rafmacalaba/datause-encoder-data huggingface dataset:
We submit this as a paradigm, i.e., from the viewpoint of efficiency, agriculture should not be taxed relative to other sectors and neither should it be favored. Nevertheless, developing countries have traditionally taxed the agricultural sector while developed countries have protected it, and both have incurred efficiency losses.

Based on Schiff and Valdes (1992), it was shown that the taxation of agriculture in developing countries resulted in a slowdown in agricultural growth and in overall economic growth, and that the slowdown was caused essentially by indirect taxation policies rather than by the direct ones. A number of LDCs have undertaken structural adjustment reforms in the last fifteen years, including trade policy reform and stabilization efforts, and this has reduced the level of indirect taxation. One reason often given for direct taxation of agriculture is to keep food prices low for urban consumers and to obtain export tax revenue. The benefits from food subsidies were captured mainly by urban households. However, the majority of the poor are often located in the rural areas rather than in the cities, and it is the rural poor who gain most from the removal of price interventions. As for export tax revenues, their contribution to fiscal revenues has fallen over time, and is, on average, small. For LDCs who depend heavily on them will have to look for alternative sources of revenue by implementing a tax reform and/or will have to raise the efficiency of the public sector and/or reduce its size.

In the developed countries, protection of agriculture has had adverse general equilibrium effects by raising costs and reducing production and employment in the manufacturing sector. A number of studies have found the cost in terms of higher unemployment to be significant. Moreover, a GATT study found that the policy had a negative impact on poor consumers.

Since the Uruguay Round, agriculture has been integrated into the trade rules of the WTO, and most LDCs have joined the WTO. This gives us hope that future multilateral negotiations will continue to lower the bias against agriculture in LDCs, and in favor of agriculture in developed countries.

30
<!-- Start of picture text --> σ 0.02<br>nfa*<br>0.015<br>)<br>σ -l<br>0.01<br>-e*v* 0.5<br>γ<br>nfa*<br>Vary Production Risk ( 0.005 ev ) γ 0.4<br>-l<br>0.4 0.2 0 0-0.2 -0.4 -0.6 -0.8 -1 0.3<br>-e*v*<br>2 0.2<br>π<br>nfa*<br>(Fraction of South Wealth) )<br>π 1.8 0.1 ev<br>-l Vary Strength of Diminishing Returns (<br>1.6<br>0<br>0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1<br>Figure 7, cont’d: Inefficient Renegotiation and Country Portfolios<br>-e*v*<br>1.4<br>1.2 ev<br>Vary North Productivity Advantage (<br>1<br>0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1<br><!-- End of picture text -->
# **Appendix Table 2: Selective Attrition**

||**Append**<br>_Surveyed ×_|**ix Table**<br>_Treatment_|**2: Selecti**<br>_Surv_|**ve Attriti**<br>_eyed_|**on**<br>_Treat_|_ment_|_Number of_|
|---|---|---|---|---|---|---|---|
||Coeff.|SE|Coeff.|SE|Coeff.|SE|_Observations_|
|Male child|-0.03|[0.03]|0.02|[0.02]|0.04|[0.02]|3,774|
|White Child|0.03|[0.03]|-0.06|[0.02]|0.01|[0.02]|3,764|
|Black Child|4.44E-3|[0.02]|0.01|[0.02]|-0.01|[0.02]|3,764|
|Mixed Race Child|-0.05|[0.03]|0.04|[0.02]|0.02|[0.02]|3,764|
|Other Race Child|0.01|[0.01]|0.01|[0.01]|-0.01|[0.01]|3,764|
|Birthweight (in kilos)|-0.02|[0.04]|0.01|[0.03]|0.04|[0.03]|3,742|
|Birth Height (in cms)|0.45|[0.28]|0.10|[0.20]|-0.21|[0.20]|3,722|
|Planned Birth|-0.05|[0.03]|0.01|[0.02]|0.05|[0.02]|3,770|
|First Born|-0.01|[0.03]|-0.04|[0.02]|-0.01|[0.02]|3,764|
|Age of the Mother at Birth|0.14|[0.32]|0.05|[0.23]|0.01|[0.23]|3,767|
|Prenatal Care|0.01|[0.02]|0.02|[0.01]|-0.01|[0.01]|3,765|
|Natural Birth Delivery|0.01|[0.03]|-0.02|[0.02]|-0.03|[0.02]|3,768|
|Premature Birth|0.03|[0.02]|4.37E-4|[0.02]|-0.01|[0.02]|3,762|
|Breastfed up to 6 Months|-0.02|[0.03]|0.03|[0.02]|-0.01|[0.02]|3,770|
|Household per Capita Income|-240.97|[150.46]|63.62|[108.09]|199.95|[108.96]|3,562|
|Household Size|0.32|[0.28]|0.19|[0.20]|-0.11|[0.20]|3,592|
|Age of Caregiver|-0.09|[0.62]|0.97|[0.45]|-0.13|[0.45]|3,776|
|Caregiver can Read and Write|0.01|[0.01]|-4.88E-3|[0.01]|0.01|[0.01]|3,768|
|Caregiver Finished Middle School|-0.01|[0.03]|0.01|[0.02]|0.04|[0.02]|3,404|
|Caregiver Graduated from High School|0.02|[0.03]|-0.03|[0.02]|0.02|[0.02]|3,404|
|<br>Caregiver has a College Degree|<br>0.01|<br>[0.01]|<br>-0.02|<br>[0.01]|<br>-4.63E-3|<br>[0.01]|<br>3,404|

