The effect of foreign direct investment on structural transformation in developing countries

Figures & data

Table 1. Definition, measurement, and expected signs of variables

Symbol	Definition & Hypothesis	Data Source
STI	It is a dependent variable in FDI-Structural transformation regression analysis. The STI is a composite index that includes the following components: (1) per capita GDP, (2) physical capital accumulation, (3) human capital, (4) service sector employment, (5) manufacturing employment, (6) urban population, (7) agriculture value-added output, and (8) manufacturing export. Because just one lag of STI (STIt-1) is included in the model, there is no need to incorporate one or more variables that were involved in STI creation as explanatory variables in the FDI-STI regression analysis. The addition of variables results in a duplication of variables. Hence, we included total population, arable land, and trade openness as control factors in our FDI-STI regression study, in addition to our interesting variable FDI.	-
lnFDI	It is the amount of equity capital, earnings reinvestment, and other intra-firm loans represented as net FDI inflows in the balance of payments. It is measured in terms of stock by USD at current prices in millions. It is projected to have a favorable effect on structural transformation because it is a source of cash, technology, and knowledge. Therefore, the study tried to test against the null hypothesis that states FDI has no effect on structural transformation in developing countries.	UNCTAD
lnOT	A country’s trade openness can be defined using the ratio of exports plus imports over GDP. It is calculated as a percentage of GDP. Liberalizing trade increases foreign direct investment, job opportunities, and economic integration with the world market, resulting in increased income for unskilled labor, capital accumulation, and technology transfers, all of which are crucial for structural transformation in developing countries. (McMillan et al., 2014). It’s also regarded as a barometer of an institution’s quality (Uy et al., 2013). Hence, it is anticipated that it will have a beneficial effect on structural transformation.	UNCTAD
lnAL	It is defined as the land under temporary agricultural crops, meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow. It is measured by hectare (thousand). Agriculture-based-MNEs need natural resource endowments, which are proxied here by the logarithm of the area of arable land, to invest directly in agribusiness like crop production and indirectly in supply chain linkage (Suleman, 2009). Agriculture is also a significant source of domestic capital, employment, and GDP growth in developing nations; so, transformation cannot be completed without considering agriculture, particularly the potential for arable land. Hence, it is predicted to have a favorable effect on structural transformation.	FAO
lnPoP	The population is an important device for attracting FDI since it provides both cheap labor and a vast market.	UNCTAD

UNCTAD = United Nations Conference on Trade and Development; FAO = Food and Agriculture Organization

Table 2. Definition, measurement, data source, and expected signs of structural transformation variables

Symbol	Definitions of Variables and Expected Signs	Data Source
PcGDP	Kuznets (1966) linked structural transformation to long-term economic growth, which he described as a country’s ability to deliver increasingly diverse economic goods to its population. Since this study uses per capita GDP as a proxy for sustainable economic growth, its effect on structural transformation is predicted to be favorable.	UNCTAD
HC	Human capital is defined as an individual’s stock of knowledge, skills, and other attributes that enable them to be more productive. The endogenous growth theory of Romer (1990) suggests that an economy with a large stock of human capital will grow faster and undergo structural transformation easily. Thus, it is thought to have a good influence on structural transformation	PenT10.0
PcCA	It is measured by per capita capital accumulation, which is the ratio of gross fixed capital formation at current prices in millions over the entire population. In Harrod (1939)-Domar (1947) growth theory and Rostow (1956) linear stage theory, capital accumulation is suggested to be an important indicator of structural transformation and sustainable growth. Hence, it is predicted to have a favorable impact on structural restructuring.	UNCTAD
UP	Kuznets (1966) defines structural transformation as a shift in population distribution between rural and urban areas, with the share of urban population increasing as the country becomes structurally transformed.	UNCTAD
MEXP	According to Chenery’s (1960) structural change hypothesis, having a higher share of manufacturing export in merchandise export is a good indicator of structural transformation. Manufacturing exports are calculated as a percentage of overall merchandise exports in this case. As a result, it is predicted to have a favorable impact on structural transformation.	WTO
ME	It is the proportion of manufacturing employment in total employment. Under Lewis’s (1954) model of dualistic development, structural change is defined as a gradual transition of employment from agricultural to manufacturing sector. Hence, a large proportion of it is a favorable signal of structural transformation and is likely to have a positive effect on it.	GGDC
SE	According to structural theory developed by H.B. Chenery and Taylor (1968) and Syrquin and Chenery (1989), a continued increase in service employment is one indicator of structural transition. It is expressed as a proportion of total employment held by the service industry. It is anticipated that it will have a positive impact on structural transformation.	GGDC
AVA	According to Syrquin and Chenery (1989) theory, structural transformation is manifested by the continual decline in agriculture’s value-added share of total output. A percentage proportion of agriculture value-added output in total output is used to calculate it. It is anticipated that it will have a positive effect on structural transformation.	GGDC

