Edwin Weber
email: edwin.weber@chemie.tu-freiberg.de

Ines M Hauptvogel

Wilhelm Seichter

Prof A Yurii

Simonov

Unusual mixed solvent supramolecular crystal framework formed of a new tecton-like tetracarboxylic building block †

Keywords: tetracarboxylic acid, dimethyl sulfoxide, chloroform, crystalline complex, x-ray crystal structure tetracarboxylic acid, dimethyl sulfoxide, chloroform, crystalline complex, x-ray crystal structure tetracarboxylic acid, dimethyl sulfoxide, chloroform, crystalline complex, x-ray crystal structure

Unusual mixed solvent supramolecular crystal framework formed of a new tecton-like tetracarboxylic building block †

Introduction

Carboxylic acids featuring a well defined shape have developed to a key position in the controlled assembly of supramolecular systems with a variety of uses in different branches of crystal engineering (1)(2)(3). This covers the carboxylic acids as hosts for crystalline inclusion formation (4) as well as their functional behaviour of a tecton (5), allowing the targeted formation of two-and three-dimensional hydrogen bond stabilized network structures (6,7), although the carboxylic building blocks are brought different into action in these particular fields. While in the object of crystalline host-guest complexation the carboxylic groups are primarily intended for hydrogen bonding to the molecular guest species (8), they are used in the network construction for cyclic hydrogen-bonded pair formation (6,7,(9)(10)(11). Moreover, rigid oligocarboxylic acid molecules are also very efficiently employed as coordinative linkers in the formation of open organic-inorganic hybrid frameworks (MOFs) (12,13). This implies that for hydrogen-bonded guest inclusion the carboxylic groups are endo in the attachment to the host molecule but at peripheral site in the tectones and linkers. With reference to the tectons, tetracarboxylic acids composed of two isophthalic acid groups connected at both ends of a linear spacer moiety, varying in the length, have been studied in their preference towards the formation of close packed, open parallel or Kagomé networks (11,14). In the case of biphenyl-3,3',5,5'- formation is observed in the solid state structure, whereas the use of a very elongated 1,4phenylene-bis(ethynylene) spacer unit between the isophthalic acid moieties produces a Kagomé assembly of the respective molecules adsorbed on highly oriented pyrolytic graphite, and the intermediate compound with a short ethynylene spacer results in the formation of a mixed pattern containing small areas of the two different motifs (14). Thus the length of the spacer unit in the tetracarboxylic molecules is shown to exert a distinct influence on the network structure to be formed.

Referring to the present compound 1 (Scheme 1), we describe the synthesis of a new member of this particular substance class of bisisophthalic acids featuring with the 1,3butadiyne-1,4-diyl spacer unit a molecule of medium length. Crystallization of 1 from a solvent mixture of DMSO and chloroform yielded an interesting stacking mode and hydrogen-bonded, three-dimensional mixed solvent containing network architecture of 1a with the two solvent species being involved in different behaviour pattern, the structure of which is reported and discussed under the specific circumstances.

Results and discussion

Synthesis

Synthesis of the title compound 1 was performed as specified in Scheme 2. This involves reaction of the starting compound dimethyl 5-iodoisophthalate (2) (15) with MEBYNOL using a Pd-catalyzed Songashira coupling procedure (16,17) to give the blocked acetylenic diester 3. The 5-ethynylisophthalic acid (4) was prepared from 3 via removal of the blocking group with KOH in n-BuOH, yielding as an intermediate the dipotassium salt of 4, which was converted into 4 by the reaction with hydrochloric acid. Sulphuric acid catalyzed esterification of 4 with methanol gave the corresponding diester 5. Alternatively, the diester 5 could also be obtained in a selective single-step deblocking reaction from 3 using sodium hydride in toluene (18). Actually, the compound 5 has already been described in the literature applying a similar synthetic route (19,20) but being based on the rather costly TMS-acetylene instead of the cheaper MEBYNOL as the blocking reagent. The diester 5 was subjected to an Eglinton coupling reaction (21,22) with copper(II) acetate in pyridine-methanol to produce the tetraester 6. This ester was

X-ray structural study

Crystallization of the tetracarboxylic acid 1 from a solvent mixture of dimethylsulfoxide and chloroform yields a ternary complex 1a containing 1, DMSO and chloroform in the stoichiometric ratio 1:2:1. Crystallographic data and selected refinement parameters of the structure are summarized in Table 1. Information regarding possible non-covalent interactions in the crystal structure are presented in Table 2. A perspective view of the molecular structure, including atom numbering scheme, is shown in Fig. 1, while an illustration of the crystal packing is given with Fig. 2. The diagram in Fig. 3 demonstrates the potential construction principle of the plain host lattice.

The crystal structure of 1a has the monoclinic space group P2 1 /c with one host molecule, two molecules of DMSO and one molecule of CHCl 3 in the asymmetric cell unit (Fig. 1). The bond lengths within the buta-1,3-diyne part of 1 are 1.432(2)/1.433(2) for C(sp 2 )-C(sp), 1.199(2)/1.201(2) for C≡C and 1.374(2) Å for the single C(sp)-C(sp) bond, and are similar to those found in 1,4-diphenylbutadiyne and its derivatives (23,24). The high degree of molecular association in the crystal structure causes a slight bend of the host molecule along its diacetylene element (25), so that the dihedral angle between the planes of the aromatic rings is 11.5°. The carboxy groups are inclined at angles between 2.7(1) and 8.3(2)° with respect to the plane of the aromatic rings to which they are attached.

It is clear that assembly of the molecules via carboxylic pairing (9,26) would result in a highly expanded and thus presumably too labile structure (27,28). As a possible consequence of this, we did not succeed to grow a crystal of 1 in its solvent free state. Instead, the distinctive donor/acceptor character of the crystal components induces a complicated network of noncovalent bonding between molecules. As depicted in Fig. 2 (34), which considers the oxygen of C=O as an "electrophil" approaching the halogen of C-X (X = Cl, Br,I) at an angle of ~100 ° ("side on"), whereas the nucleophil X approaches the oxygen of C=O at ~165 ° ("head on"). From this angle, the solvent molecules are different in their effect for stabilization of the lattice framework. While DMSO does mainly bonding like assist in stabilization of the lattice construction, the chloroform is less effective in this way but, on the contrary, may be regarded a volume stabilizing component as it fills lattice voids left by the 1 • 2 DMSO network.

A view of the crystal structure in direction of the b-axis reveals that the 1 molecules, one equivalent of DMSO solvent and the CHCl 3 molecules are arranged to corrugated layers whereas the remaining solvent molecules are located between the molecular layers. A more instructive illustration of the lattice framework is shown in Fig. 3, which represents a view along the stacking of plain 1 molecules. In this assembly, the longitudinal axes of molecules of consecutive layers are rotated by nearly 90°. From this it follows that every fourth layer in stacking direction is congruent thus producing an uncongested lattice structure. Nevertheless, the relatively close distance of 3.3 Å between the aromatic residues of consecutive molecules 1 suggests that arene stacking (35) has a decisive effect on the stabilization of the lattice structure.

Conclusions

A new molecular building block 1 comprising two isophthalic acid moieties attached in the 5position to both ends of a rigid rod-like diacetylene unit, which has been synthesized, is shown to create a complex (1:2:1) mixed solvent crystalline framework 1a with DMSO and chloroform as secondary and tertiary components, the structure of which has been solved.

While other tetracarboxylic acids with similar well-defined shapes behave as tectons for the controlled assembly of hydrogen bonded open frameworks rather than a close packed structure (11,14), the present compound 1, although corresponding to this molecular design, behaves markedly different in structure formation under the used condition. This can be attributed to geometric parameters of the molecule leading to a highly expanded labile crystal (26) which is aromatic stacking (35), allowing a partly overlap of the molecules and thus considerable shrinking of the lattice volume. The carboxylic groups being now extremely unsatisfied in terms of their hydrogen donor-acceptor property take the opportunity to interact with the coordinating solvent molecules, giving rise to a close packed structure.

Another line of reasoning is using metal ions strongly coordinating to the carboxylate groups of 1 in order to stabilize a porous framework structure (36). Actually, this has become possible as recently demonstrated by the generation and structural solution of a corresponding coordination polymer based on 1 and copper(II) (37), opening promising aspects of 1 in the actual field of metal-organic-frameworks (MOFs) (12,13). Moreover, due to the molecular shape and functionality, the compound 1 may also excite interest as tecton in the topical research area of surface assisted two-dimensional crystal engineering (6,(38)(39)(40)(41).

Experimental

Methods and materials

The melting points (uncorrected) were measured on a hot-stage microscope PHMK (Rapido Dresden). NMR spectra were recorded on a Bruker DPX 400 spectrometer ( 1 H 400, 13 C 100 MHz) in solutions using TMS as internal standard. IR spectra were determined on a Nicolet 510 FT-IR spectrometer. MS spectra were obtained using Hewlett-Packard MS 5989A (GC-MS) and Finnigan Mat 8200 (EI-MS) instruments. Elemental analyses were performed on a Heraeus CHN-Rapid analyzer. Thin layer chromatography was carried out using Merck Silica gel 60 F 254 on aluminium backed plates. Solvents were purified and dried using standard laboratory procedures [START_REF] Leonard | Praxis der Organischen Chemie[END_REF].

The following reagents were purchased from commercial sources: MEBINOL (Acros Organics) palladiumtriphenylphosphane dichloride (ABCR), copper(II) acetate, copper(I) iodide lithium hydroxide and sodium hydride (60% in mineral oil) (Aldrich). Dimethyl 5iodoisophthalate was prepared as described in the literature (15).

