Instrumental Variable Quantile Regression Research Articles

Unraveling the Effects of Economic Complexity on Governance in Developing Countries. New Evidence from Instrumental Variable Quantile Regression Estimates

Unraveling the Effects of Economic Complexity on Governance in Developing Countries. New Evidence from Instrumental Variable Quantile Regression Estimates

Internet use at work and income inequality in Ecuador

This study examines the impact of technological advancements, particularly Internet access, on labor income inequality. The Internet is a versatile technology with wide-ranging applications. We argue that in environments characterized by limited innovation and a polarized job market, Internet access can positively influence worker income, especially for those in lower income brackets. Our research conducted in Ecuador from 2010 to 2017 employs instrumental variable quantile regression to analyze how Internet access affects wages across various income levels. Our findings indicate that using the Internet at work correlates with higher wages in Ecuador, particularly benefiting workers in the higher quantiles of the income distribution. While the income gap between employees with and without Internet access is largest in the lowest quantile, it decreases in the second quantile and widens again in the third, but never surpasses the gap observed in the first quantile. Public policy should thus provide citizens, particularly in the lowest income deciles, with education on effective Internet usage to improve their conditions and reduce inequality. By concentrating on these groups, we can better distribute the advantages of Internet access, ultimately creating a more balanced and equitable society.

Which Publicly Financed Green Technology R&D Option Most Effectively Drives Carbon Productivity? Instances of Energy Efficiency, Renewables, Nuclear, Hydrogen, and Energy Storage

ABSTRACTAs a strategy, countries seek ways to improve national income and, at the same time, keep carbon emissions minimal. Such a scenario is captured by the respective economies' carbon productivity (CP) scores. Remarkably, it is expected that technological advancements could be harnessed to achieve CP. Hence, many countries have publicly invested in green technologies R&D, including energy efficiency, renewables, nuclear, hydrogen, and energy storage. However, existing studies did not verify the specific contributions of these technological advances to CP, leaving a notable void in the literature. Hence, the current research verified various green technology R&D contributions to CP. Based on panel data from 2003 to 2022, this study implemented the novel instrumental variable quantile regression technique for updated insights. The study uncovers the heterogeneous contributions of each energy innovation variant to the quantile distributions of CP. The heterogeneous effects underscore each country's changing economic structures and varied energy innovation implementation paths. Hence, policy consistency is key to driving CP and ensuring environmental compatibility. R&D on renewable, nuclear, and energy efficiency technologies contributed most significantly to CP across the distributions. R&D on hydrogen and energy storage technologies contributed the least to CP. Therefore, allocating more funds to all R&Ds that boost energy‐enhancing technologies for overall environmental sustainability is expedient. Such proactive and integrative policies consistent with SDGs 7 and 13 would reduce carbon emissions while escalating national income. Meanwhile, isolated and inconsistent funding should be discouraged for overall environmental progress. A robustness evaluation based on SIVQR produced corroborative evidence.

Gendered distributional impacts of ownership of mobile money account on farm input expenditures: A micro perspective from rural maize farmers in Ghana

AbstractThis article investigates the distributional impact of mobile money account ownership on farm input expenditures among maize farmers in southern Ghana. Using cross‐sectional data from 1044 farm households, we employed an instrumental variable quantile regression to account for endogeneity and selection bias. We find that socioeconomic, spatial location, and institutional factors significantly influence male and female‐headed household decisions to own mobile money accounts. The empirics show that male‐headed households spend more on fertilizer and pesticide relative to female‐headed households. Mobile money account ownership increases fertilizer expenditure in male‐headed households more than female‐headed households. Male‐headed families with mobile money accounts spend 13.9% and 6.5% more on fertilizer at the 40th and 60th quantiles. High 80th quantile female‐headed households spend 4.3% more on fertilizer. For male‐headed households, farming experience and education positively influence mobile money account ownership on fertilizer expenditure, while off‐farm activity at the 40th quantile positively influences female‐headed households. Our results distill useful policy implications that call for concerted efforts targeted at digital financial inclusion with an eye to bridging differential gender gaps.

