# Quantifying Subnational Economic Complexity: Evidence from Romania

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## Abstract

**:**

## 1. Introduction

## 2. Study Area

## 3. Data and Method

#### 3.1. Data Collection

#### 3.2. Methods for Analyzing the Economic Complexity Index

#### 3.2.1. Defining the Revealed Comparative Advantage of Counties

_{s,a}is the number of people working in county s in economic activity a; $\sum _{a}^{n}=1{p}_{s,a}$ represents the total number of people employed in county s; $\sum _{s=1}^{42}{p}_{s,a}$ is the total number of people employed in economic activity a throughout the country; and $\sum _{s=1}^{42}\sum _{a=1}^{n}{p}_{s,a}$ represents the total number of people employed countrywide [35]. Afterwards, this is transformed into a contiguity matrix (m

_{s,a}) where m

_{s,a}= 1 if the LQ is above a certain threshold (in our case the threshold of LQ = 1). This means that a county has a revealed comparative advantage or is specialized in a certain economic activity if the calculated index is equal to or greater than 1 (Equation (2)).

#### 3.2.2. Applying the Method of Reflection (MR) Technique

_{s,a}matrix (Equation (2)) calculates the counties’ economic diversity and the ubiquity of the respective products. The economic diversity of a county is determined by the number of products with a high RCA [21]. The ubiquity of economic activity shows the number of counties that are specialized in a product, i.e., they have the advantage of exporting the respective product (Equations (3) and (4)).

#### 3.2.3. Calculation of the Economic Complexity Index

#### 3.2.4. Applying the Multivariate Regression Analysis

_{0}+ β

_{1}X

_{i1}+ β

_{2}X

_{i2}+ ………β

_{k}X

_{ik}+ ε

_{0}is the so-called intercept of the model; β

_{1}, β

_{2}, β

_{k}are the coefficients of variable X

_{i}; and ε is the residual (error term). Thus, our multivariate regression model can be described with the following equation:

_{it}= β

_{0}+ β

_{1}X

_{1},

_{it}+ …… + β

_{k}X

_{k},

_{it}+ y

_{2}E

_{2}+ …+ y

_{n}E

_{n}+ δ

_{2}T

_{2}+ …… + δ

_{t}T

_{t}+ ε

_{it}

_{it}represents the dependent variable, where I = entity and t = time; X represents the independent variable; X

_{k,it}represents the independent variable; β

_{k}is the coefficient for the independent variables; ε is the error term; E

_{n}is the entity n; y

_{2}is the coefficient for the binary regressors; T

_{t}is time as dummy variable; and δ

_{t}is the coefficient for the binary time regressors [69].

## 4. Results and Discussion

#### 4.1. Measuring Subnational Economic Complexity (ECI)

#### 4.2. Measuring Product Complexity (PCI)

#### 4.3. The Relation between Economic Complexity and Income Inequality

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Appendix A

ID | County | Abbrev. | ID | County | Abbrev. | ID | County | Abbrev. |
---|---|---|---|---|---|---|---|---|