-(3-hydroxy-3-methyl-1-butynyl)isophthalate (3)

A mixture of dimethyl 5-iodoisophthalate (2) (10.0 g, 31.2 mmol), 2-methylbut-3-yne-2-ol (MEBINOL, 3.20 g, 37.5 mmol), Pd(PPh 3 ) 2 Cl 2 (180 mg, 0.26 mmol) and CuI (40 mg, 0.21 mmol) in pyridine (25 ml) and triethylamine (60 ml) under Ar was heated to reflux for 1 h. After cooling to room temperature, the triethylammonium chloride, which has formed, was filtered off using celite and washed with diethyl ether. The filtrate was added dropwise under cooling to 6N hydrochloric acid. The aqueous phase was separated and extracted three times with diethyl ether.

The combined organic layers were washed with 2N hydrochloric acid, aqueous sodium hydrogen carbonate and water, in this sequence, and dried with sodium sulphate. The solvent was evaporated and the crude product crystallized from toluene-n-hexane (1:1, v/v) to yield 76% of a white powder: mp 104 -106 °C. IR (KBr, cm -

5-Ethynylisophthalic acid (4)

To a solution of potassium hydroxide (11.6 g, 207.3 mmol) in n-butanol (250 ml) was added compound 3 (6.0 g, 21.7 mmol) and the mixture heated under reflux for 30 min. After cooling to room temperature, the precipitate which has formed was separated and washed with ethanol to yield in 88% the dipotassium salt of 4 as an off-white powder (mp > 350 °C). This was dissolved in water (20 ml) and acidified to pH 1 with 5N hydrochloric acid. The precipitate which has formed was separated, washed with water and dried for 4 h at 50 °C under vacuum to yield 98% of a white powder: mp 247 -250 °C. IR (KBr, cm The most part of the alcohol was evaporated and the residue added to five times the amount of iced water. The organic layer was separated and the aqueous phase extracted three times with diethyl ether. The combined organic layers were washed with conc. aqueous solution of sodium carbonate and water, in this sequence, and dried with sodium sulphate. Evaporation of the solvent and crystallization from ethanol yielded 55% of colourless needles; mp 134-136 °C (lit. (19) 130 -131 °C). Spectroscopic data correspond with the literature (19).

(b) Via deblocking of 3: To a solution of 3 (6.0 g, 21.7 mmol) in dry toluene (300 ml), sodium hydride (60% in mineral oil, 1.56 g, 39.0 mmol) was added and the mixture heated for 1 h to 75 °C. After cooling to room temperature, the mixture was carefully quenched with water. The organic layer was separated, washed with water and dried with sodium sulphate. Evaporation of the solvent and crystallization from ethanol yielded 60% of the compound.

Tetramethyl 5,5'-(1,3-butadiyne-1,4-diyl)diisophthalate (6)

A solution of the diester 5 (0.50 g, 2.30 mmol) in pyridine-methanol (10 ml, 1:1, v/v) was heated for 30 min under an atmosphere of argon, in order to remove oxygen. After cooling down to room temperature, copper(II) acetate (0.60 g, 3.22 mmol) was added and the mixture heated to reflux for 4 h under argon. The mixture was then cooled to room temperature and dropped slowly into 18N sulfuric acid under cooling with ice. The solid which has formed was dissolved in diethyl ether. The ethereal solution was washed with water and dried with sodium sulphate.

The solvent was evaporated and the crude product crystallized from acetone-chloroform (

X-ray crystallography

Single crystals of 1a suitable for X-ray crystallographic analysis were grown from a solution of pattern containing small areas of the two different motifs (14). Thus the length of the spacer unit in the tetracarboxylic molecules is shown to exert a distinct influence on the network structure to be formed.

Referring to the present compound 1 (Scheme 1), we describe the synthesis of a new member of this particular substance class of bisisophthalic acids featuring with the 1,3butadiyne-1,4-diyl spacer unit a molecule of medium length. Crystallization of 1 from a solvent mixture of DMSO and chloroform yielded an interesting stacking mode and hydrogen-bonded, three-dimensional mixed solvent containing network architecture of 1a with the two solvent species being involved in different behaviour pattern, the structure of which is reported and discussed under the specific circumstances.

Results and discussion

Synthesis

Synthesis of the title compound 1 was performed as specified in Scheme 2. This involves reaction of the starting compound dimethyl 5-iodoisophthalate (2) (15) with MEBYNOL using a Pd-catalyzed Songashira coupling procedure (16,17) to give the blocked acetylenic diester 3. The 5-ethynylisophthalic acid (4) was prepared from 3 via removal of the blocking group with KOH in n-BuOH, yielding as an intermediate the dipotassium salt of 4, which was converted into 4 by the reaction with hydrochloric acid. Sulphuric acid catalyzed esterification of 4 with methanol gave the corresponding diester 5. Alternatively, the diester 5 could also be obtained in a selective single-step deblocking reaction from 3 using sodium hydride in toluene (18). Actually, the compound 5 has already been described in the literature applying a similar synthetic route (19,20) but being based on the rather costly TMS-acetylene instead of the cheaper MEBYNOL as the blocking reagent. The diester 5 was subjected to an Eglinton coupling reaction (21,22) with copper(II) acetate in pyridine-methanol to produce the tetraester 6. This ester was

X-ray structural study

Crystallization of the tetracarboxylic acid 1 from a solvent mixture of dimethylsulfoxide and chloroform yields a ternary complex 1a containing 1, DMSO and chloroform in the stoichiometric ratio 1:2:1. Crystallographic data and selected refinement parameters of the structure are summarized in Table 1. Information regarding possible non-covalent interactions in the crystal structure are presented in Table 2. A perspective view of the molecular structure, including atom numbering scheme, is shown in Fig. 1, while an illustration of the crystal packing is given with Fig. 2. The diagram in Fig. 3 demonstrates the potential construction principle of the plain host lattice.

The crystal structure of 1a has the monoclinic space group P2 1 /c with one host molecule, two molecules of DMSO and one molecule of CHCl 3 in the asymmetric cell unit (Fig. 1). The bond lengths within the buta-1,3-diyne part of 1 are 1.432(2)/1.433(2) for C(sp 2 )-C(sp), 1.199(2)/1.201(2) for C≡C and 1.374(2) Å for the single C(sp)-C(sp) bond, and are similar to those found in 1,4-diphenylbutadiyne and its derivatives (23,24). The high degree of molecular association in the crystal structure causes a slight bend of the host molecule along its diacetylene element (25), so that the dihedral angle between the planes of the aromatic rings is 11.5°. The carboxy groups are inclined at angles between 2.7(1) and 8.3(2)° with respect to the plane of the aromatic rings to which they are attached.

It is clear that assembly of the molecules via carboxylic pairing (9,26) would result in a highly expanded and thus presumably too labile structure (27,28). As a possible consequence of this, we did not succeed to grow a crystal of 1 in its solvent free state. Instead, the distinctive donor/acceptor character of the crystal components induces a complicated network of noncovalent bonding between molecules. As depicted in Fig. 2 ) agree well with the concept of the so called "electrophil-nucleophil pairing" (34), which considers the oxygen of C=O as an "electrophil" approaching the halogen of C-X (X = Cl, Br,I) at an angle of ~100 ° ("side on"), whereas the nucleophil X approaches the oxygen of C=O at ~165 ° ("head on"). From this angle, the solvent molecules are different in their effect for stabilization of the lattice framework. While DMSO does mainly bonding like assist in stabilization of the lattice construction, the chloroform is less effective in this way but, on the contrary, may be regarded a volume stabilizing component as it fills lattice voids left by the 1 • 2 DMSO network.

A view of the crystal structure in direction of the b-axis reveals that the 1 molecules, one equivalent of DMSO solvent and the CHCl 3 molecules are arranged to corrugated layers whereas the remaining solvent molecules are located between the molecular layers. A more instructive illustration of the lattice framework is shown in Fig. 3, which represents a view along the stacking of plain 1 molecules. In this assembly, the longitudinal axes of molecules of consecutive layers are rotated by nearly 90°. From this it follows that every fourth layer in stacking direction is congruent thus producing an uncongested lattice structure. Nevertheless, the relatively close distance of 3.3 Å between the aromatic residues of consecutive molecules 1 suggests that arene stacking (35) has a decisive effect on the stabilization of the lattice structure.

Conclusions

A new molecular building block 1 comprising two isophthalic acid moieties attached in the 5position to both ends of a rigid rod-like diacetylene unit, which has been synthesized, is shown to create a complex (1:2:1) mixed solvent crystalline framework 1a with DMSO and chloroform as secondary and tertiary components, the structure of which has been solved.

While other tetracarboxylic acids with similar well-defined shapes behave as tectons for the controlled assembly of hydrogen bonded open frameworks rather than a close packed structure (11,14), the present compound 1, although corresponding to this molecular design, behaves markedly different in structure formation under the used condition. This can be attributed to geometric parameters of the molecule leading to a highly expanded labile crystal Another line of reasoning is using metal ions strongly coordinating to the carboxylate groups of 1 in order to stabilize a porous framework structure (36). Actually, this has become possible as recently demonstrated by the generation and structural solution of a corresponding coordination polymer based on 1 and copper(II) (37), opening promising aspects of 1 in the actual field of metal-organic-frameworks (MOFs) (12,13). Moreover, due to the molecular shape and functionality, the compound 1 may also excite interest as tecton in the topical research area of surface assisted two-dimensional crystal engineering (6,(38)(39)(40)(41).

Experimental

Methods and materials

The melting points (uncorrected) were measured on a hot-stage microscope PHMK (Rapido Dresden). NMR spectra were recorded on a Bruker DPX 400 spectrometer ( 1 H 400, 13 C 100 MHz) in solutions using TMS as internal standard. IR spectra were determined on a Nicolet 510 FT-IR spectrometer. MS spectra were obtained using Hewlett-Packard MS 5989A (GC-MS) and Finnigan Mat 8200 (EI-MS) instruments. Elemental analyses were performed on a Heraeus CHN-Rapid analyzer. Thin layer chromatography was carried out using Merck Silica gel 60 F 254 on aluminium backed plates. Solvents were purified and dried using standard laboratory procedures [START_REF] Leonard | Praxis der Organischen Chemie[END_REF].

