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Implementation Research in Developed and Developing Countries: an Analysis of the Trends and Directions

• Published: 10 September 2022
• Volume 23 , pages 1259–1273, ( 2023 )

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• Daniel Dramani Kipo-Sunyehzi   ORCID: orcid.org/0000-0003-3697-3333 1  

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The article examines implementation research across developed countries (North America, Europe), developing countries (Asia, Pacific) and Africa. It examines some key trends and directions of implementation research across regions. It revisits policy debate among scholars on approaches to implementation-top-down, bottom-up and mixed which characterised the developed world. Also, it adds some perspectives on the developing world including Africa. The paper has three contributions: it highlights trends in implementation research in developed and developing countries. It gives some directions on implementation research in Africa. It recommends that the policy design process should not be neglected, such neglect is inimical to implementation.

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Kipo-Sunyehzi, D.D. Implementation Research in Developed and Developing Countries: an Analysis of the Trends and Directions. Public Organiz Rev 23 , 1259–1273 (2023). https://doi.org/10.1007/s11115-022-00659-0

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• Published: 17 April 2024

The economic commitment of climate change

• Maximilian Kotz   ORCID: orcid.org/0000-0003-2564-5043 1 , 2 ,
• Anders Levermann   ORCID: orcid.org/0000-0003-4432-4704 1 , 2 &
• Leonie Wenz   ORCID: orcid.org/0000-0002-8500-1568 1 , 3  

Nature volume  628 ,  pages 551–557 ( 2024 ) Cite this article

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• Environmental economics
• Environmental health
• Interdisciplinary studies
• Projection and prediction

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

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Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References  7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs.  2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs.  2 , 18 , we use climate variables in their first-differenced form following ref.  3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs.  2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref.  19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section  1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section  2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section  2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section  3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref.  5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref.  2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs.  3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref.  2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref.  2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref.  35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section  5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs.  7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref.  15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs.  57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs.  7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section  2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, \({\bar{T}}_{x,y}\) , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, \({\widetilde{T}}_{x,y}\) :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , \({\bar{T}}_{x,m,{y}}\) is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref.  8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref.  18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L  > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs.  2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, \({\hat{T}}_{r}\) , or annual mean temperature, \({\bar{T}}_{r}\) , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref.  61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N  =  M  = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs.  2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section  1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

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Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

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Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref.  2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

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Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628 , 551–557 (2024). https://doi.org/10.1038/s41586-024-07219-0

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Biomedical research in developing countries: Opportunities, methods, and challenges

M. masudur rahman.

1 Department of Gastroenterology, Sheikh Russel National Gastroliver Institute and Hospital, Dhaka, 1212 Bangladesh

Uday C. Ghoshal

2 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, 226 014 India

Krish Ragunath

3 Curtin University Medical School, Royal Perth Hospital, Victoria Square, Perth, WA 6000 Australia

Gareth Jenkins

4 Swansea University Medical School, Institute of Life Science 1, Singleton Campus, Swansea, SA2 8PP UK

Mesbahur Rahman

5 Department of Gastroenterology, House G, Morriston Hospital, Swansea Bay University Health Board, Swansea, SA6 6LN UK

Cathryn Edwards

6 British Society of Gastroenterology, London, UK

7 Torbay & South Devon NHS Foundation Trust, Lawes Bridge, Torquay TQ2 7AA, and 3St Andrew’s Place, London, NW1 4LB UK

Mahmud Hasan

8 Bangladesh Gastroenterology Society, Dhaka, Bangladesh

9 Gastroliver Foundation, Dhaka, 1205, Bangladesh

Simon D Taylor-Robinson

10 Department of Surgery and Cancer, Imperial College London, South Kensington, London, SW7 2BU UK

Health research is essential for improving global health, health equity, and economic development. There are vast differences in the disease burden, research budget allocation, and scientific publications between the developed and the low-middle-income countries, which are the homes of 85% of the world’s population. There are multiple challenges, as well as opportunities for health research in developing countries. One of the primary reasons for reduced research output from the developing countries is the lack of research capacity. Many developing countries are striving to build their research capacity. They are trying to understand their needs and goals to solve their fundamental health problems, but the opportunity for research education and training remains low. The first joint research meeting of the Bangladesh Gastroenterology Society and the British Society of Gastroenterology took place in February 2020 at the Bangabandhu Sheikh Mujib Medical University in Dhaka, Bangladesh, aimed at providing an overview of medical research for young, aspiring medical researchers. This review article provides an outline of the research day and covers a number of useful topics. This review aims to provide a basic guide for early career researchers, both within the field of gastroenterology and, more generally, to all spheres of medical research.

Introduction

Health is a crucial factor in national prosperity. Health has been accepted as a fundamental right of all people by the constitution of the World Health Organization (WHO) and the International Declaration of Human Rights [ 1 , 2 ]. Health research is essential for improving global health, health equity, and economic development. Research capacity strengthening is one of the most potent, efficient, and sustainable ways to deal with national health problems and thus contributing to national development [ 3 ]. It is well recognized that scientific research has played a pivotal role in the advancement of technology and healthcare in the developed countries but developing countries, particularly the poor strata of the population in these countries, have benefitted little from this [ 4 – 6 ]. There are vast differences in the disease burden, research budget allocation, and scientific publications between the developed and the low- and middle-income countries (LMICs), which are the homes to 85% of world’s population. Although non-communicable disease rates are similar, the burden of communicable diseases and maternal, perinatal, and nutritional disorders is 13 times higher, and the prevalence of violence/injuries is three times higher in LMICs than in high-income countries [ 7 , 8 ]. Only about 10% of the global expenditure on health research and development is used for research in 90% of the health problems of the world mainly affecting the poor population, which is known as “the 10/90 gap” [ 4 ].

The challenges of health research in developing countries are different from the developed world, which are also the cause of low scientific output from these countries. Only 2% of the scientific publications in indexed journals come from developing countries [ 9 ]. One of the primary reasons for low-quantity and quality scientific research from the developing countries is the lack of research capacity [ 10 ]. Training and institutional development have been found as the key elements in research capacity strengthening [ 11 ]. Many developing countries are striving to build their research capacity to solve their local health problems. However, the opportunity for training and strengthening the research capacity remains low.

The collaboration and partnership between the developed and developing nations provide multiple opportunities for research and thus bridge this gap and resolve this inherent problem. The Bangladesh Gastroenterology Society in association with the British Society of Gastroenterology, which has a long track record for supporting the developing countries in research and education ( https://www.bsg.org.uk/international/ ), organized first joint research meeting for the young gastroenterologists and trainees on February 17, 2020, at Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh. The meeting covered a variety of essential research topics. This review article aims to provide an outline of the research day to present a basic guide for early career researchers, both within the field of gastroenterology and also in all spheres of medical research.

