rafmacalaba/datause-encoder-data huggingface dataset:
“business-as-usual” growth path for Angola over the next three decades. We find that, in the absence of major economic shocks or reforms, potential GDP per capita growth declines slightly in the medium term but is expected to pick up in the longer term. A growth decomposition shows that these “U-shaped” dynamics is driven by the interaction of improving demographic trends, depleting oil reserves, and a transition of the economy away from oil. Finally, we show that an unadjusted (“naïve”) calibration of the standard LTGM would generate an overly optimistic growth path for Angola. This is because a one-sector model cannot account for the depletion of oil reserves that is a drag on growth and the fact that the oil sector is much more capital intensive than the non-oil sector.

Second, we evaluate the effects of a hypothetical oil-price boom-and-bust cycle in Angola under each fiscal rule. At the end of the oil price simulation (when oil prices are back at their original level), GDP per capita is highest under the BBRHR at 20%-25% above the baseline, as all of the higher government oil revenues are invested during the boom years. However, under the other fiscal rules, GDP is only 5%-12% above baseline because of a much smaller increase in investment. The SSR-HR is the only rule that supports faster growth for several years after the end of the oil price cycle. This is because the interest from the extra savings during the oil-price cycle is recycled into the budget, releasing resources for an almost permanent increase in public investment. Third, we find that discoveries have a large and persistent impact on Angola’s growth rate, especially under HRs. As before, the LTGM-NR only simulates the supply-side effects of these price shocks and discoveries, not their short effects through aggregate demand.

**Related literature** While there is a rich literature on managing the short-term cyclical effects of commodity booms (for example, Kumhof and Laxton 2013, Mendes and Pennings 2020, Pieschacón 2012) and whether natural resources are a curse or a blessing for development (see Van der Ploeg 2011 for a survey), the mechanics of medium-to-long-term growth in individual resource-rich economies are much less frequently studied.<sup>4</sup> The closest work is the modeling sections of

> 4 For empirical evidence on the resource curse in developing countries, see Terry Lynn (1999) and Wood (1999). For individual country experience with the resource curse (Ghana and Angola), see

5
16 complex for rain-fed and irrigated farmers. They both lose because of lower maize prices, but they gain because of lower rural wages (since they are both net users of labor). These two factors are capitalized in land prices, and the balance is clear from table 2: rain-fed farmers lose substantially, while irrigated farmers experience a small gain. Note that under gradual liberalization values of rain-fed land fall less than under immediate, since protection-induced rents can be reaped for five additional years.

Figure 4 illustrates how this affects rain-fed farmers. The shaded area measures the differences in utility between immediate ('cold turkey') and gradual liberalization. The gradual path gives them additional rents during the transition (although at declining rates), but it produces no further gain once the transition is over.

<!-- Start of picture text --> Cold Turkey vs Gradualism<br>UVility of Rain4ed Farmers<br>4.2<br>4.15 -<br>4.'<br>4.0_<br>54<br>3.9<br>3.8<br>OM___ Y___w_CNA<br>| ~~-O _ Y, no CNA -- S Yrs. mFo4A<br><!-- End of picture text -->

Figure 4 Migration slows the drop in rural wages at the cost of increased pressure on urban wages. Figure 5 shows that despite lower consumer prices for maize, urban worker's real consumption wages fall relative to the base case. And with I the marginal product of capital <!-- Start of picture text --> Urban Consumption Wages<br>(no adlustment progrs)<br>)-.116- odownward<br>1.4<br>§1.I2 f w<br>0108 ^~<br>jo.os . . 4 . . . . . .<br>Yes<br>[~ 1 Y. noOL C@ 5SYrs .o nelA|<br><!-- End of picture text -->

Figure 5
If observable characteristics alone determined satisfaction, “adjusted” satisfaction measures that present the level of satisfaction after accounting for differences in these characteristics would be a reliable measure. This is the equivalent, for instance, to “value-added” measures in schoolperformance cards that present student test-scores after adjusting for student background and school characteristics. The results suggest however, that _unobserved_ rather than observed characteristics are important. There is a large and significant decline in the estimated impact of effort on satisfaction in fixed-effect specifications where unobserved doctor characteristics are adequately accounted for.

