Entrepreneurial innovativeness: when too little or too much agglomeration hurts

(1)

Entrepreneurial innovativeness: When too little or too much agglomeration hurts

Emilio Pindado, Mercedes Sánchez and Marian García Martínez

Supplementary Materials

Additional Information Effect Sizes and Robustness

Checks

(2)

Index

Additional information on the effect sizes and their confidence intervals...4 Additional robustness checks...9

Figure Index

Figure SM1. Moderating effect of entrepreneurs’ education level on the inverted U-shaped relationship between urban agglomeration and new ventures’ innovativeness (predictive margins with 95% CIs)...4 Figure SM2. Moderating effect of entrepreneurial experience for the inverted U-shaped relationship between urban agglomeration and new ventures’ innovativeness (predictive margins with 95% CIs)...4 Figure SM3. Moderating effect of entrepreneurial capabilities for the inverted U-shaped relationship between urban agglomeration and new ventures’ innovativeness (predictive margins with 95% CIs)...5 Figure SM4. Moderating effect of entrepreneurial networks for the inverted U-shaped

relationship between urban agglomeration and new ventures’ innovativeness (predictive margins with 95% CIs)...5 Figure SM5. Average marginal effect of entrepreneurs’ education level (secondary degree or higher vs. no secondary degree) across the range of Urban Agglomeration variable on

predicted new ventures’ innovativeness...7 Figure SM6. Average marginal effect of entrepreneurial experience (prior entrepreneurial experience vs. no entrepreneurial experience) across the range of Urban Agglomeration variable on predicted new ventures’ innovativeness...8 Figure SM7. Average marginal effect of entrepreneurial capabilities (entrepreneurial

capabilities vs. no entrepreneurial capabilities) across the range of Urban Agglomeration variable on predicted new ventures’ innovativeness...8 Figure SM8. Average marginal effect of entrepreneurial networks (in contact with other entrepreneurs vs. not in contact) across the range of Urban Agglomeration variable on

predicted new ventures’ innovativeness...9

Table Index

Table SM1. Tests of average marginal effects (AMEs) and second differences...6

Table SM2. Multilevel random intercept models for new ventures’ innovativeness...10

Table SM3. Multilevel random intercept models for new ventures’ innovativeness (Urban

Agglomeration S-shaped relationship)...11

Table SM4. Multilevel random intercept models for new ventures’ innovativeness (different

segments Urban Agglomeration)...12

Table SM5. Multilevel random intercept models for new ventures’ innovativeness (additional

controls)...13

(3)

Table SM6. Multilevel random intercept models for new ventures’ innovativeness

(alternative controls)...14 Table SM7. Multi-level ordered logit models...15 Table SM8. Multilevel random intercept models for new ventures’ innovativeness (Education level as scale)...16 Table SM9. Definition of the variables and descriptive statistics OECD sample Urban

Agglomeration HHI...17 Table SM10. Correlation matrix OECD sample Urban Agglomeration HHI...18 Table SM11. Country stats OECD sample Urban Agglomeration HHI...19 Table SM12. Multilevel random intercept models for new ventures’ innovativeness (Urban Agglomeration HHI)...20 Table SM13. Multilevel random intercept models for new ventures’ innovativeness (Urban Agglomeration HHI S-shaped relationship)...21 Table SM14. Test of an inverted U shaped relationship between Urban Agglomeration HHI ‐ and new ventures’ innovativeness...22 Table SM15. Multilevel random intercept models for new ventures’ innovativeness (different segments Urban Agglomeration HHI)...23 Table SM16. Two step Heckman test, self-selection into entrepreneurship. ‐ ...24 Table SM17. Multilevel random intercept models for new ventures’ innovativeness

(Innovation Driven Economies subsample)...25 Table SM18. Multilevel random intercept models for new ventures’ innovativeness

(Resource & Efficiency Driven Economies subsample)...26 Table SM19. Test of an inverted U shaped relationship between Urban Agglomeration and ‐ new ventures’ innovativeness (Innovation Driven and Resource & Efficiency Driven

Economies subsamples)...27

Table SM20. Multilevel random intercept models for new ventures’ innovativeness (based on

1,000 bootstrap samples)...28

(4)

Additional information on the effect sizes and their confidence intervals

As Maula and Stam (2020) noted, it is important to consider and report effect sizes and their confidence intervals despite there is “no single ideal test” to “measure the size of an effect or the importance of a result” and their interpretation “is always context specific”.

In this regard, it is important to note that, as Grimes et al (2018) remark, the overlap in the 95% confidence intervals (CIs) of the predictive margins (Figures SM1, SM2, SM3 and SM 4) cannot be viewed as hypotheses testing as it is remarked in the Stata base reference manual, indicating that “it is tempting to conclude from this overlap that the differences are not statistically significant. Do not fall into this trap” due to “the CIs are for the point estimates, not the differences” (StataCorp, 2017, p. 1444). Furthermore, the rule of

“confidence interval overlapping” often fails in presence of nonindependence sources such as the cross-sectional nature of our data (Belia et al. 2005).

Figure SM1. Moderating effect of entrepreneurs’ education level on the inverted U-shaped relationship between urban agglomeration and new ventures’ innovativeness (predictive margins with 95% CIs)

Figure SM2. Moderating effect of entrepreneurial experience for the inverted U-shaped relationship between urban

agglomeration and new ventures’ innovativeness (predictive margins with 95% CIs)

(5)

Figure SM3. Moderating effect of entrepreneurial capabilities for the inverted U-shaped relationship between urban agglomeration and new ventures’ innovativeness (predictive margins with 95% CIs)

Figure SM4. Moderating effect of entrepreneurial networks for the inverted U-shaped relationship between urban agglomeration and new ventures’ innovativeness (predictive margins with 95% CIs)

Hence, we follow the procedure of examining nonlinear interaction effects proposed by Mize

(2019), testing “whether an interaction effect exists or not on average” and how the effects

vary across different levels of urban agglomeration. Concretely, Mize (2019) states that when

the hypothesis is about an interaction effect that exists on average across the whole sample

like it is in our case, comparisons of average marginal effects are recommended. That is,

testing the equality of average marginal effects through second differences tests, indicating

the significance of the second difference a significant interaction effect on average. This

procedure has been used in recent papers addressing curvilinear or complex moderations

(e.g., Jünger, 2021; Cole et al., 2022; Lantz et al., 2022). The analysis of the average marginal

effects for the individual level variables addressed in this study as well as the tests of second

differences are provided in Table SM1.

(6)

Table SM1. Tests of average marginal effects (AMEs) and second differences.

AME AME Second

(No secondary degree) (Secondary degree or higher) Difference

Effect of Urban Agglomeration (t−1) 0.015*** 0.010*** 0.005***

Starti + 0.10 (0.003) (0.003) (0.001)

AME AME Second

(No entrepreneurial experience) (Prior entrepreneurial experience) Difference

Effect of Urban Agglomeration (t−1) 0.012*** 0.009*** 0.003**

Starti + 0.10 (0.003) (0.003) (0.002)

AME AME Second

(No entrepreneurial capabilites) (Entrepreneurial capabilites) Difference

Effect of Urban Agglomeration (t−1) 0.010*** 0.012*** -0.001

Starti + 0.10 (0.003) (0.003) (0.001)

AME AME Second

(Not in contact) (In contact with other entrepreneurs) Difference

Effect of Urban Agglomeration (t−1) 0.010*** 0.013*** -0.003**

Starti + 0.10 (0.003) (0.003) (0.001)

Note: Standard errors in parentheses. Level of significance: * p<0.10, ** p<0.05, *** p<0.01. Continuous variables are standardised.