Why research is necessary for clinical practice

As human beings, it is in our nature to be curious about our surroundings and explore the unknown. In the past, as hunters and gatherers, we were experimenting on techniques and processes, based on assumptions and experiences. As the traditions evolved, we have matured in our thought process to the extent that we can critically think and act, based on evidence and facts. It has become a necessity for survival as human beings in this world. This follows the Darwinian principle “survival of the fittest.” Critical thinking plays a vital role in the modern world. Clinical research involves experimentation in human health and well-being. It is the systematic study into human health and disease states by observation or interventions which give rise to new and better ways of improving the health and well-being of the population. The eighteenth century saw a breakthrough in medicine when the smallpox vaccine was invented by Edward Jenner in 1798 [ 12 ]. This was following an observational study that milkmaids who developed cowpox were subsequently free of smallpox. Although this was a simple observation, followed by experimentation in humans, its impact on medical research, inventions, and innovation was huge. As we all know, the rest is medical history with the discovery of penicillin, antisepsis, anesthesia, steroids, X-ray, organ transplantation, and so on. In the field of gastroenterology, the Nobel Prize–winning discovery of Helicobacter pylori as a causative organism for peptic ulcer disease is fresh in our minds [ 13 ]. If it was not for the inquisitive young minds of the then medical registrar, Dr. Barry Marshall, and a pathologist, Dr. Robin Warren from the Royal Perth Hospital in Western Australia, we would have been still struggling to treat peptic ulcer disease. Notable breakthroughs in the field of gastrointestinal endoscopy include the invention of the fiberoptic endoscope [ 14 ] that paved the way to several minimally invasive interventions including polypectomy, sphincterotomy, and bile duct stone extraction via endoscopic retrograde cholangiopancreatography (ERCP), thus preventing open surgery.

Research is of high value to the population and society. It provides crucial information about disease trends, risk factors, and outcomes of interventions and allows invention and innovation in healthcare. It also informs the cost of healthcare delivery. Data and sample collection can be used for secondary research in epidemiology, health service logistics, genetic study, and public health interventions, to name but a few areas. All in all, research forms the platform for evidence-based medicine. Research is also a critical tool for evidence-based clinical practice. All of us must contribute to the research output according to our capacity. We would not be what we are today without the research work put in by our forefathers. Without research, medicine would not progress. We would be relying on dogmas, intuition, and luck!

Clinicians depend on the results of medical research for the delivery of up-to-date healthcare. Therefore, all clinicians need to be conversant with current research in their specialty. To be able to understand research and interpret it in a way that can be fitted in with their clinical practice, all clinicians need to be familiar with the basics of clinical research. Taking part in clinical research is one of the best ways to learn the basics.

Asking a research question

The research question is the key parameter that focuses on any line of research enquiry. It is the what, why, who, and where to be asked. For example

• What is the prevalence of functional gastrointestinal disorders in India and Bangladesh?
• Why do people in town A die earlier than town B?
• Who was at the highest risk of death with hepatitis E during the epidemic in Kanpur, India?
• Which is the area with the highest incidence of infantile diarrhea in Chittagong?
• Is chloroquine useful in the treatment and as prophylaxis against severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection?

The question has to be clear, concise, focused, and arguable, around which subsequent lines of enquiry can be framed. Aspiring researchers need to look at the world around them and frame simple questions aimed at improving the quality of patient care for the benefit of society. It is essential not to accept the status quo. A research question helps keep research focused and on track. It informs the line of enquiry, the method of research, the research protocols used, the analysis needed, and the structure of any subsequent publication.

Medical research and ethical issues

Research ethics are the moral principles that govern how researchers should carry out their work. These principles are used to shape research regulations agreed by higher education bodies such as universities, research funding bodies, the communities in which we live, or the governments. Furthermore, all researchers should also follow any local regulations that apply to their work environment. These basic precepts include honesty (honestly reporting data, results, methods, research procedures, and publication status), objectivity, integrity, carefulness, openness, respect for intellectual property, confidentiality, and responsible publication. Other basic tenets are listed below

• Responsible mentoring of junior members of the team
• Respect for colleagues within and outside the research team
• Responsibility to the society in which we belong (including public engagement in science)
• Non-discrimination
• Competence and adherence to agreed protocols
• Legality (keeping within the law)
• Responsible animal care
• Human subject protection—respecting dignity, privacy, and autonomy

With respect to the last point, human subject protection, all participants in research studies should provide written, informed consent which conforms to the principles of the Declaration of Helsinki of 1975 [ 15 ]. Patient information leaflets detailing the research questions and procedures must be provided in transparent, lay language (funding bodies and journals may ask to see) with the opportunity for subjects to ask questions and not to feel compelled to take part. Data collected on each subject must be stored in an anonymized fashion according to the General Data Protection Regulation (GDPR) [ 16 ].

Local ethics committee approval is required to start any research. This approval governs the correctness of the research question, the feasibility of the research protocol, and the suitability of the documentation, including the consent forms and patient information leaflets. All ethical approvals need to be quoted in subsequent publications, along with a statement on conforming the guidelines upheld in the Declaration of Helsinki [ 15 ]. It is important to stress that no collaborative work can be done without full ethical approval.

It is also important in the light of the precepts outlined above to state that research misconduct includes fabrication or falsification of data and plagiarism of other people’s results or their publications. The Economic and Social Research Council (ESRC) have published useful guidelines for further consultation, which can be found at http://www.ethicsguidebook.ac.uk/EthicsPrinciples .

Research funding

The funding required depends on the research question and its scope. For individual research training, the Commonwealth awards annual scholarships to aspiring researchers from all low- and middle-income countries, including South Asian countries like Bangladesh and India. The Chevening Foundation provides funding for Master degree courses in the UK with anyone eligible from 112 countries across the globe, including Bangladesh, India, and South Asian countries. Candidates from Bangladesh are also eligible for research training fellowships from the Islamic Development Bank. Other sources of scholarship funding for well-established research ideas include the British Medical Research Council and the Wellcome Trust, both for young medical researchers, with the Royal Society additionally running schemes for pure scientists. Clinical training schemes include the Royal College of Physicians’ Medical Training Initiative (MTI) for developing clinical skills at a junior doctor level, who can take the opportunity to get into clinical research.