The impact of socioeconomic factors on water stress: insights from global analysis

ABSTRACT By utilizing a socioeconomic framework, this paper contributes to the water stress literature. Employing the instrumental variable quantile regression model on 81 countries from 2000 to 2019, our findings reveal financial development can alleviate high levels of water stress. Good governance consistently reduces water stress but human development exacerbates water stress, indicating a conflicting relationship between improving human development and reducing water stress. The moderating effect of good governance reveals that when combined with human development, it can effectively reduce water stress.

How does green investment respond to ICT and financial development?

Green investment has become the most effective option to support the efforts to mitigate worldwide climate change. Due to the efficacy of green investment in tackling climate change and global warming, researchers are searching for the factors that can boost green investment. Therefore, this analysis aims to investigate the efficacy of information and communications technology (ICT) and financial development in promoting green investment in the most polluted economies. The study analyzes aggregate data from the most polluted economies using the 2SLS, GMM, and instrumental variables quantile regression estimators. In addition, we also perform regional analysis by disaggregating the data into four regions: Asia, America, Africa, and Europe. The results reveal that ICT, financial development, financial institutions, and financial markets boost green investment in the most polluted economies. However, the regional analysis confirms the favorable influence of ICT on green investment in all the regions, while financial development boosts green investment in all the regions except Africa; financial institutions and financial market development promote green investment in America and Europe. Therefore, we suggest that policymakers must increase their reliance on ICT and the financial sector while designing a policy to promote green investment.

Urbanization and economic growth in China: empirical evidence based on a SIVQR approach

PurposeWe seek to quantify the relationship between urbanization and economic growth in China using recent advances in econometric techniques.Design/methodology/approachThis study adopts a smoothed instrumental variables quantile regression (SIVQR) estimator to obtain consistent estimates of the effects of urbanization on economic growth in China. Our approach accounts for the differential impacts of urbanization across the conditional distribution of economic growth while allowing for an identification strategy that addresses the endogeneity of urbanization. Our main findings reveal that ignoring urbanization endogeneity leads to inconsistent estimates of urbanization effects. Further, we find a positive relationship between urbanization and growth resembling an inverted U-shape. This supports the hypothesis that the beneficial effects of urbanization intensify at initial stages while diminishing beyond a certain threshold, due perhaps to weakening scale economies.FindingsOur main results indicate that the individual productivity gains brought by urbanization outweigh the negative effects thereof that impede productivity, thus contributing to the economic growth in China. Further, we find that ignoring differential impacts of urbanization underestimates the beneficial effects of urbanization for provinces whose quality of governance is in the vicinity of the center of quality distribution. Ignoring the endogeneity of urbanization generates inconsistent estimates of the elasticity of economic growth with respect to urbanization. Finally, we estimate an inverted U-shape resembling relationship between urbanization and growth.Research limitations/implicationsFirst, future studies would benefit from incorporating more data as provinces further east on the mainland become more urbanized and urbanization runs its course. Second, controlling the barriers to rural-urban mobility would contribute to the robustness of the estimated relationship between urbanization and growth once such data became available. Unveiling the impact of government-imposed barriers is key to designing optimal policies that help fuel economic growth in the country. Finally, future research could benefit from information on urbanization sources not considered here such as inter-provincial migration, as such data become publicly available.Practical implicationsQuantifying the beneficial effects of urbanization on economic growth can help guide the government in China to further fuel the growth through a set of relevant policy tools that promote urbanization.Social implicationsRural-urban migration in China lays the groundwork for economic advancement in recipient cities and economies, as it may induce scale economies. This can benefit both the economy at large and the migrants.Originality/valueThe SIVQR estimator accounts for potential heterogeneous effects of urbanization across the entire conditional distribution of growth while allowing for an identification strategy that addresses the endogeneity of urbanization. An additional distinguishing feature of the current study is our use of the most recent novel, provincial-level data obtained from the National Bureau of Statistics of China. Our focus on a single country allows sidestepping issues arising from the inconsistency of the definition of urban across different countries while accounting for intra-country urbanization drivers intrinsic to China, such as natural features and geographic characteristics. Therefore, our approach has the potential to sidestep the bias resulting from the differences in mechanisms behind urbanization-growth relationships across different countries.

