How Informal Sector Affects the Formal Economy in Pakistan? A Lesson for Developing Countries

Abstract

We explore the interaction of the informal sector with the formal economy for a developing economy, that is, Pakistan. Estimation results are obtained via autoregressive distributed lag (ARDL) bounds testing approach, which show a significantly positive effect of the informal sector in the long run whereas a negative impact of informal sector is found in the short run. We also present dynamic simulations to show the effect/contribution in terms of revised GDP per capita if the informal sector is accounted for in official statistics. The novelty of results is the varying effects of the informal sector across two time horizons that can have serious policy implications for developing and transition economies with large informal sectors. Although, the findings of this article enrich our understanding on the behaviour of the informal sector, they further fuel the debate associated with eradication of the informal sector. Efforts to curb informal activities may burden the low-skilled workforce in this sector and increase corruption opportunities in bureaucracy in the absence of institutional reforms. On the contrary, it makes the formal sector policy design and implementation a challenging task.

JEL Classification: E26, H26, K42, O17

Keywords

Informal sector shadow economy

Introduction

The dynamics and emergence of large informal economy has been a much debated issue among the policy experts and researchers in developing countries. Usually, the informal sector contains unregistered services and production activities where besides tax evasion, labour and environment regulations are compromised, leading to lower costs. In most developing countries, agriculture, animal husbandry and the production of primary commodities and non-durables are predominantly outside the formal sphere (Kar & Dutta, 2014). As it works in parallel to the formal sector, the complex interaction between the two may lead to policy complications. This effect on the formal economy has been studied from various economic and social dimensions including its implications for formal labour markets, poverty alleviation, credit markets, fiscal policy, monetary policy, etc. (Ahmed, Pasha, & Rehman, 2016; Bandaogo, 2016; Batini, Levine, Lotti, & Bo, 2011; Bowsher, 1980; Carpenter, 1999; Castillo & Montoro, 2012; Kathuria & Raj, 2016; Kolev & Morales, 2005). Moreover, Adam and Ginsburgh (1985), and Fichtenbaum (1989) developed a macro model of business cycle for Austria, Belgium and USA, respectively, to study the linkages between formal and informal sectors, while Agénor and Montiel (2015) modelled macroeconomic interactions including market equilibriums and policy transmission for developing countries while incorporating the informal sector.

The interactions between informal sector and formal economy may originate from the back and forth linkages between the two sectors via labour and capital mobility, besides the flow of goods and services. The informal sector absorbs a large part of the low and unskilled workforce from developing and transition economies where the labour productivity can have strong bearing for the wage determination and employment opportunities in the formal economy. Such a complex interaction is documented in the work of Marjit and Kar (2011) for the Indian economy. The main interaction channels are identified as labour/capital mobility and productivity shocks in either of the sectors. Likewise, trade liberalization and institutional reforms in the formal sector can also affect the prospects offered by both sectors of the economy. The authors further identified the interaction where formal sector firms may subcontract the production of intermediate goods, processed exports and import substitutes to informal sector firms.

In the presence of these complex interactions, the informal sector burdens the exchequer by evading taxes and formal regulations. On the other hand, heavy burden of taxes, bribes and inflexible bureaucratic regulations in the formal sector are the driving factors for increasing informality (De Soto, 1989). Therefore, the case of developing countries can be particularly interesting since in many such economies where corruption is rampant, the complex bureaucratic structures and low quality of public sector services equally debilitate the formal economic activity of the informal sector. As the informal sector also has significant productive channels, as already discussed in preceding paragraphs, the most pressing issue for policymakers is designing interventions in order to respond to the size of informal economy, without compromising on or hurting the overall prospects of the formal and informal economies. This becomes a challenging task, especially when the mere existence of an informal sector distorts the accuracy, detail, and sectoral coverage in national accounts data, which may lead to ineffective policies.