_Notes_ : This table investigates the hypothesis that the differential attrition between the treatment and control groups was random. Each row reports results from a separate regression of the dependent variable labeling the row on an indicator for being selected in the daycare lottery (“Treatment”), an indicator for having participated in the survey conducted to measure social preferences (“Surveyed”), an interaction of the two, and a constant. The first three set of columns reports the estimates while the last column gives the number of observations. The odd columns show the point estimates. The even columns show robust standard errors between brackets.

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# Table 1.2: Current Account Components **(1990)**

|Al|gefa<br>Mo|rocco<br>Tu|nisia|
|---|---|---|---|
|Curent Account|1,087|-2r532|1.308|
|Goods, Services, and Income||||
|Total Credit|13,535|6,321|5,300|
|Total Debit|-12,448|-8,853|-6,608|
|Merchandise Exports (f.o.b.)|12,964|4,210|3,515|
|Oil and Gas||854||
|Phosphates||437||
|Other||3,774||
|Merchandise Imports (f.o.b.)|-8,777|-6,282|-5,193|
|Trade Balance|4,187|-2,071|-1,678|
|Shipment: Credit|126|154|110|
|Shipment: Debit|-607|-559|-239|
|Passenger Services: Credit||38|213|
|Passenger Services: Debit||-31|-112|
|Other Transportation : Debit|86|21|52|
|Other Transportation : Credit|-124|-69|-85|
|Port Services<br>|||-42|
|Other|||-43|
|Travel: Credit|64|1,280|1,018|
|Travel : Debit|-149|-187|-179|
|Students|||-36|
|Other|||-148|
|Investment Income: Credit|355|177|184|
|Investment Income: Debit|-2,625|-1,522|-740|
|Other Goods and Services: Credit|303|392|173|
|Other Goods and Services: Debit|-282|-90|-74|
|Unrequested Transfers|333|2,332|**808**|
40 structure, maternal status and the economic position of family members. A graphic example is given in Wray and Aguirre's classical study in Colombia. Child nutritional status improves with smaller family size and lower parity, but the position In even small families is still very poor when compared wlth standards in the developed world, reflecting the economic insecurity of the entire community under investigation (Wray and Aguirre (1969) Tables XII and XIII).

Given that the high prevalence of premature and LBW births to older women of higher parity probably represents accumulated strain on a woman's reproductive system (Jelliffe (1966)), we might expect a similar pattern in maternal mortality and morbidity. At least 500,000 women die each year from pregnancy-related causes and many more incur permanent disability (Royston and Ferguson (1985)). Studies from the developing world show that these deaths are concentrated among the primiparous and the grand multiparous<sup>1 7</sup> , a pattern which is obviously related also to age. Maternal age is in fact generally considered to be the relevant factor (Omran and Standley (1981) pp 29-30, Chen et al (1974) Tables 6-8, pp 337-8).

# 3.3.3 The effects of birth spacing on welfare

We now turn to the effects of child spacing. Here most studies I<sup>7</sup> Primiparous refers to women having their first live birth. Grand multiparous in this context means women with 6 or more live births.
# **1 Introduction**

Taxing small and medium-sized businesses -otherwise known as small and medium-sized enterprises, or SMEs- can be challenging for a variety of reasons (e.g., see Hoy et al. 2024), with uncertain or, at best, unambiguously small fiscal gains, depending on the context. Moreover, the direct and indirect costs for governments from trying to enforce tax compliance among SMEs can be substantial. As a result, revenue authorities are increasingly investing in quasi-voluntary strategies to improve compliance among SMEs, such as greater engagement between tax officials and firms to help facilitate the tax payment process (Dom et al. 2022). However, relatively little is known about how SMEs respond to closer engagement and community presence by revenue authorities, particularly when this engagement transcends the usual audit-oriented interactions.

