rafmacalaba/datause-encoder-data huggingface dataset:
**Table A4: Descriptive Statistics - During Delegated Randomization**

|During delegated|randomizat|ion of inspe|ctor assign|ment|||
|---|---|---|---|---|---|---|
||**Ra**<br>**dec**|**ndomized**<br>**larations**||**Declara**<br>**from r**<br>**(corrupt**|**tions withh**<br>**andomizatio**<br>**ion suspect**|**eld**<br>**n**<br>**ed)**|
||**Average**|**St. dev.**|**Obs.**|**Average**|**St. dev.**|**Obs.**|
||**Excess int**|**eraction**|||||
|Excess interaction share|0.01|0.03|16,461|0.07|0.09|1,275|
|Excess interaction share - inspector logits|0.01|0.04|16,461|0.07|0.10|1,275|
||**Cont**|**rols**|||||
|Risk characteristics|||||||
|Risk score|6.14|2.97|15,925|7.31|2.11|1,242|
|Tax rate|0.29|0.14|16,461|0.38|0.09|1,275|
|Red channel dummy|0.17|0.38|16,461|0.04|0.20|1,275|
|Mixed shipment dummy|0.30|0.46|16,461|0.52|0.50|1,275|
|Diferentiated share|0.68|0.46|16,461|0.77|0.39|1,275|
|Valuation advice dummy|0.08|0.27|16,461|0.17|0.38|1,275|
|Additional characteristics|||||||
|Log initial weight|9.99|1.79|16,461|10.15|1.00|1,275|
|Log port authority weight|10.66|1.92|6,723|10.06|1.61|515|
|Log initial value|10.17|1.23|16,461|10.01|0.88|1,275|
|Log initial unit price (relative to internal prices)|-0.05|0.47|16,454|-0.24|0.53|1,272|
|Initial hypothetical tax revenue losses (internal<br>|0.05|0.46|16,454|0.25|0.52|1,272|
|prices)|||||||
|High potential tax yield dummy|0.23|0.42|12,023|0.62|0.49|730|
||**Customs o**|**utcomes**|||||
|Main outcomes|||||||
|Log clearance time (hours)|3.65|1.30|16,432|2.79|1.68|563|
|Fraud record dummy|0.09|0.28|16,461|0.12|0.32|1,275|
|∆log value|0.02|0.08|16,438|0.02|0.09|1,271|
|∆log tax|0.02|0.10|14,386|0.03|0.10|1,254|
|Hypothetical tax revenue losses (internal prices)|0.04|0.45|16,454|0.24|0.52|1,272|
|Additional outcomes|||||||
|Hypothetical tax revenue losses (external refer-|0.49|0.91|12,023|1.40|1.04|730|
|ence prices)|||||||
|Hypothetical tax revenue losses (valuation ad-<br>vice)|0.12|0.16|1,288|0.22|0.20|220|

_Notes:_ Randomized declarations are those for whom the assignment of the initial inspector was randomized by GasyNet. Declarations withheld from randomization were withheld from randomization by GasyNet by the customs IT department. The sample covers the period November 18, 2017 to November 17, 2018.

63
In what follows, we will present results using questions that aim to evaluate responses of state capacity in each of those areas. In addition to presenting results for an exhaustive set of individual outcomes, we also construct indices that aggregate those outcomes for each system. The construction of these indices follows closely the methodology in Anderson (2008). For each index, we first code all components so that higher values indicate a "better" outcome, then standardize all transformed variables and finally construct the index as a weighted average of components.<sup>15</sup> The indices for the household survey and their underlying components are as follows:

# • **Government officials’ survey:**

1. _Staff Capacity Index_ (6 items): Responses on staff skills in using computers and software.

2. _Management Index_ (5 items): Responses on management willingness and ability to attract, retain, and promote staff.

3. _Performance Culture Index_ (8 items): Responses about staff shared values, commitment to deliver, pride in serving, and ability to withstand political pressure.

# • **Household survey:**

1. _Urban Planning Systems Index_ (3 items): Responses about local government use of revenues and planning.

2. _Fiduciary Responsibility Index_ (8 items): Responses about government honesty and bribe payments.

3. _Infrastructure Management Index_ (12 items): Views on government capacity to maintain infrastructure and access to state services.

