The present study investigates the dynamic causal relationship between net inflows of skill intensive industry/sectoral FDI and different political regimes (a movement from autocratic to democratic government), to test our primary hypothesis that whether the political leaders favour their constituencies/median-voters by fiddling the sectoral composition of FDI inflows in Pakistan, to please them economically. Learning from the prior literature, we have categorised the median voter, the most populous, in low skill industry, while less populous in medium skill industry, and least populous in the high skilled industry. To address the question, that politically which industry fetch more FDI, we have employed novel Frequency Domain Spectral Granger causality (GC) test, using time series data, to our dynamic models, to gauge the cyclical fluctuations in causal relationship between political regimes and skill intensive FDI. The causal relationship between skill intensive FDI and political regimes is analysed with and without some confounding economic variables (e.g., trade openness, human capital and exchange rate) for the robustness of the results. Our findings show that, rather than political, there are some economic reasons cause to attract more FDI in low skill (sub)sectors. We have also found bi-directional causal relationship, in short as well as in the long run, for medium and high skill FDI-indicating the intentions of political interests in designing FDI sectoral distribution. However conducive economic environment is a fundamental confounding factor fetching FDI in different (sub)sectors of Pakistan economy, and in return, FDI has also instigated the development of political institutions by empowering middle class populace to participate more in the democratic process, the results reveal.

JEL Classification: PI6, CI4, H10

Keywords: Politics, Inward FDI, Frequency-Domain Granger Causality, Pakistan

1. INTRODUCTION

In the recent experience of developing world, remarkable differences in economic development have been observed, most probably ensued from the economic and regional integration of the country with rest of the economies. The empirical findings of Rodrik (2008) suggest that the capital inflows and outflows are one of the means to form such type of integration, and can be enhanced through good economic and political institutions. In order to analyse the political aspects of FDI policy, it is prime to study the political institutional framework of host country that determine the level and sectors/industry wise FDI (characterised in term of High, Medium and Low Skill Intensive FDI) as argued by Garrett (1998), Mosley (2003), Oliva and Bati (2002), Khan and Kim (2011).

The study of Zhu (2013) narrated that among political stability, institutional quality, political freedom, investors' preferences, these are the host country 'political institutions/political regimes' that create distinct tastes for different types of FDI inflows. Among others, pragmatic studies of Borner, et al. (1995), Barro (1997) have also well documented about the types of FDI flows which are more productive, and about the institutional factors attract productive capital flows.

Empirical literature regarding skill biasness of inward FDI depending upon political regime is not in surfeit. Studies including [e.g., Markusenm, et al. (1996); Mundell (1957); Yeaple (2003)] claim that capital moves into countries where factor abundance and low prices exist. On the other side, Baum and Lake (2003) argued that democratic countries invest more for median voter and attract low skilled inward FDI, while autocratic governments suppress labour unions and wages, equipping nations with greater proportion of high skill intensive FDI.

More explicitly, the studies of [Pandya (2007); Pinto (2004); Pinto and Pinto (2008); Bueno de Mesquita, et al. (1999, 2003); Milner and Kubota (2005)] showed that the relative demand of skilled and unskilled labour and income distribution is affected by distinct policies for high skill intensive and low skill intensive FDI. In order to retain office, the democratic leaders need support of majority, i.e., median voters which are mainly unskilled labour hence low skill intensive FDI inflows are favoured. In contrast authoritarian leaders rely on only a small winning coalition typically consisting of skilled workers attract high skill intensive FDI [Zhu (2013)].These aforesaid narratives, thus, arise the question that is--whether it is the democracy and/or autocracy, in which the political leaders adjust FDI inflows in different sectors of the economy to accommodate their constituencies for political support in case of Pakistan.

Concerning with the aggregated level of FDI, existing literature is, still, not conclusive on the political economy of FDI policy. Some of the studies found that democracy makes a country more attractive to foreign investors by putting institutional constraints on politicians opportunist behaviour and reducing consequent political risk [e.g., Jensen (2003)], while the others take the position on the opposite side by claiming that politician's capacity to offer favourable treatment to foreign investors is restricted due to plurality of interests [see, Li and Resnick (2003)]. Keeping in view such a controversial findings on the concerned issue, the objective of this study is to test the hypothesis that whether the political institutions/regimes are essential ingredients to shape level as well as skill intensive industry/(sub) sectoral FDI in the case of Pakistan.

