rafmacalaba/datause-encoder-data huggingface dataset:
|CI-Data Collection & Cap. Bldg. for PRS-LICUS|Emmanuel Noubissie Ngankam|
|---|---|
|CI-Data Collection & Cap. Bldg. for PRS-LICUS|Emmanuel Noubissie Ngankam|
|Strengtheningfiduciaryoversight in the Caribbean through Caribbe|aRegis Thomas Cunningham|
|Strengtheningfiduciaryoversight in the Caribbean through Caribbe|aRegis Thomas Cunningham|
|Strengtheningfiduciaryoversight in the Caribbean through Caribbe|aRegis Thomas Cunningham|
|Ethiopia: TFSCB Data Systems and economy-Wide Analysis Project|Mesfin Girma Bezawagaw|
|Ethiopia: TFSCB Data Systems and economy-Wide Analysis Project|Mesfin Girma Bezawagaw|
|Ethiopia: TFSCB Data Systems and economy-Wide Analysis Project|Mesfin Girma Bezawagaw|
|Tanzania: Extractive Industries TransparencyInitiative|Mamadou Barry|
|Tanzania: Extractive Industries TransparencyInitiative|Mamadou Barry|
|Tanzania: Extractive Industries TransparencyInitiative|Mamadou Barry|
|Public Finance and Competitiveness Development PolicyLoan with|Rashmi Shankar|
|Public Finance and Competitiveness Development PolicyLoan with|Rashmi Shankar|
|Public Finance and Competitiveness Development PolicyLoan with|Rashmi Shankar|
|Public Finance and Competitiveness Development PolicyLoan with|Rashmi Shankar|
|DR livingstandards measurement|Miguel Eduardo Sanchez Martin|
|DR livingstandards measurement|Miguel Eduardo Sanchez Martin|
|DR livingstandards measurement|Miguel Eduardo Sanchez Martin|
|DR livingstandards measurement|Miguel Eduardo Sanchez Martin|
|Protectingthe Poor under Global UncertaintyDevelopment PolicyL|Christian Yves Gonzalez Amador|
|Protectingthe Poor under Global UncertaintyDevelopment PolicyL|Christian Yves Gonzalez Amador|
|Protectingthe Poor under Global UncertaintyDevelopment PolicyL|Christian Yves Gonzalez Amador|
|RY-FLOOD PROTECTION AND EMERGENCY RECONSTRUCTION ADDI|TBrahim Ould Abdelwedoud|
|StrengtheningBrazil Subnational Audit|Joseph Kizito Mubiru|
|StrengtheningBrazil Subnational Audit|Joseph Kizito Mubiru|
|StrengtheningBrazil Subnational Audit|Joseph Kizito Mubiru|
|Argentina Basic Protection Project|Marcela Ines Salvador|
|Argentina Basic Protection Project|Marcela Ines Salvador|
|Argentina Basic Protection Project|Marcela Ines Salvador|
|Argentina Basic Protection Project|Marcela Ines Salvador|
|Argentina Basic Protection Project|Marcela Ines Salvador|
|Argentina Basic Protection Project|Marcela Ines Salvador|
|Argentina Basic Protection Project|Marcela Ines Salvador|
|Argentina Basic Protection Project|Marcela Ines Salvador|
|Argentina Basic Protection Project|Marcela Ines Salvador|
|Argentina Basic Protection Project|Marcela Ines Salvador|
|Argentina Basic Protection Project|Marcela Ines Salvador|
|Argentina Basic Protection Project|Marcela Ines Salvador|
|Argentina Basic Protection Project|Marcela Ines Salvador|
|Argentina Basic Protection Project|Marcela Ines Salvador|
|Philippines Public Health|Tomo Morimoto|
|Philippines Public Health|Tomo Morimoto|
|Philippines Public Health|Tomo Morimoto|
|Philippines Public Health|Tomo Morimoto|
|Philippines Public Health|Tomo Morimoto|
|Philippines Public Health|Tomo Morimoto|
|Philippines Public Health|Tomo Morimoto|
In summary, the data show a good balance of baseline characteristics across treatment arms and little problem with attrition in either round of data collection – particularly for the primary outcomes at the child level. We now proceed to presenting estimates of ITT effects at the 18-month follow-up. _18-month impacts_ _Primary outcomes (child level)_ At 18-months, we conducted child assessments using the MDAT, described in the Appendix, which evaluates language and fine motor/perception skills in children and was specifically created for use in rural Malawi.<sup>16</sup> Table 3 presents program effects on the overall score, as well as the two subscales (Language and Fine Motor/Perception Skills). Columns (1) and (2) present impacts by treatment arm on the total MDAT score: there were no effects in T2 or T3, but there was a significant effect of T4 compared to the control group (0.13 SD, p-value=0.06; column (2)). In columns (3) and (4), we see that this improvement in T4 is due to the intervention’s effect on language skills, which is 0.19 SD (pvalue=0.010). This improvement is significantly higher than that in T2 (T2-T4=0.22, p-value=0.001), indicating that marginal value of the parenting education is substantial over and above teacher training at the CBCC level. However, it is not significantly larger than the effect in T3, where the teachers were provided small incentives for retention and motivation (T3-T4=0.1; p-value=0.17). Columns (5) and (6) show no effects of T4 on Fine Motor/Perception Skills in comparison to the control group. T4 still performs significantly better than both T2 and T3, but this is due to a negative impact of these two interventions (only significant at the 90% level).

