A Guillot

F Lebon

D Rouffet

S Champely

J Doyon

C Collet

Muscular responses during motor imagery as a function of muscle contraction types

Keywords: Motor imagery, EMG, Muscle contraction

This study was designed to gain more insight into the mechanisms underlying motor imagery (MI). While there is ample evidence that motor performance and MI share common central neural mechanisms, the question whether MI is accompanied by subliminal electromyographic (EMG) activity remained unsolved. Thirty right-handed volunteers were asked to lift or to imagine lifting a weighted dumbbell using different types of muscle contraction, i.e. heavy concentric, light concentric, isometric and eccentric contractions. EMG activity from 9 muscles of the dominant arm (agonist, antagonist, synergist and fixator muscles) was monitored. Autonomic nervous system responses were also recorded on the non-dominant hand, thus attesting mental activity at the peripheral level. A significant increased pattern of EMG activity was recorded in all muscles during MI, when compared to the rest condition, while the goniometric data did not reveal any movement. Although being subliminal, the magnitude of this activation was found to be correlated to the mental effort required to lift a weight mentally, as more EMG activity was recorded during imaginary lifting of heavy than light concentric contractions. When considering the different types of contraction, our results provided evidence of selective changes in EMG activity. Especially, the imagined eccentric condition elicited a significant weaker muscular activity than all other conditions. In addition, the changes in the EMG pattern mirrored those usually observed during physical movement. These findings support the hypotheses of a selective effect of MI at the level of muscular activity and of incomplete inhibition of the motor command during MI.

Introduction

Motor Imagery (MI) is defined as the mental simulation of a specific action without any corresponding motor output, and shares similar mechanisms underlying movement preparation and execution [START_REF] Jeannerod | The representing brain: neural correlates of motor intention and imagery[END_REF][START_REF] Decety | Do imagined and executed actions share the same neural substrate?[END_REF]. While the effects of MI to improve motor task performance are wellestablished [START_REF] Feltz | The effects of mental practice on motor skill learning and performance: a meta-analysis[END_REF][START_REF] Driskell | Does mental practice enhance performance?[END_REF], only a few body of research has been carried out to investigate the effect of MI on strength tasks. Even though [START_REF] Herbert | Effects of real and imagined training on voluntary muscle activation during maximal isometric contractions[END_REF] have challenged the hypothesis that mental training may increase the isometric strength, strong positive effects of MI on voluntary strength of both distal and proximal muscles have usually been reported [START_REF] Cornwall | Effect of mental practice on isometric muscular strength[END_REF][START_REF] Yue | Strength increases from the motor program: comparison of training with maximal voluntary and imagined muscle[END_REF][START_REF] Smith | Impact and mechanism of mental practice effects on strength[END_REF][START_REF] Zijdewind | Effects of imagery motor training on torque production of ankle plantar flexor muscles[END_REF][START_REF] Ranganathan | From mental power to muscle powergaining strength by using mind[END_REF][START_REF] Sidaway | Can mental practice increase ankle dorsiflexor torque?[END_REF]. These results provide evidence of the neural origin of strength gain that occurs before muscle hypertrophy, hence driving the motor units to a higher intensity and/or leading to the recruitment of motor units that remain otherwise inactive. As discussed in the recent paper by [START_REF] Folland | The adaptations to strength training. Morphological and neurological contributions to increased strength[END_REF], whilst further research is clearly required, this evidence suggests that substantial increase in the strength of major ambulatory muscle groups can be made without physical activity and be independent of morphological adaptations.

Among the theories supposed to explain such positive effects, the psycho-neuromuscular theory postulates that an electromyographic (EMG) activity of the same muscles occurs during MI and actual movement, although remaining different.

1 Such hypothesis has early been proposed by [START_REF] Washburn | Movement and mental imagery: Outlines of a motor theory of the complexer mental processes[END_REF], who suggested that slight muscle movements were made during MI, and later substantiated by several authors [START_REF] Jacobson | Electrical measurements of neuromuscular states during mental activities[END_REF][START_REF] Jacobson | Electrophysiology of mental activities[END_REF][START_REF] Shaw | The distribution of muscular action potentials during imaging[END_REF], who have reported that this EMG activity occurred only in the muscles that are involved in the movement. In addition, the Golgi tendons organs may be stimulated, and thereby generate neuromuscular feedback [START_REF] Schmidt | Motor control and learning: A behavioral emphasis, 3rd edn[END_REF]. This remains a fine hypothesis, as [START_REF] Jami | Golgi tendon organs in mammalian skeletal muscle: functional properties and central actions[END_REF] has demonstrated that the Golgi tendons organs are sensitive to a single motor unit contraction, thus showing the high sensibility of these sensitive structures. Such neuromuscular effects may explain the motor performance improvement. Another hypothesis stated by [START_REF] Jeannerod | The representing brain: neural correlates of motor intention and imagery[END_REF] is that the residual EMG activity might originate from an incomplete motor command inhibition, MI being in this way considered a tiny muscular contraction [START_REF] Bonnet | Mental simulation of an action modulates the excitability of spinal reflex pathways in man[END_REF].

Therefore, several data have suggested that MI is accompanied by EMG activity and even by specific selective muscle activation [START_REF] Bird | EMG quantification of mental rehearsal[END_REF][START_REF] Jowdy | Muscular responses during mental imagery as a function of motor skill level[END_REF][START_REF] Gandevia | Mental rehearsal of motor tasks recruits a-motoneurones but fails to recruit human fusimotor neurones selectively[END_REF][START_REF] Hashimoto | Dynamic changes in corticospinal excitability during motor imagery[END_REF]). These authors systematically recorded a subliminal activity, the magnitude of the activation being a fraction of that observed during actual performance (for review, see Guillot and Collet, 2005a). The content of the mental image has also been found to be reflected in the magnitude and location of EMG pattern. Internal imagery then results in significantly higher muscles excitation than external imagery [START_REF] Harris | The effect of skill level on EMG activity during internal and external imagery[END_REF][START_REF] Bakker | Changes in muscular activity while imagining weight-lifting using stimulus or response propositions[END_REF]. Furthermore, [START_REF] Bakker | Changes in muscular activity while imagining weight-lifting using stimulus or response propositions[END_REF] and [START_REF] Boschker | Action-based imagery: on the nature of mentally imagined motor actions[END_REF] have reported that during MI of a movement involving one arm, muscular activity was higher in the active than in the passive arm, and that imagining lifting a heavy object resulted in more EMG activity than that of a lighter object (9 kg vs. 4.5 kg). These data therefore suggest that the physiological responses reflect the spatial movement organization and the quantitative characteristics of MI.

However, the question of recording EMG activity during MI has received inconsistent responses, as muscular quiescence has also been observed during MI [START_REF] Yue | Strength increases from the motor program: comparison of training with maximal voluntary and imagined muscle[END_REF][START_REF] Lotze | Activation of cortical and cerebellar motor areas during executed and imagined hand movements: an fMRI study[END_REF][START_REF] Mulder | The role of motor imagery in learning a totally novel movement[END_REF][START_REF] Mulder | Observation, imagination and execution of an effortful movement: more evidence for a central explanation of motor imagery[END_REF][START_REF] Gentili | Improvement and generalization of arm motor performance through imagery practice[END_REF], even when using nuclear magnetic resonance spectroscopy [START_REF] Decety | Central activation of autonomic effectors during mental simulation of motor actions in man[END_REF]. Some researchers also required the absence of EMG activity as a precondition to perform a specific MI task [START_REF] Naito | Internally simulated movement sensations during motor imagery activate cortical areas and the cerebellum[END_REF][START_REF] Jackson | Functional cerebral reorganization following motor sequence learning through mental practice with motor imagery[END_REF]. They considered the absence of a significant increase in EMG activity as a proof that the pattern of cerebral activation observed during MI is not due to any movement. [START_REF] Li | The effect of motor imagery on spinal segmental excitability[END_REF] have recorded selective muscle activation in the finger flexors in only four of nine subjects during MI, such an observation being later confirmed by [START_REF] Dickstein | EMG activity in selected target muscles during imagery rising on tiptoes in healthy adults and poststroke hemiparetic patients[END_REF], during a tip-toe rising task. This latter finding was rather equivocal, as in their last experiment, increased pattern of EMG activity was sometimes, but not systematically recorded during MI, in both healthy subjects and poststroke hemiparetic patients. Furthermore, the EMG activity was also equivocal across all muscles involved during motor performance.

These inconsistent results may originate from methodological problems mainly. First, using surface vs. intramuscular EMG recordings might explain why EMG activity may be recorded in some cases but not in the others, although most experiments in this research area used surface EMG. Second, the EMG signal and its properties can be affected by the measurement technique and the experimental conditions, such as electrode geometry, distance between active fibers and electrodes, or location of the muscle (for review, see [START_REF] Kleissen | Electromyography in the biomechanical analysis of human movement and its clinical application[END_REF]. Finally, differences in the literature may be explained by individual variation in motor command inhibition and could also be related to the experimental conditions [START_REF] Jeannerod | The representing brain: neural correlates of motor intention and imagery[END_REF][START_REF] Jeannerod | Mental imaging of motor activity in humans[END_REF]. Furthermore, the deep muscle fibers are known to fire during the movement preparation mainly [START_REF] Mellah | Changes in excitability of motor units during preparation for movement[END_REF], and may belong to slow tonic group fibers, thus making their activity difficult to detect by a specific configuration of surface electrodes.