The Newton Fund provides funding for scientific workshops and research exchanges, administered through the British Council. This fund is a useful step in strengthening collaborative research programs initially. Established programs may then apply to the Global Challenges Research Fund (GCRF) for larger amounts of money or bigger projects. Schemes such as the Association of Physicians of Great Britain and Ireland’s “Links with Developing Countries” scheme provide useful starter funding for collaborative research projects between the UK and any Organisation for Economic Co-operation and Development (OECD)-defined low- or middle-income country. At the same time, the Tropical Health Education Trust (THET) focuses on more clinically related schemes.

Furthermore, the Charity Commission in London has a list of charitable or philanthropic organizations, which may provide funding, dependent on the research question. Finally, it is essential not to forget that many companies, such as finance to pharmaceutical industries may be interested in funding research through so-called “corporate social responsibility” programs. Funding applications for collaborative research projects with UK universities are more likely to be successful from these organizations. Moreover, many countries have their research funding government bodies. Individual philanthropic bodies such as Trusts and Foundations in many developing countries also support research to a limited extent.

Collaborative research

Most research today (particularly in the medical field) is carried out in collaboration with other scientists, clinicians, and data analysts. Very little research is produced nowadays in a single institution with only a few authors. The nature of research has meant that bigger studies are required to perform robust analysis, and this requires multicenter studies across institutions (and countries). Collaborative research brings together the skills of diverse individuals to maximize the research project. So, clinicians work with scientists to develop molecular insights into disease. These insights can lead to recognition of the targets that chemists and pharmaceutical scientists can exploit using different drugs. Any drugs developed are then tested for safety by toxicologists. In this example, multiple skill-sets are harnessed to maximize the research effort. The synergy between the collaborators is critical to delivering the project goals. No one individual can deliver all the research aspects of a collaborative team. In collaborative research interactions are vital.

Interdisciplinary research is a growing area in science and physicists, and engineers bring substantial potential benefits to biomedical research projects by harnessing approaches and instrumentation that improve the detection and investigation of human tissues. For example, advances in nanotechnology allow printing of nanoparticles with sensors for measuring biomarkers in biological fluids and tissues. In the future, with the current advent of “Big data,” there is much to learn from mining the routine clinical data collected in standard medical practice. This involves collaboration with computer scientists, who use the new tools of “machine learning” and “artificial intelligence.” While these are highly technical and expensive, cutting-edge technology is beyond the affordability of many developing countries, and collaborative research with developed countries open up the new dawn to the young scientist of these countries. The global epidemiological study of functional gastrointestinal disorders by Rome Foundation conducted in 33 countries of 6 continents across the globe is a classic example of collaborative research between the developed and developing countries [ 17 ].

Research areas and the direction of the countries of the developed countries often differ from those of developed countries. Indeed, low-middle income countries often have enormous research potential in their healthcare services to address the research questions of the developed countries, but this should not be the sole driver [ 18 ]. Though research work carried out in collaboration with scientists of developed countries may primarily address their research questions, [ 19 ] it should ideally be a true partnership and should ideally be carried out in such a way so that both the communities are likely to benefit from the knowledge gained.

Types of studies and their designs

Choosing the appropriate design is a crucial step in undertaking any study to answer a research question. Figure ​ Figure1 1 shows the different types of study designs. A study could be on a single patient (case report), a few patients (case series), observation on a population (descriptive epidemiology) and critical statistical analysis on these observations in the population to identify factors associated with the presence of a condition (analytic epidemiology), comparison between a group of patients and controls (case-control study), observation on a group of subjects under follow up (cohort study), and well-designed randomly assigned interventional study with appropriate randomized controlled trial (RCT). RCT may be double- or single-blind (both the study subjects and the observer are blinded to the nature of intervention in the former whereas only one of them blinded in the latter).

Types of study design

Depending on the period of observation in relation to the beginning of the study, it may be prospective or retrospective. For example, if an investigator looks for the development of lung cancer in future after the study has begun among smokers, this is a prospective study; in contrast, if somebody records the history of past smoking among patients after diagnosis of lung cancer, this is a retrospective design.

Prospective design is scientifically superior to the retrospective studies as the latter ones may be biased by several known and unknown confounders. Typically, observational studies including the case report, case series, and descriptive epidemiological studies are more of hypothesis-generating in nature, the case-control and uncontrolled cohort studies help to establish an association observed in the hypothesis-generating studies, and the RCTs prove these hypothesis experimentally. Figure ​ Figure2 2 shows the levels of evidence. Randomized controlled trials and their meta-analyses, offer the best scientific evidence. For RCTs, due attention must be given to the PICO guidelines, as shown in Table ​ Table1 1 .

Types of studies in relation to their scientific merits (The evidence pyramid)

PICO guidelines

It is important to note that the primary outcome measures should not be too many. The excellent study designs have very few outcome measures (typically one or two primary and two to three secondary). If the study aim is not optimal, it would not be feasible to design a good study. The aim of a good study can be summarized by the pneumonic FINER in which “F” stands for “feasible,” “I” for “interesting,” “N” for “novel,” “E” for “ethical,” and “R” for “relevant.” Sample size calculation is an essential component of the study design. For RCTs , due attention must be given to the method of randomization (simple, block, or stratified) and concealed allocation to avoid bias. As per current guidelines, all the studies should be registered in a nationalized or international clinical trial registry after the institutional ethics clearance. A good practice is to write a summary of the study design briefly (including a flow chart) and get it reviewed by the study team members or colleagues.

The principles of statistical analysis: A primer

Before undertaking statistical analysis, one needs to ask himself/herself the following: (i) what are the types of data that are being analyzed (e.g. categorical also called nominal and discrete, or ordinal or continuous) and (ii) whether the data in question are normally distributed or not (normally distributed data are called parametric whereas the others are non-parametric). There are statistical tests to check for the normal distribution of the data (e.g. Shapiro-Wilk test). However, as a general rule, if the mean and median are quite different, the data are unlikely to be normally distributed; in contrast, if these are very close, the data are likely to be normally distributed.

Measures of central tendency and dispersion

The measures of central tendencies of the data include mean, median, and mode, and those of dispersion include standard deviation, range, and interquartile range. If the data are normally distributed, mean and standard deviations are the best ways to present these; on the other hand, data that are not normally distributed are best presented as median and range or interquartile range. The advantage of the median over mean is the lack of much influence of outliers. In medical science, the mode is not a popular method to present the data.

