rafmacalaba/datause-encoder-data huggingface dataset:
and evasion by utilizing data obtained from the NTS. In addition, this paper presents findings on tax compliance from a perception survey of taxpayers and tax practitioners such as CPAs, tax accountants, attorneys, and tax specialists in corporate or individual businesses. Lastly, some concluding insights and reflections are presented for tax authorities contemplating ETI with the benefit of hindsight from Korea’s ETI experience.

# **II. ETI EXPERIENCES ACROSS COUNTRIES**

## **1. What Is ETI?**

An invoice is a commercial document that a business issues to a customer with the agreed price, quantity, taxes paid or owed, and other information pertaining to a transaction. It constitutes mutual recognition of the occurrence of a transaction and serves important accounting purposes. An invoice plays a particularly important role in an invoice-credit VAT regime that determines the VAT due by deducting VAT paid on a purchase from VAT payable upon a sale. Businesses have an incentive to obtain an invoice for each purchase from their suppliers because they can claim the input tax credits available to them. Mindful of VAT claims by their customers, businesses also have an incentive to report their sales to the tax authority. Invoices therefore provide a transaction “trail” that encourages self-enforcement among businesses.

An electronic tax invoice or ETI is an electronically produced invoice containing transaction information much like a paper invoice that businesses issue to their customers. ETIs are widely used nowadays, but their methods and formats vary from country to country. In recent years, the EU has relaxed its stance on “e-invoice” formats in order to reduce barriers to the use and acceptance of e-invoicing, while some non-EU countries such as Chile, Korea, Mexico, and Taiwan, China, have opted for standardized formats such as structured formats using extensible markup language (XML).

The EU VAT Directive defines electronic invoice as “an invoice that contains the information required in this Directive,<sup>4</sup> and which has been issued and received in any electronic format.”<sup>5</sup> The phrase “any electronic format” includes any electronic means capable of ensuring the authenticity and integrity of the invoice, which may be achieved by any business controls that enable “a reliable audit trail between an

> 4 The directive requires in the invoice the date of issue, the sequential invoice number, the VAT identification number for the business and customer, the quantity (or the extent) and nature of goods and services transacted, the date of supply or the date of payment, the taxable amount per rate or exemption, the unit price net of VAT and any discounts or rebate, the VAT rate applied, the VAT amount payable, references to provisions indicating an exemption, any reverse charge or margin scheme, and other similar information.

> 5 Article 217, EU VAT Directive 2006/112/EC;

6
**31**

the relative contribution of agriculture and non-agriculture to poverty reduction during this period, as it coincides with the increasing liberalization and globalization of the world economy. These evolutions might affect the feedback effects of agriculture to non-agriculture, as well as the participation effects, if globalization induced a greater correlation between domestic and international food prices and a change in the farming structures through increased vertical integration. The effect of non-agricultural growth and the constant (a measure of the effect of inequality change) is retained in the decomposition, even though their estimated coefficients are not statistically significant. Observations where the share of a sector in poverty reduction exceeds | 10 | are excluded, resulting in a loss of nine observations (from 222 to 213).

**Table 8: Sectoral decomposition of changes in headcount poverty**<sup>**1)**</sup>

|**Average share to poverty**<br>**reduction of**|**No. of**<br>**obser-**<br>**vations**|**Non**<br>**agriculture**|**Agriculture**|**Inequality**<br>**change**|
|---|---|---|---|---|
|_Continent_|||||
|Sub-Saharan Africa|37|**0.342**|**0.623**|0.035|
|South Asia|20|**0.316**|**0.437**|0.247|
|East Asia & Pacific|43|**0.430**|**0.215**|0.355|
|East Europe & Central Asia|27|**0.378**|**0.451**|0.171|
|Latin America & the Caribbean|73|**0.388**|**0.403**|0.209|
|Middle East & North Africa|13|**0.593**|**-0.109**|0.515|
|Total|213|**0.393**|**0.382**|0.226|

Spells with sectoral shares exceeding | 10 | excluded. Bolded shares are based on statistically significant regression coefficients. Source: authors’ calculations