Table SM1 shows how the average effects of an increase in urban agglomeration at the country level on entrepreneurial innovativeness are larger and significant for entrepreneurs with lower levels of education or no prior entrepreneurial experience which corresponds to an accentuation of the upsides and downsides of urban agglomeration for these entrepreneurs. In other words, the concavity of the inverted u-shaped relationship is weakened for entrepreneurs with higher levels of education or prior entrepreneurial experience. Hence, these second differences test support respectively H2a and H2b. Likewise, the average effect of an increase in urban agglomeration at the country level is smaller and significant for entrepreneurs not in contact with other entrepreneurs which implies an attenuated upsides and downsides of urban agglomeration for these entrepreneurs. Thus, H2d is supported, the concavity of the inverted U-shaped relationship is strengthened for entrepreneurs in contact with other entrepreneurs. On the other hand, we do not find support for H2c due to the average effect of an increase in urban agglomeration at the country level is smaller but non- significant for entrepreneurs with lower levels of entrepreneurial capabilities. These results provide further evidence of our findings and confirm the robustness of our analysis.

Additionally, following the procedure noted by Mitchell (2012), we address if the degree of the curvature is significantly different between strong and weak entrepreneurs for the variables addressed, testing if the quadratic coefficients are significantly different for them.

We found that the degree of the curvature is significantly different for entrepreneurs with higher levels of education compared to entrepreneurs with lower levels of education (Chi squared = 9.99, p = 0.0016), for entrepreneurs with prior entrepreneurial experience compared to non-experienced entrepreneurs (Chi squared = 5.38, p = 0.0204), and for entrepreneurs knowing other entrepreneurs compared to entrepreneurs without these social ties (Chi squared = 3.96, p = 0.0465). For entrepreneurs with higher levels of entrepreneurial capabilities compared to entrepreneurs with lower levels of these capabilities, we found a non-significant difference for the quadratic coefficients (Chi squared = 0.00, p = 0.9558).

Overall, these results reinforce the findings reported in our paper about the presence of an interaction effect on average between urban agglomeration and entrepreneurs’ education level, entrepreneurial experience, and entrepreneurial capabilities.

On the other hand, as Mize (2019) indicates, it is also important to investigate the other side

of the nonlinear interaction effects. That is how the effects of our independent variables at the

individual level vary across the range of urban agglomeration at the country level. This can

be done by plotting the differences in the adjusted predictions for the different levels of our

independent variables (i.e., the average marginal effects) across different levels of urban

agglomeration. The CIs of these differences provide a direct test to evaluate the significance

(7)

of the differences between the levels of our independent variables at different values of urban agglomeration.

Figure SM5 shows the differences in the predicted entrepreneurial innovativeness between entrepreneurs’ levels of education across the range of urban agglomeration at the country level. These differences are significant across the whole range of urban agglomeration due to the differences line and CIs are above y = 0. Higher educated entrepreneurs are predicted to innovate more than lower educated ones due to the line is positive. However, the differences between higher educated entrepreneurs and entrepreneurs with lower levels of education decrease until the turning point and increase after it, which supports our H2a.

Figure SM5. Average marginal effect of entrepreneurs’ education level (secondary degree or higher vs. no secondary degree) across the range of Urban Agglomeration variable on predicted new ventures’ innovativeness

Regarding entrepreneurial experience, Figure SM6 shows the differences between

experienced entrepreneurs and novice entrepreneurs, revealing how these differences

decrease until the turning point and increase after it. The differences line is above y = 0,

however, at intermediate values of urban agglomeration, the 95% CIs include y = 0, which

suggests non-significant differences between both groups at these levels. Investigating our

dataset, we found that 32.34% of our sample are within these values (-1.04; 0.15). Having in

mind this and the nature of our curvilinear moderation hypothesis which is focused on the

differences at low and high levels and not at the turning point, we interpret this plot as

support for our hypothesis H2b.

(8)

Figure SM6. Average marginal effect of entrepreneurial experience (prior entrepreneurial experience vs. no entrepreneurial experience) across the range of Urban Agglomeration variable on predicted new ventures’ innovativeness

Figure SM7 shows the differences between entrepreneurs having start-up capabilities compared to entrepreneurs without these capabilities. The differences line is positive and above y = 0, indicating that the differences between them increase. At low levels of urban agglomeration, the 95% CIs include y = 0, revealing non-significant differences between both groups of entrepreneurs at these levels. Concretely, we found that 5.94% of our observations are within this data range (-2.25; -1.66). Hence, this figure supports the finding of our study that entrepreneurs’ start-up capabilities do not weaken the concavity of the relationship between spatial agglomeration at the country level and their innovativeness.

Figure SM7. Average marginal effect of entrepreneurial capabilities (entrepreneurial capabilities vs. no entrepreneurial capabilities) across the range of Urban Agglomeration variable on predicted new ventures’ innovativeness

Finally, Figure SM8 shows the differences between entrepreneurs in contact with other entrepreneurs and entrepreneurs not in contact with other entrepreneurs. The differences line shows how the differences between them increase until the turning point and decrease after it.

The line and CIs are above y = 0 for most of the values of this variable, however, at low

levels of urban agglomeration, they are below y = 0. This range of non-significance (-2.25;

(9)

-1.58) corresponds to 5.94% of our observations. Setting these observations aside and considering the nature of our moderation hypothesis, we interpret this plot as support for H2d.

Figure SM8. Average marginal effect of entrepreneurial networks (in contact with other entrepreneurs vs. not in contact) across the range of Urban Agglomeration variable on predicted new ventures’ innovativeness

Additional robustness checks

In what follows, we report different robustness checks that provide further evidence on the

findings of our study.

(10)

Table SM2. Multilevel random intercept models for new ventures’ innovativeness

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

Individual-level control variables

Age -0.00138 -0.00159 -0.00160 -0.00161 -0.00170 -0.00162 -0.00169 -0.00154

(0.0012) (0.0012) (0.0012) (0.0012) (0.0012) (0.0012) (0.0012) (0.0012)

Gender (male) 0.00761^*** 0.00749^*** 0.00754^*** 0.00753^*** 0.00742^*** 0.00749^*** 0.00743^*** 0.00762^***

(0.0025) (0.0025) (0.0025) (0.0025) (0.0025) (0.0025) (0.0025) (0.0025)

Individual-level variables

Education level 0.0345^*** 0.0339^*** 0.0336^*** 0.0336^*** 0.0242^*** 0.0336^*** 0.0335^*** 0.0336^***

(0.0031) (0.0031) (0.0031) (0.0031) (0.0042) (0.0031) (0.0031) (0.0031)

Entrepreneurial Experience 0.0190^*** 0.0191^*** 0.0189^*** 0.0189^*** 0.0190^*** 0.0104^* 0.0189^*** 0.0190^***

(0.0046) (0.0046) (0.0046) (0.0046) (0.0046) (0.0062) (0.0046) (0.0046)

Entrepreneurial capabilities 0.0439^*** 0.0440^*** 0.0437^*** 0.0437^*** 0.0438^*** 0.0437^*** 0.0425^*** 0.0435^***

(0.0033) (0.0033) (0.0033) (0.0033) (0.0033) (0.0033) (0.0045) (0.0033)

