Corruption, natural capital and economic development: A dynamic GMM analysis

Ang, Joshua Ping; Patalinghug, Jason Article Corruption, natural capital and economic development: A dynamic GMM analysis Review of Economic Analysis (REA) Provided in Cooperation with: International Centre for Economic Analysis (ICEA), Waterloo, Ontario Suggested Citation: Ang, Joshua Ping; Patalinghug, Jason (2025) : Corruption, natural capital and economic development: A dynamic GMM analysis, Review of Economic Analysis (REA), ISSN 1973-3909, International Centre for Economic Analysis (ICEA), Waterloo (Ontario), Vol. 17, Iss. 1, pp. 95-114, https://doi.org/10.15353/rea.v17i1.5608 This Version is available at: https://hdl.handle.net/10419/328179 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/4.0/ Review of Economic Analysis 17 (2025) 95-114 1973-3909/2025095 95 www.RofEA.org Corruption, Natural Capital and Economic Development: A Dynamic GMM Analysis Empty 15 JOSHUA P. ANG North Carolina Agricultural and Technical State University Empty 15 JASON C. PATALINGHUG Southern Connecticut State University† Empty 15 Using a dynamic panel dataset of 150 countries for the period of 2006-2018 and a twostep system GMM estimation model, this paper shows that natural resources have a positive effect on economic development while holding corruption constant. Our findings support the notion that natural resources have a positive effect on the economy of a nation. When a country has less corruption, it improves the appropriation of economic gains from natural resources which serves as natural capital that would drive further capital accumulation and further development. We also find that physical capital, human capital, and freedom from corruption show strong positive effects on economic development, controlling for other economic and institutional variables. Keywords: Corruption; Economic Development; Dynamic Panel Study; Natural Resources; Physical Capital; Human Capital JEL Classifications: O47; Q32 1 Introduction Do natural resources contribute more to the economic development of a country with low levels of corruption compared to one where corruption is rampant? Natural resources can be defined as the world's stocks of natural assets which include minerals, soil, air, water, and all living organisms. These resources can be viewed as either a blessing or a curse for economic development. There are studies (Sachs & Warner, 1999; Torvik, 2001; Krugman, 1987) which state that having natural resources could have a negative spillover effect on a country’s future economic growth compared to other forms of capital. This is called the Dutch disease or  Ang: Department of Economics, [email protected] Patalinghug: Department of Economics, [email protected] © 2025 Joshua P. Ang and Jason C. Patalinghug. Licensed under the Creative Commons Attribution - Noncommercial 4.0 Licence (http://creativecommons.org/licenses/by-nc/4.0/. Available at http://rofea.org. Review of Economic Analysis 17 (2025) 95-114 96 www.RofEA.org negative resource effect for the general economy. While others (Brunnschweiler, 2006; Allcott & Keniston, 2018) produce evidence against this theory, Mehlum et al. (2006) show mixed results (see Venables (2016), Havranek (2016) and van Der Ploeg (2011) for more information). It is important to account for the impact of corruption on countries that have an abundance of natural resources because the economic gains from these resources become the natural capital for future economic development. It is therefore imperative that these countries have good institutions (Mehlum et al.,2006) for economic growth because an abundance of resources could lead to more corruption (Leite and Weidmann, 1999; Bhattacharyya and Hodler, 2009; Gallego et. al, 2020). Aside from having human and physical capital in our growth model (Barro, 1991; Batten and Vo, 2009; Fabro and Aixala, 2012; Iqbal and Daly, 2014), we also include a variable for natural capital (Sachs and Warner, 1999; England, 2000; Brunnschweiler, 2008) to capture the country's stock of natural resources that provides it the capability to grow its economy, as well as a variable that measures the prevalence (or lack thereof) of corruption. We address the effects of natural capital and corruption on economic development with a dynamic panel data model estimated by using a two-step system GMM estimation method. This model is based on the cross-country catch-up equation by Barro and Sala-i-Martin (2003), which controls for other variables, such as institutional factors (trade freedom, labor freedom, democracy, business freedom, and property rights) and economic factors (inflation, and government spending). With this estimation, we also account