Furthermore, although we cannot fully account for unobserved patient characteristics, the reducedform of the instrumental variables specification suggests that the lack of an association between satisfaction and doctor effort is robust to unobserved patient characteristics. We are somewhat reluctant to argue for this strong conclusion, since we do not know whether order itself is related to patient characteristics, or has an independent effect on satisfaction (for instance, if patients later in the order also waited more, this could affect their satisfaction level). However, in combination with the fixed-effects results, this suggests that satisfaction level is severely contaminated by the systematic matching of patients to doctors and in ways that are unobserved by the researcher.

Finally, the differences in satisfaction reported by a comparable sample of patients interviewed at home compared to those interviewed in the clinic are an order of magnitude higher than between doctors who put in more/less effort. Hawthorne effects are unlikely to explain these differences; it is likely that other behavioral survey issues such as the “warm-glow” of being interviewed shortly after the interaction or the comfort of being interviewed at home rather than in the setting of the clinic are at play. In either case, there is a strong case for exercising caution in interpreting satisfaction results either over time or across settings when survey conditions were not identical.

There are a number of caveats and potential modifications. In the household interview we did not record the date of the facility-visit, although we ensured that the visit to the facility was during the week previous to our visit. We therefore cannot ascertain the degree to which satisfaction drops-off with time. As discussed previously, unobserved patient characteristics could bias our results; the instrumental variables discussion suggests that the estimate in the fixed-effect specification may be too high. Finally, by design our interviews were highly structured. While this does afford us the luxury of a large sample and the use of econometric tools with some precision, a component of the survey that allowed for unstructured interviews and more qualitative information could have added to our understanding of the environment.

11
While there are numerous established definitions for entrepreneurship, the concept stills lacks a common language. Furthermore, many of the current definitions focus disproportionately on industrialized countries, highlighting the assumption of risks, innovation, and high growth as necessary characteristics of entrepreneurial activity. These characteristics are typical of entrepreneurship in industrialized countries, but might be difficult to measure in developing countries. Therefore, to make the data comparable across a large number of countries, the specific type of business measured is simply _the number of limited liability corporations_ , or its equivalent in other legal systems.

The main sources of information for this study are the national business registries.<sup>1</sup> In a limited number of cases where the business registry was unable to provide the data—usually because they do not keep business registrations digitalized— the WBGES used alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and private vendors (such as Dun & Bradstreet). Given that business registration is the first step to entering the formal sector, the WBGES also gathered information on the registries’ functioning and structure to better understand their role in the business creation process.

The collection process involved telephone interviews and email/fax correspondence with business registries in over 120 countries; 112 countries responded to the survey in 2008 and data from 101countries was considered accurate and comparable according to the WBGES methodology.

The WBGES aims to provide an indicator of entrepreneurship based on an objective measure of business creation. Its nature makes the WBGES an appropriate indicator to measure the impact of regulatory, political, and macroeconomic institutional changes on the private sector, therefore becoming a valuable tool for policy making.

The data itself only provide a snapshot of the countries’ business demographics, and cannot by itself explain the factors that affect the business creation circle. However, when the WBGES is combined with other data such as the _Doing Business Report_ , Investment Climate Assessments, OECD Entrepreneurship Indicators, or the Global

> 1 The complete database and list of sources is available at: http://econ.worldbank.org/research/entrepreneurship.

