A project submitted to the graduate school in partial fulfillment of the requirements for the degree

NATIONAL REALIZED ABSORPTIVE CAPACITY: COUNTRY COMPARISONS BY LINH THI THUY DO, B.E., M.S A project submitted to the Graduate School in partial fulfillment of the requirements for the degree DOCTOR OF ECONOMIC DEVELOPMENT Major Subject: ECONOMIC RESEARCH Minor Subject: APPLIED STATISTICS NEW MEXICO STATE UNIVERSITY LAS CRUCES, NEW MEXICO NOVEMBER 2017 “National Realized Absorptive Capacity: Country Comparisons,” a project prepared by Linh Thi Thuy Do in partial fulfillment of the requirements for the degree Doctor of Economic Development, has been approved and accepted by the following: Loui Reyes Dean of the Graduate School Christopher Erickson Chair of the Examining Committee Date Committee in charge: Dr Christopher Erickson, Chair Dr James Peach Dr Robert Steiner Dr Son Tran ii ACKNOWLEDGEMENTS At first, I would like to emphasize my deepest gratitude to Dr Christopher A Erickson, who is my supervisor, for his valuable comments and guidance during the process of this study Without his help, I cannot complete this project I am also grateful to my friends for the constructive discussions and useful suggestions on any inaccuracy All of them make a great contribution to the completion of this project Finally, the sincere gratitude and appreciation go to my family for their encouragement and mental support iii VITA February 24, 1987 Born in Thai Nguyen City, Vietnam 2005 Graduated from Thai Nguyen Specialized Upper Secondary School, Vietnam 2009 Graduated from Foreign Trade University Hanoi, Vietnam 2012 Graduated from University of Essex, Colchester, the United Kingdom 2014-2017 Research Assistant and Teaching Assistant Department of Economics, Applied Statistics, and International Business, College of Business New Mexico State University Field of Study Major Field: Economic Development Minor Field: Applied Statistics iv ABSTRACT This project investigates the association between the level of economic activity and national realized absorptive capacity Absorptive capacity is a dynamic process that has four dimensions: acquisition, assimilation, transformation, and exploitation of the external knowledge From macroeconomic perspectives, national absorptive capacity can be defined as the process by which a nation internalizes external resources for its economic growth The literature shows that many variables, including development level, drive the dynamics of national absorptive capacity We distinguish between a nation’s potential and realized absorptive capacity, then use data from the World Bank to empirically identify if there are significant differences in realized absorptive capacity among nations grouped into the four income categories defined by the World Bank The association between the change in foreign direct investment net inflows as the percentage of gross domestic product and a country’s growth of real gross domestic product is used as the indicator of the nation’s realized absorptive capacity We estimate fixed effects regressions, which show that countries are different in their national realized absorptive capacity The regression results reject the null hypothesis that countries at higher level of development have higher national realized absorptive capacity We further find evidence that investment in infrastructure has positive associations with national realized absorptive capacity Keywords: