rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5b: Effect of formalizing on different channels**

||Paid<br>Taxes|Amount of<br>Taxespaid|Formal<br>Accounting|Has<br>Receipt book|Business<br>Bank A/c.|Applied for<br>Business Loan|Applied for<br> Personal Loan|
|---|---|---|---|---|---|---|---|
|Registered with the D.S.|-0.0643|-8,865|-0.103|0.352***|0.0239|-0.00421|-0.0451|
||(0.142)|(6,670)|(0.0763)|(0.130)|(0.0812)|(0.0770)|(0.0690)|
|Lag included|No|No|Yes|No|Yes|No|No|
|Observations|1,036|1,036|1,016|1,049|1,059|1,059|724|
|Survey rounds question asked|R2, R3, R4|R2, R3, R4|R1-R4|R2, R3, R4|R1-R4|R2, R3, R4|R2, R3|
|Mean for controlgroupin sample|0.66|6800|0.141|0.31|0.14|0.10|0.056|
||Share of|Electric|Applied for|Participate|Advertised|Business has||
||Sales made|Connection|Govt.|in Govt.|in Last six|clear and|Changed|
||to Govt.(%)|in Bus. Name|Contract|SMEprogram|Months|visible sign|Location|
|Registered with the D.S.|3.543|-0.152|0.000453|0.0535|0.261***|-0.0895|-0.0504|
||(2.285)|(0.116)|(0.0540)|(0.0449)|(0.0892)|(0.130)|(0.0870)|
|Lag included|No|No|No|No|Yes|Yes|No|
|Observations|1,020|724|724|724|1,036|1,030|1,016|
|Survey rounds question asked|R2, R3, R4|R2, R3, R4|R2, R3|R2, R3|R1-R4|R1-R4|R2, R3, R4|
|Mean for controlgroupin sample|0.96|0.40|0.022|0.033|0.16|0.56|0.18|

Notes: Registration with the D.S. instrumented with offer of the 10,000 Rs, 20,000 Rs, or 40,000 Rs registration treatments. Information only treatment group excluded from these regressions.

All regressions include controls for randomization strata, and for the lagged dependent variable if it was collected. Robust standard errors in parentheses, clustered at the firm level.

*, **, and *** indicate significance at the 10, 5 and 1 percent significance levels respectively.

31
**Table 4: Per capita greenhouse gas emissions in ECA by country, 1990 to 2005**

||1990|1995|2000|2005|
|---|---|---|---|---|
||------<br>|-----------------<br>|--- tCo2e -------------<br>|-------------<br>|
|Turkmenistan|19.4|13.1|14.2|18.9|
|Estonia|26.8|13.4|12.8|14.5|
|Czech Republic|18.0|14.3|14.0|13.8|
|Russian Federation|19.8|14.1|13.0|13.7|
|Kazakhstan|19.1|14.3|10.8|13.4|
|Ukraine|18.2|11.5|9.5|10.5|
|Slovenia|8.8|9.0|9.3|10.1|
|Poland|11.5|10.8|9.6|9.8|
|Slovakia|13.5|9.7|8.8|9.3|
|Bulgaria|12.8|9.2|7.4|8.6|
|Belarus|14.3|7.9|7.7|8.5|
|Hungary|9.6|7.9|7.8|8.2|
|Serbia & Montenegro|11.6|7.6|7.5|7.8|
|Croatia|6.6|5.0|5.9|6.9|
|Uzbekistan|8.6|6.9|7.3|6.9|
|Romania|10.3|7.2|5.5|6.1|
|Macedonia, FYR|6.3|5.7|5.6|5.8|
|Azerbaijan|11.6|6.1|6.8|5.7|
|Lithuania|12.5|6.4|5.4|5.7|
|Turkey|4.6|4.8|5.3|5.5|
|<br>Bosnia & Herzegovina|7.5|1.9|5.1|5.2|
|Latvia|9.5|4.9|4.2|4.7|
|Moldova|8.9|3.9|2.6|3.3|
|Albania|2.8|1.4|2.1|2.9|
|Armenia|6.9|1.8|2.0|2.4|
|Georgia|6.5|2.4|2.0|2.0|
|Kyrgyzstan|6.6|1.8|1.7|1.9|
|<br>Tajikistan|4.0|1.9|1.5|1.5|

Source: WRI (2011) and authors‘ calculations.

42
Fitch Connect data provide information on income statements and balance sheets for up to 34,400 banks since 1995. As this dataset does not report ownership information, we augment the dataset with hand-coded ownership information assembled using the procedure developed in Panizza (2021).

