Country uncertainty, power distance, and payment methods in acquisitions

Figures & data

Table 1. Deal-specific characteristics and institutional environment of target country.

Target country	Number of deals	Related	Cash	Mean toehold	Mean share sought	transaction value (US$ million)	Cross-border
Panel A: Deal-specific characteristics
Top quartile	71,028	26.44%	44.12%	27.25%	58.14%	259.85	24.75%
2nd quartile	4,855	29.19%	38.86%	30.17%	52.23%	451.65	51.28%
3^rd quartile	2,117	46.13%	36.08%	28.19%	56.40%	402.22	59.21%
Bottom quartile	926	44.27%	35.63%	30.25%	56.27%	330.93	72.34%
All countries	78,926	29.97%	43.11%	29.95%	54.08%	443.97	39.84%
Panel B: Target country institutional characteristics
Target country	CUI	CORRCTRL	CORRPER	ECOFREE	TAX	GDP Growth
Top quartile	0.710	1.082	6.963	7.241	41.07%	3.25%
2nd quartile	0.563	0.856	5.052	6.932	28.62%	3.06%
3rd quartile	0.599	0.904	4.722	7.008	26.54%	4.85%
Bottom quartile	0.607	0.569	5.004	6.915	26.11%	3.11%
All countries	0.648	0.758	5.898	7.150	29.00%	3.38%

Note: This table reports deal-specific characteristics and institutional environment of target country. Panel A shows the percentage of deals in related industries, deals that use cash payment, the mean percentage of shareholding stake by the acquirer in the target firm at the time of the bid announcement, the mean percentage of the target firm’s shares sought by the bidding firm, the mean transaction value, and cross-border deals. We present the data by the quartile of target countries, ranked by the number of deals. Panel B reports target country institutional characteristics, including Country Uncertainty index (CUI), Control of Corruption (CORRCTRL), Corruption Perceptions index (CORRPER), Quality of Economic Freedom (ECOFREE), Tax levels (TAX), and GDP growth.

Table 2. Summary statistics by target country.

