Oil majors often choose to operate in countries with weak property rights. This may appear surprising, since the lack of constraints on governments may create incentives to renege on initial promises with firms and renegotiate tax payments once investments have occurred and, in the worst case, expropriate the firm. In theory, backloading investments, production and tax payments may be used to create self-enforcing agreements which do not depend on legal enforcement. Using a new dataset covering the universe of oil majors’ assets that started production between 1974 and 1999, we indeed show in a recent CEPR Working Paper (Paltseva, Toews, and Troya-Martinez, 2022) that investments, production and tax payments are delayed by two years in countries with weak institutions relative to countries with strong institutions. Extending the dataset back to 1960 and exploiting the transition to a new world oil order where expropriation became easier, allows us to interpret our estimates as causal. In particular, prior to the transition expropriations were not feasible, due to the omnipresent and credible military threat imposed by the oil majors’ countries of origin. As the new order sat in, a new equilibrium emerged, in which expropriations became a feasible option. This transition incited an increase in expropriations and forced firms to adjust to the new reality by backloading contracts.
The Hold-up Problem
In December of 2006, when the oil price was climbing towards new heights, the Guardian reported that the Russian government was about to successfully force Shell into transferring their controlling stake in a huge liquified gas project back into the hands of the government. While officially this was motivated by environmental concerns surrounding the Sakhalin-II project, most observers agreed that this might be considered a textbook example of the hold-up problem faced by oil firms when investing in countries with limited constraints on the executive. At its core, the hold-up problem refers to the idea that the government may renege on the initial promise and appropriate a bigger share of the pie once investments have been made. Obviously, this is not an oil-specific issue and concerns any type of investment in countries with weak property rights. Academics, who worked on resolving these issues, suggest the use of self-enforcing agreements (Thomas and Worrall, 1994). These agreements use future gains from trade (as opposed to third-party enforcement) to incentivize the governments not to expropriate. And while the theoretical literature has prolifically developed over the last 30 years (Ray, 2002), to the best of our knowledge no empirical evidence has been provided on the use and dynamic patterns of self-enforcing backloaded contracts.
Data and Sample
In Paltseva, Toews and Troya-Martinez (2022), we rely on micro-level data on oil and gas projects provided by Rystad Energy, an energy consultancy based in Norway. Its database contains current and historical data on physical, geological and financial features for the universe of oil and gas assets. We focus on the assets owned by the oil majors (BP, Chevron, ConocoPhilips, Eni, ExxonMobile, Shell, and Total) using all assets that started production between 1960 and 1999, leaving us with a total of 3494 assets. An asset represents a production site with at least one well, operated by at least one firm, and with the initial property right being owned by at least one country. Being able to conduct the analysis on the asset level is particularly valuable since it allows us to control for a large number of confounding factors and rule out several alternative explanations of our main finding.
Moreover, there are three advantages of focusing our analysis on the oil and gas sector in general and the oil majors in particular. First, the sunk investments in the development of oil and gas wells are enormous, making the hold-up problem in the oil sector particularly severe. Second, oil majors have been around for many years since all of them were created before WWII. This provides us with a sufficiently long horizon to capture backloading over time. Third, the majors are simultaneously investing in many countries which provides us the necessary cross-sectional variation in institutional quality. To differentiate between countries with weak and strong institutions, we use a specific dimension from the Polity IV dataset measuring the constraints on the executive. The location of all the assets disaggregated by firm as well as a binary distinction in a country’s institutional quality is shown in Figure 1.
Figure 1. Spatial distribution of assets and institutional quality
A Stylized Fact
For the empirical analysis, our variables of interest are investment, production and tax payments normalized by the respective asset-specific cumulative sum over a period of 35 years. The resulting cumulative shares are depicted in Figure 2. We focus on physical production which, in addition to being considered the most reliable measure of an asset’s activity, does not require discounting. Real values of investment and tax payment depict a very similar picture. Most importantly, the dashed lines illustrate that 2/3 of cumulative production shares are reached approximately two years earlier in countries with strong institutions, in comparison to countries with weak institutions. The average asset size does not differ significantly between these groups. Such delays are costly for countries with weak institutions. Our back-of-the-envelope calculation suggests that the average country loses around 120 million US$ per year due to the delayed production and the respective tax payments. We confirm that the two-year delay cannot be explained by geographical, geological or financial confounders such as the location of the well, fuel type or contract features.