for the unobservable country-specific effect, and it is efficient and robust to heteroskedasticity and autocorrelation. Our findings offer support to the idea that natural resources have a positive effect on economic development assuming the free from corruption variable remains constant. We find that physical capital, human capital and property rights also have positive effects on economic development whereas labor freedom has a negative effect on development. However, we could not find any effect of democracy on economic development when we controlled for the free from corruption variable. This paper is structured as follows: Section 2 provides the review of related literature. Section 3 describes the data. Section 4 discusses the empirical framework. Section 5 reports the results and Section 6 gives us the conclusion. 2 Literature Review There have been several studies on the role that institutions play in economic growth and development. Appendix 3 provides a list of the studies discussed in this section of the paper as well as their various features (sample size, estimation method, time period covered, dependent and independent variables). Dias and Tebaldi (2012) used a panel dataset of 61 countries from AUTHOR(S) LAST NAME 4 spaces Abbreviated title 97 www.RofEA.org 1965 to 2005 and a micro-foundations model to examine the relationship between human capital, institutions and economic growth. Using system GMM to estimate their model, they found that institutions do play a role in economic growth. They also discovered that the growth of physical and human capital helps determine long-run economic growth. Mankiw et al. (1992) argued that differences in per capita GDP among nations can best be explained by using an augmented Solow model. Their paper had several main findings. First, they found no significant difference between the elasticity of income with respect to the stock of physical capital and the share of capital to income. Second, under the augmented Solow model, both the accumulation of physical capital and population growth have a larger effect on income per capita compared to the original Solow growth model. Lastly, the model in this paper predicts that countries with similar technologies and rates of accumulation and population growth should converge in income per capita. The paper thus argues that differences in per capita GDP are due to differences in saving, population growth and education among countries. Boikos et al. (2023) tries to recreate the work of Mankiw et al. (1992). They updated the dataset using the periods 1960-2015, 1970-2015 and 1990-2015. While their results are not fully consistent with the Solow and augmented Solow models, the augmented model was shown to fit their data better. Their results also show that human capital has a stronger impact on growth compared to the results of the Mankiw et al. (1992) paper. Gylfason (2001) identified four factors that cause natural resources to stunt economic development. These are (1) the Dutch disease, (2) rent seeking, (3) overconfidence and (4) neglect of education. He states that there is an inverse relationship between education spending and the share of natural resources in a country’s wealth which leads him to conclude that natural resources crowds out human capital which leads to slower economic development. Kim and Lin (2017) used panel data on 40 developing countries during the period 1960 to 2012 as well as heterogeneous panel cointegration techniques to examine the relationship between natural resources and economic development. They found that countries abundant in natural resources develop slower than those with fewer resources thus giving more proof to the curse of natural resources. Cavalcanti and Novo (2005) investigated how economic development is affected by institutions. Using output per worker as a proxy for development, their results showed that a one percent improvement in institutions leads to a five percent increase in output per worker. Their results also show that an improvement in institutions leads to a bigger increase in output per worker in lower income countries as opposed to those with higher incomes. Hashim Osman et al. (2012) examined the role institutions played in the economic development of 27 Sub-Saharan African countries. Using panel data from the period 1984 to 2003 and a fixed effects model, they found that that the quality of institutions and the stability Review of Economic Analysis 17 (2025) 95-114 98 www.RofEA.org of the government are significant factors in the economic development of these nations. However, they did not find corruption to be a significant force that affects development. Hall et al. (2010) developed a growth model where