4. _Accountability & Transparency Index_ (21 items): Views on local government transparency, participation in political meetings, ease of access to information.

5. _Views on Taxation and Fees Index_ (11 items): Normative views on taxation and fees (e.g., is it wrong not to pay taxes?) and positive views about whether individuals are punished for not paying fees and taxes.

In Table 1 we present correlations across the summary indexes from the household survey on government performance in different areas. All the measures are broadly positively correlated: positive responses or evaluations in one dimension are usually accompanied by positive evaluations in similar dimensions. It is also clear, however, that these measures are not perfectly correlated, i.e., they likely capture different dimensions of citizens’ interaction with the state.

> 15Weights for each component are equal to the sum of row-entries in the inverse covariance matrix. That means the index underweights components highly correlated with other components and over-weights those with "new" information–i.e., those with a low correlation with other components.

16
- Acemoglu, D., García-Jimeno, C., & Robinson, J. A. (2015). State Capacity and Economic Development: A Network Approach. _American Economic Review_ , _105_ (8), 2364–2409. 1, 4, 6, 7, 13, 25

- Acemoglu, D., & Robinson, J. A. (2020). _The Narrow Corridor._ . Penguin Press. OCLC: 1135576051. 7

- Anderson, M. L. (2008). Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects. _Journal of the American Statistical Association_ , _103_ (484), 1481–1495. Publisher: Taylor & Francis. 16, 43

- Andrews, M., Pritchett, L., & Woolcock, M. (2013). Escaping Capability Traps Through Problem Driven Iterative Adaptation (PDIA). _World Development_ , _51_ , 234–244. 5

- Andrews, M., Pritchett, L., & Woolcock, M. J. V. (2017). _Building state capability: evidence, analysis, action_ . Oxford ; New York, NY: Oxford University Press, first edition ed. OCLC: ocn973093519. 5, 27, 28

- Anter, A. (2020). The Modern State and Its Monopoly on Violence. ISBN: 9780190679545. 7

- Bandyopadhyay, S., & Green, E. (2016). Precolonial Political Centralization and Contemporary Development in Uganda. _Economic Development and Cultural Change_ , _64_ (3), 471–508. Publisher: The University of Chicago Press. 1, 5

- Banerjee, A. V., & Duflo, E. (2012). _Poor economics: a radical rethinking of the way to fight global poverty_ . New York: PublicAffairs, paperback first published ed. OCLC: 798933070. 5

- Bardhan, P., & Mookherjee, D. (2006). Pro-poor targeting and accountability of local governments in West Bengal. _Journal of Development Economics_ , _79_ (2), 303–327. 5

- Bardhan, P. K., & Mookherjee, D. (2000). Capture and Governance at Local and National Levels. _American Economic Review_ , _90_ (2), 135–139. 5

- Bardhan, P. K., & Mookherjee, D. (2016). Clientelistic Politics and Economic Development: An Overview. _EDI Working Paper Series_ , _WP16/10.111.5_ . 5, 28

- Battaile, W. G. (2020). What does Tanzania’s move to lower-middle income status mean? _Africa Can End Poverty blog_ . 5

- Besley, T. (2020). State Capacity, Reciprocity, and the Social Contract. _Econometrica_ , _88_ (4), 1307– 1335. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.3982/ECTA16863. 4, 6, 13

- Besley, T., & Ghatak, M. (2005). Competition and Incentives with Motivated Agents. _American Economic Review_ , _95_ (3), 616–636. 4

29
be between 20 percent in Serbia and 40–50 percent in Poland and Russia, but none of the studied countries comes anywhere close to that range (Figure 1).

**Figure 1. Building Structure and Rental Tenure in Some Transition Economies**

<!-- Start of picture text --> 80<br>Apartment & hostel buildings Non ow ner-occupied tenure<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Hungary Serbia Romania Armenia Slovakia Czech Rep Lithuania Poland Russia<br><!-- End of picture text --> _Source_ : World Bank LSMS study, Lithuanian Household Survey, national statistical offices. _Note_ : For the Czech Republic and Slovakia all units in housing cooperatives were counted as non-owner-occupied because of the lack of detailed data on renting.