Hypothesis testing

It is also called significance testing, which is used to evaluate the researchers’ belief against the null hypothesis (H0). It suggests that the observed differences between the two groups are just by chance. The researchers need to nullify the null hypothesis based on the value of the probability ( p -value). A p -value of less than 0.05 means that the probability of a null hypothesis (H0) being correct is less than 5% (less than 5 out of 100 means less than 0.05 out of 1). In medical science, only two-sided and not one-sided p -values should be used. The calculation of p -value needs statistical tests, which are chosen depending upon the type of data, and their distribution. Figure ​ Figure3 3 summarizes what statistical test to choose while comparing different types of data. The subsequent issues of the journal wish to bring a series of articles under the section “Postgraduate corner: Research techniques” on the topic.

Types of commonly used statistical tests and their choice depending on types of data and their distribution

Challenges of health research in developing countries

The challenges and opportunities for health research in developing countries are multifaceted, complex, and inextricably interlinked [ 20 – 23 ]. Table ​ Table2 2 summarizes the challenges and opportunities for healthcare research in low-middle income nations.

Challenges and opportunities for health research in developing countries

Limited facilities of research education and training for health professionals

Facilities for research education and training are fundamental requirements for the development of research infrastructure in any particular country. Training and education in research methodology are often deficient in the curricula of both undergraduate and postgraduate medical education in many developing nations. There is, therefore, a need for streamlining and modernizing the undergraduate and postgraduate curriculum. Another reason for such limitation is a relative shortage of medical workforce trained in research methodology [ 21 ]. Attainment and retention of an optimum number of researchers in biomedical research are essential for various reasons: (i) to perform research as per national priorities, (ii) to train healthcare professionals, who can evaluate health research and guide trainees and young researchers, and (iii) in the present era of evidence-based medicine, physicians should have necessary research skills to evaluate medical literature critically. All of these are lacking in many developing countries of the world as governmental priority is to feed the population, meet basic healthcare for the population, and not to train and retain skilled researchers.

Limited funding and research resources

One of the significant challenges of biomedical research is the shortage of funding and research resources to meet national health priorities. Allocation and monitoring of limited resources is another challenge. The Commission for Health Research recommended that 2% of the national health budget and 5% of the foreign aid for health program should be used for health research have been ignored by most of the LMICs [ 24 ]. Other sources of funding such as the pharmaceutical industry, trusts, foundations, and other donations are either lacking or under-utilized in many of the developing countries. In some countries, funding for medical research is non-existent.

Low priorities of health research and lack of research culture

Generally, the benefit of research is not sufficiently valued, and hence, the research is placed low on the national priority list in the LMICs. There is a lack of proper appreciation of health research as an essential tool for development among political leaders, policymakers, healthcare providers, and community groups in LMICs. The policymakers in these countries are not involved in knowledge-based and science-based decision making. Weak scientific leadership, assignment of scientists to other non-scientific works, poor remuneration or compelling the scientists to seek other sources of remuneration, inappropriate service conditions, and strong political influence on running of the institutions are some of the difficulties that may result in poor scientific research environment. Sometimes, researchers are seen as a threat to the person in higher positions rather than a matter of pride for an institution; therefore, they are not often supported. Teachers are overwhelmed with clinical work, and even teaching may be given a low priority, not to speak of research activity. The shortage of resources in developing countries paradoxically means the need for reliable healthcare evidence to prioritize the use of scarce resources [ 25 ].

Inadequate efforts for prioritization of research problems

A priority of the national research agenda needs to be developed based on national demands. The commission on Health Research for Development introduced the concept of Essential National Health Research (ENHR), which incorporates two approaches: (i) research on country-specific health problems is necessary to formulate sound policies and plans for field action, and (ii) contributions to global health research aimed at developing new knowledge and technologies to solve health problems of general significance, which are also relevant to the population of the country [ 24 ]. There are inadequate efforts for prioritization of research problems in many LMICs. Limited information is available on the disease burden and their determinants, the cross-cutting issues like poverty, gender, and health policies that affect the health of the population. Such deficiency creates difficulty in setting priorities in those countries.

Ethical standards

To create and comply with ethical guidelines for human subjects consistent with the international standard is a challenge in many developing countries. Some countries lack the infrastructure for ethical and administrative regulation of research, reducing efficiency and quality. Mostly, this is the result of decision makers not having any knowledge of research.

Limited access to health information

Access to the national and international research publications is severely restricted for researches in developing countries. This difficulty is because of the policy of pricing publications too high by the publishing houses for business purposes. Knowledge about the current status of a research question is central to the development of a good research proposal. There are also difficulties in the application of the best existing knowledge and scientific evidence to the country’s health situation, if current knowledge is unavailable.

Missing linkages

The health research system is linked in many ways to different levels and different stakeholders. Health research system needs to be integrated into the national health development plans. The national health research system needs to be linked with global and regional research systems. Linkage of academic research like thesis and dissertation with the mainstream national health problems is lacking in different developing countries. Linkage of the research community, policymakers, and health services to utilize the optimum benefit of research in clinical practice and strategy formation is another challenge.

Health inequities

The health goal of sustainable development goals (SDGs) is to “ensure healthy lives and promote well-being for all at all ages.” Equity is the heart of SDGs which are found on the concept “leaving no one behind” [ 26 ]. To reduce the inequities in health between various population groups through health research addressing the health problems of the vulnerable people and to make the benefit of research accessible to them are challenges of the developing countries.

Opportunities of health research in developing countries

Like the multiple challenges, there are also multiple opportunities for health research in developing countries. There are many unexplored health problems in developing countries. For examples, the disease burden and their risk factors are unexplored in many countries. To know the burden and determinants of these diseases do not require high-cost research projects. Descriptive studies are not expensive. Another opportunity for health research in developing countries is the availability of a substantial number of patients for clinical research. Table ​ Table2 2 summarizes the challenges and opportunities for healthcare research in low-middle income nations.

How to write a good paper

Writing a good paper relies on gaining experience in reading good papers. It is important to familiarize oneself with the relevant journals in the respective disciplines to recognize the types of papers which are published in a range of journals (from large international journals to local and national journals). Editors, when receiving papers, can reject them if they feel the paper is not suitable to the journal. The Editors send acceptable papers to the editorial board (or other reviewers), and the reviewers recommend the outcome (rejection, revision, or acceptance). A fundamental aspect of a “good” paper is the quality of the data contained within it. So, it is of primary importance to maximize the data quality before attempting to write the paper. When writing the paper, begin with the results; analyze and maximize the data quality; obtain the graphs, images, and tables; and perform statistics to identify significant effects. Then, write the discussion and introduction to shape the “story” of the paper.