Despite these inconsistent results, the detection of an EMG activity during MI remains an acute field of research. The present study is designed to explore muscular responses during actual lifting a dumbbell and MI of the same movement. Our first aim is to investigate EMG activity of muscles acting as prime movers during actual movement, but also that of antagonist, synergist and fixator muscles, which are simultaneously involved in holding the weight. The second objective is to gain insight into EMG activity characteristics recorded during MI, as a function of muscle contraction types and load constraints (dumbbell weight). Despite the wide range of factors that may affect the EMG signal and inconsistent results described in the previous section, it was expected to record a subliminal EMG activity during MI, in the muscles usually involved during the physical execution of the task. Taken into consideration the EMG activity recorded during actual movement (e.g. [START_REF] Moritani | Activity of motor units during concentric and eccentric contractions[END_REF][START_REF] Fang | Greater movement-related cortical potential during human eccentric versus concentric muscle contractions[END_REF][START_REF] Mchugh | Differences in activation patterns between eccentric and concentric quadriceps contractions[END_REF], we further hypothesized that EMG amplitude would differ as a function of the muscle contraction type during MI, i.e. the highest amplitude being recorded during concentric contraction, the lowest amplitude during eccentric contraction, and the "intermediate" amplitude during isometric contraction. In addition, the concentric contraction intensity was also expected to be differentiated through EMG activity during MI, i.e. with a heavy vs. a light weight. Finally, among the various techniques used to investigate MI, and according to the aim of the experimental work, the combination of autonomic nervous system (ANS) recording, mental chronometry and well-established psychological tests was considered the most reliable methodological approach to investigate MI directly in the field (for review, see Guillot and Collet, 2005a). Consequently, an ANS response pattern should be recorded after each MI instruction, thus attesting to mental work quality during each trial [START_REF] Roure | Imagery quality estimated by autonomic response is correlated to sporting performance enhancement[END_REF][START_REF] Guillot | Relationship between visual vs kinaesthetic imagery, field dependence-independence and complex motor skills[END_REF]. Movement duration was expected to be overestimated during MI (Guillot and Collet, 2005b), but MI duration should not be dependent upon muscle contraction type.

Methods

Participants

Thirty right-handed sport students (15 men and 15 women, aged from 18 to 25, mean = 21.2 ± 2.4) gave their informed consent to take part in the experimental procedures, which were approved by the local research ethics board. By self-report, none had any neuromuscular disorder, and all were free of any recent muscular injury. They were not made aware of the purposes or hypotheses of the experiment until after test completion.

Experimental procedures

During a preliminary session, planned 7 days before the experiment, all participants performed an incremental test to determine their maximum lifting performance (1RM) when lifting a weighted dumbbell using a bending movement of the forearm on the arm. The best mark resulting from one repetition (concentric contraction) was considered the 1RM. The isometric maximal voluntary force (IMVC) was also measured using a strain gauge force transducer with the elbow joint positioned at 90°. Finally, participants completed the Revised version of the Movement Imagery Questionnaire (MIQ-R, [START_REF] Hall | Measuring movement imagery abilities: a revision of the movement imagery questionnaire[END_REF] in a quiet room. The MIQ-R is made up of 8 items known to evaluate individual differences in visual (4 items) and kinesthetic (4 items) movement imagery. Completing each item requires 4 steps. First, the starting position is described. Second, a specific arm, leg or whole body movement is precisely described and then, the participant is required to perform it. Third, each individual is asked to reassume the starting position and to imagine the movement, using visual or kinesthetic imagery alternatively (no actual movement is made). Finally, a score is assigned using a 7-point scale regarding the ease/difficulty associated with representing each movement mentally.

During the experimental procedure, participants were seated comfortably in an experimental chair after a specific warm-up including incremental dumbbell lifting exercises. To avoid any compensatory movement, participants were fixed against the chair (Fig. 1). The experimenter then attached the EMG and ANS electrodes, on the active arm and the non-dominant hand respectively. A goniometer (SG110, Biometrics) was also strapped to the active arm and forearm, to control the initial position and check that the elbow angle (90°) remained constant during MI (Fig. 1), thus attesting that the participant did not move. The participants were then asked to lift or to imagine lifting the weighted dumbbell. The four conditions were i) heavy concentric contraction (80% of 1RM), light concentric contraction (50% of 1RM), iii) isometric contraction (at least 95% of the maximal isometric strength) with the strain gauge force transducer, and iv) eccentric contraction (120% of 1RM). A total of 8 experimental conditions were therefore counterbalanced for each contraction type. Four actual and 8 imagined trials were performed for each experimental condition, i.e. a total of 16 actual movements and 32 MI trials. As each trial was preceded by a rest-period, EMG and ANS activities were also recorded during a total of 32 rest-periods, to be compared with those recorded during actual performance and MI.

During actual performance, participants were asked to lift the dumbbell as fast as possible (concentric contractions), to "lower" it as much as possible (eccentric condition), or to hold the isometric contraction during a 5s-period. During MI, they were instructed to imagine the movement as if they were actually performing it, using an association of internal visual and kinesthetic imagery. Specifically, this latter component appears essential, as highlighted by [START_REF] Neuper | Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG[END_REF]. Each trial was separated from the next by a rest period, which was never less than 20s, thus allowing ANS variables to recover basal levels and EMG recordings to keep stable baseline. Upon mental initiation of the first body movement and at the end of the sequence, athletes pressed a button with their non-dominant hand, to start and stop the timer. A MI script was developed to standardize the instructions. Each participant was read this script and therefore received the following instructions before the first MI trial: "Try to imagine yourself doing the motor sequence with your eyes closed by imagining the different movements, as if you had a camera on your head, and perceiving the body sensations. You see and feel only what you would if you actually performed this particular skill. Start the timer with your non-dominant hand, as soon as you have left the reference point (initial position), which means at the beginning of the first imagined movement. Imagine the entire sequence and stop the timer at the end of the contraction. Make sure you use imagery in the way that is most comfortable to you, but without moving your arm".

To check that they performed exactly the correct type of imagery, athletes were regularly asked to describe the nature of the images after MI. During this debriefing, athletes were also instructed to describe the difficulty they encountered to imagine the movement and then, to rate a score of MI quality, using a 4points scale. This was set as follow: 1 = very easy to imagine/feel and 4 = very difficult to imagine/feel (2 and 3 being intermediate levels).

Mechanical recordings

Imagined and actual durations were recorded by the experimenter, during each muscle contraction type, mean actual duration being considered the reference. In the concentric and eccentric muscle contraction conditions, the actual durations were calculated from the signal recorded, while using a goniometer (SG110, Biometrics) strapped to the active arm and forearm. In the isometric contraction condition, the subjects were asked to maintain over 90% of their IMVC (recorded during the preliminary session) during 5 s with the elbow joint positioned at 90°. The angle of the elbow joint and the magnitude of the force applied were controlled using the goniometer and the strain gauge force transducer, respectively. In the imagined contraction conditions, the goniometer was used to attest the absence of any elbow movement during MI.

Electrical recordings

Before placing the EMG surface electrodes, the skin was shaved, abraded and cleaned with alcohol swabs to improve the contact between the skin and the electrodes but also to reduce skin impedance. Raw EMG signals were simultaneously recorded with the Flex-comp Infinity system (Thought technology, Montreal, Canada), from the long head of the biceps brachii (LBB), short head of the biceps brachii (SBB), brachioradialis (BR), triceps brachii (TB), flexor carpi ulnaris (FCU), flexor carpi radialis (FCR), anterior deltoid (DEL), superior trapezius (TRA) and major pectoralis (PEC) muscles. Raw EMG signals were sampled at 2048 Hz on a computer before being further processed. Then the root mean squared value of the EMG signals (EMGrms) was calculated using a time averaging period of 25 ms. The mean EMGrms values of the nine muscles were calculated over the imagined and actual durations of the different contraction types.

Autonomic nervous system recordings were aimed at evaluating MI quality through more objective data than selfevaluation [START_REF] Roure | Imagery quality estimated by autonomic response is correlated to sporting performance enhancement[END_REF]. High brain functioning may be studied through ANS effector activity at a peripheral level [START_REF] Hugdahl | Cognitive influences on human autonomic nervous system function[END_REF] which anticipate and accompany behavior, thus representing non-conscious physiological mechanisms of central mental processes. Cutaneous autonomic effectors, e.g. sweat glands are innervated by sympathetic endings only, their activation eliciting sweat release and, consequently, a decrease in skin resistance. Several experiment demonstrated that MI quality may be estimated by ANS responses, especially through skin resistance [START_REF] Roure | Imagery quality estimated by autonomic response is correlated to sporting performance enhancement[END_REF][START_REF] Guillot | Relationship between visual vs kinaesthetic imagery, field dependence-independence and complex motor skills[END_REF]Guillot and Collet, 2005a). Instrumentation was designed by the Biomedical Microsensors and Microsystems Laboratory of the French National Institute of Applied Sciences [START_REF] Dittmar | A multi-sensor system for the non-invasive measurement of the activity of the autonomic nervous system[END_REF]. Signal sampling was carried out at a frequency of 10 Hz. Skin resistance was recorded using two 30 mm 2 unpolarizable Ag/AgCl electrodes (Clark Electromedical Instruments, Ref. E243) placed on the second phalanx of the second and third digits of the non-dominant hand, and held by adhesive tape [START_REF] Fowles | Publication recommendations for electrodermal measurements[END_REF]. Skin resistance was measured with 15 μA current (current density = 0.5 μA/mm 2 ) ,a n da ni s o t o n i c conductive paste (TECA ref: 822-201210) was used to improve skin/electrode contact As response amplitude depends on the pre-stimulation value [START_REF] Furedy | The law of initial values: differentiated testing as an empirical generalization versus enshrinement as a methodological rule[END_REF], a more reliable index was defined without referring to the initial value (tonic level). The Ohmic Perturbation Duration (OPD) is measured at the beginning of the sudden drop elicited by MI, and ends when the slope, while recovering basal level, shows no more fluctuations, and when the slope is similar to that observed before the stimulation (Vernet-Maury et al., 1995).