Demand for private rental is rather strong among its typical household groups - the young and mobile - consistent with patterns found in mature market economies. Higher incomes of many younger households, typically the winners in economic transition, drive up private sector formal and informal rents in the most attractive cities. However, the lack of a consistent investment framework for formal private rental housing accommodates elastic expansion of supply, discouraging additional migration from smaller cities and rural areas. Consequently, the restricted rental tenure choice is likely to compromise macroeconomic performance in the studied countries, and by inference in the other ECA transition countries.

Residual social rental housing, decimated by the mass privatization, cannot accommodate the newly created post-privatization households who are poor, let alone the young and mobile, to any meaningful degree. Occupancy of the social housing stock in these countries is dominated by the previously allocated tenants and thus lacks socioeconomic targeting for the current dynamic situation. In the few cases where the sector has not been marginalized, highly subsidized rents effectively lock sitting tenants into their dwellings, preventing any significant adjustment, restructuring and reallocation within this stock. Inadequate public resources and programs maintain the weak supply responsiveness in the existing social housing.

With regard to the price impact of these apparent imbalances between demand and supply, the studied countries fall in three groups: iv
Figure 8.C. Interest rates, confidence, and uncertainty during global recessions

# A. Nominal interest rate

# B. Real interest rate

<!-- Start of picture text --> Percent 1975 1982 Percent 1975 1982<br>15 1991 2009 6 1991 2009<br>Average Average<br>3<br>10<br>0<br>5<br>-3<br>0 -6<br>t-4 t-3 t-2 t-1 t t+1 t+2 t-4 t-3 t-2 t-1 t t+1 t+2<br>C. Business confidence D. Policy uncertainty<br>Index, t-1 = 100 Index, t-1 = 100<br>103 1975 1982 140<br>1991 2009 1991 2009 Average<br>102 Average 120<br>101<br>100<br>100<br>80<br>99<br>98 60<br>t-4 t-3 t-2 t-1 t t+1 t+2 t-4 t-3 t-2 t-1 t t+1 t+2<br><!-- End of picture text --> Sources: Bank for International Settlements; Davis (2016); Haver Analytics; International Monetary Fund; Kose and Terrones (2015); Organisation for Economic Co-operation and Development; World Bank. Note: Year “t” denotes the year of the respective global recessions (shaded in gray). Average refers to an average of four global recessions with available data. Variables are weighted by GDP in U.S. dollars at market exchange rates. Business confidence and policy uncertainty are index numbers equal to 100 one year before year “t” (i.e., t-1 = 100). For details, see Appendix 2.

39
96 Table A8: Regional Ban Effects

||**Wo**|**rker**||**Farme**|**r**|**Average**|
|---|---|---|---|---|---|---|
|**District**|Mobile|Estate|Median|Average|Representative||
|Ampara|-2.89%|.|-22.38%|-22.38%|-22.38%|-8.47%|
|Anuradhapura|-2.93%|.|-23.08%|-23.08%|-23.08%|-13.69%|
|Baddulla|+0.68%|-16.58%|-22.48%|-22.47%|-22.47%|-11.57%|
|Batticaloa|-0.37%|.|-22.67%|-22.67%|-22.67%|-5.19%|
|Colombo|+2.55%|-16.58%|-20.41%|-20.32%|-20.35%|-0.74%|
|Galle|+2.19%|-16.58%|-9.38%|-9.37%|-9.37%|-1.31%|
|Gampaha|+2.51%|.|-21.96%|-21.94%|-21.94%|-1.34%|
|Hambantota|-0.85%|.|-19.76%|-19.76%|-19.76%|-8.45%|
|Jaffna|+2.18%|.|-19.51%|-19.50%|-19.50%|-4.05%|
|Kaluthara|+2.37%|-16.58%|-16.69%|-16.67%|-16.68%|-1.20%|
|Kandy|+2.30%|-16.58%|-15.72%|-15.70%|-15.71%|-2.98%|
|Kegalle|+2.30%|-16.58%|-16.91%|-16.89%|-16.90%|-3.57%|
|Killinochchi|-6.09%|.|-20.74%|-20.74%|-20.74%|-10.80%|
|Kurunagala|+0.53%|.|-22.78%|-22.78%|-22.78%|-9.90%|
|Mannar|-4.51%|.|-21.02%|-21.02%|-21.02%|-9.40%|
|Matale|+0.93%|-16.58%|-21.22%|-21.21%|-21.21%|-8.03%|
|Matara|+1.94%|-16.58%|-10.29%|-10.28%|-10.28%|-1.89%|
|Monaragala|-2.89%|.|-23.34%|-23.33%|-23.33%|-13.96%|
|Mullaittivu|-11.62%|.|-17.74%|-17.74%|-17.74%|-14.60%|
|Nuwareliya|+1.91%|-16.58%|-20.09%|-20.08%|-20.08%|-10.35%|
|Polannaruwa|-8.51%|.|-20.02%|-20.02%|-20.02%|-14.30%|
|Puttalam|+1.71%|.|-20.87%|-20.86%|-20.86%|-5.11%|
|Rattnapura|+2.07%|-16.58%|-14.46%|-14.45%|-14.45%|-4.12%|
|Trincomalee|-1.88%|.|-22.33%|-22.33%|-22.33%|-8.18%|
|Vavuniya|-0.32%|.|-22.15%|-22.15%|-22.15%|-10.17%|
|**Average**|-0.67%|-16.58%|-19.52%|-19.51%|-19.51%|-7.33%|

