Subnational Analysis of Economic Fitness and Income Dynamic: The Case of Mexican States
<p>Rankings of Economic Fitness for the 32 Mexican states: 2014. The average of the yearly Spearman correlations between these two rankings for the period 2004–2014 is very high (<math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>ρ</mi> <mo>¯</mo> </mover> <mrow> <mi>E</mi> <mi>x</mi> <mi>E</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mn>0.9843</mn> </mrow> </semantics></math>); however, the average of the yearly Cosine Similarity coefficients between their scores is markedly lower (<math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>C</mi> <mi>o</mi> <mi>s</mi> <mi>i</mi> <mi>n</mi> <mi>e</mi> </mrow> <mo stretchy="true">¯</mo> </mover> </mrow> <mrow> <mi>E</mi> <mi>x</mi> <mi>E</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math> = 0.8580).</p> "> Figure 2
<p>Fitness–GDP<sub>p</sub> plane in logarithmic scale (yearly observations). <span class="html-italic">ExoFit</span> index for the 32 Mexican states: 2004–2014. Income in the vertical axis is defined as per capita GDP.</p> "> Figure 3
<p>Fitness–income dynamic for the Mexican states. <span class="html-italic">ExoFit</span> index: <span class="html-italic">t</span><sub>1</sub> = 2004, <span class="html-italic">t</span><sub>2</sub> = 2014. Panel (<b>a</b>): coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>; panel (<b>b</b>): estimated versors. To obtain the coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>, we used an <span class="html-italic">x</span>-axis bandwidth of 0.86 and a <span class="html-italic">y</span>-axis bandwidth of 0.38. Income in the vertical axis is defined as per capita GDP.</p> "> Figure 4
<p>Fitness–GDP<sub>p</sub> plane in logarithmic scale (yearly observations). <span class="html-italic">EndoFit-oil</span> index for the 32 Mexican states: 2004–2014. The Endogenous Fitness indicator was calculated without raw petroleum (product code = 2709). Income in the vertical axis is defined as per capita GDP without oil mining.</p> "> Figure 5
<p>Fitness–income dynamic for the Mexican states. <span class="html-italic">EndoFit-oil</span> index: <span class="html-italic">t</span><sub>1</sub> = 2004, <span class="html-italic">t</span><sub>2</sub> = 2014. Panel (<b>a</b>): coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> <mo>;</mo> </mrow> </semantics></math> panel (<b>b</b>): estimated versors. The Endogenous Fitness indicator was calculated without raw petroleum (product code = 2709). To obtain the coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>, we used an <span class="html-italic">x</span>-axis bandwidth of 0.86 and a <span class="html-italic">y</span>-axis bandwidth of 0.38. Income in the vertical axis is defined as per capita GDP without oil mining.</p> "> Figure 6
<p>Ranking evolution of the Mexican states’ fitness. <span class="html-italic">EndoFit-oil</span>: 2004–2014.</p> "> Figure 7
<p>Fitness–income dynamic for the Mexican states, <span class="html-italic">ECI-oil</span>: <span class="html-italic">t</span><sub>1</sub> = 2004, <span class="html-italic">t</span><sub>2</sub> = 2014. Panel (<b>a</b>): coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>; panel (<b>b</b>): estimated versors. To obtain the coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>, we used an x-axis bandwidth of 0.86 and a y-axis bandwidth of 0.38. Income in the vertical axis is defined as per capita GDP without oil mining.</p> "> Figure 8
<p>Ranking evolution of the Mexican states’ fitness. <span class="html-italic">EndoFit + tourism-oil:</span> 2004–2014.</p> "> Figure 9
<p>Fitness–income dynamic for the Mexican states, <span class="html-italic">EndoFit+tourism-oil</span>: <span class="html-italic">t</span><sub>1</sub> = 2004, <span class="html-italic">t</span><sub>2</sub> = 2014. Panel (<b>a</b>): coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>; panel (<b>b</b>): estimated versors. Income is defined as per capita GDP without oil mining. To obtain the coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>, we used an <span class="html-italic">x</span>-axis bandwidth of 0.86 and a <span class="html-italic">y</span>-axis bandwidth of 0.38. Income in the vertical axis is defined as per capita GDP without oil mining.</p> "> Figure A1
<p>Exogenous Fitness without raw petroleum. <span class="html-italic">ExoFit-oil</span>: <span class="html-italic">t</span><sub>1</sub> = 2004, <span class="html-italic">t</span><sub>2</sub> = 2014. Panel (<b>a</b>): coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>; panel (<b>b</b>): estimated versors. To obtain the coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>, we used an x-axis bandwidth of 0.86 and a y-axis bandwidth of 0.38. Income in the vertical axis is defined as per capita GDP without oil mining.</p> "> Figure A1 Cont.