Data analysis

The first step of statistical EMG analysis was to test whether a difference may be observed between males and females by using a Multivariate analysis of variance (MANOVA). As the male/female comparisons did not reach the 5% threshold, male and female data were pooled and the variable related to sex was no more taken into account. A standard MANOVA test was thus performed for the 8 variables (2 movement conditions, i.e. actual performance and MI and 4 types of muscles contraction as previously described). All statistical computations were made by using a transformation (1 / y) to fit the usual normal distribution assumptions. To explore the structure of the variables, these were re-expressed using a contrast H matrix in terms of derived variables leading to better distinguish the main single effects (movement conditions and types of contraction) from their interaction (conditions ⁎ types of contraction). A suitable H matrix produced a first derived variable (not of interest here) giving the overall level of the responses. A second derived variable summarized the main effect of conditions, while 3 derived variables (3, 4 and 5) measured the main effect of the types of contraction, the 3 last ones (6, 7 and 8) summarizing the interaction effect (incidentally, this H matrix was exactly the design and analysis of the example 2.2 in the chapter about MANOVA of [START_REF] Hand | Practical Longitudinal Data Analysis[END_REF]. The nullity of the interaction was first tested by using the Hotelling's T2-test on the 3 last derived variables. Then, we examined the main effect of types of contraction by the same multivariate Hotelling's test applied to the derived variables 3, 4 and 5. In a first part, only the long biceps was considered as this is the main muscle involved in the elbow flexion. In a second part, the other muscles are considered, using the same analysis.

MIQ-R scores, ANS responses (OPD), and both physical execution and MI durations comparisons were made using Student's paired t-tests. The results are presented as mean (standard deviation values), the alpha level being set at p b 0.05.

Results

Behavioral results

The mean MIQ-R score was 43.97 (5.9). No difference was found between males and females (t = 0.641, p N 0.05, NS), mean scores being 43.27 (5.91) and 44.67 (6.04) respectively. When considering the visual and kinaesthetic subscales, the mean scores were 23.94 (3.5) and 19.87 (3.8), respectively. During the debriefing following the MI sessions, participants reported that they used the MI outlined in the script, without any switch between external and internal MI. None reported changing the imagery script to suit individual needs, but rehearsed the motor sequence as they were requested to do. The evaluation of self-reported MI accuracy did not show any difference among the experimental conditions (F =2.92, p N 0.05, NS). Similarly, these scores did not change across trials. Mean values (SD) for the 4-points scale for heavy concentric, light concentric, isometric and eccentric contraction conditions were 2.6 (0.7), 2.5 (0.7), 2.9 (0.7) and 2.8 (0.8), respectively.

The average time difference between the actual and the imagined trials was compared for each type of muscle contraction. Mean durations of actual concentric contractions were 1.21 s (0.2) and 0.78 s (1.1) for the heavy and light weights respectively. Imagined concentric contraction times were longer than actual times (2.38 s (0.1) and 1.62 s (1.1) respectively). The comparisons between imagined and physical times reached significance (t = -5.5, p b 0.001 and t = -3.96, p b 0.001). Under the 5 s isometric condition, actual and MI times did not differ (t = 0.3, p N 0.05, NS), mean MI duration being 4.89 s (2). Similarly, in the eccentric condition, MI times did not differ from physical execution times (t = 0.5, p N 0.05, NS), mean durations being 4.25 s (2.2) and 4.02 s (1.9) respectively. Interestingly, there was no significant gender difference, whatever the muscle contraction condition.

The goniometer measured the elbow angles during both the rest and the MI conditions with the aim to check whether during MI, subjects were able to keep the precise angular position that they were requested to (elbow angle of 90°), and thus to prevent any movement during imagined contractions. No significant difference was found (t = 1.39, p N 0.05, NS), average values being 0.52°(0.2) and 0.55°(0.2) during the MI and rest conditions respectively.

EMG activity during MI

All statistical computations were made by using normalized data, i.e. 1/y. When considering the EMG activity of the long head of the biceps brachii, the main muscle involved in the elbow flexion, the gender comparison did not reach the significant threshold (F (8, 28) = 2.11, [Pillai = 0.45], p N 0.05). Hence, males and females data were pooled. Similarly, none of the EMG activity provided differences between males and females for the 8 other muscles. All p-values were upper than the significant threshold. The forthcoming statistical test then used the Hotelling T2. The aim was to create derivate variables to separate interactions from simple effects (2 movement condi-tions [MI and rest condition] ⁎ 4 types of muscle contractions). The matrix of transformations H yielded to 8 derivate variables from the 8 first variables. The 3 last variables were aimed to test interaction. With reference to the matrix, it was verified whether the 3 variables showed a mean different from (0, 0, 0): T2 = 8.1, p = 0.08. As the critical probability was 8%, we considered that no interaction occurred among the 3 variables. Thus, the effect of movement conditions on the one hand, and type of muscle contraction on the other, were tested as separate variables.

The Hotelling T2 test showed a marginally effect of the muscle contraction type condition for the long head of the biceps brachii, as shown by Fig. 2 (T2 = 8.23, p = 0.07). However, the light concentric contraction condition elicited less muscular activity than the heavy concentric contraction. In addition, the eccentric condition elicited the significant weakest muscular activity (Fig. 2).

When considering EMG recordings of the 8 remaining muscles, none of the 8 p-values reached the significant threshold, although few of them indicated a marginally significant p-value (flexor carpi ulnaris, p = 0.09 and major pectoralis, p =0.06). Consequently, the effect of each condition was investigated. A significant effect of the contraction type was found in the anterior deltoidus ( p = 0.03), brachioradialis ( p = 0.01), flexor carpi ulnaris ( p = 0.002), and flexor carpi radialis levels ( p b 0.001). In addition, the Hotteling T2 test provided a marginally significant difference in the major pectoralis ( p = 0.08). The weakest muscle activity was provided during eccentric MI of lifting the weight. There was also a strong difference between the two concentric conditions, heavy concentric contraction eliciting higher muscular activity than light concentric contraction. At last, no clear difference emerged from the comparison of heavy concentric and isometric contractions.

EMG activity during MI vs. rest

Again, all statistical computations were made by using normalized data, i.e. 1/y. The statistical analysis revealed a greater EMG activity recorded during MI than during the rest condition. Such difference was also observed in the other 8 muscles, as Fig. 2. EMG activity of the long head of the biceps brachii (1 / y) as a function of the type of muscle contraction. The eccentric contraction elicited the weakest EMG activity whereas the heavy concentric contraction elicited the highest. Further, there was a significant difference between the MI of the heavy concentric muscular contraction compared with the light concentric muscular contraction. shown by Table 1. To emphasize data reliability, it was checked whether participants remained motionless during the MI sequences (at least that they did not show more muscular activity than during the rest control condition). Thus, goniometric data were compared to check that there was no significant difference between the rest and the MI conditions. Greater EMG activity were recorded in the long head of the biceps brachii during MI than during the rest condition, average normalized values being 0.82 mV (0.4) and 0.69 mV (0.4) respectively. It is noticeable that the 4 MI conditions were considered in this comparison. Similar results were obtained in each muscle recording (Fig. 3). When analyzing each type of muscle contraction in a separate way, similar results were observed. The EMG activity of the long head of the biceps was significantly greater during MI than during the rest condition the heavy concentric (t = 2.8, p b 0.01), the light concentric (t = 2.4, p b 0.02), the isometric (t = 2.5, p = 0.016) and the eccentric conditions (t = 2.6, p = 0.014). These statistical differences were also obtained when analyzing the data of all muscles, with the exception of the short head of the biceps brachii and triceps brachii during the eccentric condition, the flexor carpi radialis during the isometric condition, and the trapezius, flexor carpi ulnaris and radialis during the heavy concentric condition.

EMG activity during physical execution

A significant difference was found between males and females EMG in the long head of the biceps brachii (Pillai = 0.565, F(4,18) = 5.85, p b 0.01). Mean males EMG values were 1001 mV, 832 mV, 862 mV, and 696 mV in the isometric, heavy concentric, eccentric and light concentric conditions respectively. Weaker EMG values were recorded in women (479 mV, 478 mV, 421 mV, and 332 mV, respectively). Comparing each type of contraction to each other provided evidence of a systematic effect of the type of contraction, showing that the EMG activity recorded during the light concentric contraction was weaker than those recorded during other types of contraction (Table 2). Further, in women, a tendency was also observed when comparing the EMG activity recorded during the heavy concentric and the eccentric conditions (t =2.02, p =0.07).

When considering the other 8 muscles, different patterns of EMG activity were recorded, so that no clear conclusion may be drawn. In the male group, for example, greater EMG values were recorded during the isometric condition when compared to other types of contraction, except in the short head of the biceps brachii and the deltoïdus activity (greater EMG activity during the eccentric than during the isometric condition), as in the trapezius and the deltoïdus activity (greater activity during the heavy concentric than during the isometric condition). Similarly, the weakest EMG activity was recorded during the light concentric contraction, with the exception of the brachioradialis activity (greater activity during the eccentric than during the light concentric condition). Finally, no significant difference was observed between the heavy concentric and the eccentric conditions, except in the activity of the flexor carpi ulnaris. In the female group, no significant difference was recorded between the isometric condition and the other types of contraction, as between the eccentric and the heavy concentric Table 2 Comparison of the EMG activity as a function of muscle contraction type A systematic effect of muscle activity was observed in a way that the EMG activity recorded during both the light concentric and the eccentric contraction was significantly weaker than that recorded during other types of contraction both in men and women. Fig. 3. EMG activity (1 / y) during MI and rest conditions. The MI condition was shown to elicit a higher EMG activity than that recorded during the rest condition in all muscles.

conditions. By contrast, the weakest EMG activity was recorded during the light concentric condition, with the exception of the brachioradialis, the short head of the biceps brachii and the trapezius, when compared to the eccentric condition.

