rafmacalaba/datause-encoder-data huggingface dataset:
24

**Table 14: Expenditure Decentralization by Administrative Type of Region, %**

|**Measure of Fiscal Decentralization / Year**|**1994**|**1995**|**1996**|
|---|---|---|---|
|1. Ratio between local governments total<br>expenditures before transfers and the||||
|consolidated regional budget expenditures in:||||
|1.1 Republics||||
|-Mean|55.75|60.16|62.29|
||(1.82)|(1.9)|(2.38)|
|-Minimum|42.33|42.12|42.78|
|-Maximum|76.23|71.25|81.57|
|- Valid number of observations|19|19|19|
|1.2 Oblasts and Krais||||
|-Mean|66.29|68.66|70.14|
||(0.91)|(0.93)|(1.16)|
|- Minimum|46.07|46.21|46.15|
|-Maximum|79.24|81.87|86.58|
|-Valid number of observations|_55_|_55_|_55_|
|1.3 Autonomous Okrugs||||
|-Mean|56.39|62.29|64.65|
||(5.71)|(5.14)|(4.86)|
|-Minimum|26.81|33.34|36.59|
|-Maximum|84.27|83.80|84.84|
|- Valid number of observations|9|11|11|

Standard error of the mean in parentheses The data in Tables 13 and 14 unequivocally show the substantial and persistent gap between the degrees of fiscal decentralization on both sides of consolidated regional budgets in oblasts and krais and the other types of regions. In all years, oblasts and krais, i.e. ethnically Russian regions, boasted the highest average ratios between local governments total revenues and expenditures before transfers and the consolidated regional budget revenues and final expenditures respectively, with the pair wise differences in the corresponding means being statistically significant at the 95% level of confidence<sup>0</sup> in 1994 and 1995. Oblasts and krais are also the most homogenous group in terms of the achieved degree of fiscal decentralization - the standard error of the mean for this type of regions is more than 6 times smaller than that of autonomous okrugs and 3 times that of republics. The observed differences among the group means were somewhat subdued in 1996, when the difference in the means of the degree of revenue decentralization in oblasts and krais and republics seized to be statistically significant. The same was also true for the difference in the means of the degree of expenditure decentralization in oblasts and krais and autonomous okrugs in 1996.

Table 15 presents the values of the two measures of fiscal subordination of regional and local governments together with selected summary statistics for the period 1994 - 1996.As Table 15 shows, interbudgetary transfers from higher levels of the government were a significant source of revenues for

- 20 Comparison of means conducted with One-way Analysis of Variance supplemented with Least-significant Difference Tests.
# **Introduction**

**Background and motivation** : At the request of the National Treasury of South Africa (NT) and the South Africa Revenue Service (SARS) in 2006, the Foreign Investment Advisory Service (FIAS, a multi-donor facility of the World Bank Group) initiated a set of surveys to measure the tax compliance burden for SMMEs.<sup>2</sup> NT and SARS have been developing tax policy and tax administration reforms, including a concerted effort to reduce the tax compliance burden on SMMEs. For purposes of this study, and in line with current tax legislation in South Africa, SMMEs are defined as those with an annual turnover under R 14 million.<sup>3</sup>

International research in both developed and developing countries has documented a consistent pattern of regressivity in tax compliance costs, such that the smallest firms typically face tax compliance costs that are a significantly higher proportion of their annual turnover than larger firms (see more detail below). Anecdotal evidence in South Africa suggested that many of the smallest firms struggle with tax compliance. A number of professional tax practitioners, in interviews with FIAS, stated that they had sometimes turned away potential micro- or small businesses because they “could not afford the fees.” Meanwhile, fear of “making mistakes” on taxes and the perceived risk of heavy penalties imposed by SARS (in addition to unwillingness to pay taxes) were said to be factors deterring formalization of many informal firms.