To assess program impacts on positive and negative behaviors among the target population of children, we also administered the Strengths and Difficulties Questionnaire (SDQs, described in detail in

> 16 Detailed information on all assessments – what they measure, how they were scored, and how they were aggregated into larger indices – can be found in the appendix. All impact findings are robust to scoring child assessments using item response theory (IRT) rather than the simpler standard scoring rules that were used in our analysis (findings not shown here).

19
**Table 7:** Trust and responsibility between teachers and parents

||Parent’s|perspective|Teacher|’s perspective|
|---|---|---|---|---|
||(1)<br>Most<br>teachers<br>can be<br>trusted|(2)<br>Teacher<br>Responsibility<br>Index (SD)|(3)<br>Most<br>parents<br>can be<br>trusted|(4)<br>Parent<br>Responsibility<br>Index(SD)|
|_Double grant experim_|_ent_||||
|Treatment (1 year)|-0.096**|-0.418***|-0.110**|-0.096|
||(0.043)|(0.133)|(0.048)|(0.121)|
|WY _p_-value|0.000|0.000|0.100|0.460|
|Treatment (2 year)|-0.089***|-0.300**|-0.099**|-0.154|
||(0.034)|(0.129)|(0.040)|(0.129)|
|WY _p_-value|0.000|0.000|0.060|0.140|
|Treatment (3 year)|-0.113***|-0.217|-0.115**|-0.068|
||(0.040)|(0.142)|(0.045)|(0.135)|
|WY _p_-value|0.020|0.080|0.000|0.460|
|Control mean|0.920|0.000|0.803|-0.000|
|Observations|244|244|732|732|
|_Information experim_|_ent_||||
|Treatment (1 year)|0.149***|0.309**|0.027|0.294*|
||(0.046)|(0.152)|(0.055)|(0.164)|
|WY _p_-value|0.000|0.040|0.500|0.120|
|Control mean|0.820|0.000|0.777|-0.000|
|Observations|174|174|522|522|
|_Single grant observa_|_tion_||||
|Treatment (1 year)|-0.007|-0.446***|0.027|-0.192|
||(0.034)|(0.170)|(0.070)|(0.193)|
|WY _p_-value|0.900|0.000|0.840|0.640|
|Control mean|0.962|0.337|0.778|0.209|
|Observations|151|151|453|453|

Notes: Robust standard errors clustered by school in parentheses. Westfall and Young (WY) p-values reported. Data source: parent association survey and teacher survey.* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01
<!-- Start of picture text --> S<br>eu ||<br>8<br>4 ||<br>£8 |<br>37 |<br>a<br>2 |<br>2<br>3oa s |<br>2Fe |<br>Ei |<br>3|<br>|<br>a |<br>-10 5 0 5 10<br>t<br>—4— mean net disbursed aid for crisis countries, excl. S. Korea<br>—#— mean net disbursed aid for non-crisis countries<br><!-- End of picture text -->
components. Rubin and Tygesen (1996), Beine and Hecq (1997) and Beine, Candelon and Hecq (2000) use a codependence framework. Filardo and Gordon (1994), Beine, Candelon and Sekkat (1999) and Krolzig (2001) use a Markov Switching VAR model. This empirical work demonstrates that it is important to distinguish between short and long-run effects. Bayoumi and Eichengreen (1993), Helg et al. (1995) and Rubin and Thygesen (1996) use differenced variables in the VAR representation. However, such a specification does not allow for long-run relationship between the variables. Beine and al. (2000) overcome this by investigating simultaneously common trends and common cycles, where evidence of a common European cycle is taken as evidence of perfect synchronization of shocks. Breitung and Candelon (2001) use a frequency domain common cycle test to analyze synchronization at different business cycle frequencies.