national realized absorptive capacity, foreign direct investment, economic growth v TABLE OF CONTENTS List of Tables vii List of Figures viii Introduction Methodology Step – Estimating National Realized Absorptive Capacity Step – Identifying Determinants of National Realized Absorptive Capacity .11 Data and Estimation Techniques .13 Results 16 Tests of stationarity, cointegration, and Granger causality 16 Step 1- Estimates of National Realized Absorptive Capacity 20 Step – Determinants of National Realized Absorptive Capacity 34 Conclusions and Policy Implications .41 Appendix – List of countries included in the sample .45 Appendix –Descriptive Statistics 46 Appendix 3: Regression results for models in Table 48 Appendix 4: Estimates of national realized absorptive capacity 53 Appendix 5: STATA commands 57 References 64 vi LIST OF TABLES Table 1: Unit root tests for the %∆FDI and the %∆GDP 17 Table 2: Results of unit root tests for other explanatory variables 18 Table 3: Regression results for models with the dependent variable %∆GDP 26 Table 4: Variance Inflation Factor of variables as in the regressions of Table 30 Table 5: Regression results using the averaging method .37 Table 6: Variance Inflation Factor of variables as in the regressions of Table 39 vii LIST OF FIGURES Figure 1: National potential and realized absorptive capacity .2 Figure 2: Graph of real FDI versus real GDP for the sample of 135 countries 14 Figure 3: Visualized Regression Results of Model I from Step .32 viii Introduction Absorptive capacity is defined as “a set of organizational routines and processes by which firms acquire, assimilate, transform and exploit knowledge to produce a dynamic organizational capability” (Zahra and George, 2002, pp 186) Absorptive capacity is a dynamic process involving the internalization of external knowledge (i.e., technology) that has four dimensions: acquisition, assimilation, transformation, and exploitation The term “absorptive capacity” can be studied at individual, group, firm, sector, and national levels By aggregating upwards from the firm level, Criscuolo and Narula (2008) define national absorptive capacity as the process of internalizing external knowledge to grow an economy Zahra and George (2002) distinguish between a firm’s potential and realized capacity Potential capacity involves knowledge acquisition and assimilation; while realized capacity involves knowledge transformation and exploitation These four capabilities are dynamic and influence the firm’s ability to utilize the knowledge for organizational development In particular, realized capacity allows the firm to construct its competitive advantage; while potential capacity provides the firm with flexibility to respond operationally to changing market conditions Ben-Oz and Greve (2015) use the survey data of 252 decision makers from 129 Israeli early-stage hightech organizations and find that realized absorptive capacity is affected by short-term goals and has short-term effects, while potential absorptive capacity is mainly affected by long-term goals and has long-term effects The concept of a firm’s potential and realized capacity can be generalized into a nation’s potential and realized absorptive capacity National potential absorptive capacity is the maximum of a nation’s capability to acquire and assimilate knowledge during a particular period of time National realized absorptive capacity is the nation’s current