rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10587

# **Abstract**

This paper examines the pervasiveness of tax evasion among firms in Indonesia and the characteristics associated with higher levels of noncompliance. Tax evasion is estimated through a randomized, double-list experiment embedded in a nationally representative survey of 2,955 registered firms. This revealed whether firms pay all the taxes they owe without them having to disclose this directly. Across both list experiments, around a quarter of the firms indirectly reveal that they have evaded taxes. Firms that do not export, face intense competition from informal firms, and believe tax administration is a major obstacle to their business are the most likely to evade taxes. These findings help to inform the enforcement activities of tax authorities in middle-income countries, which face substantial challenges in estimating levels of tax evasion and identifying noncompliant taxpayers.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at choy@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility. worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**<u>Table 13.</u>** <u>LPM regressions- effects of norms and personality traits on wage employment  (vs. self-employment)- men sample</u>

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||_Norms_|_Norms+_<br>_Controls_|_Norms+_<br>_Controls+Pe_<br>_rsonality_|_Norms+_<br>_Controls+_<br>_Impulsive_|_Norms+_<br>_Controls+_<br>_Work_<br>_Central_|_Norms+_<br>_Controls+_<br>_Tenacity_|_Norms+_<br>_Controls+_<br>_Polychronici_<br>_ty_|_Norms+_<br>_Controls+_<br>_Locus of_<br>_Control_|_Norms+_<br>_Control+_<br>_Achieve_<br>_ment_|_Norms+_<br>_Control+_<br>_Power_<br>_Motivation_|_Norms+_<br>_Controls+_<br>_Organized_|_Norms+_<br>_Controls_<br>_+_<br>_Optimis_<br>_m_|
|Traditional|-0.004*|0.001|-0.000|0.001|0.003|0.002|0.001|0.012|0.013|-0.006|0.015**|0.030**|
||(-1.78)|(0.55)|(-0.02)|(0.09)|(0.35)|(0.18)|(0.13)|(1.65)|(1.21)|(-0.73)|(2.14)|(2.23)|
|Age||-0.005|-0.011|-0.006|-0.004|-0.006|-0.005|-0.010|-0.009|-0.005|-0.006|-0.007|
|||(-0.45)|(-0.89)|(-0.53)|(-0.30)|(-0.51)|(-0.45)|(-0.87)|(-0.77)|(-0.42)|(-0.48)|(-0.60)|
|Age_sq||0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|||(0.15)|(0.60)|(0.23)|(0.00)|(0.22)|(0.16)|(0.55)|(0.49)|(0.14)|(0.19)|(0.27)|
|Married||0.055|0.063|0.055|0.055|0.060|0.055|0.067|0.064|0.049|0.055|0.061|
|||(0.74)|(0.88)|(0.75)|(0.74)|(0.80)|(0.74)|(0.91)|(0.88)|(0.66)|(0.75)|(0.85)|
|At least secondaryeducation||0.134**|0.121**|0.133**|0.132**|0.135**|0.134**|0.130**|0.131**|0.136**|0.133**|0.122**|
|||(2.46)|(2.31)|(2.45)|(2.42)|(2.47)|(2.47)|(2.46)|(2.41)|(2.51)|(2.41)|(2.24)|
|Gender of head(male=1)||0.150|0.186|0.152|0.154|0.142|0.151|0.164|0.152|0.147|0.158|0.169|
|||(1.06)|(1.29)|(1.07)|(1.08)|(0.99)|(1.06)|(1.13)|(1.08)|(1.05)|(1.11)|(1.18)|
|Muslim(Christian is base)||-0.121**|-0.110**|-0.118**|-0.118**|-0.122**|-0.121**|-0.112**|-0.132**|-0.119**|-0.115**|-0.120**|
|||(-2.35)|(-2.26)|(-2.30)|(-2.29)|(-2.35)|(-2.35)|(-2.32)|(-2.59)|(-2.32)|(-2.26)|(-2.31)|
|Spousepresent in household||-0.194**|-0.228***|-0.196**|-0.193**|-0.199**|-0.194**|-0.223***|-0.204**|-0.194**|-0.193**|-0.208**|
|||(-2.35)|(-2.80)|(-2.38)|(-2.35)|(-2.41)|(-2.34)|(-2.71)|(-2.53)|(-2.40)|(-2.35)|(-2.57)|
|Household size||-0.017*|-0.014|-0.016*|-0.017*|-0.017*|-0.017*|-0.015|-0.016*|-0.018*|-0.017*|-0.016*|
|||(-1.77)|(-1.50)|(-1.73)|(-1.77)|(-1.75)|(-1.77)|(-1.55)|(-1.67)|(-1.87)|(-1.72)|(-1.70)|
|Poor||-0.061|-0.070|-0.061|-0.064|-0.061|-0.061|-0.067|-0.059|-0.060|-0.059|-0.068|
|||(-1.16)|(-1.38)|(-1.17)|(-1.23)|(-1.17)|(-1.16)|(-1.31)|(-1.12)|(-1.15)|(-1.14)|(-1.35)|
|Urban||0.080|0.069|0.080|0.080|0.082|0.080|0.065|0.085|0.082|0.083|0.060|
|||(1.22)|(1.12)|(1.23)|(1.21)|(1.25)|(1.22)|(0.99)|(1.28)|(1.29)|(1.26)|(0.95)|
|Ashanti(Northern is base)||-0.062|-0.032|-0.062|-0.055|-0.063|-0.061|-0.054|-0.058|-0.057|-0.060|-0.070|
|||(-1.08)|(-0.61)|(-1.08)|(-0.95)|(-1.10)|(-1.06)|(-0.99)|(-0.99)|(-1.00)|(-1.02)|(-1.20)|
|Impulsive|||-0.040|-0.037|||||||||
||||(-1.10)|(-0.28)|||||||||
|Work centrality|||-0.070**||-0.017||||||||
||||(-2.42)||(-0.19)||||||||
|Tenacity|||0.014|||0.028|||||||