Given the pros and cons offered by the informal sector, literature, however, needs to be enriched on the short- and long-run prospects of the informal sector in a developing country. In this regard, an appropriate policy response nonetheless demands evidence on the contribution of the informal sector in both the short and long run. To fill the gap, we take the case of Pakistan where a combination of democratic and dictatorial regimes has created an environment where policy inconsistency and complex bureaucratic formalities have led to a large informal sector. Hence, a study to explore the effect/contribution of the informal sector towards official economy may not only yield interesting results but may also be generalizable to other less developed and developing countries having large informal sectors.

There have been multiple estimates for the informal sector of Pakistan (Ahmed & Ahmed, 1995; Ahmed & Hussain, 2008; Arby et al., 2010; Aslam, 1998; Gulzar, Junaid, & Haider, 2010; Iqbal, Qureshi, & Mahmood, 1998; Kemal, 2003; Kemal, 2007; Kemal & Qasim, 2012; Kiani, Ahmed, & Zaman, 2015; Mughal, Schneider, & Hayat, 2018; Shabsigh, 1995; Yasmin & Rauf, 2003), yet most of the studies are limited to measuring the informal sector only. However, Shabsigh (1995) explored the relationship between fiscal deficit and informal sector, while Yasmin and Rauf (2003) and Kemal (2007) attempted to explore the nexus between informal and formal sectors. The estimates of the first author were based on simple ordinary least squares (OLS) without accounting for cointegration among variables. On the other hand, Kemal (2007) used vector autoregression (VAR), and his results showed unidirectional causality from informal sector to nominal GDP. Further, they used Johansen Cointegration test and Error correction model to conclude that shadow economy has a positive effect on the formal sector in short- as well as long run. We, however, argue that the effect of the informal sector on official economy may be of asymmetric in nature in the long and short run, emanating from two contrasting propositions:

First, the informal sector, being more dynamic and extensive, is considered a safe haven for informal employment and production activities stemming from its capacity to avoid the bureaucracy and legalities. This may be supporting the economic activity in the long run when the income and savings from the informal sector are spent on consumption goods being produced by the formal economy. Furthermore, countries with relatively high incidence of poverty and weak social welfare institutions may use the informal sector as a substitute for social security.

On the contrary, informality is a burden on exchequer, particularly when it comes to revenue collection in the short run; hence, it restrains the formal economic activity by raising the cost of being formal; that is, taxpayers have to bear the cost of tax evaders. Lower tax collection implies less expenditure on public utilities and lower productivity and economic growth.

The above contrasting propositions also seek strength from Khan, Khwaja, and Olken (2015) who used an experimental study on performance-based incentives to tax officials in Pakistan. Although they showed that the tax revenue increased, however, bribe requests also increased by 30 per cent, which depicts a clear burden on economic growth in the short run. Therefore, we hypothesize that the informal sector may affect the formal economy positively in the long run and negatively in the short run.

Using autoregressive distributed lag (ARDL) bounds testing approach, we present the results of long-run cointegration besides short- and long-run effects of the informal sector on the formal economy. This will help us find empirical support for our hypothesis that the informal sector affects economic growth differently in short- and long run. Additionally, for the first time, this article uses dynamic simulations to show the distortion in GDP per capita initially by considering the long-run effect of the informal sector and then by including its short-run effect.

The remainder of this article is structured as follows: Section 2 consists of methodology and explanation of variables. Section 3 presents the econometric results and shows the dynamic simulations for interaction between official and unofficial sectors, and finally section 4 concludes the article.

Methodology and Data

In order to model the effect of the informal sector on the formal economy, we estimate the following relationship:

\begin{matrix} \lg ​ d p p c_{t} = γ_{0} + γ_{1} l s e_{t} + γ_{2} l i n v p c_{t} + γ_{3} l d e v h \exp_{t} + \\ γ_{4} l t v i e n r o l l_{t} + γ_{5} l u n i e n r o l l_{t} + γ_{6} l ​ \inf l_{t} + μ_{t} \end{matrix}

(1)

All variables are used in natural logs. lgdppc_t is GDP per capita, which is taken as the dependent variable, and lse_t is the informal economy, our variable of interest taken as percentage of GDP. We use classical growth framework wherein we take total investment per capita (linvpc_t) as a proxy for capital while, to represent labour, we use ldevhexp_t, ltvienroll_t, lunienroll_t, which are development expenditures in health sector, enrolment in technical and vocational institutes and enrolment in universities, respectively. Finally, lninfl_t is the rate of inflation. Yearly data on these variables for the period 1973–2015 have been collected from State Bank of Pakistan’s publications except for informal economy series, which are the estimates of Mughal, Schneider, and Hayat (2018).

