rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 4: Stamp Taxes on Im**|**movable Property: Inter**<br>**Tax (percent of value**|**national Comparisons**||**Exchange**|
|---|---|---|---|---|
|**Country**|<br>**of transaction or**<br>**market price)**|**Exemptions (US$)**|**Lower Boundary of**<br>**Highest Rate (US$)**|**Rate**<br>**(US$/LOC as**<br>**of 1/7/04)**|
|Australia(New South Wales)|1 to 5.5 sliding||$385,000|0.77|
|The Bahamas|2 to 10 sliding|First time home buyers with<br>value upto $250,000|$250,000|1|
|China|0.05|Property donated to the<br>government, or social housing<br>for widowed, aged, injured, or<br>schools|||
|HongKong*|3.75||||
|Indonesia|5|Amount of exemption is local<br>government decision|||
|Ireland (commercial)|1to 9 sliding|Propertiesless than$12,700|$190,500|1.27|
|Ireland (residential)|0to 9 sliding|Houseslessthan$161,000|$806,451|1.27|
|Japan|No specified amount||||
|Malaysia|1 to 4 sliding||$390,000|0.26|
|Mexico|2 to 3||||
|New Zealand|Abolished in 1999||||
|Pakistan(Punjab)|6||||
|Pakistan(Sindh)|3 to 6||||
|Pakistan(Islamabad)|6||||
|Philippines|1 to 1.5||$18|0.018|
|Singapore|1to 3 sliding||$313,200|0.58|
|South Korea**|.015 to .2||$80,000|0.0008|
|Taiwan(Taipei)|0.1||||
|Thailand|No specified amount||||
|US(Florida)|0.7||||
|Vietnam|2||||
|* Tax rate is only for Real Estate Inves<br>** The slidingrate scale largelydeclin|tment & Trusts<br>es with value whereas for all ot|her slidingscales above,the rates increas|e with value.||
|Source:_Tax Notes International_, vario|us issues.||||

# **III. The Distributional Effects of Stamp Duties**

By law, stamp duties are nominally paid by the buyer involved in the transaction. However, this does not necessarily mean that it is the buyer who ultimately pays, or bears the true economic burden, of the duties. Just as an excise tax may be borne by the buyer of the good or by the seller or by both, the true incidence of the stamp duties may bear no relation to the statutory incidence.

The division of the duties between buyers and sellers depends upon the elasticities of demand and of supply. The elasticity of demand is defined as the percentage change in quantity demanded divided by the percentage change in price; the elasticity of supply is similarly defined.

11
Source: Cited in Zhang Rufei and Chreod Ltd., Shanghai Case Study, Report Prepared for Fiscal Systems and Sub-National Growth Project, World Bank (2005) The national government has sought to cut back the amount of non-reported revenues, first by greatly restricting the variety of fees that local governments can impose, on the grounds that many of the fees were “illegally” adopted and interfere with predictable business investment, and, secondly, by requiring local governments to report revenue from the list of authorized fees and some land leasing under a formal category of “off-budget revenue.” These initiatives have reduced the magnitude of unaccounted-for local revenue, but not eliminated it. Table 5 shows the modest progress being made toward reducing “other,” largely unreported revenue, The question of budget organization and re-structuring is especially important to infrastructure financing, given that the largest part of financing has come from the proceeds of land leasing, fees, and asset income, which lie outside the regular local budget. The category, “fees”, is a composite of user-fees for services like water supply and a wide variety of more arbitrary fees imposed by local governments to generate revenue. At least until the most recent round of reforms, service-related user fees were a small part of the total.

26
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27
_Risk Preferences_ are assessed using a simple, non-incentivized risk game, detailed in the Annex 9.7. Similarly, _Altruism_ is measured by a hypothetical game in which the individual is given UGX 50,000<sup>12</sup> and has to decide how much of it to share with an anonymous individual.