<mark>Next, we merge the bank-level dataset with firm-level characteristics from the World Bank’s Enterprise Survey (WBES). WBES consists of a series of firm-level surveys covering a broad range of business environment topics including</mark> _<mark>access to finance</mark>_ <mark>,</mark> _<mark>corruption</mark>_ <mark>,</mark> _<mark>infrastructure,</mark>_ <mark>and</mark> _<mark>competition</mark>_ <mark>. It includes</mark> more than 180,000 firms in more than 150 countries over a 16-year period.<sup>4</sup> Most of the countries covered by WBES have been surveyed 2 or 3 times over 2006-21. For example, the Arab Republic of Egypt was surveyed in 2013, 2016, and 2020, while Tunisia was surveyed in 2013 and 2020.

A key component of the survey is the _Access to Finance_ module. This module includes questions that focus on the various dimension of firms’ financing. Among other things, there is a question on whether the enterprise has a line of credit or if it has contracted a loan in the last 24 months. If the answer is yes, there is a follow-up question that asks the name of the bank that provided the most recent line of credit or loan.<sup>5</sup> While the answer to the second question is not available in the published version of the survey, we were able to obtain bank names on a confidential basis. We then used the answer to this question to connect firms in WBES with bank-level data from Fitch Connect.

The full WBES covers 180,000 firms in more than 150 countries. However, we only have information on banking relationships for 41 countries (27 in Europe, 8 in Asia, and 6 in the Middle East and North Africa), including 63,000 records and 108 surveys. (Table 1). Of these initial records, about one-fifth belong to surveys that did not include the bank identity question.<sup>6</sup> Of the remaining records, which are all from surveys that include the bank name question, about one-third report the name of the bank. Lack of reporting could be due to the absence of a formal banking relationship, but there are many firms that report having a banking relationship but did not provide the name of a specific bank. This could be because the firm uses multiple banks or because it prefers not to divulge such information. Overall, 25 percent of surveyed firms (15,718 records) provided the name of their bank.

To merge bank-level data with firm-level data form WBES, we first dropped records (about 3% of the total) with inconclusive bank names such as “Commercial Bank”, “Cooperative bank”, “Doesn’t know” or

> 4 The survey focuses on low- and middle-income economies, but it also includes 5 high-income countries (Cyprus, Greece, Italy, Malta, and Portugal).

> 5 The question is “Which Bank Provided the Most Recent Line of Credit or Loan?”

> 6 Surveys conducted in 2008-2009 do not feature the bank name question.

6
**Table 8. The share of minimum wage earners in survey data, imputed survey data, and tax data (only full-time employees).**

||**EU-SILC**|**Imputed EU-SILC**|**Tax data (July 2020)**|
|---|---|---|---|
|**Less than 95% of MW**|5.11%|5.44%|2.7%|
|**95 – 105% of MW**|6.84%|17.6%|24.7%|
|**More than 105% of**||||
|**MW**|88.06%|76.96%|72.6%|

_Source: Own estimation based on the imputed EU-SILC data._

# **IV. Conclusion**

**This paper focuses on assessing tax compliance and underreporting of income in Romania, with a specific emphasis on minimum wage workers.** The study aims to understand the distributional implications of tax compliance and its links with minimum wage policy and design. The paper highlights that tax evasion has significant redistributive impacts, affecting the incidence and distribution of taxes. Reducing tax evasion is crucial for ensuring the effectiveness of the tax system and promoting greater income equality. The study also emphasizes the links between tax evasion and social policy, particularly regarding the distribution of social benefits. Tax evasion can undermine the progressivity of the tax system, leading to fiscal losses and reduced social welfare for eligible recipients. The research also explores the interaction between labor tax evasion and minimum wage policy, emphasizing that payroll tax evaders may be overrepresented among minimum wage earners. This raises concerns about the effectiveness of minimum wage policy and the need to address tax compliance issues among this demographic. The study underscores the importance of addressing tax compliance, evasion, and transparency issues to enhance fiscal position and generate more revenue.

**The study provides an estimated range for non-compliance/under-reporting of income in Romania, both overall and for different population groups, and explores the characteristics associated with**

