rafmacalaba/datause-encoder-data huggingface dataset:
WPS3671

# AN ANALYSIS OF SOUTH AFRICA’S VALUE ADDED TAX

## **Delfin S. Go,** _World Bank_

**Marna Kearney,** _South Africa National Treasury_ **Sherman Robinson,** _University of Sussex, UK_

**Karen Thierfelder,** _U.S. Naval Academy_<sup>♦</sup>

## **<mark>ABSTRACT</mark>**

<mark>In this paper, we describe South Africa’s value added tax (VAT), showing that (1) the VAT is mildly regressive and (2) it is an effective source of government revenue, compared to other tax instruments in South Africa. We evaluate the VAT in the context of other distortions in the economy by computing the marginal cost of funds—the effect of raising government revenue by increasing the VAT rates on household welfare. Then we evaluate alternative, revenue-neutral tax systems in which we reduce the VAT and raise income taxes. For the analysis, we use a computable general equilibrium (CGE) model with detailed specification of South Africa’s tax system. Households are disaggregated into income deciles. We demonstrate that alternative tax structures can benefit low-income households without placing excess burdens on high-income households.</mark> World Bank Policy Research Working Paper 3671, August 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> ♦ The framework used in this paper is based on a World Bank technical assistance program for South Africa National Treasury to develop a disaggregated economy-wide model for South Africa.  Marna Kearney, while still a graduate student at the University of Pretoria, programmed the original model using the CGE model software developed by Lofgren _et. al._ (2000). In this paper we extend the South Africa CGE model to include value added taxes with rebates. The purpose of this paper is to illustrate the potential use of the South Africa CGE model for tax analysis. We thank Elias Masilela and John Page for spearheading the modeling effort at the South Africa National Treasury and at the World Bank Africa Region, respectively. We also thank various participants for comments and suggestions at several National Treasury-World Bank Workshops in Pretoria and seminars at the World Bank and the International Monetary Fund in Washington D.C. - in particular to John Page, Ritva Reinikka, Dominique Van Der Mensbrugghe, Lolette Kritzinger-van Niekerk, Tuan Minh Le, Maurizio Bussolo, Robert Gillingham, David Coady, and Cecil Morden.
Second, the roles of non‐food expenditures, net remittance and public transfers in 2007 are examined. A large amount of non‐food expenditures implies that households can potentially reallocate expenditures to food consumption to accommodate price spikes. On the other hand, if they were connected with others through remittances and/or already receiving public transfers before the crisis, it is easier for them to insure the adverse impact of price spikes with remittances (private transfers) and/or public transfers. The results show that their roles were rather at minimum to mitigate the price impact on child nutrition intake.<sup>17</sup> The Paasche and Fischer indices are also used to compare with the Leypayres index (Table A3). The Paasche index uses the 2010 quantities (or calories) as weights, and the Fischer index is the square root of the product of the Lespayres and Paasche indices. Since the 2010 quantities (or calories) are part of the household’s optimal responses to the food price crisis, the Paasche index is clearly endogenous. Similarly, for the Fischer index though the problem is milder. As presented in Figure 4, the Paasche index is generally smaller than the Laspayres index. As expected, Table 5 shows that the price spike effect using the Paasche index is insignificant. The results using the Fischer index is a mixture of the Laypeyres and Paasche results.

To sum, the above analysis confirmed potentially large negative impacts of food price spikes on child growth, measured by their heights, among food non‐producers. Differences by gender or age were not found. The point estimate in Column 1 of Table 3 shows that an exposure to the sample average Laspeyres index of 245.82 (based on quantities) leads to a reduction of the height z‐score by 1.9365. A similar exercise using calories in Column 2 of Table 3 shows a reduction of the height z‐score by 1.7723. Changes in the height z‐score by the above magnitude over 3 years are relatively large to induce a long term adverse impact on subsequent human capital outcomes unless those who were adversely affected have a chance to catch up later on. However, given that children in the non‐producer households had the initial advantage of growing fast, our finding points to convergence in child growth between the two groups.

# **6. Conclusions**

This paper examined the impact of food price spikes experienced during the 2007/08 food price crisis on child nutrition status using rural household panel data in Indonesia. The empirical results show that food price spikes had a significant negative impact on child growth, measured by the height z‐score, among the food non‐producers. There are a few important implications. First, food

> 17 Roles of formal safety net programs such as unconditional cash transfers and rice subsidy program are potentially important.  However, the data cannot identify whether households were eligible in 2007. The (self‐reported) eligibility status in 2010 is an endogenous outcome of changes in food price and household decision makings after 2007.  For this reason, we have not included this variable in the above analysis.