In order to estimate and differentiate between long- and short-run impacts of the informal sector on the formal economy, we estimate Equation (1) in ARDL setup developed by Pesaran and Shin (1996).

\begin{matrix} \begin{matrix} Δ \lg ​ d p p c_{t} = β_{0} + β_{1} \lg ​ d p p c_{t - 1} + β_{2} l s e_{t - 1} + β_{3} l i n v p c_{t - 1} + \\ β_{4} l d e v h ​ \exp_{t - 1} + β_{5} l t v i e n r o l l_{t - 1} + β_{6} l u n i e n r o l l_{t - 1} + \\ β_{7} l ​ \inf l_{t - 1} + \sum_{i = 1}^{p} δ_{1 i} Δ \ln ​ G D P P C_{t - i} + \\ \sum_{i = 0}^{q} δ_{2 i} Δ l s e_{t - i} + \sum_{i = 0}^{q} δ_{3 i} Δ l i n v p c_{t - i} + \end{matrix} \\ \begin{matrix} \sum_{i}^{q} δ_{4 i} Δ l d e v h ​ \exp_{t - i} + \sum_{i}^{q} δ_{5 i} Δ l t v i e n r o l l_{t - i} \\ + \sum_{i}^{q} δ_{6 i} Δ l u n i e n r o l l_{t - i} + \sum_{i}^{q} δ_{7 i} Δ l ​ \inf l_{t - i} + μ_{t} \end{matrix} \end{matrix}

(2)

ARDL framework can only have I(0) and I(1) variables but no I(2) variables, which is tested through Dickey Fuller (DF) test Dickey and Fuller (1979). The lag length is determined using Akaike information criterion (AIC). In order to establish cointegration among the variables in Equation (2), we employ the bounds cointegration approach introduced by Pesaran, Shin, and Smith (2001). We use F-statistic test of the joint null hypothesis of no cointegration β₁ ≠ β₂ ≠ β₃ ≠ β₄ ≠ β₅ ≠ 0 against the alternative of cointegration β₁ = β₂ = β₃ = β₄ = β₅ = 0. Under bounds testing approach, cointegration exists if calculated F-statistic is larger than the upper critical bound, whereas if it is less than the lower critical bound, no cointegration exists. The results are, however, inconclusive if the value falls in between the upper and lower critical bounds. Once cointegration is established among the variables, we can compute long-run impact of the informal sector on formal economy by $- \frac{β_{2}}{β_{1}}$ . The short-run effect can be captured by $\sum_{i = 0}^{q} δ_{2 i}$ . The variables linvpc, ldevhexp, ltvienroll and lunienroll are expected to have a positive sign, while linfl is expected to have a negative effect on the growth of formal economy. The error correction term (ECT) is expected to be significant with absolute value of less than unity.

Results

The results of the DF test for unit root are presented in Table A1 which show that all variables are I(1); hence, we can use ARDL bounds testing approach for our estimations. The detailed ARDL estimates along with bounds test for cointegration are presented in Table A2. Since, the F-statistic of 7.584 is above the upper bound critical value, the long-run cointegration is established. We present short- and long-run impacts of the informal sector on official sector in Table 1, where ‘*’ represents the level of significance.

Table 1.

Effect of Informal Sector on Formal Economy

Long-run Coefficient
lse_t _–1	0.236***
Short-run Coefficients
∆lse_t	0.015
∆lse_t_–1	–0.0503***
∆lse_t_–2	–0.037**
∆lse_t_–3	–0.0301**
ECT	–0.412***
R ²	0.806

Source: Authors own calculations.

Note: ** p < 0.05; *** p < 0.01.