The following reagents were purchased from commercial sources: MEBINOL (Acros Organics) palladiumtriphenylphosphane dichloride (ABCR), copper(II) acetate, copper(I) iodide lithium hydroxide and sodium hydride (60% in mineral oil) (Aldrich). Dimethyl 5iodoisophthalate was prepared as described in the literature (15).

-(3-hydroxy-3-methyl-1-butynyl)isophthalate (3)

A mixture of dimethyl 5-iodoisophthalate (2) (10.0 g, 31.2 mmol), 2-methylbut-3-yne-2-ol (MEBINOL, 3.20 g, 37.5 mmol), Pd(PPh 3 ) 2 Cl 2 (180 mg, 0.26 mmol) and CuI (40 mg, 0.21 mmol) in pyridine (25 ml) and triethylamine (60 ml) under Ar was heated to reflux for 1 h. After cooling to room temperature, the triethylammonium chloride, which has formed, was filtered off using celite and washed with diethyl ether. The filtrate was added dropwise under cooling to 6N hydrochloric acid. The aqueous phase was separated and extracted three times with diethyl ether.

The combined organic layers were washed with 2N hydrochloric acid, aqueous sodium hydrogen carbonate and water, in this sequence, and dried with sodium sulphate. The solvent was evaporated and the crude product crystallized from toluene-n-hexane (1:

1, v/v) to

5-Ethynylisophthalic acid (4)

To a solution of potassium hydroxide (11.6 g, 207.3 mmol) in n-butanol (250 ml) was added compound 3 (6.0 g, 21.7 mmol) and the mixture heated under reflux for 30 min. After cooling to room temperature, the precipitate which has formed was separated and washed with ethanol to yield in 88% the dipotassium salt of 4 as an off-white powder (mp > 350 °C). This was dissolved in water (20 ml) and acidified to pH 1 with 5N hydrochloric acid. The precipitate which has formed was separated, washed with water and dried for 4 h at 50 °C under vacuum to yield 98% of a white powder: mp 247 -250 °C. IR (KBr, cm

X-ray crystallography

Single crystals of 1a suitable for X-ray crystallographic analysis were grown from a solution of

•••A H•••A D-H•••A 1 O(2)-H(2)•••O(1B) -1+x, 1.5-y,-1.5+z 2.637(2) 1.82 163 O(4)-H(4)•••O(1B) 1-x, 1-y,1-z 2.606(2) 1.77 174 C(2C)-H(2C1)•••O(1B)
x, y, z

  two directly connected isophthalic acid groups), open parallel network

  in THF-H 2 O followed by acidification with hydrochloric acid to yield the tetracarboxylic acid 1.

  , all strong donor atoms of the tetracarboxylic acid are associated in the same fashion with DMSO molecules via O-H•••O hydrogen bonds (29) [d(O•••O) 2.598(3)-2.651(3) Å], while a part of the acidic hydrogens of the DMSO molecules are coordinated to the carboxyl oxygens O(3), O(4) and O(7) of 1 or fulfil DMSO•••DMSO interaction (30). Nevertheless, the donor-acceptor character of the tetracarboxylic acid 1 requires the presence of a second solvent species having a complementary coordination behaviour compared with DMSO. This is realized by the chloroform molecule, through C=O•••H-C (31) and C=O•••Cl-C contacts (32,33) to different molecules of 1. In more detail, the hydrogen atom of the chloroform molecule is connected to O(5) of 1 by a relatively strong C-H•••O=C hydrogen bond [d(O•••H 2.20 Å]. The observed angular parameters of the C=O•••Cl-C contacts contributing to the chloroform coordination (C=O•••Cl 85.6, 126.6°; C-Cl•••O 158.2, 167.9°) agree well with the concept of the so called "electrophil-nucleophil pairing"

  pairing. Hence, as a way out, the molecule uses its secondary mode of synthon formation

4 (

 4 1 ): 3452 (OH), 3084 (C-H, Ar), 2981, 2955 (C-H), 2231 (C≡C), 1734 (C=O), 1636, 1605 (Ar), 1440 (C-H), 1347, 1259, 1202, 1176 (C-O), 1005, 917, 866 (Ar); 1 H NMR (CDCl 3 ): δ 1.63 (s, 6 H, CCH 3 ), 2.05 (s, 1 H, OH), 3.95 (s, 6 H, OCH 3 ), 8.25 (d, 4 J = 1.5 Hz, 2 H, Ar-H), 8.60 (t, 4 J = 1.5 Hz, 1 H, Ar-H); 13 C NMR (CDCl 3 ): δ 31.CCH 3 ), 52.5 (OCH 3 ), 65.6 [C(CH 3 ) 2 OH], 80.3 (Ar-C≡C), 95.7 (Ar-C≡C), 123.9, 130.1, 130.9, 136.6 (Ar-C), 165.6 (COOCH 3 ). MS (GC): m/z calcd for C 15 H 16 O 5 : 276.28. Found 276 [M + ]. Anal. calcd for C 15 H 16 O 5 : C, 65.21,; H, 5.84. Found: C, 64.86; H, 5.97%.

  Via esterification of 4: A mixture of the diacid 4 (5.70 g, 30.0 mmol) in dry methanol(25 ml) and conc. sulfuric acid (1.20 g, 12.2 mmol) was heated to reflux for 5 h under dry conditions.

Figure 1 .

 1 Figure 1. Perspective view of the asymmetric unit of 1a (1 • 2 DMSO • CHCl 3 ). Thermal ellipsoids are drawn at 50% probability level. Broken lines represent hydrogen bonds.

Figure 2 .

 2 Figure 2. Packing excerpt of 1a showing the mode of intermolecular interactions. Chlorine atoms are displayed as cross-hatched circles, oxygen atoms as dotted and sulfur atoms as shaded circles. Broken lines represent hydrogen bond type interactions.

Figure 3 .Introduction

 3 Figure 3. Illustration of the plain lattice of 1 (excluding solvent molecules) viewed along the crystallographic a-axis. Oxygen atoms are displayed as shaded circles. Only the hydrogens of the carboxy groups are displayed for clarity.

  in THF-H 2 O followed by acidification with hydrochloric acid to yield the tetracarboxylic acid 1.

1 .

 1 , all strong donor atoms of the tetracarboxylic acid are associated in the same fashion with DMSO molecules via O-H•••O hydrogen bonds (29) [d(O•••O) 2.598(3)-2.651(3) Å], while a part of the acidic hydrogens of the DMSO molecules are coordinated to the carboxyl oxygens O(3), O(4) and O(7) of 1 or fulfil DMSO•••DMSO interaction (30). Nevertheless, the donor-acceptor character of the tetracarboxylic acid 1 requires the presence of a second solvent species having a complementary coordination behaviour compared with DMSO. This is realized by the chloroform molecule, through C=O•••H-C (31) and C=O•••Cl-C contacts (32,33) to different molecules of In more detail, the hydrogen atom of the chloroform molecule is connected to O(5) of 1 by a relatively strong C-H•••O=C hydrogen bond [d(O•••H 2.20 Å]. The observed angular parameters of the C=O•••Cl-C contacts contributing to the chloroform coordination (C=O•••Cl 85.6, 126.6°; C-Cl•••O 158.2, 167.9°

  pairing. Hence, as a way out, the molecule uses its secondary mode of synthon formation(26) which is aromatic stacking(35), allowing a partly overlap of the molecules and thus considerable shrinking of the lattice volume. The carboxylic groups being now extremely unsatisfied in terms of their hydrogen donor-acceptor property take the opportunity to interact with the coordinating solvent molecules, giving rise to a close packed structure.

Figure 1 .

 1 Figure 1. Perspective view of the asymmetric unit of 1a (1 • 2 DMSO • CHCl 3 ). Thermal ellipsoids are drawn at 50% probability level. Broken lines represent hydrogen bonds.

Figure 2 .

 2 Figure 2. Packing excerpt of 1a showing the mode of intermolecular interactions. Chlorine atoms are displayed as cross-hatched circles, oxygen atoms as dotted and sulfur atoms as shaded circles. Broken lines represent hydrogen bond type interactions.

Figure 3 .

 3 Figure 3. Illustration of the plain lattice of 1 (excluding solvent molecules) viewed along the crystallographic a-axis. Oxygen atoms are displayed as shaded circles. Only the hydrogens of the carboxy groups are displayed for clarity.

  Fig.1

  The tetraester 6 (0.20 g, 0.46 mmol) was dissolved in THF (15 ml). Lithium hydroxide (0.80 g, 19.0 mmol) was added and the suspension mixed with water (4.5 ml). The mixture was stirred for 15 h at room temperature and then diluted with water (20 ml). The solution was acidified with 3N hydrochloric acid and the precipitate, which has formed, dissolved with a mixture of diethylether and THF. The ethereal solution was dried with sodium sulphate and evaporated to

	yield after drying at 100 °C under vacuum 83% of a colourless solid: mp > 350 °C. IR (KBr, cm -
	1 ): 3415 (br, H 2 O), 3038 (C-H, Ar), 2637, 2551 (COOH), 1703 (C=O), 1595, 1542, 1510 (Ar),
	1142 (C-O), 914 (Ar). 1 H NMR ([D 6 ]DMSO): δ 8.30 (s, 4 H, Ar-H), 8.50 (s, 2 H, Ar-H), 13.62 F r (s, 4 H, COOH); 13 C NMR ([D 6 ]DMSO): δ 74.5 (C-C≡C), 80.6 (C-C≡C), 121.4, 131.0, 132.3, o 136.6 (Ar-C), 165.6 (COOH). MS (EI): m/z calcd for C 20 H 10 O 8 : 378.29. Found: 378.1 [M + ].
	P
	e
	e r
	R
	e
	v i e
		w
	1:1, v/v) to yield 88% of a colourless solid; mp 221-222 °C. IR (KBr, cm -1 ): 3079, 3037, 3008 (C-H, O n l Ar), 2951 (C-H), 1729 (C=O), 1634, 1595 (Ar), 1438 (C-H), 1331, 1271, 1131, 1110 (C-O), 999, y
	914, 875 (Ar); 1 H NMR (CDCl 3 ) δ 3.97 (s, 12 H, CH 3 ), 8.37 (s, 4 H, Ar-H), 8.67 (s, 2 H, Ar-H);
	13 C NMR (CDCl 3 ) δ 52.6 (CH 3 ), 75.1 (C-C≡C), 80.2	(C-C≡C), 122.6, 131.2, 131.3, 137.4
	(Ar-C), 165.3 (COOCH 3 ); MS (GC): m/z calcd for C 24 H 18 O 8 : 434.39. Found: 434 [M + ]. Anal.
	calcd for C 24 H 18 O 8 : C, 66.36; H, 4.18. Found: C, 66.18; H, 4.15%.