47
Africa and Brazil (around 20,000) then by Pakistan, Bulgaria, and Iran (with more than 13,000 each) whereas the smallest pools of exporters (around and below 300) are found in Niger and Mali, followed by Burkina, Laos, Yemen, and Cambodia. This pattern seems to mirror the countries’ level of development, and this link will be studied further in Section IV.

Average exports per firm are the highest (in the range of 7-8 million USD) for Belgium, Brazil, Chile, and Mexico followed by Cambodia, Sweden and Peru.<sup>33</sup> Albania, Lebanon, Macedonia, Yemen, and Kenya exhibit the lowest average export values per exporter (less than 800,000 USD). The tremendous difference between mean and median exports per firm indicates very skewed exporter size distributions in all countries with some very large exporters driving total exports.<sup>34</sup> For Botswana, Chile, Mali, Peru, South Africa, Mexico, and Malawi the skewness in the exporter size distribution is further confirmed by the very high share of exports – more than 90 percent– accounted for by just the top 5 percent of exporters. Interestingly, for most other developing countries the shares for developing countries are smaller than those for Norway, Sweden, Norway, and New Zealand and are smaller than those for the U.S. and other European countries for which the top 5 percent of firms account for 80 percent or more of trade, as shown by Bernard, Jensen, and Schott (2009) and Mayer and Ottaviano (2008).

The average number of products per exporter also exhibits a high degree of heterogeneity across countries, ranging from 2 in Laos to 15 in South Africa. In terms of markets, the average number of destinations per exporter is more similar across countries ranging from 1.4 in Botswana to 4.8 in Cambodia and 6.8 in Belgium. Interestingly, the average or median of these

> 33 The very large average exporter size in Cambodia is driven by a small number of extremely large formerly state-owned apparel and textiles producers, as gathered from statistics based on the World Bank Enterprise Surveys for Cambodia.

> 34 Using the same cross-country input dataset used in the construction of the Database, Freund and Pierola (2012) show that exports are dominated by a small group of very large exporters (so-called ‘superstars’). These firms are remarkably larger than the rest, they participate in many sectors and most importantly, they define the productive structures and drive the export growth observed in most countries.

19
climate in (mostly) developing countries and to understand the determinants of firm performance. The richest country in the sample is Sweden with a GDP per capita of US$46,000, and the poorest countries (i.e., Burundi, Liberia, Malawi, Ethiopia) have a GDP per capita around US$200. In each country, the survey is based on the universe of eligible non-agricultural firms obtained from the country’s statistical office with stratified random sampling with replacement, and the result is a representative sample of the nonagricultural private economy in the country.<sup>2</sup> Stratification is based on two criteria: the sector of activity and firm size. Typically, the stratified sampling yields between 100 and 1,000 firms per country, with 108 firms for the median city. Industries range from manufacturing and construction to services and retail and wholesale trade,<sup>3</sup> and some industries such as mining and financial services are excluded in the survey. Each survey is largely a cross-sectional data set.