This paper examines how SMEs react to increased local presence by tax officials by drawing on a novel field experiment in Tanzania. The World Bank and the Tanzania Revenue Authority (TRA) implemented a broadly representative in-person survey of SMEs across mainland Tanzania in December 2022. Among the 119 urban and peri-urban wards that were included in the study, half were randomly assigned to have tax officials present during the data collection exercise. This clustered randomized controlled trial introduced exogenous variation in the visibility and physical presence of tax officials among SMEs. While the tax officials did not directly survey the businesses (the survey was conducted by a local survey research firm and trained enumerators were tasked with conducting the interviews), simply having a tax official present was expected to have repercussions on respondents’ self-reported behavior (captured through the survey) as well as declaration behavior (captured through administrative data). As a consequence, it is valuable to examine both the impact of TRA presence on survey outcomes and ward-level indicators of compliance.

Tanzania is a useful context to explore strategies for improving SME taxation. Tanzania’s SMEs are overwhelmingly informal. Those that are registered with the TRA are primarily subject to a simplified, presumptive tax regime. However, evidence has shown that there are 2
Figure 2 <!-- Start of picture text --> Hungary: Bank Credits to the Enterprise Sector<br>(In billion Forlnts, year end)<br>Total<br>Credit<br>(485 bF)<br>Consumer ToTal<br>Total Ptices Crit<br>Credi TItbF-400<br>(360b te) Credit (341 bP) (391 F- ~~~~~~~~Short<br>Short Tr g TunShort 8 Loe~ TemTr Short TLXi Teo(1 P L=<br>Tam Sho~~Trt (1992bF -200b<br>(149W<br>-100<br>1987 1988 1989 1990<br>L Large Baiks Small & Medium Banks Savings Banks & SFis<br>Source: Natonal Bank of Hungary<br>Nominal Credit Growth, 1987 to 1990<br>Total Credit Short Term Long Term Consumer Prices<br>Total 35% 53% 17% 75%<br>Large Banks 1% 5% -2%<br>Producer Prices<br>Small & Liedium Banks 261% 341% 81%<br>52%<br>Savings Banks & SFis 1033% 1200% 903%<br>Source: National Bank df Hungary,<br>Market Share, 1987 versus 1990<br>Total Credit Short Term Long Term<br>1987 1990 1987 1990 1987 1990<br>Large Banks 92% 69% 89% 61% 94% 79%<br>Small & Medium Banks 7% 19% 10% 28% 4% 7%<br>Savings Banks&SFis 2%h 13% 1% 11% 2% 15%<br>Source: National Bank of Hungary<br>Nominal Credit Grow-th, 1988 to 1990<br>Total Credit Short Term Long Term Consumer Prices<br>Total 42% 53% 9% 51%<br>Large Banks 14% 35% -2% Producer Prices<br>Small & Medium Banks 235% 319% 60% 470/o<br>Savings Banks & SFs 190% 366% 110%<br>6aSouros: National Sank d Hunry<br><!-- End of picture text --> 6a
**Figure 7. The evolution of the total LVAX share in exports, by sector**

<!-- Start of picture text --> All countries, backward linkages Developing countries, backward linkages<br>100000 0.6 40000 0.45<br>35000 0.4<br>0.5<br>80000 30000 0.35<br>0.4 0.3<br>60000 25000<br>0.25<br>0.3 20000<br>0.2<br>40000 15000<br>0.2 0.15<br>20000 10000 0.1<br>0.1<br>5000 0.05<br>0 0 0 0<br>agriculture extractive manufacturing services agriculture extractive manufacturing services<br>Values Shares Values Shares<br>All countries, forward linkages Developing countries, forward linkages<br>90000 1.2 20000 1.4<br>80000 1 1.2<br>70000 15000<br>1<br>60000 0.8<br>50000 0.8<br>0.6 10000<br>40000 0.6<br>30000 0.4<br>0.4<br>20000 5000<br>10000 0.2 0.2<br>0 0 0 0<br>agriculture extractive manufacturing services agriculture extractive manufacturing services<br>Values Shares Values Shares<br>LVAX share LVAX share<br>LVAX (million USD) LVAX (million USD)<br>1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011<br>LVAX share LVAX share<br>LVAX (million USD) LVAX (million USD)<br>1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011<br><!-- End of picture text --> Note: Based on export-weighted data for 31 countries that are present in the database every year since 1995. Income group is defined according to the World Bank country group 2011 classification. High-income countries are represented by Australia, Canada, Denmark, Finland, Germany, Hong Kong SAR, China, Japan, the Republic of Korea, New Zealand, Singapore, Sweden, Taiwan, China, United Kingdom, and United States.