Entrepreneurial Networks 0.0282^*** 0.0282^*** 0.0284^*** 0.0284^*** 0.0284^*** 0.0284^*** 0.0283^*** 0.0329^***

(0.0026) (0.0026) (0.0026) (0.0026) (0.0026) (0.0026) (0.0026) (0.0035)

Country-year level control variables

GDP per capita (t−1) 0.0362^*** 0.0304^*** 0.0234^** 0.0239^** 0.0233^** 0.0232^** 0.0235^**

(0.0095) (0.0099) (0.0098) (0.0098) (0.0098) (0.0098) (0.0098)

Institutional Quality (t−1) 0.0219^* 0.0204^* 0.0197^* 0.0194 0.0198^* 0.0197^* 0.0196^*

(0.012) (0.012) (0.012) (0.012) (0.012) (0.012) (0.012)

Financial System (t−1) 0.0211^*** 0.0197^** 0.0200^** 0.0199^** 0.0199^** 0.0198^** 0.0199^**

(0.0081) (0.0080) (0.0079) (0.0079) (0.0079) (0.0079) (0.0079)

Innovation System (t−1) -0.0376^*** -0.0296^*** -0.0324^*** -0.0315^*** -0.0325^*** -0.0323^*** -0.0324^***

(0.011) (0.011) (0.012) (0.012) (0.012) (0.012) (0.012)

Land Area (t−1) -0.0183^*** -0.0175^*** -0.0240^*** -0.0239^*** -0.0240^*** -0.0241^*** -0.0241^***

(0.0068) (0.0067) (0.0078) (0.0078) (0.0078) (0.0078) (0.0078)

Total Population (t−1) 0.0305^*** 0.0423^*** 0.0436^*** 0.0428^*** 0.0436^*** 0.0436^*** 0.0435^***

(0.0080) (0.0084) (0.0083) (0.0083) (0.0083) (0.0083) (0.0083)

Country-year level means

Education level 0.0992^** 0.0669 0.0562 0.0673 0.0678 0.0659

(0.044) (0.044) (0.044) (0.044) (0.044) (0.044)

Entrepreneurial Experience 0.469^** 0.549^*** 0.573^*** 0.549^*** 0.553^*** 0.551^***

(0.19) (0.19) (0.19) (0.19) (0.19) (0.19)

Entrepreneurial capabilities -0.177^** -0.148^* -0.140^* -0.149^* -0.148^* -0.147^*

(0.073) (0.076) (0.076) (0.076) (0.076) (0.076)

Entrepreneurial Networks 0.287^*** 0.368^*** 0.362^*** 0.367^*** 0.371^*** 0.368^***

(0.095) (0.094) (0.094) (0.094) (0.094) (0.094)

Country-year level variables

Urban Agglomeration (t−1) 0.0291^*** 0.0318^*** 0.0288^*** 0.0165^* 0.0252^***

(0.0089) (0.0093) (0.0089) (0.0094) (0.0091)

Squared Urban Agglomeration (t−1) -0.0203^*** -0.0272^*** -0.0209^*** -0.0201^*** -0.0170^***

(0.0061) (0.0065) (0.0061) (0.0067) (0.0063) Interactions

Urban Agglomeration (t − 1) x Education

level -0.00474

(0.0031) Squared Urban Agglomeration (t − 1) x

Education level 0.00960^***

(0.0030) Urban Agglomeration (t − 1) x

Entrepreneurial Experience 0.00799

Entrepreneurial Experience 0.00906^**

Entrepreneurial capabilities 0.0152^***

Entrepreneurial capabilities 0.000180

Entrepreneurial Networks 0.00606^**

(0.0026) -0.00472^**

(0.0024)

Constant 0.435^*** 0.430^*** 0.199^** 0.155^* 0.168^* 0.157^* 0.152^* 0.152^*

(0.0094) (0.0093) (0.090) (0.090) (0.090) (0.090) (0.090) (0.090)

Model fit statistics

Variance of random intercept country-year 0.0220 0.0196 0.0189 0.0180 0.0180 0.0180 0.0180 0.0180

Num. of groups country-year 579 579 579 579 579 579 579 579

ICC country-year 0.075 0.068 0.065 0.062 0.063 0.062 0.062 0.063

Observations 190,046 190,046 190,046 190,046 190,046 190,046 190,046 190,046

Log likelihood -146148.4 -146117.0 -146106.3 -146093.35 -146086.18 -146090.39 -146083.69 -146088.07

Chi-square 21005.87 18054.42 16140.09 14898.34 14788.13 14876.45 14853.41 14887.05

Probability > Chi-square ^*** ^*** ^*** ^*** ^*** ^*** ^*** ^***

AIC 292356.8 292306.0 292292.6 292270.7 292260.4 292268.8 292255.4 292264.1

LR test of model fit ^a - ^*** ^*** ^*** ^*** ^* ^*** ^***

Industry Controls Yes Yes Yes Yes Yes Yes Yes Yes

a. Likelihood ratio test (LRT) was conducted comparing Models 1 through 4 between each other to test the improvement of the goodness of fit when we introduced country-year variables. Likewise, we carried out a LRT comparing Model 4 with each of the interactions considered (Models 5 to 8) to test the significance of the interaction effect.

(11)

Table SM3. Multilevel random intercept models for new ventures’ innovativeness (Urban Agglomeration S-shaped relationship)

Model 1 Model 2

Age -0.00161 -0.00162

(0.0012) (0.0012)

Gender (male) 0.00753^*** 0.00752^***

(0.0025) (0.0025)

Education level 0.0336^*** 0.0336^***

(0.0031) (0.0031)

Entrepreneurial Experience 0.0189^*** 0.0189^***

(0.0046) (0.0046)

Entrepreneurial capabilities 0.0437^*** 0.0437^***

(0.0033) (0.0033)

Entrepreneurial Networks 0.0284^*** 0.0284^***

(0.0026) (0.0026)

GDP per capita (t−1) 0.0234^** 0.0228^**

(0.0098) (0.0098)

Institutional Quality (t−1) 0.0197^* 0.0219^*

(0.012) (0.012)

Financial System (t−1) 0.0200^** 0.0194^**

(0.0079) (0.0079)

Innovation System (t−1) -0.0324^*** -0.0335^***

(0.012) (0.012)

Land Area (t−1) -0.0240^*** -0.0242^***

(0.0078) (0.0078)

Total Population (t−1) 0.0436^*** 0.0425^***

(0.0083) (0.0083)

Education level 0.0669 0.0594

(0.044) (0.044)

(0.19) (0.19)

Entrepreneurial capabilities -0.148^* -0.135^*

(0.076) (0.077)

(0.094) (0.094)

Urban Agglomeration (t−1) 0.0291^*** 0.0197

(0.0089) (0.012)

Squared Urban Agglomeration (t−1) -0.0203^*** -0.0209^***

(0.0061) (0.0061)

Cubic Urban Agglomeration (t−1) 0.00497

(0.0043)

Constant 0.155^* 0.152^*

(0.090) (0.090)

Variance of random intercept country-year 0.0180 0.0180

Num. of groups country-year 579 579

ICC country-year 0.062 0.062

Observations 190,046 190046

Log likelihood -146093.35 -146092.7

Chi-square 14898.34 14899.46

Probability > Chi-square ^*** ^***

AIC 292270.7 292271.4

LR test of model fit ^a - -

Industry Controls Yes Yes

a. Likelihood ratio test (LRT) was conducted comparing Model 1 with Model 2 to test the significance of the S-shaped relationship.