To document the extent of the problem in South Africa, FIAS worked with NT and SARS to design a set of three surveys:

- A survey of professional tax practitioners (TP) about their SMME clients

- A survey of SMMEs registered with SARS

- A survey of informal SMMEs regarding their perceptions of tax compliance costs

There have been two reports based on the TP survey – “Tax Compliance Burden for Small Businesses – a Survey of Tax Practitioners”, focused on different turnover bands within SMMEs<sup>4</sup> , and “Tax Compliance Costs for Small Businesses – Provincial Analysis” focused on provincial differences.<sup>5</sup> The first report documented that SMMEs who use tax practitioners to prepare their filings for the four key business taxes in South Africa (CIT, provisional tax, VAT and employees’ taxes) pay an average of just over R 7,600 per year (about US$1000), with

> 2 Small Medium and Micro Enterprises

> 3 The exchange rate as of August 2008 was R7.7 = US$1

> 4 FIAS (2007) “South Africa: Tax Compliance Burden for Small Businesses: A Survey of Tax Practitioners,” available at http://www.fias.net

> 5 Coolidge, Ilic and Kisunko (2008) “South Africa: Tax Compliance Burden for Small Businesses: Provincial Analysis”, forthcoming

2
Chatterjee et al. (2022) combine surveys and income tax data to estimate wealth inequality in South Africa, using a replacement method. They use incomes from household surveys and tax data and replace the top of the distribution in the surveys with tax data at the point where survey data incomes are lower than in the tax data, which they find to be around the 25<sup>th</sup> to 30<sup>th</sup> percentile. The authors then use the income data to capitalize asset classes where capitalization can be applied and use survey and tax data to estimate capital for other asset classes. The key finding is that South Africa has an extremely unequal wealth distribution. The top 1% own 55% of total wealth, whilst the net wealth held by the bottom 50% is negative due to liabilities exceeding assets.

Gaps in the Research on Measuring the Upper Tail of the Income and Wealth Distributions In this section we document gaps in the research that has been conducted on measuring the top tail of the income distribution in LMICs. In the next section we discuss how these gaps can be filled by outlining a possible research agenda.

Chancel et al. (2023) highlighted the limited availability of household surveys with income data in Africa, as well as the lack of harmonized consumption data. This is very clearly an important gap- there is no way to directly investigate the top of the income distribution without income data from surveys.

We have noted that a few papers using tax data from LMICs have shown important differences between administrative and survey data at the top of the income distribution. But there is a need for more studies in a wider variety of LMICs to further investigate the top of the income distribution in surveys and admin data. This requires the availability of administrative tax data, at least in tabulation form for different income groups. This type of data is not easily accessible and may best be pursued on a country-by-country basis.

The use of other forms of data to check surveys in the absence of tax data is still uncommon. Van der Weide et al. (2018) concluded that the Egyptian Income and Expenditure household survey severely under-estimated inequality using external house price data but given the number of strong assumptions required for their methods to be valid, it is possible that some of differences may be due to the inadequacies of the method and/or the administrative house price data used rather than the surveys.

Researchers finding solutions to the challenges of measuring the top of the income or wealth distributions that use admin data generally do not investigate _why_ the surveys and admin data find substantially different incomes at the top of the distribution. To improve the methods for solving the measurement challenges described above, to establish new methods and to improve the quality of 25
14 engagement. They confirm that farming households are already more actively engaged in land markets than commonly assumed, especially in land rental markets (Table 3). Land sales activity remains limited, though information was only available for Niger and Uganda.

Rental market access proves to have significant and beneficial effects for the equalization of land endowments and farm productivity. It permits land‐poor but labor‐rich households to raise their resource base by renting in land. It facilitates other farmers to diversify their activity by renting out their land and taking up non‐farm employment (without the risk of losing their land assets). These are profound gains in a process of structural change. The effects are strongest in Malawi, Nigeria and Uganda.

The authors suggest that institutional reforms (especially within the legal framework) are needed and effective in strengthening the role that land markets play in enhancing farmer welfare. They especially call for a more differentiated and empirically grounded view of the reality farmers face on land, which should be combined with a thorough understanding of the institutional context. They make a number of suggestions on how household questionnaires could be improved to achieve this.

_Farmers rarely use credit when purchasing farm inputs ._ The role of credit in rural transformation is well understood, but do African farmers make use of credit when purchasing modern inputs? Adjognon, Liverpool‐Tasie, and Reardon (2017) show that credit use for fertilizer, pesticide or seed purchases is extremely low, across credit type (formal, informal, tied), crop (food or cash crop) and countries. They estimate that on average only 6 percent of farmers use any form of credit to buy these inputs—at least in the four countries they cover (Malawi, Nigeria, Tanzania and Uganda). Larger farms are more likely to use credit, but interestingly, even there, the use of informal credit is found to be rare. Modern inputs are primarily financed through cash from nonfarm activities and crop sales instead.

