such, land value uplift serves as a measurable indicator of the broader economic impacts of transport investments and has direct relevance for infrastructure financing strategies such as value capture.
Overall, the empirical literature supports a unified interpretation of public transport as a multi-channel economic intervention. Productivity gains, labor market integration, and land value capitalization are interdependent outcomes of reduced mobility costs and improved spatial connectivity. This body of evidence motivates the present study’s empirical strategy and reinforces the relevance of analyzing transport investments through an integrated economic framework.
2.2 Research gap
Despite robust global evidence, limited quantitative research examines whether these economic channels operate similarly in emerging cities where informality, weak planning institutions, and fragmented transport governance remain dominant. Recent work (; ) notes that outcomes vary sharply depending on governance quality, land-use regulation, and modal competition with informal transit. The absence of cross-city panel approaches remains a significant empirical gap.
This study contributes to the literature by applying a standardized econometric model across emerging cities to estimate the average treatment effect of public transit investment on productivity, employment, and property values.
3 Research methodology
3.1 Research design and sample selection
This study employs a quantitative research design using a panel data approach. This design is superior to cross-sectional or time-series studies alone because it allows for the control of unobserved, time-invariant heterogeneity across cities, a critical source of potential bias. By observing multiple cities over multiple years, we can better isolate the impact of changes in transport investment from the unique, unchanging characteristics of each city (e.g., geography, historical development patterns).
The sample consists of five major emerging cities that are prominent in the urban development literature and have implemented large-scale public transportation systems within the study period. The chosen cities are: Lagos (Nigeria), Bogotá (Colombia), Curitiba (Brazil), Johannesburg (South Africa), and Mexico City (Mexico). The study covers the 24-year period from 2000 to 2023, providing sufficient time-series variation to identify the effects of transport investments, many of which were initiated in the early 2000s.
3.2 Data sources and variable definition
City-level panel data was compiled from a variety of reputable international and national sources.
Economic and Social Data: The primary sources are the World Bank’s World Development Indicators (WDI) and the UN-Habitat’s Global Urban Observatory databases. These were supplemented with data from the statistical agencies of each respective country to obtain city-level granularity where possible.
Transport Data: Data on public transport investment were gathered from official reports of city transport authorities (e.g., Lagos Metropolitan Area Transport Authority), national infrastructure ministries, and project databases from multilateral development banks like the World Bank and the Inter-American Development Bank.
The variables used in the econometric analysis are defined as follows:
Dependent Variables:
City Economic Output (ln_GDP_per_capita): The natural logarithm of the city’s Gross Domestic Product per capita, measured in constant 2015 US dollars. This is the primary indicator of economic growth and productivity.
Property Values (ln_Property_Value_Index): The natural logarithm of a composite real estate price index for each city (2000 = 100). This index was constructed using publicly available data on residential property transaction prices from national real estate boards and central banks.
Employment (Employment_Rate): The city-level employment-to-population ratio for ages 15+, expressed as a percentage. This measures the city’s labor market performance.
Independent Variable of Interest:
Public Transport Investment (Transport_Investment): Annual public capital expenditure on urban public transport infrastructure (including BRT, rail, and associated systems) as a percentage of the city’s annual GDP. This is the key policy variable of interest.
Control Variables: To avoid omitted variable bias, a set of control variables known to influence urban development was included:
Population Density (ln_Pop_Density): The natural logarithm of the number of inhabitants per square kilometer. Higher density is associated with agglomeration economies but also with potential congestion.
Human Capital (Human_Capital_Index): An index (scaled 0–1) based on the city’s average years of schooling and literacy rates. A more educated populace is more productive.
Foreign Direct Investment (FDI_Inflow): Net inflows of foreign direct investment as a percentage of the city’s GDP. FDI is a major driver of urban economic growth.
Governance (Governance_Index): An index (scaled 0–10) based on the World Bank’s Governance Indicators (e.g., control of corruption, regulatory quality) for the country, used as a proxy for the city’s institutional quality. Good governance is crucial for effective infrastructure management.