(12)

Table SM4. Multilevel random intercept models for new ventures’ innovativeness (different segments Urban Agglomeration).

Model 1 Individual-level control variables

Age -0.00159

(0.0012)

Gender (male) 0.00750^***

(0.0025) Individual-level variables

(0.0031)

Entrepreneurial Experience 0.0189^***

(0.0046)

(0.0033)

Entrepreneurial Networks 0.0284^***

(0.0026) Country-year level control variables

GDP per capita (t−1) 0.0253^**

(0.010)

Institutional Quality (t−1) 0.0168

(0.012)

Financial System (t−1) 0.0209^***

(0.0079)

Innovation System (t−1) -0.0293^***

(0.011)

Land Area (t−1) -0.0205^***

(0.0075)

Total Population (t−1) 0.0414^***

(0.0082) Country-year level means

Education level 0.0523

(0.044)

(0.19)

Entrepreneurial capabilities -0.182^**

(0.076)

(0.093) Country-year level variables

Urban Agglomeration (t−1) Quantile 2 0.130^***

(0.026)

Quantile 3 0.124^***

(0.028)

(0.027)

(0.029)

(0.032)

(0.030)

Quantile 10 0.111^***

(0.035)

Constant 0.00881

(0.095) Model fit statistics

Variance of random intercept country-year 0.0172

Num. of groups country-year 579

ICC country-year 0.06

Observations 190046

Log likelihood -146080.9

Chi-square 13617.05

Probability > Chi-square ^***

AIC 292259.8

Industry Controls Yes

Note: Standard errors in parentheses. Level of significance: * p<0.10, ** p<0.05, *** p<0.01.

Continuous variables are standardised. Following Haans et al. (2016), we introduce as a robustness of check categorical dummies that indicate different segments of our independent variable at country level (baseline category: quantile 1). The results are consistent with the proposed inverted u-shaped relationship (Bothner et al., 2012).

(13)

Table SM5. Multilevel random intercept models for new ventures’ innovativeness (additional controls).

Age -0.00164

(0.0012)

(0.0031)

(0.0046)

(0.0033)

GDP per capita (t−1) 0.0257^**

(0.010)

Institutional Quality (t−1) 0.0214^*

(0.012)

Higher education and training (t-1) 0.00136

(0.012)

Financial System (t−1) 0.0189^**

(0.0088)

Business sophistication (t-1) 0.0165

(0.017)

Innovation System (t−1) -0.0523^***

(0.014)

Unemployment (t-1) 0.00581

(0.0061)

Land Area (t−1) -0.0223^***

(0.0083)

Urban Agglomeration (t−1) 0.0226^**

(0.010)

Squared Urban Agglomeration (t−1) -0.0153^**

(0.0061)

Constant 0.448^***

ICC country-year .065

Observations 190046

Chi-square 16052.33

AIC 292294.5

Continuous variables are standardised.

(14)

Table SM6. Multilevel random intercept models for new ventures’ innovativeness (alternative controls).

Age -0.00201

(0.0014)

(0.0036)

Entrepreneurial Experience 0.00942

(0.0058)

(0.0038)

GDP per capita (t−1) 0.0106

(0.0095)

Economic Freedom of the World (t-1) 0.0543^***

(0.011)

Financial System (t−1) 0.000643

(0.0080) Gross domestic expenditures on research and

development (t-1)

-0.0247^***

(0.0082)

Land Area (t−1) -0.0173^**

(0.0074)

Urban Agglomeration (t−1) 0.0204^**

(0.0093)

Squared Urban Agglomeration (t−1) -0.0201^***

(0.0071)

Constant 0.446^***

ICC country-year 0.06

Observations 142816

Chi-square 10765.17

AIC 223677.9

Continuous variables are standardised. We control for the quality of the institutional environment measured by the index of Economic Freedom of the World (EFW) from the Fraser Institute (Gwartney et al., 2019).

(15)

Table SM7. Multi-level ordered logit models.

Model 1 Model 2 Model 3 Model 4 Model 5 Individual-level control variables

Age -0.00794^* -0.00839^* -0.00796^* -0.00824^* -0.00771^*

(0.0044) (0.0044) (0.0044) (0.0044) (0.0044)

Gender (male) 0.0212^** 0.0208^** 0.0212^** 0.0207^** 0.0215^**

(0.0089) (0.0089) (0.0089) (0.0089) (0.0089) Individual-level variables

Education level 0.123^*** 0.0817^*** 0.123^*** 0.123^*** 0.123^***

(0.011) (0.015) (0.011) (0.011) (0.011)

Entrepreneurial Experience 0.0683^*** 0.0688^*** 0.0403^* 0.0684^*** 0.0685^***

(0.016) (0.016) (0.022) (0.016) (0.016)

Entrepreneurial capabilities 0.159^*** 0.159^*** 0.159^*** 0.145^*** 0.159^***

(0.012) (0.012) (0.012) (0.016) (0.012)

Entrepreneurial Networks 0.103^*** 0.103^*** 0.103^*** 0.103^*** 0.115^***

(0.0092) (0.0092) (0.0092) (0.0092) (0.012) Country-year level control variables

GDP per capita (t−1) 0.0890^** 0.0913^*** 0.0888^** 0.0881^** 0.0894^**

(0.035) (0.035) (0.035) (0.035) (0.035)

Institutional Quality (t−1) 0.0522 0.0506 0.0526 0.0522 0.0517

(0.042) (0.042) (0.042) (0.042) (0.042)

Financial System (t−1) 0.0800^*** 0.0798^*** 0.0798^*** 0.0795^*** 0.0799^***

(0.028) (0.028) (0.028) (0.028) (0.028)

Innovation System (t−1) -0.112^*** -0.108^*** -0.112^*** -0.111^*** -0.112^***

(0.041) (0.041) (0.041) (0.041) (0.041)

Land Area (t−1) -0.0899^*** -0.0895^*** -0.0900^*** -0.0906^*** -0.0904^***

(0.028) (0.028) (0.028) (0.028) (0.028)

Total Population (t−1) 0.170^*** 0.167^*** 0.170^*** 0.170^*** 0.170^***

(0.029) (0.030) (0.029) (0.029) (0.029)

Education level 0.224 0.178 0.226 0.229 0.221

(0.16) (0.16) (0.16) (0.16) (0.16)

Entrepreneurial Experience 1.921^*** 2.021^*** 1.917^*** 1.932^*** 1.925^***

(0.67) (0.68) (0.67) (0.67) (0.67)

Entrepreneurial capabilities -0.484^* -0.449^* -0.487^* -0.482^* -0.478^*

(0.27) (0.27) (0.27) (0.27) (0.27)

(0.33) (0.33) (0.33) (0.33) (0.33)

Urban Agglomeration (t−1) 0.0982^*** 0.107^*** 0.0975^*** 0.0469 0.0829^**

(0.032) (0.033) (0.032) (0.033) (0.032)

(0.022) (0.023) (0.022) (0.024) (0.022)

Interactions

Urban Agglomeration (t − 1) x Education level -0.0170

(0.011) Squared Urban Agglomeration (t − 1) x Education level 0.0436^***

(0.011)

Urban Agglomeration (t − 1) x Entrepreneurial Experience 0.0204

(0.017) Squared Urban Agglomeration (t − 1) x Entrepreneurial Experience 0.0292^**

(0.014)