<p>Exogenous Fitness without raw petroleum. <span class="html-italic">ExoFit-oil</span>: <span class="html-italic">t</span><sub>1</sub> = 2004, <span class="html-italic">t</span><sub>2</sub> = 2014. Panel (<b>a</b>): coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>; panel (<b>b</b>): estimated versors. To obtain the coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>, we used an x-axis bandwidth of 0.86 and a y-axis bandwidth of 0.38. Income in the vertical axis is defined as per capita GDP without oil mining.</p> "> Figure A2
<p>Exogenous Fitness (<span class="html-italic">ExoFit</span>) without oil-dependent states: <span class="html-italic">t</span><sub>1</sub> = 2004, <span class="html-italic">t</span><sub>2</sub> = 2014. Panel (<b>a</b>): coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>; panel (<b>b</b>): estimated versors. To obtain the coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>, we used an <span class="html-italic">x</span>-axis bandwidth of 0.86 and a <span class="html-italic">y</span>-axis bandwidth of 0.38. Income in the vertical axis is defined as per capita GDP.</p> "> Figure A2 Cont.
<p>Exogenous Fitness (<span class="html-italic">ExoFit</span>) without oil-dependent states: <span class="html-italic">t</span><sub>1</sub> = 2004, <span class="html-italic">t</span><sub>2</sub> = 2014. Panel (<b>a</b>): coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>; panel (<b>b</b>): estimated versors. To obtain the coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>, we used an <span class="html-italic">x</span>-axis bandwidth of 0.86 and a <span class="html-italic">y</span>-axis bandwidth of 0.38. Income in the vertical axis is defined as per capita GDP.</p> "> Figure A3
<p>Endogenous Fitness (EndoFit) without oil-dependent states: <span class="html-italic">t</span><sub>1</sub> = 2004, <span class="html-italic">t</span><sub>2</sub> = 2014. Panel (<b>a</b>): coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>; panel (<b>b</b>): estimated versors. To obtain the coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>, we used an <span class="html-italic">x</span>-axis bandwidth of 0.86 and a <span class="html-italic">y</span>-axis bandwidth of 0.38. Income in the vertical axis is defined as per capita GDP.</p> "> Figure A3 Cont.