UNCTAD = United Nations Conference on Trade and Development, WTO = World Trade Organization, PenT10.0 = Pen Table 10.0, FAO = Food and Agriculture Organization, GGDC = Groningen Growth and Development Centre.

Table 3. Engenvalues of structural transformation

Component	Eigenvalue	% of variance explained	Cumulative %
Comp1	4.43218	0.5540	0.5540
Comp2	1.09951	0.1374	0.6915
Comp3	0.940133	0.1175	0.8090
Comp4	0.569173	0.0711	0.8801
Comp5	0.348905	0.0436	0.9237
Comp6	0.272825	0.0341	0.9578
Comp7	0.213103	0.0266	0.9845
Comp8	0.12417	0.0155	1.0000

Table 4. Common factors and weight analysis

Variables	Common factor loading			Squared Factors loading (Scaled to sum to one)			Weight
	Comp1	Comp2	Comp3	Comp1	Comp2	Comp3
HC	0.4193	0.0409	0.1636	0.1757	0.0017	0.0267	0.0687
PcCA	−0.0102	0.0162	0.9664	0.0001	0.0003	0.9335	0.3112
PcGDP	0.418	−0.0677	0.0688	0.1746	0.0046	0.0047	0.0611
SE	0.4656	−0.0125	0.0258	0.2166	0.0001	0.0006	0.0725
MEXP	−0.0672	0.7368	0.0691	0.0045	0.5423	0.0048	0.1825
UP	0.5085	−0.0946	−0.1053	0.2584	0.0089	0.0111	0.0916
AVA	−0.41	−0.1387	0.1167	0.1679	0.0192	0.0136	0.0679
ME	0.0461	0.6506	−0.0697	0.0021	0.4228	0.0048	0.1433

Table 5. Structural transformation status of the countries

No	Country	STI	No		STI	No	Country	STI
1.	Taiwan	0.8363	17.	Brazil	0.5325	33.	Ghana	0.3278
2.	Hong Kong	0.8296	18.	Argentina	0.5305	34.	Senegal	0.317
3.	South Korea	0.8079	19.	South Africa	0.4951	35.	Nepal	0.3146
4.	Singapore	0.8012	20.	Bangladesh	0.481	36.	Kenya	0.286
5.	Malaysia	0.7983	21.	Pakistan	0.4786	37.	Zambia	0.2531
6.	Mexico	0.6993	22.	Morocco	0.4643	38.	Cameroon	0.2526
7.	Cambodia	0.6932	23.	Colombia	0.4633	39.	Myanmar	0.2521
8.	Mauritius	0.6802	24.	Bolivia	0.4586	40.	Tanzania	0.228
9.	Turkey	0.6363	25.	Lesotho	0.4431	41.	LAO	0.2088
10.	China	0.6304	26.	India	0.4386	42.	Uganda	0.1978
11.	Thailand	0.5911	27.	Viet Nam	0.4367	43.	Nigeria	0.1915
12.	Tunisia	0.5906	28.	Indonesia	0.4324	44.	Rwanda	0.143
13.	Sri Lanka	0.5752	29.	Peru	0.4137	45.	Burkina Faso	0.136
14.	Philippines	0.5607	30.	Ecuador	0.4006	46.	Mozambique	0.136
15.	Botswana	0.5561	31.	Egypt	0.395	47.	Malawi	0.1294
16.	Costa Rica	0.5543	32.	Namibia	0.3726	48.	Ethiopia	0.0951

No = Role number.