the productivity and allocation of capital depends on the quality of a nation’s institutions. Using a dataset of 96 countries that covers the period of 1980 to 2000, their results show that increases in physical and human capital can lead to a country having economic growth only if it has good institutions. Vedia-Jerez and Chasco (2015) used a dataset of 10 South American countries over the period of 1960 to 2008 to examine the long-run determinants of economic growth in the region. Using system GMM to estimate their model, they discovered that human and physical capital accumulation are vital conditions for increasing economic growth. Also, efficient political institutions are a key component for growth as they help stimulate productivity and attract capital. Nasreen et al. (2015) used both ordinary least squares (OLS) and panel GMM to investigate the long-term impact of institutions on investment and economic growth. Using a dataset of 94 countries for the period 1985-2009, their results showed that countries with more economic freedom have higher economic growth per unit of input as well as higher levels of spending on physical and human capital investment. Their study also points out that an independent and unbiased judicial system that protects property rights is crucial for economic growth. Tavares and Wacziarg (2001) looked at the role of democracy in economic growth. They used a diverse dataset of 65 developed and developing countries over the period of 1970-1989. Using three-stage least squares (3SLS), they found that democracy has a negative but moderate effect on economic growth. This is because more democracy increases human capital accumulation and lowers physical investment rates. This means that democracy helps lowerincome groups by expanding access to education, but it comes at the expense of accumulating physical capital. Democracy also leads to lower income inequality which can lead to higher growth. The authors of the study point out that democratic institutions provide a trade-off between economic costs and social benefits. Dawson (1998) analyzes the empirical relationship between institutions, investment and growth. Using a dataset of 85 countries over the period of 1975-1990 and fixed effects estimation, he showed that economic freedom has a significant and positive effect on growth. This is because economic freedom has a direct effect on growth through total factor productivity and an indirect effect through investment. Thus, promoting economic freedom is important in achieving growth. The study also shows that free-market institutions as well as political and civil liberties are contributors to growth. Fatas and Mihov’s (2013) paper shows that policy volatility has a strong and negative impact on growth. They can show this by creating measures of policy volatility out of a dataset of 93 countries over the period 1960-2007. Using pooled OLS and fixed effects models, they AUTHOR(S) LAST NAME 4 spaces Abbreviated title 99 www.RofEA.org discover that countries that aggressively use discretionary fiscal policy when it is unnecessary tend to have lower economic growth. One of their main findings is that institutions affect economic growth through their impact on policy rather than their use as a constraint on the chief executive of a nation. Gwartney et al. (2006) examine the relationship between institutions and investment and how institutional quality affects growth through its impact on the productivity and level of investment. Using a dataset of 94 countries during the period 1980-2000, their findings show that countries with better quality institutions have more growth per unit of investment and attract a higher level of private investment as a share of GDP. The paper states that institutional quality has a sizeable indirect impact on private investment. Their analysis also shows that higher growth does not lead to better institutions. In fact, it goes the opposite direction as lower growth would lead to bigger improvements in institutional quality. Issahaku et al. (2018) tried to test for two related hypotheses. The first one is the growth importation hypothesis which states that countries with weak institutions will import growth in the form of international remittances. The second one is the urgency hypothesis which states that there is a high urgency to apply remittances efficiently in countries with weak institutions because of limited alternatives. It thus implies that there is a high opportunity cost of misallocating remittance revenues. Using data for 106 countries during the period 1996-2013 and two-stage least squares (2SLS) estimation, they were able to show strong evidence for the existence of these two hypotheses. They