Figure 5: Virtually all purchases of modern inputs are financed from non‐credit sources <!-- Start of picture text --> 100 Of those who bought external inputs,<br>90 share of farmers buying on credit (%)<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>11<br>10 5 6<br>3<br>0<br>Malawi Nigeria Tanzania Uganda<br><!-- End of picture text --> <!-- Start of picture text --> 5<br>3<br><!-- End of picture text --> Source: Liverpool‐Tasie, Adjognon, and Reardon, 2017.
6. _Control of Corruption (CC)_ – measuring perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests.

In brief, our methodology consists of identifying many individual sources of data on governance perceptions that we can assign to these six broad categories. We then use a statistical methodology known as an unobserved components model to construct aggregate indicators from these individual measures. These aggregate indicators are weighted averages of the underlying data, with weights reflecting the precision of the individual data sources. Crucially our methodology also generates margins of error for the estimates of governance for each country, which need to be taken into account when making comparisons of governance across countries and over time.

# **2.1  Underlying Data Sources**

We rely on a large number of individual data sources that provide us with information on perceptions of governance of a wide range of stakeholders. These data sources consist of surveys of firms and individuals, as well as the assessments of commercial risk rating agencies, non-governmental organizations, and a number of multilateral aid agencies and other public sector organizations. A full list of these sources is presented in Table 1. For the 2007 round of the WGI, we rely on a total of 340 individual variables measuring different dimensions of governance. These are taken from 35 different sources, produced by 32 different organizations. Appendices A and B provide a detailed description of each data source, and document how we have assigned individual questions from these data sources to our six aggregate indicators. Almost all of our data sources are available annually, and we use the data only from the most recent year available from each source in our aggregate indicators. In a few cases, as noted in Appendix A, we use data lagged one or two years if current data are not available. In some cases we use several individual variables from a single data source in our aggregate indicators. When we do so, we first compute a simple average of these variables from a single source, and then treat the average of these individual questions as a single observation from that data source.

8
|4<br>(10)|0.017<br>(0.029)|Yes<br>Yes|570|0.02|-60 months..|
|---|---|---|---|---|---|
|3<br>(9)|0.037**<br>(0.019)|Yes<br>Yes|1213|0.02|between 49|
|Polio<br>2<br>(8)|0.030*<br>(0.017)|Yes<br>Yes|1021|0.04|months; 4 is|
|0<br>1<br>(6)<br>(7)|0.095**<br>0.047*<br>(0.039)<br>(0.026)|Yes<br>Yes<br>Yes<br>Yes|560<br>1010|0.07<br>0.03|tchment areas.<br>; 3 is between 37-48|
|4<br>(5)|0.024<br>(0.028)<br>0.89***<br>(0.020)|No<br>No|570|0.002|d within ca<br>5-36 months|
|3<br>(4)|0.042*<br>(0.023)<br>0.87***<br>(0.018)|No<br>No|1213|0.004|evel.<br>s are clustere<br> is between 2|
|Polio<br>2<br>(3)|0.034<br>(0.026)<br>0.84***<br>(0.02)|No<br>No|1021|0.002|0) percent l<br>bance term<br> months; 2|
|1<br>(2)|0.053*<br>(0.031)<br>0.79***<br>(0.02)|No<br>No|1010|0.005|the 1 [5] (1<br>esis. Distur<br>tween 13-24<br>Table A.8.|
|Dependent<br>variable<br>Age<br>0<br>Speci...cation<br>(1)|Program impact<br>0.082<br>(0.051)<br>Constant<br>0.59***<br>(0.04)|Controls<br>No<br>District ...xed e¤ects<br>No|Observations<br>560|R2<br>0.007|a. *** [**] (*) denote signi...cance at <br>b. Robust standard errors in parenth<br>c. Age: 0 is below 12 months; 1 is be<br>d. Control variables: see note (d) in|

48
highest income group). The transit subsidy as a percent of income will, however, be highest for the lowest income group, which spends the highest proportion of income on transit.