31
**Table 2: GHG mitigation potential between pre-existing vs. new stock**

|GHG Mitigation<br>option|Total mitigation potential<br>during the 2015-2035<br>|% of total mitigation<br>from the pre-existing<br>|% of total mitigation<br>from the new stock|
|---|---|---|---|
||period (Thousand tons)|stock||
||**(a) **<br>|**Armenia**<br>||
||Residentia|lbuildings||
|Lightbulbs|5,862|0.2%|99.8%|
|Refrigerators|2,894|20.6%|79.4%|
|Air conditioners|1,089|6.6%|93.4%|
|TV sets|1,538|16.5%|83.5%|
|Insulation|4,588|100%|0%|
||Commercia|lbuildings||
|Airconditioners|116|74.3%|25.7%|
|Publiclighting|354|0.5%|99.5%|
||**(b) **|**Georgia**||
||Residentia|l buildings||
|Lightbulbs|975.0|1.3%|98.7%|
|Refrigerators|99.5|0.0%|100.0%|
|Washingmachine|27.9|0.0%|100.0%|
|TV sets|168.6|0.0%|100.0%|
|Windowinsulation|1639.3|88.1%|11.9%|
|Wall insulation|843.5|16.9%|83.1%|
|Roof insulation|507.8|43.7%|56.3%|
||Commercia|lbuildings||
|Indoor Lightbulbs|540.3|0.1%|99.9%|
|PublicLighting|108.7|1.2%|98.8%|
|Windowinsulation|118.3|55.4%|44.6%|
|Wall insulation|549.6|9.4%|90.6%|
|Roof insulation|399.3|16.3%|83.7%|

# **5. Sensitivity Analysis**

The calculations of marginal costs, GHG mitigation potential and the MAC curves are sensitive to several parameters, such as assumptions on the penetration rates of energy efficient devices and processes in future years, evolutions of costs of energy efficient technologies overtime, etc. Among these variables, penetration rate would be very sensitive because it influences both the baseline and mitigation or energy efficient scenario. To measure the level of sensitivity of penetration rates, we carried out analyses with varying rates of penetration in both baseline and mitigation scenarios.

# **5.1 Penetration of energy efficient technologies in the baseline scenario**

It is very important to distinguish between the penetration of efficient technologies that occur by itself and as a result mitigation policies or programs or incentives. The penetration that occurs 15
**Table 4.5. Results for agropastoral zone in Western Africa**

|||**NDVI**||
|---|---|---|---|
||**(1)**|**(2)**|**(3)**|
|**Landed ho**|**useholds**|||
|**No TLU**||||
|Hazard|-0.046|-0.047|-0.031|
||[-0.817]|[-0.843]|[-0.329]|
|Hazard<sup>2</sup>||-0.002|-0.001|
|||[-0.068]|[-0.025]|
|Hazard<sup>3</sup>|||-0.009|
||||[-0.224]|
|**Few(less t**|**han 2.1) TLU**|||
|Hazard|**0.052***|**0.052***|0.064|
||**[1.658]**|**[1.693]**|[1.240]|
|Hazard<sup>2</sup>||0.022|0.022|
|||[1.034]|[0.970]|
|Hazard<sup>3</sup>|||-0.006|
||||[-0.292]|
|**Many (mor**|**e than 2.1) TLU**|||
|Hazard|0.039|0.039|-0.010|
||[0.908]|[0.906]|[-0.192]|
|Hazard<sup>2</sup>||-0.010|-0.008|
|||[-0.235]|[-0.215]|
|Hazard<sup>3</sup>|||0.032|
||||[1.044]|
|**Landless ho**|**useholds**|||
|Hazard|**-0.212*****|**-0.214*****|0.112|
||**[-3.393]**|**[-3.553]**|[0.453]|
|Hazard<sup>2</sup>||0.018|0.023|
|||[0.265]|[0.352]|
|Hazard<sup>3</sup>|||-0.186|
||||[-1.391]|
|Obs.|4,593|4,593|4,593|
|R-sq|0.132|0.133|0.137|

_Source:_ World Bank.

_Note:_ NDVI = Normalized Difference Vegetation Index. Results are from a weighted regression such that each household is assigned its survey-specific population weight. Countries and years included are Niger (2018) and Nigeria (2011, 2013, 2016). Dependent variable is the log of per capita (or adult equivalent) consumption. Other variables included and not shown are education of household head, gender of household head, whether the household location is remote, whether the household was surveyed during the lean season, survey year, country, country x survey year, and administrative fixed effects. Robust t-statistics are shown in parenthesis. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

# 4.2. Exploring different areas over which to aggregate the hazard data

Initial analysis suggested that a 20 km level of spatial aggregation of hazards was appropriate. This radius is able to reflect the local shock to a household’s crop yields and may be large enough to capture some localized impacts on prices and wages in the local market area. We tested this level of aggregation again at the end of the analysis for all countries and found that the level of spatial aggregation made very little difference. In some specifications taking a 24
# **2.2 Components of EPRD programs**

As illustrated in Figure 1, an EPRD program requires that the program comprises two separate but related components: a _performance rating_ component, and a _public disclosure_ component.