ANS activity

Skin resistance responses (measured through the OPD index) were recorded during both physical and imagined contractions. During MI, the responses were significantly shorter than those recorded during actual practice in all muscle contraction conditions ( p b 0.001), as shown by Table 3. As MI quality was estimated through ANS responses, especially skin resistance responses [START_REF] Guillot | Relationship between visual vs kinaesthetic imagery, field dependence-independence and complex motor skills[END_REF]Guillot and Collet, 2005a;[START_REF] Roure | Imagery quality estimated by autonomic response is correlated to sporting performance enhancement[END_REF], good and poor MI trials were differentiated with respect to the OPD of each MI trial. However, the EMG activity did not statistically differ between these "correct and incorrect" MI trials, with the exception of the long head of the biceps brachii during light concentric contraction (t =2.39, p b 0.05) and the brachoradialis during heavy concentric contraction (t = 3.5, p b 0.001). In these muscles, the mean EMG activity was significantly greater during correct than during incorrect MI trials (4.31 (4.2) and 2.97 (3.1) in the biceps brachii activity, 3.55 (1.8) and 2.74 (1.6) in the brachioradialis activity). Finally, the EMG activity tended to be significantly greater during correct heavy concentric than during incorrect MI trials in the long head of the biceps brachii activity (t =1.77,p = 0.09), mean values being 4.88 (4.6) and 3.53 (3.12) respectively.

Discussion

The present study was designed to investigate the muscular responses during the actual lifting of a dumbbell and the MI of the same movement. Two main independent variables were considered, the type of muscle contraction and the weight to lift. The main result was that MI was accompanied by a subliminal specific EMG activity of muscles acting as prime movers during actual movement, but also that of antagonist, synergist and fixator muscles. Further, the statistical analysis revealed a greater EMG activity during MI than during the rest condition, whatever the gender, the muscles and the type of contraction. In addition, there was an effect of the weight being mentally lifted under the concentric condition, and the eccentric condition was found to elicit a significant weakest muscular activity than during all other conditions, hence suggesting that the low magnitude of the physiological response may interact with the type of contraction being imagined. These results provide further evidence that MI and motor execution share common neural mechanisms. This was confirmed through mental chronometry as no difference emerged from actual and imagined duration, perhaps with the exception of the concentric conditions. This latter difference is easily explained with reference to the very short actual duration (less than 1.5 s), as rapid movement duration is always overestimated during MI (Guillot and Collet, 2005b).

First, our results support the previous findings by [START_REF] Bakker | Changes in muscular activity while imagining weight-lifting using stimulus or response propositions[END_REF] and [START_REF] Boschker | Action-based imagery: on the nature of mentally imagined motor actions[END_REF] who found that the MI of lifting 9 kg weights resulted in a larger EMG activity in the active arm than the MI of lifting 4 kg 1/2. In the present study, the MI of a heavy concentric contraction (80% of 1RM) resulted in a greater pattern of EMG activity than during MI of a light concentric condition (50% of 1RM). Especially, the association of visual internal and kinesthetic imagery agrees with the assumptions stating that a larger EMG activity is recorded when subjects use internal rather than external visual imagery [START_REF] Hale | The effects of internal and external imagery on muscular and ocular concomitants[END_REF][START_REF] Harris | The effect of skill level on EMG activity during internal and external imagery[END_REF]. Although some authors have reported that the EMG activity was necessarily equivocal across all muscles that contract during actual performance [START_REF] Dickstein | EMG activity in selected target muscles during imagery rising on tiptoes in healthy adults and poststroke hemiparetic patients[END_REF], we recorded an overall activation in the 9 muscles being monitored. These results corroborates the view stating that the content of the mental image (i.e. the intensity of the contraction) may be related to the magnitude of the subliminal EMG activity, which is thought to partially reflect, in turn, the quantitative characteristics of this mental image. As outlined by [START_REF] Boschker | Action-based imagery: on the nature of mentally imagined motor actions[END_REF], however, it still remains an empirical question to find out how detailed the EMG response mirrors the image. Especially, such a difference during MI may also be linked to the relationship between physical effort and perceived exertion arising from the tacit knowledge of how difficult is the heavy contraction by comparison to the light condition. Altogether, such a modification in the EMG activity during MI seems more than an epiphenomenon, even though it remains difficult to explain. According to [START_REF] Jeannerod | The representing brain: neural correlates of motor intention and imagery[END_REF] and [START_REF] Bonnet | Mental simulation of an action modulates the excitability of spinal reflex pathways in man[END_REF], this physiological response is supposed to result from an incomplete inhibition of the motor command.

Second, our results provide evidence, for the first time, of an effect of the contraction type. The EMG activity was found larger during the heavy concentric contraction than during both the light concentric and eccentric conditions. More, the pattern of EMG activity recorded during imagined eccentric contractions was systematically weaker than those observed during all other types of contraction. Interestingly, these results are in keeping with previous literature findings showing that the EMG activity recorded during actual eccentric contractions is weaker than that observed during concentric and isometric contractions [START_REF] Moritani | Activity of motor units during concentric and eccentric contractions[END_REF][START_REF] Tesch | Force and EMG signal patterns during repeated bouts of concentric or eccentric muscle actions[END_REF][START_REF] Fang | Greater movement-related cortical potential during human eccentric versus concentric muscle contractions[END_REF][START_REF] Mchugh | Differences in activation patterns between eccentric and concentric quadriceps contractions[END_REF]. This difference may be explained by a fewer activation of the motor units during eccentric than during concentric contractions at the same force, more fast-twitch motor units being however activated during actual eccentric contractions. Even though a spectral analysis should confirm this hypothesis, a similar physiological mechanism may occur during MI. In contrast, while previous studies investigating physical execution have shown that a greater EMG activity was recorded during concentric than during isometric or during eccentric contractions [START_REF] Komi | Force and EMG power spectrum during eccentric and concentric actions[END_REF][START_REF] Linnamo | Maximal force during eccentric and isometric actions at different elbow angles[END_REF], we found that the EMG activity was greater during actual isometric than during heavy concentric contractions. This difference may result in the difference between the designs of the two studies: participants were requested to lift 80% of 1RM in our experiment while previous studies investigated maximal voluntary contractions. Finally, our results support the findings by [START_REF] Komi | Force and EMG power spectrum during eccentric and concentric actions[END_REF] and [START_REF] Linnamo | Maximal force during eccentric and isometric actions at different elbow angles[END_REF] with regards to MI, as the pattern of activity during imagined contractions was greater during concentric than during isometric conditions. The sensation of effort, which has usually been reported during MI [START_REF] Decety | Effect of brain and spinal cord injuries on motor imagery[END_REF][START_REF] Decety | Sensation of effort and duration of mentally executed actions[END_REF], may explain these results, especially when subjects have to imagine lifting a heavy weighted dumbbell.

The outcomes also indicated that the pattern of EMG activity was recorded in all muscles involved during the physical flexion of the forearm, i.e. the agonist muscles but also the antagonists, synergists and muscles with a fixation function. Agonists create the range of movement in a joint by contracting while the antagonists act in opposition about the axis of the joint. The antagonists which show eccentric activity have often been found activated during MI (e.g. [START_REF] Bakker | Changes in muscular activity while imagining weight-lifting using stimulus or response propositions[END_REF][START_REF] Boschker | Action-based imagery: on the nature of mentally imagined motor actions[END_REF][START_REF] Slade | Muscular activity during real and imagined movement: a test of inflow explanations[END_REF]. Interestingly, the synergists and the muscles with a fixation function, eliciting single muscle action to reinforce the agonist activity and concerted activation of several muscles respectively, were also activated during MI, in our experiment. This result emphasizes the functional equivalence between MI and motor performance, which has already been well-established with regards to the central and peripheral functional equivalence (e.g. [START_REF] Decety | Mapping motor representation with positron emission tomography[END_REF][START_REF] Pfurtscheller | Motor imagery activates primary sensorimotor area in humans[END_REF][START_REF] Holmes | The PETTLEP approach to motor imagery: a functional equivalence model for sport psychologists[END_REF]Guillot and Collet, 2005a;Guillot et al., submitted for publication), as the behavioral (temporal) equivalence (Guillot and Collet, 2005b). Even though earlier studies found a functional equivalence within the EMG activity [START_REF] Jacobson | Electrical measurements of neuromuscular states during mental activities[END_REF][START_REF] Shaw | The relation of muscular action potentials to imagine weight lifting[END_REF], none has reported an activation of all muscles involved in the production of the actual movement.