4
# 4.3 Revenue Mobilization

We shall show that Kenya has managed a successful transition from a high tax rate, low tax base system with considerable reliance on import taxes and seigniorage to a more efficient and equitable revenue system based on broad-based taxes like income tax and VAT. This accomplishment is considered difficult to achieve—countries marked by political instability, social division and weak governance, which is the stereotypical image of Kenya, are more likely to rely on seigniorage and import tariffs, taxes which are relatively easy to collect but highly distortionary, and probably regressive in the case of seigniorage.<sup>23</sup> The rise in the primary surplus in the wake of Goldenberg was achieved in part by raising tax rates. This plus high inflation boosted the tax take and pushed the revenue-to-GDP ratio to about 25 percent in the early-to-mid 1990s.<sup>24</sup> It was felt that maintaining such a high rate of revenue mobilization would deter growth, so tax rates were cut and the revenue-to-GDP target lowered to 21-22 percent of GDP. Considerable reform in tax policy and administration has been implemented since the mid-1990s. After 2003, governance improvements have boosted direct tax collections notwithstanding large reductions in the CIT rate and the top PIT rate to 30 percent (from peak levels 45 percent each earlier in the decade). VAT rates and import tariffs have also been cut significantly. A ‘large taxpayers office’ was set up in 2006 whereby 800 companies pay some 70 percent of total direct taxes.

Figure 4 plots four important revenue components: import and excise duties, income tax and VAT as a percentage of GDP. Revenues fell as tax rates were cut but then picked up after 2003 as a result of stronger tax administration. The steady decline in import duty collection is by design, given the trade liberalization policy. Income tax displays a u-shaped pattern, falling in the late 1990s, but then, unlike import duty, recovering over the last three years. This is encouraging to the extent that it captures lower tax rates combined with a broadening of the tax base and

> 23 Cukierman, Edwards and Tabellini (1992). 24 Source: Meeting with Kenya Revenue Authority officials, February 2007.

16
program to expand and began to provide funding for the program in poor areas; by the end of 2006 roughly 80 percent of the provinces and counties in China had adopted some form of rural dibao program (Ministry of Civil Affairs 2007, World Bank Social Protection Group 2010, Xu and Zhang 2010).

In early 2007 the central government announced that the rural dibao program was to be implemented nationwide in all counties and with central subsidies (Xinhua 2007a, 2007b; World Bank Social Protection Group 2010; Xu and Zhang 2010). Under this new national initiative, the program would become more standardized and would absorb or complement several pre‐ existing programs that had provided subsidies for poor households, such as the five‐guarantee ( _wubao_ ) program and the subsidy program for destitute households ( _tekun jiuzhu_ ). Although central funding of the program increased, the program was to be co‐funded by local governments based on their fiscal capacity, and the minimum income thresholds and subsidy amounts continued to be set locally at the county level in light of local fiscal capacity (World Bank 2010).

Official statistics indicate that the rural dibao program grew quickly after 2006 (Table 1). In 2007, the first year of nationwide implementation, the rural dibao program provided transfers to 36 million rural individuals (4.9% of the rural population) and accounted for three‐quarters of the rural recipients of social relief, followed in a far second place by the five‐guarantee program, which covered 5 million recipients (Department of Social, Science and Technology Statistics of the National Bureau of Statistics 2008, p. 330; National Bureau of Statistics 2009, pp. 89, 939). By 2010‐11 program participation had leveled off at about 53 million individuals, equivalent to 8% of the rural population. This is more than double the size of the urban dibao program (23 million) and far outnumbers the sum total of participants in all other rural poverty relief programs (17.9 million in 2010; does not include disaster relief) (Ministry of Civil Affairs 2011; National Bureau of Statistics 2011).

Spending on the program also grew. According to official statistics shown in Table 1, in 2007 government spending on the rural dibao program was 11 billion yuan, with an average transfer amount of 466 yuan per recipient. In 2009 spending on the program was 36 billion yuan or, with an average transfer of 816 yuan per recipient. Although the number of recipients leveled off after 2010‐11, program spending continued to expand, implying further increases in transfers 5
To ascertain the significance of different factors in<sup>explainingthegrowthof</sup> mutual funds we estimated an empirical model that<sup>regressedthesizeof the mutualfund</sup> sector, given by the level of net assets in relation to<sup>nationalincome,on anumber of</sup> independent variables. Following established practice we<sup>includedamong the explanatory</sup> variables indicators of the level of economic development,<sup>securitiesmarketdevelopment</sup> and efficiency, financial stability, and regulatory effectiveness<sup>as wellasrelevant return</sup> variables. We did not include tax rules because they<sup>aredifficultto document.Dataon</sup> most of these variables were collected from the World<sup>Bank'sDatabaseforEconomic</sup> Indicators except for stock market returns, which were<sup>collectedfrom the Datastreamand</sup> Bloomberg databases.