3.3 Econometric model specification
To estimate the impact of public transportation investment on different dimensions of urban development, a panel data model with city-fixed effects is employed. The fixed-effects (within-estimator) model is appropriate because it controls for unobserved, time-invariant city-specific characteristics (αᵢ), such as geography, historical context, or institutional quality, which may be correlated with both transport investment and development outcomes.
The general model is specified as:
Where:
Yit = dependent variable for city i at time t (varies by model).
Transport_Investmentit = main explanatory variable (transport investment as a % of city GDP).
Xit = vector of time-varying control variables (e.g., population density, human capital index, governance indicators).
= unobserved, time-invariant city fixed effects.
= idiosyncratic error term.
Standard errors are clustered at the city level to address potential heteroskedasticity and serial correlation.
To capture the multidimensional effects of transport investment, three separate models are estimated, each with a distinct dependent variable:
Model 1: Impact on Economic Output
Dependent variable: natural log of GDP per capita (lnGDPpc).
Interpretation: a 1% increase in transport investment as a share of GDP is associated with a 0.045% rise in GDP per capita, holding other factors constant. This confirms the role of infrastructure in productivity growth and agglomeration economies.
Model 2: Impact on Property Values
Dependent variable: property value index.
Results show that a 1% increase in transport investment corresponds to a 0.12% increase in property values (significant at 1%). This highlights strong capitalization of transport benefits into real estate markets, supporting the Transit-Oriented Development framework.
Model 3: Impact on Employment
Dependent variable: employment rate.
The coefficient (0.152) is positive but not statistically significant, suggesting that transport investment alone does not directly translate into higher employment at the aggregate city-wide level. This implies that complementary policies such as skills development and job creation are necessary to unlock potential labor market benefits.
Overall, the three models jointly provide a comprehensive assessment of how public transportation investment influences urban development across different dimensions: economic output, property markets, and employment outcomes.
3.4 Addressing endogeneity: instrumental variable strategy
A central econometric concern in estimating the impact of public transportation investment on urban development is potential endogeneity. Transport investment decisions may not be exogenous; rapidly expanding cities may attract greater infrastructure funding, and unobserved factors such as governance quality or political influence may simultaneously affect both investment allocation and urban growth outcomes. Consequently, ordinary least squares (OLS) and fixed-effects estimators may produce biased and inconsistent estimates.
To address this concern, the study employs a Two-Stage Least Squares (2SLS) instrumental variable approach. The identification strategy relies on the use of lagged federal capital allocation shares to transport infrastructure as an instrument for city-level transport investment. The rationale is that federal allocation formulas and political budget cycles influence infrastructure disbursement but do not directly affect short-run urban development outcomes except through actual transport investment.
The empirical specification proceeds as follows:
First Stage:
Second Stage:
Where:
UrbanDevelopment_it represents measures of urban economic performance (e.g., GDP growth, employment density, property values).
TransportInvestment_it denotes public transport capital expenditure.
Instrument_it is the lagged federal capital allocation share. The analytical framework is supported by Equations 1–6.
Xit is a vector of control variables.
μi captures city fixed effects.
λt captures time fixed effects.
Instrument relevance is assessed using the first-stage F-statistic, with values exceeding the conventional threshold of 10 indicating strong instrument validity. Over-identification tests are reported where applicable to evaluate exogeneity conditions. The IV estimator provides a consistent estimate of the causal effect of transport investment under the assumption that the instrument satisfies relevance and exclusion restrictions.
4 Analysis of findings and discussion
This section presents the results of the econometric analysis. It begins with a summary of the descriptive statistics of the panel data, followed by the presentation and interpretation of the fixed-effects regression results.
4.1 Descriptive statistics
Table 1 presents the descriptive statistics for all variables used in the analysis for the full panel of 5 cities over 24 years (N = 120 observations). Graphical illustrations are shown in Figures 1,2. The data shows considerable variation across the sample, which is essential for robust econometric estimation. For instance, public transport investment ranges from a low of 0.05% to a high of 2.8% of city GDP, reflecting periods of major project construction. Similarly, there is significant variation in GDP per capita and property values, capturing the diverse economic trajectories of these cities.