Averaging Estimation for Instrumental Variables Quantile Regression

AbstractThis paper proposes two averaging estimation methods to improve the finite‐sample efficiency of the instrumental variables quantile regression (IVQR) estimator. I propose using the usual quantile regression for averaging to take advantage of cases when endogeneity is not too strong. I also propose using two‐stage least squares to take advantage of cases when heterogeneity is not too strong. The first averaging method is to apply a recent proposal for GMM averaging to the IVQR model based on this proposed intuition. My implementation involves many computational considerations and builds on recent developments in the quantile literature. The second averaging method is a new bootstrap model averaging method that directly averages among IVQR, quantile regression, and two‐stage least squares estimators. More specifically, I find the optimal weights from bootstrapped samples and then apply the bootstrap‐optimal weights to the original sample. The bootstrap method is simpler to compute and generally performs better in simulations, but uniform dominance results have not been formally proved. Simulation results demonstrate that in the multiple‐regressors/instruments case, both the GMM averaging and bootstrap estimators have uniformly smaller risk than the IVQR estimator across data‐generating processes with a variety of combinations of different endogeneity levels and heterogeneity levels.

The effect of renewable energy aid and governance quality on environmental tax effort in Sub-Saharan Africa

The literature on foreign aid and tax efforts largely overlooks the specific role of environmental aid and taxation. To fill this gap, we examine the effects of renewable energy development assistance (EDA) and governance quality on climate change tax effort, air pollution tax effort, and aggregate environmental tax effort in Sub-Saharan Africa (SSA). Utilizing panel data from 15 SSA countries over 20 years (2000–2019), the sample is divided into two groups based on income levels: seven low-income countries (LIC) and eight middle-income countries (MIC). The instrumental variable generalized method of moments (IV-GMM) regression technique is employed to estimate the parameters of the baseline model specifications. The study also uses the Smoothed Instrumental Variable Quantile Regression (SIVQR) technique to test the robustness of the baseline model. Among middle-income SSA countries, the findings suggest that renewable energy aid inflows reduce climate change tax efforts but increase air pollution and aggregate environmental tax efforts. However, among low-income SSA countries, renewable energy aid negatively impacts all three categories of environmental tax efforts. Governance quality tends to enhance all three categories of environmental tax effort in both low-income and middle-income SSA countries. The implications of these results are discussed.

The Impact of Information and Communication Technology (ICT) on Pesticides Use of Potato Farmers in Indonesia

Reducing pesticides is an important driver in preserving a healthy and sustainable environment and protecting human health. In the digital era, understanding the role of information and communication technology (ICT) on pesticide use in crucial. Therefore, This study aim to estimate the impact of ICT on farmers’ pesticide used. This study uses a cross-sectional data from a survey to 150 farmers in Indonesia. Furthermore, the data is analyzed by ordinary least square (OLS) and instrumental variable quantile regression (IVQR). The main results indicate that ICT has an essential impact on pesticide used reduction. Farmers who has access on the ICT tend to use lower pesticide that the farmers who did not use the ICT. The IVQR results, make the claim more robust, which is show a negative and significant impact of ICT on farmers pesticide use in all quartile groups. Therefore, this finding implies that there is a need to develop agriculture related ICT continuously among smallholder farmers to reduce the pesticide use.

Testing in smoothed GMM quantile models with an application to quantile Euler equation

Within the framework of smoothed generalized method of moments for quantile models, including instrumental variables quantile regression as a special case, a distance metric hypothesis test and an overidentification test are proposed. The asymptotoic validity including the consistency of the distance metric hypothesis test are established. Simulations show reasonable finite-sample performance of both proposed tests in terms of size and power. In an empirical example, the proposed tests are applied to test the discount factor and elasticity of intertemporal substitution parameters in quantile Euler equations and overall the quantile Euler equation itself.