GDP per capita is used as an indicator of economic development.<sup>Manystudies</sup> have shown that financial intermediaries tend to be larger,<sup>moreactive and moreefficient</sup> in high-income countries (Demirguc-Kunt and Levine<sup>1999).</sup> Capital market development is represented by the total<sup>valueof listeddomestic</sup> equities and issued bonds in relation to national income.<sup>Alternativemodel specifications</sup> use separately quantitative indicators of equity and bond<sup>marketdevelopment.However,</sup> as pointed out by Levine and Zervos (1998) large markets<sup>donotnecessarilyfunction</sup> efficiently. Taxes and other regulations may distort the<sup>incentive to list on theexchange,</sup> resulting in little trading activity and low levels of<sup>liquidity.</sup> Market efficiency is measured by two indicators: the equity<sup>market turnoverratio</sup> (which measures the value of traded shares divided<sup>bymarket capitalization)andthe</sup> value of traded shares in relation to national income. The<sup>first indicatorsuffersfromthe</sup> use of market capitalization as the denominator. It may<sup>show as highlyefficientmarkets</sup> with a low level of market capitalization and low<sup>absolutetrading valuesbutahigh</sup> turnover ratio. The value of traded shares is a better<sup>indicatorofmarketliquidity,</sup> although it is also not free from weaknesses. It tends<sup>to be higher when equity pricesare</sup> rising and lower when prices are falling, even though<sup>marketliquidity andefficiency may</sup> not change. A more relevant measure of market efficiency<sup>andliquiditywouldbe</sup> provided -by data on trading costs and price impact,<sup>butsuchdataarenotreadily</sup> 29
||GNP Per Capita, Dollars, 1990|
|---|---|
||Previous Atlas Measures<br>SACF Estimate|
|Romania|1,640<br>1,750|
|Poland|1,690<br>1,960|
|Bulgaria|2,250<br>2,400|
|Hungary|2,780<br>2,930|
|Czechoslovalda|3,140<br>3,170|
|FSU|NA<br>2,870|

It seems likely that the necessity for a shift to SACF will become more apparent when 1991 estimates are being prepared. Tentative figures suggest that "egregious" undervaluations will become more widespread (certainly affecting Bulgaria in 1991); that declines in nominal per capita income will far exceed what can be explained by real output ?~clines, if standard _Atlas_ methodology is maintained. However, a separate study will have tk be prepared on this.

# IV. National Accounting Issues

The break-up of the FSU presents a rare set of problems, in estimating the relative per capita incomes of constituent states, even in ruble terms. To deal with these, the Bank commissioned two independent compilations of national accounts estimates in rubles'<sup>7</sup> to check the intemational comparability of estimates provided by national authorities to Bank missions. While further work with national compilers is essential, particularly for assessments beyond 1990, the margin of error for ruble figures appear minor compared to uncertainties regarding the ruble-dollar conversion factor--in that year.

Even with the two commissioned studies, considerable work was required to assemble national accounts for each FSU economy. Positions had to be taken on traditional concerns about FSU national accounts but, insofar as possible, these are documented elsewhere (see Steinberg 1992) or relegated to Annexes to this report, in order to focus on operationally relevant concerns about transition. As the Union-wide central planning process decayed, more than the relevance of the information generated by the p:ocess declined. There is growing 171a addition to Steinberg 1992, the Bank commissioned a study by the US Census Bureau's Center for International Research, which provides 1987-90 esfimates using income-ouday approaches. Complete results are due by October 1992.