<p>Endogenous Fitness (EndoFit) without oil-dependent states: <span class="html-italic">t</span><sub>1</sub> = 2004, <span class="html-italic">t</span><sub>2</sub> = 2014. Panel (<b>a</b>): coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>; panel (<b>b</b>): estimated versors. To obtain the coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>, we used an <span class="html-italic">x</span>-axis bandwidth of 0.86 and a <span class="html-italic">y</span>-axis bandwidth of 0.38. Income in the vertical axis is defined as per capita GDP.</p> "> Figure A4
<p>Economic Complexity (ECI) without oil-dependent states: <span class="html-italic">t</span><sub>1</sub> = 2004, <span class="html-italic">t</span><sub>2</sub> = 2014. Panel (<b>a</b>): coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>; panel (<b>b</b>): estimated versors. To obtain the coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>, we used an <span class="html-italic">x</span>-axis bandwidth of 0.86 and a <span class="html-italic">y</span>-axis bandwidth of 0.38. Income in the vertical axis is defined as per capita GDP.</p> "> Figure A5
<p>Endogenous Fitness with Tourism (<span class="html-italic">EndoFit+Tourism</span>) without oil-dependent states: <span class="html-italic">t</span><sub>1</sub> = 2004, <span class="html-italic">t</span><sub>2</sub> = 2014. Panel (<b>a</b>): coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>; panel (<b>b</b>): estimated versors. To obtain the coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>, we used an <span class="html-italic">x</span>-axis bandwidth of 0.86 and a <span class="html-italic">y</span>-axis bandwidth of 0.38. Income in the vertical axis is defined as per capita GDP.</p> "> Figure A6
<p>Ranking evolution of the Mexican states’ fitness, <span class="html-italic">EndoFit~Pop</span>: 2004–2014.</p> "> Figure A7
<p>Endogenous Fitness with Tourism normalized by population (<span class="html-italic">EndoFit+Tourism~Pop</span>): <span class="html-italic">t</span><sub>1</sub> = 2004, <span class="html-italic">t</span><sub>2</sub> = 2014. Panel (<b>a</b>): coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>; panel (<b>b</b>): estimated versors. To obtain the coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>, we used an <span class="html-italic">x</span>-axis bandwidth of 1.3 and a <span class="html-italic">y</span>-axis bandwidth of 1.0. Income in the vertical axis is defined as per capita GDP.</p> "> Figure A7 Cont.
<p>Endogenous Fitness with Tourism normalized by population (<span class="html-italic">EndoFit+Tourism~Pop</span>): <span class="html-italic">t</span><sub>1</sub> = 2004, <span class="html-italic">t</span><sub>2</sub> = 2014. Panel (<b>a</b>): coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>; panel (<b>b</b>): estimated versors. To obtain the coefficients <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi>D</mi> <mo>˜</mo> </mover> <mi>k</mi> </msub> </mrow> </semantics></math>, we used an <span class="html-italic">x</span>-axis bandwidth of 1.3 and a <span class="html-italic">y</span>-axis bandwidth of 1.0. Income in the vertical axis is defined as per capita GDP.</p> "> Figure A8
<p>Growth predictions with random effects estimations: These graphs are drawn with the adjusted values obtained from regressions of <a href="#entropy-20-00841-t0A5" class="html-table">Table A5</a>; only predicted income growth for 2010–2014 is presented (annualized); income growth is defined with per capita GDP, inflation adjusted; predicted values come from using xb; the point estimates for Campeche are excluded for improving the clarity of the illustrations (this is the only state with a negative growth in the sample).</p> "> Figure A9
<p>Arch elasticities for growth calculated from the random effect estimations. These graphs are drawn with the adjusted values obtained from regressions of <a href="#entropy-20-00841-t0A5" class="html-table">Table A5</a>; only arch elasticities for predicted growth in the period 2010–2014 are presented, these are calculated with a 10% increase in the fitness/complexity indicator; income growth is defined with per capita GDP, inflation adjusted; predicted values come from using xb.</p> ">
Abstract
:1. Introduction
2. Productive Capabilities and Regional Development
3. Two Forms of Measuring Economic Fitness
4. The Dynamic of Income and Exogenous Fitness
5. The Dynamic of Income and Endogenous Fitness without Raw Petroleum
6. The Dynamic of Economic Complexity and Income
7. How Sensitive is the Fitness Index to the Inclusion of Tourism?
8. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
Appendix A1. Remarks
Appendix A2. Databases
Appendix A3. The Income–Fitness Dynamic Excluding Raw Petroleum
Appendix A4. The Income–Fitness Dynamic Excluding Oil-Dependent States
Appendix A5. How Sensitive is Endogenous Fitness to a Non-Monetary Normalization Procedure?
