rafmacalaba/datause-encoder-data huggingface dataset:
welfare of the top deciles of the distribution. This is principally due to the marginal propensity to consume that declines as household welfare increases (McCarthy 1995; Dynan et al. 2004; Jappelli and Pistaferri 2014; Gandelman 2017). Second, inequality calculated on consumption is likely to be biased downward if the set of goods in the consumption measure does not include items consumed by the rich (Beegle et al. 2016). Third, there may be households with zero annual income who, for example, finance their current spending out of previously accumulated savings, whereas people with zero annual consumption cannot exist. Finally, there are many income-rich people who save a part of their income; thus, their income can be greater than their consumption and, consequently, the high end of the distribution would be more elongated in the case of income (Milanovic 2010). This makes the distribution according to income more “elongated” both around the bottom and the top and thus more unequal: the consumption distribution will be “truncated” at some minimum amount necessary to survive and at the top since not all what households earn will be immediately spent.

Our empirical strategy, first of all, is to present the extent of the problem by conducting a multivariate analysis on a data set of developing and developed countries; this can show, all other things being equal, how much inequality is underestimated in SSA by means of using consumption. In the second part we propose our way to attain a better estimation of inequality in SSA. To our knowledge, this is the most reasonable way to correct the bias, since just substituting consumption with income would require a very expensive upgrade in the capacity of many national statistical offices in SSA and, second, because in many SSA countries’ consumption is still the best indicator for measuring the welfare of many households whose revenues are from the informal sector and very seasonal (Clementi et al. 2020).

# 3.2 Empirical Estimations

Figure 1 illustrates this latter point using data from Povcalnet. High-income countries, although they still collect consumption data, tend to measure their official inequality figures with income; official inequality measured on consumption are practically nonexistent. Official consumption-based measures start to appear among the upper-middle income countries, although less numerous than those income-based, and eventually become the majority within the low-middle income group (figure 1, panel b). Among lowincome countries, inequality is predominantly measured with consumption. Finally, looking at the different regions of the developing world, only in Latin America and the Caribbean do we have a predominance of inequality measured with income, while in other regions only the most advanced 9
56 FIGURE **1.** TRENDS IN INFANT MORTALITY ( **q** ) **AND** CHILD MORTALITY (3 q0 ) IN 10 **DHS** COUNTRIES OF SUB-SAAHA AFRICA IN THE 1970s AND i980s <!-- Start of picture text --> 5 qo<br>300 IL<br>I *4 o0X + 40~~~ as.<br>*MA<br>i ~~~~'"<br>I ~ ~ ~ ~ ~ ~ L lqo<br>t + N LI 1 q 0<br>200 %,04.k<br>-Xg *',SE :<br>^ N9 UG<br>~T 0<br>lb *GH<br>100<br>4<br>ZI<br>,<br>1 BO<br>1960 1970 1980 1960 1970 1980<br>Year Year<br><!-- End of picture text -->
## 2.4 Pros and cons of effective progressivity metrics

Effective progressivity metrics have significant advantages. Most obviously, effective progressivity considers the interaction of both the tax schedule and the underlying income distribution. Effective progressivity measures can be used to examine not just the PIT and social contributions, but also the VAT and excise taxes. The key drawback of the effective progressivity approach is that it is data intensive. It requires information on both the tax system and on the underlying distribution of income. The difficulty in obtaining data for IDA countries was illustrated by the fact that even the wide ranging CEQ analyses cover only six IDA countries, and only two based on income survey data. Furthermore, any microdatabased analysis is resource intensive.

In addition to the difficulty obtaining data, there are also a wide range of data limitations that impact effective progressivity analysis. In particular, top incomes are typically underreported in income surveys. Evasion, particularly by high-income taxpayers, will also not be captured in income survey data. If consumption data is used for PIT modelling, it will provide only an imperfect proxy for income data due to savings patterns. Additional problems arise when modelling indirect taxes (beyond the conceptual difficulties noted above). For example, expenditure is typically underreported in household budget surveys, due to a range of factors including imperfect recall and intentional underreporting of some expenditure categories. VAT exemptions are difficult to model, and ideally input-output data is needed to estimate the impact of the denial of input tax credits. The impact of informality can also pose modelling challenges in developing countries, particularly if a place of purchase variable is not available. There are also challenges associated with modelling _ad quantum_ excise taxes with expenditure data.

