rafmacalaba/datause-encoder-data huggingface dataset:
istrations to make timely and data-based decisions such as forecasting revenues, measuring progress, and monitoring staff performance. However, these internal functions have limited empirical evidence in the literature, and we do not include them in the scope of this paper. Instead, we focus on technology applications that directly affect interactions between taxpayers and the tax administration. The rest of this paper proceeds as follows. Sections 2, 3, 4 and 5 provide an overview of consumption taxes, real estate taxes, taxes on trade and income taxes, respectively, in Africa and discuss the possible role of technology in improving the administration of these taxes. Section 6 provides insights from interviews with revenue administrators in Africa on their practical experiences and challenges in implementing technology solutions. Section 7 concludes.

# **2 Technology and Consumption Taxes**

Consumption taxes include sales tax, value added tax (VAT) and excise taxes. VAT is becoming increasingly common in Africa, as in the rest of the world, accounting for about 40 percent of total tax revenue (ATAF, 2019). Albeit quite sophisticated and complex to administer (Slemrod and Velayudhan, 2022), the VAT holds great potential to improve compliance, mostly due to its in-built self-enforcing mechanism as buyers must report their input purchases to be able to deduct the VAT they paid from their own VAT liability from their sales. This generates opposite incentives for buyers and sellers in reporting the same transaction: buyers have an incentive to maximize the amount paid, whereas sellers have an incentive to minimize the amount received, thereby creating less room for evasion. Importantly, the VAT generates a paper trail or a record of transactions from the different trading partners in the value chain. Especially when digitized, such a trail provides a wealth of information to tax administration which they can use in their monitoring function.<sup>4</sup> Technology can significantly help in the correct administration of such a complex tax

> 4Another crucial use of VAT data is that it can be very informative about the state of the economy and help government measure trends and shocks, as done in Mascagni and Lees (2022) for Rwanda.

7
||5|–0.164 (.124)|–0.260 (.117)|–0.115 (.092)|
|---|---|---|---|---|
||6+|–0.248 (.111)+|–0.524 (.139)*|0.0002 (.109)|
|**_Proportion wasted_**|2|-.107 (.029)*|.068 (.021)*|-.013 (.033)|
||3|-.112 (.029)*|-.064 (.033)+|-.069 (.028)+|
||4|-.028 (.017)|.001 (.018)|-.052 (.019)*|
||5|-.043 (.019)+|.026 (.013)+|.034 (.013)+|
||6+|-.0007 (.012)|-.024 (.013)~|-.004 (.005)|

_Note_ : Standard errors in parentheses. ~p<.10 +p<.05 *p<.01.

**Table 10. Distribution of Significant Positive Effects by Age and Months of Exposure to** **<u>Treatment for Group I (Predominately Positive Impacts)</u>**

||Mon|ths of Expo|sure|Total|Mean Impact<br>(Sample SDs)|
|---|---|---|---|---|---|
|Age in Years(Round 3)|4-12m|13-16m|17+ m|||
|2|9|7|7|23|0.90|
|3|4|7|7|18|0.49|
|4|2|3|3|8|0.26|
|5|4|2|3|9|0.24|
|6+|1|3|2|6|0.29|
|**Total by duration**|20|22|22|64||
|**Mean impact by duration**|0.60|0.70|0.72|||

Among children below age four at the time of Round 3, there has been a substantial improvement in cognitive, social, motor and language development in all seven domains for those in program areas relative to non-program areas. Table 9 indicates that for twoyear-olds, gross motor skills are about 1.1 to 1.5 of a standard deviation higher in program areas than in non-program areas. For two- and three-year olds exposed to the program for at least 17 months, expressive and receptive language skills are about .92 to 1.8 standard deviations higher. Program impacts on cognitive skills at young ages range from .92 to 1.2 standard deviations (for two year olds) and .28 to .43 standard deviations (for three year olds). The weight-for-height Z-score among older children (those four and older) are significantly higher among program compared to non-program children by about .16 to .27 of one standard deviation (though similar positive impacts are not evident among children who have been exposed to the program for 17+ months).

