informal work in a post-transition country - some evidence from poland

Informal work in a post-transition country – some evidence from Poland

Stanisław Cichocki

Social boundaries of work. The varieties of informal work in contemporary societies , 13.11.2015

Group for Research in Applied Economics

Outline

1. Introduction

2. Definition

3. Facts

4. Examples of research

5. Conclusions

2

Introduction

• Informal work is common around the world

• Research mostly focuses on developing countries i.e. Brazil (Bosch, Maloney 2010), Mexico (Bosch, 2015), Argentina (Pratap and Quintin, 2006), Latin America (Lehmann and Muravyev, 2012)

• Research for developed economies is rarer i.e. OECD countries (Schneider, 2013), UK (Williams and Windebank, 2002), Denmark (Graversen and Smith, 2002)

• In case of transition and post-tranistion countries i.e Russia (Slonimczyk, 2012; Lehmann and Zaiceva, 2013), Ukraine (Lehmann and Pignatti, 2007), Baltic states (Merikuell and Staehr, 2008), CEE countries (Williams and Horodnic, 2015)

3

Introduction

• Poland: IBnGR (1995), Kalaska and Witkowski (1996), CSO (1995, 1998, 2004, 2009, 2010), Ministry of Labor and Social Affairs (2008), Cichocki and Tyrowicz (2010, 2011)

• The biggest challenge in case of Poland: lack of data

4

Definition

• Informal employment:

• individuals for whom only part of the compensation is declared to the tax authorities

• individuals who are officially inactive (not registered as unemployed) and obtain earned income

• individuals who are officially registered as unemployed and obtain earned income

• individuals who dispose of an official job and have an additional, informal source of earned income

• individuals who run their own/family company, either without registration or not reporting the corporate revenues

5

Definition

• Informal employment:

6

Wholly unregistered

Partially unregistered

Wage employed

Oral contract (without a written

contract); Written contract but without

paying any taxes and contributions

Written contract to a lower

amount than indeed paid.

Self-employed

Activity wholly uregistered

Registered activity but

without reporting a part of

income to tax authorities

Source: Undeclared Employment survey by Ministry of Labor and Social Affairs, 2007.

Facts

• For post-transition countries….

• … much bigger size of shadow economy than for established market economies, Friedman et al. (2000), Schneider et al. (2011), Herwartz et al. (2013)

• … size of underground economy for EU-15 is estimated on average 18-19% of GDP, it is about 31-32% of GDP for CEE, Schneider et al. (2011)

• …. for unregistered employment in other CEE countries Renooy et al. (2004) conducted a study estimating its size from 9% of GDP in Estonia and Czech Republic to 30% of GDP in Bulgaria

7

Facts

• Problems….

• ……. analyses focus on size of shadow economy and/or unoffcial employment rarely providing insight into the verification of determinants of this phenomenon, Gerxhani (2003), Kriz et al., (2008), Merikuell and Staehr (2010), Cichocki and Tyrowicz (2010), Tyrowicz and Cichocki (2010), Williams and Horodnic (2015)

• ... Poland typically comprised in cross-country studies, data usually from CSO, Johnson et al. (1998), Schneider et al. (2011)

8

Facts

• CSO estimates of informal labor based on LFS:

• 2004: 9.6% of working labor force



• Estimates for Ministry of Labor and Social Affairs(2008):


9

Examples of research

• Twofold reasons for shadow employment...

• ... official market distortions make it ineffective for agents to engage in registered employment (revenues from shadow employment are higher than the corresponding official ones)

• ... for a particular group of workers regular employment may be unattainable (labour market segmentation)

• Goal: identify if informal employees have a higher or lower wage as compared to their statistical ”twins” in the formal sector

10


• We use 52 consecutive labor force surveys (Study no. I):

• period: 1995q1-2007q4

• each data set: circa 50 000 individuals

• altogether: 10 994 individuals who officially report unemployment status but at the same time declare wage employment

• individual level characteristics (education, age, gender, residence, marital status, industry, occupation + declared net wages and number of hours worked)

• We use a dedicated survey on 18 000 individuals, with wider

definition of “gray” employment (Study no. II)

• What we do: match otherwise identical “gray” to “non-gray” individuals, to learn how declared wage patterns differ

11


• Figure: Wage employed, unemployed and undeclared employed over 1995-2007 in Poland

12



13



14


• Method: Propensity Score Matching

• Run logit: shadow employment Y = 1, no shadow employment Y = 0 on appropriate conditioning variables

• These include: education, age, gender, residence, marital status, industry, occupation and their interactions

• Obtain propensity score: predicted probability p for each observation

• Match each “gray” individual with a “non-gray” one of a similar score

• Obtain groups: treated and control groups (“statistical twins”)

• Compare wages between treated, control and reference groups

15


• Table: Propensity score matching results - pooled data

16

Before matching

Variable Difference Standard error t-statistic Significance

Wage -306.34 7.10 -43.11 ***

Hours 0.33 0.107 3.06 ***

Hourly wage -7.85 0.21 -36.77 ***

After matching

Variable Difference Standard error t-statistic Significance

Wage -140.22 4.92 -28.47 ***

Hours -0.79 0.14 -5.63 ***

Hourly wage -2.87 0.14 -20.25 ***


• Formally employed declare higher net employment-related earnings than informally employed

• After matching the differential is reduced by about 50%

• Informally employed work slightly shorter hours than their formally employed counterparts

• All differentials significant

17


• Data from a dedicated research project (Ministy of Labor and Social Affairs) in the period May-June 2007:

• ... individual interviews with statistically representative subsample of population

• ... over 18 000 observations

• ... about 1000 declaring shadow employment (non-direct declarations!)

• ... sectoral (industrial) composition of ”gray” similar to ”non-gray”

• ... some differences in education and professions

18


• Table: Propensity score matching results

19

Declared earnings (PLN)

Shadow economy

Official economy

Difference of means

S.E. of difference

t-statistic of difference

Total 1057.69 1364.43 -309.44 43.27 -7.20***

”Twins” 1057.69 1298.97 -247.30 65.09 -3.75***

No of observations

344 4 994


• Formally employed declare higher net earnings than informally employed by approx. 30%

• Earning gap reduced to approx. 24% when only matched individuals are considered as reference group

• Results statistically significant

• Standard errors point to larger heterogeneity in earnings for control group

20


• Both studies seem to suggest segmentation as a strong determinant of informal employment ⇒ tax evasion

isimportant but not the key reason for unregistered employment, where it contributes to marginalisation

• Policy recommendations:

• tax incentives will only address the “top” half of the informal employees – the “bottom” half will still be in the shadow because of the the lack of alternatives

• focusing on measures tackling shadow employment which stems from labour market tightness

21

Conclusions

• Research on informal employment mostly focuses on developing countries

• However informal employment is also strongly present in CEE

• The main limitation in case of Poland is lack of data

• Labor market segmentation seems to be the most important reason for undertaking informal employment in case of Poland

22

Thank you for your attention! Author: Stanislaw Cichocki, e-mail: [email protected]

More about our research on

http://grape.uw.edu.pl

Twitter: @GrapeUW

informal work in a post-transition country - some evidence from poland

Economy & Finance