Figure 2. Years to reach 66% of cumulative flows in 35 years
The Transition to a New World Order
To push towards a causal interpretation of the results, we exploit the global transition to a new world oil order. This change affected the probability of expropriations in countries with weak institutions while leaving countries with strong institutions unaffected. In particular, the post-WWII weakening of the OECD members as political and military actors provides a natural experiment of global proportions. Expropriations are first viewed as impossible due to the military threat of British, French and US armies, and then become possible due to a global movement aiming at returning sovereignty over natural resources to the resource-rich economies. In the words of Daniel Yergin (1993): “The postwar petroleum order in the Middle East had been developed and sustained under American-British ascendancy. By the latter half of the 1960s, the power of both nations was in political recession, and that meant the political basis for the petroleum order was also weakening. […] For some in the developing world […] the lessons of Vietnam were […] that the dangers and costs of challenging the United States were less than they had been in the past, certainly nowhere near as high as they had been for Mossadegh, [the Iranian politician challenging UK and US before the coup d’etat in 1953], while the gains could be considerable.” Consequently, the number of expropriations has grown substantially since 1968, marking the transition to a new world order (Figure 3). However, Kobrin (1980) finds that even during the peak of expropriations in 1960-1976, only less than 5 % of all foreign-owned firms in the developing countries were expropriated. We suggest that this is, at least partly, thanks to the use of backloaded self-enforcing contracts.
Figure 3. Transition to a new world order
Indeed, focusing on the years around the transition to the new world oil order, we show that there have not been any differences in investment, production or tax payments dynamics between countries with weak and strong institutions in the early years of the 1960s. But investment, production and the payments of taxes started experiencing significant delays after 1968 in the countries with weak institutions, using countries with strong institutions as a control. Intuitively, the omnipresence of a credible military threat in response to an expropriation served as an effective substitute for strong local formal institutions and eliminated the need for contracts to be self-enforced and backloaded in countries with weak institutions. Once this threat disappeared, contracts had to be self-enforcing and investment, production and tax payments had to be backloaded to decrease the risk of being expropriated by the governments of resource-rich economies. Theoretically, these initial differences in contract backloading between countries with strong and weak institutions should disappear in the long run, because the future gains from trade need to materialize eventually. We confirm empirically that this point is reached on average 20 years after firms start a contractual relationship with a country.
We provide evidence that oil firms seem to backload contracts in countries with weak institutions. We show that such backloading appears in the data during the transition to a new world order since 1968, when firms were in need of a new mechanism to deal with weak property rights and the risk of expropriations. We estimate the cost of such delays to be around 120 US$ per country and year. While this cost is high, it is important to emphasize that in the absence of such backloading, forward-looking CEOs of oil majors would often choose not to invest in the first place, since they would anticipate the severe commitment problems (Cust and Harding, 2020). Thus, as a second-best, the cost of the backloading may be marginal compared to the value added from trade when oil majors are willing to invest in countries with weak institutions and questionable property rights.
- Cust, J. & Harding, T. (2020). “Institutions and the location of oil exploration”. Journal of the European Economic Association, 18(3): 1321–1350.
- Kobrin, S. J. (1980). “Foreign enterprise and forced divestment in LDCs”. International Organization, 65–88.
- Kobrin, S. J. (1984). “Expropriation as an attempt to control foreign firms in LDCs: trends from 1960 to 1979.” International Studies Quarterly, 28(3): 329–348.
- Paltseva, E, Toews, G & Troya Martinez, M. (2022). ‘I’ll pay you later: Relational Contracts in the Oil Industry‘. London, Centre for Economic Policy Research.
- Debraj, R. (2002). “The time structure of self-enforcing agreements.” Econometrica, 70(2): 547–582.