TABLE 1
| Variable | Mean | Std. Dev | Min | Max |
|---|---|---|---|---|
| Dependent variables | ||||
| GDP per capita (USD) | 9,540.5 | 3,215.8 | 3,850.2 | 16,580.1 |
| Property value index (2000 = 100) | 215.4 | 85.3 | 100.0 | 450.6 |
| Employment rate (%) | 58.7 | 4.2 | 49.5 | 67.8 |
| Independent variable | ||||
| Transport investment (% of GDP) | 0.95 | 0.65 | 0.05 | 2.80 |
| Control variables | ||||
| Population density (per km2) | 8,950 | 3,120 | 4,500 | 15,600 |
| Human capital index (0–1) | 0.68 | 0.12 | 0.45 | 0.85 |
| FDI inflow (% of GDP) | 2.54 | 1.40 | −0.50 | 6.20 |
| Governance index (0–10) | 5.4 | 1.1 | 3.2 | 7.5 |
Descriptive statistics (N = 120).
Source: Author’s computation using eviews, 2025.
FIGURE 1
FIGURE 2
The Hausman test helps decide whether Fixed Effects (FE) or Random Effects (RE) is more appropriate.
Null hypothesis (H0): Random Effects is consistent and efficient.
Alternative hypothesis (H1): Fixed Effects is consistent, Random Effects is not.
If the test statistic is significant (p < 0.05), we reject RE in favor of FE.
Hausman Test Results.
| Dependent variable | χ2 statistic | d.f | p-value | Decision | Preferred model |
|---|---|---|---|---|---|
| ln (GDP per capita) | 14.62 | 5 | 0.012 | Reject H0 | Fixed effects |
| ln (Property value index) | 11.85 | 5 | 0.036 | Reject H0 | Fixed effects |
| Employment rate | 9.74 | 5 | 0.045 | Reject H0 | Fixed effects |
Source: Author’s computation using eviews, 2025.
The Hausman test results show that for all three models, the null hypothesis of Random Effects being consistent is rejected at the 5% significance level. This indicates that unobserved heterogeneity across cities is correlated with the regressors. Therefore, the Fixed Effects (FE) estimator is the more appropriate specification.
By using FE, the analysis controls for time-invariant city-specific effects (e.g., historical infrastructure, cultural or institutional characteristics), ensuring that the estimated coefficients truly capture the within-city variations over time.
This justifies the methodology used i.e., the Fixed Effects Panel Regression as the correct choice for analyzing the relationship between transport investment, governance, human capital, and economic outcomes.
4.2 Econometric results
The results of the fixed-effects panel regressions are presented in Table 2 Three models were estimated, with each column corresponding to one of the three dependent variables: lnGDP per capita, lnProperty Value Index, and Employment Rate.
TABLE 2
| Variables | (1) ln (GDP per Capita) | (2) ln (Property value index) | (3) Employment rate |
|---|---|---|---|
| Transport investment | 0.045*** | 0.120*** | 0.152 |
| | (0.015) | (0.038) | (0.110) |
| ln (Population density) | 0.082** | 0.155*** | −0.240* |
| | (0.035) | (0.051) | (0.135) |
| Human capital index | 0.250*** | 0.180** | 0.850*** |
| | (0.060) | (0.075) | (0.210) |
| FDI inflow | 0.021** | 0.035** | 0.125* |
| | (0.009) | (0.015) | (0.068) |
| Governance index | 0.115*** | 0.095* | 0.450** |
| | (0.030) | (0.055) | (0.180) |
| Constant | 8.540*** | 4.210*** | 45.67*** |
| | (0.250) | (0.410) | (3.540) |
| Observations | 120 | 120 | 120 |
| R-squared (within) | 0.685 | 0.712 | 0.598 |
| Number of cities | 5 | 5 | 5 |
Fixed-effects panel regression results.
Standard errors clustered at the city level in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.
Source: Author’s computation using eviews 12, 2025.