Table 10 summarizes the significant and positive (at the .05 level) impact estimates for Group I across all the indicators. It gives the number of significant and positive estimates for each of the 15 age-duration groups, as well as the marginal totals for the three duration and the five age groups. In addition, the last column gives the mean estimated impact in terms of sample deviations by age and the last row gives the mean estimated impact in terms of sample deviations by the three duration categories. Though for some of the indicators the estimates in Table 9 indicate significant positive responses primarily after fairly long duration of program exposure (e.g., after 17 months for gross motor skills, after 13 months for social-emotional skills), the last two rows of Table 10 suggest only 22
the government, which can reduce poverty and improve the income distribution provided spending is efficient and its composition benefits the poor.

**Figure 6. Tax Revenues and Spending on Health and Education, by Country Group** _(percent of GDP, 2010-19 average)_

As countries become richer, the government can raise more fiscal revenue and increase spending on public goods and services.

<!-- Start of picture text --> 25 7<br>6<br>20<br>5<br>15 4<br>10 3<br>2<br>5<br>1<br>0 0<br>Tax revenue Public health Education<br>(left scale) spending spending<br>(right scale) (right scale)<br>AEs EMs LIDCs AEs EMs LIDCs AEs EMs LIDCs<br>Percent of GDP, 2010-19 average<br><!-- End of picture text --> _Source_ : Authors’ calculations based on data from World Bank. _Note_ : AEs = advanced economies; EMs = emerging market economies; LIDCs = low-income developing countries. Country groups are defined according to WEO Methodology. For details see https://www.imf.org/external/pubs/ft/weo/faq.htm#q4b2.

# **_Redistribution_**

As with public goods, the impact of growth on poverty and inequality through redistribution depends on social preferences. If poverty and inequality are considered social ills, people may be willing to “purchase” reductions in poverty and inequality through redistribution policies as overall incomes rise (that is, poverty and inequality reduction function as “normal goods,” in which demand increases with income). Indeed, cross-country evidence shows that higher-income countries engage in more redistribution than developing countries (Figure 7), where redistribution is measured as the difference between the Gini before and after taxes and transfers. But the composition and incidence of taxes and transfers is important. For example, developing countries have high energy subsidies. This policy may be intended to support the poor, but instead largely benefits the rich who spend more on energy products (see Cerra et al. 2021, Chapter 12 and 13 for elaboration on taxation and spending policies).

14
**Figure 6:** Intergenerational Educational Mobility in LAC

<!-- Start of picture text --> Mexico Cuba Mexico Cuba<br>Jamaica Dominican Republic Jamaica Dominican Republic<br>Honduras Haiti Honduras Haiti<br>Guatemala St. Lucia Guatemala St. Lucia<br>El Salvador Nicaragua Trinidad and Tobago El Salvador Nicaragua Trinidad and Tobago<br>Costa Rica Costa Rica<br>Panama Venezuela Panama Venezuela<br>Colombia Suriname Colombia Suriname<br>Ecuador Ecuador<br>Peru Peru<br>Brazil Brazil<br>Bolivia Bolivia<br>Upward Mobility Paraguay Downward Mobility Paraguay<br>0.66 to 0.87 −0.004 to 0.046<br>0.59 to 0.66 0.046 to 0.072<br>Uruguay Uruguay<br>0.49 to 0.59 Argentina 0.072 to 0.115 Argentina<br>0.37 to 0.49 Chile 0.115 to 0.154 Chile<br>0.18 to 0.37 0.154 to 0.226<br>(a) Upward Mobility (b) Downward mobility<br><!-- End of picture text --> Notes: Upward mobility reflects the likelihood that children, aged 14-18, whose parents have not completed primary schooling will manage to complete at least primary education. Downward mobility reflects the likelihood that children, aged 14-18, whose parents have completed primary schooling or higher will not manage to complete primary education. Both estimates are net of cohort and census year effects.

significantly negatively correlated (see Figure A13 in the Appendix).

Country-level estimates of intergenerational mobility focused on secondary education can be found in Table A7 of the Appendix. The level of upward (downward) mobility is considerably lower (higher) and the samples smaller. Similar to the estimates using primary education, there is significant variation across countries. In the case of upward mobility measured as the likelihood that children complete at least secondary education when their parents were not able to complete primary, we see lower levels of mobility at the country level as one may have expected (see Table A10 in the Appendix).