# **B. Fare Structure**

The fact that all but the highest income households spend more per month on bus than on rail (see Table 8) reflects the fact that bus fares are higher, per kilometer traveled, than rail fares. Table 9 shows the current bus fares in Mumbai.<sup>16</sup> A person commuting 15 km each way to work paid a fare of Rs. 18 per day or Rs. 450 per month, assuming 25 workdays per month. Although BEST has recently introduced monthly passes, they did not exist at the time of the Baker et al. survey or during the period 2005-2006, for which we compute transit subsidies.

Monthly and quarterly passes are available for rail. Table 9 shows the cost of a second-class monthly rail pass as well as the cost of one-way fares, by distance traveled. The cost of travel by rail, per kilometer, is lower than the cost by bus, even if no monthly pass is purchased. The availability of monthly passes makes calculating the marginal cost of travel by rail difficult; however, the average cost of rail travel, computed from information on revenues and passenger kilometers traveled (see Table 2) is approximately 13 paise per kilometer, or Rs. 4.1 for a 30 km trip.

**Table 9. Cost of Rail (Second Class) and Bus (Regular Service)**

||||**Ra**<br>|**il Fare**<br>|**Bus**|**Fare**<br>|
|---|---|---|---|---|---|---|
|**Di**<br>|**stan**<br>**(km)**|**ce**<br>|**Monthly**<br>**Pass**<br>**(Rs.)**|<sup>**One Way**</sup><br>**(Rs.)**|**Monthly**<br>**Pass**<br>**(Rs.)**|**One Way**<br>**(Rs.)**|
|1|-|3|60|4|180|3|
|3|-|5|60|4|210|4|
|5|-|7|60|4|240|5|
|7|-|10|60|4|390|6|
|11|-|15|75|5|480|9|
|16|-|20|90|6|-|10|
|21|-|25|105|7|-|11|
|26|-|30|105|7|-|12|
|31|-|35|120|8|-|13|
|36|-|40|135|9|-|15|
|41|-|45|150|10|-|17|
|46|-|50|165|11|-|19|
|51|-|55|180|11|-|21|
|56|-|60|195|12|-|23|

Source: Indian Railways and BEST Undertaking.

> 16 A more complete listing of fares appears in Appendix Tables A.2 and A.3.

12
Agriculture is still the main employer in dispersed rural areas. In Table 3.2, the economically active population in dispersed rural areas is broken down by sector of principal activity (occupation). In 2003, 71.6 percent of the working population was engaged in agricultural activities, the vast majority in cultivation. Moreover, the greater part was males, 77.1 and 50.4 percent of the rural males and females respectively were employed in agricultural activities (Table 3.2). Turning to rural nonfarm activities, we observe that 4.4 percent of the working age population was primarily engaged in manufacturing, 15.3 percent in services and commerce, and 4.0 percent in the public administration in 2003.<sup>10</sup> In total, about 28.4 percent of the rural working population was engaged in nonagricultural activities as a primary activity. These estimates are likely to be conservative estimates of the importance of nonagricultural activities because they do not include nonfarm activities that are secondary.

The nonpoor people are slightly more likely to be employed in agricultural activities than the poor and indigent in dispersed rural areas. While 67.6 percent of the working poor are employed in agricultural activities, more than 76 percent of the nonpoor are active in this sector (Table 3.3).

Table 3.3: Sector of Employment and Poverty Condition Dispersed Rural Areas in Argentina, 2003 <u>(percent)</u>

||Indigent|Poor|Nonpoor|
|---|---|---|---|
|Industry|4.7|5.4|2.9|
|Services and Commerce|16.7|15.9|14.6|
|Agriculture and Livestock|71.2|67.6|76.3|
|Public Administration|1.9|4.3|3.6|
|OtherSectors|5.5|6.5|2.6|
|Source: Own calculation based o|n RHS 2003.|||

Considering the distribution of rural labor by status and farm and nonfarm households, the RHS reveals that the majority of household heads of the nonfarm households are salaried workers and a few are self-employed (Figure 3.1). Of the farm households 30 percent of household heads also offer their labor to the market.

> 10 Unfortunately, there is no employment data sets, annual household surveys, census data, etc. for rural Argentina on rural sector of employment. Therefore, we are not able to compare this information with other sources.

11
17

# Annex B: Metadata

Table B 1 shows each variable coded in the metadata of each FDP dataset identified in UNHCR/WB MDL.