The results confirm that despite its slower (direct) growth record, agriculture played a major role in the evolution of poverty during the 1980-2000 period (Table 8). On average just under 40 percent of the change in poverty incidence across the world was attributable to growth in agricultural GDP—as much as growth in both industry and services combined. Even so, this is likely an underestimate, as the decompositions are based on contemporaneous growth rates in agriculture and non-agriculture. As a result, the contribution of agriculture to poverty reduction through its effect on growth in non-agriculture is attributed to the non-agricultural sector in this decomposition exercise. To the extent that the indirect feedback effect from agriculture to non-
**<mark>Table IA4 – Impact of FD Law on States’ GDP and Employment for States Below and Above Ex ante Public Debt</mark>** <mark>This table reports OLS estimates of the impact of the FD Law on GDP and employment of states above the ex-ante public indebtedness.</mark> _<mark>GDP</mark> s,q_ is the GDP growth rate of state _s_ in quarter _q_ . _Employment Totals,q_ is the growth rate of employment in state _s_ in quarter _q_ . _Employment Primarys,q, Employment Secondarys,q and Employment Tertiarys,q_ are the growth rates of employment in the primary, secondary, and tertiary sectors of state _s_ in quarter _q_ . _I(DebtState)s,16Q1_ is an indicator variable that equals one if a state's measure of public indebtedness in the quarter prior to the FD Law (calculated as the ratio of public debt of a state over its net income) is above the median state and zero otherwise. _Postq_ an indicator variable that equals one from 2016Q2 onwards. Observations at the state-quarter level. Standard errors are reported in parentheses and are doubled clustered at the state and quarter levels. *, **, *** denote significance at the 10, 5 and 1 percent levels.  Detailed variable <u>definitions are provided in Table A1 in the Appendix.</u>

||GD|Ps,q|Emplo<br>Tot|yment<br>als,q|Emplo<br>Prim|yment<br>arys,q|Employ<br>Second|ment<br>arys,q|Empl<br>Terti|oyment<br>arys,q|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Postq|0.003||0.001||0.017||-0.009***||-0.002||
||(0.012)||(0.004)||(0.013)||(0.001)||(0.006)||
|Postq*I(DebtState)s,16Q1|0.005**|0.006*|0.004*|0.004*|-0.009|-0.009|0.008**|0.008**|0.007*|0.007|
||(0.002)|(0.004)|(0.002)|(0.002)|(0.015)|(0.017)|(0.003)|(0.003)|(0.003)|(0.004)|
|Observations|480|480|420|420|420|420|420|420|420|420|
|R-squared|0.044|0.473|0.016|0.176|0.014|0.092|0.027|0.037|0.017|0.140|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Quarter FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|