Appendix A6. Regional Growth Regressions
Appendix A6.1. Estimation Results for Endogenous Fitness
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | |
---|---|---|---|---|---|---|---|---|
Log(GDPp) | −0.0173255 *** (0.0039995) | −0.0175403 *** (0.003894) | −0.0183869 *** (0.0039537) | −0.0200634 *** (0.0037319) | −0.0144554 (0.0104048) | −0.015769 (0.0101421) | −0.017082 * (0.0097757) | −0.0186439 ** (0.0091743) |
EndoFit | −0.0023644 (0.0014368) | −0.0073397 ** (0.003434) | 0.0025256 (0.0095901) | 0.0060215 (0.009006) | 0.0007554 (0.0012829) | 0.0047724 * (0.0028566) | 0.0197269 ** (0.0089051) | 0.0213824 ** (0.009532) |
EndoFitSq | 0.0005771 (0.0003638) | −0.0022698 (0.0026109) | −0.0031274 (0.0024451) | −0.0004149 (0.0002832) | −0.0046572 ** (0.0021766) | −0.0049994 ** (0.0023128) | ||
EndoFitCu | 0.0002034 (0.0001847) | 0.0002612 (0.0001728) | 0.0002908 ** (0.0001357) | 0.0003113 ** (0.0001439) | ||||
Log(Pop) | 0.0066581 * (0.003531) | 0.0079357 ** (0.003529) | 0.0056816 (0.0040674) | 0.0044504 (0.0038024) | 0.0004058 (0.003067) | −0.0014418 (0.0035538) | −0.0039679 (0.0038233) | −0.0046521 (0.0041124) |
Edu | 0.0104072 *** (0.0032049) | 0.0114063 *** (0.0031815) | 0.009516 ** (0.0036038) | 0.0090339 ** (0.0033415) | 0.0015736 (0.0024411) | 0.0008942 (0.0023368) | −0.0000913 (0.0022483) | −0.0000355 (0.0021719) |
DTabasco | 0.023672 ** (0.010386) | 0.0203133 *** (0.0066683) | ||||||
Constant | 0.0384033 (0.0840078) | 0.0162002 (0.0829336) | 0.0709979 (0.0964312) | 0.1101432 (0.0908727) | 0.1645297 (0.1274021) | 0.210625 (0.1337973) | 0.2679194 ** (0.1360862) | 0.2942927 ** (0.137251) |
Obs. | 32 | 32 | 32 | 32 | 64 | 64 | 64 | 64 |
R2 | 0.4606 | 0.4893 | 0.4934 | 0.5662 | 0.1953 0.0637 | 0.1918 0.2251 | 0.2252 0.2578 | 0.2577 0.2649 |
Statistic | 7.62 | 6.94 | 6.03 | 6.78 | 2.59 | 4.63 | 9.27 | 16.35 |
Prob. > Stat. | 0.0003 | 0.0003 | 0.0005 | 0.0002 | 0.6279 | 0.4626 | 0.1587 | 0.0221 |
Appendix A6.2. Estimation Results for Exogenous Fitness and Economic Complexity
Appendix A6.3. Estimated Results for Oil-dependence Corrected Regressions
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | |
---|---|---|---|---|---|---|---|---|
Log(GDPp) | −0.0165319 * (0.0089899) | −0.0148992 * (0.0086574) | −0.0140956 (0.0089671) | −0.0149223 ** (0.0069951) | −0.0143415 (0.0088832) | −0.0139801 (0.0095308) | −0.0142191 (0.0096114) | −0.015892 * (0.0087936) |
ExoFit | 0.0102867 ** (0.0041612) | 0.0209965 ** (0.0092185) | 0.0288682 (0.02022) | 0.0489177 *** (0.0171303) | ||||