## Table B 1: Metadata

|Name|Description|Source|
|---|---|---|
|id|Unique numeric ID|UNHCR/WB|
|idno|Unique stringID|UNHCR/WB|
|nation_abbreviation|ISO-3 countryabbreviation|UNHCR/WB|
|statement_title|Dataset title|UNHCR/WB|
|data_url|URL|UNHCR/WB|
|nation_name|Countryname|UNHCR/WB|
|year|Year of data collection|UNHCR/WB|
|producers_name|Name(s)of dataproducer(s)|UNHCR/WB|
|entity_name|Name(s)of entity/entities involved in data collection|UNHCR/WB|
|entity_contact|Contact information of involved entities|UNHCR/WB|
|abstract|Brief description and scope of the dataset|UNHCR/WB|
|coll_dates|Year,month,and daydata collection began and ended|UNHCR/WB|
|analysis_unit|Unit of analysis or data|UNHCR/WB|
|geog_coverage|Geographical coverage|UNHCR/WB|
|data_kind|Type of survey (e.g.,sample survey,census,event,multi-frame,no sampling)|UNHCR/WB|
|notes|Description of topics and modules covered in the survey|UNHCR/WB|
|method_data_collectors_name|Method of data collection|UNHCR/WB|
|method_sampling|Description of sampling|UNHCR/WB|
|method_coll_mode|Mode of data collection(e.g.,face-to-face,CAPI,CATI,Telephone,other)|UNHCR/WB|
|method_weight|Description of samplingweights|UNHCR/WB|
|topic_id|Numerical identifiers for which topics are covered in the survey based on nlp4dev<br>(https://www.nlp4dev.org/)|Authors|
|topic_words|Text identifiers for which topics are covered in the survey based on nlp4dev<br>(https://www.nlp4dev.org/)|Authors|
|topic_main|Indicator of the main topic covered in the survey based on nlp4dev<br>(https://www.nlp4dev.org/)|Authors|
|project_specific|Binaryindicator of whether the dataset isproject specific or not: Yes or no|Authors|
|idp|Binaryindicator of whether the dataset includes IPDs or not: Yes or no|Authors|
|Longitude|Longitude of countryof data collection|Authors|
|Latitude|Latitude of countryof data collection|Authors|
|Region|Name of the region where the survey took place: Middle East & North Africa, Sub-<br>Saharan Africa,South-Asia,Latin America & Caribbean|Authors|
|income_group|The income status of the country: Low income, Lower middle income, Upper middle<br>income|Authors|
|country_of_birth|Binary indicator of whether the survey asks a respondent their country of birth: Yes or<br>no|Authors|
|country_of_citizenship|Binaryindicator of whether surveyasks countryof citizenship: Yes or no|Authors|
|acquisition_of_citizenship|Binaryindicator of whether surveyasks aquisition of citizenship: Yes or no|Authors|
|date_arrival|Binary indicator of whether the survey asks a respondent their date of arrival: Yes or<br>no|Authors|
|reason_for_migration|Binaryindicator of whether the surveyasks their reason for migration: Yes or no|Authors|
10 derived from the Stone-Geary utility indicator allows for non-unitary income elasticities of demand and non-zero cross-price elasticities of demand between domestically produced and foreign produced consumpti-n good.

Traded goods are imperfect substitutes by country of origin. In each sector, goods produced domestically are imperfect substitutes for Imports. As in the case analyzed by Snape (1977), changes in trade policy will shift the demand curve of domestic firmq. Likewise, goods supplied on the domestic market are imperfect substitutes for goods supplied for export. This assumption is maintained also for sectors with IRTS. In those sectors, goods are produced by Ni ileptical firms. Thus, all goods produced for domestic sales in the same sector are perfect substitutes, allowing us to aggregate sectoral demand and supplies.

The assumption that product differentiation is modelled at the national level rather than at the firm level has three implications for the welfare estimates reported below. First, '¢cause all domestic firms are identical and supply a homogeneous product, one cannot capture product variety and hence we may underestimate the benefits of trade liberalization as additional product variety occurs.

The next two implications bear on the monopolistic competitive approach to modelling imperfectly competitive behavior. The assumption of national product differentiation implies that the domestic firms' perceived elasticity of demand only depends on the number of competing domestic firms rather than on the total number of competing firms in the world. Our numerical results, however, show that the value of the perceived elasticity of demand is quite insensitive to firm entry/exit. The other implication of national product differentiation is that adjustment to achieve zero profits occurs by firm entry/exit. In the case of