We use annual data from 1965 to 2002 for real GDP and trade figures, and monthly data on industrial production and economic activity from 1995 to 2002. GDP data is from IFS, data on industrial production are from Central Bank statistics. Trade data is from WITS and Direction of Trade Statistics.

The key variable in our study is the degree of business cycle synchronization between countries _i_ and _j_ . To measure this variable, we follow Frankel and Rose (1998) and compute the correlation between the cyclical component of the output in countries _i_ and _j_ , where a higher correlation implies a higher degree of business cycle synchronization. The cyclical component of output is obtained using different de-trending methods. Given the lack of consensus on the optimal procedure and the sensitivity of the cycle to the de-trending method, this approach should provide a robustness check of our results. For annual data we use first-differencing and bandpass filtering (Baxter and King, 1999). Spectral analysis is used to assess business cycle synchronization with monthly data.

# **2.1.1 Annual Data: 1965 –2002**

Band pass filtered data, our preferred method for business cycle extraction in this section, shows that in Central America business cycle synchronization is highest between Costa Rica, Guatemala, El Salvador and Honduras. Nicaragua and Panama appear to be follow a different 4
|P077477|2003|2006|8/15/2005|6 Closed|Uganda|
|---|---|---|---|---|---|
|P077477|2003|2006|1/31/2006|7 Closed|Uganda|
|P077477|2003|2007|11/16/2006|8 Closed|Uganda|
|P077477|2003|2007|6/13/2007|9 Closed|Uganda|
|P077477|2003|2008|11/30/2007|10 Closed|Uganda|
|P077491|2002|2008||0 Closed|Morocco|
|P077491|2002|2009||0 Closed|Morocco|
|P077491|2002|2010||0 Closed|Morocco|
|P077491|2002|2011||0 Closed|Morocco|
|P077491|2002|2012||0 Closed|Morocco|
|P077491|2002|2013||0 Closed|Morocco|
|P077513|2004|2004|6/15/2004|1 Closed|Congo,Republic of|
|P077513|2004|2005|3/21/2005|4 Closed|Congo,Republic of|
|P077513|2004|2006|8/30/2005|5 Closed|Congo,Republic of|
|P077513|2004|2006|3/22/2006|6 Closed|Congo,Republic of|
|P077513|2004|2007|12/23/2006|7 Closed|Congo,Republic of|
|P077513|2004|2007|6/29/2007|8 Closed|Congo,Republic of|
|P077513|2004|2008|7/19/2007|9 Closed|Congo,Republic of|
|P077513|2004|2008|1/16/2008|10 Closed|Congo,Republic of|
|P077513|2004|2008|6/28/2008|11 Closed|Congo,Republic of|
|P077513|2004|2009|12/28/2008|12 Closed|Congo,Republic of|
|P077513|2004|2009|6/29/2009|13 Closed|Congo,Republic of|
|P077513|2004|2010|12/5/2009|14 Closed|Congo,Republic of|
|P077513|2004|2010|6/24/2010|15 Closed|Congo,Republic of|
|P077513|2004|2011|3/28/2011|16 Closed|Congo,Republic of|
|P077513|2004|2012|11/5/2011|17 Closed|Congo,Republic of|