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example of a rating methodology score card SNDO is applying, adopted from Moody’s). To acquire information and skills, SNDO subscribes to Moody’s and S&P for access to rating methodologies, and sometimes staff attends credit risk analysis courses offered by rating agencies.

**Table 3.1: Rating methodology score card for government owned infrastructure projects used in Sweden**

|**Broad Rating**<br>**Factor**<br>**Factor**<br>**Weighting **|**Rating Sub-Factor**|**Sub-Factor**<br>**Weighting**|
|---|---|---|
|Market Position<br>40%|Asset Type|10%|
||OperatingHistory|10%|
||Competition|10%|
||Service Area Characteristics|10%|
|30% <br>Performance|Annual Traffic|5%|
|Trends|Traffic Profile|5%|
||5 ‐ Year Traffic CAGR|5%|
||Abilityand Willingness to Raise Rates|15%|
|Financial Metrics<br>20%|Debt Service Coverage Ratio|10%|
||Debt to OperatingRevenue|10%|
|10%<br>Capacity, Capital|Capital Needs|5%|
|Plan and|Limitations to Growth / Operational|5%|
|Leverage|Restrictions||
|Total<br>100%|Total|100%|

_Source_ : Moody’s Investor Service; Swedish National Debt Office When developing a simulation model for a specific guarantee, SNDO analysts follow a seven-step approach. These include the identification of significant risk drivers with respect to the specific guarantee or loan; the construction of a loss function from the relationship between these risk drivers (e.g. construct a dependent variable that determines default); make assumptions about the stochastic behavior of the risk factors (i.e. probability distributions), factoring in potential correlations between risk factors; collect and adapt available data to estimate parameter distributions; estimate the expected value of the resulting loss function through simulations; and stress-test the model to validate it.

SNDO has developed a simulation model for the Öresund link, a bridge between Denmark and Sweden. At an exposure of about USD 2.5 bn. this bridge constitutes the largest guarantee in portfolio managed by the Guarantee and Loan department at SNDO. The key risk drivers identified were traffic revenues, operational and financial costs, extraordinary events (e.g. disasters), and dividends. SNDO contracted several consulting firms to model behavior of the respective risk drivers and then simulate their behavior in a loss function to estimate the probability of default, and hence the guarantee being called.

In addition to analyzing risks of individual guarantees/loans, the Swedish government has tasked SNDO to conduct an analysis of the government’s aggregated guarantee and on-lending portfolio. This analysis includes an analysis of credit risk and liquidity risk (i.e. the risk of marginally higher borrowing costs if the payment of a guarantee is very large and/or needs to be executed in an extraordinary short period of time). The analysis includes behavior of risk drivers with respect to idiosyncratic risk (i.e. concentration 24 | P a g e
that it is relevant to separate the responses of instructors and the direct interaction of effects between peers when evaluating tracking in other contexts. In addition, the differences in the results from this paper with those from Duflo et al. (2011) also suggest the lack of incentives to club instructors, compared to school teachers, to target material to the particular needs of the students.

## **Explaining the puzzling outcomes for less violent children**

It is puzzling that the effects on academic outcomes for low-violence students are greater under tracking even when mixing improves their attitudes toward school and learning. One explanation is that the time dedicated to each part of the session was conditional on the group composition. For instance, tutors in Low-HM clubs may have had to use less time on social skills training than on the particular club’s curriculum, compared to the High-HM or HT groups. Thus, it may be expected that Low-HM clubs with academic curricula are driving the improved academic results compared to the HT clubs. I test this channel by including in specification (3) an interaction between each HM treatment and a dummy for academic clubs on academic outcomes. I find that in the comparison of the Low-HM and HT groups, the effects on academic outcomes are driven by students enrolled in clubs that focus on academic topics.

# **6. Conclusions**

This paper provides experimental evidence of group composition effects of an ASP on participants’ academic outcomes, behavior, violence, and neurophysiological outcomes. The intervention occurred in schools located in highly violent communities in a developing country, El Salvador. By exploiting the direct variation from the experimental design, I find that in terms of behavioral and stress-related outcomes, tracking generates adverse attitudes toward school and learning, particularly for low-violence students, and increases the probability of bad behavior reports, 34
on requirements for proper record keeping (e.g., securing the integrity of data) for cash registers (Revenue Irish Tax and Customs 2015). Many countries, including Argentina, Brazil, Bulgaria, Greece, Hungary, Latvia, Lithuania, Malta, Poland, the Russian Federation, Turkey, the República Bolivariana de Venezuela, and Canada Quebec, have increasingly made mandatory the use of certified electronic cash registers or POS systems in targeted business sectors (e.g., restaurants), which can prevent cash receipt skimming by adding monitoring devices to the POS system, securing data with digital signatures, or transferring transaction data to tax authorities (OECD 2013). This approach may not ultimately solve underreporting fraud, however, in cases where business sellers and customers agree to off‐the‐record cash transactions or when business sellers simply do not register cash transactions without receipts. Other problems with the certified cash register approach are the potential for too much interference by governments in private businesses and the high cost of the registers, which will restrict the scalability of mandatory adoption to certain business sectors (Ainsworth and Hengartner 2009).