While this may appear self-evident, it is important to note that performance rating may exist without public disclosure, and that public disclosure may exist without performance rating. Indeed, environmental regulators often maintain a list of industrial facilities deemed to be ‘large polluters’ or significantly ‘out of compliance’ with regulatory standards while not publicly disclosing the name of these industrial facilities. These lists generally aim at guiding monitoring and enforcement activities and are meant to be for internal use only (Box 2).

On the other hand, a number of public disclosure programs are not based on the actual rating of the firms’ environmental performance. For example, public disclosure programs along the lines of the United States Environmental Protection Agency’s (USEPA) _Toxics Release Inventory_ (TRI) relies _not_ on a _rating_ of industrial facilities’ environmental performance against specific criteria, but instead on a _ranking_ of industrial facilities from largest to smallest discharge sources of specific pollutants.<sup>10</sup> The EPRD programs referred to in this paper are those where both of these components (performance rating _and_ public disclosure) are integral parts of the program, and where performance rating is undertaken explicitly for the purpose of public disclosure.

Furthermore, a number of programs focus on the public disclosure of only one type of industrial facilities: those whose environmental performance is deemed to be lacking (such as ‘black list’ of polluters; see Box 3) or those whose environmental performance is deemed to be superior (such as ‘green awards’ schemes).

The EPRD programs referred to in this paper are those where both types of environmental performance (‘good’ _and_ ‘bad’) are part of the program (Box 4).

# **III. Setting Up**

Prior to their setting up, most EPRD programs have had to tackle two important issues, one pertaining to the legality of the public disclosure of firms’ environmental performance, and one pertaining to the expected sustainability of the program over the long term. We discuss each of these issues in turn.

# **3.1 Legal system**

In the course of its inception phase, an EPRD program is likely to have to tackle whether or not it is legal<sup>11</sup> to publicly disclose the environmental performance of targeted facilities: 5
# **Table of Contents**

|1.<br>INTRODUCTION................................................................................................................................ 3|
|---|
|2.<br>LITERATURE REVIEW AND DEFINITIONS.................................................................................. 4|
|_2.1 Measurement of Diversification_.......................................................................................................... 5|
|_2.2 Data_.................................................................................................................................................... 7|
|_2.3 Stylized facts_........................................................................................................................................ 8|
|3.<br>ECONOMETRIC SECTION................................................................................................................ 8|
|_3.1 Potential Drivers of Concentration/Diversification_............................................................................ 9|
|_3.2_<br>_Estimation Methodology - Generalized Method of Moments_...................................................... 11|
|_3.3 Empirical Findings_........................................................................................................................... 12|
|4.<br>FDI IN T&T........................................................................................................................................ 15|
|_4.1 Stylized Facts_.................................................................................................................................... 15|
|_4.2 Public Policies_.................................................................................................................................. 16|
|_Box 1. Dutch Disease_.............................................................................................................................. 19|
|5.<br>CONCLUSION................................................................................................................................... 20|
|6.<br>REFERENCES................................................................................................................................... 21|
|7.<br>ANNEX............................................................................................................................................... 23|

2
_Figure 14: Self‐reported well‐being changes of the households (share of households, %)_ <!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2014 2015 2016 2017<br>Well-being worsened Well-being improved<br><!-- End of picture text -->

# Source: Authors’ estimates based on HSS data.

The main factor determining the reduction of the well‐being of the population is inflation, which exceeded the growth rate of nominal income and thus led to a reduction in real incomes. However, in 2015–2016 significant contributors to the deterioration of well‐being were loss of employment and falling nominal wages, the latter registering a particularly sharp reduction. Post‐recession, in 2017, these trends were less pronounced and the inflation‐related reduction in real incomes became again the dominant reason of well‐ being loss for the population. Note, in particular, that this is against the background of relatively low inflation rates in 2017, but wages did not start registering increases until the second half of 2017.

Individuals vulnerable to the risk of poverty appear to be more affected by nominal cuts in wages than the population on average – a tendency that was exacerbated during the crisis, especially in 2016. They also faced a higher risk of losing their job compared to other population groups, although by 2017 these differences leveled off (Figure 15). In other words, the crisis resulted in the increased share of those who lost jobs or suffered a deterioration of terms of employment among vulnerable groups of the population. The effect of the crisis on the poor (based on the official definition) through loss of labor income is even more pronounced. Those below the poverty threshold had a substantially higher than average risk of losing their jobs and of falling nominal wages (see Annex, Table A6). Moreover, the risk of losing one’s job among the poor in 2016 was higher than the nominal wages reduction risk, stressing the severity of possible well‐being losses for poor households.