# 3 Measuring structural tax progressivity

Structural progressivity focuses purely on the income tax rate schedule itself, ignoring the underlying distribution of income, and produces local measures of progressivity. This section first presents a range of local measures of structural progressivity before presenting examples of their use, and finally summarizing their strengths and weaknesses.

## 3.1 Structural measures

A simple measure of structural progressivity linked to the basic increasing average tax rate definition, is where 1 is tax paid on income 1 , and 0 is tax paid on income 0 (with 1 0) , while 1 and 0 are the average tax rates for incomes 1 and 0 . While the name derives from Musgrave and Thin (1948), this TT YY TT YY YY > YY AA AA structural progressivity measure has been proposed by various authors including Pigou (1928) and Slitor YY YY (1948). For a progressive tax, the average rate progression coefficient will be positive, while it will be equal to zero for a proportional tax, and negative for a regressive tax. Musgrave and Thin (1948) also show that average rate progression can be written as: 17
# Appendix I.A.3 Historical Demographic Data, Belgium **1846-1981**

**0'i**

|Year|1846|1856|1866|1880|1890|1900|
|---|---|---|---|---|---|---|
|Population aged 15 to 64/<br>population over 65 years old|_10.51_|11.37|9.79|9.31|9.44|10.11|
|Population aged 20 to 59/<br>population over 60 years old|5.56|5.85|4.99|4.86|4.91|5.23|
|Percentage of population<br>over 60 years old|8.94|8.79|9.95|9.78|9.72|9.44|
|Percentage of population<br>under 15 years old|32.33|30.3|31.64|33.5|32.78|31.6|
|Women over 60 years old/<br>Men over 60 years old|1.16|1.19|1.09|1.09|1.11|1.13|
|**Year**|**1910**|**1920**|**1930**|**1947**|**1961**|**1981**|
|Population aged 15 to 64/<br>population over 65 years old|9.88|10.55|9.15|6.49|5.23|4.57|
|Population aged 20 to 59/<br>population over 60 years old|5.36|5.42|4.84|3.61|2.88|2.88|
|Percentage of population<br>over 60 years old|9.44|10.2|11.80|_15.54_|17.93|18.52|
|Percentage of population<br>under 15 years old|30.63|25.00|22.99|20.61|23.84|20.02|
|Women over 60 years old/<br>Men over 60 years old|1.17|1.20|1.16|1.18|1.30|1.43|

Ratios calculated using data found in Mitchell, B. R. 1990 "International Historical Statistics: Europe 1750-1988".
First, the infrastructural limits and connectivity hiccups within a given tax administration inevitably affect the capacity of taxpayers to use a technology. As a recurring theme across the selected countries, when connectivity is poor, taxpayers are often forced to revert back to manual practices. For instance, when EFDs are not functioning, firms end up recording transactions manually, limiting the benefits coming from this technology. In Ethiopia, up to recent times, due to poor internet connectivity, taxpayers were forced to go to the local tax office to file electronically.

The same pattern emerges in Eswatini, where e-filing became mandatory in 2021, but taxpayers in remote, poorly connected areas still have to visit the local offices or internet kiosks to access the web portal and file their returns (see also Santoro, Amine and Magongo, 2022). In Rwanda, on the other hand, the RRA introduced the USSD-based M-declaration application that works on simple feature phones as a simpler tool for tax declaration and payment for presumptive taxpayers (Section 5) instead of the e-tax platform that requires internet connection, with the clear policy intent of reaching less connected taxpayers and facilitating their compliance. The app also easily enables tax payments through a direct link to mobile money platforms, which function smoothly on USSD phones. Second, IT literacy and high adoption costs must be factored in when implementing a new technology. In Uganda, for instance, we learned that lack of sufficient consultation with taxpayers on the new e-registration system, integrated with the national ID database, resulted in overwhelming feedback from taxpayers that URA’s registration process was too cumbersome. As another example from Uganda, the biggest challenge with e-filing is that the average taxpayer does not have adequate knowledge of the tax system and familiarity with IT tools to fill in online forms. Also, language barriers play a role, as e-returns are only in English. This means that most taxpayers need a middleman or tax agent to register or file returns, a situation that the interviewees fear may lead to agents preying on ignorance of taxpayers and overcharging them.