Target Country	No. deals	Related	Cash	Mean toehold	Mean share sought (%)	Transaction value (US$ mil)	Cross- border	CUI	CORRCTRL	CORRPER	ECOFREE	TAX
Panel A: Developed markets
United States	24243	0.32	0.42	0.04	90.03	404.82	0.16	0.71	1.51	7.51	8.19	0.53
Japan	7285	0.26	0.65	0.32	44.40	112.78	0.07	0.71	1.37	7.55	7.69	0.48
Canada	6150	0.41	0.30	0.11	82.32	170.58	0.25	0.52	1.98	8.13	8.09	0.54
South Korea	5651	0.12	0.42	0.19	46.37	88.96	0.11	1.62	0.50	6.17	7.47	0.28
Australia	5520	0.29	0.37	0.14	68.41	104.25	0.27	0.43	1.97	8.51	8.06	0.65
Singapore	2170	0.19	0.51	0.28	60.11	106.44	0.33	0.33	2.18	9.08	8.74	0.32
United Kingdom	1662	0.25	0.55	0.25	72.55	841.22	0.32	0.82	1.85	7.94	8.18	0.37
New Zealand	1110	0.28	0.31	0.16	72.63	69.42	0.45	0.48	2.24	9.03	8.40	0.53
France	960	0.26	0.52	0.43	42.71	571.19	0.33	0.75	1.37	6.97	7.34	0.25
Taiwan	920	0.36	0.33	0.21	57.13	135.64	0.32	0.52	0.67	6.37	7.53	0.07
Israel	660	0.23	0.30	0.15	66.33	105.11	0.45	0.85	0.94	6.14	7.21	0.31
Germany	625	0.32	0.54	0.45	45.57	525.26	0.37	0.26	1.82	7.57	7.66	0.16
Norway	460	0.18	0.44	0.54	40.76	245.01	0.33	0.62	2.10	8.15	7.55	0.30
Italy	451	0.31	0.37	0.44	41.45	971.56	0.26	0.79	0.32	4.84	7.29	0.33
Sweden	361	0.22	0.54	0.39	60.74	311.26	0.36	0.63	2.23	8.72	7.54	0.15
Spain	337	0.22	0.40	0.30	33.20	809.75	0.28	0.64	1.04	6.33	7.53	0.41
Switzerland	227	0.42	0.51	0.40	42.83	1410.44	0.37	0.48	2.01	8.06	8.33	0.20
Netherlands	211	0.28	0.62	0.27	61.77	1633.47	0.43	0.44	2.11	8.33	7.70	0.25
Denmark	164	0.39	0.45	0.35	57.11	451.54	0.33	0.71	2.37	8.90	7.85	0.40
Belgium	145	0.25	0.52	0.43	49.33	578.03	0.45	0.37	1.33	6.95	7.39	0.36
Finland	120	0.25	0.39	0.44	44.17	292.23	0.42	0.27	2.36	9.03	7.87	0.20
Portugal	115	0.34	0.54	0.52	27.28	388.15	0.31	0.52	1.11	6.40	7.38	0.22
Austria	85	0.21	0.48	0.48	36.20	647.78	0.54	0.54	1.67	7.52	7.71	0.27
Ireland-Rep	72	0.20	0.59	0.16	72.11	1530.25	0.49	0.61	1.57	7.11	7.96	0.38
Luxembourg	50	0.42	0.49	0.35	58.16	3856.27	0.78	0.71	1.42	7.16	7.33	0.29
Iceland	22	0.23	0.29	0.34	40.52	357.14	0.21	0.58	2.25	8.36	7.79	0.29
Panel B: Developing markets
China	4911	0.24	0.36	0.19	50.27	114.11	0.21	0.32	−0.45	4.33	6.26	0.23
India	2821	0.26	0.42	0.28	47.85	88.11	0.40	0.28	−0.41	4.49	6.44	0.41
Malaysia	2443	0.23	0.63	0.29	62.93	75.24	0.19	0.57	0.20	5.52	6.73	0.47
Thailand	1191	0.28	0.31	0.33	48.15	69.24	0.27	0.77	−0.31	4.37	6.69	0.34
South Africa	1143	0.26	0.45	0.17	69.55	155.12	0.29	1.19	0.27	5.02	6.82	0.51
Indonesia	1021	0.33	0.35	0.23	56.13	95.10	0.51	0.81	−0.75	4.25	6.55	0.35
Mexico	809	0.37	0.30	0.19	77.52	359.20	0.55	0.96	−0.37	3.75	6.77	0.30
Vietnam	556	0.27	0.76	0.22	38.11	29.23	0.38	0.59	−0.55	3.68	6.20	0.35
Philippines	543	0.33	0.31	0.24	55.86	118.24	0.30	0.72	−0.62	3.82	7.07	0.40
Brazil	399	0.38	0.44	0.52	40.01	549.85	0.28	0.87	−0.03	4.32	6.14	0.25
Russian Fed	396	0.34	0.26	0.41	35.27	710.26	0.23	0.76	−0.99	3.41	6.45	0.04
Kuwait	291	0.25	0.30	0.20	54.06	100.14	0.40	0.76	−0.11	4.37	6.34	0.03
Poland	290	0.23	0.41	0.45	40.89	117.11	0.47	0.53	0.57	5.41	7.05	0.13
Egypt	290	0.32	0.34	0.25	52.47	205.62	0.46	0.76	−0.64	3.85	6.15	0.26
Turkey	239	0.31	0.56	0.34	35.50	276.18	0.42	1.05	−0.07	4.49	6.75	0.17
Utd Arab Em	231	0.25	0.33	0.11	67.06	209.00	0.63	0.63	1.04	6.24	7.59	0.00
Greece	215	0.34	0.31	0.36	50.27	445.22	0.52	0.30	0.01	4.11	7.01	0.21
Saudi Arabia	166	0.21	0.26	0.23	53.88	99.71	0.53	0.41	−0.11	4.48	4.78	0.20
Chile	141	0.31	0.48	0.41	41.14	278.53	0.31	0.22	1.47	7.20	7.73	0.28
Morocco	125	0.35	0.42	0.30	48.32	261.17	0.56	0.30	−0.36	3.90	6.08	0.27
Kazakhstan	121	0.44	0.35	0.21	60.73	400.93	0.61	0.27	−0.96	3.61	6.40	0.34
Peru	115	0.33	0.45	0.40	39.67	130.22	0.45	1.01	−0.33	4.04	7.50	0.29
Nigeria	92	0.38	0.46	0.13	61.64	371.53	0.52	1.41	−1.14	2.75	6.21	0.31
Jordan	85	0.25	0.33	0.12	54.72	55.57	0.47	0.29	0.16	5.49	7.34	0.11
Panama	83	0.44	0.32	0.10	82.67	163.38	0.68	0.53	−0.31	3.75	7.40	0.07
Argentina	83	0.37	0.28	0.41	33.22	357.16	0.52	1.46	−0.36	3.63	6.23	0.15
Puerto Rico	63	0.26	0.52	0.05	89.94	229.11	0.70	0.68	0.72	−0.01	NA	NA
Qatar	61	0.36	0.28	0.23	52.53	226.21	0.50	0.12	0.63	5.52	4.18	0.29
Bulgaria	61	0.33	0.30	0.25	56.11	95.94	0.68	0.56	−0.34	4.33	6.49	0.19
Venezuela	58	0.47	0.84	0.35	57.17	473.20	0.24	1.22	−0.87	3.25	5.26	0.03
Colombia	54	0.35	0.41	0.48	48.42	377.25	0.42	0.90	−0.29	3.99	6.21	0.20
Czech Republic	53	0.30	0.47	0.42	52.72	424.48	0.72	0.77	−0.15	4.24	6.45	0.15
DEV	59,776	0.28	0.45	0.31	54.59	645.62	0.34	0.62	1.64	7.60	7.78	0.33
EMG	19,150	0.31	0.41	0.28	53.46	249.68	0.45	0.68	−0.12	4.32	6.52	0.24
GLB (Total)	78,926
GLB (Mean)		0.30	0.43	0.29	53.95	420.34	0.40	0.65	0.64	5.73	7.07	0.28
GLB (Std.Dev)		0.44	0.49	0.38	37.42	141.48	0.42	0.52	0.89	1.88	0.76	0.13

Note: This table reports deal-specific characteristics and institutional environment of target country for developed and developing market subsamples over 2000-2015. The acquisition-related variables (Related, Cash and Cross-border) are proportions expressed in decimal form and index values are provided for the country-level institutional attributes (CUI, CORRCTRL, CORRPER, ECOFREE, and TAX).