The EMG activity recorded during the MI of lifting weighted dumbbells was shown to mirror actual execution, and to distinguish among the different types of contraction elicited by the movement under actual conditions of execution. This lends support that this activity originates from the high central nervous system. Moreover, as EMG activity has been sometimes, but not systematically, recorded during MI [START_REF] Dickstein | EMG activity in selected target muscles during imagery rising on tiptoes in healthy adults and poststroke hemiparetic patients[END_REF], it seems not reasonable to explain the effects of MI on motor performance and learning in terms of bottom-up mechanisms only [START_REF] Mulder | The role of motor imagery in learning a totally novel movement[END_REF]. These authors thus argued that it was more plausible to explain such effects in terms of a top-down mechanism based on the activation of a central representation of the movement [START_REF] Mulder | Observation, imagination and execution of an effortful movement: more evidence for a central explanation of motor imagery[END_REF]. They also interpreted that the occurrence of peripheral activity in other studies could not be satisfactorily explained by such a central theory. However, central operations (planning and program-ming) are paralleled by peripheral responses such as ANS responses which anticipate and accompany behavior, thus representing non-conscious physiological mechanisms of central mental processes [START_REF] Collet | Programming or inhibiting action: autonomic nervous system control of anticipation[END_REF][START_REF] Plascencia-Alvarez | Effect of muscle contraction on heart rate and skin blood flow. Preliminary results[END_REF][START_REF] Sequeira | Pyramidal control of heart rate and arterial pressure in cats[END_REF]. Therefore, these physiological responses appear compatible with a central origin of MI effects. More, the occurrence of a pattern of EMG activity during mental practice has been interpreted as an incomplete inhibition of the motor output [START_REF] Jeannerod | The representing brain: neural correlates of motor intention and imagery[END_REF][START_REF] Bonnet | Mental simulation of an action modulates the excitability of spinal reflex pathways in man[END_REF]. Especially, the cerebellum is thought to be involved in the inhibition of movement execution during MI, the inhibitory process being possibly controlled by the Larsell lobule HVII ipsilateral to the movement [START_REF] Lotze | Activation of cortical and cerebellar motor areas during executed and imagined hand movements: an fMRI study[END_REF]. Furthermore, the experimental data providing evidence of substantial increase in muscle strength through MI, as mentioned in the introduction, lend also support to the central explanation theory of MI. These results were explained with reference to the neural origin of strength gain, hence driving the motor units to a higher intensity and/or leading to the recruitment of motor units that are otherwise inactive, without any peripheral changes such as hypertrophy. Similar neural reorganizations are supposed to explain the limit of strength loss during immobilization [START_REF] Newsom | Use of mental imagery to limit strength loss after immobilization[END_REF]. It may thus be concluded that the effect of MI may be explained by the cerebral and cerebellar plasticity following mental practice [START_REF] Lafleur | Motor learning procedures parallel dynamic functional changes during the execution and the imagination of sequential foot movements[END_REF][START_REF] Lacourse | Cerebral and cerebellar sensorimotor plasticity following motor imagery-based mental practice of a sequential movement[END_REF], hence resulting from a central origin.

Finally, no clear difference was found when comparing the EMG activity recorded during correct and incorrect trials, i.e. when MI was considered, or not, being vivid, with the exception of the agonist muscles (the long biceps brachii in our experiment). Although there was the same tendency in some other muscles towards a larger pattern of EMG during correct trials, the present results do not support the hypothesis stating that subjects who produce more specific patterns of muscular activity are better imagers than those who produce less specific patterns of muscular activity. This finding thus agrees with previous literature data that failed to find a relationship between MI ability and EMG activity [START_REF] Hale | The effects of internal and external imagery on muscular and ocular concomitants[END_REF][START_REF] Hecker | Application of imagery theory to sport psychology: some preliminary findings[END_REF][START_REF] Jowdy | Muscular responses during mental imagery as a function of motor skill level[END_REF][START_REF] Boschker | Action-based imagery: on the nature of mentally imagined motor actions[END_REF], with the exception of the paper by [START_REF] Lutz | Covert muscle excitation is outflow from the central generation of motor imagery[END_REF]. Conversely, this is probably not the case when central nervous system activity is considered (Guillot et al., submitted for publication). Consequently, the recording of EMG activity may be considered a reliable indicator of MI accuracy with reference to the EMG activity of agonist muscles only (Guillot and Collet, 2005a).

To conclude, the present study shows that a pattern of EMG activity was recorded during MI in all muscles interested in the movement we considered, as a function of the weight to be lifted and to the muscle contraction type. This pattern of muscular response was found to overall mirror the configuration of the EMG activity recorded during different types of physical contraction, even though the magnitude was strongly weaker during MI, as expected. Such equivalence still needs, however, further experimental investigation, using spectral analysis, for example to check whether the configuration of activation of the motor units is similar during MI and motor execution. Altogether, these data lend support to the high central nervous system origin of that activity, hence suggesting that the effect of MI may be explained by the central nervous system plasticity following mental practice.

Fig. 1 .

 1 Fig.1. Measurement equipment and subject positioning. The part of the goniometric system located in the forearm is not visible in this drawing.

Table 1

 1 Mean EMG activity recorded during the rest and motor imagery conditions

		Mean rest EMG	Mean MI EMG	P-value
		activity (1 / y)	activity (1 / y)
	Biceps brachii	0.82	0.69	b0.001
	(long head)
	Biceps brachii	0.79	0.66	b0.0001
	(short head)
	Anterior deltoidus	0.66	0.59	b0.0001
	Superior trapezius	0.28	0.26	b0.01
	Triceps brachii	0.86	0.78	b0.01
	Brachioradialis	0.68	0.51	b0.01
	Flexor carpi ulnaris	0.83	0.73	b0.02
	Flexor carpi	0.73	0.65	b0.01
	radialis
	Major pectoralis	0.35	0.32	b0.002
	Results clearly indicated higher, although subliminal, muscular activity during
	MI than during rest.

Table 3

 3 Ohmic perturbation duration recorded during physical execution and motor imagery Contraction type Condition OPD (s) Standard deviation tp

	Heavy concentric Actual	5.26	2.1	7.14 b0.001
		MI	2.13	1.11
	Light concentric Actual	4.86	2.38	5.98 b0.001
		MI	1.48	1.2
	Isometric	Actual	7.91	2.38	11.4 b0.001
		MI	2.21	1.33
	Eccentric	Actual	9.04	2.38	12.7 b0.001
		MI	1.26	0.89
	ANS responses recorded during MI were found to be significantly shorter than
	those recorded during physical execution.


Théophile T Azomahou
email: theophile.azomahou@aercafrica.org

Njuguna Ndung'u A

Mahamady Ouédraogo

Coping with a dual shock: The economic effects of COVID-19 and oil price crises on African economies *

Keywords: COVID-19, oil-dependence, growth forecast, Africa. JEL Codes: I15, O47, P28

Oil-dependent countries face a twin-shock: in addition to the COVID-19 outbreak, they are facing an oil price collapse. In this paper, we study the impact of this dual shock on the forecasted GDP growth in Africa using the COVID-19 outbreak as a natural experiment. We use the IMF World Economic Outlook's GDP growth forecasts before and after the outbreak. We find that COVID-19 related deaths result in -2.75 percentage points forecasted GDP growth loss in the all sample while oil-dependence induces -7.6 percentage points loss. We document that the joint shock entails higher forecasted growth loss in oil-dependent economies (-10.75 percentage points). Based on oil price forecasts and our empirical findings, we identify five recovery policies with high potential: social safety net policy, economic diversification, innovation and technological transformation, fiscal discipline, and climate-friendly

Introduction

The COVID-19 outbreak is inducing an unprecedented economic crisis in Africa even though the continent remains the least affected in terms of recorded cases. 2 According to the April 2020 forecast of the IMF, the continent's GDP is expected to decrease by 1.6 percent in 2020, the lowest on record and 5.2 percentage points drop compared to the October 2019 forecast (IMF, 2020). It would require three times the 2018' Official Development Aid (ODA) for Sub-Saharan African countries to fully recover from the COVID-19 shock [START_REF] Adam | After the lockdown: Macroeconomic adjustment to the COVID-19 pandemic in Sub-Saharan Africa[END_REF].

Worldwide, no country will be spared; and the economic and social consequences remain uncertain. However, the magnitude of the effect will be disproportionate both within and between countries and regions [START_REF] Furceri | Will COVID-19 affect inequality? Evidence from past pandemics[END_REF]. The impacts of the crisis depend, among others, on the pre-COVID-19 economic conditions and governance systems. While some sectors may experience either a supply or demand shock, others will experience both shocks (del [START_REF] Del Rio-Chanona | Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective[END_REF]. African economies in general and oil-dependent economies, in particular, are at the heart of this asymmetry.

Oil-dependent countries face a twin-shock: the current health crisis and its attendant impact on their economy and a collapsing oil price. Not only does the pandemic induces a negative oil price for the first time in history, it also creates uncertainty around the future of the oil economy.

The COVID-19 crisis predominantly impacted the carbon-intensive industries (fossil fuel industry and transport among others) [START_REF] Mukanjari | Charting a "green path" for recovery from COVID-19[END_REF]. A major shift in investment from carbon-intensive sectors to climate-friendly sectors is expected [START_REF] Dutta | COVID-19 and oil market crash: Revisiting the safe haven property of gold and Bitcoin[END_REF]. The investment in the oil and gas industry cut down by around $30 billion in 2020. 3 This time is an opportunity to push forward the climate agenda [START_REF] Hepburn | Will COVID-19 fiscal recovery packages accelerate or retard progress on climate change?[END_REF]. Therefore, oil-dependent economies have to deal with a permanent shock beyond the initial price collapse. Since the onset of the health emergency, the call for green recovery measures is strengthening [START_REF] Mukanjari | Charting a "green path" for recovery from COVID-19[END_REF]. As William McDonough stresses early in 2005, "the Stone Age did not end because humans ran out of stones. It ended because it was time for a re-think about how we live". 4 The Oil Age may not end because the world ran out of oil. Also, oil-intensive countries5 in Africa are vulnerable and less resilient to shocks for at least three reasons. Firstly, their economies are poorly diversified and the oil price collapse will induce a substantial decline in export revenues. Secondly, oil revenue constitutes an important share of the government budget in oil-dependent countries. Since the health crisis requires major urgent fiscal responses, and oil price collapse has a significant impact on government revenue, countries will have less fiscal space to adequately respond to the crises. Thirdly, natural resource-rich countries in Africa, specifically oil-dependent economies, tend to have weaker governance and less fiscal discipline [START_REF] Busse | The resource curse revisited: Governance and natural resources[END_REF][START_REF] Knutsen | Mining and local corruption in africa[END_REF][START_REF] Ross | What have we learned about the resource curse?[END_REF]. In fact, African oil-dependent countries had little fiscal space even before the COVID-19 outbreak.