discover that remittances foster growth in low income and lower middle-income countries but not in high income or upper middle-income countries. The authors point out that low income and lower middle-income countries have not yet benefitted from the growth dividends of institutions and that institutions and remittances are substitutes for one another. Sidek and Asutay (2021) analyze the impact of government expenditures on economic growth while controlling for institutional factors using a dataset that included 30 developed and 91 developing countries over the period 1984-2017. Using two-step system GMM estimation, they discovered that government expenditures, whether they are government development or government consumption expenditures, have a positive effect on economic growth if good institutions are present. This implies that good governance leads to the efficient use of public funds. 3 Data We collected data on 150 countries for 12 years (2006-2018) from different sources. We use the real per capita Gross Domestic Product (GDP) (in constant 2010 US$) with the data collected from The World Bank (WB) and smoothened with a centered moving average of a year. We also collected data on the mean years of schooling from the United Nations (UN) as Review of Economic Analysis 17 (2025) 95-114 100 www.RofEA.org a proxy for human capital. This represents the average number of years of education received by people aged 25 and older. We use gross capital formation (% of GDP), which consists of outlays on additions to the fixed assets of the economy plus net changes in the level of inventories, as a proxy for physical capital. We also use total natural resources rents (% of GDP), where the total natural resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents, as a proxy for natural capital. Data on gross capital formation and total natural resources rents were obtained from The World Bank. The CorruptFree variable is the Corruption Index collected from Transparency International’s Corruption Perceptions Index that measures the level of corruption in a nation with zero as the most corrupt and 100 as the least corrupt. The Democracy variable comes from the Economist Intelligence Unit Democracy Index which measures the quality of democracies based on factors such as electoral pluralism, form of government, political participation, civil liberty and political culture, with zero as the least democratic and 100 as the most democratic. The data on the annual inflation rate (derived from the consumer price index) is collected from the International Monetary Fund (IMF). We also collected data from The Heritage Foundation. This data includes the Government Spending Index that measures all levels of government expenditures, the Labor Freedom Index that measures regulations concerning labor employment, the Trade Freedom Index that measures the absence of tariff and non-tariff barriers that affect imports and exports of goods and services, the Business Freedom Index that measures the ability to have a business, the overall burden of regulation as well as the efficiency of government in the regulatory process, and Property Rights that measures the degree to which a country’s laws protect private property rights and the degree to which its government enforces those laws. All the indices are measured with zero as having the least freedom and 100 as having the most freedom. 4 The Model Our baseline model is a two-step system Generalized Method of Moments (GMM) estimation model on dynamic panel data. It can be re-written from the cross-country catch-up equation of Barro and Sala-i-Martin (2003) to assess the institutional factors, with our main focus on freedom from corruption and natural resources, as: ln𝑌 𝑖𝑡 =  ln 𝑌 𝑖𝑡−1 +  1𝐻𝑢𝑚𝑎𝑛𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 +  2𝑃ℎ𝑦𝑠𝑖𝑐𝑎𝑙𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 +  3𝑁𝑎𝑡𝑢𝑟𝑎𝑙𝐶𝑎𝑝𝑖𝑡𝑎𝑙𝑖𝑡 +  4𝐶𝑜𝑟𝑟𝑢𝑝𝑡𝐹𝑟𝑒𝑒𝑖𝑡 +  5𝑇𝑟𝑎𝑑𝑒𝐹𝑟𝑒𝑒𝑑𝑜𝑚𝑖𝑡 +  6𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛𝑖𝑡 +  7𝐿𝑎𝑏𝑜𝑟𝐹𝑟𝑒𝑒𝑑𝑜𝑚𝑖𝑡 +  8𝐷𝑒𝑚𝑜𝑐𝑟𝑎𝑐𝑦𝑖𝑡 +  𝑖+ 𝑑𝑡+  𝑖𝑡 (1) AUTHOR(S) LAST NAME 4 spaces Abbreviated title 101 www.RofEA.org where  = 1 +  and  is the conditional convergence factor, 𝑌 𝑖𝑡 is the real GDP per capita, 𝑌 𝑖𝑡−1 is the previous year real GDP per capita,  𝑖 the unobservable country-specific effect, 𝑑𝑡 is the yearly time dummy, εit is the error term, and  are the coefficients. In Equation (1), 𝐻𝑢𝑚𝑎𝑛𝐶𝑎𝑝𝑖𝑡𝑎𝑙, 𝑃ℎ𝑦𝑠𝑖𝑐𝑎𝑙𝐶𝑎𝑝𝑖𝑡𝑎𝑙, and 𝑁𝑎𝑡𝑢𝑟𝑎𝑙𝐶𝑎𝑝𝑖𝑡𝑎𝑙 are the capitalrelated explanatory variables for human capital, physical capital, and natural capital respectively. For the baseline model, we have 𝑇𝑟𝑎𝑑𝑒𝐹𝑟𝑒𝑒𝑑𝑜𝑚, 𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛, 𝐿𝑎𝑏𝑜𝑟𝐹𝑟𝑒𝑒𝑑𝑜𝑚, and 𝐷𝑒𝑚𝑜𝑐𝑟𝑎𝑐𝑦 as control variables