After the precise introduction in the first section, rest of the paper follows: Section 2 is consists of trends and patterns of sectoral FDI under different political regimes. Review of the prior work is made in the Section 3. Section 4 is based on model, data and methodology. Interpretations of results and discussions are made in the Section 5, while Section 6 concludes.

2. SKILL INTENSIVE FD I INFLOWS UNDER DIFFERENT POLITICAL REGIME: TRENDS AND PATTERNS

By disaggregating FDI inflows on the basis of skill intensities of workers engaged in specific industry in Pakistan, we have observed distinct patterns of FDI distribution across different (sub) sectors/economic groups. Some sectors, like sugar, leather and leather products, rubber and rubber products, basic metals and metal products, electrical machinery, paper and pulp and machinery, have received a very minor share of FDI over the years. Metal products stand at bottom in receiving FDI in selected years. In years 1999 to 2006, during the autocratic era, all these sectors were receiving less FDI as compared to the years in which country has improved in terms of polity score and moved towards democracy as shown in Figure 2.1. We observed a sharp surge in FDI inflows to this high skill intensive sectors. However FDI inflows touched bottom in 2001 and then started rising slowly.

After the year 2006, when country was on way to regain democracy, FDI to this sector started declining again, touched bottom in 2011 and became even negative in 2012. Hence this trend of machinery other than electrical, show that authoritarian leaders favour high skill intensive FDI to benefit the few elite capitalists form whom they get support to gain and retain their office as shown in Figure 2.3. In the sectors of textile, cement, electronics, petro chemicals and petroleum refining, transport equipment, social and personal services and tourism, we observed very moderate FDI flows.

Food and beverages which is a low skill intensive sector, FDI inflows to this sector remained low on average in all time period of autocracy, touched bottom in 2002 with $ -5.1 million inflows. On the other hand, with highest inflows in 2007 and 2015 with $515 million and $553 million respectively. Electronic sector which is a medium skill intensive sector received total of $ 251 million of net FDI in 20 years since 1997, with highest inflow of $ 27 million in 2008 and lowest in 2015 on average inflows to this sector were high in democratic regime.

Inflows to medium skill intensive sectors of petro chemicals and petroleum refining and transport equipment both experienced increasing trend since 2008. Total of $571 million of net FDI has been received by transport equipment sector since 1997, and $ 497 million of which has been received from 2007 to 2016 as shown in Figure 2.2. Similarly petro chemicals and petroleum refining has received 579.72 million US dollars since 1997. $595.1 million of which was received during years of democratic government and rest during authoritarian regime.

Chemicals, pharmaceuticals and fertilisers, trade and construction received almost equal total FDI in 20 years with $1277.78, 1018.77 and $ 1110.88 million inflows. $722.41 million of FDI in chemicals, pharmaceuticals and fertilisers sector is received since 2006. Similarly maximum of inflows were received in trade and construction during democratic regimes. FDI to power sector was not encouraged during autocratic regime and inflows to this sector were high in democratic era.

Transport, storage and communication, mining and quarrying-oil and gas and financial business are the highest receiver of FDI in twenty years. Transport, storage and communication has received highest FDI inflows in all 24 economic groups of around $7678.06 million with highest influx of 1633.98 million in 2008 along with Mining and Quarrying-Oil and Gas and financial business reaching at the top in same year, as shown in Figure 2.3. All the sectors, from low to high skill intensive, have received higher inflows in years of high democratic regime.

3. LITERATURE REVIEW

A large body of prior literature focus on exploring institutions effects of FDI, considering it a major determinant of FDI inflows [Alfaro, et al. (2008); Cuervo-Cazurra (2008); Javorcik and Wei (2009); Kinda (2010); Wei (2000)]. Yet, how does FDI influences institutions have not been under greater academic scrutiny.

Michel (2000) has explored the impact of domestic institutions on capital flows which is mostly positive and directs country's capital. He also found positive impact of institutional quality, trade openness, market potential and infrastructure development, while negative impact of natural resources and human capital on FDI.

Zhu (2013) tested for 38 developing economies and found that democracy and high skill intensive FDI were negatively associated supporting his claim of political regime creating difference in sectoral structure of inward FDI.

In case of China, the institutions (like lower level of tax, less arbitrariness in tax burdens and better legal protection) of host regions of china were found to be positively associated with FDI [Yang and Zhang (2003)], indicating simultaneity between FDI and institutions.