To assess _cognitive ability_ , the survey uses a digit span memory test, in which an increasingly longer string of numbers is read to the respondent, who is in turn asked to repeat it. We use the number of digits the respondent can correctly recall as a proxy for cognitive ability.<sup>13</sup> . We complement this measure, following Dal Bo, Finan and Rossi (2013), with an indicator assessing whether the respondent chooses a dominated option in the risk game described above.

Tables 1 and 2 present descriptive statistics on the survey sample, highlighting differences in means between politicians (including non-elected contenders) and bureaucrats. Table 1 presents basic socio-demographics characteristics and confirms the intuition that politicians are very different from bureaucrats in basic observable characteristics. Politicians are much more likely to be females, to have been born in the same District where they currently work<sup>14</sup> and also to come from less wealthy households (they have larger families and hold less assets). The most striking distinction between bureaucrats and politicians is in educational attainment: whereas half of politicians have not completed college, 96 percent of bureaucrats did, including half with postgraduate degrees. These numbers are broadly consistent with Raffler’s (2017) data on subcounty politicians and officials in Uganda, and paint broadly the same scenario of policy environment: local politicians oversee project execution led by highly educated and centrally appointed bureaucrats.

Table 2 presents descriptive summaries of selection traits. Panel A reports, for each of the components on the Integrity module, the share of individuals who agree or strongly agree with each statement. For all components a larger share of politicians than bureaucrats agree with statements such as "People shouldn’t be held accountable for doing ques-

> Finan and Rossi (2013) and Callen et al. (2015)

> 12Approximately USD 15, at the conversion rate of USD 1 = UGX 3,400 (average during the survey period).

> 13This is the same methodology used in Weaver (2016).

> 14Which is not surprising given that bureaucrats are centrally appointed.

12
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predictions based on the sum of a sequence of regression forests, which are estimated by iteratively predicting the residual from the sum of the previous regression forests.

To our knowledge, Krennmair et al. (2022) is the first paper to specify a model that combines a conditional random effect with tree-based machine learning, specifically random forests. When applied to Austrian income data, the mixed effect random forest model tends to generate more accurate predictions of mean income than traditional EBP. The authors conclude that random forest models offer substantial advantages over linear models in the presence of complex and non-linear interactions between covariates.

Merfeld and Newhouse (2023) compare estimates produced using gradient boosting applied to geospatial data to those from a linear mixed effect EBP model. This differs from Krennmair et al. (2022) in two ways: It uses extreme gradient boosting instead of a single random forest model, and it assumes an unconditional random area effect instead of a conditional random area effect. The predictions are evaluated against census aggregates (for the asset index) or whether predicted welfare in the census falls below a threshold (for poverty in Malawi). Uncertainty is estimated using a block random effects booststrap approach, as proposed by Chambers and Chandra (2013) and applied in Krennmair et al. (2022). The results in Table 5 show that XGboost, even without the conditional random effect, is more accurate than EBP in each case. For out-of-sample areas, the greater flexibility of the XGboost algorithm eliminates much of the selection bias associated with out-of-sample prediction using linear models under informative sampling. In results not shown here, coverage rates vary from 94 to 97 percent, reflecting the success of the bootstrap procedure in estimating uncertainty accurately.

_Table 5: R_<sup>_2_</sup> _by method and in or out of sample_

|Country|Indicator|In-sampl|e|Out-of-S|ample|
|---|---|---|---|---|---|
|||EBP|XGboost|EBP|XGboost|
|Madagascar|Asset Index|0.83|0.87|0.62|0.76|
|Malawi|Asset Index|0.79|0.91|0.54|0.71|
|Mozambique|Asset Index|0.84|0.91|0.70|0.81|
|Sri Lanka|Asset Index|0.80|0.83|0.64|0.81|
|Malawi|Estimated headcount<br>poverty|0.75|0.91|0.63|0.78|