1 | Alba | AB | 15 | Constanta | CT | 29 | Mures | MS |

2 | Arges | AG | 16 | Covasna | CV | 30 | Neamt | NT |

3 | Arad | AR | 17 | Dâmbovita | DB | 31 | Olt | OT |

4 | Bucuresti | B | 18 | Dolj | DJ | 32 | Prahova | PH |

5 | Bacău | BC | 19 | Gorj | GJ | 33 | Sibiu | SB |

6 | Bihor | BH | 20 | Galati | GL | 34 | Sălaj | SJ |

7 | Bistrita-Năsăud | BN | 21 | Giurgiu | GR | 35 | Satu Mare | SM |

8 | Brăila | BR | 22 | Hunedoara | HD | 36 | Suceava | SV |

9 | Botosani | BT | 23 | Harghita | HR | 37 | Tulcea | TL |

10 | Brasov | BV | 24 | Ilfov | IF | 38 | Timis | TM |

11 | Buzău | BZ | 25 | Ialomita | IL | 39 | Teleorman | TR |

12 | Cluj | CJ | 26 | Iasi | IS | 40 | Vâlcea | VL |

13 | Călărasi | CL | 27 | Mehedinti | MH | 41 | Vrancea | VN |

14 | Caras-Severin | CS | 28 | Maramures | MM | 42 | Vaslui | VS |

Global Industries Clusters | |||
---|---|---|---|

1 | Aerospace Vehicles and Defense | 27 | Leather and Related Products |

2 | Agricultural Inputs and Services | 28 | Lighting and Electrical Equipment |

3 | Apparel | 29 | Livestock Processing |

4 | Appliances | 30 | Marketing, Design, and Publishing |

5 | Automotive | 31 | Medical Devices |

6 | Biopharmaceuticals | 32 | Metal Mining |

7 | Business Services | 33 | Metalworking Technology |

8 | Coal Mining | 34 | Music and Sound Recording |

9 | Communications Equipment and Services | 35 | Non-metal mining |

10 | Construction Products and Services | 36 | Oil and Gas Production and Transportation |

11 | Distribution and Electronic Commerce | 37 | Paper and Packaging |

12 | Downstream Chemical Products | 38 | Performing Arts |

13 | Downstream Metal Products | 39 | Plastics |

14 | Education and Knowledge Creation | 40 | Printing Services |

15 | Electric Power Generation and Transmission | 41 | Production Technology and Heavy Machinery |

16 | Environmental Services | 42 | Recreational and Small Electric Goods |

17 | Financial Services | 43 | Textile Manufacturing |

18 | Fishing and Fishing Products | 44 | Tobacco |

19 | Food Processing and Manufacturing | 45 | Transportation and Logistics |

20 | Footwear | 46 | Upstream Chemical Products |

21 | Forestry | 47 | Upstream Metal Manufacturing |

22 | Furniture | 48 | Video Production and Distribution |

23 | Hospitality and Tourism | 49 | Vulcanized and Fired Materials |

24 | Information Technology and Analytical Instruments | 50 | Water Transportation |

25 | Insurance Services | 51 | Wood Products |

26 | Jewelry and Precious Metals | ||

Local industries clusters | |||

1 | Agriculture, Forestry and Fishing | 10 | Financial and Insurance Activities |

2 | Manufacturing | 11 | Real Estate Activities |

3 | Electricity, Gas, Steam and Air Conditioning Supply | 12 | Professional, Scientific and Technical Activities |

4 | Water Supply; Sewerage, Waste Management and Remediation Activities | 13 | Administrative and Support Service Activities |

5 | Construction | 14 | Public Administration and Defense; Compulsory Social Security |

6 | Wholesale and Retail Trade; Repair of Motor Vehicles and Motorcycles | 15 | Education |

7 | Transportation and Storage | 16 | Human Health and Social Work Activities |

8 | Accommodation and Food Service Activities | 17 | Arts, Entertainment and Recreation |

9 | Information and Communication |

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**Figure 3.**Romania’s regional economic complexity. (

**A**)—Territorial distribution of Romania’s Economic Complexity Index in 2018. (

**B**)—Diagram representing the Diversity Ubiquity relations of economic activities. The full names of the counties are given in the Appendix A (Table A1). (

**C**)—Evolution in time of the counties according to the values of the ECI in 2008, 2013, and 2018. (

**D**)—The correlation between counties’ rankings according to the ECI in 2008 and 2018.

**Figure 4.**Product Complexity Index rankings between 2008 and 2018 (blue color—most complex economic activities, red color—least complex economic activities, green color—emerging industries).

Dependent Variable: Gini | |||||||
---|---|---|---|---|---|---|---|

(I) | (II) | (III) | (IV) | (V) | (VI) | (VII) | |

ECI | −0.009 *** (0.009) | −0.002 * (0.007) | −0.009 ** (0.009) | −0.009 ** (0.009) | −0.009 * (0.009) | −0.007 * (0.009) | |

logGDPpc | −0.191 * (0.090) | −0.024 *** (0.067) | −0.022 ** (0.069) | −0.018 * (0.087) | −0.190 * (0.089) | −0.209 * (0.088) | |