|P077513|2004|2012|3/11/2012|18 Closed|Congo,Republic of|
|P077513|2004|2012|6/20/2012|19 Closed|Congo,Republic of|
|P077526|2004|2004|3/30/2004|1 Closed|Albania|
|P077526|2004|2005|5/18/2005|3 Closed|Albania|
|P077526|2004|2006|12/7/2005|4 Closed|Albania|
|P077526|2004|2007|12/4/2006|5 Closed|Albania|
|P077526|2004|2008|12/21/2007|6 Closed|Albania|
|P077526|2004|2008|6/30/2008|7 Closed|Albania|
|P077526|2004|2009|6/27/2009|8 Closed|Albania|
|P077526|2004|2010|6/8/2010|9 Closed|Albania|
|P077526|2004|2011|4/27/2011|10 Closed|Albania|
|P077526|2004|2012|12/26/2011|11 Closed|Albania|
|P077533|2002|2002||0 Closed|Afghanistan|
|P077533|2002|2003|6/29/2003|2 Closed|Afghanistan|
|P077533|2002|2004|6/21/2004|4 Closed|Afghanistan|
|P077533|2002|2005|10/5/2004|5 Closed|Afghanistan|
|P077539|2002|2008||0 Closed|Ecuador|
|P077539|2002|2009||0 Closed|Ecuador|
|P077539|2002|2010||0 Closed|Ecuador|
|P077539|2002|2011||0 Closed|Ecuador|
|P077539|2002|2012||0 Closed|Ecuador|
<!-- Start of picture text --> Figure 1: District Targeting of Vaso de Leche Expenditures<br>Based on Stunting Rates In Peru, 1999<br>400O<br>35 o 0<br>0 0 o 0<br>30, S 0 0<br>3 0<br>83 ff 20 ° X oo°OO~00 0<br>} 15 I 0 ; °e ¢ o O0v~00 0 gojO0<br>0*0O Oo 0<br>ffiOdOO O O° O<br>30O Base on tntn atsI Peru 0 1999-<br>00 40 O S ~ 0o ~ 0 O0O0 ~ ~ ~ ~ ~ 0 OO 0d Di 0t n O ou<br>00 0 O,a0 0.2 OO;go0 03 04 05 06 0.7 a 0 0 .9 10<br>0 00 o 0 0<br>140 o 0 00 O<br>0 0 ~ ~ ~ ~ ~ ~ ~ o.<br>0 ~ ~~~~~~~~~ O<br>- O<br>I. 00 0 -10~~~~0 1 0 00 02 03 0 00 0i-6 Os 0 o 07 0 : 0O 09 0<br>.a1 0<br>0O0<br>Os 4.3~ ~ ~ .~004 00 0<br>00 0 3 3 04 5 0O07 0 00 0<br>30 O0 tot~ o oo~~~~~~~~~DO&<br>O 0 P. 0~~~1<br><!-- End of picture text -->
the same definition used by the World Bank’s Doing Business report. It is also the most prevalent formal business form in most countries around the world (Doing Business, 2010).<sup>6</sup> The main variable of interest is new business “entry density”, calculated as the ratio of newly registered limited liability firms per 1,000 working age population (those ages 15-64).<sup>7</sup> The final dataset includes an unbalanced panel of 494 observations from 91 countries over the six-year period 2004 to 2009. Figure 2, left panel, shows the distribution of entry density across countries; it has a minimum value of 0.0021 (in Niger) and a maximum value of about 10.<sup>8</sup> Entry density varies significantly by income groups: it ranges from 4.21 in high-income countries to less than one in lower-middle and lower income countries (Figure 2, right panel). In other words, there are on average about four limited-liability firms registered annually per 1,000 working age individuals in industrialized countries, as compared to about one firm per 1,000 individuals in developing countries.<sup>9</sup>