Figure 1. Resources shift among different sectors in both NICs and LDCs.

Note: ______Agricultural Share,——-Manufacturing Share,−∙∙−ServicesShare

Figure 2. The trend of per capita capital stock (1990–2018).

Figure 3. The trend of per capita income (1990–2018).

Table 6. Descriptive statistics

Variables		LDCs	NICs	Variables	LDCs	NICs
Value-Added Share (%)	Agriculture	18.67	1.58	Manufacturing Export (%)	44.97	87.23
	Manufacturing	16.27	21.31	FDI-GDP (%)	24.09	135.67
	Industry	23.90	28.38	Per Capita CA (‘000 USD)	227.38	323.97
	Services	37.02	49.67	Per capita GDP (USD)	2500	24,814
Employment share (%)	Agriculture	46.06	4.37	Per Capita Arable land (ha)	0.961	0.0158
	Manufacturing	10.39	19.09	Openness of Trade (%)	51.28	213.80
	Industry	25.78	28.64	Urbanization (%)	42.32	87.99
	Services	22.15	41.14	Human Capital Index	2.02	3.00

LDCs are all 44 developing countries, ha = hectare, USD = USA dollar.

Figure 4. Trends of structural transformation (1990–2018).

Note: 1 = Argentina, 2 = Burkina Faso, 3 = Bangladesh, 4 = Bolivia, 5 = Brazil, 6 = Botswana, 7 = China, 8 = Cameroon, 9 = Colombia, 10 = Costa Rica, 11 = Ecuador, 12 = Egypt, 13 = Ethiopia, 14 = Ghana, 15 = Indonesia, 16 = India, 17 = Kenya, 18 = Cambodia, 19 = LAOs, 20 = Sri Lanka, 21 = Lesotho, 22 = Morocco, 23 = Mexico, 24 = Myanmar, 25 = Mozambique, 26 = Mauritius, 27 = Malawi, 28 = Malaysia, 29 = Namibia, 30 = Nigeria, 31 = Nepal, 32 = Pakistan, 33 = Peru, 34 = Philippines, 35 = Rwanda, 36 = Senegal, 37 = Thailand, 38 = Tunisia, 39 = Turkey, 40 = Tanzania, 41 = Uganda, 42 = Viet Nam, 43 = South Africa, 44 = Zambia, 45 = Taiwan, 46 = Hong Kong, 47 = South Korea, and 48 = Singapore.

Table 7. Fisher unit root test

	Inverse chi-square (P)		Inverse Normal (Z)		Inverse Logit (L)		Modified inv.Chi-squared (Pm)
	Statistic	p-value	Statistic	p-value	Statistic	p-value	Statistic	p-value
lnSTI	313.994	0.0000	−10.4827	0.0000	−11.9114	0.0000	17.035	0.0000
lnFDI	264.784	0.0000	−7.9431	0.0000	−9.4379	0.0000	13.325	0.0000
lnAL	215.262	0.0000	−7.0131	0.0000	−7.5376	0.0000	9.5928	0.0000
lnOT	285.015	0.0000	−10.9793	0.0000	−11.5289	0.0000	14.850	0.0000
lnPoP	1461.85	0.0000	−25.8815	0.0000	−54.8006	0.0000	103.55	0.0000
lnPcGDP	120.168	0.0129	−2.7038	0.0034	−2.5133	0.0063	2.4248	0.0077
lnCA_GDP	226.359	0.0000	−7.8688	0.0000	−8.1571	0.0000	10.429	0.0000
lnHC	695.269	0.0000	−5.9966	0.0000	−13.1849	0.0000	45.774	0.0000
lnMVA	212.674	0.0000	−7.5378	0.0000	−7.7980	0.0000	9.3977	0.0000
lnSVA	273.452	0.0000	−10.1586	0.0000	−10.7595	0.0000	13.979	0.0000
lnMEm	284.147	0.0000	−8.7327	0.0000	−10.6896	0.0000	14.785	0.0000
lnSE	264.920	0.0000	−7.1087	0.0000	−8.7091	0.0000	13.335	0.0000
lnMEXP	398.256	0.0000	−13.6284	0.0000	−16.2213	0.0000	23.386	0.0000
lnUP	1064.78	0.0000	−17.7602	0.0000	−36.6276	0.0000	73.627	0.0000