5,5'-(1,3-Butadiyne-

1,4-diyl)diisophthalic acid (1) Anal. calcd for C 20 H 10 O 8 • H 2 O: C, 60.61; H, 3.05. Found: C, 60.28; H, 2.93%.

  To a solution of 3 (6.0 g, 21.7 mmol) in dry toluene (300 ml), sodium hydride (60% in mineral oil, 1.56 g, 39.0 mmol) was added and the mixture heated for 1 h to 75 °C. After cooling to room temperature, the mixture was carefully quenched with water. The organic layer was separated, washed with water and dried with sodium sulphate. Evaporation of the solvent and crystallization from ethanol yielded 60% of the compound.The tetraester 6 (0.20 g, 0.46 mmol) was dissolved in THF (15 ml). Lithium hydroxide (0.80 g, 19.0 mmol) was added and the suspension mixed with water (4.5 ml). The mixture was stirred for 15 h at room temperature and then diluted with water (20 ml). The solution was acidified with 3N hydrochloric acid and the precipitate, which has formed, dissolved with a mixture of diethylether and THF. The ethereal solution was dried with sodium sulphate and evaporated to

	Dimethyl 5-ethynylisophthalate (5)
	(a) Via esterification of 4: A mixture of the diacid 4 (5.70 g, 30.0 mmol) in dry methanol (25 ml)
	and conc. sulfuric acid (1.20 g, 12.2 mmol) was heated to reflux for 5 h under dry conditions.
	F o r The most part of the alcohol was evaporated and the residue added to five times the amount of yield after drying at 100 °C under vacuum 83% of a colourless solid: mp > 350 °C. IR (KBr, cm -iced water. The organic layer was separated and the aqueous phase extracted three times with 1 ): 3415 (br, H 2 O), 3038 (C-H, Ar), 2637, 2551 (COOH), 1703 (C=O), 1595, 1542, 1510 (Ar), diethyl ether. The combined organic layers were washed with conc. aqueous solution of sodium carbonate and water, in this sequence, and dried with sodium sulphate. Evaporation of the solvent and crystallization from ethanol yielded 55% of colourless needles; mp 134-136 °C (lit. (19) 130 -131 °C). Spectroscopic data correspond with the literature (19). 1142 (C-O), 914 (Ar). 1 H NMR ([D 6 ]DMSO): δ 8.30 (s, 4 H, Ar-H), 8.50 (s, 2 H, Ar-H), 13.62 F (s, 4 H, COOH); 13 C NMR ([D 6 ]DMSO): δ 74.5 (C-C≡C), 80.6 (C-C≡C), 121.4, 131.0, 132.3, 136.6 (Ar-C), 165.6 (COOH). MS (EI): m/z calcd for C 20 H 10 O 8 : 378.29. Found: 378.1 [M + ]. o r Anal. calcd for C 20 H 10 O 8 • H 2 O: C, 60.61; H, 3.05. Found: C, 60.28; H, 2.93%.
	P e e r R e P e e r R (b) Via deblocking of 3: Tetramethyl 5,5'-(1,3-butadiyne-1,4-diyl)diisophthalate (6) e
	v i e w A solution of the diester 5 (0.50 g, 2.30 mmol) in pyridine-methanol (10 ml, 1:1, v/v) was heated for 30 min under an atmosphere of argon, in order to remove oxygen. After cooling down v i e to room temperature, copper(II) acetate (0.60 g, 3.22 mmol) was added and the mixture heated to reflux for 4 h under argon. The mixture was then cooled to room temperature and dropped w
	O n l y slowly into 18N sulfuric acid under cooling with ice. The solid which has formed was dissolved in diethyl ether. The ethereal solution was washed with water and dried with sodium sulphate. The solvent was evaporated and the crude product crystallized from acetone-chloroform (1:1, O n l v/v) to yield 88% of a colourless solid; mp 221-222 °C. IR (KBr, cm -1 ): 3079, 3037, 3008 (C-H, Ar), 2951 (C-H), 1729 (C=O), 1634, 1595 (Ar), 1438 (C-H), 1331, 1271, 1131, 1110 (C-O), 999, y
	914, 875 (Ar); 1 H NMR (CDCl 3 ) δ 3.97 (s, 12 H, CH 3 ), 8.37 (s, 4 H, Ar-H), 8.67 (s, 2 H, Ar-H); -1 ): 3297 (C-H, ethynyl), 3076 (C-H, Ar), 2983, 13 C NMR (CDCl 3 ) δ 52.6 (CH 3 ), 75.1 (C-C≡C), 80.2 (C-C≡C), 122.6, 131.2, 131.3, 137.4 2933, 2876 (C-H), 2116 (C≡C), 1706 (C=O), 1599 (Ar), 1278, 1246 (C-O), 917, 860 (Ar); 1 H (Ar-C), 165.3 (COOCH 3 ); MS (GC): m/z calcd for C 24 H 18 O 8 : 434.39. Found: 434 [M + ]. Anal.
	NMR ([D 6 ]DMSO): δ 4.43 (s, 1 H, C≡C-H), 8.18 (s, 2 H, Ar-H), 8.47 (s, 1 H, Ar-H), 13.49 (s, 2 calcd for C 24 H 18 O 8 : C, 66.36; H, 4.18. Found: C, 66.18; H, 4.15%. H, COOH); 13 C NMR ([D 6 ]DMSO): δ 81.7 (C≡C-H), 82.7 (C≡C-H), 122.8, 130.1, 132.1, 136.0 (Ar-C), 165.8 (COOH). MS (EI): m/z calcd for C 10 H 6 O 4 : 190.15. Found: 189.9 [M + ]. Anal. calcd 5,5'-(1,3-Butadiyne-1,4-diyl)diisophthalic acid (1)
	for C 10 H 6 O 4 : C, 63.16; H, 3.18. Found: C, 62.88; H, 3.42%.

Table 1 .

 1 Crystallographic and structure refinement data of the compound studied (estimated standard deviations are in parentheses).

	Compound

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Table 2 .

 2 Distances (Å) and angles(°) of possible hydrogen-bond type interactions of the compound studied.

	Atoms involved	Symmetry	Distance	Angle
			D

Table 1 .

 1 Crystallographic and structure refinement data of the compound studied (estimated standard deviations are in parentheses).

	3.313(3)	2.56	134

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Table 2 .

 2 Distances (Å) and angles(°) of possible hydrogen-bond type interactions of the compound studied.

	Page 33 of 38
	Atoms involved	Symmetry	Distance		Angle
			D•••A	H•••A	D-H•••A
	1
	O(2)-H(2)•••O(1B)	-1+x, 1.5-y,-1.5+z	2.637(2)	1.82	163
	O(4)-H(4)•••O(1B)	1-x, 1-y,1-z	2.606(2)	1.77	174
	C(2C)-H(2C1)•••O(1B)	x, y, z	3.313(3)	2.56	134
	O(6)-H(6)•••O(1C)	x, y, z	2.598(2)	1.78	163
	O(8)-H(8)•••O(1C)	1-x, 0.5+y,1.5-z	2.651(2)	1.81	176
	C(2C)-H(2C2)•••O(3)	1-x, 0.5+y, 1.5-z	3.310(3)	2.43	149
	C(2B)-H(2B2)•••O(4)	1-x, 0.5+y, 0.5-z	3.535(3)	2.72	141
	C(1A)-H(1A)•••O(5)	x, y, z	3.093(3)	2.20	155
	C(1B)-H(1B2)•••O(7)	1-x, 2-y, 1-z	3.143(3)	2.48	125

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URL: http:/mc.manuscriptcentral.com/tandf/gsch Email: suprachem@mail.cm.utexas.edu Supramolecular Chemistry


Dierk Herzer
email: dierkherzer@wiwi.uni-frankfurt.de

Michael Grimm

-Financial Markets|Saving and Capital Investment < O1 -Economic Development < O -Economic Development, Technological Change, and Growth, C23 -Models

Keywords:

This paper uses panel cointegration and causality techniques to examine the long-run relationship between foreign aid and private investment. Our main result is that aid has a statistically significant negative effect on private investment. This result is robust to outliers, different measures and forms of aid, sample selection, and the sample period. In addition, we find that long-run causality runs in both directions, suggesting that an increase in aid reduces private investment and that, in turn, higher private investment leads to lower aid inflows.