Table 3. Descriptive statistics.

	Cash				Non-Cash
	(1)				(2)
Variable characteristics	N	Mean	Med	SD	N	Mean	Med	SD	Diff (2)-(1) t-ratio
Target CUI	33,218	0.617	0.529	0.452	45,708	0.659	0.504	0.538	3.37***
Target CORRCTRL	33,196	1.075	1.218	0.822	44,563	1.053	1.222	0.917	−4.56***
Target CORRPER	33,196	6.774	7.315	1.802	44,489	6.407	7.295	1.886	−6.58***
Target EPU	27,750	111.591	117.121	39.884	36,283	114.775	119.400	39.958	9.99***
Target ECOFREE	32,463	7.607	7.630	0.721	42,885	7.699	7.846	0.825	3.46***
Target MCGDP	32,528	1.030	1.004	0.474	42,907	1.022	1.041	0.466	−3.33***
Target TAX	33,218	0.454	0.487	0.130	42,722	0.468	0.511	0.150	3.58***
Target SIZE	31,642	12.303	12.310	3.214	33,125	12.215	12.241	3.098	−1.19
Target RELSIZE	31,642	0.339	0.342	0.128	33,125	0.314	0.307	0.144	−1.88*
Target MB	31,642	1.894	1.775	0.085	33,125	1.709	1.771	0.082	−1.62
Target HTECH	33,218	0.316	0.002	0.462	45,708	0.281	0.002	0.439	−7.89***
Acquirer CUI	33,218	0.721	0.665	0.518	45,708	0.692	0.598	0.547	−5.36***
Acquirer CORRCTRL	33,196	1.165	1.377	0.825	44,563	1.194	1.404	0.870	3.26***
Acquirer CORRPER	33,196	7.017	7.502	1.715	44,477	7.002	7.395	1.841	−0.82
Acquirer ECOFREE	31,907	7.724	7.841	0.758	42,625	7.779	7.965	0.793	8.16***
Acquirer MCGDP	31,886	1.057	1.010	0.481	41,596	1.050	1.042	0.450	−0.62
Acquirer TAX	31,857	0.457	0.471	0.135	41,442	0.463	0.476	0.124	9.52***
Acquirer SIZE	31,642	15.746	15.642	4.031	31,642	15.018	15.067	4.988	−2.24**
Acquirer CF/TA	31,642	0.058	0.054	0.007	31,642	0.042	0.044	0.003	−2.11**
Acquirer HTECH	32,360	0.251	0.000	0.431	45,240	0.245	0.000	0.430	−1.34
Diff CUI	33,218	−0.008	0.000	0.284	45,708	0.003	0.000	0.276	3.27***
Diff ECOFREE	31,907	−0.072	0.000	0.581	42,625	−0.099	0.000	0.626	−6.15***
Diff MCGDP	31,886	−0.024	0.000	0.352	41,596	−0.032	0.000	0.333	−2.89***
Diff TAX	31,857	0.009	0.000	0.078	41,442	0.003	0.000	0.081	−9.65***
RELATED	33,218	0.274	0.000	0.442	45,211	0.286	0.000	0.407	1.92*
TOEHOLD	33,218	0.215	0.000	0.411	45,211	0.151	0.000	0.350	−22.17***
SHARE SOUGHT (%)	33,218	61.958	69.742	37.006	45,211	70.886	100.000	35.642	36.15***
CROSS-BORDER	33,218	0.241	0.000	0.421	45,211	0.244	0.000	0.425	0.41
DEALVALUE ($mil)	33,218	224.137	19.004	921.332	45,211	283.015	19.462	1563.023	5.585***

Note: This table reports the summary statistics of the variables in our data for two groups: Cash and Non-Cash paid acquisitions. Country-level variables include CUI, CORRCTRL, CORRPER, ECOFREE, MCGDP, and TAX. Deal-specific and firm-level characteristics: RELATED, TOEHOLD, SHARE SOUGHT, CROSS-BORDER, DEALVALUE, SIZE, RELSIZE, MB, CF/TA, and HTECH. Difference variables (Diff) indicate the difference between the target and acquirer countries. For variable definitions, see the Appendix A. We use Target or Acquirer prefixes to indicate whether the variable is for the target country or bidder country. Symbols ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

Table 4. Country-specific uncertainty and payment methods in acquisitions.