77
**Table 5. Adequacy regressions controlling for migration self-selection**

|||**Subje**|**ctive adequacy**|**of:**|||
|---|---|---|---|---|---|---|
|**Isolation**|**Food**|**Clothing**|**Housing**|**Schooling **|**Health care**|**Income**|
|Travel time to local markets (log)|-0.151|-0.065|-0.128|-0.214|-0.244|-0.051|
||(4.22)**|(1.92)|(3.55)**|(5.31)**|(6.45)**|(1.37)|
|Urban population within 2 hrs travel time|1.188|0.920|1.777|1.301|1.789|0.913|
||(5.04)**|(4.24)**|(7.14)**|(4.79)**|(7.00)**|(4.04)**|
|Population Density (per sqkm)|0.000|0.000|-0.001|0.000|0.000|0.000|
||(1.60)|(0.45)|(5.04)**|(1.10)|(0.34)|(1.93)|
|**Household consumption and controls**|||||||
|<br>Consumption expenditures (log)|0.501|0.393|0.136|0.189|0.065|0.257|
||(7.35)**|(5.63)**|(2.01)*|(2.48)*|(0.95)|(3.56)**|
|Value of assets (log)|0.214|0.171|0.149|0.178|0.131|0.194|
||(9.25)**|(8.63)**|(7.55)**|(7.21)**|(6.57)**|(8.32)**|
|Household size (log)|-0.349|-0.197|-0.003|-0.139|-0.011|-0.131|
||(3.83)**|(2.28)*|(0.03)|(1.34)|(0.12)|(1.41)|
|Share of adult females|-0.041|0.172|0.190|0.656|0.126|-0.188|
||(0.13)|(0.59)|(0.63)|(1.71)|(0.41)|(0.61)|
|Share of children 6 and under|-0.403|-0.008|-0.561|0.156|-0.591|-0.772|
||(1.28)|(0.03)|(1.82)|(0.42)|(1.86)|(2.43)*|
|Share of youths aged 7 to 20|-0.262|-0.166|-0.306|0.289|-0.271|-0.713|
||(1.02)|(0.69)|(1.22)|(0.94)|(1.04)|(2.78)**|
|Share of elderly 65 and above|-0.347|0.283|-0.041|0.466|-0.177|-0.008|
||(1.01)|(0.90)|(0.12)|(1.08)|(0.51)|(0.02)|
|Age of household head|-0.013|0.001|-0.017|-0.033|-0.019|-0.001|
||(0.99)|(0.08)|(1.33)|(2.36)*|(1.45)|(0.09)|
|Age of household head squared|0.000|0.000|0.000|0.000|0.000|0.000|
||(1.10)|(0.08)|(1.38)|(2.23)*|(1.34)|(0.10)|
|Female head dummy|0.073|0.075|0.004|-0.059|0.003|0.056|
||(0.66)|(0.72)|(0.03)|(0.49)|(0.03)|(0.49)|
|**Prices**|||||||
|House rental premium in district (predicted)|0.034|0.038|0.151|0.066|0.114|0.093|
||(1.03)|(1.21)|(4.43)**|(1.81)|(3.38)**|(2.60)**|
|Median wage rate in ward (log)|-0.195|-0.035|-0.081|-0.148|0.004|0.000|
||(3.25)**|(0.63)|(1.41)|(2.33)*|(0.06)|(0.00)|
|Median rice price in ward (log)|-0.247|-0.033|0.099|-0.273|-0.045|-0.243|
||(2.06)*|(0.30)|(0.85)|(1.98)*|(0.37)|(2.00)*|
|**Regional and belt dummies**|||included but|not shown|||
|<br>Intercept|-5.471|-6.050|-3.665|-2.739|-2.534|-4.588|
||(7.40)**|(8.15)**|(5.12)**|(3.36)**|(3.50)**|(6.10)**|
|Number of observations|2867|2867|2863|2421|2851|2862|
|**Likelihood ratio test of independence between**|**the selection**|**and adequ**|**acy equations**||||
|Chi2(1) test-statistic|2.69|8.51|5.03|3.66|1.47|4.5|
|p-value|0.100|0.000|0.020|0.060|0.220|0.030|

Estimator is Heckman (selection) probit. Absolute value of t statistics in parentheses: * significant at 5%; ** significant at 1% The corresponding selection regressions are shown in Table A2.
level. We do the currency conversion at Purchasing Power Parity (PPP).<sup>3</sup> We also allow for extra costs of living in the US not fully reflected in the PPPs, such as higher housing costs.

Using these data, we can now explore the impact of creating a market for work permits by calibrating the model and going through a series of simulations.

**Supply of work permits: Calibrating the disutility of work** To calibrate supply – curve (2), we need some estimate of the disutility of work the parameter _γ_ . To that end, we draw on the surveys that have been done to determine what share of pre-retirement income will make a retiree no worse off on a pension. Binswanger and Schunk (2012) report their estimates from such surveys for the US. The mean replacement rate is 80%, but they find that it is higher for lower-income workers, as one would expect. Based on their results, we set that rate to 0.9. Namely, we henceforth assume that a US worker _i_ will sell her work permit at any price that exceeds 90% of her income.

**Demand for work permits** To calibrate the demand for work permits, we proceed in two steps. First, we need to estimate the wages that Mexican migrants would earn if they were to migrate and work in the US. Second, we calibrate the other parameters in equation (3) such as migration costs or the cost of living in the US.

