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Does urbanization improve health outcomes: a cross country level analysis

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Abstract

Though rapid escalating urbanization has a positive effect on economic growth and employment, its impacts on health outcomes need to be analyzed. Using panel quantile regression models: the present study examined the impact of urbanization on health outcomes by considering 204 countries from 1960 to 2021. We measured urbanization by considering three proxy variables; total urban population, percentage of the urban population, and percentage of the population living in million-plus agglomerations. Overall estimated results suggested a negative effect on the fertility rate and a positive impact on life expectancy at birth due to urbanization. A mixed impact of urbanization was perceived on the infant mortality rate. Urbanization quality and management variables such as percentage of the urban population having access to clean fuel and technologies for cooking, electricity, basic drinking water, sanitation services, and hand washing facilities showed an asymmetric impact on health outcomes. The study additionally deployed feasible generalized least square (FGLS) and bias corrected least square dummy variable (LSDV) regressions to confirm the robustness of outcomes. The Granger causality test indicated that the relationship between urbanization and health outcomes is bidirectional. The panel cointegration test suggested that there is a long-run relationship between them. Order logit regression results suggested that the impact of urbanization on health outcomes may vary with the different stages of development a country is experiencing. The results indicate that well managed urbanization is beneficial for achieving higher health outcomes. Most importantly, developing countries are yet to promote and manage urbanization from this perspective, thus urgent attention is needed. Finally, we suggest appropriate policies for sustainable urbanization to achieve higher health outcomes in the world.

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Notes

  1. Accessed on 05/06/2022 from https://www.unfpa.org/publications/state-world-population-2007.

  2. More details about each test can be found at https://www.stata.com/manuals/xtxtcointtest.pdf (Accessed on 2.nd June, 2022).

  3. In compare to earlier econometrics models, the order logit models use less number of sample as some of countries are deleted as they have higher missing values.

  4. Due to the space limit, we only present the marginal effects of the estimated urbanization variables.

  5. As health expenditure data is available for a shorter period from the year 2000 onwards, we could not use it for our analysis.

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Authors and Affiliations

  1. Department of Economics, Adamas University, Kolkata, India

    Sabyasachi Tripathi

  2. Department of Finance, National Research University-Higher School of Economics, Saint Petersburg, 194100, Russia

    Moinak Maiti

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  1. Sabyasachi Tripathi
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Appendix

Appendix

See Tables 9, 10, 11, 12, 13 and 14 and Figs. 3 and 4.

Table 9 Fisher-type Phillips-Peron unit root test
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Table 10 Correlation coefficients
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Table 11 Panel quantile regression results for 50th quantile
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Table 12 Panel quantile regression results for pooled quantile model
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Table 13 Panel regression results for feasible generalized least squares (FGLS) method
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Table 14 Order logit regression results
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Fig. 3
figure 3

Histogram of dependents variables for different counties

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Fig. 4
figure 4

Dependent variables by quantiles

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Tripathi, S., Maiti, M. Does urbanization improve health outcomes: a cross country level analysis. Asia-Pac J Reg Sci 7, 277–316 (2023). https://doi.org/10.1007/s41685-022-00268-1

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