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21 Indeed, the results at the bottom of Table 7(c) for this sample of countries show that inequality would decline in all three developing country regions following full trade liberalization of all goods, or just agricultural products, and both for own-country and rest-ofworld reform. The effect of non-farm trade reform on its own is more mixed, providing another reason to urge trade negotiators not to neglect agricultural reform in trade negotiations. Rest-of-world and global agricultural reform both lead to a reduction in inequality in every country in the sample except Thailand (plus the Philippines slightly for global reform), whereas unilateral agricultural reform reduces (or leaves constant) inequality in a small majority of countries with China, the Philippines and Thailand being the exceptions (but the latter effects are small). Non-farm global reform increases inequality slightly in three countries. In the case of Indonesia the inequality-increasing impact of nonfarm reform more than offsets the egalitarian effect of farm trade reform, whereas both types of reform increase inequality in the case of the Philippines and Thailand.

Inequality within the rural or urban household grouping is not altered very much by trade reform as compared with overall national inequality (compare parts (a) and (b) with part (c) of Table 7). This underlines the point that trade reform would tend to reduce urban-rural inequality predominantly rather than inequality within either region.

Several of the national studies investigate impacts of reforms that could complement trade reforms, most notably different approaches to deal with the elimination of trade tax revenues. If these revenues can be recouped through taxes that do not bear on the poor, then the impacts of reform for poverty reduction are more favorable. The China study focuses on the important issue of reducing the barriers to migration out of agriculture, by improving the operation of land markets and reducing the barriers to mobility created by the _hukou_ system. These measures, and international trade liberalization that increases China’s market access,
for all poverty measures; for example, the elasticity is about -4.3 for growth in GDP per capita over 1981-2005 and using the $1.25 a day line, while for China the corresponding elasticity is about -0.8.<sup>8</sup> For India it is -0.4 (-0.8 using the mixed method).<sup>9</sup> These are large differences in the impact of a given rate of growth on poverty, notably between Brazil (on the one hand) and China and India (on the other). To put the differences in perspective, Table 3 gives the proportionate rates of poverty reduction implied by each combination of growth rate and elasticity. (These calculations illustrate the size of the differences in elasticities; it is not claimed that it was feasible, on economic or political grounds, for Brazil, say, to attain China’s growth rate while keeping its own elasticity.) Suppose, for example, that India had Brazil’s elasticity; then India’s growth rate would have delivered a rate of poverty reduction of 15% per annum—well above even China’s rate. Even with China’s elasticity, India’s rate of poverty reduction would have been more than double that implied by the surveys (though similar to that implied by the mixed method), and certainly enough to bring down the number of poor. Or if China had India’s elasticity (based on the surveys) it would have seen a rate of poverty reduction less than half its actual rate.

What lies behind these large differences in the elasticity of poverty reduction to economic growth? Later I will examine the roles played by initial conditions and policies. But one factor is already evident in the summary statistics in Table 1. Inequality, as measured by the Gini index, rose over time in the (initially) low inequality countries (China and India) and fell in the highinequality country (Brazil).<sup>10</sup> Naturally, rising inequality will tend to dampen the impact of growth on poverty, while falling inequality will tend to enhance that impact. This pattern is suggestive of “inequality convergence,” as implied by neoclassical growth theory,<sup>11</sup> although an equally plausible explanation is “policy convergence”: pre-reform policy regimes in some countries kept inequality “artificially” low while in others they kept it high (Ravallion, 2003a).

> 8 Even accepting Maddison’s (1998) downward revision to China’s growth rate (see Appendix), the elasticity is only -1.0.

> 9 Slightly higher elasticities are obtained using consumption from NAS or (even higher) survey means for measuring growth rates; using the national poverty line (of about $1.00 per day) also gives a slightly higher elasticity; for further details see Datt and Ravallion (2009).

> 10 The Gini index is not necessarily the best way of measuring inequality from the point of view of explaining differences in progress against poverty (as explained in Datt and Ravallion, 1992). However, it is the most widely understood measure of inequality.

> 11 See Benabou (1996). The cross-country evidence is also suggestive of inequality convergence, even after allowing for likely biases in standard tests; see Ravallion (2003a).