# **_3.2 Business Environment Indicators_**

Our indicators of business environment regulations come from the _Starting a Business_ section of the World Bank’s _Doing Business_ database, which provides country-level indicators to measure the efficiency of the business registration process. The first indicator, Starting Costs, captures all official fees and additional fees for legal and professional services involved in incorporating a business, and is measured as a percentage of the economy’s income per capita.

> 6 We exclude countries identified by the IMF (Zorome, 2011) as off-shore financial centers, since information provided by these countries likely reflects a nontrivial number of shell companies, defined as companies that are registered for tax purposes, but are not active businesses. For example, in 2007 the British Virgin Islands had 77,022 newly registered corporations, which translates to approximately four new firms for each working age individual.

> 7 The complete database and list of sources is available at: http://econ.worldbank.org/research/entrepreneurship. 8 We restricted entry density to be less than 10. Effectively this excludes two countries that are outliers on the number of new registrations (Iceland and New Zealand). In addition, we exclude the top and bottom 1 percent of the distribution of new density in all regressions.

> 9 Summary Statistics, by country, are shown in Appendix 1.

11
In Table 7, we analyze the effects of district-level increases of employment in tradable services on employment and number of firms in the non-tradable services sector for women and men separately. To this end, in columns (1) and (2) respectively, we only keep either female employees or male employees in the sample, before aggregating at the district level. For the number of firms in columns (3) and (4), we only keep either female-owned or maleowned businesses. Overall, the effects for women are much stronger. Column (1) shows a coefficient on non-tradable employment of 0.91, compared to 0.43 for men, as shown in column (2). The gender difference is even more pronounced when analyzing the number of firms in columns (3)-(4), with a coefficient of 1.38 for female-owned business, albeit only significant at the 10% level. Conversely, the coefficient for male-owned businesses is close to zero and insignificant. These results suggest that district-level growth in tradable services employment increases both female employment and female-owned firms (entrepreneurship). This is important because Chiplunkar and Goldberg (2021) show that promoting female entrepreneurship can in turn lead to higher female labor force participation because women entrepreneurs hire more females.

Table 7: Impact of tradable services on non-tradable services, by gender

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Emplo|yment|No. of|Firms|
||Women|Men|Women|Men|
|Log tradable services|0.910**|0.425**|1.376*|0.160|
||(0.411)|(0.206)|(0.709)|(0.193)|
|Education|0.012|0.001|0.027|0.001|
||(0.013)|(0.007)|(0.019)|(0.006)|
|Observations|1,173|1,173|1,173|1,173|
|District FE|yes|yes|yes|yes|
|Year FE|yes|yes|yes|yes|

*** ** * Standard errors (in parentheses) are clustered at the district level. _p <_ 0.01, _p <_ 0.05, _p <_ 0.1.

Lastly, we now turn to the heterogeneous effects by firm size. The average size of establishments in tradable services, such as ICT, is comparable to the manufacturing sector across countries at different levels of per capita income. However, the average services establishment in non-traded services is relatively small. In developing economies, informality plays an important role here because many services firms across, for example, small-scale retail and personal services are unregistered. Even when the analysis is restricted to formal firms, non-tradable services, such as retail, vehicles trade, and real estate, have the smallest average firm size, which is about four to five times smaller than a manufacturing firm in the same country (Nayyar et al., 2021b). Therefore, analyzing the heterogeneous effects by firm 18
Table 3B. Correlations between interest rates and output (correlation coefficient)

|||**Lags**||||**Leads**||
|---|---|---|---|---|---|---|---|
||-3|-2|-1|0|1|2|3|
|**Nominal short-ter**|**m interest r**|**ate**||||||
|1971:1-2016:4|-0.10|0.04|0.21|0.35|0.42|0.42|0.36|
|1971:1-1984:4|-0.23**|-0.11***|0.08**|0.22|0.26|0.28|0.21|
|1985:1-2016:4|0.00|0.13|0.28|0.40|0.46|0.44|0.37|
|**Nominal long-term**|**interest ra**|**te**||||||
|1971:1-2016:4|-0.17|-0.06|0.06|0.17|0.21|0.20|0.15|
|1971:1-1984:4|-0.44***|-0.30***|-0.13***|0.09|0.25|0.29|0.24|
|1985:1-2016:4|-0.08|0.02|0.12|0.20|0.21|0.18|0.12|
|**Real short-term int**|**erest rate**|||||||
|1971:1-2016:4|0.05|0.08|0.11|0.12|0.09|0.06|0.01|
|1971:1-1984:4|0.03|0.08|0.19|0.24|0.16|0.07|-0.07|
|1985:1-2016:4|0.05|0.05|0.07|0.08|0.08|0.07|0.06|
|**Real long-term int**|**erest rate**|||||||
|1971:1-2016:4|0.04|0.00|-0.05|-0.12|-0.18|-0.23|-0.24|
|1971:1-1984:4|0.02|0.03|0.05*|0.05**|-0.02*|-0.13|-0.25|
|1985:1-2016:4|0.00|-0.06|-0.11|-0.17|-0.21|-0.22|-0.20|

Note: The average within-country correlation between the year-over-year interest rate changes and output growth is presented. The sample consists of 18 advanced economies. Lags (leads) indicate that output is shifted one or more quarters forward (backward) relative to interest rates. ***, ** and * indicate that the average correlations for the period Q1 1971 to Q4 1984 are statistically different from those for the period Q1 1985 to Q4 2016 at the 1%, 5% and 10% levels, respectively.

52
_Fanty Planning Success in 1Aw Cites In Zaire_ _16_ Table 4.