**Mincer regression** To predict the expected wages of Mexican migrants in the US, we first estimate the coefficients ( _β_<sup>_US_</sup> ) of a Mincer earning regression for the log yearly earnings of US worker _i_ on a set of her productive characteristics using the CPS data:

where _εi_ is a standard error term with mean zero and variance _σ_<sup>2</sup> . We predict the expected ˆ earnings of a Mexican migrant, which we denote _wMX_<sup>_US_, using the estimated coefficients (</sup><sup>_βUS_)</sup> and her characteristics ( _Xj_<sup>_MX_</sup> ) from the ENOE data. When predicting migrant wages in the

> 3We use a Mexico PPP rate for 2018 of 7.17 based on Argente et al. (2023).

9
Policy ReseaRch WoRking PaPeR 4455

# **Abstract**

This paper analyzes the effect of facilities and infrastructure available at the market place on a farmer's decision to sell at the market using a comprehensive survey of farmers, markets and villages conducted in Tamil Nadu, India in 2005. The econometric estimation shows that the likelihood of sales at the market increases significantly with an improvement in market facilities and a decrease in travel time from the village to the market.

The results suggest that wealth reduces a farmer's cost of accessing market facilities more than it increases her/ his opportunity cost of leisure. The wealthy farmers are able to capture a disproportionate share of the benefits of facilities available at congested markets. The policy simulation, however, shows that the marginal benefits from an improvement in market facilities will favor poorer farmers in the context of India.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to understand the impact of investment in infrastructure and facilities on agricultural commercialization and development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at fshilpi@worldbank.org.

_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
more employees. The impact on sales in the third round is positive, but not statistically significant, although an aggregate index of sales and profits shows a significant increase. By this last survey round, existing firm applicants are 19.6 percentage points more likely to be operating a firm, have 4.4 more workers per firm, and are 20.6 percentage points more likely to have reached 10 or more workers. Winning also significantly increases sales by 338,000 naira per month (US$2153), which is a 66 percent increase on the control mean.

This setting has several favorable characteristics for considering the accuracy of counterfactuals. It involves a large treatment effect, and a relatively educated set of individuals. The intervention is a relatively simple one – the main benefit of winning being the $50,000 grant winners receive – so that it should be reasonably easy for the control group to also understand what exactly the treatment is. This contrasts with job or business training programs, where the control group is unlikely to have accurate knowledge of what the exact content of the intervention is, making it harder for them to think about likely impacts.

# **3.2 Eliciting Business Owner Counterfactuals**

The last follow‐up survey elicited counterfactuals for business ownership, employment, and sales for both groups. Applicants in the control group were asked the following questions: _Imagine you had won the YouWin! Competition in the first round. I would like you to tell me what you think you would be doing right now in terms of running a business or working._ _a) What do you think is the percent chance (out of 100) that you would be running a business today if you had won YouWin!?_____________________ _b) If you had won YouWin!, and were running a business today, how many employees in total do you think you would have today?_ _c) If you had won YouWin!, and were running a business today, how much do you think your sales would have been for the last month?_

# Winning applicants in the treatment group were asked:

_Imagine you had NOT won the YouWin! Competition in the first round. I would like you to tell me what you think you would be doing right now in terms of running a business or working._ _a) What do you think is the percent chance (out of 100) that you would be running a business today if you had NOT won YouWin!?_____________________ 6
P a g e <u>|</u> **29**