Another approach countering underreporting fraud is incentivizing customers to request receipts from businesses. Taiwan, China, introduced “receipt lottery” in 1951 to encourage people to get receipts when purchasing goods or services from businesses. It was intended to broaden the tax base by providing an incentive (a lottery prize) to request receipts from businesses and hence prompt businesses to file their sales correctly according to their receipts issued. Mainland China also announced a guideline on lottery receipts in 1989 and introduced mandatory lottery receipts in all tax bureaus in China in 2009 after experimenting with the concept in more than 80 big‐city‐ level tax bureaus, including Beijing, Shanghai, and Tianjin (Wan 2010). According to Wan’s analysis, lottery receipts increased sales tax and total tax revenues significantly in the experiment areas (2010). Puerto Rico also introduced a sales‐and‐use tax lottery in 2011. In contrast to this program of indirect incentives in the form of lottery prizes, Sao Paulo, Brazil, introduced VAT rebates for receipts. End‐consumers collecting receipts could take tax rebates of 30 percent (currently 20 percent) of the VAT paid by the final sales establishment. In addition, lottery tickets are given for every US$50 purchase. This Nota Fiscal Paulista program in Sao Paulo increased firms’ reported revenues by at least 22 percent (Naritomi 2015).

Table 3 offers a summary of policy measures for reducing informal firms’ or individuals’ undeclared work (UW) or formal firms’ underreporting (UR). Policy measures can be categorized according to targets: “seller” or “buyer.” A further classification is by policy approaches: “deterrent,” “encouraging,” or “normative.” The policy measures mapped in this matrix reduce undeclared work, underreporting, or both. Normative approaches can be divided into “seller” and “buyer” categories but they are presented here without distinction.

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# <u>Table 7: Panel B: The Relationship between Trade Credit Demand and Trade Credit Supply</u>

The reported estimates are from logit in Columns (1), (3), (5), (7), OLS in Columns (2) and (6) and tobit regressions in Columns (4) and (8) AR_d is a dummy indicating the use of accounts receivable; AR_per is the percent of monthly sales sold on credit. We transform AR_per in a continuous variable by using the inverse function of the logistic model. AR_discount is a dummy indicating if the firm offers a pre-payment discount on credit to its customers; and AR_days the average number of days customers are allowed to use the credit before the firm imposes penalties. Columns (3-4) and (7-8) include only the subsample of firms that use accounts receivable. See Table 1 for variable definitions. All regressions include 12 sector dummies and 17 city dummies. Robust p-values are shown in parentheses, ***, **, * indicate significance at the 1%, 5%, and 10% level, respectively.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|||_Retained Ea_|_rnings (D_RE_|_)_|_Family &_|_Informal F_|_inancing (D__|_Fam_Inf)_|
||_AR_d_|_AR_per _|_AR_discount_|_AR_days_|_AR_d_|_AR_per _|_AR_discount _|_AR_days_|
|L_Age|-0.19|-0.56|0.18|-4.62|-0.22|-0.59|0.13|-2.02|
||[0.08]*|[0.06]*|[0.49]|[0.22]|[0.04]**|[0.05]**|[0.61]|[0.59]|
|L_emp|0.06|0.20|-0.08|2.26|0.08|0.25|-0.07|2.46|
||[0.27]|[0.16]|[0.54]|[0.22]|[0.16]|[0.08]*|[0.57]|[0.18]|
|D_Foreign|0.60|0.75|0.29|-8.56|0.51|0.27|0.36|-6.55|
||[0.02]**|[0.42]|[0.52]|[0.23]|[0.06]*|[0.77]|[0.45]|[0.37]|
|D_State|0.02|0.40|-0.41|-15.03|-0.03|0.35|-0.51|-18.89|
||[0.91]|[0.40]|[0.39]|[0.02]**|[0.86]|[0.47]|[0.29]|[0.00]***|
|D_Export|-0.40|-0.30|-0.42|5.67|-0.47|-0.30|-0.28|8.38|
||[0.10]|[0.70]|[0.41]|[0.46]|[0.05]*|[0.70]|[0.58]|[0.29]|
|D_RE|0.14|0.24|0.29|-2.08|||||
||[0.32]|[0.57]|[0.31]|[0.65]|||||
|D_Fam_Inf|||||-0.13|-0.05|-0.36|6.47|
||||||[0.47]|[0.92]|[0.34]|[0.25]|
|AP_d|1.50<br>[0.00]***||||1.44<br>[0.00]***||||
|AP_per||0.09<br>[0.00]***||||0.09<br>[0.00]***|||
|AP_discount|||0.54<br>[0.25]||||0.64<br>[0.16]||
|AP_days||||0.13<br>[0.01]***||||0.14<br>[0.00]***|
|Constant|-3.26|-13.59|-1.14|40.54|-2.90|-13.48|0.44|52.36|
||[0.00]***|[0.00]***|[0.54]|[0.15]|[0.00]***|[0.00]***|[0.77]|[0.06]*|
|Observations|1,207|1,203|453|388|1,187|1,182|445|381|
|PseudoR-squared|0.16|0.2|0.18|0.01|0.15|0.2|0.17|0.02|