- Jonathan, T. & Worrall, T. (1994). “Foreign direct investment and the risk of expropriation.” The Review of Economic Studies, 61(1): 81–108
- Yergin, D. (2011). The prize: The epic quest for oil, money & power. Simon and Schuster.
The low economic diversification in Russia is commonly blamed on the abundance of energy resources. This brief summarizes the results of our research that investigates the presence of Dutch disease effects across Russian regions. We compare manufacturing subsectors with different sensitivity to the availability of natural resources across Russian regions with varying natural resource endowments. We find no evidence of differential deindustrialization across subsectors, thereby offering no support for a Dutch disease. This finding suggests that the impact of energy resources on Russian manufacturing is more likely to go through the “institutional resource curse” channel. Thereby, we argue that more efficient policies to counteract the adverse effect of resources on the Russian economy should focus on improving the institutional environment.
Russian abundance in oil and gas, and the ways it could negatively affect long-term economic performance and institutional development is not a new debate. One of the key concerns is the influence of energy resources on Russian industrial structure. Energy resources are often blamed for the low diversification of the economy, with an extensive resource sector and the dominant oil and gas export share.
In a forthcoming chapter (Le Coq, Paltseva and Volchkova), we contribute to this debate by exploring the channels through which abundance in energy resources influences the industrial structure in Russia. Our main focus is on the deindustrialization due to the expansion of the natural resource sector, the so-called ‘Dutch disease’. Specifically, we explore the impact of energy resources on the growth of manufacturing subsectors in Russian regions. Adopting a regional perspective allows us to separate the Dutch disease mechanism from the main alternative channel of the institutional ‘resource curse’. This brief summarizes our findings.
Dutch disease vs. institutional resource curse
The Dutch disease and the institutional resource curse are, perhaps, the most discussed mechanisms proposed to explain the influence of natural resources on economic performance (see e.g., earlier FREE brief by Roine and Paltseva for a review). In an economy facing a Dutch disease, a resource boom and resulting high resource prices shift production factors from manufacturing industries towards resource and non-tradable sectors. As a result, a country experiencing a resource boom would end up with a slow-growing manufacturing and an under-diversified economic structure. Since the manufacturing sector is often the main driver of economic growth, the economic development may be delayed. If, instead, an economy is suffering from the institutional ‘resource curse’, it is the interplay of weak institutions and adverse incentives created by resource rents that leads to a slow growth of manufacturing and delayed development.
Importantly, offsetting the potential negative impact of these two channels requires different policy interventions. In the case of a Dutch disease, a state can rely on direct industrial policy mechanisms targeted towards increasing the competitiveness of the manufacturing sector and isolating it from the effect of booming resource prices. For example, it can use subsidies or targeted trade policy instruments, or channel money from increased resource prices out of the economy through reserve fund investments abroad.
In the case of an institutional resource curse, on the other hand, resource rents and weak institutions may undermine and disrupt the effect of such policies. In this case, state policies should be targeted, first and foremost, towards promoting good institutions such as securing accountability and the transparency of the state, and protecting property rights. This suggests that properly understanding the channels through which resource wealth impacts the economy is necessary for choosing appropriate remedial measures.
In our analysis, we address the differential impact of energy resources in Russian regions. This regional perspective allows us to single out the Dutch disease effect, and disregard the mechanisms of a political resource curse to the extent that the relevant institutions do not differ much across regions.
Resource reallocation effect vs. spending effect
The mechanism of a Dutch disease implies two channels through which a resource boom negatively affects the manufacturing sector. First, a resource boom implies the reallocation of production factors from other sectors of economy such as manufacturing or services to the resource sector, a so-called ‘resource reallocation effect’. Second, an additional income resulting from a boom in the resource sector leads to an increase in demand for all goods and services in the economy. This increase in demand will be accommodated differently by different sectors, depending on their openness to world markets. Namely, in non-tradable sectors, isolated from international competition, there will be an increase in prices and output. This, in turn, will increase the prices on domestic factor markets. For tradable manufacturing sectors the price is determined internationally and cannot be adjusted domestically. As a result, production factors will also reallocate away from manufacturing to non-tradable sectors, a so-called “spending effect”.