ability to transform and exploit the knowledge that has been acquired and assimilated up to the current time Visually, at a point in time, a national potential absorptive capacity is the external bound, while its realized absorptive capacity lies within the bound (Figure 1) The larger the green circle, the higher the efficiency ratio – the ratio of realized absorptive capacity to potential absorptive capacity (Zahra and George, 2002) Figure 1: National potential and realized absorptive capacity National potential absorptive capacity is the maximum of a nation’s capability to acquire and assimilate the knowledge during a particular period of time National realized absorptive capacity is the nation’s current ability to transform and exploit the knowledge that has been acquired and assimilated up to the current time The ratio of realized absorptive capacity to potential absorptive capacity is called the efficiency ratio (Zahra and George, 2002) The larger the realized absorptive capacity, the higher the efficiency ratio Source: Authors Dummy Model I Model II Papua New Guinea 92 -0.1439 -0.1363 Lower middle Lesotho 69 -0.0822 -0.0749 Lower middle Guatemala 47 -0.0423 -0.0360 Lower middle Cameroon 22 -0.0208 -0.0229 Lower middle Tonga 121 0.0263 -0.0691 Lower middle Bolivia 14 0.0707 0.0713 Lower middle Congo, Rep 31 0.0958 0.0337 Lower middle Djibouti 36 0.1490 0.1633 Lower middle Swaziland 114 0.1520 0.1556 Lower middle El Salvador 40 0.1578 0.1605 Lower middle Yemen, Rep 134 0.1602 0.2902 Lower middle Mongolia 79 0.1605 0.1603 Lower middle Mauritania 76 0.1665 0.1632 Lower middle Solomon Islands 106 0.2075 0.2025 0.1985 Lower middle Samoa 100 0.2387 0.2429 0.2506 Lower middle Lao PDR 68 0.2606 0.2837 Lower middle Egypt, Arab Rep 39 0.2858 0.2989 Lower middle Tunisia 123 0.2970 0.3007 Lower middle Vietnam 133 0.2999 0.3100 Lower middle Tajikistan 117 0.3644 0.3738 Lower middle Kyrgyz Republic 67 0.3680 0.5830 Lower middle Kiribati 64 0.4014 Lower middle Ghana 45 0.4775 0.4913 Lower middle Vanuatu 131 0.5236 0.5941 Lower middle Armenia 0.5874 0.5304 Lower middle Ukraine 126 0.6677 0.9997 Lower middle Cambodia 21 0.7041 0.7153 Lower middle Cabo Verde 20 0.7180 0.6718 Lower middle Nicaragua 85 0.7419 0.7555 0.6983 Lower middle Morocco 80 0.8272 0.8447 0.8687 Lower middle Zambia 135 0.9063 0.9460 0.2543 Lower middle Honduras 51 0.9807 1.0086 Lower middle Kenya 63 1.1502 1.2131 Lower middle Bangladesh 10 1.1648 Lower middle Sri Lanka 108 1.2954 1.2116 Lower middle Pakistan 90 1.5504 1.6219 Lower middle Indonesia 55 2.0023 2.0179 Lower middle Cote d'Ivoire 33 3.0301 2.9758 Group Country Lower middle 55 Model III Model IV -0.1819 0.0872 0.1122 0.3277 0.4378 0.6731 3.6071 0.8007 1.5681 Dummy Model I Model II Niger 86 -0.5914 -0.7353 Low income Chad 25 -0.2560 -0.2567 Low income Burkina Faso 18 -0.2104 -0.5524 Low income Senegal 102 -0.1793 -0.1674 Low income Mozambique 81 -0.0846 -0.0977 Low income Comoros 29 -0.0835 -0.0686 Low income Sierra Leone 104 -0.0728 -0.0541 -0.0450 Low income Malawi 73 0.1852 0.1647 0.2157 Low income Togo 120 0.2004 0.2030 0.2547 Low income Tanzania 118 0.2061 0.2021 Low income Guinea 48 0.2293 0.2271 Low income Benin 13 0.2892 0.0903 Low income Congo, Dem Rep 30 0.2962 0.3053 0.0503 Low income Gambia, The 44 0.3438 0.3446 0.5272 Low income Madagascar 72 0.4969 0.5342 Low income Burundi 19 0.6319 0.3975 Low income Mali 75 0.6345 0.6403 Low income Uganda 125 1.1490 1.1740 Low income