In Malawi, taxpayer education is a key priority of the MRA. However, due to COVID-19, 28
## **Table 3.5. Macro SAM II: Additional calculations**

|**# Item or SAMcell**|**Definition**|**Formula/Source**|
|---|---|---|
|_New data inputs to c_<br>|_alculations for Macro SAM II (intermediate data_<br>|_)_<br>|
|1 gintmed/VA|Ratio bt. government intermediate demand|GTAP (2016)|
||and value‐added||
|_New cells in Macro S_|_AM II compared to Macro SAM I_||
|Government activity<br>|and commodity<br>||
|2 [com‐gov,gov]|Government consumption|=[com,gov]|
|3 [f‐lab,act‐gov]|Government payment to labor*|=[com‐gov,gov]/ (1+gintmed/va)|
|4 [com‐prv,act‐gov]|Government payment for intermediates|=[com‐gov,gov]‐[f‐lab,act‐gov]|
|5 [act‐gov,com‐gov]|Sales of government output|=[com‐gov,gov] (=[f‐lab,act‐gov]+[com‐prv,act‐gov])|
|Private activity and c|ommodity||
|6 [f‐lab,act‐prv]|Private payment to labor|=[f‐lab,act]‐[f‐lab,act‐gov]|
|7 [com‐prv,act‐prv]|Private payment for intermediates|=[com,act]‐[com‐prv,act‐gov]|
|8 [f‐cap,act‐prv]|Private payment to capital|=[f‐cap,act]|
|9 [tax‐act,act‐prv]|Private payment to activity taxes|=[tax,act]|
|10 [act‐prv,com‐prv]|Sales of private output|=[act,com]‐[act‐gov,com‐gov]|
|11 [row,com‐prv]|Imports|=[row,com]|
|12 [tax‐com,com‐prv]|Sales taxes|=[tax‐com,com]|
|13 [tax‐imp,com‐prv]|Import tariffs|=[tax‐imp,com]|
|14 [tax‐exp,com‐prv]|Export taxes|=[tax‐exp,com]|
|15 [com‐prv,hhd]|Household consumption|=[com,hhd]|
|16 [com‐prv,row]|Exports|=[com,row]|
|17 [com‐prv,sav‐inv]|Investment|=[com,sav‐inv]|
|18 [com‐prv,dstk]|Stockchange|=[com,dstk]|
|*Derivation of the formu|la for government payment to labor:||
|Known relationships:|||
|(1) [com‐prv,act‐gov]|=gintmed/va*[f‐lab,act‐gov]||
|(2) [com‐gov,gov]=[|f‐lab,act‐gov]+[com‐prv,act‐gov]||
|Substitutingfor interm|ediate demand in 2 using1:||
|[com‐gov,gov]=[f‐la|b,act‐gov]+gintmed/va*[f‐lab,act‐gov]=[f‐lab,a|ct‐gov]*(1+gintmed/va)|
|Solving for governmen|t payment to labor:||
|[f‐lab,act‐gov]=[com|‐gov,gov]/ (1+gintmed/va)||

Source: Authors’ elaboration.

# **Macro SAM III**

Compared to Macro SAM II, Macro SAM III replaces the aggregate savings‐investment account by multiple capital and investment accounts. Such a SAM for an archetype country is shown in Table 3.6. Except for its disaggregation of the “taxes” account, its structure is identical to that of the SAM in Section 2.

New data and computations are shown in Table 3.7. With reference to the row numbers in this table, the only new data that are used (i.e., data other than the information in Macro SAM II) is information on government and foreign direct investment (1‐2), government foreign borrowing (3), and change in foreign reserves (4). Government and private investment are defined using new investment data and the adding‐up constraints for total ‐16‐
**Figure 2.1: Left: Wind diagram for Bangladesh; Right: Climate diagrams for Dhaka: Seasonality governed by the monsoon (2000-2017).**

<!-- Start of picture text --> 25 35 400<br>30 350<br>20<br>300<br>25<br>15 250<br>20<br>200<br>10 15<br>150<br>10<br>100<br>5<br>5 50<br>0 0 0<br>Average wind speed<br>Precipitation (mm)<br>Max. and min. wind speed<br>Temperature (C)<br>Wind speed (km/h)<br>Average monthly temperature (°C) Total Monthly Precipitation (mm)<br>January February March April May June July August September October November December January February March April May June July August September October November December<br><!-- End of picture text --> _Source: authors based on ERA5 data Note: All values represent averages over the period 2000-2017._ Lightning data from the NASA Lightning Imaging Sensor (LIS).