Madani and Nobakhtin (1990) found a positive impact of democracy in contrast to autocracy on FDI inflows. The impact is transmitted through political risk perceived by foreign firms. Previous literature can be divided into three section regarding impact of political regime type on FDI inflows. Firstly, institutions and political procedures under democracy are intended to encourage business foe multinationals and reduce political risk for foreign investors, e.g., audience and reputation cost, checks and balances, large number of veto players and transparency (1) which increase credibility of democratic governments for their commitments in sight of foreign investors. Foreign investors in return give financial support to the leaders in their election campaign who commit to liberal and market friendly policies to benefit multinationals. The audience cost for such leaders can make begging campaign contributions, a startling challenge, because of the fear to lose financial support of multinationals [Hansen and Mitchell (2000); Hillman and Ursprung (1988)].

Turning to the relationship between multinationals and dictatorship, Alsina and Dollar (2000) showed that authoritarian leaders give no ear to political pressure and rivals, and can present liberal fiscal and financial incentives to multinationals. Market friendly policies can easily be adopted and implemented to attract FDI by these leaders without any fear of domestic actors acting against these policies. This ability of authoritarian leaders lends flexibility and timely responsiveness of policies to macroeconomic environment by such regimes and makes FDI more attractive for foreign investors.

In contrast, large number of veto players becomes hurdle in passing new resolutions and decisions regarding foreign investors due to diversity of preferences, hence democracy fails to attract higher FDI inflows [Mathur and Singh (2013)]. On the other hand autocratic leaders can ensure contract enforcement to stimulate growth [Olson (1993); Geh Ibach and Keefer (2011)]. Similarly, empirical studies of Oneal (1994), Li and Resnick (2003), Gehlbach and Keefer (2011), and Resnick (2001) supporting the argument of positive relation between autocracy and multinationals activities. There are also studies which provide ambiguous picture on this relation. The argument claims that higher level of motivation on part of the politicians under both type of regimes increase political risk. Thus leaders both in autocracy and democracy expropriate multinationals in host countries [Li (2009)].

Decomposing FDI into manufacturing, primary and services sector, Schulz (2009) found that there is a significant relation between manufacturing sector FDI inflows and democracy, rejecting the relationship between FDI inflows to primary and services sector with democracy.

Pandya (2007) explored relation between FDI inflows and regime type by distinguishing between vertical and horizontal FDI. Regime characteristics matter in opposing or supporting horizontal FDI because politicians have to face a tradeoff between different interest groups and consumer welfare. However vertical FDI is favoured by left wing parties because it extends employment opportunities for domestic labour. In addition, Dorobantu (2010) argued that to benefit workers of their constituencies, democratic leaders tend to support FDI inflows (under assumption that FDI hurt domestic capital but benefits domestic labour). On the other hand authoritarian leaders, who gain and retain their offices with support of only a few capitalists, try to restrict FDI inflows.

Although above-mentioned studies has advanced FDI literature in the field of politics of FDI to a new level but still analysis of FDI at disaggregated level remains to be explored in detail.

Literature shows that different countries attract different type of FDI, and as a result FDI policy varies across industries. Mostly studies examine the supply side factors of FDI and role of external and internal institutions, but demand side determinants like domestic interest groups, political competition, partisanship, government turnout etc. have been ignored. That is the reason, this study intends to fill this gap by focusing on political economy of FDI policy with some new composition of FDI inflows and methodological framework.

4. DATA AND METHODOLOGY

In this Section, we present model, data and methodology. Dynamic model is formulated to gauge the impact of different political regimes (PR) on Skill intensive FDI (FDI) in case of Pakistan, employing time series data. The study follows a novel Granger causality infrequency domain, taking into account the time series properties of data, i.e., the cyclical and time varying characteristics.

From the macroeconomic perspectives, New Classical trade theory, and H-0 theory suggest that factor endowment differentials across countries, and the return to capital are the primary intuition for capital to flow from one to another country considering perfect capital mobility. While Lucas (1990), negating the perfect capital mobility, argues that these are the economies of scale, systemic distortion, regulations and political factors, along with economic factors that determine FDI in the host country. Keeping in view these arguments, we have constructed the following VAR model for the estimating GC in frequency domain in the next section.

