Table 6: Specifications using shortest distance to a seaport as accessibility proxyPF<sup>6</sup> FP

|Sector|Ln(ACCESSr<br>)|Ln(NPs,r,t<br>)|Ln(Nr,t<br>)|Ln(URBANr,t<br>)|R<br>2<br>|P-value|Obs.|
|---|---|---|---|---|---|---|---|
|All|-0.033<br>a<br>|0.124<br>a<br>|0.078<br>a<br>||0.376|0.000|6112|
||-0.038<br>a<br>|0.173<br>a<br>||0.194<br>a<br>|0.374|0.000|6112|
|Food & beverages|0.041<br>a<br>|0.134<br>a<br>|0.114<br>a<br>||0.450|0.000|1296|
||0.036<br>a<br>|0.293<br>a<br>||0.086|0.438|0.000|1296|
|Chemicals|-0.117<br>a<br>|0.305<br>a<br>|0.002||0.371|0.000|628|
||-0.143<br>a<br>|0.080<br>c<br>||1.424<br>a<br>|0.432|0.000|628|
|Basic metals|-0.121<br>a<br>|0.156<br>a<br>|0.039||0.519|0.000|476|
||-0.123<br>a<br>|0.239<br>a<br>||-0.435<br>a<br>|0.535|0.000|476|
|Fabricated metals|-0.026<br>a<br>|0.296<br>a<br>|0.006||0.619|0.000|840|
||-0.025<br>a<br>|0.338<br>a<br>||-0.280<br>a<br>|0.622|0.000|840|

> <sup>a</sup> significant at 1%;<sup>b</sup> significant at 5%;<sup>c</sup> significant at 10%. See Table 1 for Sector codes.

To understand the factors underlying the unusual positive coefficient for the distance to the shortest major airport yielded by some sectors, we use another accessibility proxy: the shortest distance to a major sea port. This helps to sort out the industries with a consistent unusual positive coefficient for accessibility proxy (see Table 5). Indeed, except for the Food and Beverages industry, this adjustment turns the estimated coefficient of the distance variable to the expected negative sign in all the other sectors. Therefore, these sectors (Chemicals and chemical Products, Petroleum, Coal, Rubber and Plastic Products (35); Basic Metal industries (37); and Fabricated Metal Products (38)) add to the third group of sector-specific regressions that depicts a negative and significant coefficient for the accessibility proxy and a positive and significant coefficient for the localization economies proxy.

With the appropriate accessibility proxy, we end up with industry 31 (Food and Beverages) in group 1, industry 36 (Non-Metallic Mineral) in group 2, and the remaining industries in group 3. As can be seen in Table 7, This classification is confirmed when using the five-year average database which includes additional proxy variables to approximate urbanization economies (total amount of loan at the provincial level, total consumption of electricity at the provincial level, ratio of asphalt roads in the villages located in the province, and rate of university graduates in the 25 years and over population in the province). The following comments focus first on sectors in the group 3, and then turn to the specificities of the Food and Beverages, and the Non-Metallic Mineral sectors. The preferred specifications used to do these comments are the one based on the database adjusted with moving average excluding missing observations, and the five-year simple average.

The productivity of the textile industry appears to be positively impacted by a good accessibility to local, national and international market. Indeed, the estimated coefficient in Table 6 indicated that halving the distance to the nearest major airport will increase textile firms’ productivity by 40%, and a 10% increase of ratio of asphalt roads in villages will increase their productivity by 2.5%. The proximity of firms operating in the same sector is also a productivity-enhancer factor since all the specifications yield a

> <sup>6</sup> The database used is the one adjusted with a moving average excluding missing observations.

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