ExoFitSq | −0.0036485 (0.0024707) | −0.0100866 (0.0140177) | −0.0214376 * (0.0124) | |||||
ExoFitCu | 0.0013129 (0.0026414) | 0.0032552 (0.0023666) | ||||||
Eci | 0.0089863 ** (0.0038962) | 0.0088548 ** (0.0043927) | 0.0159825 ** (0.0073132) | 0.0186952 ** (0.007804) | ||||
EciSq | 0.0000689 (0.0042466) | 0.0030141 (0.0035541) | 0.0036416 (0.0037432) | |||||
EciCu | −0.0064342 * (0.0034704) | −0.0075883 ** (0.0036262) | ||||||
Log(Pop) | −0.0055626 (0.0043632) | −0.0050557 (0.0043232) | −0.0049136 (0.0043282) | −0.0066017 (0.004778) | −0.0022973 (0.0042544) | −0.0021 (0.0042271) | −0.0020939 (0.0045071) | −0.0027713 (0.0048939) |
Edu | 0.0001228 (0.002164) | −0.0001628 (0.0021382) | −0.0001617 (0.0021671) | −0.0003775 (0.0020473) | 0.0000613 (0.0023212) | 0.0001785 (0.0022342) | 0.0000171 (0.0020751) | 8.63 × 10 −6 (0.0019351) |
DTabasco | 0.0358943 *** (0.007187) | 0.0264273 *** (0.0079933) | ||||||
Constant | 0.2784872 ** (0.1321611) | 0.2498717 * (0.1276447) | 0.2366058 * (0.1325907) | 0.2633407 ** (0.1268572) | 0.212915* (0.1257222) | 0.2049188 (0.1310565) | 0.208004 (0.1401495) | 0.2357233 (0.144123) |
Obs. | 64 | 64 | 64 | 64 | 64 | 64 | 64 | 64 |
R2 | 0.2497 0.2733 | 0.2895 0.2889 | 0.2970 0.2560 | 0.4039 0.1947 | 0.2806 0.2257 | 0.2830 0.2216 | 0.2712 0.3279 | 0.3258 0.3260 |
Wald Chi | 8.33 | 11.87 | 12.71 | 44.80 | 14.14 | 14.02 | 11.11 | 23.70 |
Prob > Chi2 | 0.0802 | 0.0367 | 0.0479 | 0.0000 | 0.0069 | 0.0155 | 0.0849 | 0.0013 |
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | |
---|---|---|---|---|---|---|---|---|
Log(GDPp) | −0.0145268 (0.0136423) | −0.0164121 * (0.0099913) | 0.0210509 *** (0.0073088) | −0.0178092 * (0.0095139) | −0.0170732 (0.0142267) | −0.0170839 ** (0.0074607) | −0.0128544 (0.0129291) | 0.0182204 *** (0.0066289) |
EndoFit | 0.0186449 * (0.009982) | 0.0061016 (0.006722) | . | |||||
EndoFitSq | −0.0044425 * (0.0023349) | −0.0022026 (0.0018052) | ||||||
EndoFitCu | 0.0002783 * (0.0001437) | 0.0001587 (0.0001248) | ||||||
EndoFit~Oil | 0.0161024 ** (0.0073745) | 0.0173203 ** (0.0078194) | ||||||
EndoFit~OilSq | −0.0036825 ** (0.001652) | −0.0039153 ** (0.0017375) | ||||||
EndoFit~OilCu | 0.0002192 ** (0.0000964) | 0.0002321 ** (0.0001011) | ||||||
ExoFit | 0.0345633 * (0.0209359) | |||||||
ExoFitSq | −0.0125525 (0.0128991) | |||||||
ExoFitCu | 0.001683 (0.0023846) | |||||||
ExoFit~Oil | 0.0458676 ** (0.0200682) | |||||||
ExoFit~OilSq | −0.0177393 (0.0137968) | |||||||
ExoFit~OilCu | 0.0024239 (0.0026225) | |||||||
Eci | 0.0155941 * (0.0080645) | 0.0106833 ** (0.0049313) | ||||||