# **III.1.a Urban-rural**

Given that an important feature of most developing countries is the gap in living standards between rural and urban residents (see Lagakos, 2020), I explore the heterogeneity 18
<!-- Start of picture text --> N<br>2<br>Ss -"<br>_®Oo -“~ -_ Lo<br>or i<br>a) ae<br>i] _-<br>1 oT<br>To oT<br>L ma ae<br>o “ ao<br>oOa U A Za Lect _ ae<br>c oe ao<br>® U --7<br>oO ~ oe Z<br>ee a<br>TO oo<br>io))@o ! oo<br>i.) Z<br>= 7 7<br>7<br>N nn P24<br>2<br>t<br>2013 2014 2015 2016 2017<br>——®—_ Difference —-—--—- Confidence Interval<br><!-- End of picture text -->
|Share of trade volume identified as vehicle<br>trades,γ(net of False Discoveries)<sup>25</sup>|**3.8%**|
|---|---|
|Cross-border trades<sup>26</sup>/ identified crypto vehicle-<br>trades|17%|
|Cross-border trades / identified crypto vehicle-<br>trades (in USD) Volume)|20%|
|Cross-border trades / identified crypto vehicle-<br>trades (in USD) Volume) when excluding RMB<br>and RUB|27%|

Source: LocalBitcoins.com API, Authors’ Calculations Table 3 also shows that of the 7.4% of all trade we identify as crypto vehicle trades, just under one-fifth are cross-currency/cross border; that is, where two different fiat currencies are used in the two transactions of exactly matching Satoshi size. In Appendix A.2, we perform a Monte Carlo simulation where we draw random samples of trades from the data set.<sup>27</sup> Applying the algorithm on these randomly constructed data sets, only about 1% are individually identified as vehicle trades. And exactly 0% remain identified as vehicle trades after deducting the false discovery control discussed in section II.

Table A.3 in the Appendix looks at the robustness of our results to the time window, using twohour, five-hour and ten-hour windows. As can be seen in the table, as the time-window increases, the number of candidate vehicle trades increases. However, because the number of trades encompassed

> 25 See the Appendix for an explanation of how the methodology is adjusted to calculate the share of the trade volume.

> 26 Note that here we consider only trades with two different currencies as cross-border. However, during limited field experiments one of the authors found the US dollar to be used for cross-border crypto vehicle trades in countries whose primary legal tender is not the US dollar.

> 27 For details see Appendix A2.

19
what is driving this growth; and finally what, if anything, can be done to prevent the risk of flooding from being realized in the form of the frequent devastation of cities and towns worldwide.

Disaster statistics appear to show flood events are becoming more frequent. At the outset we can state that studies based on reported disasters suggest that there has been considerable shift in the pattern and intensity of flood events, resulting in increased hazard for the growing world urban population. Figure 1 shows the statistic for water related hazards over the last three decades. Floods are seen to be increasing faster than other impacts. Floods are also growing faster than non-climate related hazards. Projected future increases in hazard due to climate change are predicted to impact differently in different regions, but an increase in flood hazard is a common future expectation.

**Figure 1: Trends in reported water-related disasters (after Adikari and Yoshitani 2009)**

<!-- Start of picture text --> Drought Epidemic Flood Mass Movement Wet Storm<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>1981-1983 1984-1986 1987-1989 1990-1992 1993-1995 1996-1998 1999-2001 2002-2004 2005-2007 2008-2010<br>Number of Disasters<br><!-- End of picture text --> Increased hazard is thus compounded by the global trend in urban population, as more people, infrastructure and resources will be affected by each event. Half the world’s population currently lives in urban areas, with two-thirds of this in low- to middle-income nations (IFRC 2010). Urban population is growing at a much higher rate than the world rural population at 2.1%, especially in the developing world, with rates of 3.3% in the Middle East and Africa, and 2.7% in Asia-Pacific. It is expected that it will reach 6.2 billion by 2050 as indicated by the United Nations in 2009, and is projected to be double the rural population at that time (see Figure 2).

2
equivalent scenario for lending flows, and discounted debt relief by 96 percent (leaving the four percent average debt service flow) while still netting out all technical cooperation.

The table below presents inequality indices with (base case) and without aid flows for the year 2002, using the three scenarios described above. The difference between the minimum calculation and the intermediate calculation is so small that we have chosen not to include it in the table or figure below. Figure 2 shows the impact of aid on the mean GNI per decile of the international distribution.