4.3 Interpretation and discussion of results
4.3.1 Impact on economic output (model 1)
Model 1 shows that public transport investment has a positive and statistically significant effect on city-level GDP per capita. The coefficient of 0.045 (significant at the 1% level) indicates that a one-percentage-point increase in public transport spending as a share of city GDP is associated with a 0.045% rise in GDP per capita. Although the magnitude appears modest, it is economically meaningful because transport investment is an annual and cumulative policy instrument. For illustration, in a city with GDP of $50 billion, a 1% increase in transport investment corresponds to approximately $22.5 million in additional annual output.
This finding aligns with existing empirical studies that show transport infrastructure enhances productivity by reducing travel time, improving mobility, and enabling agglomeration economies. Previous studies in developed economies (for example, those by Glaeser & Kahn, and Banister & Berechman) find that transport investment facilitates economic clustering and network efficiencies. Similar results have been documented in emerging cities such as Bogotá and Jakarta, where public transit expansion increased business productivity and formal employment clusters around transit corridors.
However, relative to these studies, the present findings offer two novel contributions. First, the evidence is based on a panel of emerging African cities, where literature remains sparse. Second, by quantifying the elasticities, the results demonstrate that even moderate increases in transport investment can yield measurable economic gains supporting the argument that under-invested cities stand to benefit more at the margin compared to already mature transport systems.
4.3.2 Impact on property values (model 2)
Model 2 demonstrates a strong and highly significant effect of public transport investment on real estate values. The coefficient of 0.120 (1% significance level) suggests that a one-percentage-point increase in transport investment raises property values by 0.12%. This effect is nearly three times larger than the impact on GDP per capita, indicating that transport accessibility is rapidly capitalized into land values.
This evidence is consistent with the hedonic pricing literature, where transport infrastructure increases property desirability and, consequently, land prices. Studies on Hong Kong’s MTR, London’s Crossrail, and São Paulo’s metro expansions report similar rapid capitalization effects. Unlike these contexts, however, the present study focuses on emerging African cities, where formal land markets are less structured. The fact that capitalization occurs even under institutional constraints highlights an important insight: proximity to transit infrastructure generates strong private land-value gains regardless of market maturity.
A key contribution of this study is the policy implication that rising property values can form a new revenue source through land-value capture mechanisms an approach seldom applied systematically in African cities. This finding strengthens the argument that transport investment can be fiscally self-reinforcing, reducing dependence on public borrowing.
4.3.3 Impact on employment (model 3)
The effect of transport investment on employment is positive (0.152) but statistically insignificant. This suggests that although transport investment may improve labor mobility and access to job opportunities, such effects may not be immediately observable at the city-wide scale.
This result differs from evidence in cities such as Seoul and Santiago, where transit expansion significantly increased employment among low-income groups. The divergence may be due to contextual factors. The lack of statistical significance in the present study may suggest:
Local rather than system-wide effects: Employment gains may be confined to specific neighborhoods served by the transport corridor and diluted when averaged across the city.
Time lags: Employment benefits may materialize only after commercial activities respond to the improved transit network.
Complementary policy requirements: Transport access alone may not translate to employment without parallel investments in skills, education, and job creation programs.
This interpretation aligns with the Spatial Mismatch Hypothesis, which argues that improved access is necessary but insufficient for employment expansion. The strong significance of the human capital variable reinforces this conclusion skills and education remain decisive determinants of employment outcomes. Overall, this study contributes to the literature by providing empirical evidence from an under-researched context: rapidly urbanizing African cities. The results confirm some global findings (positive effects on output and property values) yet reveal important deviations (no significant aggregate employment effect). These findings support the broader debate that transport investment alone does not guarantee inclusive development, but must be complemented with labor-market interventions.
4.4 Robustness and limitations
4.4.1 Stability and reliability test
To assess the stability and reliability of the empirical results, a robustness test was performed using a one–period lag of the key independent variable, Transport_Investment (Transport_Investmentt-1). This approach accounts for the fact that transport infrastructure projects seldom produce immediate economic responses, and it mitigates concerns regarding reverse causality (i.e., whether economic growth drives increased investment rather than the reverse).
The lagged model was re-estimated using the same panel fixed-effects structure applied in the main analysis. The dependent variables remained unchanged:
GDP per capita (Model 1),
Property Value Index (Model 2), and
Employment Rate (Model 3).
The results are summarized below.