Urban Agglomeration (t − 1) x Entrepreneurial capabilities 0.0622^***

(0.012)

Squared Urban Agglomeration (t − 1) x Entrepreneurial capabilities 0.0112

(0.012)

Urban Agglomeration (t − 1) x Entrepreneurial Networks 0.0239^***

(0.0092)

Squared Urban Agglomeration (t − 1) x Entrepreneurial Networks -0.0132

(0.0085)

cut1 0.832^*** 0.775^** 0.828^*** 0.832^*** 0.844^***

(0.32) (0.32) (0.32) (0.32) (0.32)

cut2 2.089^*** 2.032^*** 2.084^*** 2.089^*** 2.101^***

(0.32) (0.32) (0.32) (0.32) (0.32)

cut3 3.631^*** 3.574^*** 3.627^*** 3.632^*** 3.643^***

(0.32) (0.32) (0.32) (0.32) (0.32)

cut4 5.093^*** 5.036^*** 5.089^*** 5.094^*** 5.105^***

(0.32) (0.32) (0.32) (0.32) (0.32)

Variance of random intercept country-year 0.227 0.228 0.227 0.227 0.227

Num. of groups country-year 579 579 579 579 579

ICC country-year 0.065 0.065 0.065 0.065 0.065

Observations 190046 190046 190046 190046 190046

Log likelihood -253832.0 -253821.4 -253829.8 -253819.2 -253827.0

Chi-square 14987.22 14900.69 14966.04 14946.94 14971.34

Probability > Chi-square ^*** ^*** ^*** ^*** ^***

AIC 507752.0 507734.8 507751.5 507730.3 507745.9

LR test of model fit ^a - ^*** - ^*** ^***

Industry Controls Yes Yes Yes Yes Yes

a. Likelihood ratio test (LRT) was conducted comparing Model 1 with each of the interactions considered (Models 2 to 5) to test the significance of the interaction effect.

(16)

Table SM8. Multilevel random intercept models for new ventures’ innovativeness (Education level as scale).

Model 1 Model 2 Model 3 Model 4 Model 5 Individual-level control variables

Age -0.00120 -0.00127 -0.00120 -0.00128 -0.00113

(0.0012) (0.0012) (0.0012) (0.0012) (0.0012)

Gender (male) 0.00734^*** 0.00727^*** 0.00730^*** 0.00724^*** 0.00743^***

(0.0025) (0.0025) (0.0025) (0.0025) (0.0025) Individual-level variables

Education level 0.0233^*** 0.0196^*** 0.0233^*** 0.0233^*** 0.0233^***

(0.0013) (0.0017) (0.0013) (0.0013) (0.0013)

Entrepreneurial Experience 0.0192^*** 0.0193^*** 0.0106^* 0.0192^*** 0.0193^***

(0.0046) (0.0046) (0.0062) (0.0046) (0.0046)

Entrepreneurial capabilities 0.0418^*** 0.0419^*** 0.0418^*** 0.0405^*** 0.0416^***

(0.0033) (0.0033) (0.0033) (0.0045) (0.0033)

(0.0026) (0.0026) (0.0026) (0.0026) (0.0035) Country-year level control variables

GDP per capita (t−1) 0.0216^** 0.0222^** 0.0215^** 0.0214^** 0.0217^**

(0.0098) (0.0098) (0.0098) (0.0098) (0.0098)

Institutional Quality (t−1) 0.0193 0.0192 0.0194^* 0.0193 0.0191

(0.012) (0.012) (0.012) (0.012) (0.012)

Financial System (t−1) 0.0201^** 0.0200^** 0.0201^** 0.0200^** 0.0201^**

(0.0078) (0.0079) (0.0078) (0.0078) (0.0079)

Innovation System (t−1) -0.0315^*** -0.0311^*** -0.0316^*** -0.0313^*** -0.0315^***

(0.012) (0.012) (0.012) (0.012) (0.012)

Land Area (t−1) -0.0248^*** -0.0247^*** -0.0248^*** -0.0249^*** -0.0249^***

(0.0078) (0.0078) (0.0078) (0.0078) (0.0078)

Total Population (t−1) 0.0434^*** 0.0427^*** 0.0435^*** 0.0434^*** 0.0434^***

(0.0083) (0.0083) (0.0083) (0.0083) (0.0083) Country-year level means

Education level 0.0455 0.0362 0.0459 0.0464 0.0445

(0.044) (0.044) (0.044) (0.044) (0.044)

Entrepreneurial Experience 0.526^*** 0.544^*** 0.526^*** 0.529^*** 0.527^***

(0.19) (0.19) (0.19) (0.19) (0.19)

Entrepreneurial capabilities -0.145^* -0.138^* -0.146^* -0.145^* -0.144^*

(0.076) (0.076) (0.076) (0.076) (0.076)

(0.094) (0.094) (0.094) (0.094) (0.094)

Urban Agglomeration (t−1) 0.0289^*** 0.0301^*** 0.0286^*** 0.0163^* 0.0250^***

(0.0089) (0.0098) (0.0089) (0.0094) (0.0091) Squared Urban Agglomeration (t−1) -0.0205^*** -0.0330^*** -0.0212^*** -0.0204^*** -0.0174^***

(0.0061) (0.0072) (0.0061) (0.0067) (0.0063) Interactions

Urban Agglomeration (t − 1) x Education level -0.000736

(0.0013) Squared Urban Agglomeration (t − 1) x Education level 0.00393^***

(0.0012)

Urban Agglomeration (t − 1) x Entrepreneurial Experience 0.00811^*

(0.0049) Squared Urban Agglomeration (t − 1) x Entrepreneurial

Experience 0.00924^**

(0.0039)

Urban Agglomeration (t − 1) x Entrepreneurial capabilities 0.0153^***

(0.0035) Squared Urban Agglomeration (t − 1) x Entrepreneurial

capabilities 0.000291

(0.0032)

Urban Agglomeration (t − 1) x Entrepreneurial Networks 0.00605^**

(0.0026)

Squared Urban Agglomeration (t − 1) x Entrepreneurial Networks -0.00465^**

(0.0024)

Constant 0.120 0.139 0.122 0.118 0.117

(0.090) (0.090) (0.090) (0.090) (0.090)

Variance of random intercept country-year 0.0180 0.0180 0.0180 0.0180 0.0180

Num. of groups country-year 579 579 579 579 579

ICC country-year 0.062 0.062 0.062 0.062 0.062

Observations 190,046 190,046 190,046 190,046 190,046

Log likelihood ‐

145989.33 ‐

145983.53 ‐

145986.27 ‐

145979.56 ‐

145984.12

Chi-square 14926.78 14738.10 14904.51 14883.85 14915.87

Probability > Chi-square ^*** ^*** ^*** ^*** ^***

AIC 292062.7 14738.10 292060.5 292047.1 292056.2

LR test of model fit ^a - ^*** ^** ^*** ^***

Industry Controls Yes Yes Yes Yes Yes

a. Likelihood ratio test (LRT) was conducted comparing Model 1 with each of the interactions considered (Models 2 to 5) to test the significance of the interaction effect.

(17)

Table SM9. Definition of the variables and descriptive statistics OECD sample Urban Agglomeration HHI.

Variable Description Mean Std. Dev Min Max

Dependent Variable New ventures’

Innovativeness Average of:

Item 1: “Do all, some, or none of your potential customers consider this product or service new and unfamiliar?” (GEM)

0. None 1. Some 2. All

Item 2: “Right now, are there many, few, or no other businesses offering the same products or services to your potential customers?”