# **Table 1.  Impact of aid flows, 2002**

|_Case (all income inequality measures_<br>_population -weighted)_|#ofobs.|_Gini_|_Theil_<br>_Entropy_|_Mean_<br>_Log_<br>_Dev_|_e=0.5_|_Atkinson_<br>_e=2 _|_e=5 _|
|---|---|---|---|---|---|---|---|
|Base (after aid)|159|0.5380|0.5305|0.5316|0.2378|0.6187|0.8291|
|“Maximum Scenario”(aid netted out)<br>-0.8 grant equivalent of imputed<br>multilateral<br>-All debt relief included<br>-All technical cooperation included|159|0.5424|0.5391|0.5507|0.2427|0.6468|0.8805|
|“Preferred Scenario”(aid netted out)<br>-0.5 grant equivalent of imputed<br>multilateral<br>-96 percent of debt relief deducted<br>-All technical cooperation deducted|159|0.5404|0.5351|0.5415|0.2404|0.6319|0.8498|

Source: Authors’ estimates based on data from World Bank (2004b) and DAC (2004) database on aid flows.

The impact of aid on international inequality is extremely small when one is using inequality measures sensitive to high and middle incomes. It is slightly bigger, but remains very limited for inequality measures giving more weight to the poor – i.e. the mean logarithmic deviation or the Atkinson measure with high inequality aversion, _e_ . The explanation of this result is given by Figure 2. In the maximum scenario, aid produces a 12
documents as a risk free rate of 6.5% plus a risk premium of 4%, giving a total real rate of 10.5%. The variable rate was set as the monthly average real risk free rate of the financial system in monthj, plus a 4% risk premium. Table 5.3 shows that only one bidder declared a preference for the variable rate, while the winner opted for a fixed rate.

**Table 5.3 Type of discount rate chosen by each bidder**

|**Firm**|**Type of discount rate**|
|---|---|
|CICASA Chile|fixed|
|ECORUTAS|variable|
|Rutas del Pacifico|fixed|
|Autopistas de Peajes SA|fixed|

As was mentioned earlier, minimum income guarantees were offered as an option at a price of 0.75% of the value of the outstanding guarantees. It is interesting to note that two of the four bidders-including the winner-did not request the guarantees (see Table 5.4). This is suggestive of the advantages of the LPVR auction in-lowering income uncertainty and therefore reducing the need to grant guarantees.

**Table 5.4 Minimum income guarantee requests**

|**Firm**<br>**Request of minimum income guarantee**|
|---|
|CICASA Chile<br>yes|
|ECORUTAS<br>yes|
|Rutas del Pacifico<br>no|
|Autopistas de Peajes SA<br>no|
|nally, the government has the option of terminating the concession early after 12 years if<br>nsiders that traffic growth justifies the development of a new project. If the concession i<br>rminated early in month_m,_the concessionaire will be compensated according to the followin<br>rmula:<br>_C._<br>_COMP=TRC- P VI_<br>_-__<br>**_tij1_**<br>**_*A_**<br>_(+ r)_|

Finally, the government has the option of terminating the concession early after 12 years if it considers that traffic growth justifies the development of a new project. If the concession is terminated early in month _m,_ the concessionaire will be compensated according to the following formula: where _COMP_ = future value of the required compensation in Unidades de Fomento (UF).

_C;_ = estimated operating and maintenance cost in month i.

_s_ = estimated end of the concession if early termination is not applied.

_r_ = discount rate of 10.5%.

A = forward value factor given by, <!-- Start of picture text --> 11(I + t,<br>j=l<br><!-- End of picture text --> 40
|**_Table_**|**_1a: Gender G_**<br>|**_ap in Firm_**<br>|**_Closures_**<br>|**_: Time Seri_**<br>|**_es Repea_**<br>|**_ted Cross_**<br>|**_-Section fo_**<br>|**_r 7 Survey W_**<br>|**_aves in 20_**<br>|**_20 and 202_**<br>|**_1_**<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
||||B<br>(|usiness close<br>1=Yes; 0=No|d<br>)||||Time Spent<br>(H|on Paid Wor<br>ours per da|k Activities<br>y)||
||_Wave 1_<br>_May_|_Wave 2_<br>_June_|_Wave 3_<br>_July_|_Wave 4_<br>_Aug_|_Wave 5_<br>_Sept_|_Wave 6_<br>_Oct_|_Wave 7_<br>_Sept_|_Wave 3_<br>_July_|_Wave 4_<br>_Aug_|_Wave 5_<br>_Sept_|_Wave 6_<br>_Oct_|_Wave 7_<br>_Sept_|
|_Wave_|_2020_|_2020_|_2020_|_2020_|_2020_|_2020_|_2021_|_2020_|_2020_|_2020_|_2020_|_2021_|
||0.072***|0.014*|0.036***|0.030***|0.040***|0.039***|0.046***|-0.997***|-1.024***|-0.847***|-0.649***|-0.747***|
|Female Respondent|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.11]|[0.10]|[0.10]|[0.11]|[0.13]|
|Constant|0.188***|0.126***|0.084***|0.085***|0.081***|0.104***|0.093***|8.398***|8.376***|8.463***|8.127***|7.776***|
||[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.01]|[0.09]|[0.09]|[0.09]|[0.10]|[0.11]|
|Observations|24397|20612|21961|22266|21093|21010|23119|12281|12409|11746|10982|14361|
|Adjusted R-squared|0.04|0.03|0.04|0.03|0.03|0.02|0.01|0.04|0.04|0.04|0.04|0.03|
|Mean Male|0.25|0.18|0.15|0.14|0.14|0.14|0.12|7.83|7.86|7.87|7.75|6.97|