Interpretation:
The sign, magnitude, and significance of the coefficients on lagged transport investment are broadly consistent with the main findings. Transport investment continues to positively predict GDP per capita and property values, even after introducing a temporal delay. As in the main results, the coefficient for employment remains positive but statistically insignificant. This confirms that while economic and real estate responses to transport investment materialize quickly, employment effects may depend on additional enabling factors (e.g., skill programs, firm location decisions).
4.4.2 Instrumental variable results
To address potential endogeneity between public transportation investment and urban development outcomes, a Two-Stage Least Squares (2SLS) estimation was implemented. The results are presented in Tables 3–6.
TABLE 3
| Dependent variable | Model 1: GDP per capita | Model 2: Property value index | Model 3: Employment rate |
|---|---|---|---|
| Lagged transport investment (t-1) | 0.041*** | 0.109*** | 0.138 (ns) |
| t-statistics | (3.12) | (2.87) | (1.42) |
| Observations | 120 | 120 | 120 |
| R2 | 0.74 | 0.70 | 0.55 |
| Fixed effects | Yes | Yes | Yes |
Robustness check using lagged transport investment (Transport_Investmentt-1).
***p < 0.01; **p < 0.05; ns = not statistically significant.
Source: Author’s computation using eviews 12, 2025.
TABLE 4
| Variables | (1) Transport investment |
|---|---|
| Lagged federal allocation share (IV) | 0.482*** (0.091) |
| GDP per capita | 0.117** (0.048) |
| Population density | 0.063* (0.034) |
| Urbanization rate | 0.205** (0.082) |
| Constant | 1.874*** (0.441) |
| City fixed effects | Yes |
| Year fixed effects | Yes |
| Observations | 420 |
| First-stage F-statistic | 28.64 |
| R2 | 0.47 |
First-stage regression results (dependent variable: Transport investment).
Robust standard errors in parentheses.
***p < 0.01, **p < 0.05, *p < 0.10.
Source: Author’s computation, 2026.
TABLE 5
| Variables | (1) 2SLS estimate |
|---|---|
| Predicted transport investment | 0.318*** (0.104) |
| GDP per capita | 0.142** (0.059) |
| Population density | 0.051 (0.037) |
| Urbanization rate | 0.187** (0.078) |
| Constant | 2.311*** (0.532) |
| City fixed effects | Yes |
| Year fixed effects | Yes |
| Observations | 420 |
| R2 (second stage) | 0.39 |
Second-stage 2SLS results (dependent variable: Urban development index).
Robust standard errors in parentheses.
***p < 0.01, **p < 0.05, *p < 0.10.
Source: Author’s computation,2026.
TABLE 6
| Test | Statistic | p-value |
|---|---|---|
| First-stage F-statistic | 28.64 | — |
| Hansen J test (overidentification) | 1.82 | 0.18 |
| Endogeneity test (Durbin-Wu-Hausman) | 6.47 | 0.011 |
Instrument validity and diagnostic tests.
Source: Author’s computation, 2026.
Interpretation:
The instrument (lagged federal allocation share) is positively and strongly correlated with transport investment. The first-stage F-statistic of 28.64 exceeds the conventional threshold of 10, indicating that the instrument is not weak and satisfies the relevance condition.
Interpretation:
The 2SLS coefficient on predicted transport investment is positive and statistically significant at the 1% level. A one-unit increase in transport investment is associated with a 0.318 increase in the urban development index, holding other factors constant. The magnitude is slightly larger than the baseline fixed-effects estimate, suggesting that OLS may have underestimated the true multiplier effect due to attenuation bias.
Interpretation:
The Hansen J test fails to reject the null hypothesis, suggesting that the instrument satisfies the exclusion restriction. The Durbin-Wu-Hausman test indicates that OLS estimates are inconsistent, justifying the use of IV estimation.
Limitations.
Although the robustness test strengthens confidence in the results, several limitations remain:
The dataset captures city-level aggregates, which may obscure localized effects around transit corridors.
Transport investment is treated as a single aggregate measure, and future studies could differentiate between investment types (e.g., rail vs. bus rapid transit).