Nor'westers – a type of Severe Local Convective Storms (SLCS) – occur in Bangladesh between April and May, when humidity and temperatures rise. They are the South Asian equivalent of Derecho events in the USA<sup>13</sup> . They cause considerable damage to assets and livelihoods as they result in short, intense, and very localized thunderstorms, combined with lightning and strong wind gusts. They are known in Bangladesh as Kalbaisakhi – which translates roughly to “evil summer”. By reviewing thousands of local newspaper reports, Yamane et al. (2010) compile a database of 2,324 SLCS events covering the years 1990 to 2005. Their data illustrates the strong seasonality of SLCS, the majority of which are concentrated in April and May (Figure 2.2).

While Yamane _et al._ (2010) provide a clear picture of the seasonality of SLCS, their data does not cover the relevant period up to 2017. However, ERA5 climate data is not well suited to capture short, intense, and very localized storms, not least since wind speeds over 50 km/h cannot be reflected due to model restrictions and methodology. While the variance of ERA5 based wind speeds increases significantly in April, average wind speed from ERA5 do not capture well Nor'wester storms. Monthly average wind speeds do not peak during the Nor'wester season (Figure 2.1 (Left)).

> 13 <u>https://www.spc.noaa.gov/misc/AbtDerechos/derechofacts.html</u>

7
# **Figure 5.3 Sigma Convergence for Rio Grande do Norte Municipalities' Real Per-capita Income (1970-96)**

<!-- Start of picture text --> 0.48<br>0.46<br>0.44<br>0.42<br>0.40<br>0.38<br>0.36<br>1970 1975 1980 1985 1990 1996<br><!-- End of picture text --> _Source_ : author’s calculations.

Several papers have identified a number of econometrics problems with this type of analysis. According to Kuper (1997), the traditional tools used to analyze convergence are not appropriate because “convergence is, by definition, a dynamic concept which cannot by captured by cross section studies”. Moreover, according to Durlauf and Quah (1998), σ-convergence does not permit to evaluate the dynamics of cross section distribution over time and says nothing on whether clusters form within the cross section and on whether transitions occur within distribution. To avoid the above-mentioned problems, we apply the spatial statistic methodology and ask whether geography within RN plays a role in the growth process.

# **5.3 Convergence by Spatial Statistic Perspective**

According to Quah (1993), it is necessary to evaluate convergence to understand the role of the space in the growth path. In addition, Gallup _et al._ (1998) argue that location and climate have large effects on income levels and growth. It is also argued that economic activity may agglomerate at some locations with the striking implication that two regions with similar characteristics may end up developing totally different economic structure (see Mossi _et al_ . 2001).

Tools of spatial econometrics have been developed and recent studies have presented several ways to analyze the possibility of the existence of spatial correlation in a variable across distinct areas. A variable is called “spatially correlated” when the observed values in adjacent areas are more similar.

14
There is now a broad consensus that development is more than just income poverty alleviation, but that it also entails expanding access to crucial publicly provided social services such as basic education and health care.' Both privately provided and publicly provided goods matter for individual well-being. However, we know surprisingly little about the access of the poor to social services in developing countries.

With increasing concern about the role of the public sector in poverty alleviation, we need to look more closely at how the benefits of public spending are distributed. Many public services are publicly provided private goods and for them utilization is the key determinant of benefits derived. To get at this, one must clearly go beyond the aggregate social indicators such as school enrollment rates. With household level data sets we can see how utilization of social services varies with other aspects of living standards, such as consumption of private goods, and other variables which may be relevant, including for example, urban/rural location or region of residence. Another variable which may affect incidence is gender; for example: are girls "catching up" to boys in their rates of utilization of schools?