_Notes: * significant at 10% level, ** significant at 5% level, *** significant at 1% level._ _[1] OLS regression where Female is a dummy variable where 1 = female-led firm and 0 = male-led firm. Regression includes region of the world fixed effects. [2] Outcome Business Closed = 1 if the firm owner reported their firm was closed at the time of the survey. Outcome Time spent on paid work activities in hours per day was only collected from July 2020 survey wave (wave 3) onwards._ 36
Table 3 where we show, for example, that the estimated number of children requiring Alternative 1 (i.e. additional behavioral management intervention) with one sibling is 1.92, Alternative 2 (-2.44 (negative sign signifying a reduction in children requiring intervention)), Alternative 3 (-1.44), and Alternative 4 (-1.44).

We then applied the outcome data with the cost data described above into the enhanced CBA APP. Since the provision of PIP did not affect the RBRI score of some of the subgroups (non-statistically significant effect), no benefits could be attributed to those groups. As such, the economic benefits for these groups were not estimated (see Table 3 column 3, row 5). For other groups, however, we found positive and significant benefits (resulting in a positive avoided cost), and also negative and significant benefits (resulting in additional future treatment cost). The results presented in Table 3 show the number of children under each category (i.e. by number of siblings) multiplied by the corresponding per participant costs of the management programs to generate an estimate of the costs, benefits, and net benefits of PIP (i.e. the avoided costs of behavioral management). As discussed above, PIP was most beneficial for those PIP participants that had 1 or 2 siblings. Table 3 reveals the net benefits to PIP participants with 1 or 2 siblings of $ $18,234.39 and $74,049.29, respectively.

**Table 3. Estimated CBA results according to the no. of siblings**

|No. of<br>siblings|Costs<br>(AUD)|Benefits<br>(AUD)|Net benefit<br>(AUD)|Estimated<br>no. of<br>children<br>requiring<br>Alternative<br>1|Estimated<br>no. of<br>children<br>requiring<br>Alternative<br>2|Estimated<br>no. of<br>children<br>requiring<br>Alternative<br>3|Estimated<br>no. of<br>children<br>requiring<br>Alternative<br>4|
|---|---|---|---|---|---|---|---|
|0|$4,611.92|-$20,928.98|-$25,540.90|-2.00|1.20|-1.00|1.00|
|1|$13,835.76|$32,070.15|$18,234.39|1.92|-2.44|-1.44|-1.44|
|2|$2,8824.5|$102,873.79|$74,049.29|-7.42|-9.42|0.74|-4.27|
|3|$15,565.23|$-|-$15,565.23|-|-|-|-|
|4|$10,376.82|$-|-$10,376.82|-|-|-|-|
|5|$6,917.88|-$27,133.43|-$34,051.31|-1.80|5.00|-1.40|1.00|
|6|$6,341.39|$-|-$6,341.39|-|-|-|-|
|7|$1,729.47|-$2,876.68|-$4,606.15|-1.00|2.00|0.00|0.00|
|8|$4,611.92|-$4,646.32|-$9,258.24|-1.00|3.00|1.00|0.00|

The above economic estimates (derived from the enhanced CBA APP) are consistent with the findings of our regression analyses, both in strength and direction, where there were positive estimated benefits of the PIP intervention for children with 1 or 2 siblings and 27