We include data collected after 2006, although some enterprise surveys were conducted earlier. Prior to that year, there was considerable heterogeneity across countries in terms of the questionnaire format, sectors covered, and sampling methodology. Moreover, the samples for surveys conducted before 2006 were not generally representative. After dropping observations that have missing values for whether the firm is headed by a female manager, our final sample consists of more than 80,000 firm-year observations covering 567 cities in 117 countries.<sup>4</sup> A complete list of variables and data sources is shown in Table 2; the summary statistics for our key variables used in regressions are in Table 3.

# **4. Basic patterns for the relationship between FDI and female corporate leadership**

How do we define female entrepreneurship? Ideally, we want to capture firms with women being the primary owner and running the firm; that is, women act as both owners and chief executives. Female-owned firms may not be a good indicator for entrepreneurs since family ownership is prevalent and important in both developed and developing countries

> 2 Thus, wholly state-owned firms are not in the sample.

> 3 See http:\\www.enterprisesurveys.org for a more detailed description of the WBES.

> 4 Because of the missing values of other variables, for each dependent variable in the regressions, the number of observations differs. In the baseline regressions, the number of observations when the dependent variable is female-manager and female-entrepreneur are 50,472 and 47,702 respectively.

8
_UaAwWu be de Arab Ca _Iiu_

|_Count_|_Uniwuit & Locadon_|_Ye_|_Lan5age_|_Acad_<br>_Staff_|_Sfdents_|
|---|---|---|---|---|---|
||M<br>amnud V Univ.,<br>Rabat-Chesah|1957|A,F|c. 1330<br>90 P.T.|39,160|
||U. Sidi Mohammed Ben<br>Abdclth,<br>Atlas-Fes|1975||c350|C!5,000|
||Qadi Ayyad U.,<br>Mariskoch|1978|F|||
||Q<br>taacyinU.,<br>Fes|859<br>_AD._|A|c 90|C. 5,000|
|Oman|Sultan Qaboos U.,<br>Muscat|1986||c. 130|c 540|
|Qatar|U. of Qatar,<br>DlDha|1973/<br>1977|A|84-85<br>405|84-8<br>4A58|
|saud<br>Axabi|King Saud U,<br>RiyKUl|1957|A, E|83-84<br>2,419|83-84<br>21,246|
||King Faisal U,|1974|A, E|c. 500|c. 1,430|

MOB- Minitary of Educad; MOHESR-MNly of Hier Educatio and Scientific Reseac b AS-AlAh;U Ar-Ahlteumn; **A-AMt** Q-Cmputer Ssc D-Dentistry Ed-Educaion; E-Ennrng Ft-Food TedIn4 **I.ALanpas** L-Lw **N-Medkcle; P-Puhaacr, R-Retiglou** Shdics; **S-Sdencu T-Tecnohogy,** Vm-Vetedny Med.

_46_
maternal and infant mortality rates and high prevalence of infectious and parasitic diseases.<sup>25</sup> _<u>Table 15: Initial poverty levels</u>_<sup>_26_</sup> _<u>and percent changes due to trade reforms</u>_

||||<br>**Initial**|<br><br>**distribution**|<br><br>||||
|---|---|---|---|---|---|---|---|---|
||||**H**|**PG**|**P2**||||
||||**_Abso_**|**_lute levels_**|||||
|**Initial levels**|**All**|**PovLine**|49.8|21.0|11.6||||
|||**ExtremePL**|21.0|6.9|3.3||||
||**Urban**|**PovLine**|39.8|14.9|7.6||||
|||**ExtremePL**|12.5|4.1|1.9||||
||**Rural**|**PovLine**|63.9|29.5|17.1||||
|||**ExtremePL**|32.9|10.9|5.3||||
||||**Full d**|**istribution**||**Neutral**|**distributi**|**on**|
||||**H**|**PG**|**P2**|**H**|**PG**|**P2**|
|||||**_Percent_**|**_changesfro_**|**_m initial level_**|**_s_**||
|**Full unilateral lib.**|**All**|**PovLine**|-1.6|-3.7|-4.9|-1.3|-2.0|-2.4|
|**(segmented)**||**ExtremePL**|-4.4|-6.9|-9.6|-2.5|-3.0|-3.1|
||**Urban**|**PovLine**|-2.5|-6.3|-8.4|-1.6|-2.8|-3.2|
|||**ExtremePL**|-7.0|-11.3|-15.5|-2.5|-3.5|-3.8|
||**Rural**|**PovLine**|-0.9|-1.8|-2.8|-0.4|-1.0|-1.2|
|||**ExtremePL**|-3.1|-4.6|-6.7|-1.0|-1.6|-1.7|
|**CAFTA bilateral lib. **|**All**|**PovLine**|-0.3|-1.2|-1.4|-0.7|-1.2|-1.5|
|**(segmented)**||**ExtremePL**|-0.9|-1.8|-2.5|-1.1|-1.8|-2.0|
||**Urban**|**PovLine**|-0.4|-2.3|-2.8|-1.2|-1.7|-2.0|
|||**ExtremePL**|-1.0|-3.4|-4.4|-1.3|-2.1|-2.4|
||**Rural**|**PovLine**|-0.2|-0.4|-0.5|-0.3|-0.6|-0.8|
|||**ExtremePL**|-0.8|-1.0|-1.5|-0.7|-1.1|-1.2|