4. Small area estimates using geospatial data can help improve on poor targeting systems A key application of small area estimation is assisting the identification of the poorest households through geographic targeting. Traditionally, cash transfer programs use Proxy Mean Tests (PMT) as a way to identify the poorest households, which utilize a registry of verifiable characteristics to assign each household a score (Coady, Grosh, and Hoddinot 2004). The weight applied to each characteristic is typically determined through by regressing these proxy welfare indicators on log per capita consumption. However, registries are typically very costly and time-consuming to collect and update. In
- 12 -

# TOM 7: **Trends hI MStea** d **Female** Partkipation Rat". **hdla** 1961-81

||Female<br>Workers<br>per 1,000<br>Populatlon|Female<br>Nonfarm Workers<br>per 1,000<br>Population|Female<br>Workers<br>as a percent<br>of all<br>Fulltbiie Workers|
|---|---|---|---|
|Rural||||
|1961|154|22|34.2%|
|1971|64|7|18.9%|
|1981|78|9|22.5%|
|Total National||||
|1961|135|25|31.4X|
|1971|57|10|17.3%|
|1981|68|13|20.2X|

~ hndis (Iak)i 1971a-b, 1581c). See Appendix Table A.5 for a detailed disaggregation.

our **understanding** of poverty given the low-return, part-tkie, labor-intensive, often home-based activities In which women predomInate.

E. Trend 17. The years nice 1961 have witnessed several changes In the Indlan nonfarm economy. As Table 1 Indlcates, the decade of the 19709 represents a key turning point. Up until 1970, India's agricultural share of natlonal employment remained constant, constant for a century or more accordIng to some.<sup>7</sup> Not until the 1981 census did the first ovidence emerge of an Increase In the national share of nonfarm omployment; It rose from 28 percent In 1971 to 33 percent In 1981. The Income profiles tracked by NCAER researchers likewise Identify the first substantial boost In nrral nonfarm icomes durin the 1970s (Table 2). Thls coincides with the widespread ' **See Sinha** (1962), Vynn **and** Mathei **(1978) and** D..hpande and **D.ehpand. (1985).**
In sum, the literature suggests that a meaningful analysis of government expenditures should take into account societal factors, portraying social demand and consensus, together with indicators of the government ability to pursue own policy priorities and overcome financial constraints. Change in any of these three factors may lead to significant shifts in the size and composition of government expenditure. Comprehensive empirical analysis (Heller and Diamond, 1990, and Tait and Heller, 1982) has shown that all these three areas are affected by myriad exogenous factors, which are outlined above, and some of which may be much more significant than the level of national income per capita. This is in contrast to the claim that the size of the state in post-transition economies remains vastly "oversized", which appear to be based on empirical evidence limited to regressions that use per-capita income as _the_ explanatory variable.

# Empirical Results **for** the **Spending Adequacy**

Based on the above discussion, we proceeded to develop an empirical model to explain the current spending ratios of the transition countries. We ran a series of regressions between general government expenditure to GDP ratios (gg) and a number of explanatory variables that proxy existing needs for, constraints to, social disposition toward, and the outcome of, government spending in the countries. We used data for 47 OECD and developing countries, focusing on the 1993-94 averages, to predict government spending ratios to transition countries.'<sup>2</sup> Results of our most significant regressions are shown in Box 1.

In our selected regressions, the old age dependency ratio (old) serves as a proxy for social need of public spending'<sup>3</sup> ; per capita GDP evaluated at purchasing power parity prices (gdp) refers to both social needs and revenue constraint of public programs; public debt to GDP ratio (d) directly illustrates the current and, also, the past financial constraints; infant mortality (mor) and secondary education enrollment (sec) illuminate the quality of government services; and commonalties in historical, philosophical and political experience are roughly expressed by the regional dummies of Europe (eu), and Asia and Latin America Asia (las).'4 We also tested many other variables, such as primary school enrollment, access to health care, population per physician and per hospital bed, private expenditure on health care, 12 For the sample of countries and figures, used in the selected regressions see Appendix. **13** For empirical findings of a strong relationship between the percentage of the population over 60, see (World Bank, 1995).