Writing papers takes time and effort, the data generated can take several years, and it is worth planning the paper-writing when collecting data. The submission and review process can take several months (2–6 months) in itself. The writing stage can also take several months to finalize a paper. So, it is best to plan and factor in the time taken, as a rushed paper is more likely to be rejected as the reviewers (and Editor) notice the haste.

A significant reason why papers get rejected is the use of poor English in the paper. If English is not your first language, it is useful to ask for language proofreading to improve the written word. It may be possible to rely on co-authors to improve the English. It is certainly appropriate to engage all the co-authors in the writing of the paper. They have to help if they are named in the paper. Other reasons for paper rejection include the lack of novelty in the data generated. Editors have to support the reputation of the journal and are keen to publish novel findings that receive high citation rates. Studies describing a well-known phenomenon without any novelty are dimly viewed by the Editor. Another reason for rejection is when the conclusions are not supported by the data; this can occur when authors overclaim the significance of their findings.

While choosing a journal for submission of the paper, consider the appropriateness of the journal with respect to the data. Over-reaching and submitting papers to leading journals can waste author's time and effort. Approaching the Editors or members of the editorial board is an excellent way to assess the “fit” for the paper in the journal in question.

Finally, think of the Editors (and reviewers) when writing your paper. Make it easy to read and easy to review and do not make it easy to reject—avoiding the obvious problems (English language, data analysis, highlight the novelty).

Editing and publishing a research study

Medical research papers currently are generally written in IMRAD format; IMRAD stands for introduction, method, result, and discussion. Each journal, however, may have some specific requirements, including the length of the paper. Hence, it is essential to carefully follow the instruction to the authors of that journal while writing and editing the paper. Introduction section should state the purpose of the work and provide a pertinent summary of the rationale for the study. This section should be brief, but at the same time, it should be able to draw the attention of the readers. It is good to state the hypothesis of the study, followed by its aims at the end of the introduction section. The method section should present how the work was done. This section should be stated in sufficient detail to allow other workers to reproduce the study. The statistical methods used should be outlined with enough details. A schematic diagram may be used to present the methods and the results. Result section, which reports what was found in the study, should be presented in logical sequence in the text, tables, and illustrations. It is worth reiterating that “a picture is more than 1000 words.” Discussion section typically presents what do the results mean? It should present the strengths and weaknesses of the study; strengths and weaknesses of the present data in the background of the other studies; consideration of essential differences in results; the meaning of the study, including possible explanations and implications for clinicians and policymakers; and commentary considering un-answered questions and future research.

The “7-point discussion” is a practical way to write the discussion, as shown in Table ​ Table3. 3 . The following points require consideration while writing a paper, (i) novelty, (ii) clarity, (iii) brevity, and (iv) avoiding verbosity and plagiarism (high degree of similarity in language with other published papers). Attention should be given to write good English, which is particularly essential for the authors whose first language is not English. A good practice is to write short sentences in active voice and avoiding a combination of sentences and dividing each section into multiple sub-sections. There are different guidelines for reporting different types of research ( www.equator-network.org ), as shown in Table ​ Table4 4 .

Seven-point discussion

Reporting guidelines for main study types

Selection of the journal is important. Factors to be considered in selecting a journal for publication include focus and purpose of the journal; local, regional, or global readership or whether the readers are scientists or clinicians; review process; acceptance rate; impact factor; publication schedule; print vs. online publication; credibility; and the cost of the publication. Though every author would like to publish the study in high-impact international journal, the journal editors and the reviewers also look at the novelty and the scope of the paper and whether it would be cited by others. Hence, it is good not to be over-ambitious. Revision of the paper and responding to the reviewers’ comment are keys to success. It is important to remember that most reviewers are quite positive, and they are trying to improve the paper and respond accordingly.

Active research life is an essential component of the modern physician’s portfolio to improve scientific knowledge, implement clinical treatment protocols, and promote high-quality, evidence-based service provision for the communities that we serve. Research does not have to be complicated, but it does need to adhere to the principles of scientific rigor, using a validated approach. Many research questions are purely observational, with the most straightforward ideas often being the best and the most achievable. An appreciation of statistics helps design a realistic and deliverable research protocol, but collaboration through a research network allows input from experts in data analysis at an early stage of planning. With a network of support, research is practical even for the busiest of clinicians contributing to a range of activities from sample collection for laboratory studies to clinical documentation in epidemiological or audit work. The importance of validated clinical phenotype cannot be over-emphasized. A research-active clinical community is evidenced to deliver improved patient outcomes and reduce mortality [ 27 – 29 ]. Being mindful of the opportunities for research in clinical practice is a key to the delivery of a better future for our patients. The role of National Specialty Societies in supporting this ambition is to promote the engagement of our members in such research activity and to support the publication of the resulting data through our peer-reviewed journals.

Acknowledgments

The authors are thankful to the Bangladesh Gastroenterology Society and British Society of Gastroenterology for organizing the research meeting and are also thankful to Prof. Abdur Rahim Mia, Prof. Anowarul Kabir, and Prof. Mozammel Hoque of Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh, for supporting the research meeting and to the Gastroenterology Training Academy ( www.gtaswansea.org ) for the financial assistance in organizing the research meeting. SDT-R is grateful to the UK National Institute of Healthcare Research at Imperial College London for the infrastructure support.

Authors’ contribution

MMR, UCG, MR, MH, CE, and SDT-R conceptualized the research meeting idea. MMR, UCG, KR, GJ, CE, MH, MR, and SDT-R conducted the literature search and drafted and critically revised the review article.

Compliance with ethical standards

MMR, UCG, KR, GJ, MR, CE, MH, and SDT-R declare that they have no conflict of interest.

The authors are solely responsible for the data and the contents of the paper. In no way, the Honorary Editor-in-Chief, Editorial Board Members, or the printer/publishers are responsible for the results/findings and content of this article.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

What’s the latest in development economics research? Microsummaries of 150+ papers from NEUDC 2018

David evans, almedina music.