In the long run, an open and transparent trade policy enhances growth opportunities by – among other things – facilitating access to new technologies, promoting foreign direct investment, and increasing incentives for pursuing balanced domestic macro policies. In general, it can help reduce poverty. Nonetheless, in this paper, only the short-term impacts of trade reforms on poverty are considered, focusing on the potential immediate problems or benefits for the poor, which helps formulate any compensatory measures.

Overall, both liberalization scenarios marginally reduce poverty in Nicaragua, with the non-discriminatory one being more pro-poor than CAFTA. A positive aspect is

> 25 For a full analysis of the poverty situation and its evolution in the last decade see World Bank Report No. 20488 NI, titled “Nicaragua Poverty Assessment, volumes 1 and 2.

> 26 Although informative, the rural and urban poverty measurement obtained using a single national poverty line may be to some extent misleading. Price levels and the corresponding purchasing powers in the rural and urban sectors of the economy can be quite different so the zone specific poverty lines should be used to guarantee more accurate estimates.

38
Provinces, which are administrative units roughly similar to states in the US; and 2) Districts, which are fine administrative units similar to counties in the US. These variables contain the geographies for every country harmonized spatio-temporally to provide spatially consistent boundaries across samples in each country.

**Migration.** IPUMS International reports, when available in the original source, the administrative unit in which the individual was born, the previous location of the previous residence, and the number of years living in the current location. For some censuses, this information is all at the level of province (e.g., Brazil 2010 has only state-level information) and for others, information at the district level is also available (e.g., Cuba 2012 has municipality-level information). I use these variables to identify a sample of movers, i.e. individuals who at the time of the interview live in a place different than the one in which they were born, as well as a sample of permanent residents (or non-movers) that correspond to individuals living in the same place in which they were born. Hence, I consider the place of origin as the place where they were born, the current place as the destination, and I construct a variable that records age at move using current age minus the time they have been living in the current place.

Table 1 provides a set of summary statistics comparing the characteristics of individuals (and their families) that are non-movers versus those that are movers.<sup>5</sup> In terms of family characteristics, movers show slightly higher income percentile (according to average income of parents) and urban status (at destination in the case of movers). However, permanent residents have a higher share of dwelling ownership and are more likely to be couple families. In the case of educational outcomes, individuals who are permanent residents complete primary education with exactly the same likelihood but exhibit lower years of schooling than movers. Overall, the table suggests that permanent residents are relatively similar to those individuals who move (or at least they are not remarkably different).<sup>6</sup>

> 5A caveat of this comparison is that some characteristics such as household income and years of schooling are not available in all the surveys, hence, the sample composition may vary across rows.

> 6Table A2 in the Appendix describes the sample size by census sample distinguishing between movers

7
# Figure 1

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# **3. Data**

To better understand what characteristics help explain the effectiveness of IPAs, this paper brings together new data sources on sectoral FDI and IPA characteristics. This section describes these various data sets as well as the study sample used.