(GEM)

0. Many 1. Few 2. No

0.669 0.58 0 2

Individual Level Variables

Age Age of respondents measured in years. (GEM) 39.068 11.47 18 64

Gender Gender of respondents. (GEM) 0.596 0.491 0 1

Education level Dummy variable indicating whether individual has at least secondary

education or higher. (GEM) 0.834 0.372 0 1

Entrepreneurial

Experience Dummy variable indicating whether individual has shut down a business,

which he/she owned and managed, in the in the last 12 months. (GEM) 0.056 0.229 0 1

Entrepreneurial

capabilities Dummy variable indicating whether the respondent believes that he or she

“Has the knowledge, skills and experience required to start a business.”

(GEM)

0.845 0.362 0 1

Entrepreneurial

Networks Dummy variable indicating whether the respondent knows someone who

has started a business in the last two years. (GEM) 0.63 0.483 0 1

Country Level Variables GDP per capita

(t−1) Gross Domestic Product (GDP) per capita, constant at 2017 $USD. (WBI) 34964.701 15783.743 10622.812 113396.75 Institutional

Quality (t−1) 1^st Pillar GCI conformed by items covering the country’s quality of the

Public and Private Institutions, normalized on a 1-7 (best) scale. 4.521 0.78 3.299 6.182 Financial System

(t−1) 8^th Pillar GCI conformed by items covering the country’s financial market efficiency, trustworthiness and confidence, normalized on a 1-7 (best) scale.

4.593 0.624 2.524 6.4

Innovation System (t−1)

12^th Pillar (GCI) conformed by items covering the country’s context that is conducive to innovative activity, normalized on a 1-7 (best) scale.

4.007 0.867 2.567 5.838

Land Area (t-1) Country's total area sq. km. (WBI) 1170951.3 2253746.2 2430 9161920

Total Population

(t−1) Total population, all residents regardless of legal status or citizenship.

(WBI) 48704433 63761693 530946 3.229e+08

Urban Agglomeration HHI (t−1)

Urban Agglomeration HHI = [Herfindahl-Hirschman-Index HHI]*[Domestic market size]

HHI is the sum of the squared shares of each Functional Urban Areas (FUAs) contribution to the overall urban population in the beginning of period p (Frick and Rodríguez-Pose, 2018). (OECD)

Domestic market size as the sum of gross domestic product plus value of imports of goods and services, minus value of exports of goods and services, normalized on a 1-7 (best) scale. (GCI)

1.183 .767 .179 3.115

Source: Authors based on GEM, WBI, World Economic Forum’s GCI and OECD.

(18)

Table SM10. Correlation matrix OECD sample Urban Agglomeration HHI.

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1. New ventures’ Innovativeness 1.000

2. Age -0.018*** 1.000

3. Gender -0.012*** -0.019*** 1.000

4. Education level 0.039*** -0.083*** 0.022*** 1.000

5. Entrepreneurial experience 0.025*** 0.015*** 0.017*** -0.006* 1.000

6. Entrepreneurial capabilities 0.048*** 0.047*** 0.065*** 0.041*** 0.036*** 1.000

7. Entrepreneurial Networks 0.040*** -0.083*** 0.053*** 0.088*** 0.026*** 0.128*** 1.000

8. GDP per capita (t−1) -0.069*** 0.120*** 0.037*** 0.105*** -0.036*** 0.024*** 0.051*** 1.000

9. Institutional Quality (t−1) 0.052*** 0.101*** 0.022*** 0.130*** -0.005 0.031*** 0.067*** 0.710*** 1.000

10. Financial System (t−1) 0.044*** 0.082*** -0.002 0.060*** 0.023*** 0.038*** 0.018*** 0.450*** 0.704*** 1.000

11. Innovation System (t−1) -0.057*** 0.120*** 0.027*** 0.110*** -0.026*** 0.007** 0.045*** 0.758*** 0.753*** 0.571*** 1.000

12. Land Area (t-1) 0.015*** 0.031*** -

0.018*** 0.026*** 0.015*** 0.012*** 0.000 0.188*** 0.062*** 0.261*** 0.269*** 1.000

13. Total Population (t−1) -0.042*** 0.042*** -

0.019*** -0.031*** 0.004 0.003 -0.015*** 0.173*** -0.078*** 0.160*** 0.331*** 0.725*** 1.000

14. Education level, country-year mean 0.059*** 0.049*** 0.046*** 0.257*** -0.005 0.005 0.028*** 0.408*** 0.545*** 0.327*** 0.440*** 0.100*** -0.121*** 1.000 15. Entrepreneurial experience, country-year mean 0.226*** -0.053*** -

0.049*** -0.015*** 0.079*** 0.004 -0.016*** -

0.429*** -0.118*** 0.107*** -0.288*** 0.194*** 0.066*** -0.059*** 1.000

16. Entrepreneurial capabilities, country-year mean 0.018*** 0.024*** 0.017*** 0.058*** -0.010*** -0.007** 0.123*** 0.415*** 0.545*** 0.283*** 0.346*** 0.003 -0.126*** 0.227*** -0.127*** 1.000

17. Entrepreneurial Networks 0.059*** 0.030*** -

0.015*** 0.010*** 0.003 0.119*** -0.008** 0.204*** 0.238*** 0.232*** 0.081*** 0.103*** 0.031*** 0.038*** 0.033*** -

0.062*** 1.000

18. Urban Agglomeration HHI (t−1) 0.077*** -0.029*** 0.028*** 0.088*** 0.009*** -0.024*** 0.030*** -

0.029*** 0.109*** -0.117*** -0.180*** -

0.345*** -0.567*** 0.342*** 0.114*** 0.247*** -0.212***

Source: GEM 2007-2017 APS surveys, WBI, GCI, OECD.

(19)

Table SM11. Country stats OECD sample Urban Agglomeration HHI.

N New ventures’ Innovativeness Age Gender Education level E. Experience E. Capabilities E. Networks GDP p.c. (t−1) Institutional Quality (t−1) Financial System (t−1) Innovation System (t−1) Land Area (t-1) Total Population (t−1) Urban Agglomeration HHI(t−1)

Australia 1133 0.62

4 40.68 0.58 7 0.86

8 0.04 4 0.84

3 0.64

5 46944.00 6 5.28

5 5.41 9 4.46

7 7683887.

1 23182179 0.98 4

Austria 1222 0.58

7 38.96 5 0.57

6 0.83 8 0.05

1 0.84 3 0.74

4 53029.23 1 5.18

1 4.49 8 4.82

4 82539.62

8 8491135.

8 1.72 3

Belgium 801 0.62

7 39.50 1 0.64

4 0.92 8 0.05

1 0.83 8 0.55

4 48315.42 9 5.03

1 4.81 8 4.78

3 30280 10936722 1.32 9

Canada 1468 0.72

3 39.96 5 0.60

2 0.94 3 0.06

5 0.85 3 0.68

3 47181.25 6 5.42

8 5.32 1 4.57

6 8965590 35410080 0.81 3

Chile 1198

7 1.03

5 38.78 0.55 2 0.85

4 0.08 6 0.85

8 0.64

8 23052.63 5 4.79

1 4.76 4 3.47

2 743532 17490476 1.77 9

Colombia 1004

8 0.69 8 36.61

5 0.56 3 0.79

4 0.07 3 0.84

2 0.53

7 12333.04 7 3.42

5 4.11

7 3.18 1109500 45528598 0.70 8 Czech Rep. 562 0.59

3 36.68

3 0.68 0.94 7 0.03

9 0.79 0.56

8 33735.67 3.72 2 4.32

4 3.84

2 77232.91

8 10500170 1.54 3

Denmark 672 0.79

6 38.97 3 0.65

5 0.92 3 0.05

4 0.81 5 0.76

8 52068.07 7 5.89

6 5.26 5 4.63

1 42028.61

6 5525555.