The saturation rate of communities with planting material for highly nutritious crop technologies (vitamin-A-rich orange sweet potato and high-iron biofortified beans) substantially increases the average probability of adopting the crops and increased spillovers to neighboring households by 16-19 percent. A treatment in which opinion leaders were invited to promote the technologies did not lead to no more diffusion of either technology. ( Baird, Gilligan, and McNiven ) #RCT

How much are women willing to sacrifice to avoid sexual harassment? In India (Delhi), “women  are  willing  to  choose  a  college  in  the  bottom  half  of  the  quality  distribution over  a  college  in  the  top  quintile  in  order  to  travel  by  a  route  that  is  perceived  to  be  one  standard deviation safer.” ( Borker )

Getting married one year later in India results in “a significant decline in physical violence, although it has no impact on sexual or emotional violence.” ( Dhamija & Roychowdhury )

The opening of all women police stations in India “increased reported crime against women by 22 percent. This is due to increases in reports of female kidnappings and domestic violence.” ( Amaral, Bhalotra, & Prakash )

A multi-year intervention that “engaged adolescents in classroom discussions about gender equality” improved gender attitudes and reported gender-equitable behavior (e.g., “boys report helping out more with household chores”). ( Dhar, Jain, & Jayachandran ) #RCT

Does your daughter think she’s bad at math? It’s probably because of her classmates’ parents. Evidence from China. ( Eble & Hu )

In recent years in Bangladesh, researchers find “a pro-female bias in enrollment decision but a pro-male bias in the decisions on the conditional expenditure and core share in education expenditure.” A program providing stipends to females helped with enrollment but didn’t overcome gender bias overall. ( Xu, Shonchoy, & Fujii )

Cash transfers in Kenya reduced physical violence against wives regardless of whether the husband or wife received them, but they reduced sexual violence against wives only when the wives received them. ( Haushofer et al. )

Participants in a lab experiment in Ethiopia “are ten percent less likely to follow the same advice from a female leader than an otherwise identical male leader, and female-led subjects perform .33 standard deviations worse as a result.” ( Ayalew, Manian, & Sheth )

Across 42 countries, what are the differences in infant feeding patterns by wealth, parental education, and community infrastructure? ( Choudhury, Headey, & Masters )

Teaching mothers how to improve children’s diets improved children’s diets in Ethiopia, but providing vouchers for them to afford more food did not. Combining the two was the most effective. ( Park, Han, & Kim ) #RCT

Give a multi-layered child nutrition program or its equivalent in cash? Mixed bag in Rwanda. But giving a lot more cash makes a real difference. ( McIntosh & Zeitlin ) #RCT

“Mothers who received free meals during primary school are less likely to have stunted children compared to mothers who did not receive free meals” in India. ( Chakrabarti et al. )

“Women exposed to cow slaughter bans” in India “in their year of birth have lower levels of hemoglobin (Hb) and are up to 10% more likely to be anemic in their prime reproductive ages between 15 and 35, particularly those who have not completed primary schooling or who come from poorer families.” ( Dasgupta, Majid, & Orman )

What’s the optimal level of health insurance subsidy? In Ghana, a one-time partial subsidy affects long-term health care service use more than a one-time full subsidy. This seems to be due to selection – sicker people opted into the partial subsidy program. ( Asuming, Kim, & Sim ) #RCT

Public anti-malaria investments in Senegal did not crowd out household investments in health. ( Rossi & Villar )

“Peers are… more effective than health workers in bringing in new suspects for testing” for tuberculosis in India. “Low-cost incentives of about $3.00 per referral considerably increase the probability that current patients make referrals.” ( Goldberg, Macis, & Chintagunta ) #RCT

The cost of low effort among clinicians in Nigeria is about US$350 million annually. Peer monitoring increases effort. ( Okeke )

A soda tax in Mexico increased gastrointestinal disease because of low-quality drinking water. ( Gutierrez & Rubli )

“Piped water at home reduces childhood” obesity in Morocco and the Philippines. ( Ritter )

Providing double-fortified salt to primary school children in India reduced anemia but didn’t affect test scores on average. However, for the kids who complied best with treatment, test scores did rise. ( Krämer, Kumar, & Vollmer )

In China, regulating salt to make sure it contained iodine resulted in higher test scores for girls but not for boys, reducing the math ability gap. ( Deng & Lindeboom )

Games in Kenya show that spouses don’t totally trust each other. Letting them communicate increase trust a bit. ( Castilla, Masuda, & Zhang ) #LabInField

When a carpet manufacturer offered jobs to women in India, their likelihood of taking the job was unchanged whether they received the offer directly or their husbands got the information to share with their wives. When couples discussed the opportunity together, women were less likely to take the job. ( Lowe & McKelway ) #RCT

Households differ in who makes decisions but also in why that person makes the decisions. Among farmers in Senegal, “households achieve greater milk production, higher hemoglobin levels among children, and more satisfaction with decisions when the most informed member or members of the household make the relevant decision.” ( Bernard et al. )

In Bangladesh, “women, children, and the elderly face significant probabilities of living in poverty even in households with per-capita expenditure above the poverty threshold.” ( Brown, Calvi, & Penglase )

“A drop of 1 percentage point in the earnings gap” between husbands and wives in Mexico led to “an increase in the divorce rate of 2 percent.” ( Davila )

Christian missionaries settled in healthier, safer and more developed locations in 43 sub-Saharan African countries (early 20 th century) and in Ghana (18 th -20 th century) – this endogeneity led to an overly optimistic account of the importance of colonial missions for long-term development. ( Jedwab, Meier zu Selhausen, and Moradi ) #RDD

Greater suitability for opium cultivation in India under British Rule is associated with lower present-day literacy outcomes and a lower rate of public good provision. In opium-growing districts, the Colonial administration spent less on education and health, while spending more on police forces. ( Lehne ) #RDD

The United Fruit Company (UFCo), active in Costa Rica from 1889 to 1984, had positive, large and persistent effects even after it stopped production: households in former UFCo areas have better housing, sanitation, education, and consumption capacity. UFCo invested in physical and human capital, such as sanitary and health programs, housing for its employees, and vocational training. ( Méndez-Chacón and Van Patten ) #RDD

In locations where plantation estates were ruled by private, foreign enterprises during the Dutch colonial period in Java (Indonesia) weaker economic outcomes and institutions persist to this day. ( Fetzer and Mukherjee )

A novel index of ethnic segregation – taking into account both ethnic and spatial distances between individuals and computed for 159 countries - reveals that countries where ethnically diverse individuals lived far apart, have higher-quality government, higher incomes and higher levels of trust. ( Hodler, Valsecchi, and Vesperoni )

Information can break the political resource curse: Giving information - related to a recent discovery of natural gas in Mozambique - only to community leaders increases elite capture and rent-seeking, while information targeted at the general population increases mobilization, trust, demand for political accountability and decreases conflict.  ( Armand et al.)  