[mathematical expression not reproducible] (4.1)

Where, the [x.sub.t] is a vector consists of control variables, e.g., Human capital (SEE), Exchange Rate (ER), and Trade Openness (TOP). The variable fdi indicates the foreign direct investment, and its superscript T indicates the type of FDI i.e., Low/Medium/High Skill intensive FDI, and pr represents the political regimes. The terms [epsilon]t and [upsilon]t are the white noise errors, and Mis the lag order of the series.

(i) Econometric Methodology

Extensive literature is available on the econometric methods/techniques that have been developed to investigate the causal relationship among the concerned time series variables. Yet there exist, still, a missing area that is to analyse the time varying characteristics of time series data. In order to analyse the causal relationship and taking into account the cyclical fluctuation in the data, a novel spectral Granger Causality (GC) in frequency domain is used in this study. For the unit root test we have used augmented Dickey-Fuller test (ADF) and Phillips-Perron test. Following Lemmens, et al. (2008), we employed bivariate GCS pectral analysis in order to address the cyclical characteristics of the data.

Spectral Granger Causality (GC) in Frequency Domain

Starting from the original work proposal by Pierce (1979), GC test measures the coefficient of coherence (CoQ estimated in a non-parametric fashion. The application this novel methodology to our analysis would provide the nonlinear causality patterns, and cyclical fluctuation in causal relationship, i. e., the long run, medium, and short run causality patterns corresponding to low, medium and high frequency domains respectively [Tiwari, et al. (2105)].

For the spectral GC analysis, the series must be stationary if these are not cointegrated (i.e., their linear combination must turn to be of order zero). For the series, if the co-integration exist, the series at level with logarithm can be used for the analysis [Lemmens, et al. (2008)].

Stating with our concerns variables, we used political regimes (PR), skilled intestine foreign direct investment (FDI) consisting on Low, Medium and high skill intensive FDI of T time periods, to test out primaty hypothesis. If there exist cointegration between FDI and PR, then goal is to test our primary hypothesis, that whether PR Granger cause skill intensive FDI at a given level of frequency co. For this sake, following Pierce (1979) framework, frequency domain is applied in the univariate innovation series, Ut and Vt, obtained from filtering FDI and PF from ARMA processes, i.e.,

[[PHI].sup.pr] (L)[fdi.sub.t] = [C.sup.pr] + [[THETA].sup.pr] (L)[[zeta].sub.t]

[[PHI].sup.fdi] (L)[fdi.sub.t] = [C.sup.fdi] + [[THETA].sup.fdi] (L)[[zeta].sub.t] ... (4.2)

Where [[PHI].sup.pr] (L), [[PHI].sup.fdi] (L) are the autoregressive polynomials, [[PHI].sup.fdi] (L), [[PHI].sup.pr] (L) are the moving average polynomials, and [C.sup.pr], [C.sup.fdi] are the deterministic components.

Following Lemmens, et al. (2008), the obtained series [zeta]' and [[xi].sup.t] are white-noise errors with zero mean and correlated, at different lags and leads, with each other. In defining [[zeta].sup.t] and [[xi].sup.t] in term of frequency domain; [[zeta].sub.t] ([omega]) and ([xi]t), also termed as the spectra, or density functions of [[zeta].sub.t] and at frequency [omega][member of] [R.sub.+], we have;

Spectra[zeta]([omega]) = 1 / 2[pi], [[infinity].summation over (k-[infinity])][GAMMA][[zeta].sub.t](k) [e.sup.-[omega]k]

Spectra[zeta]([omega]) = 1 / 2[pi], [[infinity].summation over (k-[infinity])][GAMMA][[zeta].sub.t](k) [e.sup.-[omega]k] ... (4.3)

[GAMMA][[zeta].sub.t], (k) is the Cov ([[zeta].sub.t], [[zeta].sub.t-k]at kth lag of innovation series [zeta] and [GAMMA][xi],(k) is the Cov

([[zeta].sub.t], [[zeta].sub.t-k]) at kth lags of series [xi]. Thus, the above function shows that the spectral/density representation is actually the decomposition of time series into the sum of unrelated components, where each unrelated component occurs [C] times (i.e., indicating particular frequency of happening a specific events). So interpreting the spectrum GC precisely, its each spectrum indicates the decomposition of the series variances in term of frequency.

