Trade liberalization and informality nexus: Evidence from Pakistan

ABSTRACT

This study investigates the impact of trade openness on informal sector employment during the drastic 1988s trade reforms of Pakistan. It is generally perceived that increased external competition in less developed countries results in as an expansion in informal sector, which has less compliance with labor market regulations. Using micro-level data of Pakistan, we study the adjustments in the employment of informal sector due to trade openness. We find that informality and trade openness are associated. In Pakistan, trade reforms have given rise to employment in the informal sector. Our findings are robust to different trade-related measures. A substantial flexibility in labor market is required to benefit from the gains of liberalization.

KEYWORDS:

• Trade liberalization
• informality
• developing country

JEL CODES:

• F14
• F16
• O17
• O53

Acknowledgements

I would like to thank Hubert Visas, Xingle Long, Valerie Marleen Hunstock, Dragana Ostic and seminar participants at School of Finance & Economics, Jiangsu University China for helpful comments and suggestions. The paper also benefited greatly from the comments of the anonymous referee and the editor.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Jabbar Ul-Haq http://orcid.org/0000-0002-5314-6092

Huaping Sun http://orcid.org/0000-0002-9328-7850

Notes

1. Pakistan joined GATT/WTO in 1948 and 1995, respectively. However, article XVIII of GATT permitted less developed countries to continue having high tariffs as GATT members.

2. For details, see paper.

3. This model is extension of models discussed in Shapiro and Stiglitz (1984), Bulow and Summers (1986).

4. See Zaidi (2015) for details about Pakistan’s trade reforms and their exact time.

5. The source of tariff information is Pakistan Customs Tariff (PCT) (Various Issues), which is publishing by Central Board of Revenue (CBR) every year. Tariffs are at 8-digit ISIC level. See Ul-Haq (2016) details about the tariff data of Pakistan. We used tariffs and other industry related variables of Ul-Haq (2016).

6. Number of industries varies a little bit in various LFS datasets. LFS reports industry at 2-digit only during our study sample, which is our one of the limitations.

7. Williams, Shahid, and Martínez (2016) and Kahyalar et al. (2018) also used this criteria to consider enterprise or worker as informal in their studies.

8. One of the reasons, inter alia, behind lower wages in informal sector is that firms in informal sector have low bargaining power. A firm having low bargaining power will benefit less from total gains (Tsao et al. 2018).

9. In our other work (Ul-Haq et al. 2018), we found that trade liberalization give rise to formal-informal wage differentials in Pakistan. We used two measures for formal-informal wage gap. First one is formal-informal log weekly wage-gap and second one is formal-informal wage differentials following Aleman-Castilla (2006). The studies on Trade liberalization and skill premiums found that liberalization increases skill premiums (Ul-Haq 2016; Mamoon and Murshed 2013; Cigno, Giovannetti, and Sabani 2018; Behar 2016; Wu et al. 2018).

10. A few examples Pablo Acosta and Leonardo Gasparini (2007), Attanasio, Goldberg, and Pavcnik (2004), Goldberg and Pavcnik (2005), Ul-Haq (2016), Kumar and Mishra (2008), Krueger and Summers (1988).

11. In addition to this, we also applied Probit model to calculate sector informality differentials. The correlation coefficient between sector informality differentials of both models is 0.95. Therefore, we move ahead with LPM. Goldberg and Pavcnik (2003) also used Probit model to calculate informality differentials and found high positive correlation (i.e. 0.8) between differentials obtained from both models. Ajefu (2015) found similar results from LPM and Probit model.

12. A 20% tariff is denoted in our data as 20.

Additional information

Funding

This research is supported by Natural Science Foundation of China, NSFC-71774071, China Postdoctoral Science Foundation funded project [2015M571708].