for the Trade Freedom Index, the inflation rate, the Labor Freedom Index, and the Democracy Index respectively. We also include 𝑃𝑟𝑜𝑝𝑒𝑟𝑡𝑦𝑅𝑖𝑔ℎ𝑡, 𝐵𝑢𝑠𝑖𝑛𝑒𝑠𝑠𝐹𝑟𝑒𝑒𝑑𝑜𝑚 , and 𝐺𝑜𝑣𝑒𝑟𝑛𝑚𝑒𝑛𝑡𝑆𝑝𝑒𝑛𝑑𝑖𝑛𝑔 as other control variables for property rights, the Business Freedom Index, and government spending. There exists unobserved heterogeneity in the cross-section of countries which is persistent and has higher variance than the error terms. We thus estimate the dynamic panel data using fixed effects and GMM models to control for endogeneity and unobserved heterogeneity. However, we could possibly encounter the problem of having severely weak instruments if we use the difference GMM model (Arellano and Bond, 1991). Therefore, we employ the system GMM estimator (Arellano and Bover, 1995; Blundell and Bond, 1998). According to Roodman (2009), having a sample with the time dimension being greater than the number of countries can weaken the instruments which could lead to invalidating some asymptotic results and specification tests (Teixeira and Queiros, 2016; Iqbal and Daly, 2014). Hence, our two-step system GMM estimation is done with panel data consisting of 150 countries over a period of 12 years. We use two-step, rather than one-step system GMM, because it is more efficient and robust to heteroskedasticity and autocorrelation (Roodman, 2009). We use the collapsed instrument matrix to resolve the issue of overfitting endogenous variables. 5 Results In this section, we present the results from the dynamic panel data analysis in Table 1 that are estimated by Blundell and Bond (1998)’s two-step system GMM estimation method using a collapsed instrument matrix that includes time dummy variables and using robust standard errors corrected for finite samples (Windmeijer-corrected standard errors). Table 1 shows the estimation results for Equations 1 to 4 for the dependent variable which is real GDP per capita (in natural log). Arellano-Bond Tests AR (1) show that the specified dynamic model is appropriate when we reject the null hypothesis at the 1% significance level for all results. Arellano-Bond Tests AR (2) show that the GMM lag is a good instrument when we fail to reject that there exists second-order serial-correlation at the 10% significance level for all results. The Hansen tests show that the results are with valid instruments being specified because we fail to reject the null hypothesis that the instruments are invalid at the 10% Review of Economic Analysis 17 (2025) 95-114 102 www.RofEA.org significance level. All results have a time period of 12 years that is less than the number of groups, and this ensures valid asymptotic results and specification tests, according to Roodman (2009). The results show that the accumulation of both human and physical capital have strong positive effects with statistical significance (at either the 1% or 5% level) on economic development for all equations, similar to the findings of Batten and Vo (2009), Fabro and Aixala (2012), and Iqbal and Daly (2014). Holding everything else constant, a one-unit increase in physical capital is associated with an approximately 0.06% increase in real GDP per capita in Equations 1 to 4. Holding everything else constant, a one-unit increase in human capital is associated with a 2.17%, 2.12%, 1.62%, and 1.55% increase in real GDP per capita in Equations 1 to 4, respectively. With a population that has more educational attainment and more capital formation over time, the higher level of human capital accumulated has a greater effect of specialization in capital-intensive industries that would give rise to a greater gain on economic development. The natural capital variable shows a positive effect on growth (similar to Brunnschweiler, 2006; Allcott and Keniston, 2018) with statistical significance at either the 5% or 10% level. Holding everything else constant, every one-percentage point increase in natural resources as a percent of GDP is associated with a 0.26%, 0.28%, 0.3%, and 0.29% increase in the level of economic development in Equations 1 to 4, respectively. We also find that being less corrupt has a strong positive effect, which is statistically significant at the 5% or 10% level, on economic development. Holding everything else constant, every one-unit increase in the free from corruption variable is associated with a 0.33%, 0.32%, 0.23%, and 0.24% increase in the level of economic development in Equations 1 to 4 respectively. This is consistent with Mehlum et al. (2006), which finds a positive natural resources effect when a country endowed with natural resources has good institutions. When a country has an abundance of natural resources, corruption causes an uneven appropriation on the gains from these