_Notes_ : for each district, the table shows the % welfare changes of five types of agents between the baseline equilibrium with no QRs and the counterfactual equilibrium with QRs. The median (resp. average) farmer is defined as a farmer whose landholding size is equal to the median (resp. average) of her district. The representative farmer of district _i_ is defined as a farmer whose utility is equal to the district’s average farmer utility ( _Vi_<sup>_F_). Welfare changes are defined as EVs: starting from the agent’s baseline (net-of-taxes) income, the EV is the % change in (net-of-taxes) income</sup> that would leave her with the same utility level as she has in the counterfactual equilibrium, keeping prices fixed at baseline levels. The table also displays each district’s average welfare change, which is computed as the weighted average of types’ EVs, with weights given by types’ population shares in the district.
# **Long-Run Impacts of Increasing Tobacco Taxes: Evidence from South Africa**

**Alan Fuchs, Giselle Del Carmen, and Alfred Kechia Mukong**<sup>**1**</sup>

JEL Codes: H23, H31, I18, O15

> 1 Fuchs: Poverty and Equity Global Practice, World Bank (email: <u>afuchs@worldbank.org); Del Carmen: Poverty and</u> Equity Global Practice, World Bank (email: <u>gdelcarmen@worldbank.org); Kechia Mukong: Economics of Tobacco</u> Control Project, SALDRU, University of Cape Town (email: mukalf83@gmail.com). Support in the preparation of this report has been provided by the World Bank’s Global Tobacco Control Program, co-financed by the Bill and Melinda Gates Foundation and Bloomberg Philanthropies. The authors are grateful to the International Development Research Centre (IDRC), the Medical Research Councils (MRC) of South Africa, Corne van Walbeek, Patricio Marquez, Paolo Belli, Marek Hanusch, Ingrid Woolard, Gabriela Inchauste and Nicole Vellios for providing comments and support. The findings, interpretations, and conclusions in this research note are entirely those of the authors. They do not necessarily represent the views of the World Bank Group, its Executive Directors, or the countries they represent.
Policy ReseaRch WoRking PaPeR 4424

# **Abstract**

This paper provides evidence of the effects of a largescale intervention that focuses on the quality of nutritional and child care inputs during the early stages of life. The empirical strategy uses a combination of double-difference and weighting estimators in a longitudinal survey to address the purposive placement of participating communities and estimate the effect of the availability of the program at the community level on nutritional outcomes. The authors find that the program helped 0-5 year old children in the participating communities to bridge the gap in weight for age z-scores and the incidence of underweight. The program also had significant effects in protecting long-term nutritional outcomes (height for age z-scores and incidence of stunting) against an underlying negative trend in the absence of the program. Importantly, the effect of the program exhibits substantial heterogeneity: gains in nutritional outcomes are larger for more educated mothers and for villages with better infrastructure. The program enables the analysis to isolate responsiveness to information provision and disentangle the effect of knowledge in the education effect on nutritional outcomes. The results are suggestive of important complementarities among child care, maternal education, and community infrastructure.