In the similar fashion, now the covariance between two innovation series, cov ([[zeta].sub.t], [[xi].sub.t]), also termed as cross-spectrum at lags M(= [square root of]T).sup.2], establishes the relationship between two series as a function of frequency co, given as under;

Spectra[zeta][zeta]([omega]) = 1/2[pi] [[M.summation over (k=M)][w.sub.k][GAMMA][zeta][zeta](k)[e.sup.-i[omega]k]] ... (4.4)

Now, the coefficient of coherence (CoC, obtained from dividing absolute value of cross spectrum, [asolute value of [Spectra.sub.[zeta][zeta]] ([omega])], over the squared root of the product of spectra of individual innovation series i.e., [Spectra.sub.[zeta][zeta]]([omega])] and [Spectra.sub.[xi]](([omega]). So, Granger causality coefficient of coherence, which value varies from 0 to 1, interpreted same as R-square of ordinary regression, is then given by,

CoC[zeta][zeta](w) = [absolute value of ([Spectra.sub.[zeta][zeta]]([omega]))]/[square root of ([Spectra.sub.[zeta]]([omega]) x [[Spectra.sub.[xi]]([omega]))] ... (4.5)

The hypothesis that the political regime (PR) does not GC skill intensive FDI, if [CoC.sub.[zeta][xi]] ([omega]) = 0 for every [omega] in ]0, [pi] [, that follows the chi-square distribution.

Following the Breitung and Candelon (2006), we further extend this test in a VAR framework;

[mathematical expression not reproducible] (4.6)

For such VAR specification, now, the Null hypothesis for testing Spectral GC can be stated as;

[mathematical expression not reproducible]

Here, [bar.[omega]] is the vector of coefficients of x, and F-Statistics against this restriction follows chi- distribution as F(2, T-2M) for [omega] [member of]]0, [pi][. And in the co-integrated VAR system, the values of low frequency (high frequency) represent the long run causality (short run causality) as suggested by Breitung and Candelon (2006). Further, to check the Spectral GC in cointegrated VAR systems, we have replaced x by [DELTA][x.sub.t]. It is also noted that the frequency [omega] can be observed into periodicity or a cycle of time T years by the formula; T = 2[pi]/[omega] (in years, for annual data).

(ii) Data

The study uses annually time series data ranges from 1980 to 2016, depending upon the availability of the data. The data on the dependent variable; economic group/sectoral Foreign Direct Investment net Inflows (FDI) measured in Millions of US Dollar, are collected from Liabilities and Assets and Foreign Investment in Pakistan (various issues), State Bank of Pakistan, Khan and Kim (1999), and World Investment Directory, Vol. I and Vol. Ill, United Nations Conference on Trade and Investment, UNCTI. While the data on independent variables; Gross Domestic Product (GDP) measured in US Dollar, Trade Openness (TOP) measured as percentage of GDP, Nominal Exchange rate (ER), and Human Capital (SEE) measured with number of individuals with Secondary School Education, are taken from World Development Indications (WDI) and Economic Survey of Pakistan (various issues). The data on Political Regimes (PR), proxied by Polity II, is collected from the Integrated Network for Societal Conflict Research (INSCR) Database, Centre for Systematic Peace. The index ranges from -10 (autocratic government) to +10 (full democratic government).

Learning from the prior literature [for detail, see Nunn and Trefler (2006); Antweiler and Trefler (2002); Nunn (2007) and Milner and Mukherjee (2009)], we have followed Zhu (2012) to categorised industries/(sub)sectors into low, medium and high skill industries, while the median voter, the most populous, is engaged professionally in low skill industry, while less populous in medium skill industry, and least populous in the high skilled industry. Following this classification, reported in Table 4.1, we have re-aggregated economic group/sectoral FDI inflows to obtain skill intensive FDI in Low/Medium/High skill categories, measured in Millions of US Dollars.

5. EMPIRICAL FINDINGS

This segment provides the results of GC in the frequency domain among the different political regimes and skill intensive FDI in order to investigate our prior hypothesis. For the robustness of causal relationship between Political Regimes (PR) and FDI, we further extent our analysis from bivariate to multivariate modelling by including economic variables, i.e., Human capital (SEE), Exchange rate (ER), and Trade openness (TOP). The results (2) of ADF and PP unit root test indicate that the series have mixed order of integration. While the co-integration tests show that the series hold a long run relationship, so we have applied the spectral GC on the co-integrated series.