The strength of either effect is likely to be different across different subsectors of manufacturing depending on the sectoral specificities. In particular, subsectors with higher economies of scale are likely to be more affected by the outflow of factors towards the resource sector through the “resource reallocation effect”. Similarly, subsectors that are more open to international trade are likely to be affected by the “spending effect”.
These observations give raise to our empirical strategy: we access differences in growth of regional manufacturing subsectors with different sensitivity to the availability of energy resources, where sensitivity reflects economies of scale, for the first mechanism, and openness to the world market, for the second mechanism. In other words, we test whether manufacturing subsectors with higher economies of scale (or openness) grow slower than subsectors with lower economies of scale (or openness) in regions rich in energy resources, as compared to the regions poor in energy resources. Observing differential deindustrialization, depending on the industry’s exposure to the tested mechanism, would offer support to the presence of a Dutch disease.
Note that the validity of our empirical strategy relies on the fact that there is high variation in resource abundancy and structure of the manufacturing sectors across Russian regions (as illustrated by Figures 1 and 2).
Figure 1. Geographical distribution of fuel extractions relative to gross regional product; 2014, percent.
Figure 2. Regional diversity in manufacturing structure, 2014.
Data and results
Our empirical investigation covers the period 2006—2014. The data on manufacturing subsector growth and regional energy resource abundancy come from Rosstat, the sensitivity measures across different manufacturing sectors are approximated based on data from Diewert and Fox (2008) (economies of scale in US manufacturing), and OECD (sectoral openness to trade).
The results of our estimation show that the differences in growth rates of manufacturing subindustries across Russian regions with varying natural resource endowments cannot be explained by the sensitivity of these subindustries to the availability of energy resources. This can be seen from Table 1, where the coefficient of interest – the one of the interaction term between the measure of sectoral sensitivity if resource abundance and regional energy resource wealth – is not significantly different from zero, no matter how we measure the sensitivity: by the returns to scale or by openness to international trade.
Table 1. Estimation of Dutch disease effect with different sensitivity measures.
|Dependent variable: average annual growth index of sectoral output|
|Sensitivity measure: Economies of scale||Sensitivity measure: Openness|
|Subsector sensitivity * Size of the fuel extraction sector in the region
|Subsector fixed effect||YES||YES|
|Region fixed effect||YES||YES|
Source: Authors’ calculations.
These results hold true if we control for differences in regional taxes, labor market conditions, and other region-specific characteristics by including regional and sectoral dummy variables, if we consider alternative measures of energy resource wealth, and if we use other, non-parametric estimation methods.
In other words, our data robustly offers no support for the presence of a Dutch disease in Russian regions.
Conclusion and policy implications
Diversification is often mentioned by the Russian government, as one of the top economic policy priorities, and the need for ‘diversification’ has been used in the political debate as an argument for an active industrial policy.
However, the policy measures that are necessary to counter the effect of abundant energy resources on diversification and, more generally, on economic development may be highly dependent on the prevailing channel through which resources affect the economy. In particular, while active industrial policy may be justified as a remedy in the case of a Dutch disease, industrial policy may well be ineffective, or even harmful, in the presence of an institutional resource curse mechanism.
In our study, we find no support for the Dutch disease effect when looking at the impact of energy resources on the growth of regional manufacturing sectors. Thereby, to counterbalance the resource curse effect on the Russian economy, we argue that it may be more efficient to improve the institutional environment than to use active government policies affecting industrial structures.
- Diewert, W. E and Fox, K. J. (2008) ‘On the estimation of returns to scale, technical progress and monopolistic markups’, Journal of Econometrics, 145(1-2): 174-93.
- Le Coq, C., Paltseva E., and Volchkova N., forthcoming. “Regional impacts of the Russian energy sector”, in Perspectives on the Russian economy under Putin, eds. Becker and Oxenstierna, London, Routledge.
Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.