Variables	(1)	(2)	(3)	(4)
Target CUI	−0.053***	−0.071***	−0.201***	−0.179***
	(−5.52)	(−6.92)	(−16.22)	(−14.08)
RELATED		0.043***	0.043***	0.052***
		(4.06)	(3.75)	(4.31)
TOEHOLD		0.106***	0.055***	0.041***
		(8.05)	(4.07)	(3.00)
SHARE SOUGHT		−0.004***	−0.004***	−0.004***
		(−28.16)	(−29.12)	(−29.41)
lnDEALVALUE		0.030***	0.032***	0.032***
		(12.70)	(13.55)	(13.27)
CROSS-BORDER		−0.011	0.011	0.001
		(−1.18)	(1.07)	(0.15)
Target SIZE		0.067	0.067	0.072
		(1.15)	(1.15)	(1.23)
Target RELSIZE		0.013**	0.012**	0.013**
		(2.04)	(2.04)	(2.04)
Target MB		0.008*	0.008*	0.008*
		(1.85)	(1.85)	(1.85)
Target HTECH		0.034***	0.051***	0.034**
		(2.87)	(4.11)	(2.39)
Target ECOFREE			−0.132***	−0.130***
			(−7.42)	(−4.98)
Target MCGDP			0.270***	0.181***
			(16.75)	(7.80)
Target TAX			0.052	0.271***
			(0.76)	(3.11)
Acquirer SIZE				0.084**
				(2.23)
Acquirer CF/TA				1.126***
				(3.25)
Acquirer ECOFREE				−0.035
				(−1.04)
Acquirer MCGDP				0.134***
				(6.11)
Acquirer TAX				−0.346***
				(−4.55)
Acquirer HTECH				0.058**
				(2.41)
Fixed-effects	YIG	YIG	YIG	YIG
Obs	78,926	75,524	68,411	64,767
Pseudo R^2	0.032	0.047	0.067	0.089

Note: This table reports the results from Probit regressions of the payment method variable on the target country’s level of uncertainty. The dependent variable, CASH, is an indicator variable taking on the value of one if an acquisition is paid by cash and zero if it is paid by acquirer’s stock or a mixed cash and stock payment. The main test variable is the level of target country-specific uncertainty (CUI). Control variables include: (i) Deal-specific and firm-level characteristics: RELATED, TOEHOLD, SHARE SOUGHT, CROSS-BORDER, lnDEALVALUE, SIZE, RELSIZE, MB, CF/TA, and HTECH; (ii) Country-level variables: ECOFREE, TAX, and MCGDP. The empirical model is as follows: (1) $\begin{array}{r}CAS{H}_{i,t}={\beta }_0+{\beta }_{1}CU{I}_{j,t-1}+{\beta }_{2}CONTROL{S}_{t-1}+{\gamma }_s+{\delta }_t+{\Omega }_g+{\zeta }_{i,t}\end{array}$(1) We use Target or Acquirer prefixes to indicate whether the variable is for the target country or bidder country. See the Appendix A for variable definitions. In all model specifications, we also include year, industry, and geographic location dummies to control for year (Y), industry (I), and geographic (G) effects, respectively. These models are chosen after checking for multicollinearity and model specification error. z-ratios are shown in parentheses. Symbols ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

Table 5. Results for subsamples.

	Panel A: The target country’s level of policy uncertainty across subsamples
	DEVELOPED	DEVELOPING	NON-US	CRISIS	NON-CRISIS	DOMESTIC	CBMA
Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	−0.204***	−0.028	−0.226***	−0.157***	−0.218***	−0.086***	−0.033**
	(−12.57)	(−1.14)	(−16.79)	(−6.83)	(−13.99)	(−6.80)	(2.14)
Firm-level controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Target country controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Acquirer country controls	Yes	Yes	Yes	Yes	Yes	No	Yes
Fixed-effects	YIG	YIG	YIG	YIG	YIG	YIG	YIG
Obs	52,255	12,421	43,107	9,711	55,056	42,897	21,870
Pseudo R^2	0.095	0.086	0.068	0.089	0.105	0.092	0.086
	DEVELOPED		DEVELOPING		NON-US		CRISIS		NON-CRISIS
	High	Low	High	Low	High	Low	High	Low	High	Low
Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
Panel B: The target country’s level of policy uncertainty across subsamples when uncertainty is high and low
	−0.499***	0.214***	−0.081	0.212*	−0.377***	0.449***	−0.517***	0.512***	−0.355***	0.428***
Target CUI	(−20.46)	(2.89)	(−1.60)	(1.93)	(−16.88)	(8.02)	(−11.39)	(3.22)	(−15.05)	(7.15)
Firm-level controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Target country controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Acquirer country controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Fixed-effects	YIG	YIG	YIG	YIG	YIG	YIG	YIG	YIG	YIG	YIG
Obs	29,929	25,325	4,796	7,625	21,194	21,913	6,276	3,435	26,527	28,529
Pseudo R^2	0.095	0.066	0.080	0.057	0.092	0.054	0.075	0.050	0.089	0.067