Group size of minorities has no relation with its representation in national government under proportional electoral systems, while it shows an inverted-U shaped relationship in majoritarian electoral systems (i.e., if “too small” or “too large” they suffer a disadvantage against the majority group) based on 421 ethno-country minority groups across 92 democracies spanning the period 1946–2013. ( Chaturvedi and Das )

The majority of citizens in Bangladesh prefer taking common decisions via democratic and inclusive institutions, and these positive evaluations of participatory governance are reinforced by the exposure to a Community-Driven Development program. ( Cocciolo ) #LabInField

Rewarding politicians by making their political effort more visible to citizens - either through public recognition or by increasing their access to public funds - improves citizens’ wellbeing in south Indian state Tamil Nadu.  ( Mansuri et al. )

Caste quotas lead to political candidates with lower wealth, lower criminal records, but similar education levels. Quotas also increase women’s representation in politics. There is no difference in the level of public goods between quota-bound and non-quota-bound areas. ( Jogani ) #RDD

Presence of political opposition in the city council improves mayors’ performance in Brazil: it increases legislative oversight, reduces corruption, increases the probability that a physician will be present at the local health clinic, and decreases the infant mortality rate by 3.4 per 1000 births for uneducated mothers. ( Poulsen and Varjão ) #RDD

Registered citizens in Tanzania are more likely to work in the formal economic sector, have higher education, bank accounts, and pay taxes. ( Bowles ) #IV

A land certification program in Zambia improved perceptions of tenure security, but it had no impact on investment. ( Huntington and Shenoy ) #RCT

Improved schools increased satisfaction with government’s education policy, voter registration and vote share for incumbent representatives in Liberia. Electoral gains were concentrated in places where test score gains were largest, suggesting that voters perceive and reward school quality. ( Romero, Sandefur, and Sandholtz )

An alcohol ban led to an increase in crime in the Indian State of Bihar. Since state capacity and supply of police is fixed, diverting law enforcement resources towards implementing the alcohol ban effectively reduces capacity to prevent crimes. ( Dar and Sahay )

But wait! Alcohol regulation policies in the Indian State of Bihar led to a 0.21 standard deviation reduction in the incidence of violent crimes but had no significant impact on non-violent crimes. ( Chaudhuri et al. )

Brazil’s 2007 voter re-registration reform, intended to curb voter-buying, increased political competition and healthcare expenditures, which in turn led to better health outcomes: a 6.6 percent increase in prenatal visits, a 15 percent decrease in the incidence of low birthweight, and 5.3 percent reduction in the infant mortality rate.  ( Karim ) #RDD

Electing “parachuters” (those who have hereditary/dynastic background) leads to 0.2 percentage point lower GDP growth per year compared to constituencies where “climbers” are elected (those who have made their way up on their own). Impact is likely driven by misallocation of bureaucratic resources. ( Dar ) #RDD

Workers will privately accept jobs at a wage below the prevailing norm in India, but not when other workers can observe them making the choice. “Workers give up 38% of average weekly earnings in order to avoid being seen as breaking the social norm.” ( Breza, Kaur, & Krishnaswamy )

A youth training intervention subsidizing skills training and employment placement services in Nepal showed increased non-farm employment, hours worked and earnings one year after the program. The effects are mainly driven by women, who engage in non-farm self-employment activities carried out inside (but not outside) the house.  ( Chakravarty et al .) #RDD

The decline in Mexican net migration from 2006 to 2012 reduced employment for lower educated men and increased wages for higher education men and women. Informality does not change, and women switch from unpaid to salaried jobs (likely because of reduced remittances). ( Conover, Khamis, and Pearlman ) #IV

Fear of sexual assault reduces women’s labor market participation in India: a one standard deviation increase in sexual assault reports within one’s own district reduced women’s employment probability by 0.36 percentage points, especially among highly educated married urban women. There is no effect of lagged physical assault reports on employment outside home. ( Siddique )

Tax rate changes do not increase formal employment in Ghana, Rwanda, Tanzania and Uganda. ( McKay, Pirttilä, and Schimanski )

Risk averse children in Kenya are more likely to make an independent decision to work, as opposed to being sent by or negotiating with parents over the decision. This suggests a strategic decision by risk averse children who face a risky outside option in semi-nomadic pastoralism. ( Walker and Bartlett ) #LabInField

Effects of local labor demand shocks can differ significantly by gender. In 1991-2010 Brazil, male labor demand shocks, relative to equivalent female shocks, lead to larger increases in population (migration), own-gender wages, and the gender economic gap, particularly for those without high school education. ( Chauvin )

In the short run, job application workshops and transport subsidies increase the probability of finding employment for young job seekers in Addis Ababa, Ethiopia. The workshop also helped young people access stable jobs with an open-ended contract. Four years later, the workshop has a large and significant impact on earnings, while the effects of the subsidy have dissipated. ( Abebe et al. ) #RCT

Decision-making responsibilities shift towards women during the seasonal migration period in Bangladesh. Seasonal migration brings clear changes in some beliefs with respect to gender and income inequality, but no accompanying behavioral change. ( Mobarak, Reimão, and Shenoy ) #RCT

Migration generates bilateral cultural convergence even if migrants are excluded from the pool of respondents (hence eliminating social mixing). International migration appears as a stronger and more robust driver of cultural convergence than trade. ( Rapoport, Sardoschau, and Silve )

In the agrarian sector in the Philippines, self-selection effects accounts for 60% of the productivity difference between fixed wage and individual piece rate contracts. Social norms significantly alter the decision-making by workers: Guilt aversion and kinship taxation discourage workers to choose the remunerative option, whereas enviousness facilitates them to opt for it. ( Goto et al. )

Employment Protection Laws decreased employment of the disabled by 9 percentage points, particularly for women and employees. Employers reduce their demand for disabled labor to avoid the cost of workplace accommodations for disabled workers. ( Palmer and Williams )

What happens when a single sector is hit with a negative shock? The EU ban on black tiger shrimp pushed some workers out of the industry in Thailand, increasing incomes of those who stayed. But those who were pushed out also received a benefit in their children’s education. ( Banternghansa & Giannone )

“Countries’ dollar-denominated net external debt (dollar debt) helps explain the large differences in risk premia across currencies and how U.S. monetary policy affects the global economy.” ( Wiriadinata )

With novel data from Mexico, “larger firms (in terms of sales and employees) tend to use more interfirm trade credit relative to bank credit… These firms use interfirm trade credit as a mechanism to smooth variations in their prices. All else equal, firms with a higher trade-to-bank credit ratio tend to lower prices.” ( Shapiro et al. )

In China, the road network veers towards the birthplaces of top officials who were in power when it was built. ( Alder & Kondo )

When the poorest households in a cash transfer program in Kenya experience monetary penalties failing to comply with conditions, consumption drops significantly. Less poor households are better able to avoid getting fined in the future. ( Heinrich & Knowles )