This paper—a product of the Poverty Team, Development Research Group—is part of a larger effort in the department to promote rigorous impact evaluations of anti-poverty programs. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at egalasso@worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. 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
|Economy|Year|A|B|C|D|E|F|G|H|I|J|K|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Uruguay|2004|11.5|3.9|||18.2|||18.7|||19.1|
|<br>Uruguay|2005|11.4|3.8|||17.7|||17.3|||18.8|
|Uruguay|2006|11.2|3.8|4.4|5.6|19.2|5.3|6|19.9|4|5.6|20.1|
|Uruguay|2007|11.3|3.8|4.4|5.7|19.4|4.5|6|19.2|5.9|6|21.1|
|Uruguay|2008|10.1|3.8|7.1|5.2|17|9.3|5.6|18.7|3.7|5.8|17.7|
|Uruguay|2009|11.1|3.8||5.4|18.2||5.6|17.2||6.2|20|
|Uruguay|2010|10.9|3.7|9.9|5.4|18.9|11.2|5.3|18.6|8.6|6.5|20.6|
|<br>Uruguay|2011|10.2|3.8|8.5|5|17.2|8|5|16.6|13.6|6.1|19.2|
|Uruguay|2012|9.8|3.6|4|4.8|15.7|5.4|4.5|15.4|2.5|6.1|17.5|
|Venezuela, RB|1989|9.5|4.2|12.8|7|15.4|13.1|6.3|15|14.3|9.6|16.3|
|Venezuela, RB|1992|8.1|4.3|||0.8|||0.8|||0.8|
|Venezuela, RB|1995|8.3|4.2|10.7|5.6|14.5|11.6|5.6|14.4|11|7|15.3|
|Venezuela, RB|1998|8.8|4.2|8.8|5.5|15.9|9.6|5.6|16.6|8.9|6.9|16.5|
|Venezuela, RB|1999|9.1|4.3|9|6|16|10.6|5.8|15.5|4.9|7.9|17.7|
|Venezuela, RB|2000|7.5|4.4|6.7|4.9|13.7|6.3|5|12.7|8.6|5.2|15.2|
|Venezuela, RB|2001|9.2|4.3|8.6|5.2|17|9|5.1|16.3|8.6|6.4|18.5|
|Venezuela, RB|2002|9.5|4.3|9.4|5.8|16.8|9.6|5.7|16.4|10.7|6.9|18.1|
|Venezuela, RB|2003|9.1|4.3|8.8|5.7|15.9|8.9|5.6|15.7|9.3|6.7|16.9|
|Venezuela, RB|2004|8.9|4.3|9.9|5.7|14.9|9.8|5.5|14.9|12.1|6.8|15.7|
|<br>Venezuela, RB|2005|8.2|4.3|10.3|5.2|13.5|10.2|5|13.3|12.7|6.8|14.7|
|Venezuela, RB|2006|7.3|4.3|8.1|4.3|12.6|8.2|4.1|12.3|10.8|6.3|14.2|
|West Bank and Gaza|1998|1.4|3.9|7.9|0.7|0.1|4.5|1.1|0.2|11.3|0.3|12.7|
|West Bank and Gaza|1999|1.8|3.8|6.1|1.2|0.2|3.7|1.4|0.4|7.8|2.9|12.7|
|West Bank and Gaza|2000|1.6|3.9|5.8|0.5|0.7|3.2|0.8|0.7|9.7|3.5|11.5|
|West Bank and Gaza|2001|0.7|3.9|1.5||1.6|||1.9|8.9|2.3|4.8|
|West Bank and Gaza|2002|2.8|3.9|11.6||5.1|9||5.1|10||14.5|
|West Bank and Gaza|2003|3|3.9|10.5|0.4|4.2|9.9|0.7|4.1|5.6|1.3|12.9|
|West Bank and Gaza|2004|4.3|3.9|11|1|5.6|6.3|1.3|5.4|22.6|3.1|15|
|West Bank and Gaza|2005|4|3.8|17|1.1|5|11|1.2|5.3|22.6|2.6|14.4|
|West Bank and Gaza|2006|5|3.9|13.4|1.7|5.8|12.4|1.9|5.7|6.7|1.8|17.6|
|West Bank and Gaza|2007|5|3.8|8.4|1.6|5.9|7.4|1.7|6|1.1|4.5|12.9|
|West Bank and Gaza|2008|3.8|3.8|28.7|0.2|5.5|29.7|0.9|5.2|14||21|
|Yemen, Rep.|2005|5.4|3.9||3.8|||3.7|||8||
|<br>Zambia|1998|12.9|3.4|9.2|10.6|12|9.5|10|12.4|6.3|13.2|9.7|
|Zambia|2003|19.2|3.2|13.6|12.1|24.2|14|11.6|25.6|10.3|13.7|23.4|
|Zambia|2010|12.6|4||8.2|18.2||9.5|19.6|2.9|8.1|16.6|