13
in the mid-1920s focusing on mobilizing peasants as the key political support base (Ch’en, 1983). When famines forced peasants to sell their meager land, this would only make them more prone to revolution propaganda. Mao’s assessment of the impact of economic hardship on support of Communism echoed the views of Lenin, who viewed the 1891-1892 Volga famine in Russia as fuel for revolutionary fervor. Lenin, as quoted by Vodovozov in Ellman (2005), hailed the Russian famine as able to “force the peasant to reflect on the bases of the capitalist system, demolish faith in the tsar and tsarism, and consequently in due course make the victory of the revolution easier.” (Ellman, 2005, page 823). We thus expect: _The famines-and-communism hypothesis_ : The regions experiencing warlord-era famines would be more likely to witness the spread of communism due to the appeal of Communist ideology as well as its policy of land redistribution and reduction in land inequality.

# **3. Data and Measurements**

In this section we discuss our data and our key measurements.

# **3.1 Warlords and Border Ruggedness**

Information about warlords is from The Chronicles of Beiyang Government Officials, which contains details on the appointment and ranks of all government officials between 1912 and 1927. In the post-1916 warlord era, there were 78 warlords that controlled the 289 prefectures in 18 provinces. A warlord’s rule was fragile and often short-lived. They were frequently replaced by other warlords, or could lose control of some of their territories due to military conflicts, or even assassination. In our sample, the average ruling period of the warlord in a prefecture was only four years. However, the overall scale and casualty-count of the wars during this period were relatively small (Chi, 1976). Most of the time, soldiers in the battlefield had little fighting spirit or loyalty to their generals, and wars usually served as a negotiation tool for a calculating warlord. The low casualty rate could also be the result of poor weaponry.

Topography has always played an important role in wars, especially in low-intensity warfare, where natural barriers could deter military actions. This could have profound impact on the incentives and behavior of warlords. According to Olson (1993), warlords in China in 12
# **Table 7 OLS Estimations based on country specific variables**

||**1985 **<br>|**arrival **<br>||**1975 a**|**rrival**||
|---|---|---|---|---|---|---|
|**EDUCATION INDEX 1**|**1990**|<br>**-2000**|**198**|<br>**-1990**|**199**|<br>**0-2000**|
||**predicted**<br>**(lnOP1990)**|**(1/10)**<br>**Δpredicted**<br>**(lnOP1990-2000)**|**predicted**<br>**(lnOP1980)**|**(1/10)**<br>**Δpredicted**<br>**(lnOP1980-1990)**|**predicted**<br>**(lnOP1990)**|**(1/10)**<br>**Δpredicted**<br>**(lnOP1990-2000)**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|**Log of GDP per capita**|**0.112*****<br>**[0.0205]**||**0.0999****<br>**[0.0500]**||0.0369<br>[0.0227]||
|**Openness Index**|-0.00006<br>[0.000650]|0.00001<br>[1.69e-05]|-0.00019<br>[0.00124]|-0.00002<br>[2.38e-05]|-0.00015<br>[0.000684]|0.00001<br>[2.61e-05]|
|**Log of Distance to the US**|**0.142*****<br>**[0.0463]**|**0.00258*****<br>**[0.000895]**|0.0923<br>[0.0623]|0.00272<br>[0.00198]|**0.0913****<br>**[0.0443]**|0.000705<br>[0.00113]|
|**English**|0.0566<br>[0.0446]||**0.123***<br>**[0.0731]**||0.0393<br>[0.0422]||
|**Military conflict**|**-0.0977****<br>**[0.0464]**|-0.000152<br>[0.00129]|-0.00147<br>[0.127]|-0.00133<br>[0.00214]|-0.0358<br>[0.0508]|0.000404<br>[0.00163]|
|**Communism index**|-0.0399<br>[0.0733]|**0.00458***<br>**[0.00242]**|0.0406<br>[0.152]|-0.00267<br>[0.00238]|-0.0353<br>[0.0697]|0.00166<br>[0.00207]|
|**Predicted Log of OP in 1990**||**-0.0285*****<br>**[0.00419]**||||**-0.0280*****<br>**[0.00938]**|
|**Predicted Log of OP in 1980**||||**-0.0519*****<br>**[0.00460]**|||
|**Constant**|4.291***<br>[0.454]|0.175***<br>[0.0226]|4.583***<br>[0.672]|0.335***<br>[0.0279]|5.494***<br>[0.431]|0.184***<br>[0.0563]|
|**Observations**|71|71|72|72|72|72|
|**R-squared**|0.637|0.695|0.282|0.844|0.221|0.377|
|***** p<0.01, ** p<0.05, * p<0.1**<br>**Robust standard errors in bracket**|**s**||||||

35