|Illustrati|on of TDR Schemes at City Scale: Mumba|Numbers<br>i|Units|
|---|---|---|---|
|A|# of Slum Households in 2001|1,295,088|Households|
|B|Entitlement to a Flat per Govt<br>policy|270|Sqft|
|C|Total Floor Space Entitlement for<br>Those Living in Slums in 2001:<br>AXB|349,673,760|Sqft|
|D|Floor Space Allowed to be sold on<br>the market per space provided to<br>slum dwellers: 70-133%|102%|Average %<br>allowed|
|E|Total Floor Space Sold on Free<br>Market Needed to Provide Floor<br>Space to those Living in Slums:<br>DXC|356,667,235|Sqft|
|F|Per Cent of Households that can<br>Afford a New Formal Unit|25%||
|G|Total Number of Households in<br>Mumbai|2,515,589||
|H|# of Households that Can Afford<br>a New Formal Unit:FXG|628,897||
|I|Annual New Household<br>Formation|3.50%||
|J|Total Number of New Formal<br>Units Demandedperyear: IXH|22,011||
|K|Average New Formal Apartment<br>Size|700|Sqft|
||Annual Floor Space in New<br>Formal Apartments with Potential<br>to Provide for Affordable|||
|L|Apartments: JXK|15,407,983|SqFt|
|M|Annual Yearly Provision of<br>Affordable Apartment Units if<br>ALL Units use TDR L/B|57,067|Households|
||Number of Years until all<br>Households in Slums Receive a|||
|N|TDR Apartment:A/M|23|Years|
|O|Average Construction Cost Per<br>Square Foot|700|INR|
||Average Implicit Tax on New<br>Home Purchasers for||INR Per|
|P|ConstructingExtra Floor Space|480,392|New Unit|

**Table 4: Could FAR incentives in the form of TDR solve Mumbai’s slum problem?**

29
# **3.1.3 Evaluating AFTA: A Wrong Turn**

The sudden upsurge in FTAs around the world notwithstanding, on balance, the AFTA is likely to contribute only marginally to prosperity in the region. Indeed, the net effect of it may well be negative. As a forum for promoting political and cultural harmony and as an instrument of encouraging cooperation on projects of regional interest, the ASEAN has served the member countries well. But the preferential trading and investment promoted by the ASEAN and planned by the AFTA are likely to be counterproductive. Several points may be noted.

(i) Small Internal Markets. The case against the AFTA lies primarily in the small size of the regional market. Because Singapore already has complete, nondiscriminatory free trade, the AFTA can, by definition, involve no greater access to its market than what exists currently. Therefore, the gains from AFTA, if any, must come from integration of the remaining five countries' markets. But markets in these countries are quite small in relation to the world. As shown in Table 2, the share of ASEAN 4 (the ASEAN exclusive of Singapore and Brunei) in the world GDP has declined from a low 1.5% in 1980 to 1.3% in 1990. The share in the world exports is bigger--2.4% in 1990--but iiot big by any stretch of imagination. If we include Singapore in our calculations, 1990 shares of the ASEAN in the world GDP and exports, respectively, rise to 1.43% and 3.87%. These ratios are still quite small so that the possibility that the most efficient producers are located outside the region is high.

(ii) Low Levels of Intra-Regional Trade. An analysis of intra-regional trade flows tells us a similar story. Tables 6 and 8, respectively, show destinations of exports and origins of imports of the ASEAN countries. Because Singapore is already a free-trading country, exports to that country are shown separately from the remaining ASEAN countries. Remarkably, the 26
Policy Research Working Paper 8452

# **Abstract**

Many e-government initiatives introduce technology to improve efficiency and avoid potential human bias. Electronic tax filing (e-filing) is an important example, as developing countries increasingly adopt online submission of tax declarations to replace in-person submission to tax officials. This paper examines the impact of e-filing on compliance costs, tax payments, and bribe payments using experimental variation and data from Tajikistan firms. Firms that e-file have lower compliance costs, spending five fewer hours each month on fulfilling tax obligations. There are no significant average effects of e-filing on tax or bribe payments, but significant heterogeneity exists across firms by their baseline likelihood of tax evasion. Among firms previously more likely to evade, e-filing doubles tax payments, likely by disrupting collusion with officials. Conversely, among firms less likely to have been evading, e-filing reduces tax payments, suggesting that officials had previously required them to pay more. These firms also pay fewer bribes, as e-filing reduces opportunity for extortion. In all, the results indicate that e-filing reduces compliance costs and makes the distribution of tax payments across firms arguably more equitable.

This paper is a product of the Development Research Group, Development Economics. 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/research. The authors may be contacted at ookunogbe@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