Note: This table reports the results from Probit regressions of the payment method variable on the target country’s level of policy uncertainty for subsamples (Panel A) and when uncertainty is high and low (Panel B). Our subsamples include target firms operating in developed versus developing markets and non-US firms ([1] through [3]). We also limit our sample to the crisis and non-crisis years of our overall sample period ([4] and [5]). We further test the relationship between policy uncertainty and cash payment for domestic and cross-border subsamples separately ([6] and [7]). In Panel B, for each acquisition announcement year in the sample period, we sort target firm country into two groups (high and low policy uncertainty) based on the median value. The target country’s level of policy uncertainty for each acquisition announcement year is defined as high if the uncertainty value of this year is higher than the median uncertainty of the sample in a given year. The dependent variable, CASH, is an indicator variable taking on the value of one if an acquisition is paid by cash and zero if it is paid by acquirer’s stock or a mixed cash and stock payment. The main test variable is the level of target country-specific uncertainty (CUI). Control variables include: (i) Deal-specific and firm-level characteristics: RELATED, TOEHOLD, SHARE SOUGHT, CROSS-BORDER, lnDEALVALUE, SIZE, RELSIZE, MB, CF/TA, and HTECH; (ii) Country-level variables: ECOFREE, TAX, and MCGDP. See the Appendix A for variable definitions. In all model specifications, we include the deal-specific, target firm and acquirer firm control variables and year, industry, and geographic location dummies to control for year (Y), industry (I), and geographic (G) effects, respectively. Note we only report the results for the main variable of interest (Target CUI) in the table. These models are chosen after checking for multicollinearity and model specification error. z-ratios are shown in parentheses. Symbols ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

Table 6. Multinomial Logit regression for the three subsamples (cash, stock and mixed payment).

	Cash vs. Stock			Mixed payment vs. Stock
Multinomial Logit Regression	Coef.	SE	z	Coef.	SE	z
Target CUI	−0.045	0.021	−2.14	0.072	0.021	3.31
RELATED	0.015	0.020	0.75	−0.015	0.020	−0.75
TOEHOLD	0.053	0.027	1.92	−0.043	0.028	−1.55
SHARE SOUGHT	0.000	0.000	0.65	0.001	0.000	4.14
lnDEALVALUE	0.005	0.005	1.07	−0.006	0.005	−1.21
CROSS-BORDER	−0.077	0.025	−3.03	0.055	0.025	2.17
Target SIZE	0.034	0.031	1.11	0.002	0.002	1.03
Target RELSIZE	0.014	0.007	1.78	0.001	0.001	1.24
Target MB	0.009	0.008	1.02	−0.003	0.002	−1.32
Target HTECH	−0.027	0.027	−0.98	0.002	0.027	0.08
Acquirer SIZE	0.093	0.040	2.31	0.011	0.007	1.63
Acquirer CF/TA	1.075	0.291	3.69	0.095	0.046	2.13
Acquirer HTECH	0.017	0.026	0.64	−0.035	0.027	−1.32
Target country controls	Yes
Acquirer country controls	Yes
Fixed-effects	YIG
Obs	64,767
Wald χ^2	2266.91***
Pseudo R^2	0.046
Stock payment sample is the base outcome

Note: This table presents the multinomial Logit regression results of the relation between country uncertainty and payment methods decisions. The dependent variable, METHODS, takes the value of zero for stock payment (base), one for cash, and two for mixed cash and stock payment. The main test variable is the level of target country-specific uncertainty (CUI). Control variables include: (i) Deal-specific and firm-level characteristics: RELATED, TOEHOLD, SHARE SOUGHT, CROSS-BORDER, lnDEALVALUE, SIZE, RELSIZE, MB, CF/TA, and HTECH; (ii) Country-level variables: ECOFREE, TAX, and MCGDP. See the Appendix A for variable definitions. We also include year, industry, and geographic location dummies to control for year (Y), industry (I), and geographic (G) effects, respectively. The LR Chi-squared value confirms the significance and the explanatory power of the estimated specification. Symbol *** indicates significance at the 1%level.

Table 7. Robustness check with alternative independent variables

Variables	(1)	(2)	(3)	(4)
Panel A: Institutional transparency
Target CORRCTRL	0.669***	0.743***
	(14.92)	(15.17)
Target CORRCTRL^2	−0.173***	−0.179***
	(−12.98)	(−13.62)
Target CORRPER			0.296***	0.327***
			(9.54)	(9.87)
Target CORRPER^2			−0.020***	−0.021***
			(−8.86)	(−8.86)
Target country controls	No	Yes	No	Yes
Acquirer country controls	No	Yes	No	Yes
Fixed-effects	YIG	YIG	YIG	YIG
Obs	68,411	64,767	68,411	64,767
Pseudo R^2	0.056	0.071	0.044	0.069
Variables	(1)	(2)
Panel B: National Elections
Target ELECTION	−0.082***	−0.046**
	(−5.16)	(−2.40)
Target country controls	No	Yes
Acquirer country controls	No	Yes
Fixed-effects	YIG	YIG
Obs	68,411	64,767
Pseudo R^2	0.046	0.072
Panel C: Economic Policy Uncertainty
Target EPU	−0.001***	−0.002***
	(−4.97)	(−6.75)
Target country controls	No	Yes
Acquirer country controls	No	Yes
Fixed-effects	YIG	YIG
Obs	58,033	54,982
Pseudo R^2	0.052	0.056