I. Introduction

The financing of aid is a key component of international development strategies. Developing countries, particularly the poorest, have insufficient domestic resources to finance their investment and development needs, while rich countries have, for various reasons (humanitarian, economic, and political), a desire to help countries that are less well off. Aid serves these purposes as rich donors provide concessional financing to poor recipients. The fundamental assumption underlying the allocation of aid is that it effectively contributes to economic growth and development in the recipient country. The 3 institutions to calculate aid requirements for target growth rates [START_REF] Dalgaard | Reasonable expectations and the First Millennium Development Goal: how much can aid achieve?[END_REF]. Obviously, aid can affect overall investment by financing public investment (since aid is typically given to governments). However, not all aid is intended for investment, and not all investment is financed by aid [START_REF] Herzer | The long-run effect of aid on domestic output[END_REF]. Hence, an important question is, to what extent does aid affect private investment, particularly in the long run? This is the subject of the present paper.

The focus on private investment is interesting, since many studies suggest that private investment is associated with larger growth effects than is public investment (Khan and Reinhard, 1990;[START_REF] Greene | Private investment in developing countries[END_REF][START_REF] Dollar | The search for the key: aid, investment and policies in Africa[END_REF][START_REF] Devarajan | Low investment is not the constraint on African development[END_REF]. Consistently, [START_REF] Dollar | The search for the key: aid, investment and policies in Africa[END_REF] find that total investment is not a good predictor of growth in Africa. Moreover, [START_REF] Pritchett | Mind your p's and q's. The cost of public investment is not the value of public capital[END_REF] finds that high levels of public investment do not necessarily translate into higher levels of physical capital.

Despite the importance of this subject, there have been very few empirical studies investigating the effect of aid on private investment in the recipient country. The results of the few studies which have been done are mixed. [START_REF] Mosley | Aid, the public sector and the market in less developed countries[END_REF], in a time-series study of eight countries, find that the private investment effect of aid is negative in one recipient country, statistically significant in three, and positive in four. [START_REF] Mahdavi | The effects of foreign resource inflows on composition of aggregate expenditure in developing countries: a seemingly unrelated model[END_REF] uses cross-country data and finds a positive but insignificant association between aid and private investment. The pooled time-series cross-sectional regressions by [START_REF] Snyder | Foreign aid and private investment in developing economies[END_REF] suggest that aid has a statistically significant negative effect on private investment. [START_REF] Hadjimichael | The long-run relationship between outward FDI and domestic output: evidence from panel data[END_REF] investigate the impact of aid on private investment in a sample of Sub-Saharan African countries and find that the impact is positive for countries under structural adjustment and negative for countries with negative per-capita growth. Finally, [START_REF] Dollar | The search for the key: aid, investment and policies in Africa[END_REF] use a sample of 49 countries with up to six four-year periods.

Employing pooled ordinary least squares (OLS) regressions, they find a positive effect of aid on private investment in a good economic policy environment, but this effect is subject to diminishing returns; the marginal impact of aid declines and becomes negative at high volumes of aid. If policies are poor, aid reduces private investment.

A limitation of all these studies, with the exception of [START_REF] Dollar | The search for the key: aid, investment and policies in Africa[END_REF], is that they do not adequately account for the potential endogeneity of aid, and this failure may bias the results. If, for example, donors are more willing to give aid to countries with high private investment rates, possibly because they believe that such countries use foreign aid more effectively, the results are biased towards a positive relationship between aid and private investment. If, in turn, donors prefer to provide aid to those countries in which private investment has declined-possibly due to factors such as natural disasters, economic shocks, or debt overhang-the results are biased towards a negative correlation between aid and private investment. Dollar and Easterly attempt to circumvent this problem by instrumenting aid through population and 'donors'-interest' variables.

However, the quadratic aid-policy interaction term loses significance when instrumented.

Moreover, the results seem to be sensitive to outliers, period length, and the exact definition of good economic policies.

A second problem is that, with the exception of [START_REF] Mosley | Aid, the public sector and the market in less developed countries[END_REF], all studies use standard cross-sectional regression techniques. These techniques, however, are known to be unable to control for country-specific factors, and this inability gives rise to omittedvariable bias.

This paper examines the relationship between aid and private investment using panel cointegration and causality techniques to control for endogeneity and omittedvariable bias. Given that private investment is considered one of the main drivers of economic growth in developing countries, the paper contributes to the understanding of the mechanisms underlying the impact of aid on economic growth. Our results suggest that aid has a statistically significant negative effect on private investment, and that long-run causality runs in both directions, i.e., larger amounts of aid lead to lower private investment and higher levels of private investment lead to lower inflows of aid.

The remainder of this paper is organised into four sections. In Section II, we discuss the potential ways in which aid can affect private investment. Section III sets out the basic empirical model and describes the data. The econometric implementation and the estimation results are presented in Section IV, while Section V concludes.

II. Theoretical Framework

The basic set up

In what follows we present a theoretical framework that will guide our empirical analysis.

It is well known that modelling investment behaviour of private firms is a serious challenge. Investment is the most volatile component of aggregate demand. Investment behaviour is a forward-looking activity with irreversible aspects and is driven by expectations, which are difficult to observe. As we will show below, the developingcountry context adds many other particularities that make it difficult to come up with a straightforward model of investment behaviour. For industrial countries much of the literature relies upon the 'flexible-accelerator approach' [START_REF] Agénor | Development macroeconomics[END_REF]. The flexible-accelerator approach models investment as being mainly dependent upon expected aggregate demand, the cost of capital and labour, and the initial capital stock. Assuming the profit function where π are profits, p is the price of output, y is output, k is capital stock, w is the wage rate, and n(•) is the level of employment, derived from profit maximisation conditioned upon the existing capital stock, we find that the optimal capital stock k* must satisfy:

k p k ) ( ) ( ' * δ ρ π + = ( 2
)
where ρ is the discount rate, δ the rate of depreciation and p k the price of capital. The righthand side of Equation 2 reflects the user cost of capital.

Given an initial capital stock k 0 , net investment in this model represents a gradual adjustment of the actual to the desired capital stock. Gross investment is derived by adding to this an amount of replacement investment that is proportional to the initial capital stock.

Hence, relying on this type of model, a structural investment function needs to include expected future values of aggregate demand, the user cost of capital, the wage rate, and the initial stock of capital, which will all interact in a non-linear way. Since the above representation is static in the sense that it ignores short-run frictions (through short-run adjustment costs, for example), we adopt an empirical specification that captures the longrun relationship between aid and private investment. In doing so, we do not need to be concerned about the lags through which aid affects private investment. However, before we translate this model into a parsimonious empirical specification, we must discuss the aspects of the model that need to be adapted to fit the circumstances of a typical developing country [START_REF] Rama | The impact of public investment on private investment spending in Latin America: 1980-1995[END_REF][START_REF] Servén | Private investment and macroeconomic adjustment: a survey[END_REF][START_REF] Agénor | Development macroeconomics[END_REF], and to form assumptions about how foreign aid may intervene in this context. Third, many developing countries suffered in the past from a serious debt overhang, which is seen by many as a factor, if not the main factor, inhibiting private investment, since it may imply confiscatory future taxation. Given that aid in recent years is often paid in the form of debt relief, aid may also boost private investment via this channel.

F

Fourth, macroeconomic instability, which is often induced by political instability, has been shown to be an important determinant of private investment [START_REF] Rodrik | Policy uncertainty and private investment in developing countries[END_REF][START_REF] Aizenmann | Macroeconomic uncertainty and private investment[END_REF][START_REF] Larrain | Investment and macroeconomic adjustment: the case of East Asia[END_REF]. [START_REF] Alesina | External debt, capital flight, and political risk[END_REF], and financial resources that would otherwise be available to the private sector. On the other hand, public investment to maintain or expand infrastructure and the provision of public goods and services is likely to be complementary to private investment and may actually increase the productivity of capital invested by the private sector. Therefore, it is a priori unclear which effect will dominate. [START_REF] Bleaney | Adjustment to external imbalance and investment slumps in developing countries[END_REF] and Ramirez (2000),

F
for example, find a positive effect of public investment on private investment and attribute this effect to complementarities. [START_REF] Agénor | Labour market reforms, growth, and unemployment in labour-exporting countries in the Middle East and North Africa[END_REF] examine specifically the effect of public infrastructure investment on private investment in three countries-Egypt, Jordan, and Tunisia-using time-series analysis. They find that the effects are positive, but very small and short-lived. They explain these effects by the rather unfavourable environment for private investment in these three countries. Other studies, such as [START_REF] Nazmi | Public and private investment and economic growth in Mexico[END_REF] and [START_REF] Mitra | Has government investment crowded out private investment in India?[END_REF], find clear evidence of crowding out. investment? To answer this question, we assume for the moment that public investment, I Pub , is financed by international aid, A, and domestic savings, S:

S A a I Pub β + = (3)
where α and β are the shares of foreign aid and domestic savings, respectively, that are used for public investment. Thus, not all aid finances public investment and not all public investment is financed by aid. Hence, Equation 3shows that public investment is financed by foreign aid and domestic savings if α > 0 and β > 0. From this, it follows that the share of domestic savings available for private investment is ) 1 ( β and that government consumption, G, can be written as the sum of the remaining part of aid and income, R:

R A a G + - = ) 1 ( (4)
According to Equation 3, a major channel through which aid can affect private investment is aid-financed public investment (if α > 0). Crowding-out effects occur when aid encourages the public sector to undertake investment projects that would otherwise be undertaken by the private sector, or when competition from aid-financed government projects for scarce resources, such as skilled labour, increases the costs facing private investors. If, on the other hand, foreign aid is used to finance public investment projects that would otherwise have been financed using scarce domestic financial resources, aid can eliminate a potential source of crowding out; β would decline such that S A )

( β ∆ - = ∆
. In addition, aid may generate positive externalities to private firms, thus stimulating private The inflow of aid may also create incentives for governments to relax their tax effort, a phenomenon that is widely recognized in the theoretical and empirical literature on the fiscal consequences of foreign aid [START_REF] Mcgillivray | Fiscal effects of aid[END_REF]. As a result, tax-financed public investment may be reduced. A weaker tax effort, in turn, reduces domestic distortions and transfers resources to the private sector (lower taxation means more income available for the private sector, implying that ) 1 ( β increases). This may then increase private sector investment.