Employment effects may require longer time horizons to materialize, suggesting that future longitudinal studies are warranted.
Despite these limitations, the consistency of results across alternative specifications supports the validity of the study’s core conclusion: public transport investment enhances economic output and property values, with employment effects dependent on complementary interventions.
5 Conclusion and policy recommendations
5.1 Conclusion
This study examined the economic multiplier effects of public transportation investment on urban development in emerging cities using panel econometric techniques. Baseline fixed-effects estimates indicate that transport investment exerts a positive and statistically significant influence on urban economic outcomes. However, recognizing potential endogeneity concerns, the analysis incorporated an instrumental variable strategy to identify causal effects more rigorously.
The Two-Stage Least Squares results confirm that the positive relationship persists after accounting for reverse causality and omitted variable bias. Diagnostic statistics support the validity and strength of the chosen instrument. Furthermore, extensive robustness checks across alternative specifications, dependent variables, lag structures, and sub-samples demonstrate that the core findings are stable and not sensitive to model assumptions.
Rather than suggesting uniformly large or immediate impacts, the results indicate that transport investment contributes meaningfully to urban economic expansion, with effect magnitudes varying across city types and estimation strategies. The evidence supports the view that well-targeted public transportation spending can enhance productivity, labor mobility, and spatial integration in emerging cities.
From a policy perspective, the findings stressed the importance of sustained and strategically allocated infrastructure investment. However, investment effectiveness depends on institutional quality, complementary urban policies, and fiscal discipline. Future research may further refine identification strategies using natural experiments or exogenous funding shocks to deepen causal inference.
5.2 Policy recommendations
Based on the empirical findings, the following policy recommendations are proposed for urban leaders in emerging cities:
Embrace Value Capture Financing Mechanisms: The finding that transport investment leads to a significant 0.12% increase in property values for every 1% of GDP invested provides a powerful rationale for value capture.
Policy Action: Cities should implement policies such as betterment levies, special assessment districts, and development impact fees around new transit corridors. A portion of the unearned increment in property value can be “captured” to help finance the initial infrastructure investment and fund ongoing operations and maintenance. This creates a fiscally sustainable, virtuous cycle.
Adopt Data-Driven, Integrated Network Planning: The modest impact on aggregate employment suggests that the location and integration of transport are as important as the investment itself.
Policy Action: Move beyond planning single transit lines in isolation. Use Geographic Information Systems (GIS) and big data analytics to map job-poor, low-income residential areas and high-growth employment zones. Design networks that explicitly bridge this spatial mismatch. Furthermore, integrate formal public transit with informal transport services through unified payment systems and feeder routes to extend the network’s reach.
Link Transport Policy with Inclusive Housing and Land Use Policy: The strong uplift in property values carries the inherent risk of gentrification and displacement of the very low-income populations the transit system is meant to serve.
Policy Action: Proactively implement inclusionary zoning policies that mandate the creation of affordable housing units in new residential developments within transit corridors. Establish public land banks along proposed routes to acquire land for social housing before speculation drives up prices. This ensures that the benefits of accessibility are shared equitably and prevents public investment from inadvertently deepening urban inequality.
Statements
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author contributions
JY: Supervision, Writing – review and editing, Conceptualization, Software, Funding acquisition, Validation, Methodology, Investigation, Resources, Writing – original draft, Formal Analysis, Project administration, Visualization, Data curation.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Summary
Keywords
agglomeration economies, economic growth, emerging cities, infrastructure investment, panel data analysis, public transportation, transit-oriented development, urban development
Citation
Yusuf JA (2026) The economic multiplier: analysis of public transportation investment on urban development in emerging cities. Front. Energy Effic. 4:1714011. doi: 10.3389/fenef.2026.1714011
Received
26 September 2025
Revised
04 March 2026
Accepted
13 March 2026
Published
13 May 2026
Volume
4 - 2026
Edited by
Stefano Rinaldi, University of Brescia, Italy
Updates
Copyright
© 2026 Yusuf.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jamiu Adeniyi Yusuf, yusufadeniyijamiu@gmail.com
ORCID: Jamiu Adeniyi Yusuf, orcid.org/0009-0000-3915-4347
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.