Guinea-Bissau 49 2.1220 2.1714 Low income Central African Rep 24 3.3929 3.4074 Low income Rwanda 99 3.6675 3.7554 Group Country Low income 56 Model III Model IV 0.6798 1.1405 3.4622 Appendix 5: STATA commands clear all clear matrix drop _all set memory 500m set matsize 800 mata: mata set matafavor speed, perm use "D:\Documents and Settings\linh\Desktop\WDI_data_clean.dta" gen r_gdp = gdp_cur_lcu / ppp gen r_fdi = fdi_netifl_cur * exchange_rate / ppp bys country_id (year): gen d_rgdp= r_gdp[_n] - r_gdp[_n-1] bys country_id (year): gen d_lngdp = d_rgdp / r_gdp[_n-1] bys country_id (year): gen d_rfdi = r_fdi[_n] - r_fdi[_n-1] bys country_id (year): gen d_fdigdp = d_rfdi / r_gdp[_n-1] Sample selection (countries with many observations – 135 countries) /*keep country country_id year d_fdigdp d_lngdp r_gdp r_fdi Income keep if d_fdigdp~= bys country_id: gen nyear=[_N] sort nyear keep if nyear>20 save "D:\Documents and Settings\linh\Desktop\135_real_mostobs.dta*/ merge m:1 country using "D:\Documents and Settings\linh\Desktop\135_real_mostobs.dta" keep if _merge == drop _merge Create dummy variables – change in real FDI as the percentage of real GDP in a previous year by country tabulate country_id, generate(dum) foreach var of varlist dum1-dum135 { generate fdi`var'=`var'*r_fdi } foreach var of varlist dum1-dum135 { generate d_`var'=`var'*d_rfdi } foreach var of varlist d_dum1-d_dum135 { generate `var'_gdp =`var'/ r_gdp[_n-1] } save "D:\Documents and Settings\linh\Desktop\panel_135.dta" 57 * Draw Figure twoway scatter r_gdp r_fdi || lfit r_gdp r_fdi * Regressions use "D:\Documents and Settings\linh\Desktop\panel_135.dta", clear drop if year presence of hetoroskedasticity xtreg d_lngdp d_fdigdp lag1a lag2a lag2b lag2c d_dum*_gdp, fe robust xttest3 * Test for serial correlation -> presence of first order autocorrelation xtserial d_lngdp d_fdigdp lag1a lag2a lag2b lag2c d_dum*_gdp * Test for normality -> OK xtreg d_lngdp d_fdigdp lag1a lag2a lag2b lag2c d_dum*_gdp, fe robust pantest2 year • Descriptive statistics (Appendix 2) sum r_gdp r_fdi d_rgdp d_rfdi d_lngdp d_fdigdp distcap gov_debt biz_proc exp_per /// 59 unemp inf_def tel_subs lfpart_ilo inc_tax pop_tot r_exrate /// r_irate r_eduexp d_eduexp deducenr sum d_pop d_tel d_inf d_exp d_lfp d_eduexp d_irate d_tax d_exrate d_biz d_unemp d_gdebt • Regression Results (Appendix 3) and check for multicollinearity (Table 4) xtreg d_lngdp d_fdigdp lag1a lag2a lag2b lag2c d_dum*_gdp, fe vce(cluster country_id) estimate store mod1 estat ic vif, uncentered drop if dum60==1 xtreg d_lngdp lag1a lag2a lag2b lag2c d_fdigdp d_pop d_lfp d_inf d_dum*_gdp, fe vce(cluster country_id) estimate store mod2 estat ic vif, uncentered xtreg d_lngdp lag1a lag2a lag2b lag2c /// d_fdigdp d_pop d_lfp d_inf d_tel d_exp d_irate d_exrate d_eduexp d_biz d_unemp /// d_tax d_gdebt d_dum*_gdp, fe vce(cluster country_id) estimate store mod3 estat ic vif, uncentered xtreg d_lngdp lag1a lag2a lag2b lag2c /// d_fdigdp d_inf d_exp d_exrate d_irate d_dum*_gdp, fe vce(cluster country_id) estimate store mod4 estat ic vif, uncentered estimate table mod1 mod2 mod3 mod4, star stats (N r2 r2_a) • Graphing regression results (Figure 3) xtreg d_lngdp d_fdigdp /*lag1a lag2a lag2b lag2c*/ d_dum*_gdp, fe vce(cluster country_id) predict yhat Graph of 10 first countries separate yhat, by(country_id) twoway (line yhat d_fdigdp if country_id==2, sort) (line yhat d_fdigdp if country_id==3, sort) /// (line yhat