However, it may also happen that aid is just used to increase government consumption, implying that 0

= a so that G A ∆ = ∆
, according to Equation 4. In this case aid just replaces domestic savings. This was an important argument against international aid put forward by the dependency school [START_REF] Griffin | Foreign capital, domestic savings, and economic development[END_REF][START_REF] Weisskopf | The impact of foreign capital inflows on domestic savings in underdeveloped countries[END_REF]. Positive externalities of the type described above are generated only if aid does not substitute domestic savings, but instead finances additional public investment projects that would not have been undertaken without aid. Given that the literature on aid effectiveness largely concludes that aid is effective only under certain conditions, such as in the presence of good institutions and policies, and that aid has hardly made development miracles possible (although in most cases, economic growth would have been lower without aid [START_REF] Mcgillivray | Controversies over the impact of development aid: it works; it doesn't; it can, but that depends[END_REF]), one may expect that a substantial portion of aid is indeed consumed [START_REF] Boone | Politics and the effectiveness of foreign aid[END_REF].

A sixth characteristic of developing countries is that institutions are often weak or through which aid affects private investment. Our focus is thus primarily on the net longrun effect, which we will investigate using estimation techniques that are robust (under cointegration) to a variety of estimation problems, such as omitted variables and endogeneity;

to our knowledge these techniques have not been used thus far in the existing literature.

Moreover, we analyse whether aid-financed public investment crowds in or crowds out private investment.

III. Model and Data

The analysis will examine the long-run relationship between aid and private investment. In this section, we present the basic empirical model, discuss some econometric issues, and describe the data.

Following common practice in panel cointegration studies [START_REF] Drine | Do panel data permit the rescue of the Balassa-Samuelson hypothesis for Latin American countries[END_REF]Herzer, 2008a this same stationary relationship will also be found in an enlarged variable set [START_REF] Johansen | Modelling of cointegration in the vector autoregressive model[END_REF].

Another assumption underlying Equation 5 is that private investment is endogenous in the sense that, in the long run, changes in aid cause changes in private investment.

However, although the existence of cointegation implies long-run Granger causality in at least one direction, long-run causality may also run from private investment to aid, as discussed in the beginning of this paper. The empirical implication is that it is not only crucial to examine the integration and cointegration properties of the variables, but it is also important to deal with this endogeneity problem and to investigate the direction of causality. , which will also be considered in the next section. As shown by [START_REF] Pagan | Econometric issues in the analysis of regressions with generated regressors[END_REF], the use of such generated regressors produces asymptotically efficient estimates and the correct values for the standard errors.

F

The identification and estimation of cointegrating relationships requires the use of data over a sufficiently long period of time. Since a period of 30 years should be long enough for this purpose, we select a sample of countries for which continuous data are available over a 30-year period from 1970 to 1999. This sample selection results in a balanced panel of 540 observations on 18 countries: Argentina, Brazil, Chile, Colombia, the Dominican Republic, Ecuador, El Salvador, Guatemala, India, Kenya, Malaysia, Mexico, Pakistan, Paraguay, Thailand, Tunisia, Turkey, and Uruguay. Obviously, this is not a random sample of aid recipients. For example, it does not include any countries from Sub-Saharan Africa, except Kenya. Moreover, most of the countries in the sample are currently relatively stable politically, and almost all have well-developed institutions and a well-performing private sector. They also receive less aid, as measured by its share of GDP, than the average aid recipient, among other things, because many of these countries have good access to international capital markets. Therefore, we provide an additional robustness check by using a second sample with 585 observations on 39 countries over the period from 1985 to 1999. Admittedly, this period is possibly too short to capture the long-run relationship between aid and private investment. Given, however, that panel cointegration methods can be implemented with shorter data spans than their time-series counterparts (due to exploitation of both the timeseries and cross-sectional dimensions of the data), the use of this short sample period should not seriously affect the results. Of the countries in this sample, Malawi is the most aid-dependent economy, with aid amounting to more than 24 percent of GDP on average in the period from 1985 to 1999, followed by Madagascar, Dominica, Haiti, Kenya, Grenada, Belize, El Salvador, Egypt, and St. Lucia. All these countries have an average ratio of aid to GDP of more than five percent. In Argentina, Barbados, Brazil, Chile, China, Colombia, India, Malaysia, Mexico, Panama, South Korea, Thailand, Trinidad and Tobago, Turkey, Uruguay, and Venezuela, aid accounts for less than one percent of GDP, while in Bangladesh, the Dominican Republic, Ecuador, Guatemala, Indonesia, Mauritius, Morocco, Namibia, Pakistan, Paraguay, Peru, the Philippines, and Tunisia, the aid-to-GDP ratio is between one and five percent. To also test whether there are significant differences in the effects of aid on private investment between highly aid-dependent countries, moderately aid-dependent countries, and minimally aid-dependent countries, we will divide the sample into three sub-samples: countries with aid inflows above five percent of GDP, countries with aid-to-GDP ratios between one and five percent, and countries with aid inflows below 0.1 percent.

IV. Empirical Analysis

This section examines the long-run impact of aid on private investment. Specifically, we use panel data techniques that allow us to control for omitted-variable and endogeneity bias. We begin this section by first examining the basic time-series properties of the data.

Then, we test for the existence of a long-run or cointegrating relationship between it A and Finally, we test the direction of causality between the two variables.

Time series properties

To examine the time-series properties of the data, we use the panel unit root test of [START_REF] Im | Testing for unit roots in heterogeneous panels[END_REF] (IPS), which is based on the augmented Dickey-Fuller (ADF) regression for the ith cross-section unit:

it i p j j it ij it i it it x x z x ε ϕ ρ γ + ∆ + + = ∆ ∑ = - - 1 1 ' ( 6
)
where p i is the lag order and z it represents deterministic terms, such as fixed effects or fixed effects combined with individual time trends. The IPS test tests the null hypothesis of a unit root for all i, 0 :

0 = i H ρ
, against the alternative of (trend) stationary, 0 :

1 < i H ρ , i = 1, 2, …, 1 N ; 0 = i ρ , 1 1 + = N i , 2 1 + N , …, N, using the standardised t-bar or IPS statistic [ ] v t N NT t µ - = Γ (7)
where NT t is the average of the N = 18 cross-sectional ADF t-statistics, and µ and ν are, respectively, the mean and variance of the average of the individual t-statistics, tabulated by [START_REF] Im | Testing for unit roots in heterogeneous panels[END_REF].

However, the standard IPS test can lead to spurious inferences if the errors, ε it , are not independent across i, for example, due to common shocks or spillovers between countries. Therefore, we also employ the cross-sectionally augmented IPS test proposed by augmenting the ADF regression with the cross-section averages of lagged levels and firstdifferences of the individual series. Accordingly, the cross-sectionally augmented ADF (CADF) regression is given by

it i p j j t ij t i i p j j it ij it i it it v x x x x z x + ∆ + + ∆ + + = ∆ ∑ ∑ = - - = - - 0 1 1 1 ' η α ϕ ρ γ (8)
where t

x is the cross-section mean of

x it , t x = ∑ = - N i it x N 1 1
. The cross-sectionally augmented IPS statistic is the simple average of the individual CADF statistics and is defined as

CIPS = t-bar = ∑ = - i N i i t N 1 1 (9)
where i t is the OLS t-ratio of i ρ in Equation 8. Critical values are tabulated by [START_REF] Pesaran | A simple panel unit root test in the presence of cross-section dependence[END_REF].

The test results for the variables in levels and in first differences are presented in Table 1. Both the IPS and the CIPS test statistics are unable to reject the hypothesis that all countries have a unit root in levels. Since the unit root hypothesis can be clearly rejected for the first differences, we conclude that it I and it A are integrated of order 1, I(1)-the necessary condition for cointegration in a bivariate context. Thus, the next step in our analysis is an investigation of the cointegration properties of the variables.

[Table 1 In addition, we compute the Fisher statistic proposed by [START_REF] Madalla | A comparative study of unit root tests with panel data and a new simple test[END_REF], which is defined as

∑ - = N i i p ) log( 2 λ (12)
where p i is the p-value of the trace statistic for country i, calculated from the response surface estimates in [START_REF] Mackinnon | Numerical distribution functions of likelihood ratio tests for cointegration[END_REF]. The Fisher statistic is distributed as χ 2 with 2×N degrees of freedom.

However, these test procedures do not take into account potential error crosssectional dependence, which could bias the results. To test for cointegration in the presence of possible cross-sectional dependence we follow [START_REF] Holly | A spatio-temporal model of house prices in the US[END_REF] and adopt a residualbased two-step approach in the style of [START_REF] Pedroni | Critical values for cointegration tests in heterogeneous panels with multiple regressors[END_REF]. But unlike Pedroni, we use the common correlated effects (CCE) estimation procedure developed by [START_REF] Pesaran | Estimation and inference in large heterogeneous panels with a multifactor error structure[END_REF] in the first-step regression. This procedure allows for cross-sectional dependencies that potentially arise from multiple unobserved common factors by augmenting the cointegrating regression with the cross-sectional averages of the dependent variable and the observed regressors as proxies for the unobserved factors. Accordingly, the crosssectionally augmented cointegrating regression we estimate for each country is given by

it t i t i it i i i it e I g A g A t a I + + + + + = 1 0 β δ (13)
where t I and t A are the cross-sectional averages of it I and it A in year t. In the second step, we compute the cross-sectionally augmented IPS statistic for the residuals from the individual CCE long-run relations,

it it i it it A t I β δ µ - - =
, including an intercept. Thus, we The results of these tests are presented in Table 2. For completeness, we also report the standard panel and group ADF test statistics suggested by [START_REF] Pedroni | Critical values for cointegration tests in heterogeneous panels with multiple regressors[END_REF]. As can be seen, all tests strongly suggest that it I and it A are cointegrated. The standardized trace statistics and the Fisher χ 2 statistics clearly support the presence of one cointegrating vector. Also, the CIPS, the panel ADF, and the group ADF statistics reject the null hypothesis of no cointegration at least at the five percent level, implying that there exists a long-run relationship between aid and private investment.

