Productivity and Taxes as Drivers of FDI Heckman Selection model. Rahul Anand Econ 764 class presentation. Outline. Theoretical framework of the determinants of FDI flow Analytical framework with productivity as a driving force M&A FDI flows Greenfield FDI flows

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Productivity and Taxes as Drivers of FDI Heckman Selection model

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Outline • Theoretical framework of the determinants of FDI flow • Analytical framework with productivity as a driving force • M&A FDI flows • Greenfield FDI flows • Extending the framework to include corporate taxation as an additional driving force • Econometric Approach: Heckman Selection model • Empirical Evidence

Theoretical Framework • Focus on bilateral FDI flows among members of OECD • Study of two set of driving forces- • Productivity • Taxation • Important feature of this model: fixed set up costs of new investments (distinguishing FDI flows from portfolio flows)

Two margins of FDI decision- • Intensive margin: determining the magnitude of flows, based on standard marginal productivity conditions • Extensive margin: whether to make new investment at all • Productivity and Taxes may affect the two margins in different, possibly conflicting ways • Set up costs can be industry-specific, giving rise to two way rich-rich, as well as rich-poor, FDI flows

A Stripped-Down Model of Foreign Direct Investment • Source to host FDI flows typically include many observations with zero flows, may be an indication of existence of fixed set up costs • A stripped down model of FDI with fixed set up costs

Model • Consider a pair of source and host country • Free capital mobility, fixing world interest rate at r • Host country: denoted by H • Source country : denoted by S • A representative industry whose product serves for both consumption and investment • Firms last for two periods

In 1st period: continuum of NH firms that differ by an idiosyncratic productivity factor, ε • Firm with productivity factor of ε is referred to as ε –firm • G(.): Cumulative distribution of ε • g(.) : Density function • Number of ε-firms = NH*g(ε) • KH0 = initial net capital stock of each firm (assumed same for all firms) • If invest I, augmented capital stock K = KH0 +I

Gross output in period 2: AHF(K,L)(1+ε) • AH = country (H) specific aggregate productivity parameter • Assume, CH = fixed set up cost of investment (same for all firms, independent of ε) • Fixed cost has two components- • CSH = cost borne by FDI investor in his own country (management time and other expenses at home headquarters) • Cost incurred in host country: assume it involves labor input only, LHC

Therefore a cutoff ε exists(ε0) such that an ε–investment firm will make a new investment if and only if ε >ε0 • V+(AH,KHO, ε0,wH) –CH= V-(AH,KHO, ε0,wH) • wH is determined in equilibrium by a clearance in the labor market

Labor abundance is manifested in wage differences • If labor per firm in host > labor per firm in source (i.e, LHo> LSo ) • In addition no. of firms –a measure of abundance of entrepreneurship- abundance of labor means scarcity of entrepreneurship • If wages equal then demand per firm is same in both countries and market clearing condition could not hold for both countries • Hence, wH< wS (Host employ more worker per firm, in equilibrium Source firm effectively more productive)

Mergers and Acquisitions FDI • M & A: acquisitions of existing host firms • Source country entrepreneurs endowed with some intangible capital, or know how, stemming from their specialization/expertise • comparative advantage: Set up cost by source FDI investors in host country < Set up cost by host investors in host country • CH*= CSH* + wH LHC < CH

Foreign investor can bid up the direct investors of the host country in the purchase of the investing firm in the host country • Each firm (with ε >ε0 ) is purchased at its market value, V+(AH,KHO, ε0,wH) –CH • New owner also invests: K+(AH,ε,wH) –KHO in the firm

Aggregate Productivity Shock: Flow and Selection • How shock to AH affects FDI flows to the host country? • Case 1: wH Fixed • Three positive effects of positive shock on the notiaonal FDI on Flow equation • Raises the marginal productivity of capital, increasing the amount of investment made by each investing firm • It raises the value of each such firms, increasing the acquisition price which is a part of notional FDI • Increasing the number of firms purchased by FDI investors (by lowering ε0 )

Selection Condition Equation: • A rise in AH reduces the likelihood that ε0 exceeds ε¯ (as profitability of investment increases, the threshold condition for investment decreases) • So the likelihood of satisfying the selection condition increases implying the realization of notional FDI flow • Thus a positive aggregate productivity shock raises actual FDI through both the flow and selection condition equation

Case 2: wH not fixed • Wage is determined through market clearing conditions • Increase in demand of labor raises wH , raising the fixed set up cost wH LHC COUNTERING THE ABOVE THREE EFFECTS • With unique equilibrium the initial effects are likely to dominate these subsequent counter effects, so the notional FDI still rises (governed by the flow equation)