## **3.1 Data on sectoral FDI**

We create a new sectoral FDI database by combining data on FDI positions from EUROSTAT’s BPM6 database with the US Bureau of Economic Analysis (BEA). EUROSTAT’s database reports information on outward- and inward FDI from 39 OECD countries for the years 2013 to 2018. However, the amount of information reported differs significantly across countries due to different country-level reporting standards. As a result, some countries only report aggregate FDI stock (e.g. Switzerland) or report on only a small number of partner countries or sectors (e.g. Japan and the United States). We therefore only include data from 29 reporting countries. To create the widest available data on a country’s FDI, we use their information on outward FDI positions (as our interest is in both OECD and non-OECD countries). The sectoral data is available for a range of NACE<sup>4</sup> sectors at the 1-digit to 3-digit-level (country depending).

This data is then combined with the sectoral FDI database from the US BEA, which provides information on US FDI positions across the world since the 1980s. However, this relies on a distinct set of sectors that do not align directly to any NACE code. We therefore create our own concordance table to harmonize this with the EUROSTAT data. We also only keep the years 2013-2018 from this database, for which US BEA has information on over 200 countries. All FDI values are then transformed into constant 2018 USD terms. The summary statistics on FDI reporting countries is given in table 2, showing each country’s count, number of unique sectors and their unique country-pairs (i.e. the number of countries for which they report their sectoral FDI position).

**Table 2: FDI reporting countries– breakdown by country**

|Data Source|CountryISO|CountryName|Total Count|Unique Sectors|Unique Country-Pair Count|
|---|---|---|---|---|---|
|EUROSTAT|BEL|Belgium|12,181|79|36|
||BGR|Bulgaria|10,930|79|34|
||CYP|Cyprus|10,207|77|26|
||CZE|Czech Republic|11,149|75|32|
||DEU|Germany|8,293|79|35|
||DNK|Denmark|12,186|78|35|
||ESP|Spain|1,313|48|31|
||EST|Estonia|11,246|79|35|
||FIN|Finland|9,674|77|35|
||FRA|France|3,525|66|34|
||GBR|United Kingdom|6,269|77|35|
||GRC|Greece|10,950|79|34|
||HRV|Croatia|11,024|73|26|
||HUN|Hungary|8,598|74|30|
||IRL|Ireland|6,994|79|31|
||ITA|Italy|7,615|75|35|
||LTU|Lithuania|9,859|79|26|
||LVA|Latvia|9,779|82|23|
||MLT|Malta|9,572|79|32|

> 4 NACE is the industry standard classification system used in the European Union, and comes from the French term

- " **_n_** _omenclature statistique des_ **_a_** _ctivités économiques dans la_ **_C_** _ommunauté_ **_e_** _uropéenne_ ."

9
# **Political Stability and Absence of Violence/Terrorism:  Individual Indicators in 2024 WGI Update**

|**Repres**|**entative Sources**|
|---|---|
|**EIU**|Orderly transfers<br>Armed conflict|
||Violent demonstrations|
||Social unrest|
||International tensions / terrorist threat|
|**HUM**|Political terror scale|
|**IJT**|Security risk rating|
|**IPD**|Intensity of internal conflicts: ethnic, religious or regional|
|**PRS**|Intensity of violent activities...of underground political organizations<br>Intensity of social conflicts (excluding conflicts relating to land)<br>Government stability<br>Internal conflict<br>External conflict<br>Ethnic tensions|
|**WMO**|_Protests and riots._The risk that the nature and impact of protests and riots (excluding those related to labour) cause damage to assets or<br>injure or detain people, particularly if these disrupt normal movement, business operations, and activity.|
||_Terrorism._The risk that the activities of any non-state armed group or individual cause (or are likely to cause) property damage and/or<br>death/injury through violence. This risk definition includes terrorism, which uses violence (or the threat of) to advance a political cause, and<br>similar tactics used by "for profit" organised crime.<br>_Interstate war._This risk measures resultant impacts (death/property damage) and means, covering the spectrum from targeted military strikes<br>against limited targets to full-scale war with the aim of changing the government and/or occupation.<br>_Civil war._The risk of intra-state military conflict, in the form of an organised insurgency, separatist conflict, or full- blown civil war, in which<br>rebels/insurgents attempt to overthrow the government, achieve independence, or at least heavily influence major government policies.|
|**Non-re**|**presentative Sources**|
|**HRM**|Right to Freedom from Disappearance|
||Right to Freedom from Extrajudicial Execution|
||Right to Freedom from Arbitrary Political Arrest<br>Right to Freedom from Torture and Ill-Treatment|
|**WBS**|Political instability as an obstacle to business|
|**WCY**|The risk of political instability is very low|
|**WJP**|Factor 5.2: Civil conflict is effectivelylimited|

46