5 1.72 2

Estonia 1545 0.65

1 36.48 5 0.63

1 0.94 6 0.03

9 0.81 9 0.71

1 30453.02 5.00 2 4.63

6 3.95

7 43150.54

4 1318540.

5 2.63 7

Finland 1227 0.52

2 39.43 6 0.64

2 0.91 8 0.03

7 0.84 7 0.78

6 46328.82 6.06 7 5.46

2 5.58 303943.6

9 5373136.

3 1.91 4

France 614 0.68

6 39.46 4 0.63

8 0.82 9 0.05

4 0.80 6 0.68

9 42837.51 8 4.91

4 4.80 3 4.71

2 547557 65623844 1.01 2

Germany 2569 0.54

8 40.93 0.61 5 0.91

1 0.04 5 0.84

4 0.65

7 49068.59

6 5.39 4.82

7 5.37 348748.1

7 81415118 0.20 9

Greece 1425 0.55

3 38.66 2 0.62

1 0.88 1 0.04

5 0.82 6 0.56

2 32796.54 2 3.81

2 3.55 7 3.10

2 128900 11005563 2.83 9

Hungary 1554 0.45 39.41

1 0.66 6 0.80

4 0.04 1 0.79

8 0.55

5 25490.98 2 3.79

8 4.17 7 3.59

3 90424.15

7 9962316.

3 2.79 4

Ireland 1463 0.73

9 40.59 0.64 0.92 3 0.05

9 0.85 4 0.64

3 59423.63 6 5.33

1 4.28 8 4.65

8 68890 4584163.

8 2.69 9

Italy 871 0.61

3 40.05 9 0.65

1 0.76 0.04 1 0.78

2 0.56

9 42479.14 3.56 2 3.59

8 3.59 294140 59574475 0.68 5

Japan 688 0.50

5 44.45 1 0.68

6 0.96 4 0.03

3 0.65 4 0.56

3 38300.47 3 5.12

4 4.75 4 5.30

2 364500 1.28E+08 1.04 1

Latvia 1977 0.57

8 35.34 4 0.64

1 0.96 3 0.05

6 0.82 1 0.63

4 24352.82 5 3.99

4 4.39 4 3.16

9 62175.00

2 2061071.

1 2.88 9

Lithuania 812 0.55

7 35.10 3 0.65

6 0.98 3 0.03

3 0.66 1 0.66

4 26658.97 2 4.00

4 3.87

4 3.48 62674.53 3014501.

5 1.71 8 Luxembourg 740 0.79

7 41.37 8 0.61

8 0.89

5 0.07 0.86 1 0.73

1 108849.3

1 5.68 5.09 8 4.85

3 2430 555306.1

7 2.6

Mexico 3940 0.51

5 36.56 3 0.51

5 0.53 2 0.06

5 0.76

2 0.68 18773.04 7 3.43

6 4.20 1 3.27

7 1943950 1.18E+08 0.52 Netherlands 2083 0.61

9 39.76 5 0.60

5 0.85 9 0.03

8 0.87 7 0.69

5 52604.16 4 5.63

7 4.89 4 5.02

9 33716.13

5 16693771 0.65 6

Norway 997 0.55

1 41.42 1 0.69

9 0.95 3 0.06

9 0.81 1 0.68

9 62472.50 7 5.78

8 5.41 7 4.67

6 365214.5

8 4917716.

7 1.56 8

Poland 1425 0.57

1 36.74 5 0.64

6 0.95 1 0.05

1 0.82 2 0.66

5 26508.80 2 4.06

9 4.47 4 3.29

8 306214.5

4 38019944 0.48 1

Portugal 1121 0.53

4 37.95 2 0.62

8 0.65 6 0.03

9 0.84 4 0.57

1 30896.94 8 4.34

6 3.77 6 3.89

8 91597.59

5 10481274 2.12 1

Slovakia 1169 0.54

6 38.68 3 0.63

7 0.77 2 0.06

2 0.86 1 0.64

8 27507.67

9 3.41 4.47 6 3.11

5 48084.43

8 5415279.

6 1.95 7

Slovenia 1340 0.64

2 37.73 0.68

1 0.88 0.02 8 0.89

3 0.76

5 33863.26 4 4.18

1 3.81 1 3.75

4 20142.87

4 2042326.

7 1.66 3 South Korea 799 0.59

8 44.79

1 0.66 0.90

7 0.03 0.69 1 0.67

1 38060.53 2 3.90

6 3.86 1 4.83

3 97390.79

3 50706815 1.90 6

Spain 1415

3 0.55 3 39.69

9 0.59 2 0.77

5 0.03 6 0.88

2 0.61

2 37781.05 8 4.25

1 4.32 2 3.58

1 499523.6

9 46014031 0.67 1

Sweden 1284 0.59 41.12 0.63

9 0.97 1 0.04

7 0.82 5 0.75

1 49429.51 5.75 4 5.19

8 5.48

2 408168.0

4 9620495.

8 1.63 2 Switzerland 1119 0.60

7 42.16 8 0.57

2 0.97 1 0.02

7 0.83 0.61

3 65178.02 7 5.77

6 5.24 3 5.70

3 39516.00

9 8052072.

8 1.15 United

Kingdom 5514 0.62

7 41.17 4 0.60

4 0.81 8 0.05

3 0.88 0.6 43853.48 5 5.35

6 5.58 9 5.14

4 241930 62781323 0.57 2 United States 3640 0.67

3 41.39 2 0.58

3 0.91 6 0.06

4 0.87 8 0.62

3 55847.73 6 4.73

3 5.19 7 5.49

8 9149312.

2 3.12E+08 0.18 3

(20)

Table SM12. Multilevel random intercept models for new ventures’ innovativeness (Urban Agglomeration HHI)

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

Age -0.00178 -0.00173 -0.00160 -0.00168 -0.00184 -0.00169 -0.00169 -0.00171

(0.0020) (0.0020) (0.0020) (0.0020) (0.0020) (0.0020) (0.0020) (0.0020)

Gender (male) 0.00227 0.00249 0.00257 0.00255 0.00288 0.00254 0.00248 0.00250

(0.0040) (0.0040) (0.0040) (0.0040) (0.0040) (0.0040) (0.0040) (0.0040)

Education level 0.0213^*** 0.0212^*** 0.0207^*** 0.0208^*** 0.00140 0.0208^*** 0.0208^*** 0.0207^***

(0.0055) (0.0055) (0.0055) (0.0055) (0.0086) (0.0055) (0.0055) (0.0055)

Entrepreneurial Experience 0.0227^*** 0.0224^*** 0.0216^*** 0.0216^*** 0.0213^** 0.0193 0.0217^*** 0.0216^***

(0.0084) (0.0084) (0.0084) (0.0084) (0.0084) (0.012) (0.0084) (0.0084)

Entrepreneurial capabilities 0.0597^*** 0.0594^*** 0.0591^*** 0.0591^*** 0.0590^*** 0.0591^*** 0.0738^*** 0.0592^***