A educyT Return to another year of schooling B edusdT Standard deviation of return to another year of schooling C educyL_P_T Returns to education total primary D educyL_S_T Returns to education total secondary E educyL_T_T Returns to education total tertiary F educyL_P_M Returns to schooling male primary G educyL_S_M Returns to schooling male secondary H educyL_T_M Returns to schooling male tertiary I educyL_P_F Returns to schooling female primary J educyL_S_F Returns to schooling female secondary K educyL_T_F Returns to schooling female tertiary 36
Figure 2: Rainfall and price Graphs here <!-- Start of picture text --> Burkina Faso Ghana<br>Average monthly ra infall Cassava<br>Average monthly rainf all Maize Maize Plantain_Apentu<br>Millet Sorghum Rice_Local Yam<br>2008m7 2009m7 2010m7 2011m7 2012m7<br>2000m72001m72002m72003m72004m72005m72006m72007m72008m72009m72010m72011m72012m7<br>300 250 200<br>250<br>250 150<br>200 200<br>200 100<br>150 Rainfall(mm)<br>Rainfall(mm)<br>150<br>150 Average nominal price(XOF/KG) 50 Average nominal price(GHS/unit*)<br>100<br>100<br>100 0<br>50<br><!-- End of picture text --> Source: Authors’ own calculation using VAM and TRMM data.

As the graphs show, the rainy season for all these countries falls around the same time of year – June to September. The exception is northern Ghana where the wet season starts one month early, and ends a month later – that is, the rainy season lasts from May to October. The graphs show that for many of the countries, price volatility is very high and highly correlated with the growing season. There is a tendency for prices to fall sharply immediately following the harvest – probably because there is a huge surge in supply in the market – and then rise slowly and reach a peak just before the rainy season, especially for certain key staples. There are also two additional observations. One is that periods of droughts are notable for the sharp increases in prices, not surprisingly. This is the case in 2002, 2005 and 2010, especially in Burkina Faso. The other is that the global food price shocks in 2008 and 2009 show up in these price data for countries where there is substantial dependence on imports to meet the main staple, as is evident in Nigeria and Senegal for rice. But even in a country like Mali, where domestic rice production is substantially higher, there was visible upward pressure on domestic prices from international transmission of shocks.

7
- Category-A taxpayers are businesses with an annual gross income of 1,000, 000 ETB (Ethiopian birr) and above.

- Category-B taxpayers have an annual gross income between 1,000,000 and 500,000 ETB.

- Category-C taxpayers have an annual gross income of less than 500,000 ETB.

Each category is subject to varying reporting and tax payment requirements. For example, Category A taxpayers must maintain detailed accounting records following financial reporting standards. They are required to record business assets and liabilities, including acquisition date, costs, and current book values. They must also keep records of daily income and expenditure, sales and purchases of goods and services with names and Tax Identification Number (TIN), and relevant documents. Category B taxpayers have less stringent reporting requirements, primarily maintaining records of daily revenue and expenses, as well as purchase and sale records. Category C taxpayers may maintain records of gross income and other necessary information. In most cases, the information for Category C taxpayers will not be sufficient to determine the liabilities based on the documents kept by the taxpayer, hence, they are liable to presumptive income tax.

A taxpayer is required to submit their income tax self-assessment declarations within a specified time-frame as outlined by the tax authority.<sup>2</sup> The deadlines for tax declarations vary: four months from the end of the tax year for category A and two months for category B. The tax office has the authority to modify assessments based on available information, at any time in cases of fraud and willful neglect, and within five years for other instances.

Another aspect of the business tax structure with implications for gender equality is the progressivity of the tax code. A progressive tax system is 2This directive is in accordance with the Federal Tax Administration Proclamation No. 983/2016 (see FDRE, 2016b) and the Income Tax Proclamation (see FDRE, 2016a).

7