Low Skill Intensive FDI and Political Regimes

Estimating the time varying GC relationship among political regimes and low skill FDI, we have found that, firstly--in the bivariate analysis i.e., considering only PR and low skill FDI, there exist no GC relationship running from political regime to low skill FDI. While there are strong evidences of reverse causation from low skill intensive FDI to political regimes. These findings show, albeit this is one side of the picture, that democratic leader do not intends to influence the FDI policy for their constituencies to win the support of their voters in Pakistan. While foreign investment distributed in low skill sector of the economy reshape the political institutions, as show in Figure 1. In analysing the other side of the picture, we have extended our causality analysis by including some economic variables i.e., human capital (SEE), exchange rate (ER), trade openness (TOP), to investigate whether these economic variables are the confounding variables, rather than political factors, that fetch FDI in low skill sectors of the Pakistan economy.

The findings indicate, as shown in Figure 2, that there involve no political interest in formulating the FDI policy, rather these are the favourable economic conditions responsible for allocation of FDI in low skill intensive sectors. Such sectors, like Beverages Tobacco, Sugar, Textiles, fetch FDI because of cheap labour and input cost in these sectors.

Medium Skill Intensive FDI and Political Regimes

Now, we extend our analysis by investigating the causal relationship between medium skill intensive FDI and political regime. We have found a strong bidirectional causal relationship between medium skill intensive FDI and political regime, in case of bivariate analysis. The results are robust in case of controlling the causal relationship by including some economic variables like trade openness (TOP), exchange rate (ER), and human capital (SEE), indicating that the political leaders take care of occupants of medium skill (sub)sectors, i.e., Electronics, Chemicals, Pharmaceuticals and Fertilisers, Petro Chemicals and Petroleum Refining, Machinery, Paper and Pulp, Transport Equipment, Transport, Storage and Communication sectors etc. On the other hand, the evidence of reverse causal relationship shows that social and economic wellbeing of middle class populace strengthen its ability of being more participatory in the democratic process--by striving for the achievement of political rights/freedom, and competing for the public office, so that to make the democratic process more forte, in reverse, for feasible FDI policy.

High Skill Intensive FDI and Political Regimes

The findings of causal relationship are mixed in case of high skill FDI, as shown in Figure 5. In the short run (in the high frequency areas), and long run (in the low frequency areas) the evidences of causality from political regimes to high skill FDI indicates that, in short and long run, political leaders have favoured the high skill industry in formulating FDI policy regarding FDI distribution across different sectors of Pakistan economy.

While, after controlling the causal relationship with economic conditions of the economy, we have made our analysis sturdier and found that better economic conditions, along with political reasons, are also responsible for the increase in the volume of FDI in high skill industry. The existence of reverse causation i.e., from high skill FDI to political regimes, may also indicate the presence of patron-client relationship between specific (economic) interest group and political coalitions.

Aggregated FDI and Political Regimes

Now, finally, we have investigates the GC relationship between aggregated FDI and political regimes. Figure 7 shows that there exist time varying bi-directional causal relationship between aggregated FDI and political regime; running from political regime to FDI, and from FDI to political regimes. The results are verified by including economic variable to make clear whether the relationship is direct or there are any economic reasons that establish this relationship. The prudent outcomes showed that, along with economic reasons, there are strong political reasons cause to fetch FDI. The reason may be that the strong democratic political institutions avoid a number of political risks including the risks of expropriation and nationalisation that involve the loss of ownership of transnational firms. Similarly, democratic governments establish cooperation between states being more credible in term of dealings with multinationals and by forming Regional Trade Agreements (RTAs), which permit a high collaboration among multinationals and the state.

The results are reveal that FDI flows also develop institutions of a country via improving property rights. Our results are in line with the findings of Jensen (2013), Zhu (2013), Dorobantu (2010), Olney(2013), Macdonald (2011), Dang (2013), among others.

6. CONCLUSIONS

The present study have investigated our prime hypothesis--that whether the political leaders (autocrats/democrats) adjust the FDI inflows in favour of the (sub)sectors where their majority of voters are engaged professionally, in order to please the constituencies/voters, to get their political favour. Using time series data from 1980-2016, we test this hypothesis, by employing a novel GC in frequency domain methodology, keeping in view the time varying and cyclical fluctuating properties of time series data. We have disaggregated the net inflows of FDI in term of Low/medium/high skill intensive industries of Pakistan economy, considering the low skill industry the most populous of low-cost work force, medium skill industry the less populous of low-skill workers, while high skill industry the least populous of low-skill workers.