d_fdigdp if country_id==6, sort) (line yhat d_fdigdp if country_id==7, sort) /// (line yhat d_fdigdp if country_id==8, sort) (line yhat d_fdigdp if country_id==9, sort) /// (line yhat d_fdigdp if country_id==11, sort) (line yhat d_fdigdp if country_id==14, sort) /// (line yhat d_fdigdp if country_id==15, sort) (line yhat d_fdigdp if country_id==16, sort) Graph of all 134 countries (Japan is omitted) twoway line yhat d_fdigdp • Store coefficients from Step (Appendix 4) 60 Example: xtreg d_lngdp d_fdigdp lag1a lag2a lag2b lag2c d_dum*_gdp, fe vce(cluster country_id) drop if dum60==1 gen a=_b[d_fdigdp] gen b= _b[d_dum1_gdp]* dum1 + _b[d_dum2_gdp]* dum2 + _b[d_dum3_gdp]* dum3 + /// _b[d_dum4_gdp]* dum4 + _b[d_dum5_gdp]* dum5 + _b[d_dum6_gdp]* dum6 + /// _b[d_dum7_gdp]* dum7 + _b[d_dum8_gdp]* dum8 + _b[d_dum9_gdp]* dum9 + /// _b[d_dum10_gdp]* dum10 + _b[d_dum11_gdp]* dum11 + _b[d_dum12_gdp]* dum12 + /// _b[d_dum13_gdp]* dum13 + _b[d_dum14_gdp]* dum14 + _b[d_dum15_gdp]* dum15 + /// _b[d_dum16_gdp]* dum16 + _b[d_dum17_gdp]* dum17 + _b[d_dum18_gdp]* dum18 + /// _b[d_dum19_gdp]* dum19 + _b[d_dum20_gdp]* dum20 + _b[d_dum21_gdp]* dum21 + /// _b[d_dum22_gdp]* dum22 + _b[d_dum23_gdp]* dum23 + _b[d_dum24_gdp]* dum24 + /// _b[d_dum25_gdp]* dum25 + _b[d_dum26_gdp]* dum26 + _b[d_dum27_gdp]* dum27 + /// _b[d_dum28_gdp]* dum28 + _b[d_dum29_gdp]* dum29 + _b[d_dum30_gdp]* dum30 + /// _b[d_dum31_gdp]* dum31 + _b[d_dum32_gdp]* dum32 + _b[d_dum33_gdp]* dum33 + /// _b[d_dum34_gdp]* dum34 + _b[d_dum35_gdp]* dum35 + _b[d_dum36_gdp]* dum36 + /// _b[d_dum37_gdp]* dum37 + _b[d_dum38_gdp]* dum38 + _b[d_dum39_gdp]* dum39 + /// _b[d_dum40_gdp]* dum40 + _b[d_dum41_gdp]* dum41 + _b[d_dum42_gdp]* dum42 + /// _b[d_dum43_gdp]* dum43 + _b[d_dum44_gdp]* dum44 + _b[d_dum45_gdp]* dum45 + /// _b[d_dum46_gdp]* dum46 + _b[d_dum47_gdp]* dum47 + _b[d_dum48_gdp]* dum48 + /// _b[d_dum49_gdp]* dum49 + _b[d_dum50_gdp]* dum50 + _b[d_dum51_gdp]* dum51 + /// _b[d_dum52_gdp]* dum52 + _b[d_dum53_gdp]* dum53 + _b[d_dum54_gdp]* dum54 + /// _b[d_dum55_gdp]* dum55 + _b[d_dum56_gdp]* dum56 + _b[d_dum57_gdp]* dum57 + /// _b[d_dum58_gdp]* dum58 + _b[d_dum59_gdp]* dum59 + _b[d_dum60_gdp]* dum60 + /// _b[d_dum61_gdp]* dum61 + _b[d_dum62_gdp]* dum62 + _b[d_dum63_gdp]* dum63 + /// _b[d_dum64_gdp]* dum64 + _b[d_dum65_gdp]* dum65 + _b[d_dum66_gdp]* dum66 + /// _b[d_dum67_gdp]* dum67 + _b[d_dum68_gdp]* dum68 + _b[d_dum69_gdp]* dum69 + /// _b[d_dum70_gdp]* dum70 + _b[d_dum71_gdp]* dum71 + _b[d_dum72_gdp]* dum72 + /// _b[d_dum73_gdp]* dum73 + _b[d_dum74_gdp]* dum74 + _b[d_dum75_gdp]* dum75 + /// _b[d_dum76_gdp]* dum76 + _b[d_dum77_gdp]* dum77 + _b[d_dum78_gdp]* dum78 + /// _b[d_dum79_gdp]* dum79 + _b[d_dum80_gdp]* dum80 + _b[d_dum81_gdp]* dum81 + /// _b[d_dum82_gdp]* dum82 + _b[d_dum83_gdp]* dum83 + _b[d_dum84_gdp]* dum84 + /// _b[d_dum85_gdp]* dum85 + _b[d_dum86_gdp]* dum86 + _b[d_dum87_gdp]* dum87 + /// _b[d_dum88_gdp]* dum88 + _b[d_dum89_gdp]* dum89 + _b[d_dum90_gdp]* dum90 + /// _b[d_dum91_gdp]* dum91 + _b[d_dum92_gdp]* dum92 + _b[d_dum93_gdp]* dum93 + /// _b[d_dum94_gdp]* dum94 + _b[d_dum95_gdp]* dum95 + _b[d_dum96_gdp]* dum96 + /// _b[d_dum97_gdp]* dum97 + _b[d_dum98_gdp]* dum98 + _b[d_dum99_gdp]* dum99 + /// _b[d_dum100_gdp]* dum100 + /// _b[d_dum101_gdp]* dum101 + _b[d_dum102_gdp]* dum102 + _b[d_dum103_gdp]* dum103 + /// _b[d_dum104_gdp]* dum104 + _b[d_dum105_gdp]* dum105 + _b[d_dum106_gdp]* dum106 + /// _b[d_dum107_gdp]* dum107 + _b[d_dum108_gdp]* dum108 + _b[d_dum109_gdp]* dum109 + /// _b[d_dum110_gdp]* dum110 + _b[d_dum111_gdp]* dum111 + _b[d_dum112_gdp]* dum112 + /// _b[d_dum113_gdp]* dum113 + _b[d_dum114_gdp]* dum114 + _b[d_dum115_gdp]* dum115 + /// _b[d_dum116_gdp]* dum116 + _b[d_dum117_gdp]* dum117 + _b[d_dum118_gdp]* dum118 + /// _b[d_dum119_gdp]* dum119 + _b[d_dum120_gdp]* dum120 + _b[d_dum121_gdp]* dum121 + /// _b[d_dum122_gdp]* dum122 + _b[d_dum123_gdp]* dum123 + _b[d_dum124_gdp]* dum124 + /// _b[d_dum125_gdp]* dum125 + _b[d_dum126_gdp]* dum126 + _b[d_dum127_gdp]* dum127 + /// _b[d_dum128_gdp]* dum128 + _b[d_dum129_gdp]* dum129 + _b[d_dum130_gdp]* dum130 + /// _b[d_dum131_gdp]* dum131 + _b[d_dum132_gdp]* dum132 + _b[d_dum133_gdp]* dum133 + /// _b[d_dum134_gdp]* dum134 + _b[d_dum135_gdp]* dum135 replace b= if b==0 gen coefdep= a + b 61 • Create dummies by income level gen group=1 replace group=2 if Incomegroup=="Lower middle income" replace group=3 if Incomegroup=="Upper middle income" replace group=4 if Incomegroup== "High income" gen gp1=0 replace gp1=1 if group==1 gen gp2=0 replace gp2=1 if group==2 gen gp3=0 replace gp3=1 if group==3 gen gp4=0 replace gp4=1 if group==4 save "D:\Documents and Settings\linh\Desktop\step2_Sep17.dta", replace Regressions in Step use "D:\Documents and Settings\linh\Desktop\step2_Sep17.dta", clear drop if coefdep== gen netex = exports_cur - imports_cur gen netex_r= netex * exchange_rate / ppp egen mrd = mean(rd_expd), by (country_id) egen minfr = mean(tel_subs), by (country_id) egen mnetex = mean(netex_r), by (country_id) egen mgini = mean(gini), by (country_id) egen mpov = mean(poverty), by (country_id) egen mbiz = mean(biz_proc), by (country_id) egen mreser = mean(researcher), by (country_id) egen meduc = mean(educ_enr), by (country_id) egen gdp2 = mean(d_lngdp), by (country_id) egen mpop = mean(pop_tot), by (country_id) egen mhealth = mean(healthexp), by (country_id) keep country country_id Incomegroup group gp1 gp2 gp3 gp4 coefdep mreser mrd minfr mnetex /// mgini mpov mbiz* meduc* gdp2 mpop mhealth* duplicates drop • Descriptive statistics (Appendix 2) sum gp1 gp2 gp3 gp4 coefdep gdp2 mpop minfr meduc mnetex mbiz mhealth mgini mpov mrd mreser • Regression results using differenced data (Table 5) and the related VIF (Table 6) reg coefdep gp2 gp3 gp4, robust estimate store mod1c estat ic vif 62 reg coefdep gdp2 mpop minfr mnetex gp2 gp3 gp4, robust estimate store mod2c estat ic vif reg coefdep gdp2 mpop minfr mnetex meduc mbiz mhealth mgini mpov mreser mrd gp2 gp3 gp4, robust estimate store mod3c estat ic vif reg coefdep meduc mbiz mhealth mrd mgini mpov mreser gp2 gp3 gp4, robust estimate store mod4c estat ic vif estimate table mod1c mod2c mod3c mod4c, star stats (N r2 r2_a) /* notice countries included in the model, an 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Academy of Management Review 27, no (2002): 185-203 68 ... variables are said to cointegrate if there exists a linear combination of the variables that has a lower order of integration than the original 18 variables themselves10 If the two variables are... 3,510 In the regressions, we base on two criteria to add the explanatory variables into the regression models: the findings from literature and the availability of the data This means the variables... data of all the explanatory variables; hence, the total observation for those variables could be fewer than 134 Furthermore, due to the problem of missing data, the base to calculate the mean