A new model suggests the existence of a network-level poverty trap. “Transfer programs can be made more cost-effective by targeting communities at the threshold of the aggregate poverty trap.” Based on data in Bangladesh. ( Advani )

Not all marginalized groups in India are catching up! Mobility in India has remained the same overall since before the early 1990s, but in fact it has risen among some groups (the traditionally lowest castes) and fallen among others (Muslims). ( Asher, Novosad, & Rafkin )

Cash transfers in Indonesia decreased suicides by 18%. ( Christian, Hensel, & Roth )

How to incorporate ordinal measures (e.g., ranked positions rather than levels of income) into multidimensional poverty measurement. ( Seth & Yalonetsky )

Introducing formal insurance can crowd-out private redistributive transfers in Ethiopia’s rural communities. To donors, new information based on insurance decisions allows them to place recipients of private funds in a different light, and reduce their support. ( Anderberg and Morsink )

Increases in the generosity of in-kind food subsidies led to lower labor supply and higher wages, mostly in the low-skilled casual labor market in India. ( Shrinivas, Baylis, and Crost )

Insurance is an important factor in explaining effort supply and fertilizer use. Going from no sharing to full insurance, effort supply decreases by more than six times and fertilizer use drops by almost 50 percent in rural India. ( Pietrobon )

A large-scale HIV prevention program in public secondary schools in Malawi provided free circumcision and transport subsidies to clinics. Demand for circumcision increased in addition to positive peer effects among untreated students. In the long run, the preventive effect of circumcision is mitigated through risk compensation behavior in the group that got circumcised due to the intervention, but not for those induced by peer effects. ( Kim et al. ) #RCT

Access to a new financial product, offering guaranteed credit access after a shock, improves household welfare in Bangladesh through two channels: an ex-ante insurance effect where households increase investment in risky production and an ex-post effect where households are better able to maintain consumption and asset levels after a shock.  ( Lane ) #RCT

Without financial incentives such as discounts or rebates, farmers in Bangladesh do not use insurance to manage production risk during the monsoon season, even at actuarially-favorable prices. Purchasing insurance yields both ex ante risk management effects as well as ex post income effects on production practices ( Hill et al. ) #RCT

In India, the association between yield losses and rainfall index losses are stronger for large deviations. Therefore, demand for commercially priced rainfall insurance is more likely to be positive when coverage is restricted to extreme losses. ( Negi and Rawasmani )

A large-scale environmental disaster in 2016, when toxic industrial waste contaminated the marine ecosystem of Vietnam’s central coast, reduced fishing activities by 23 percent and fishermen’s income by 45 percent. ( Hoang et al. )

Do you want practical advice for your farm? Go to your local church or mosque! In Kenya, “shared attendance of two peers at” a religious institution “increases the likelihood of seeking out and receiving advice from their peer by 33 percentage points.” ( Murphy, Lee, & Nourani )

Based on a field experiment in Ethiopia, “conventional job referrals through social networks can reinforce labour market inequalities and prevent less socially connected individuals from getting access to jobs. However, when given referral opportunities, individuals can manage to escape exclusion.” ( Witte ) #RCT

Matching employers and employees using social networks can lead to bad matches, particularly among “less productive, poorer workers and firms” in Ethiopia. ( Matsuda & Nomura )

Business training for micro-entrepreneurs in Uganda rewires social networks, as entrepreneurs who don’t receive the training seek to network with trained peers. ( Stein )

When the social network is not completely informative, any self-report which is not supported by a third party must be discarded. ( Bloch and Olckers )

In Filipino villages with high social fragmentation, workers earn higher wages and occupations are disproportionately less likely to be dominated by a single social group. ( Caria and Labonne )

Households which experience climate shocks tend to invest more in family-caste (formal and informal) and vertical network relationships. Those networks bring benefits which are key to mitigating the impact of negative climate shocks. ( Ramsawak )

Income shocks facilitate altruistic giving that better targets the least well off within one’s network in Ghana. ( Barrett et al. )

What does major bridge construction do for economic activity (in Bangladesh)? In the formerly disconnected area, workers move from agriculture to services, population grows, and agricultural productivity rises. ( Blankespoor et al. )

Big, surprising oil and gas discoveries lead to lots of additional foreign direct investment (FDI). In Mozambique, each FDI job leads to between 4.4 and 6.5 additional other jobs. ( Toews & Vézina )

India’s Freight Equalization Scheme “contributed to the decline of industry in eastern India” but it took time. But repealing it reversed the decline, at least in some states. ( Firth & Liu )

In Brazil, trade with China reduced unemployment for areas exporting stuff and increased unemployment for areas importing stuff. ( Brummond & Connolly )

In Indonesia, “each percentage point of additional agriculture-driven poverty reduction also corresponds to around three percent of district area in forest loss since 2000.” ( Edwards )

New state-built formal housing on the outskirts of Addis Ababa, Ethiopia, is popular among slum dwellers, despite longer, and slightly more expensive commutes. Moving to formal housing on the outskirts of the city has no effects on labor supply and earnings. ( Franklin )

Public housing projects in South Africa decrease close-by formal residential home prices by 16 percent. While there is greater access to services and improved home quality within project areas, surrounding neighborhoods experience substantial growth in informal housing which exacerbates congestions and generate declines in formal home prices.  ( Bradlow, Polloni, and Violette ) #DiD

Air pollution substantially lowers productivity among industries with labor intensive technology while industries that rely less on labor inputs are less affected. ( Hansen-Lewis ) #IV

The TransJakarta Bus Rapid Transit (BRT) system, a public transport initiative designed to improve mobility for the greater Jakarta metropolitan area, did not increase transit ridership and exacerbated congestion on the routes it served, leading to increased travel times for other modes. Motorcycle vehicle ownership increased substantially. ( Gaduh, Gračner, and Rothenberg )

Two light-touch psychological interventions – one that helped women improve planning and another that helped reduce impatience – both had impacts on sanitation behavior and health outcomes after ten weeks in Kenya. ( Haushofer, John, & Orkin ) #RCT

After training street food vendors in India on food safety, vendors knew a lot more but didn’t change what they actually did. ( Daniele, Mookerjee, & Tommasi ) #RCT

Providing subsidies lead more households to purchase latrines in India. “A household becomes more likely to invest if a larger fraction of its community are also offered a subsidy.” ( Guiteras, Levinsohn, & Mobarak ) #RCT

Agriculture and Food

Climate Change

Fragility Conflict and Violence

Senior Fellow, Center for Global Development

Senior Economist, Education Global Practice

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