6
completing the picture of the environment in which microfinance is operating. The experts need not be providers, indeed conflict of interest considerations suggests that it is probably better when they are not. (The _Doing Business_ survey has used central bankers among those responding to questions about credit availability, and registrars for questions relating to registries.)

# **6. The way forward: much known, much missing**

# _State of knowledge_

We have proposed a basic framework within which the analysis of the interesting questions: (i) how does access affect poverty and productivity and (ii) what hinders access, can be considered. These questions need information from user surveys, both household and business, and from surveys of providers and their regulators. Expert surveys will be a necessary complement.<sup>32</sup> It will be evident from the discussion so far that quite a lot of relevant information has been collected, that its reliability, timeliness, comprehensiveness and comparability all remain somewhat deficient. On the other hand, less efforts have so far been made to bring together what is available – the _Doing Business_ study and The Mix’s effort and Christen et al. (2004) being notable exceptions. Doing so will help raise awareness of the access issue in general, as well as allowing countries to begin to benchmark their financial systems even on simple criteria such as percentage of households with deposits or loans from the financial sector.

The program of business user surveys is moving ahead well. While the questionnaire would benefit from an overhaul, the extent of country coverage and the usefulness of the information collected in the PICS is indisputable.

The position with regard to household surveys is somewhat less satisfactory, despite recent advances for a few countries. Even if the right questions are being asked, they are not being asked in a sufficiently large number of countries. Perhaps now is the time to launch a new wave of _specialized_ household surveys of financial service usage, building on what has been learnt from the specialized cases mentioned in Section 2 above, and seeking co-financing from financial service providers who could make use of the results for market development.

# _National basic access indicators_

In addition to providing the essential raw material for research, both of the fundamental, academic type, the policy-oriented and that useful for the market, there is a need for a set of basic high-profile national summary indicators – the GDP, M2 and CPI of the world of access. These indicators will not by themselves prove anything, but in the early years of

> used.  (By the qualification ‘independent’ I mean disregarding multiple respondents from the same law firm or accountancy practice).  Nor is the Chinese case unrepresentative in this regard.

> 32 Recent co-ordinated efforts to document the flow of international remittances provide a good example of the combined use of multiple sources (cf. the papers prepared for a recent APEC conference at <u>http://www1.worldbank.org/finance/html/amlcft/ARS%20Research%20Papers.htm).</u>

22
# **2 Historical background**

## **2.1 The rise and fall of apartheid**

Apartheid was enforced through several pieces of legislation introduced by National Party governments that ruled South Africa from 1948 to 1994. It considerably strengthened the racial segregation that began under Dutch and British colonial rule.

The Population Registration Act of 1950 classified inhabitants into four racial groups— white, black, colored and indian—and introduced an identity card for all adult citizens specifying their race.<sup>4</sup> In the same year, the Group Areas Act institutionalized racial segregation of residential areas. Each race was allotted its own territory, which was later used as a basis for forced removals.

Public goods provision was highly segregated. The Reservation of Separate Amenities Act of 1953 made it possible to reserve municipal grounds according to race, leading to separation of buses, hospitals, beaches and park benches. Black people were offered services markedly inferior to those of white people, and to a lesser extent to those of indian and colored people. The Bantu Education Act of 1953 introduced a separate education system for black pupils, directed to preparing them for lives as a laboring class. In 1959 separate universities were created for black, colored and indian people, while existing universities were prevented from enrolling additional black students.

Through the homeland system, the National Party government sought to divide South Africa into separate nation-states. The Bantu Authorities Act of 1951 created separate government structures for white and black citizens. The Promotion of Black SelfGovernment Act of 1959 proposed self-governing Bantu units, which would have devolved administrative powers with the promise of later autonomy and self-government. The Black Homeland Citizenship Act of 1970 deprived black people of their citizenship, who legally became citizens of one of ten tribally based self-governing homelands. Figure 1 depicts the geographic location of each of these ten homelands.

The homelands accounted for about 13 percent of the land, a small share compared to the population (Clarck and Worger, 2011). Four homelands were declared independent states by the South African government: Transkei in 1976, Bophuthatswana in 1977, Venda in 1979, and Ciskei in 1981. In parallel with the creation of the homelands, there was a massive program of forced relocation: between the 1960s and 1980s, millions of inhabitants were forced from their homes, many being resettled in the homelands. The government aimed for a total removal of the black population to the homelands (Clark and Worger, 2011).

> 4For further details on the content of each piece of legislation described in this section see Horrell (1978), Clark and Worger (2011) and the references there.

6