Average and heterogeneous effects of smallholder farm sizes on dietary diversity in northern Ghana

AbstractThe relationship between farm size and dietary diversity has recently gained attention among development practitioners and policymakers. To study this issue, we utilized cross‐sectional data from 900 farm households in Northern Ghana. The analysis employs three econometric approaches—ordinary least squares, two‐stage least squares, and instrumental variable (IV) quantile regression. We subjected our estimates to Conley‐Hansen‐Rossi bound tests to assess the validity of total farmland under household control as an IV for farm size, which serves to illustrate the robustness of the causal inferences linking farm size to household dietary diversity. The results show that a 1‐ha increase in cultivated land improves household dietary diversity score by 3.221 units. The effect of cultivated land on dietary diversity is enhanced by market information, use of improved seeds, chemical fertilizers and herbicides, and overall household asset value. In conclusion, the results indicate that farm size increases household dietary diversity when moving along the dietary diversity distribution. This highlights the importance of considering the influence of farm size at different points along the conditional dietary diversity score distribution, as relying solely on mean effects may obscure important counteracting effects.

The temporal dimensions of policy responses to capital surges

The temporal dimensions of policy responses to capital surges

A dynamic quantile model for distinguishing intertemporal substitution from risk aversion

This paper uses a dynamic quantile model to estimate the elasticity of intertemporal substitution (EIS) and risk attitude using large disaggregated data from the NielsenIQ Consumer Panel. This data is transactional at the consumption purchase level, which minimizes measurement error, and the final sample contains more than two million observations. In the quantile model, the risk attitude is captured by the quantile and is, therefore, separable from the EIS. To estimate the parameters of interest we use the Euler equation along with instrumental variables quantile regression. First, we estimate the model across different levels of risk attitude. Empirical results document evidence of monotonically decreasing EIS along quantiles. For large risk aversion, the EIS is greater than one, whereas for small risk aversion it descends into negative values. Subsequently, we estimate the risk attitude and the EIS simultaneously. The results substantiate a risk attitude close to the median, with EIS consistently positive and smaller than one.

The Impact of Village Cash Transfer on Rural Households’ Economic Well-being

Indonesia applied adjustments and refocused the policy of using the Village Funds budget to minimize the impact of the Covid-19 pandemic in 2020, especially in the rural area. The Village Funds have been used as a social safety net as Village Cash Transfer. The researchers carried out this study to present an overview of the evaluation of the effects of Village Cash Transfer on the economic well-being of rural communities in every household quintile. This study uses data on the characteristics of 35,759 households obtained from merging results of the March & September 2020 Susenas data with the Village Fund data in each district/city area. Data were analysed using Smoothed Instrumental Variables Quantile Regression, which overcame endogeneity issues and produced robust estimates. The study results show that the Village Cash Transfer has a positive and significant impact on the economic well-being of rural households. Still, their impact is felt more by the upper middle quintile households, which benefit more from Village Cash Transfer households in the Java region. As a result, it is advisable for Governments to be more generous in providing benefits to families in the lowest quantile and to monitor the program by the requirements of beneficiaries closely.

The Impact of Community-Driven Development Program on Rural Households Economic Well-being: Evidence from Village Funds

Indonesia has implemented the concept of community-driven development in rural areas through village funds since 2015. Village funds are one of the instruments to minimize the impact of Covid-19 in rural areas. This research was conducted to provide an overview of the impact evaluation of village funds on the economic well-being of rural communities in each household quantile. This study uses data on the characteristics of 35,759 households obtained from merging results of the March & September 2020 Susenas data with the village fund data in each district/city area. Data were analyzed using smoothed instrumental variables quantile regression, which overcame endogeneity issues and produced robust estimates. The study results show that the village fund has a positive and significant impact on the economic well-being of rural households. Still, their impact is felt more by the upper middle class and households in the Sumatra region.