This paper aims to shed some light on the impact of COVID-19 outbreak and the oil price shock on economic growth in Africa. Specifically, it investigates the effect of the COVID-19 shock, the oil price shock on the forecasted GDP growth on the one hand, and the joint shock on the other hand. The paper is related to two strands of the previous literature. The first strand is the literature on the effects of a pandemic on economic growth, which mostly focuses on developed countries. Evidence from this literature shows that a pandemic can result in a substantial growth loss [START_REF] Barro | The coronavirus and the great influenza pandemic: Lessons from the "spanish flu" for the coronavirus's potential effects on mortality and economic activity[END_REF][START_REF] Beach | The 1918 influenza pandemic and its lessons for COVID-19[END_REF][START_REF] Carillo | Pandemics and local economic growth: Evidence from the great influenza in Italy[END_REF][START_REF] Dahl | The 1918 epidemic and a v-shaped recession: Evidence from municipal income data[END_REF]. [START_REF] Barro | The coronavirus and the great influenza pandemic: Lessons from the "spanish flu" for the coronavirus's potential effects on mortality and economic activity[END_REF] using panel-data from 43 countries from 1901 to 1929 find that the Influenza pandemic resulted in a 6% real GDP per capita decline. [START_REF] Carillo | Pandemics and local economic growth: Evidence from the great influenza in Italy[END_REF] using Italian municipality data find that the 1918 Great Influenza had a strong and significant adverse effect on regional growth. The most affected Italian regions by Influenza experienced a 6.5% decline in real GDP compared to the least affected regions. [START_REF] Dahl | The 1918 epidemic and a v-shaped recession: Evidence from municipal income data[END_REF] find similar results using 76 Danish municipality data. Evidence on developing countries is scant and the existing studies on COVID-19 rely on a single-country and simulation model based on different scenarios of COVID-19 [START_REF] Adam | After the lockdown: Macroeconomic adjustment to the COVID-19 pandemic in Sub-Saharan Africa[END_REF][START_REF] Kinda | How could the COVID-19 pandemic impact the economy of Burkina Faso[END_REF]. In this paper, we investigate the effect of the COVID-19 pandemic in addition to an oil shock on a sample of developing countries in Africa.

The paper is also related to the well-known literature on the resource curse [START_REF] Sachs | The curse of natural resources[END_REF], and particularly the vulnerability of oil-dependent countries to price shocks (Van der Ploeg and Poelhekke, 2009).

Based on our difference-in-differences strategy, we find that COVID-19 shock results in -2.75 percentage points forecasted GDP growth loss in the all sample while oil-dependence induces -7.60 percentage points loss. The joint shock engenders -10.75 percentage points forecasted GDP growth loss, supporting our intuition that the shock is dual and more severe in oil-dependent countries in Africa. The results are robust to alternative measure of oildependence.

The remaining of the paper is organized as follows: The second section discusses the pre-COVID-19 macroeconomic conditions in Africa, presents briefly the history of oil shock episodes and why this time could be different. The third and fourth sections respectively describe the data and our empirical strategy. The fifth section lays out the results. The sixth section undertakes out a robustness analysis. The last section concludes and draws policy recommendations.

African economies under the wake of COVID-19 crisis

COVID-19 in Africa

Despite the continent poor health system, 6 Africa remains the least affected in terms of the number of confirmed cases and fatalities. As of 19th May 2020, the continent has 88 700 confirmed cases of COVID-19 roughly representing 66.17 confirmed cases per million people. The cases are still rising in some countries and the statistics are fragile due to limited testing capacities in many countries but, the trends show that the pessimistic predictions made earlier will fortunately not materialize. The reasons for these seemingly unexpected lower cases are unknown. Although, some speculations are rife that the weather conditions, the demographic structure (relatively younger population), the low urbanization rate, and the lack of public transport play a critical role [START_REF] Chitungo | COVID-19: Unpacking the low number of cases in Africa[END_REF][START_REF] Lawal | Africa's low COVID-19 mortality rate: A paradox?[END_REF]. 7 In some sense, one can say that Africa's relative deprivation saved people's life (at least for the direct impact). Table 1 shows the debt, oil revenue as a share of government fiscal revenue, revenue from oil export as a share of total export, and the average GDP growth over the last ten years (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019) in Africa on average as compared to the oil-dependent countries.

The government debt data reveal that some countries (Angola, the Republic of Congo, and Gabon) are already highly indebted. This situation limits countries' fiscal space at a time where governments are in dire need to respond to both the health as well as the looming economic and social crisis.

Debt sustainability and financial stability could thus be problematic in these countries. Furthermore, previous debt relief efforts have been jeopardized.

The current international solidarity, including the debt service suspension ini- tiative, may be insufficient to compensate previous limited fiscal discipline.9

Oil revenue represents a large share of the government's fiscal revenue in all oil-dependent economies in Africa. It represents more than 60% in all the seven countries for which the data are available and even reaches 95% in South Sudan. Oil revenue collapse will significantly affect the government's revenue and hence undermine its ability to recover. As long as it takes for the oil price to restore, governments' budgets in oil intensive countries will be substantially affected.

Oil export revenue also constitutes a large share of total export revenue in oil-intensive countries. This makes these economies vulnerable to oil price instability. Since their export basket is concentrated and with oil price decreasing, oil-dependent economies may run out of foreign currency to cover its import. This may result in the current account deficit and financial crisis.

The average GDP growth in the previous decade shows that some countries (Equatorial Guinea, South Sudan) were already in recession before the outbreak even though the oil price was relatively high. The growth rate was lower than 3% in most of the oil-dependent countries. This economic gloom before the crisis limits countries' capacity to respond at a hard time.

Governance

Governance is key to the response to the COVID-19 pandemic. Figure 3 displays the evolution of the average control of corruption in oil-dependent countries compared to other countries. The trend shows, not only does the control of corruption is sharply declining in oil-intensive countries, it also illustrates that control of corruption is much lower than in other countries.

Data on government effectiveness display similar patterns (Figure 4).

While good governance is crucial to an effective response to the health crisis and the post-COVID-19 economic stimulus policy, oil-dependent economies in Africa exhibit lower and declining institutional quality.

- At the beginning and the end of the 1990s, oil price also experiences two downward shocks. First, crude oil price slightly decreases in 1990-1991 due to the First Persian Gulf War [START_REF] Kilian | Exogenous oil supply shocks: How big are they and how much do they matter for the US economy?[END_REF]. Then, the Asian financial crisis started in the summer of 1997 in countries such as Thailand, South Korea, increasing investors' doubt about Asian growth prospects and oil price collapse, falling below $12 per barrel by the end of 1998 [START_REF] Hamilton | Historical oil shocks[END_REF].

Oil prices began to bounce back between 1999 and 2000 and since 2000, Secondly, the post-COVID-19 oil price shock is historically one of the largest prices collapse in a short period. The oil price decreases by more than 60%

since January before falling to its lowest record in history in April 2020.

Thirdly, there is great uncertainty about the outcome of the oil economy as it emerges from this crisis. The world is already facing a climate emergency that calls for consistent responses which have so far been slow to come.

The COVID-19 crisis could accelerate a change in lifestyle that will have a long-lasting impact on oil demand. Indeed, according to the U.S. Energy

Information Administration (EIA)'s forecast, the oil price will remain low (Figure 6). We use two dependent variables. For our cross-sectional regression, the dependent variable is the forecasted gap of GDP growth forecast between October, 2019 and April, 2020 measured as ∆ growth i = |growth April2020 igrowth October2019 i | for country i. For the difference-in-differences method, the outcome variable is the forecasted GDP growth. Our variables of interest are oil-dependence, the incidence of COVID-19 which is the number of COVID-19 cases divided by the total population (expressed in terms of cases per million people), and COVID-19 related deaths per million people. Also, we measure COVID-19 deaths as a dummy variable which is equal to 1 if a country reports at least one COVID-19 related death by the time of the forecast and 0 otherwise. Oil-dependence is a dummy variable equal to 1 if the country oil export revenue is greater or equal to 30% of the total export revenue as classified by the IMF. Alternatively, we use the oil revenue as a share of government revenue for robustness check. We create these dummy variables because oil price crash predominantly affects the oildependent economies.

Table 2 presents the descriptive statistics. The expected average GDP growth in Africa before the outbreak is 4.02% with a minimum of -5% and a maximum of 8.2%. After the outbreak, the expected average growth is -23%.

The minimum is -58.66% (Libya) and the maximum 4.9% (South Sudan). Before the COVID-19 outbreak, the average GDP growth is 2.7% in oildependent countries while it is 4.3% in non-oil-dependent countries. However, the difference between these two means is not statistically different from zero. After the outbreak, the average forecasted GDP growth is -7.6% in oil-dependent countries whereas it is -1.1% in non-oil-dependent ones.

For the pre-COVID-19 forecast, we test the difference in forecasted GDP growth for oil-dependent and non-oil-dependent countries assuming unequal variances between the two samples. The null hypothesis is that this difference is equal to zero against three alternative hypotheses: the difference is less than zero; different from zero; and greater than zero. In these three cases, we failed to reject the null hypothesis that the difference in growth forecast in the two groups before the outbreak is equal to zero at 5% level. The probabilities are respectively P r(T < t) = 0.91; P r(|T |> |t|) = 0.19; and P r(T > t) = 0.094. Hence, we can use the non-oil-dependent countries as a control group for our difference-in-differences estimation.

Empirical strategy

We aim to evaluate the extent and the magnitude of the impact of both the COVID-19 outbreak and the oil price crash on African economies. To do so, we use the forecasted growth before and after the outbreak as our outcome variable. We use two specifications (cross-sectional and difference-in-differences model) to estimate the effect of COVID-19 and oil-dependence on forecasted growth loss.