The Breusch–Godfrey LM test for autocorrelation and Breusch–Pagan/Cook–Weisberg test for heteroskedasticity show that there is no problem of heteroskedasticity or serial correlation in the series (Table A3). All the modelled variables appear with expected signs in short- and long run, except lunienroll, which is not statistically significant. In the long-run total investment per capita, technical and vocational institutes’ enrolment and size of the informal economy as percentage of GDP have significant impact on the formal economy at 1 per cent level of significance.

As hypothesized, the informal sector has a significant negative impact in the short run, which is as per expectations. The informal sector is a burden on the formal economy because of tax evasion that results in greater tax burden on the official sector; that is, a negative impact occurs in the form of less expenditure on public utilities, increased taxation, lower productivity and economic growth in the short run. However, the positive impact of the informal sector on economic growth of the formal sector in the long run emerges from the fact that first it is a safety net for poor population in developing and transition economies, which is highly likely in a developing country like Pakistan with growing population while having minimal social protection system. In such countries, many rural areas are deprived of basic facilities coupled with high unemployment rate. Hence, in the long run, the informal sector, being more dynamic and extensive, is considered a safe haven for informal employment and production activities stemming from its capacity to avoid the bureaucracy and legalities. This may be supporting the economic activity in the long run when the income and savings from the informal sector are spent on regular consumption goods being produced by the formal economy. Furthermore, countries with relatively high incidence of poverty and weak social welfare institutions may use the informal sector as a substitute for social security. The positive long-run impact can also be associated with increased trade in recent years. Marjit and Kar (2011) used general equilibrium model and showed that trade liberalization in the official sector also has strong implications for informal economy by possibility of increasing employment and wages conditional upon free capital among the two sectors. When the documentation requirements in an economy are not stringent, like in case of Pakistan and most of the developing countries, the interaction of firms in both sectors leads to ease of capital mobility, which can have positive effects on both sectors in the long run. This phenomenon is further authenticated by the recent events in 2015–2016, when owing to the varying increase in bank withholding tax rates on tax filers and non-filers¹, many businesspersons went on strikes against the government (Iqbal, 2015, July 5).

Based on the above model, our long-run normalized equation is having following parametric values:

\begin{matrix} \lg ​ d p p c_{t} = 4.428 + 0.573 l s e + 1.054 l i n v p c + 0.123 l d e v h ​ \exp + \\ 0.395 l t v i e n r o l l + 0.024 l u n i e n r o l l - 0.048 l ​ \inf l \end{matrix}

The above equation shows that a 1 percentage increase in the size of informal economy in the long run would lead to 0.573 percentage increase in GDP per capita. By using a dynamic simulation, the difference between official and calculated real GDP per capita can be determined. By multiplying yearly variation in the informal sector with its estimated long- and short-run coefficients and then subtracting long-run and adding the short-run effects in the official recorded GDP per capita gives us the influence of the informal sector on the formal economy in Pakistan. This is equivalent to answering the question ‘What would be the official GDP per capita if the informal sector economy does not exist?’ The simulation results are shown in Figure 1. The figure consists of three columns: the official GDP per capita, long-run effect of the informal sector, while, the last column is one where the short-run effects are also accounted for by adding back the negative influence of informal sector. The results show that when the influence of informal sector is removed/added using the estimated coefficients, the official statistics change by a significant extent. Since, the informal sector has had positive effect in the long run, therefore, if there had been no informal economy, the actual GDP per capita would have been lower.

Figure 1.

Source: Authors own calculations.

The same results are presented in the Table 2 for selected years, while the complete results are presented in Table A4.

Table 2.

Influence of the Informal Sector on the Formal Economy

Year	Official GDP Per Capita (thousand Pak rupees)	With no Shadow Economy (LR effect only)	With no Shadow Economy (LR and SR effect)
1980	24,917.80	21,233.24	21,540.01
1985	29,562.80	25,759.60	26,193.74
1990	33,320.60	31,748.93	32,501.56
1995	38,512.40	29,920.80	30,414.39
2000	41,114.90	37,271.55	37,594.46
2005	47,803.90	45,611.52	45,961.90
2010	51,251.30	40,917.82	41,481.42
2015	56,061.20	48,208.04	48,879.25

Source: Authors own calculations.