[Table 2 about here]

The long-run relationship between aid and private investment

Having tested for cointegration, the next step of the analysis is to estimate the long-run effect of aid on private investment-the parameter β in Equation 5. To this end, we use the within-dimension panel DOLS estimator suggested by [START_REF] Kao | On the estimation and inference of a cointegrated regression in panel data[END_REF]. The DOLS regression in our case is given by

it p p j j it ij it i i it A A t a I ε β δ + ∆ Φ + + + = ∑ - = - (14)
where Φ ij are coefficients of current, lead, and lag differences, which account for possible serial correlation and endogeneity of the regressor(s), thereby yielding unbiased estimates.

Thus, an important feature of the DOLS procedure is that it generates unbiased estimates for variables that cointegrate even with endogenous regressors. Consequently, in contrast to cross-sectional and conventional panel approaches, this approach does not require exogeneity assumptions nor does it require the use of instruments. In addition, the (panel) DOLS estimator is superconsistent under cointegration, and is robust to the omission of variables that do not form part of the cointegrating relationship.

In Table 3 we present panel DOLS point estimates of the long-run effect of aid on private investment for the raw data as well as for data that have been demeaned over the cross-sectional dimension (i.e., in place of it I and it A , we use

t it it I I I - = ' , t it it A A A - = '
,

where t I = ∑ = - N i it I N 1 1 and t A = ∑ = - N i it A N 1 1
). The latter allows us to account for potential cross-sectional dependence through common time effects. As can be seen, the results for both the unadjusted and the demeaned data show a significant negative relationship between aid and private investment. More precisely, the point estimates imply that, in the long run, a one-percentage-point increase in the aid-to-GDP ratio decreases the private-investment-to-GDP ratio by about 0.27 to 0.34 percentage points. As noted earlier, possible channels are that, in the long run, aid crowds out private investment and/or undermines institutional quality by weakening accountability, encouraging rent seeking and corruption, and alleviating pressure to reform inefficient policies.

[Table 3 about here]

Given the small number of countries in this sample, we need, however, to ensure that the negative effect on private investment is not due to outliers. To this end, we reestimate the DOLS regression (with cross-sectionally demeaned data), excluding one country at a time from the sample. The estimated coefficients and their t-statistics are presented in Figure 1. Since the coefficients are fairly stable around -0.33 and always statistically significant at least at the five percent level, we conclude that the negative effect of aid on private investment is not the result of possible outliers.

[Figure 1 ) in place of it A . The results are presented in Table 4. Regardless of which aid measure is used, the long-run relationship between aid and private investment remains negative and statistically significant. Particularly worth noting is that the coefficient on Invested it A is highly significant and of a similar magnitude as its counterpart in Table 3 (column 2). From this it can be concluded that the negative net effect of aid on private investment is primarily due to the presence of crowding out by aidfinanced public investment. The fact that the coefficient on Consumed it A is also significant and negative could suggest that aid also reduces private investment by weakening institutions and encouraging rent-seeking activities; these two mechanisms, however, seem of minor importance since the effect of consumed aid is smaller in magnitude than the effect of invested aid.

[Table 4 about here]

Finally, we examine whether the negative long-run relationship between aid and private investment is due to sample-selection bias. Sample-selection bias occurs when the selected sample is not random and thus not representative. A potential problem with our sample could be that it does not include any countries from Sub-Saharan Africa, except Kenya, and that it consists mainly of countries with low aid-to-GDP ratios, as discussed in Section III. We therefore re-estimate the DOLS regression (with demeaned data) for a second sample of 39 countries over the period from 1985 to 1999. In addition, we split this sample into three sub-samples: countries with aid inflows above five percent of GDP, countries with aid-to-GDP ratios between one and five percent, and countries with aid significant at least at the five percent level (although somewhat smaller than the estimates for the original sample), suggesting that the negative effect on private investment is not due to sample-selection bias. Moreover, given that all aid-coefficients in Table 5 have the same magnitude, it can be concluded that there are no significant differences in the effects of aid on private investment between highly aid-dependent countries, moderately aiddependent countries, and minimally aid-dependent countries. And lastly, the fact that the results for the period 1985 to 1999 (in Table 5) are qualitatively similar to those for the period 1970 to 1999 (in Table 3) suggests that the estimated negative coefficient on A it is not sensitive to the sample period.

[Table 5 about here]

Long-run causality

Even though estimation by DOLS does not require the regressor(s) to be exogenous (and even though cointegration implies long-run Granger causality in at least one direction), we are interested in detecting the direction of long-run causality. Specifically, given that a negative correlation between it A and it I could also imply that countries in which private investment declined (for example, due to natural disasters or economic shocks) get more aid, it could well be that causality runs in both directions, that is, not only from aid to private investment but also from private investment to aid. Thus, the final step in our analysis is to examine the direction of causality.

Our causality test involves estimating a panel vector error correction model

   +       +         ∆ ∆ Γ +       =       ∆ ∆ - - - = ∑ it it it j it j it p j j i i it it ec a a A I c c A I 2 1 1 2 1 1 2 1 ε ε (15) where ] [ it i i i it it A t a I ec β δ + + - =
are the residuals from the DOLS long-run relations of the individual countries. A statistically significant coefficient on 1 it ec (a 1,2 ) implies long-run Granger causality from the independent to the dependent variable(s), where long-run Granger non-causality and weak exogeneity can be regarded as equivalent. Following Herzer (2008aHerzer ( , 2008b)), we test for weak exogeneity by first imposing zero restrictions on the statistically insignificant short-run parameters (Г j ) and then using a conventional Wald test of the null hypothesis a 1,2 = 0. As in the previous section, we use both the raw and the cross-sectionally demeaned data for this purpose.

The Wald statistics and the corresponding p-values are presented in Table 6. As can be seen from the table, the unadjusted and demeaned data produce similar results. In both cases, the null hypothesis of weak exogeneity is rejected for both it I and it A at the one percent significance level. Thus, private investment is endogenous in the long run, implying that the estimates in the previous section reflect a causal long-run effect of aid on private investment, as expected. However, this long-run causality is bidirectional, suggesting that increased private investment also reduces (quite plausibly) the amount of aid given to a country.

[Table 6 about here]

V. Conclusion

This paper has examined the relationship between development aid and private investment in developing countries using panel cointegration and causality techniques that are Specifically, an important conclusion of our robustness analysis is that the effect of aid on private investment is qualitatively the same for highly aid-dependent countries, moderately aid-dependent countries, and less aid-dependent countries. Moreover, we find evidence that the negative effect of aid on private investment is primarily due to the crowding out of private investment by aid-financed public investment. Given that private investment must be considered as a key determinant of economic growth, our findings suggest that development aid does not exploit its full growth potential. Furthermore, our results show that the long-run causality between aid and private investment runs in both directions, specifically, larger amounts of aid lead to lower levels of private investment, and correspondingly, higher levels of private investment lead to lower inflows of aid.

If we find that aid has rather negative effects on private investment and that this is at least partly due to crowding out through public investment, then the implication can or should of course not be that aid is bad for private investment per se. It suggests rather that aid is clearly not used in a way that would generate optimal complementarities between public and private investment. This implies in turn that if aid were more systematically used for investment in infrastructure, research and development, improvements in the quality of institutions, and for a better skilled work force, private investment would likely respond positively to aid. This is consistent with the broader view that aid is effective if economic policies are good (Burnside andDollar, 2000, 2004;[START_REF] Collier | Development effectiveness: what have we learnt?[END_REF].

Another implication of our findings is that the link between aid and private investment can be strengthened if policy enhances the development of capital markets and it is accordingly The number of degrees of freedom υ in the standard χ 2 (υ) tests corresponds to the number of zero restrictions. The models were estimated with up to three lags.

  aid intervene in the relationship between public and private

  it is used to finance public investment in human and physical capital and in the economic infrastructure.

  , we estimate this relationship and examine the robustness of the results.

  common factors that could be correlated with the observed regressors in both steps.

  examine whether the negative relationship between aid and private investment is robust to different forms or measures of aid. To this end, we re-estimate the DOLS regression (with demeaned data) using net ODA, excluding technical cooperation and food aid ( Financial it A), invested aid by the government (aid-financed public investment)

(

  Fig. 1. DOLS estimation with single country excluded from the sample

  human capital or adopt more efficient technologies, implying that resources are misallocated and private investment opportunities go unexploited. Thus, private investment is likely to depend strongly upon the quality of institutions, and aid may therefore affect private investment through its effects on institutions. The conventional argument is that by expanding a government's resource envelope, aid may reduce the government's need to explain its actions to citizens, which may in turn alleviate pressure on it to govern well. Poor governance is generally associated with a weakening of institutions necessary for a good investment climate, given that the government falters in

	F o r desire of different factions to control the large amount of food aid the country was receiving. In summary, aid may stimulate private investment by relaxing foreign exchange constraints (thus reducing p k ), reducing the debt overhang, and increasing macro-economic stability (this should raise positive expectations among entrepreneurs). In addition, and perhaps most importantly, if aid is used by the recipient government to finance public investment in education, research and development, and physical infrastructure, such in physical or F o r investment may generate positive externalities for the private sector and thus increase the productivity of private investment (by raising ' F o r
	P P P
	e e e
	e r e r e r
	R R R
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	v i e v i e v i e
	w w w
	11 12 13

non-existent. Institutions lower transaction costs by reducing uncertainty and establishing a stable economic structure to facilitate interactions, thus helping to allocate resources to their most efficient uses. Without institutions, individuals do not have incentives to invest its responsibilities to protect property rights, ensure a predictable judiciary, and limit corruption. The hypothesis of an aid-induced weakening of institutions is supported by several studies.