The findings of the study indicate that there are strong economic reasons causing to affect the FDI inflows in the low skill intensive industry/sectors. While, in high skilled industrial sector, besides some significant economic factors, historically the political leaders have favoured their constituencies in short as well as in long run, to uplift the economic condition of middle working class in order to sustain their political reliability, and to drive the political power from supporters who are benefited from the investment of the transnational firms. While in the authoritarian regimes, where autocrats have found it hard to balance the economic power, and undermine the privileged position of median voters resulted in upsetting FDI distribution in favour of wealthy domestic elites. The findings of the study leaves a vibrant massage that economic planners should focus on regional cooperation and collaboration via feasible FDI policy keeping in view the economic structure and position of the Pakistan economy in order to achieve sustainable and inclusive economic development, and to enrich the political power of people for strengthening the democratic process.

Muhammad Zahir Faridi, and Ghulam Murtaza <GM.QAUI@gmail.com> are affiliated with School of Economics, Bahauddin Zakariya University, Multan. Rabia Nazir is Assistant Professor, Islamia University of Bahawalpur and PhD scholar at Pakistan Institute of Development Economics, Islamabad. Fareeha Armaghan is Sr. Project Manager, HEC, and PhD scholar at Pakistan Institute of Development Economics, Islamabad.

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(1) See [Jensen (2008), p. 9, 81; Harms and Ursprung (2002); Jensen and Schmith (2005); Olson (1993); Weingast (1995); North and Weingast (1989); Tsebelis (1999); Keefer and Stasavage (2003) and Straub (2008)].

(2) The results of ADF and PP unit root tests, and tests for co-integration are not reported here in the study, as it is beyond the scope of this study, yet are available on demand.

Caption: Fig. 2.1. Low Skill Intensive FDI Inflows in Pakistan (Million $)

Caption: Fig. 2.2. Medium Skill Intensive FDI Inflows in Pakistan (Million $)

Caption: Fig. 2.3. High Skill Intensive FDI Inflows in Pakistan (Million $)

Caption: Fig. 1. Low Skill Intensive FDI and Political Regimes

Caption: Fig. 2. Low Skill Intensive FDI and Political Regimes

Caption: Fig. 3. Medium Skill Intensive FDI and Political Regimes

Caption: Fig. 4. Medium Skill Intensive FDI and Political Regimes

Caption: Fig. 5. High Skill Intensive FDI and Political Regimes

Caption: Fig. 6. High Skill Intensive FDI and Political Regimes

Caption: Fig. 7. Aggregated FDI and Political Regimes

Caption: Fig. 8. Aggregated FDI and Political Regimes

Table 4.1
Skill Intensity of Industries/(Sub)Sectors

       Industries/Sub-sectors            Skill Intensity
       Low Skill Intensive FDI        Sectors/Economic Group

* Food, Beverages Tobacco              Low Skill Intensive

* Sugar                                Low Skill Intensive

* Textiles                             Low Skill Intensive

* Leather and Leather Products         Low Skill Intensive

* Rubber and Rubber Products           Low Skill Intensive

* Cernent                              Low Skill Intensive

* Basic Metals                         Low Skill Intensive

* Metal Products                       Low Skill Intensive

* Construction                         Low Skill Intensive

* Trade                                Low Skill Intensive

* Tourism                              Low Skill Intensive

* Others                               Low Skill Intensive

       Industries/Sub-sectors            Skill Intensity
     Medium Skill Intensive FDI       Sectors/Economic Group

* Electronics                         Medium Skill Intensive

* Chemicals, Pharmaceuticals          Medium Skill Intensive
  and Fertilisers

* Petro Chemicals and                 Medium Skill Intensive
  Petroleum Refining

* Electrical Machinery                Medium Skill Intensive

* Paper and Pulp                      Medium Skill Intensive

* Transport Equipment                 Medium Skill Intensive

* Transport, Storage and
  Communication

High Skill Intensive FDI Sectors/Economic Group

* Financial Business                   High Skill Intensive

* Machinery other than Electrical      High Skill Intensive

* Mining and Quarrying-Oil and Gas     High Skill Intensive

* Power                                High Skill Intensive

* Social and Personal Services         High Skill Intensive