Note: In this table, we employ a number of alternative proxies for a country’s uncertainty. In Panel A, we use the levels of corruption control (CORRCTRL) and of corruption perceptions (CORRPER) as proxies for country-specific uncertainty. The CORRCTRL variable relates to the levels of control of corruption and captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as capture of the state by elites and private interests. The CORRPER variable relates to the levels of corruption perceptions by perceived levels of public sector corruption according to experts and business people. In Panel B, we use national elections (ELECTION) as another proxy for country-specific uncertainty. The ELECTION variable is a dummy that equals 1 if country i holds presidential (or parliamentary) election in year t, and 0 otherwise. In Panel C, we use Economic Policy Uncertainty Index (EPU) as another proxy for country-specific uncertainty. This index is constructed by Baker et al. (2016) based on combining three types of underlying components, including newspaper coverage of policy-related economic uncertainty, the number of federal tax code provisions set to expire in future years, and the disagreement among economic forecasters. The dependent variable, CASH, is an indicator variable taking on the value of one if an acquisition is paid by cash and zero if it is paid by acquirer’s stock or a mixed cash and stock payment. Control variables are as given in the previous table. See the Appendix A for variable definitions. We use Target or Acquirer prefixes to indicate whether the variable is for the target country or bidder country. In all model specifications, we include deal-specific, target firm and acquirer firm control variables and year, industry, and geographic location dummies to control for year (Y), industry (I), and geographic (G) effects, respectively. Similar to Table 5, we only include the results for the main variable(s) of interest. z-ratios are shown in parentheses. Symbols *** and ** indicate significance at the 1% and 5% levels, respectively.

Table 8. Policy uncertainty difference and payment methods in acquisitions.

Variables	(1)	(2)	(3)
Diff CUI	−0.042***	−0.045***	−0.045***
	(−2.74)	(−2.91)	(−2.91)
RELATED		0.042***	0.058***
		(4.21)	(5.19)
TOEHOLD		0.108***	0.074***
		(8.21)	(4.96)
SHARE SOUGHT		−0.004***	−0.004***
		(−28.32)	(−27.13)
lnDEALVALUE		0.031***	0.037***
		(12.73)	(13.37)
CROSS-BORDER		−0.006	0.018
		(−0.58)	(1.27)
Target SIZE		0.016	0.016
		(1.22)	(1.22)
Target RELSIZE		0.008	0.007
		(1.07)	(1.07)
Target MB		0.003	0.003
		(0.55)	(0.55)
Target HTECH		0.016*	0.016*
		(1.68)	(1.68)
Acquirer SIZE		0.047*	0.044*
		(1.85)	(1.82)
Acquirer CF/TA		1.079**	1.082**
		(2.33)	(2.35)
Acquirer HTECH		0.059**	0.064**
		(2.44)	(2.48)
Diff ECOFREE			−0.052**
			(−2.35)
Diff MCGDP			−0.009
			(−0.46)
Diff TAX			0.372***
			(5.11)
Fixed-effects	YIG	YIG	YIG
Obs	78,926	68,411	64,767
Pseudo R^2	0.028	0.043	0.059

Note: This table reports the results from Probit regressions of the payment method variable on the difference in uncertainty between the target and acquirer countries. The dependent variable, CASH, is an indicator variable taking on the value of one if an acquisition is paid by cash and zero if it is paid by acquirer’s stock or a mixed cash and stock payment. The main test variable is the level of target country-specific uncertainty (CUI). Control variables include: (i) Deal-specific and firm-level characteristics: RELATED, TOEHOLD, SHARE SOUGHT, CROSS-BORDER, lnDEALVALUE, SIZE, RELSIZE, MB, CF/TA, and HTECH; (ii) Country-level variables: ECOFREE, TAX, and MCGDP. The empirical model is as follows: (2) $\begin{array}{r}CAS{H}_{i,t}={\beta }_0+{\beta }_{1}DiffCU{I}_{j,t-1}+{\beta }_{2}CONTROL{S}_{t-1}+{\gamma }_s+{\delta }_t+{\Omega }_g+{\zeta }_{i,t}\end{array}$(2) We use Target or Acquirer prefixes to indicate whether the variable is for the target country or bidder country. See the Appendix A for variable definitions. In all model specifications, we also include year, industry, and geographic location dummies to control for year (Y), industry (I), and geographic (G) effects, respectively. These models are chosen after checking for multicollinearity and model specification error. z-ratios are shown in parentheses. Symbols *** and ** indicate significance at the 1% and 5%, respectively.

Table 9. The role of power distance.

Panel A:		Panel B:
Target CUI	−0.187***	Target CUI	−0.114***
	(−14.08)		(−9.65)
Diff PDI	0.000	Diff PDI	0.000
	(0.53)		(0.97)
Target CUI*Diff PDI	0.002*	Target CUI*Diff PDI	0.003**
	(1.81)		(2.40)
RELATED	0.048***	RELATED	0.053***
	(4.21)		(4.80)
TOEHOLD	0.040***	TOEHOLD	0.066***
	(2.62)		(4.38)
SHARE SOUGHT	−0.004***	SHARE SOUGHT	−0.004***
	(−30.01)		(−28.65)
lnDEALVALUE	0.038***	lnDEALVALUE	0.038***
	(13.54)		(13.55)
CROSS-BORDER	0.007	CROSS−BORDER	0.009
	(0.56)		(0.86)
Target SIZE	0.016	Target SIZE	0.008
	(1.22)		(1.10)
Target RELSIZE	0.007	Target RELSIZE	0.007
	(1.07)		(1.07)
Target MB	0.003	Target MB	0.003
	(0.55)		(0.55)
Target HTECH	0.016*	Target HTECH	0.016*
	(1.68)		(1.68)
Acquirer SIZE	0.044*	Acquirer SIZE	0.017
	(1.82)		(1.43)
Acquirer CF/TA	1.082**	Acquirer CF/TA	1.008*
	(2.35)		(1.71)
Acquirer HTECH	0.064**	Acquirer HTECH	0.034*
	(2.48)		(1.82)
Target ECOFREE	−0.181***	Diff ECOFREE	−0.091***
	(−5.41)		(−2.90)
Target MCGDP	0.186***	Diff MCGDP	−0.029
	(6.16)		(−1.06)
Target TAX	0.280***	Diff TAX	0.466***
	(2.91)		(6.00)
Fixed-effects	YIG	Fixed-effects	YIG
Obs	61,852	Obs	61,852
Pseudo R^2	0.069	Pseudo R^2	0.064