Cross-sectional model

The first model is a cross-sectional regression where we specify the following equation (Equation 1 below):

(1)

∆ growth i = α + β 1 Oildependent i + β 2 COV ID i + β 3 Oildependent i × COV ID i + β 4 SSA + i
where ∆ growth

Impact analysis: difference-in-differences model

The second model is a difference-in-differences regression using COVID-19 related death (having reported at least one death) and being oil-dependent countries as treatment variables. We assume that the oil-dependence is not endogenous to the difference in the forecast. Also, the mean test shows that there is no systematic difference between the average forecasted GDP growth between oil-dependent and non-oil-dependent countries before the outbreak (October 2019). With the COVID-19 being an exogenous shock, we use the COVID-19 outbreak as a natural experiment [START_REF] Cameron | Microeconometrics: Methods and Applications[END_REF] to estimate the causal effect of both the oil-dependence and the COVID-19 outbreak on the forecasted GDP growth.

We specify the following model (Equation 2 below):

∆ DID = [Y 1 t -Y 0 t |D = 1] -[Y 0 t -Y 0 t |D = 0] ( 2
)
where Y is our outcome variable (GDP growth forecast) with Y 1 and Y 0 for treated and untreated outcomes. The subscripts t and t are respectively the period before and after the COVID-19 outbreak. D = 0 and D = 1 denote the group of untreated and treated respectively.

The identifying assumption of Equation 2is that [START_REF] Heckman | Characterizing selection bias using experimental data[END_REF]. This assumption is the crucial identifying restriction in difference-in-difference regression [START_REF] Cameron | Microeconometrics: Methods and Applications[END_REF][START_REF] List | Why economists should conduct field experiments and 14 tips for pulling one off[END_REF]Wolpin et al., 2000). It implies that, in the absence of the treatment, the average outcome would have been parallel conditional to the covariates. Regarding the short period of time and the unpreparedness of countries worldwide, the change in the forecast between October 2019 and April 2020 is mainly driven by the COVID-19 shock. The April forecasts are the latest data available and best suit the analysis since these data could not consider countries' responses.

E(Y 0 t -Y 0 t |D = 1) = E(Y 0 t -Y 0 t |D = 0)

Results

Results for the cross-sectional regression

Table 4 reports the results of our estimates of the cross-sectional regression. We compute the net marginal effects of oil-dependence and those of COVID-19 on the forecasted GDP growth gap. Oil-dependence is positively correlated with forecasted GDP growth loss. The coffefficient is greater when we control for the COVID-19 incidence (6) as compared to the related deahts (9.6). The net effect is, however, not significant for the COVID-19 incidence.

The net marginal effect of COVID-19 is not statistically significant. Subsaharan Africa membership is associated with lower forecasred growth loss compared to the Northern Africa.

Net marginal effects of oil-dependence

Figures 7 and8 show the marginal effects of oil-dependence on the growth revision conditional to the COVID-19 incidence and COVID-19 related deaths per million people respectively (β 1 +β 3 COV ID). These graphs show that oildependent countries exhibit higher growth losses. These gaps are lower and closer to zero for non-oil-dependent countries. The marginal effect decreases as the incidence increases for oil-dependent countries. The effect remains unchanged following the COVID-19 cases. Oil-dependent economies suffer not only from the COVID-19 crisis and its primary consequences on the economy 1) is for COVID-19 related deaths and column (2) is for its incidence.

b The net effects are computed using bootstrap.

as any other country in the world, but they are also affected by the oil-price collapse. Oil-dependent countries are experiencing higher downward growth revision both in North Africa and in Sub-Saharan Africa. For non-oil-dependent countries, the net marginal effects are constant over the incidence (Figure 9) and the number of COVID-19 related deaths (Figure 10). By contrast, in the oil-dependent countries the net marginal effects are decreasing.

Results for the difference-in-differences regression

Table 5 shows the change in growth forecasts with three treatment variables: being an oil-dependent country, having at least one confirmed COVID-19 related death at the date of the forecast release, and having both.

Using the COVID-19 deaths as our treatment, we find that having recorded at least one COVID death by the time of the forecast results in 2.75 percentage points growth loss. For oil-dependence as a treatment variable, the results

show that it induces 7.6 percentage point forecasted growth loss. Turning to the joint effect, we use the interaction between the COVID-19 related deaths and oil dependence. The joint effect is -10.75 percentage points, larger than each exclusive effect. Overall, these results suggest that oil-dependent countries in Africa are facing a twin shock as the forecasted GDP loss in these countries is higher relative to the non-oil-dependent economies. We use oil-revenue as a percent of government revenue as a measure of oil-dependence for robustness analysis12 . We create a dummy variable, which is equal to one if the oil revenue is greater than the 30% threshold and zero otherwise. The results for our cross-sectional and difference-in-differences regressions are in Tables 6 and7 respectively. The cross-sectional regression results show that oil-dependence is associated with greater forecasted growth loss 6.27 and 10.21 respectively when controlling for COVID-19 deaths and COVID-19 incidence (Table 6). Sub-Saharan African dummy is associated with less forecasted growth loss compared to other countries as in our previous results.

In the difference-in-differences estimations, oil-dependence is associated with -9.08 percentage points forecasted growth loss, higher than the previous results (-7.6 percentage points). Similarly, the joint shock entails a larger forecasted growth loss (-12.01 percentage points) as compared to the previous results (-10.75 percentage points) (see Table 7).

Overall, using oil-revenue as a share of government revenue yields similar results as oil exports as a share of total exports. However, oil revenue as a share of government revenue better captures the oil-dependence. 1) is for COVID-19 related deaths and column (2) is for its incidence.

b The net effects are computed using bootstrap.

Table 7: Difference-in-differences estimates

Conclusion and policy options

The COVID-19 outbreak is inducing unprecedented economic and social disruptions. Although all countries are experiencing this crisis, the size of the effects are different across countries and regions regarding the economic conditions and the policy responses. This paper documents the of both COVID-19 and oil price collapse on the forecasted GDP growth in Africa. We find a negative effect of the COVID-19 crisis on the forecasted GDP growth in the continent. However, the joint shock is higher in oildependent economies. In addition to the urgent need to address the health issues (in both the short and the long run), we identify five high potential economic policies for a sustainable and speedy recovery: social safety net policy, economic diversification and structural transformation, innovation, public finances management and green and climate-friendly policies.

Redistribution policy toward vulnerable groups

The crisis could undermine decades of efforts toward poverty reduction.

Evidence on the current and previous pandemics shows that a pandemic disproportionately affects the poorest: they are more likely to be infected and less resilient [START_REF] Adams-Prassl | Inequality in the impact of the coronavirus shock: Evidence from real time surveys[END_REF][START_REF] Alon | The impact of covid-19 on gender equality[END_REF][START_REF] Galletta | The effect of the 1918 influenza pandemic on income inequality: Evidence from Italy[END_REF]. The effect on inequality is even persistent after a century (Galletta and Giommoni, 2020). The issue is more problematic in oil-dependent economies where growth is less inclusive [START_REF] Leamer | Does natural resource abundance increase latin american income inequality[END_REF]. In the shortterm governments should help smooth the impact of the crisis on the most vulnerable and prevent a humanitarian distress. Technologies such as mobile money services can help targeted programs to reach the most vulnerable.

Diversification and structural transformation

African economies rely heavily on commodity export specifically natural resources dependent countries. The 1990s liberalization policies under the aegis of the "Washington consensus" contribute to weakening the embryonic industrial sector. For instance, oil exports and oil revenue represent respectively more than 80% of total exports and 60% of total domestic revenue in most oil-rich countries in Africa (Algeria, Angola, Gabon and Nigeria).

Oil sector investments also constitute an important part of the investments.

The low oil price will dry up a larger share of investments in particular foreign direct investment in Africa. Thus, economic diversification and structural transformation policy are key to speed up the recovery and build back strong and resilient economies. To do so, governments can build forward and backward linkages between the natural resources sector and the rest of the economy. This requires exploiting the value chains in sectors with high growth and job creation potentials such as agriculture and livestock.

Regional integration, in particular deepening intra-regional trade, is essential to structural transformation. The newly launched African Continental

Free Trade Area will be ineffective without supporting infrastructures including physical and virtual insfrastructure. African governments should increase efforts toward infrastructure development across the continent.

Innovation and new technology

Africa is showing creativity in addressing the health crisis. Since the COVID-19 outbreak, several innovations ranging from handwashing facilities to robotic nurses have been seen across the continent. These dynamics across African countries should be mobilized toward other economic activities (industrial, social, medical, etc.). Also, the deployment and use of digital technology in financial services, E-government services and online learning have supported most economic activities. This is particularly important since evidence from the literature shows that pandemic curbs productivity [START_REF] Azomahou | HIV/AIDS and development: A reappraisal of the productivity and factor accumulation effects[END_REF][START_REF] Boucekkine | Growth economics of epidemics: A review of the theory[END_REF].

Smart and transparent public finances management

The crisis has shown solidarity within and across countries. Organizations such as the World Bank, the IMF13 , the G-20, and bilateral donors have taken measures to support developing countries including new assistance and debt service suspension. This solidarity helps some countries to regain fiscal space. However, governments should further increase transparency in revenue management regarding the level of governance in particular in oil-dependent countries.

Also, countries should be ingenious in mobilizing domestic resources.

Most countries can broaden their tax base without increasing the tax rate.

Reforms of tax administrations are needed for more efficiency in revenue mobilization. In the long-run, the reforms should be expended to address public debt sustainability.

Green and climate-friendly economy

Unlike previous oil price crashes, the current crisis is likely to span over a long period of time and the price of oil could remain low, not only because of the fall in demand but also because of the climate emergency. Many experts stress that the crisis is an opportunity to push the climate agenda forward.

After all, the parallelism between the COVID-19 crisis and the climate crisis clearly shows how much we need to rethink our way of life [START_REF] Allan | A net-zero emissions economic recovery from COVID-19[END_REF].