[START_REF] Knack | Aid dependence and the quality of governance[END_REF]

finds that higher aid levels erode the quality of governance;

[START_REF] Bräutigam | Foreign aid, institutions, and governance in sub-Saharan Africa[END_REF]

and

Rajan and Subramanian (2007)

suggest that aid is associated with weak governance, while

[START_REF] Djankov | The course of aid[END_REF]

find that aid reduces the level of democratisation.

Similarly, it can be argued that aid can affect private investment if it encourages a diversion of resources from productive activities into unproductive rent seeking. More specifically, aid may induce self-interested individuals to engage in rent-seeking activities aimed at appropriating part of the resource windfall, and, as a result, scarce resources are withdrawn from other productive activities, as several theoretical models predict

[START_REF] Hodler | Rent seeking and aid effectiveness[END_REF][START_REF] Economides | Does foreign aid distort incentives and hurt growth? Theory and evidence from 75 aid-recipient countries[END_REF]

. Rent-seeking may, in extreme cases, directly harm the economy by provoking civil conflicts. Such conflicts, of which war is the extreme example, affect the investment climate of a country in a number of ways, such as by generating uncertainty in the economic environment and/or by destroying the economic and political institutions and infrastructure that facilitate an efficient allocation of resources.

[START_REF] Maren | The road to hell: the ravaging effects of foreign aid and international charity[END_REF]

, for example, argues that Somalia's civil war was caused by the π ). Aid may also eliminate a possible source of crowding out if it is used to finance public investment projects that would otherwise have been financed using scarce domestic financial resources (p k declines). In addition, foreign aid can be used to reduce taxes, thereby reducing distortions in the economy and increasing the income of the private sector, which may then have a similar positive effect on private investment (by reducing p k ). However, the other side of the coin is that foreign aid may reduce private investment when the recipient government uses the aid to finance investment projects that would otherwise be undertaken by the private sector. Crowding-out effects may also occur when aid-financed public investment competes with private investment for scarce resources, such as skilled labour. Finally, high levels of aid over long periods of time can have substantial negative effects on private investment by weakening institutions and encouraging rent-seeking activities.

Thus, aid can affect private investment in several ways, perhaps the most important of which is aid-financed public investment. From this, it follows that the net effect of aid is theoretically ambiguous and becomes an empirical question. Although our objective is to examine the long-run relationship between aid and private investment in detail, we have to admit that the available data do not adequately allow identification of the exact channels

  GDP ratio. Necessary conditions for this assumption to hold, and thus for our model to be a correct description of the data, are that both the individual time series for aid (relative to GDP) and the individual time-series private investment (relative to GDP) are nonstationary or, more specifically, integrated of the same order, and that it I and

	specific omitted factors which are relatively stable in the long run or which evolve
	smoothly over time are captured by country-specific fixed effects, i a , and country-specific
	time trends, t i δ .
	Equation 5 assumes a long-run bivariate relationship between permanent
	movements in the aid-to-GDP ratio and permanent movements in the private-investment-
	to-
			F o r
														P
															e
																	e r
																	R
																	e
	I	it	=	i a	+	δ	i	t	+	β	it A	+	ε	it	v i e	(5)
																	w
	where it I is the private-investment-to-GDP ratio over time periods	t	=	, 1	, 2	...	,	T	and
	countries	i	=	, 1	, 2	...	,	N	, and it A is represented by the standard measure of aid-the
	percentage share of net Official Development Assistance (ODA) in GDP. The coefficient
	β can be interpreted as the long-run elasticity of private investment with respect to aid,
	measuring the net long-run effect of aid on private investment. As the principal interest is
																	14

;[START_REF] Crowder | Does investment lead to greater output? A panel error-correction model analysis[END_REF]

, we consider a bivariate long-run relationship of the form on the long-run effect, it is not essential to be concerned about the variable lags through which aid will impact private investment (as discussed in Section II). Finally, any country-it A form a cointegrated pair. A regression consisting of two cointegrated variables has a stationary error term, it ε , in turn implying that no relevant integrated variables are omitted; any omitted nonstationary variable that is part of the cointegrating relationship would enter the error term, thereby producing nonstationary residuals and thus leading to a failure to detect cointegration. If, on the other hand, cointegration between a set of variables is detected,

  The data on private investment as a percentage of GDP are from the International Finance Corporation (IFC)'s Trends in Private Investment in Developing Countries:Statistics for 1970-2000, authored by Everhart and Sumlinski (2001). The data on aid come from the World Development Indicators of the World Bank. Since the World Development Indicators do not report data on net ODA as a percentage of GDP, we calculate the aid-to-GDP ratio by multiplying ODA per capita (in current US dollars) by population, divided by GDP (in current US dollars). ODA consists of net disbursements of loans and grants made on concessional terms by official agencies of the members of the Development Assistance Committee (DAC) and certain Arab countries to promote economic development and welfare in recipient countries listed as developing by the DAC. However,

	IFC's Trends in Private Investment in Developing Countries) on aid for each individual
	country,	I	Pub it	=	i b	it A	+	ξ	it	. The estimated residuals, it ξ ˆ, from this regressions represent the
	part of public investment that is not attributed to aid. Then, we subtract it ξ ˆ from Pub it I	to
	obtain that part of public investment that is financed by aid, Invested it A	. The difference
	between it A and Invested it A		is, in turn, a measure of consumed aid, Consumed it A
				o r F o r
								P P
											e e
											e r e r
											R R
											e e
	it A on Financial	v i e w v i e are from the International Development Statistics 2010 CD-ROM, published Financial it A . The data w
	by the OECD.
	The fundamental problem, however, is that ODA does not identify the aid used for
	public investment; the coefficient on it A captures the (net) effect of total aid and thus not
	necessarily the effect of aid-financed public investment on private investment. To
											16

although the DAC collects and reports only non-military grant and low-interest loan assistance to developing countries, this standard measure has limitations. For example, it also includes non-financial aid in the form of technical assistance, which may contribute to capacity building and aggregate productivity (although much of this aid is actually spent in the donor, rather than the recipient, country) and food aid (which could be considered as investment in human capital in the broadest sense). Since these two types of aid might not directly affect private investment, we use a second measure of aid to check the robustness of our results: net ODA, excluding technical cooperation and food aid, explicitly test whether public investment financed by aid crowds in or crowds out private investment, we construct a third measure of aid: We regress public investment (from the

about here]

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Page 27 of 37 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  deal with key problems (endogeneity and omitted variables) plaguing previous studies of the aid-private investment nexus. Our main result is that aid has a statistically significant negative effect on private investment. This result is robust to outliers, different measures or forms of aid, sample selection, and the sample period.

	Submitted Manuscript
	specifically designed to
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Table 1 . Panel unit root tests

 1 28 less likely that public and private investors would compete for the same financial resources. Obviously, these conclusions are suggestive and need further investigation, but this is beyond the scope of this article.Notes: c (t) indicates that we allow for different intercepts (and time trends) for each country. Three lags were selected to adjust for autocorrelation. The IPS statistic is distributed as N(0, 1). The relevant 5% (1%) critical value for the CIPS statistics is -2.67(-2.83) with an intercept and a linear trend, and -2.15 (-2.32) with an intercept. ** denote significance at the 1% level.

	F o r
	P
	e
	e r
	R
	e
	v i e
	w

Table 2 . Panel cointegration tests

 2 The panel trace statistic, the panel ADF statistic, and the group ADF statistic are distributed as N(0, 1). The Fisher statistic is distributed as χ 2 with 2×N degrees of freedom. It has a critical value of 58.62 (51) at the 1% (5%) level. The relevant 5% (1%) critical value for the CIPS statistic is -2.15(-2.32). The number of lags was determined by the Schwarz criterion with a maximum of three lags. ** (*) indicate a rejection of the null hypothesis of no cointegration at the 1% (5%) level.

	Cointegration rank
	r = 0	r = 1

Notes:

Table 3 . DOLS estimates of the long-run effect of aid on private investment for the period from 1970 to 1999 (coefficients on A it )

 3 The dependent variable is I it . t-statistics in parenthesis. ** (*) indicate significance at the 1% (5%) level. The DOLS regression was estimated with one lead and lag.

	Raw data	Demeaned data
	-0.265*	-0.338**
	(-2.11)	(-2.69)

Notes:

Table 4 . Parameter estimates using alternative measures of aid

 4 The dependent variable is I it . t-statistics in parenthesis. ** (*) indicate significance at the 1% (5%) level. The DOLS regression was estimated with one lead and lag.

	Financial it A	Invested it A	Consumed it A
	-0.260**	-0.344**	-0.033*
	(-3.12)	(-4.54)	(-2.54)

Notes:

Table 5 . Estimates of the long-run impact of aid on private investment using different samples over the period from 1985 to 1999

 5

	Coefficients on	t-statistics Number of countries
	A it	in the sample

Notes: ** (*) indicate significance at the 1% (5%) level. The DOLS regression was estimated with one lead and lag.

Table 6 . Weak exogeneity tests / long-run causality tests

 6

	Variable	I it	A it
	(Coefficient)	(α 1 )	(α 2 )
	Raw data
	χ 2 (1)	101.97	8.08
	(p-values)	(0.000)	(0.005)
	Demeaned data
	χ 2 (1)	120.23	8.39
	(p-values)	(0.000)	(0.004)

Notes:

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Acknowledgement

We are grateful to two anonymous referees for excellent suggestions that considerably improved this paper.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Cointegration tests

We first test for cointegration using the [START_REF] Larsson | Likelihood-based cointegration tests in heterogeneous panels[END_REF] approach, which is based on [START_REF] Johansen | Statistical analysis of cointegrating vectors[END_REF]

and then standardising it as follows:

( )