Note: This table presents the empirical analysis results on the effect of power distance on the relation between country-specific uncertainty and payment method. We employ the difference in Hofstede’s Power distance Index (Diff PDI) between the target and acquirer countries to proxy for power distance between host and home countries. PDI measures the extent to which the less powerful members of organizations and institutions accept and expect that power is distributed unequally. This represents inequality (more versus less), but defined from below, not from above. It suggests that a society’s level of inequality is endorsed by the followers as much as by the leaders. The dependent variable, CASH, is an indicator variable taking on the value of one if an acquisition is paid by cash and zero if it is paid by acquirer’s stock or a mixed cash and stock payment. The main test variable is the level of target country-specific uncertainty (CUI). Control variables include: (i) Deal-specific and firm-level characteristics: RELATED, TOEHOLD, SHARE SOUGHT, CROSS-BORDER, lnDEALVALUE, SIZE, RELSIZE, MB, CF/TA, and HTECH; (ii) Country-level variables: ECOFREE, TAX, and MCGDP. The empirical model is as follows: (3) $\begin{array}{r}CAS{H}_{i,t}={\beta }_0+{\beta }_{1}CU{I}_{j,t-1}+{\beta }_{2}DiffPD{I}_{t-1}+{\beta }_{3}CU{I}_{j,t-1}\ast DiffPD{I}_{t-1}+{\beta }_{4}CONTROL{S}_{t-1}+{\gamma }_s+{\delta }_t+{\Omega }_g+{\zeta }_{i,t}\end{array}$(3) We use Target or Acquirer prefixes to indicate whether the variable is for the target country or bidder country. In all model specifications, we also include year, industry, and geographic location dummies to control for year (Y), industry (I), and geographic (G) effects, respectively. z-ratios are shown in parentheses. Symbols ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

Table

Variables	Acronym	Description	Data sources
A. Dependent variable
Payment methods	CASH	An indicator variable taking on the value of one if an acquisition is paid by cash and zero if it is paid by acquirer’s stock or a mixed cash and stock payment.	SDC Platinum
B. Independent variables
Country-specific uncertainty	CUI	This index indicates country-specific uncertainty developed by Ahir, Bloom, and Furceri (2018) and defined using the frequency of the word ‘uncertainty' in the quarterly Economist Intelligence Unit country reports. These reports discuss major political and economic developments in each country, along with analysis and forecasts of policy implications, and political and economic conditions. The index captures uncertainty related to economic and political developments.	Ahir, Bloom, and Furceri (2018) policyuncertainty.com
Economic Policy Uncertainty	EPU	This index is constructed by three types of underlying components. One component quantifies newspaper coverage of policy-related economic uncertainty. A second component reflects the number of federal tax code provisions set to expire in future years. The third component uses disagreement among economic forecasters as a proxy for uncertainty.	Baker et al. (2016); policyuncertainty.com
Control of corruption	CORRCTRL	This index relates to the levels of control of corruption and captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as capture of the state by elites and private interests. Its value is from −2.5 to +2.5, where the higher the index the less the corruption indicated.	Worldwide Governance Indicators, World Bank
Corruption perceptions	CORRPER	This index relates to the levels of corruption perceptions. It ranks 180 countries and territories by their perceived levels of public sector corruption according to experts and business people, and uses a scale of 0–100, where 0 is highly corrupt and 100 is very clean.	Transparency International
C. Control variables
C.1. Firm-level characteristics
Firm size	SIZE	The natural logarithm of total market value of a firm’s equity in the preceding year of M&A event.	Worldscope
Relative size	RELSIZE	The ratio of transaction value to market value of a target firm’s equity.	SDC Platinum, Worldscope
Growth potential	CF/TA	The ratio of the acquiring firm’s operating cash flow to total assets in the prior year of acquisition.	Worldscope
Market-to-book ratio	MB	The ratio of a firm’s market value to book value in the prior year.	Worldscope
High-tech acquisitions	HTECH	An indicator variable taking on the value of one if target firm operates in a high-tech industry and zero otherwise.	SDC Platinum
C.2. Deal-specific characteristics
Relatedness acquisitions	RELATED	An indicator variable taking on the value of one if the target and the acquirer are in same areas of operations and zero for unrelated acquisitions.	SDC Platinum
Acquisition locations	CROSS-BORDER	An indicator variable taking on the value of one if the bidder and target firm come from different countries and zero otherwise.	SDC Platinum
Payment methods	SHARE SOUGHT	The percentage of the target firm’s shares sought by the bidding firm.	SDC Platinum
Toehold transactions	TOEHOLD	The percentage of target equity held by the bidder before the acquisition.	SDC Platinum
Transaction value	DEALVALUE	The value of the transaction (US$ million) in logarithm.	SDC Platinum
C.3. Institutional environments and other country-level characteristics
Economic freedom	ECOFREE	This index relates to A country’s quality of economic freedom. The index measures the country’s degree of economic freedom in five wide-ranging areas: (1) Size of government, (2) Legal structure and security of property rights, (3) Access to sound money, (4) Freedom to trade internationally, and (5) Regulation of credit, labor, and business.	Fraser Institute
Market capital to GDP	MCGDP	The stock market capitalization to GDP ratio.	Global Financial Development Database
Corporate income tax ratio	TAX	This variable relates to the tax level on income and capital gains.	World Development Indicators, World Bank
Power distance value	PDV	This index is a measurement of the acceptance of a hierarchy of power and wealth by the individuals who make up the general population of a nation, culture, or business.	geerthofstede.com