A New Instrumental-Type Estimator for Quantile Regression Models

This paper proposes a new instrumental-type estimator of quantile regression models for panel data with fixed effects. The estimator is built upon the minimum distance, which is defined as the weighted average of the conventional individual instrumental variable quantile regression slope estimators. The weights assigned to each estimator are determined by the inverses of their corresponding individual variance–covariance matrices. The implementation of the estimation has many advantages in terms of computational efforts and simplifies the asymptotic distribution. Furthermore, the paper shows consistency and asymptotic normality for sequential and simultaneous asymptotics. Additionally, it presents an empirical application that investigates the income elasticity of health expenditures.

Can geopolitical risk improve energy efficiency in European countries?

The European countries are highly dependent on energy imports, making them vulnerable to international geopolitical threats. In this context, improving energy efficiency emerges as a cost-effective solution. The primary purpose of this study is to investigate the impact of geopolitical risk (GPR) on energy efficiency for 18 European countries during 1991–2020 using the Dumitrescu Hurlin Panel Causality test, instrumental variable quantile regression (IVQR) model, and Data Envelopment Analysis (DEA). The study found that GPR has a positive impact on energy efficiency in higher quantities. This trend might be due to the fact that countries with high energy efficiency are better equipped to face GPR, thanks to their investments in infrastructure, particularly renewable energy technology.

Distributional impacts of anchor borrower program on the welfare of rice farming households in Oyo State, Nigeria

PurposeThis study assessed the heterogeneous impact of the Anchor Borrower Program (ABP) on the welfare distribution of rice farming households in Nigeria.Design/methodology/approachSelf-selection bias and treatment endogeneity were accounted for by employing the Instrumental Variable Quantile Regression (IVQR) model. The estimates obtained from the IVQR model were further compared with those from the conventional quantile regression, and quantile regression using Propensity Score Matching. This was to highlight the extent to which endogeneity bias has been purged from the treatment, in order to establish a consistent causal link between participation in the ABP and the welfare of a cross-section of rice farming households.FindingsABP farmers had significantly higher rice yields across all quantiles of the yield distribution under treatment exogeneity assumption, and in only two quantiles upon controlling for observable confounders. However, this yield gain did not translate to higher Per capita Consumption Expenditure (PCE). The estimates of the more robust IVQR model provided further evidence that the rice yield and PCE of ABP farmers are not statistically different from that of non-ABP farmers across all quantiles of the welfare distribution.Social implicationsThe negligible impact of ABP was relatively higher for lower-yielding households. Thus, implying that, although the ABP is a pro-poor development intervention, the program has not been sufficiently implemented to significantly improve the welfare of the dominant resource-poor farming households in Nigeria.Originality/valueThis study assessed the impact of ABP beyond the conventional potential mean outcome framework by accounting for heterogeneity in treatment effect.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0083

Metropolitan area heterogeneity and the impact of road infrastructure improvements on VMT

Past studies have found that the average elasticity of vehicle miles traveled (VMT) to the stock of highways is close to one. This result is often interpreted to mean that an increase in the stock of highway miles is likely to be accompanied by a commensurate increase in VMT, leaving congestion unaffected. In this study, we explore the heterogeneity in this elasticity due to both observed and unobserved city characteristics. We begin by using a simple model to demonstrate how cities with different initial congestion levels may respond differently to added road capacity. These differences give rise to heterogeneity in the elasticity of VMT to highway capacity. We then conduct an empirical analysis using the instrumental variable quantile regression (IVQR) model to incorporate variation in the elasticity due to the presence of unobserved differences across metropolitan statistical areas (MSAs). The IVQR results imply that expanding road capacities has a greater impact on MSAs with low levels of VMT than on MSAs with high VMT. Estimates of the elasticity using generalized quantile regression (GQR) mirror the IVQR estimates. We further simulate the mechanisms using a spatial equilibrium model with an extensive road network calibrated to the Greater Los Angeles region by treating commuting and shopping trips, mode, and route choices. We find that the elasticity of VMT to capacity—which decreases with initial VMT—in LA is 0.32. One important policy implication is that, when building more roads, the mean or median congestion level across cities remains unchanged, although it can reduce the number of cities experiencing high congestion levels.