Prevalence of Current Use of Modern Contraceptives Among Married<sup>Women</sup> of Reproductive Age In 10 Sites In Zaire

|Location|Year of<br>Survey|Percent Using<br>Modern Method|Research Group|
|---|---|---|---|
|URBAN:||||
|Matadi|1981|4%|PRODEF/Tulane|
||1984|17%|PRODEFlTulane [5]|
||1989|13%|PRODEF/Tulane|
|Kinshasa|1982|4%|INS/Westinghouse [3]|
||1988|6%|PSNDirulane|
|Lubumbashi|1982|8%|INS/Westinghouse [3]|
||1982|3%|INS/Westinghouse [3]|
||1987|4%|IMCK 18]|
|Kananga|1990|17%|IMCK [201|
||1982|2%|INS/Westinghouse [3]|
|Mbuji Mayi|1986|3%|PRODEF/PSNDlllulane [23]|
||1986|1%|PRODEF/PSND/rulane|
|RURAL:||||
|Nsona Mpangu|1981|3%|PRODEF/CBZO/Tulane|
|(Bas Zaire)|1984|12%|PRODEF/CBZO/Tulane|
||1988|8%|PRODEF/CBZOlTulane|
|Vanga (Bandundu)|1983|11%|INS/Westinghouse [3]|
|Nkara (Bandundu)|1984|2%|INS/Westinghouse [31|
|Sona Bata (Bas Zaire)|1986<br>1988|3%<br>6%|PRODEF/CBZO/Wulane<br>PRODEF/CBZO/Whlane|

listed above (PRODEP, AZBEF/PSND, tubal ligation service, and pharmacies) to contraceptive use in Matadi for two reasons. First, both PRODEF and AZBEF/PSND supported FP<sup>activities</sup> in clinics. Second, both PRODEF and the AZBEF/PSND centers may have been<sup>a source of</sup> referrals for tubal ligations at the hospital.

Data on the source of different contraceptives provide further insight into use<sup>patterns</sup> among this population. Among pill users (n= 86), the most commonly cited sources<sup>were a clinic</sup> (52 percent), pharmacy (21 percent), hospital (14 percent), and distributor (7 percent).<sup>All</sup> (n=62) women who had undergone tubal ligation cited the hospital as their source.<sup>Women</sup> whose partners used condoms (n= 16) named the pharmacy (38 percent), hospital and<sup>clinic (19</sup>
accurately than most other studies (which often consider all self-employed informal).<sup>3</sup> Importantly, for 25 countries, our analysis includes the early stage of the economic crisis of 2008-2010: field work of the round 4 of the ESS has been completely or mostly performed in 2008/q4 for 14 countries, and in 2009 for 11 countries in our sample.

Second, we show that both on average and after controlling for a rich set of individual characteristics, informal employees in all parts of Europe are having the largest financial difficulties among all categories of employed population (yet they fare much better than the unemployed and discouraged), whilst informal self-employed are at least as well off as formal employees.

Third, we find a negative and significant effect of the individual-level satisfaction with the national government on the propensity to work without contract in Eastern Europe, as well as in Western Europe.

The rest of the paper is organized as follows. Section 1 briefly outlines the predictions of search and matching labor market model (Pissarides 2000) with regards to workers‘ sorting between formal and informal jobs. Section 2 describes prevalence and dynamics of informal employment in Europe. Section 3 compares profiles of informal and formal employees, as well as informal self-employed in terms of key personal characteristics and job profile (Section 3.1), exclusion factors (Section 3.2), and household income (Section 3.3). Section 4 presents econometric analysis of individual level determinants of work without contract among employees<sup>4</sup> . Section 5 amends this analysis by adding worker satisfaction with the national government to the explanatory variables and applying instrumental variable techniques. Section 6 concludes.

# **1 Theoretical framework**

The literature provides several models describing the behavior or workers and firms, as well as the role of institutions and other macro factors in an economy with formal and

> 3 ILO (2002) states that „the self-employed ...include high-end professionals and employers of registered enterprises, who are not considered to be informally employed. These categories are assumed to be small worldwide...‖. We consider a self-employed person belonging to formal sector if he/she either works in a professional occupation (like lawyer, doctor,  consultant, etc.) or has more than five employees. This approach is similar to the one found in Henley et al. (2006) and consistent with the ILO (2003) guidelines requiring that ―The enterprise of informal employers must fulfill _one or both_ of the following criteria: size of unit below a specified level of employment, and non-registration of the enterprise or its employees‖. We show further (see Table 3) that in Eastern Europe formal self-employed account for about 2% or labor force, whilst in the rest of Europe this proportion is 3% and thus cannot be claimed negligible.

> 4 See Hazans (2011a) for a more general analysis of determinants of labor market status, including employment formality.

4