Summary and Conclusion

We model the interaction of the informal with formal economy across two time horizons. The estimation results are obtained via ARDL bounds testing approach, which show a significantly positive association between the two sectors in the long run whereas negative impact of the informal sector is found in the short run. The novelty of results is varying response of informal economy across two time horizons that can have serious policy implications for developing and transition economies with large informal sectors, in addition to presentation of dynamic simulations. The findings of this article enrich our understanding of the behaviour of informal sector across two time horizons and may be a guide for policymakers who may attempt to supress informal activities.

The reason for considering informal sector as a nuisance is that it undermines the quality of national accounts statistics, resulting in undesired effects of economic policies. Further, the official sector is also bearing the burden of informal sector firms and workers by paying taxes and license fees. Contrary to the general belief, the informal sector may also boost economic activities due to its dynamic nature and ability to change with economic conditions, as against the formal sector that is marred with bureaucratic formalities. This is evident from long-run positive impact of informal sector on the official economy. Further, varying impact of the informal sector on the formal economy across two time horizons demands that policies to curb this sector should be designed cautiously. Most importantly, the cost of formality needs to decrease with eradication of corruption in the system, which will simultaneously increase the cost of informality. Tax amnesty schemes for long-time tax evaders, facilities payment of land prices by land grabbers, etc. lead to increase in opportunities for bribes by concerned officials, which once institutionalized ultimately increases the cost of formality. Hence, it may be appropriate to consider that reforms in institutional values are the first step before cracking down on the informal sector.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Appendix

Table A4.

Interaction Between Official and Unofficial Sectors

Year	Official GDP per Capita	GDP per Capita with no Shadow Economy (LR effect only)	GDP per Capita with no Shadow Economy (LR and SR effect)
1974	22,183.50	16,325.90	—
1975	22,359.00	19,685.46	—
1976	22,401.40	22,019.95	—
1977	22,353.20	21,761.26	22,274.79
1978	23,367.70	21,640.42	21,866.75
1979	23,928.40	22,233.46	22,450.35
1980	24,917.80	21,233.24	21,540.01
1981	25,728.70	24,955.37	25,504.99
1982	26,841.80	25,245.95	25,669.32
1983	27,802.00	25,266.62	25,680.68
1984	28,038.20	26,198.32	26,573.00
1985	29,562.80	25,759.60	26,193.74
1986	30,497.40	28,777.51	29,395.13
1987	31,301.70	27,803.85	28,322.99
1988	32,311.90	27,666.72	28,317.74
1989	32,846.80	30,014.05	30,759.25
1990	33,320.60	31,748.93	32,501.56
1991	34,118.30	15,114.97	15,701.06
1992	35,644.60	35,380.10	37,389.99
1993	35,392.20	32,551.94	33,918.05
1994	35,923.10	32,926.70	34,247.15
1995	38,512.40	29,920.80	30,414.39
1996	40,062.90	36,809.46	37,975.02
1997	39,772.10	37,969.11	38,991.65
1998	40,189.50	37,612.53	38,450.95
1999	40,913.90	40,265.57	40,788.25
2000	41,114.90	37,271.55	37,594.46
2001	41,078.50	38,806.49	39,323.06
2002	41,525.40	41,228.37	41,711.58
2003	42,427.00	41,180.93	41,565.24
2004	44,717.90	42,126.28	42,391.72
2005	47,803.90	45,611.52	45,961.90
2006	49,660.70	43,271.09	43,730.74
2007	51,482.40	49,225.21	50,113.25
2008	51,920.00	49,067.64	49,817.77
2009	51,016.70	47,994.62	48,727.56
2010	51,251.30	40,917.82	41,481.42
2011	52,024.10	47,089.40	48,355.77
2012	52,933.10	45,162.97	46,447.70
2013	53,778.60	50,539.75	52,126.38
2014	54,844.30	54,575.57	55,651.15
2015	56,061.20	48,208.04	48,879.25

Source: Authors own calculations.

Notes

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