URB | −0.001 ** (0.001) | −0.001 * (0.001) | −0.001 (0.001) | −0.001 * (0.001) | −0.001 (0.001) | −0.001 * (0.001) | |

GER | −0.001 (0.001) | −0.001 (0.001) | −0.016 (0.001) | −0.001 (0.001) | −0.001 (0.001) | −0.001 (0.001) | |

LEB | 0.005 * (0.005) | 0.005 (0.005) | 0.006 (0.006) | 0.004 (0.005) | 0.006 (0.005) | 0.003 (0.005) | |

UR | 0.003 (0.003) | 0.002 (0.003) | 0.004 (0.003) | 0.002 (0.003) | 0.003 (0.003) | 0.002 (0.003) | |

Observations | 42 | 42 | 42 | 42 | 42 | 42 | 42 |

R^{2} | 0.649 | 0.638 | 0.691 | 0.646 | 0.646 | 0.639 | 0.637 |

Adjusted R^{2} | 0.572 | 0.574 | 0.521 | 0.583 | 0.584 | 0.575 | 0.573 |

Res. Std. Error | 0.037 | 0.037 | 0.039 | 0.037 | 0.037 | 0.036 | 0.033 |

F-Statistic | 7.12 *** (df = 6.35) | 8.39 *** (df = 5.36) | 6.97 *** (df= 5.36) | 8.66 *** (df = 5.36) | 8.69 *** (df = 5.36) | 8.45 *** (df = 5.36) | 8.37 *** (df = 5.36) |

**Table 2.**Fixed-effect panel regression result: the effect of the Gini coefficient on ECI and Human Development.

Dependent Variable: Gini | |||||||
---|---|---|---|---|---|---|---|

(I) | (II) | (III) | (IV) | (V) | (VI) | (VII) | |

ECI | −0.014 ** (0.006) | −0.014 *** (0.006) | −0.013 * (0.006) | −0.014 * (0.006) | −0.012 ** (0.007) | −0.012 * (0.006) | |

logGDPpc | 0.016 * (0.069) | 0.022 * (0.071) | −0.001 * (0.069) | 0.017 ** (0.069) | 0.206 ** (0.067) | 0.029 (0.069) | |

URB | 0.005 ** (0.003) | 0.004 ** (0.003) | 0.005 * (0.003) | 0.005 * (0.003) | 0.006 * (0.003) | 0.005 * (0.003) | |

GER | 0.007 * (0.001) | 0.001 * (0.001) | 0.016 * (0.001) | −0.001 * (0.001) | 0.001 ** (0.001) | 0.001 (0.001) | |

LEB | 0.015 *** (0.003) | 0.016 (0.003) | 0.004 *** (0.002) | 0.016 *** (0.003) | 0.015 *** (0.003) | 0.017 *** (0.002) | |

UR | 0.004 (0.002) | 0.003 (0.002) | 0.004 (0.002) | 0.004 * (0.002) | 0.004 (0.002) | 0.008 (0.002) | |

Observations | 126 | 126 | 126 | 126 | 126 | 126 | 126 |

R^{2} | 0.545 | 0.487 | 0.542 | 0.491 | 0.539 | 0.418 | 0.512 |

Adjusted R^{2} | 0.522 | 0.466 | 0.523 | 0.470 | 0.520 | 0.394 | 0.492 |

Res. Std. Error | 0.034 | 0.035 | 0.034 | 0.035 | 0.034 | 0.038 | 0.035 |

F-Statistic | 23.78 *** (df = 6.119) | 22.85 *** (df = 5.120) | 28.48 *** (df = 5.120) | 23.21 *** (df = 5.120) | 28.13 *** (df = 5.120) | 17.27 *** (df = 5.120) | 25.27 *** (df = 5.120) |

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## Share and Cite

**MDPI and ACS Style**

Török, I.; Benedek, J.; Gómez-Zaldívar, M.
Quantifying Subnational Economic Complexity: Evidence from Romania. *Sustainability* **2022**, *14*, 10586.
https://doi.org/10.3390/su141710586

**AMA Style**

Török I, Benedek J, Gómez-Zaldívar M.
Quantifying Subnational Economic Complexity: Evidence from Romania. *Sustainability*. 2022; 14(17):10586.
https://doi.org/10.3390/su141710586

**Chicago/Turabian Style**

Török, Ibolya, József Benedek, and Manuel Gómez-Zaldívar.
2022. "Quantifying Subnational Economic Complexity: Evidence from Romania" *Sustainability* 14, no. 17: 10586.
https://doi.org/10.3390/su141710586