EciSq | 0.0066555 (0.0039978) | 0.0066555 (0.0054479) | ||||||
EciCu | −0.006685 * (0.0035449) | −0.0089164 * (0.0048503) | ||||||
Log(Pop) | −0.0036141 (0.0041484) | −0.0026159 (0.0035318) | 0.0019717 (0.0030873) | −0.003159 (0.003765) | −0.0058348 (0.0057272) | −0.0072285 (0.0052079) | −0.0019833 (0.0047752) | −0.0000662 (0.002424) |
Edu | −0.0003489 (0.0022064) | 0.0004749 (0.0022924) | −0.0036114 * (0.0018429) | 0.0005514 (0.0022155) | −0.0000164 (0.0021688) | −0.0003506 (0.0019459) | −0.0002263 (0.002007) | −0.0043241 ** (0.001863) |
DOil | −0.0075064 (0.0256846) | 0.0093994 (0.0299999) | −0.0048651 (0.0307612) | |||||
DTabasco | 0.0182323 *** (0.0067492) | 0.0239798 *** (0.0059947) | ||||||
Constant | 0.2363029 (0.1890923) | 0.2374505 * (0.1331446) | −0.2198894 ** (0.1031526) | 0.259925 * (0.1341542) | 0.2796539 (0.214558) | 0.2969323 ** (0.1376459) | 0.1928784 (0.1856774) | −0.1548453 * (0.08371) |
Obs. | 64 | 64 | 60 | 64 | 64 | 64 | 64 | 60 |
R2 | 0.2407 0.2475 | 0.2156 0.1976 | 0.2768 0.0232 | 0.2433 0.2054 | 0.2916 0.2585 | 0.3602 0.2642 | 0.2721 0.3361 | 0.2927 0.0843 |
Wald Chi | 10.92 | 10.19 | 12.82 | 19.78 | 17.97 | 29.63 | 11.03 | 15.93 |
Prob > Chi2 | 0.1423 | 0.1168 | 0.0459 | 0.0061 | 0.0121 | 0.0001 | 0.1371 | 0.0141 |
Appendix A6.4. Estimation Results for Models with Fixed Effects
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
---|---|---|---|---|---|
Log(GDPp) | −0.0856704 *** (0.0181986) | −0.0798948 *** (0.0201228) | −0.0786688 *** (0.0195105) | −0.0876209 ** (0.0319398) | −0.0998928 *** (0.0204312) |
EndoFit | 0.0461811 *** (0.0152243) | ||||
EndoFitSq | −0.0077373 ** (0.0029663) | ||||
EndoFitCu | 0.000408 ** (0.0001807) | ||||
ExoFit | −0.0111011 (0.0567444) | 0.0142936 (0.011174) | |||
ExoFitSq | 0.023465 (0.0367806) | ||||
ExoFitCu | −0.004571 (0.0063557) | ||||
Eci | 0.0191758 * (0.0106878) | 0.0164638 ** (0.0077017) | |||
EciSq | 0.0045546 (0.0088639) | ||||
EciCu | −0.0024511 (0.0063875) | ||||
Log(Pop) | −0.1520162 *** (0.0455606) | −0.1467475 *** (0.0503463) | −0.1524682 *** (0.0483571) | −0.1218414 ** (0.0499547) | −0.1253733 ** (0.0469399) |
Edu | 0.0103553 *** (0.0037082) | 0.0110999 ** (0.0041726) | 0.0116899 *** (0.0039414) | 0.0094653 ** (0.0046066) | 0.0106828 *** (0.0037743) |
Constant | 3.142839 *** (0.7072579) | 3.000767 *** (0.7813192) | 3.06343 *** (0.7554937) | 2.737751 *** (0.8476219) | 2.923602 *** (0.7213618) |
Obs. | 64 | 64 | 64 | 64 | 64 |
R2 | 0.0009 0.6022 | 0.0021 0.4985 | 0.0029 0.4850 | 0.0011 0.5362 | 0.0029 0.5314 |