_15_
# **Figure 18. Scenario 1. PIT conditional allowance**

# **a. Cumulative share of PIT**

<!-- Start of picture text --> 100.0<br>90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>.0<br> 1 2 3 4 5 6 7 8 9 10<br>Per capita MYPP deciles<br>Baseline<br>PIT reform (2022)<br>PIT reform (2022) conditional<br>Cumulative share of taxes<br><!-- End of picture text -->

# **c. Poverty, poverty gap, inequality change (percentage points)**

<!-- Start of picture text --> Child -0.7<br>-2.7<br>-0.3<br>National -0.2<br>-1.1<br>-3.5 -2.5 -1.5 -0.5 0.5<br>Percentage points povety headcount AROP<br>Inequality (Gini points) Poverty gap Poverty<br><!-- End of picture text -->

# **b. Fiscal incidence of taxes (% of MYPP)**

<!-- Start of picture text --> 7.00<br>6.00<br>5.00<br>4.00<br>3.00<br>2.00<br>1.00<br>0.00<br> 1 2 3 4 5 6 7 8 9 10<br>Per capita MYPP deciles<br>Baseline<br>PIT reform (2022)<br>PIT reform (2022) conditional<br>% dor MYPP<br><!-- End of picture text -->

# **d. Fiscal cost (EUR million)**

<!-- Start of picture text --> 2,000<br>1,977<br> 1,950 176<br>addtional<br> 1,900 cost<br> 1,850 285<br> 1,800 1,801 addtional<br>cost<br> 1,750<br> 1,700 1,692<br> 1,650<br> 1,600<br> 1,550<br> 1,500<br>Tax collection Child tax reform<br>PIT reform (2022)<br>PIT reform (2022) conditional<br>EUR million<br><!-- End of picture text --> _Source:_ Authors’ estimates based on 2021 EU-SILC, 2021 HBS and CEQ Methodology.

Note: PIT reform refers to child deduction reform 2022, and PIT reform conditional is the alternative targeted version for poorer households 44
Figure 7 <!-- Start of picture text --> 100<br><!-- End of picture text --> <!-- Start of picture text --> Fundamental Rights Cases<br>Percentage of Cases in Favour of Advantaged Classes vs. Others<br>100<br>80<br>60<br>40<br>20<br>0<br>1950 1960 1970 1980 1990 2000 2010<br>Year<br>Advantaged Classes Not Advantaged Classes<br>Percentage<br><!-- End of picture text -->

# Figure 8

# **Fundamental Rights Cases**

<!-- Start of picture text --> Percentage of Cases in Favor of ST/SC/OBC Complainants vs. Others<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>1950 1960 1970 1980 1990 2000 2010<br>Year<br>Not SC/ST/OBC SC/ST/OBC<br>Percentage<br><!-- End of picture text --> 20
coefficient of private credit increases even to 1.94, it is no longer significant (p-value: 0.135).

As another robustness check, we analyze the impact of private credit on the _growth_ rate of cereal yields. Regression (5) shows that at the 5% level, a 1 percent increase in private credit is associated with a 0.003% additional growth in cereal yields. Note that the coefficients for private credit in the cereal yields regressions are generally lower than in Table 6, where we analyzed its impact on overall agricultural productivity. This suggests that the effect of financial sector development on increase in cereal yields is perhaps important, but not the only means by which financial sector development drives the increase in agricultural productivity.

We next investigate the association with the growth in livestock production and crop production. Since both indices, by construction, have a value of 100 in 1999-2001, we use the initial values for the indexes as the dependent variables, meaning that if the initial value was low, growth over the next years was high for that country. Consequently, we would expect a negative sign for private credit if that spurs production. We indeed find this result of private credit for both the livestock and crop production indexes, and very significant as well (at the 1% level). This shows that financial sector development leads to high productivity growth in crops and livestock.

# _E. Private credit and productivity enhancing inputs_

We have shown that financial development indeed spurs agricultural productivity. We next want to investigate the channels. We expect that financial development to be associated with an increase in the use of productivity enhancing inputs that require some upfront financing or outlays. This would provide further evidence of the specific channels of financial sector development on undernourishment. We are constrained in the data we have for a large set of countries. Therefore, we use data on fertilizer use and the number of tractors per agricultural worker, inputs which require upfront outlays. When we assess the impact of private credit, Regression (1) in Table 8 shows that private credit is significantly (at the 1% level) associated with fertilizer use, even after controlling for the 23