Table 8. The one-step arallano-bond (1991) estimation output (Developing Countries)

lnSTI	Coefficient	Robust Std.Err.	Z	P>/z/	[95% Conf. Interval]
lnSTIt-1	0.6581***	0.0666	9.88	0.000	0.5274683	0.7886619
lnFDI	0.0214***	0.0079	2.71	0.007	0.0058955	0.0368593
lnAL	0.1370***	0.0582	2.35	0.019	0.0229704	0.2511151
lnOT	0.2007	0.0213	0.94	0.345	−0.0216174	0.061759
lnPoP	0.0461	0.0534	0.86	0.389	−0.0586279	0.1507366

Table 9. Fixed effect model estimation output (Developing Countries)

Variables	Output		Labor shift		Manufacturing Export	Urbanization
	lnMVA	lnSVA	lnMEm	lnSE
lnFDI	0.0156* (0.0091)	0.0236*** (0.0048)	0.0766** (0.0124)	0.0232*** (0.0083)	−0.0125 (0.0244)	0.0335**** (0.0048)
lnPcGDP	0.0096 (0.0205)	0.0287*** (0.0108)	−0.0253 (0.0279)	0.0577*** (0.0187)	−0.1672*** (0.0502)	0.0297*** (0.0108)
lnCA_GDP	0.1550*** (0.0163)	0.0378*** (0.0086)	−0.069*** (0.0221)	0.0064 (0.0148)	−0.0798** (0.0389)	−0.0047*** (0.0086)
lnHC	−0.2401** (0.1047)	0.2141*** (0.0551)	−0.854*** (0.1427)	0.0133 (0.0956)	1.0965*** (0.2515)	0.0940* (0.0552)
lnAL	0.0930** (0.0373)	0.1016*** (0.0196)	0.3442*** (0.0508)	0.2824*** (0.0340)	0.0960 (0.0902)	0.1465*** (0.0196)
lnOT	−0.0161 (0.0224)	−0.0068 (0.0118)	0.2333*** (0.0305)	0.0925*** (0.0205)	0.0071 (0.0555)	−0.0091 (0.0118)
lnPoP	−0.3590*** (0.0644)	−0.1308*** (0.0339)	0.1805*** (0.0877)	0.8977*** (0.0588)	0.2422 (0.1633)	0.3103*** (0.0339)
Hausman test	X^2 = 82.15 P = 0.000	X^2 = 32.91 P = 0.000	X^2 = 144.6 P = 0.000	X^2 = 260.1 P = 0.000	X^2 = 116.46 P = 0.0000	X^2 = 104.29 P = 0.0000
F-test	F = 126 P = 0.000	F = 70.35 P = 0.000	F = 105.3 P = 0.000	F = 82.76 P = 0.000	F = 146.85 P = 0.000	F = 315.68 P = 0.0000

MVA = the share of manufacturing value added in total value-added output, MEm = share of manufacturing employment in total employment, SE = share of services employment, MEx = share of manufacturing export in total merchandise export, ln = natural logarithm, X² = chi-square, P = probability, and F = F-value. The sign *, **, and *** indicates it is significant at 10, 5, and 1 percent significance level, respectively. Standard errors are reported in parentheses.

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