resources that would otherwise serve as the natural capital to allow more people to be better off from accumulating other forms of capital to achieve higher economic development. We did find an interesting note on democracy. We failed to find its connection with economic development when we controlled for freedom from corruption. We also found that trade freedom has a moderate positive effect on development in equation 4. Our results reaffirm the findings of Leite and Weidmann (1999) which said that the establishment of strong, corruption-free institutions that secure property rights plays an important role in promoting economic development when a country is endowed with natural resources. AUTHOR(S) LAST NAME 4 spaces Abbreviated title 109 www.RofEA.org Hall et al. (2010) 19802000 96 countries OLS growth of output per worker Human Capital: average years of schooling per worker from Baier, Dwyer, and Tamura (2006) Physical Capital: perpetual inventory method used to calculate the physical capital stock per worker using annual investment data from Heston et al. (2000) Institutions: data on "risk of expropriation" from the International Country Risk Guide (ICRG) Dias and Tebaldi (2012) 19652005 61 countries one-step system GMM with robust covariance matrix GDP growth rate per capita Human Capital: rate of return from education Psacharopoulos (1994) Physical Capital: perpetual inventory system (Easterly and Levine (2001) Institutions: ratio of people with post-secondary education to people with no schooling, Polity-IV measure of democracy and autocracy Hashim Osman et al. (2012) 19842003 27 countries Fixed effects Growth rate of real per capita GDP Institutional quality: (a) socioeconomic conditions, (b) corruption, (c) government stability and (d) ethnic tensions, data comes from International Country Risk Guide Fatas and Mihov (2013) 19602007 93 countries pooled OLS and fixed effects growth rate of output per capita Human capital: primary school enrollment Physical capital: investment price Institutions: political volatility Review of Economic Analysis 17 (2025) 95-114 110 www.RofEA.org Nasreen et al. (2015) 19852009 94 countries OLS and panel GMM growth rate of real GDP per capita Human capital: adult literacy rate Physical capital: investment as share of GDP Institutions: political liberty, civil liberty and economic freedom indices taken form Freedom House Vedia-Jerez and Chasco (2015) 19602008 10 countries system GMM Growth: interquadrennial growth of log GDP per capita, FDI: log foreign direct investment as a percentage of GDP Human Capital: Average percentage of the log of working age population with secondary education Physical capital: Log gross fixed capital formation Institutions: institutional quality index form Norris (2009), institutional constraints on chief executives index from Polity IV, contractintensive money from IFS Kim and Lin (2017) 19902012 40 countries Multi-factor regression Log of real GDP per capita Natural resources: (a) share of primary exports in GDP and (b) revenues from natural resources (including energy, minerals, and forestry) as a share of GDP Issahaku et al (2018) 19962013 106 countries 2SLS growth in per capita GDP Human capital: labor force participation rate Physical capital: gross fixed capital formation Institutions: composite measure of institutional quality derived from the six governance indicators development by Kaufmann, Kraay and Mastruzzi (2011) AUTHOR(S) LAST NAME 4 spaces Abbreviated title 111 www.RofEA.org Sidek and Asutay (2021) 19842017 121 countries system GMM GDP growth rate Human capital: annual change in population, Physical capital: investment Institutions: 12 variables (government stability, socio economic conditions, investment profile, internal conflict, external conflict, corruption, military in politics, religious tensions, law and order, ethnic tensions, democratic accountability, bureaucracy) from ICRG Boikos et al. (2023) 19602015 139 countries OLS ln GDP per working-age person in 2015 Human capital: (a) the secondary educational attainment as a % of the population aged 15-64 (total) from the Barro-Lee dataset (Barro and Lee 2013) and (b) the human capital index, based on years of schooling and returns to education from the Penn Table References Allcott, H. and Keniston, D. (2018). Dutch disease or agglomeration? The local economic effects of natural resource booms in modern America. Review of Economic Studies 85, 695731. https://doi.org/10.1093/restud/rdx042 Arellano, M. and Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277– 97. https://doi.org/10.2307/2297968 Arellano, M. and Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. 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