As the COVID-19 crisis is an opportunity to address the climate emergency, African countries should be taking a step forward moving out of the fossil fuel economy and considering climate-friendly packages in their recovery policies. A promising policy step would be the implementation of 'A science panel for the Congo Basin'. The Congo Basin is the world's secondlargest rainforest after the Amazon. 14 The idea of a scientific panel for the Congo Basin is based on the "science panel for the Amazon forest" launched in 2019 under the sponsorships of the Sustainable Development Solutions Network (SDSN) [START_REF] Bruna | Scientific Framework to Save the Amazon[END_REF].

Under a similar framework, a science panel on the Congo Basin could gather leading experts from different disciplines working on issues related to the Congo Basin forest. Their tasks may include collecting better data on the forest, building indicators, connecting scientific evidence to the public discourse and policy through advocacy, being a collective and audible voice to inform the public, and monitoring policy.

14 Yale University: https://globalforestatlas.yale.edu/region/congo

Figure 1 :

 1 Figure 1: Number of confirmed cases per million people

Figure 2 :

 2 Figure 2: Average GDP per capita growth by decades

Figure 3 :Figure 4 :

 34 Figure 3: Control of corruption in Oil-dependent countries compared to others

Figure 5

 5 Figure 5 shows the evolution of the crude oil price in US dollars per barrel since 1960. Hamilton (2011) identifies six major episodes of oil price downfall between 1960 and 2010. In December of 1968, a strike by east coast oil delivers in the US contributes to the end of a decade of oil price increases. The period 1981-1986 corresponds to the so-called "the great price collapse". The nominal oil price decreases by 25% (much higher in real terms) due to the Iran-Iraqi war. Between 1985 and 1986 oil price collapsed from $27 per barrel in 1985 to $12 per barrel.

  Figure 5: Brent crude oil price ($US per barrel-yearly average)

Forecasts

Figure 6 :

 6 Figure 6: Brent crude oil price ($US per barrel-monthly average)

  By the time of the forecast, the number of COVID-19 cases and deaths are 40.65 and 0.81 per million people respectively in Lybia and South Sudan. As of the date of the publication of the forecasts, (April 20, 2020), 38 out of the 54 African countries (70%) recorded at least one COVID-19 related death.

i

  is the difference between GDP growth forecast before and after the COVID-19 outbreak as previously defined. Our variables of interest are Oildependent i and COV ID i , measured by the COVID-19 incidence or COVID-19 related deaths per million people by the time of the forecast, for a country i. SSA denotes the Sub-Saharan Africa dummy taking the value 1 if the country is in SSA and 0 otherwise. The identification assumption is that E( i |x) = 0 with x denoting the explanatory variables Oildependent, COV ID and the interactive term.This model estimates a correlation between the variables of interest and the dependent variable. The net correlation of oil-dependence on the forecasted GDP growth loss after the outbreak is given by β 1 + β 3 COV ID while the net correlation of COVID-19 is given by β 2 + β 3 Oildependent. COV ID and Oildependent denote respectively the average COVID-19 incidence or COVID-19 deaths per million people and the proportion of oil-dependent countries in the sample. In the next model, we go beyond the correlation to estimate the causal impact of the joint shock on the forecasted GDP growth.

Figure 7 :

 7 Figure 7: Margins of oil dependence conditional to COVID-19 incidence (95% confidence interval)

Figure 8 :

 8 Figure 8: Margins of oil dependence conditional to the COVID-19 deaths (95% confidence interval)

Figure 9 :Figure 10 :

 910 Figure 9: Margins of COVID-19 incidence (95% confidence interval)

  0.1 ; **p < 0.05; ***p < 0.01; t statistics in parentheses T = 0 T = 1 are respectively time before and after the COVID-19 outbreak 6. Robustness check 11

  0,1 ; **p < 0,05; ***p < 0,01

Table 1 :

 1 Macroeconomic conditions in Africa

	Countries	Government	Oil revenue (% of	Oil export % of	Average	GDP
		Debt in 2019 (%	Government rev-	total export	growth	(2010-
		of GDP)	enue) 2018		2019)
	African average	50.1*	0.20	51	4
	Algeria	46.1	76.3	95	2,86
	Angola	110	75	96	2,34
	Cameroon	34	0.12	43	4,53
	Chad	44.2	0.24		3,37
	Congo, Rep.	95.3	0.47	45	2,96
	Equatorial Guinea	41.4	80		-3,08
	Gabon	58.8	60	83	3,88
	Libya	16.5	86.5		1,38
	Nigeria	29.4	65.9	94	3,82
	South Sudan	34.4	95		-4,47

Data source: IMF (2019) for GDP growth, AEC (2020) for oil export and WDI (2019) for Debt data *Sub-Sahran Africa. Left in blank are missing data.

Table 2 :

 2 Descriptive Statistics

	Variables	mean st. dev. min	max
	GDP growth forecast October	4.024	2.55 -4.992 8.213
	GDP growth forecast April	-2.293	8.58 -58.66 4.89
	COVID-19 deaths per million people	40.65	120.6	0 856.3
	COVID-19 Incidence (per million people)	0.81	1.62	0 8.55
	Oil dependence based on exports (dummy)	0.185	0.39	0	1
	Oil dependence based on oil revenue (dummy) 0.148	.357	0	1
	COVID-19 death (dummy)	0.70	0.46	0	1
	Forecasted GDP growth loss	6.317	7.65 0.500 58.63
	Notes: Number of countries (N=54)

Table 3

 3 presents the average forecasted GDP growth of oil-dependent and non-oil-dependent countries in Africa before and after the COVID outbreak.

Table 3 :

 3 Forescasted GDP growth: Mean difference test

	GDP	growth	GDP	growth	Difference	Mean difference
	October		April			test
	Oil-dependent countries	2.69		-7.60	10.29	1.75 (9.68)
	Non oil-dependent countries	4.33		-1.08	5.41	8.43 (70.92)
	Difference	1.64		6.52
	Mean difference test	1.4		1.3
		(10.65)		(9.16)

Notes: Standard erros in parentheses, mean test with unequal variance

Table 4 :

 4 Effect of Oil-dependence and COVID-19 on forecasted growth loss (OLS)

	Dependent variable: Gap in GDP growth forecast
		(1) a	(2) a
	Oil dependence	6.004**	9.586***
		(2.761)	(3.371)
	COVID-19 deaths per million people	-0.0666
		(0.838)
	Oil dependence x COVID-19 per million people	-1.337
		(1.211)
	COVID-19 incidence		-0.0053
			(0.008)
	Oil dependence x COVID-19 incidence		-0.213**
			(0.0942)
	Sub Saharan Africa	-8.685***	-8.464***
		(2.925)	(2.782)
	Net effect of oil dependence b	4.922*	0.933
		( 2.95)	(3.033)
	Net effect of COVID-19 b	-0.96	-0.0447
		(1.888)	(0.0387)
	Constant	12.96***	12.96***
		(2.933)	(2.746)
	Observations	54	54
	R-squared	0.216	0.266
	Standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1

a Column (

Table 5 :

 5 Difference-in-differences estimatesRegression with COVID-19 deaths as treatment variable

Table 6 :

 6 Effect of Oil-dependence and COVID-19 on forecasted growth loss

	Dependent variable: Gap in GDP growth forecast
		(1)	(2)
	Oil dependence	6.267**	10.21***
		(3.009)	(3.664)
	COVID-19 deaths per million people	-0.0551
		(0.834)
	Oil dependence x COVID-19 per million people	-1.326
		(1.217)
	COVID-19 incidence		-0.00529
			(0.00829)
	Oil dependence x COVID-19 incidence		-0.225**
			(0.0992)
	Sub Saharan Africa	-8.417***	-8.250***
		(2.944)	(2.805)
	Net effect of oil dependence b	1.08	-.0468
		(43.855)	(0.208)
	Net effect of COVID-19 b	5.193	-.0300
		( 43.647)	16.67
	Constant	12.86***	12.90***
		(2.957)	(2.770)
	Observations	54	54
	R-squared	0.211	0.263
	Standard errors in parentheses; *** p <0.01, ** p <0.05, * p <0.1

a Column (

As to the number of COVID-19 cases and related deaths at May 19, 2020

https://www.energylivenews.com/2020/03/02/coronavirus-to-cut-2020-oil-and-gasinvestments-by-30bn

/ 4 http://news.bbc.co.uk/2/hi/science/nature/4682011.

stm 5 Oil-intensive economies or oil-dependent economies refer to countries where net oil exports are greater or equal to 30 percent of total export as considered by the IMF.

https://aercafrica.org/latest-news/the-consequential-impacts-of-the-covid-19-crisisand-fragile-growth-in-africa/

https://www.brookings.edu/blog/africa-in-focus/2020/04/18/covid-19-and-debt-st andstill-for-africa-the-g-20s-action-is-an-important-first-step-that-must-be-complemen ted-scaled-up-and-broadened/

https://www.imf.org/external/np/fin/tad/extarr11.aspx?memberKey1=ZZZZ&dat

We thank the anonymous referee for suggesting this robustness check.

An alternative measure suggested by the reviewer is the foreign reserves before and after the COVID-19 outbreak. We did not find the data on this variable.

https://www.worldbank.org/en/news/press-release/2020/04/02/world-bank-grouplaunches-first-operations\-for-covid-19-coronavirus-emergency-health-\support-strength ening-developing-country-responses

 Van der Ploeg, F. and Poelhekke, S. (2009). Volatility and the natural resource curse. Oxford Economic Papers, 61(4):727-760. Wolpin, K. I., Rosenzweig, M. R., et al. (2000). Natural "natural experiments" in economics. Journal of Economic Literature, 38(4):827-874.