Table

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)	(16)	(17)	(18)	(19)	(20)	(21)	(22)	(23)	(24)
Target CUI	1
CASH	−0.03	1
RELATED	−0.03	−0.01	1.00
TOEHOLD	0.00	0.08	−0.03	1
SHARE SOUGHT	−0.04	−0.13	0.06	−0.40	1
CROSS-BORDER	−0.08	0.00	0.01	0.01	−0.05	1
lnDEALVALUE	−0.02	0.02	0.05	0.00	0.17	0.06	1
Target SIZE	0.01	0.03	0.03	0.06	0.11	−0.01	0.04	1
Target RELSIZE	0.00	0.01	0.01	0.02	0.04	−0.01	0.02	0.32	1
Target MB	0.01	0.00	0.04	0.08	0.09	0.03	0.06	0.26	0.05	1
Acquirer SIZE	0.01	0.14	0.15	0.09	0.12	0.09	0.08	0.04	0.12	0.14	1
Acquirer CF/TA	0.06	0.28	0.17	0.03	0.14	0.12	0.13	0.01	0.09	0.15	0.26	1
Target ECOFREE	−0.04	−0.01	0.04	−0.13	0.29	−0.11	0.06	0.03	0.02	0.03	0.03	0.05	1
Target MCGDP	−0.08	0.01	0.01	−0.13	0.27	−0.06	0.05	0.01	0.01	0.02	0.01	0.03	0.52	1
Target TAX	−0.20	−0.01	0.07	−0.20	0.33	−0.09	−0.03	0.01	0.00	0.02	0.03	0.14	0.55	0.39	1
Target HTECH	0.03	0.03	0.00	−0.04	0.04	0.00	0.00	0.12	0.04	0.01	0.03	0.03	0.09	0.03	0.02	1
Target CORRCTRL	−0.07	0.02	0.05	−0.09	0.25	−0.09	0.05	0.01	0.00	0.02	0.01	0.04	0.84	0.41	0.43	0.06	1
Target CORRPER	0.00	0.03	0.03	−0.07	0.19	−0.10	0.01	0.02	0.01	0.03	0.01	0.04	0.68	0.37	0.46	0.05	0.42	1
Target EPU	0.25	0.04	0.01	0.01	−0.02	−0.02	−0.01	0.00	0.00	0.01	0.02	0.06	−0.07	−0.22	−0.10	0.01	−0.01	0.03	1
Diff CUI	0.31	−0.01	0.00	0.01	0.00	−0.01	0.00	0.01	0.00	0.01	0.02	0.03	0.00	−0.02	−0.03	0.00	−0.03	−0.01	0.03	1
Diff ECOFREE	0.02	0.02	−0.01	−0.04	0.07	−0.26	0.01	0.02	0.02	0.03	0.00	0.02	0.45	0.22	0.17	0.03	0.34	0.30	0.05	−0.02	1
Diff MCGDP	0.00	0.01	−0.01	−0.03	0.05	−0.16	0.00	0.01	0.02	0.01	0.00	0.02	0.20	0.36	0.10	0.03	0.20	0.19	0.01	−0.05	0.57	1
Diff TAX	−0.04	0.04	−0.01	−0.03	0.02	0.13	0.01	0.03	0.02	0.02	0.01	0.02	0.14	0.12	0.31	0.02	0.14	0.12	0.02	−0.04	0.29	0.19	1
Diff PDI	−0.04	−0.02	0.03	0.03	−0.03	0.17	0.00	0.01	0.01	0.02	0.03	0.04	−0.23	−0.08	−0.15	−0.02	−0.27	−0.22	−0.06	−0.10	−0.50	−0.14	−0.25	1

Baker, S. R., N. Bloom, and S. J. Davis. 2016. “Measuring Economic Policy Uncertainty.” Quarterly Journal of Economics 131 (4): 1593–1636.

 Web of Science ®Google Scholar

Ahir, H., N. Bloom, and D. Furceri. 2018. “World Uncertainty Index.” Unpublished.

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