Access to and utilization of public services has been a long standing issue in many developing countries, and Indonesia is no exception. Here too, the dimensions along which one is concerned about utilization and access are between "poor" and "non poor", but also along characteristics such as whether one lives in a rural or urban area, in Java or the Outer Islands, and gender.

Indonesia has a well respected household consumption survey, the National Socio-Economic Survey (SUSENAS), which records information at the household level on whether, and at what level, children attend school, as well I This view has been most recently articulated in the World Bank's World DeveloDment Re2ort 1990: Poverty, UNDP's 1990 Human Develonment Report, and in Hunaer and Public Action by Dreze and Sen (1989).
# **Table 2:  Summary Statistics on Governance Indicators**

||**Voice and**<br>**Accountability**|**Political**<br>**Stability**|**Government**<br>**Effectiveness**|**Regulatory**<br>**Quality**|**Rule of Law**|**Control of**<br>**Corruption**|**Overall**|
|---|---|---|---|---|---|---|---|
|**Number of Co**|**untries**|||||||
|1996|193|179|181|183|168|152|176|
|1998|192|166|184|185|186|184|183|
|2000|192|166|187|188|188|187|185|
|2002|199|186|202|197|197|197|196|
|2003|201|186|202|197|197|197|197|
|2004|207|207|208|204|208|204|206|
|2005|208|213|210|203|208|204|208|
|**Median Numbe**|**r of Sources Pe**|**r Country**||||||
|1996|4|4|4|4|6|4|4|
|1998|4|4|4|4|7|5|5|
|2000|5|6|6|5|9|7|6|
|2002|7|7|8|7|11|8|8|
|2003|7|7|8|7|11|8|8|
|2004|8|8|9|8|12|8|9|
|2005|8|7|9|8|11|8|9|
|**Proportion of**|**Countries with O**|**nly One Data**|**Source**|||||
|1996|15|18|22|15|7|18|16|
|1998|14|10|19|13|11|18|14|
|2000|14|6|7|7|5|7|8|
|2002|10|10|5|7|7|8|8|
|2003|10|9|5|7|7|7|7|
|2004|6|6|8|8|8|8|7|
|2005|6|7|9|8|8|7|7|
|**Average Stand**|**ard Error**|||||||
|1996|0.28|0.40|0.30|0.37|0.28|0.34|0.33|
|1998|0.26|0.32|0.36|0.41|0.27|0.29|0.32|
|2000|0.26|0.34|0.27|0.40|0.23|0.26|0.29|
|2002|0.22|0.27|0.23|0.23|0.19|0.21|0.23|
|2003|0.19|0.28|0.23|0.21|0.18|0.20|0.22|
|2004|0.18|0.26|0.23|0.22|0.19|0.21|0.21|
|2005|0.17|0.28|0.20|0.20|0.19|0.19|0.21|

37
inefficiencies in government production. If this has occurred, higher vacancy rates, increased worker turnover, more absenteeism, reductions in work effort, etc. should be evident. Unfortunately, such independent information on government worker behavior has not been systematically collected. Instead, we must rely on anecdotal evidence which suggests that these respenses to wage movements have, in fact, taken place. Especially for skilled staff recent pay trends have redntced the attractiveness of government work.<sup>7</sup> At the lower end of the pay structure, the situation is usually the reverse. With few exceptions (e.g., Sudan), unskilled government worker real pay has been relatively protected. The fact that there remain sizeable queues for such government jobs reflects their attractiveness. Private sector wage opportunities and returns to informal sector activities often are below government unskilled wage offers. Many governments in Africa are, therefore, pursuing policies which subsidize their most abundant labor input. Such practices are especially costly given the frequent expansion of government employment at the lowest ranks. The provision of unskilled government jobs with relatively rigid real wage floors has become a costly transfer payment program which undermines the government's ability to finance efficient production of goods and services.

Outside of the Sub-Saharan region the problems resulting from pay erosion and wage compression are also voiced. (See Table 3 reproduced from Nunberg (1987).) However, even simple statistics on changes in real starting salaries or on the pay structure are rarely presented. The fiv cases for which information is available include Bangladesh, Jamaica, Sri-Lanka and Thailand (Table 4). The Bangladesh and Jamaica results share much in common 15