F Statistic | 6.56 | 4.31 | 6.59 | 5.01 | 7.94 |
Prob > Stat | 0.0003 | 0.0038 | 0.0007 | 0.0016 | 0.0002 |
Hausman | 21.36 | 17.18 | 14.54 | n.a. | 22.39 |
Prob > Stat | 0.0016 | 0.0086 | 0.0058 | n.a. | 0.0002 |
Appendix A6.5. Predicted Growth with Different Metrics
Model A1.8 (EndoFit) | Model A2.4 (ExoFit) | Model A2.8 (Eci) | ||
---|---|---|---|---|
(1) | Log(GDPp) | −0.0161203 ** (0.0078185) | −0.0136732 ** (0.0059063) | −0.0138542 ** (0.0064373) |
(2) | EndoFit | 0.0178243 ** (0.0079498) | ||
(3) | EndoFitSq | −0.0043577 ** (0.0020221) | ||
(4) | EndoFitCu | 0.0002745 ** (0.0001277) | ||
(5) | ExoFit | 0.024116 *** (0.008316) | ||
(6) | ExoFitSq | −0.0053179 * (0.002752) | ||
(7) | Eci | 0.0170175 *** (0.0065092) | ||
(8) | EciCu | −0.0058726 * (0.0033072) | ||
(9) | DTabasco | 0.0183141 *** (0.0062984) | 0.0272618 *** (0.0069967) | 0.0244084 *** (0.0066153) |
(10) | Constant | 0.197893 ** (0.0881094) | 0.1586653 ** (0.0655722) | 0.1737042 ** (0.0732403) |
(11) | R2 | 0.2615 0.2089 | 0.3570 0.1772 | 0.3414 0.2233 |
(12) | Wald Chi | 16.28 *** | 16.80 *** | 18.13 *** |
(13) | Prob > chibar2 (Breush & Pagan) | 0.0028 | 0.0125 | 0.0046 |
(14) | Mean adjusted growth, 2010–2014 | 0.0180172 (0.0092687) | 0.0180025 (0.0100728) | 0.0176993 (0.0104244) |
(15) | Mean standard deviation of adjusted growth, 2010–2014 | 0.0046942 (0.0031006) | 0.0039256 (0.002623) | 0.0043876 (0.0023375) |
(16) | Mean square error, 2004–2008 & 2010–2014 | 0.0002025 (0.0002549) | 0.0001755 (0.000223) | 0.0001803 (0.0002412) |
EndoFit versus ExoFit | EndoFit versus Eci | ExoFit versus Eci | ||
---|---|---|---|---|
(1) | Growth forecast: p-value | 0.9841 | 0.7828 | 0.6976 |
(2) | St.Dev. forecast: p-value | 0.0001 | 0.2740 | 0.0772 |
(3) | Square errors: p-value | 0.0443 | 0.1297 | 0.6787 |
(4) | Elasticities: p-value | 0.0634 | 0.9618 | 0.0804 |
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Castañeda, G.; Romero-Padilla, J. Subnational Analysis of Economic Fitness and Income Dynamic: The Case of Mexican States. Entropy 2018, 20, 841. https://doi.org/10.3390/e20110841
Castañeda G, Romero-Padilla J. Subnational Analysis of Economic Fitness and Income Dynamic: The Case of Mexican States. Entropy. 2018; 20(11):841. https://doi.org/10.3390/e20110841
Chicago/Turabian StyleCastañeda, Gonzalo, and Juan Romero-Padilla. 2018. "Subnational Analysis of Economic Fitness and Income Dynamic: The Case of Mexican States" Entropy 20, no. 11: 841. https://doi.org/10.3390/e20110841