(0.0054) (0.0054) (0.0054) (0.0054) (0.0054) (0.0054) (0.0077) (0.0054)

Entrepreneurial Networks 0.0398^*** 0.0396^*** 0.0396^*** 0.0395^*** 0.0396^*** 0.0395^*** 0.0396^*** 0.0481^***

(0.0041) (0.0041) (0.0041) (0.0041) (0.0041) (0.0041) (0.0041) (0.0059)

GDP per capita (t−1) 0.00594 0.000142 0.00684 0.00695 0.00688 0.00696 0.00712

(0.011) (0.0096) (0.0093) (0.0093) (0.0093) (0.0093) (0.0093)

Institutional Quality (t−1) 0.0817^*** 0.0528^*** 0.0509^*** 0.0509^*** 0.0509^*** 0.0511^*** 0.0510^***

(0.018) (0.016) (0.016) (0.016) (0.016) (0.016) (0.016)

Financial System (t−1) -0.0158 -0.0368^*** -0.0368^*** -0.0368^*** -0.0368^*** -0.0368^*** -0.0368^***

(0.011) (0.0100) (0.0096) (0.0096) (0.0096) (0.0096) (0.0096)

Innovation System (t−1) -0.0751^*** -0.0272^** -0.0403^*** -0.0400^*** -0.0403^*** -0.0403^*** -0.0405^***

(0.015) (0.014) (0.013) (0.013) (0.013) (0.013) (0.013)

Land Area (t−1) 0.0194^* 0.00437 -0.00199 -0.00192 -0.00203 -0.00205 -0.00213

(0.010) (0.0093) (0.0089) (0.0089) (0.0089) (0.0089) (0.0089)

Total Population (t−1) 0.0130 0.00388 0.0296^** 0.0290^** 0.0296^** 0.0300^** 0.0297^**

(0.012) (0.011) (0.013) (0.013) (0.013) (0.013) (0.013)

Education level 0.245^*** 0.308^*** 0.304^*** 0.308^*** 0.309^*** 0.309^***

(0.083) (0.083) (0.083) (0.083) (0.083) (0.083)

Entrepreneurial Experience 4.924^*** 4.709^*** 4.718^*** 4.713^*** 4.712^*** 4.718^***

(0.53) (0.51) (0.51) (0.52) (0.51) (0.52)

Entrepreneurial capabilities 0.117 -0.0136 -0.0101 -0.0138 -0.0150 -0.0144

(0.11) (0.11) (0.11) (0.11) (0.11) (0.11)

Entrepreneurial Networks 0.221 0.426^*** 0.409^*** 0.426^*** 0.419^*** 0.424^***

(0.14) (0.14) (0.14) (0.14) (0.14) (0.14)

Urban Agglomeration (t−1) 0.0403^*** 0.0595^*** 0.0407^*** 0.0336^** 0.0386^***

(0.013) (0.014) (0.013) (0.014) (0.013)

(0.0075) (0.0098) (0.0075) (0.0088) (0.0080)

Interactions -0.0224^***

Urban Agglomeration (t − 1) x Education level (0.0076)

0.0190^***

Squared Urban Agglomeration (t − 1) x Education level

(0.0069)

-0.00936 Urban Agglomeration (t − 1) x Entrepreneurial

Experience

(0.010) 0.00267 Squared Urban Agglomeration (t − 1) x

Entrepreneurial Experience

(0.0088)

0.00804 Urban Agglomeration (t − 1) x Entrepreneurial

capabilities (0.0069)

-0.0146^***

Squared Urban Agglomeration (t − 1) x

Entrepreneurial capabilities (0.0055)

0.00222 Urban Agglomeration (t − 1) x Entrepreneurial

Networks (0.0052)

-0.00851^**

(0.0042)

Constant 0.408^*** 0.413^*** -0.311^* -0.399^** -0.367^** -0.400^** -0.406^** -0.405^**

(0.015) (0.015) (0.17) (0.16) (0.16) (0.16) (0.16) (0.16)

Variance of random intercept country-year .0165858 .014149 .009929 .0088804 .0088626 .0088875 .0088839 .0088994

Num. of groups country-year 271 271 271 271 271 271 271 271

ICC country-year .0530242 .0455886 .0324313 .0291062 .0290529 .0291292 .0291198 .0291685

Observations 81962 81962 81962 81962 81962 81962 81962 81962

Log likelihood -66779.6 -66760.6 -66720.2 -66707.855 -66702.827 -66707.417 -66704.1 -66705.217

Chi-square 7842.50 4794.86 2413.63 2008.13 2006.13 2008.89 2008.39 2010.13

Probability > Chi-square ^*** ^*** ^*** ^*** ^*** ^*** ^*** ^***

AIC 133619.3 133593.2 133520.5 133499.7 133493.7 133502.8 133496.2 133498.4

LR test of model fit ^a - ^*** ^*** ^*** ^*** - ^** ^*

Industry Controls Yes Yes Yes Yes Yes Yes Yes Yes

a. Likelihood ratio test (LRT) was conducted comparing Models 1 through 4 between each other to test the improvement of the goodness of fit when we introduced country-year variables. Likewise, we carried out a LRT comparing Model 4 with each of the interactions considered (Models 5 to 8) to test the significance of the interaction effect.

(21)

Table SM13. Multilevel random intercept models for new ventures’ innovativeness (Urban Agglomeration HHI S-shaped relationship)

Model 1 Model 2

Age -0.00168 -0.00168

(0.0020) (0.0020)

Gender (male) 0.00255 0.00255

(0.0040) (0.0040)

Education level 0.0208^*** 0.0208^***

(0.0055) (0.0055)

(0.0084) (0.0084)

Entrepreneurial capabilities 0.0591^*** 0.0591^***

(0.0054) (0.0054)

(0.0041) (0.0041)

GDP per capita (t−1) 0.00684 0.00676

(0.0093) (0.0093)

Institutional Quality (t−1) 0.0509^*** 0.0510^***

(0.016) (0.016)

Financial System (t−1) -0.0368^*** -0.0371^***

(0.0096) (0.0098)

Innovation System (t−1) -0.0403^*** -0.0400^***

(0.013) (0.013)

Land Area (t−1) -0.00199 -0.00230

(0.0089) (0.0091)

Total Population (t−1) 0.0296^** 0.0308^**

(0.013) (0.014)

Education level 0.308^*** 0.309^***

(0.083) (0.083)

(0.51) (0.52)

Entrepreneurial capabilities -0.0136 -0.00574

(0.11) (0.12)

(0.14) (0.15)

Urban Agglomeration HHI (t−1) 0.0403^*** 0.0389^***

(0.013) (0.015)

Squared Urban Agglomeration HHI (t−1) -0.0373^*** -0.0397^**

(0.0075) (0.016)

Cubic Urban Agglomeration HHI (t−1) 0.00142

(0.0083)

Constant -0.399^** -0.410^**

(0.16) (0.17)

Variance of random intercept country-year .0088804 .0088818

Num. of groups country-year 271 271

ICC country-year .0291062 .0291105

Observations 81962 81962

Log likelihood -66707.855 -66707.8

Chi-square 2008.13 1996.74

Probability > Chi-square ^*** ^***

AIC 133499.7 133501.7

LR test of model fit ^a - -

Industry Controls Yes Yes

(22)

a. Likelihood ratio test (LRT) was conducted comparing Model 1 with Model 2 to test the significance of the S- shaped relationship.