Effect on Selection Condition Equation • Increase in set up cost reduces the advantage of carrying out positive FDI flows at all • As wH rises ε0 rises, reducing the likelihood of satisfying the selection condition • So, the follow up effects of a positive shock works in the opposite direction and may dominate it • AS has no effect : as free capital mobility

Greenfield FDI • Establishing a new firm (where KHO =0) • Newcomer doesn’t know in advance ε of the potential firm and takes G(.) as the CDF. • Assumption- ε is revealed before he decides whether or not to make new investment

Effect of Positive Productivity Shocks • Positive Shock to AH • Positive effects on both the notional FDI flows and on the likelihood of these flows to actually materialize • Positive Shock to AS • Doesn’t affect the notional flows but reduces the likelihood of such flows to occur at all • Positive Shock to AH’(productivity of other potential hosts) • Likelihood of having greenfield FDI is negatively affected

Source Country and Host Country Corporate Taxation • Tax rates of the source country and host country may have different effect on the two decisions (flow and selection condition equations) • Case of a parent firm developing a new product line • Develop it at home and produce it at a subsidiary abroad • Determined by productivities and tax considerations

Issue of Double Taxation • Income of a foreign affiliate typically taxed by the host country • If source country also taxes: double taxation • Double taxation is typically relieved at the source country level (by granting exemption or granting tax credits) • In practice, foreign source income is far from being taxed at the source country rate • Various reduced rates for foreign source income • Taxed only upon repatriation

Effect of tax rates • Tax rate in source country ςs affects positively the decision by a parent firm to invest in host • ςH has a negative effect on this decision • ςs is irrelevant for the determination of the magnitude of FDI flows which are negatively affected by ςH

Econometric Approach • FDI flow two fold decisions: • Whether to invest: “threshold” selection eq. • How much to invest: “flow/gravity” eq. • There may be zero actual flows • Hence, selection of actual (s,h) countries endogenous (as opposed to exogenous in traditional gravity models) • So, Heckman Selection Model used

Heckman Selection Model • Jointly estimate the likelihood of surpassing the threshold and the magnitude of the FDI flow (provided threshold is surpassed)

Y*ijt can be positive or negative • Yijt is zero not only when Y*ijt is negative but also when Y*ijt is positive but below the threshold profit • π*ijt = π*’ijt / σ π*’ = (Wijtρ– Cijt) / σ π*’ • π*’ijt : indicates if FDI will be made or not (depending on positive or negative) • Wijt : explanatory variables • Cijt : fixed cost of setting up new investment • ρ : vector of coefficients • σπ*’ : standard deviation of π*’

Set up cost • C*ijt = Aijtδ + vijt • Aijt : explanatory variables • δ : vector of coefficients • vijt : error term • Substituting for C*ijt in the previous equation

Maximum Likelihood method is employed to estimate the flow coefficient vector β and the selection coefficient vector θ • λijt depends on Xijt . So from equation 7.8. OLS estimates of β confined to positive observations of Yijt is biased because such estimates include also the effect of Xijt on Yijt through the term βλ λijt

Yijt Y*=βXX B B’ Y=bXOLSX R M’ A’ M A T T’ S XH XL Xijt

The Tobit Model • Actual FDI flow may be zero even when notional FDI flows are not. A significant portion of a typical sample is zero, but is roughly continuously distributed for positive values. In such cases Tobit model is often employed.

Empirical Analysis Productivity and Tax Rate as Determinants of FDI flows

Data and Descriptive Statistics • Standard Mass Variables • Source and host population sizes • Distance Variables • Physical distance • Whether two countries share a common language • Economic Variables • Source and host GDP per capita • Difference in years of schooling • Financial risk rating

Control for country and time fixed effects • Dependent Variable in all flow equations: Log of FDI flow • The main variables are grouped as follows: • Standard Country Characteristics • Real GDP per capita • Source and host GDP per capita • Difference in years of schooling • Financial risk rating

Source and Host Characteristics • Physical distance • Whether two countries share a common language • Productivity • Corporate tax rates • Productivity • Approximated by output per worker- measured by purchasing power parity-adjusted real GDP per worker • At times instrumented by capital to labor ratio and years of schooling

Corporate Tax Rates • Statutory rates or by the effective average rates compiled by Devereux, Griffith, and Klemm • At times instrumented by the statutory corporate tax rates and GDP per capita • No smoothing of data done: to investigate the effects of the explanatory variables over the business cycle • Data on FDI flows from International Direct Investment (IDI): bilateral flows among 18 OECD countries during 1987-2003 • Deflated by US CPI (Urban Consumers)