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balance rule. Together, our results suggest that during global crises, credit markets internalize the fact that temporary rule abandonments generally do not lead to long-term debt insolvency. Thus, our results provide evidence that credit markets functioned properly through the pandemic in the sense that sovereign governments possessing a reputation of fiscal responsibility faced borrowing costs which facilitated meeting rising short-term needs. Our results have strong implications for policymakers in EMDEs who seek policies that signal fiscal responsibility and compress borrowing costs, especially during global crises such as the COVID-19 pandemic. The results not only suggest that a spread-compressing effect of fiscal rules exists, but that complex rules attempting to achieve flexibility through the inclusion of complicated contingencies can be greatly simplified given that credit markets do not perceive any difference between suspensions and escape clause usage during global crises.

There are several interesting issues concerning the spread-compressing effect and the fiscal responsibility channel of fiscal rules that future work can focus on. For instance, does the spread-compressing effect of fiscal rules hold during idiosyncratic, country-specific crises? If so, do markets internalize temporarily rule abandonments during idiosyncratic negative shocks the same way they do during periods of global crisis? Which type of fiscal rule is most effective in simultaneously signaling fiscal responsibility and enforcing fiscal discipline - simple debt rules, spread break rules as in Hatchondo et al. (2022), or a rule promising low government spending in the future as in Bianchi et al. (2023)? What would constitute an adequate escape clause for this fiscal rule, if any? Our analysis suggests that the spread-compressing effect of fiscal rules is worth consideration, and, thus, we view the questions posed as promising avenues for future research. Specifically, extending our empirical framework to countryspecific crises and enhancing sovereign debt models with strategic enforcement of fiscal rules to explore the issues raised above are interesting areas for future research.

# **References**

Arellano, Cristina, Yan Bai, and Gabriel Mihalache. "Deadly Debt Crises: COVID-19 in Emerging Markets." _The Review of Economic Studies, 2023_ .

Angeletos, George-Marios, Manuel Amador, and Ivan Werning. 2006. "Commitment vs Flexibility." _Econometrica_ 74.2 (2006):365-396.

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**Table 18: The foreign household's occupancy status of the public housing in municipalities where foreign residents concentrate to live (the end of March, 2002)**

||The total number of households|The number of foreign<br>households|The proportion of foreign<br>households|
|---|---|---|---|
|Ota City,Gunma|3,505|355|10.1%|
|Oizumi Town, Gunma|698|96|13.8%|
|Iida City, Nagano|1,103|145|13.1%|
|Hamamatsu City, Shizuoka|7,413|951|12.8%|
|Iwata City, Shizuoka|623|116|18.6%|
|Kosai City, Shizuoka|516|146|28.3%|
|Fuji City, Shizuoka|2,765|183|6.6%|
|Toyohashi City, Aichi|6,694|1,159|17.3%|
|Toyota City, Aichi|7,176|1,450|20.2%|
|Ogaki City, Gifu|1,197|27|2.3%|
|Kani City, Gifu|227|4|1.8%|
|Minokamo City, Gifu|266|26|9.8%|
|Yokkaichi City, Mie|5,639|670|11.9%|
|Suzuka City, Mie|2,084|129|6.2%|
|Total|39,906|5,457|13.7%|

Source: Gaikokujin Shuju Toshi Kaigi (Congress of Major Cities where Foreign Residents Concentrated to Live).

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