14 For defining the dummies for countries of a common socio-historical experience, we tried various country groups., ad hoc, based on the shared political, philosophical, cultural, and religeous traditions. Most explanatory power was reached by the dummy representing all European countries. Dummies including countries of Latin America and Asia showed coefficients of nearly the same size and significance.

14
example, 81% of men and 70% of women are involved in the decision making on whether to use birth control. Composite scores (ψw and ψm) are constructed with eight raw indicators for women and men separately, reflecting the degree of their autonomy. The scale for both scores ranges from 0 to 8, and the summary statistics are appended to the bottom of Table 1. Women’s relative decision-making power (θw) is constructed using the share of the women’s decision making (θw=ψw / (ψw+ψm )).There are two traditional measures of relative spousal power. The first commonly used is the female income share. A number of studies find that female income share has a significant effect on household expenditure patterns. For example, Hoddinott and Haddad (1995) found that when women’s share of household income increased, so did the budget share spent on food; and such households also spent less on more male-specific consumption items such as alcohol and cigarettes. Maitra and Ray (2002) found that the identity of the income earner has an important effect on expenditure share. However, as Basu (2006) points out, a measure based on income share might be endogenous to household decisions because a woman’s earnings are dependent upon her representation in the labor force, which is a choice variable for households and is influenced by the household’s decisions. In fact, the female labor force participation rate is very low (roughly 20 percent) in Pakistan, which makes using the earning share as a measure of women’s decision-making power almost impossible in this context. The second measure is the relative years of education or share of years of education (Gitter and Barham 2008). These measures assume that as women’s education level increases they are likely to have more decision-making power. I compare women’s decision-making power θw with years of education share and find a high correlation between years of education share and θw. Figure 1 illustrates the relationship of these two measures in the household consumption sample. The relationship using the child sample shows a similar pattern, and the results are available upon 9
exports was the loss of immigrant farm labor<sup>13</sup> , which was exacerbated by the abandonment of the farms in search of more lucrative employment in the oil and related sectors and, the poor yields of aged plantations.

The traditional export sectors which has always been in difficulty given the recurrent shortage of labor, is further affected by the real exchange rate appreciation, consequence of favorable terms of trade developments particularly world oil prices and other fundamentals, such as the strong overall fiscal position which itself is good for the overall economy; given the current structure of Equatorial Guinea economy. The appreciation of the real exchange rate is likely to accelerate the shifting of production inputs ( _capital and labor - resource movement effect_ ). The Dutch Disease, by reallocating resource may be doing what the government would have been otherwise reluctant to do, therefore facilitating the transformational process of the economy.

<!-- Start of picture text --> Equatorial Guinea: GDP by Selected Sector 1994 -<br>2005<br>100<br>90<br>80<br>70<br>60 Agriculture<br>Forestry<br>50<br>Fishing<br>40<br>Oil<br>30<br>20<br>10<br>0<br>Years<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Percentage of GDP<br><!-- End of picture text --> Source: Equatoguinean authorities and IMF.

Despite the enormous resources derived from oil exports which have increased the per capita GDP from US$ 346 in 1995 to US$ 3391 in 2004, Equatorial Guinea has not yet been able to significantly improve living conditions of the majority of the population. Sixty-six percent of the population still does not have access to potable water and the illiteracy rate stands at 60 percent. Infant mortality is about 97/1,000. The 2005 UNDP Human Development Index ranked the country 121<sup>st</sup> over 177 countries for which the index has been calculated. This poor performance reflects the absence of a comprehensive development and poverty reduction framework as well as poor human resource capacity which impedes the translation of the main policy objectives of the government into a set of budget priorities and expenditures.

> 13 Indeed, there is continued shortage of labor in EQG.

11