rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7. Cross-validation of predictions across classifications**

||||||Vali<br>|dation<br>||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||||**World**|**GWU st**|**udents**|**MTurk**|**- India**|**MTurk**|**- USA**|**Predictio**|**n accuracy**|
|||**Pooled**|**Bank**<br>**analysts**|Impromptu<br>judgment|Structured<br>protocol|Impromptu<br>judgment|Structured<br>protocol|Impromptu<br>judgment|Structured<br>protocol|Average|Std. Dev.|
||**Pooled**|88.1|84.7|86.5|88.3|87.8|86.6|87.8|88.1|87.2|1.24|
||**World Bank analysts**|||||||||||
||Structured protocol|84.6|87.2|80.3|82.3|83.6|86.8|85.8|88.0|84.8|2.67|
||**GWU Students**|||||||||||
||Impromptu judgment|85.8|80.3|88.7|91.1|88.5|80.9|85.1|82.5|85.4|3.92|
|Train|Structured protocol|87.0|81.8|90.1|89.2|88.0|82.9|86.3|84.4|86.2|2.98|
||**MTurk- India**|||||||||||
||Impromptu judgment|88.5|85.1|89.5|90.4|88.3|86.4|88.6|88.2|88.1|1.68|
||Structured protocol|86.0|86.3|81.0|82.3|85.3|88.9|86.7|88.3|85.6|2.72|
||**MTurk- USA**|||||||||||
||Impromptu judgment|89.2|87.1|87.5|88.9|88.7|88.1|87.6|90.2|88.4|1.04|
||Structured protocol|88.8|87.5|83.8|85.5|86.4|89.5|89.2|88.6|87.4|2.00|

_Note_ : Train indicates the classification used for prediction model development and validation indicates the classification used for prediction accuracy evaluation. The random forests method is used.

25
time, corporate, bank, and sovereign balance sheets have remained relatively insulated from the impact of the occasional reversals of capital and sharp adjustments in exchange rate.<sup>30</sup> While portfolio equity and FDI inflows have been liberalized steadily over time, the pace of liberalization of debt flows, and outward investment, has synchronized with the underlying capital flow cycles. Further liberalization of debt inflows slowed down during the period of capital surge, and picked up right after the sudden stops. On the other hand, the pace of liberalization of outflows picked up during the surge, but slowed down or even reversed during sudden stops. For example, the quantitative ceilings on foreign investment in government and corporate bonds were liberalized slowly until 2008, but the pace of liberalization picked up after 2009, the phase of slower and more volatile capital flows. Starting from a $ 1 billion limit on foreign investment in government bonds and $ 1⁄2 billion in private bonds in 1998, the limit was enhanced to $ 5 billion and $ 6 billion respectively by 2008. The ceilings were then relaxed rapidly in the aftermath of the sudden stop in 2008-09 to their current levels of $ 30 billion for government debt and $ 51 billion for corporate debt. Are these the current levels?

**Figure 17: Ceilings on Investment by Foreign Institutional Investors in Government and Corporate debt were liberalized rapidly after the sudden stop of capital in 20082009**

<!-- Start of picture text --> Ceiling on Government Securities Outstanding Ceiling on Corporate Debt Outstanding<br>50<br>50<br>Ceiling was relaxed<br>40 Ceiling was relaxed 40 rapidly after 2009<br>30 rapidly after 2009 30<br>20 20 Ceiling was kept<br>Ceiling was kept low low during the surge<br>10 during the surge 10<br>0<br>0<br>US $, Billion<br>US $, Billion<br><!-- End of picture text --> Source: RBI Notifications on Foreign Investment in India, and Kapur and Mohan (2012).

> 30 Countercyclical macroprudnetial measures—especially sectoral provisions requirements and risk weights-- have been implemented, complementing countercyclical liberalization of capital flows. See Kapur and Mohan, and Patel et al for details on these.

27
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||`(0.68)`|`(1.04)`|`(0.84)`|`(1.20)`|
|---|---|---|---|---|
|`South Kalimantan`|`-0.111`|`-0.146`|`-0.120`|`-0.153`|
||`(1.34)`|`(1.74)`|`(1.43)`|`(1.80)`|
|`South Sulawesi`|`0.036`|`-0.010`|`0.022`|`-0.023`|
||`(0.33)`|`(0.09)`|`(0.20)`|`(0.20)`|
|`Moved Province`|`-0.243`|`0.929`|`-0.263`|`0.869`|
||`(1.84)`|`(1.29)`|`(2.05)*`|`(1.22)`|
|`Moved Kabupaten/Kota`|`0.136`|`0.100`|`0.133`|`0.062`|
||`(1.82)`|`(0.24)`|`(1.80)`|`(0.15)`|
|`Moved Kecamatan`|`0.033`|`0.637`|`0.037`|`0.620`|
||`(0.75)`|`(2.36)*`|`(0.83)`|`(2.31)*`|
|`Moved Prov x Initial Income`||`-0.181`||`-0.175`|
|||`(1.69)`||`(1.64)`|
|`Moved Kab x Initial Income`||`0.010`||`0.015`|
|||`(0.16)`||`(0.25)`|
|`Moved Kec x Initial Income`||`-0.097`||`-0.094`|
|||`(2.31)* `||`(2.25)* `|
|`Non-farm dummy`|`0.362`|`0.373`|||
||`(1.99)*`|`(2.18)*`|||
|`Stayed Non-farm`|`-0.269`|`-0.282`|`-0.143`|`-0.155`|
||`(1.56)`|`(1.75)`|`(0.72)`|`(0.80)`|
|`Moved to non-farm`|`0.082`|`0.077`|`0.082`|`0.077`|
||`(0.92)`|`(0.87)`|`(0.93)`|`(0.88)`|
|`Moved to farm`|`-0.474`|`-0.499`|`-0.329`|`-0.352`|
||`(2.49)*`|`(2.76)**`|`(1.55)`|`(1.69)`|
|`Mining_Quarry`|||`-0.082`|`-0.067`|
||||`(0.22)`|`(0.18)`|
|`Manufacturing`|||`0.132`|`0.143`|
||||`(0.65)`|`(0.72)`|
|`Electricity`|||`0.126`<br>|`0.144`<br>|
||||`(0.52)`|`(0.59)`|
|`Construction`|||`0.052`|`0.059`|
||||`(0.25)`|`(0.29)`|
|`Wholesale`|||`0.292`|`0.302`|
||||`(1.42)`|`(1.50)`|
|`Transport`|||`0.218`|`0.225`|
||||`(1.04)`|`(1.10)`|
|`Finance`|||`0.535`|`0.527`|
||||`(2.21)*`|`(2.20)*`|
|`Social_services`|||`0.450`|`0.455`|
||||`(2.16)*`|`(2.21)*`|
|`Constant`|`3.104`|`2.826`|`3.218`|`2.950`|
||`(8.71)**`|<br>`(7.63)**`|`(9.07)**`|<br>`(7.94)**`|
|`Observations`|`4225`|`4225`|`4225`|`4225`|
|`R-squared`|`0.32`|`0.33`|`0.33`|`0.34`|
|Robust t statistics in parentheses|||||

* significant at 5%; ** significant at 1%

Note: All variables which are not difference variables are from the IFLS 1993.

37
level, even though we only used in the regressions the values at the microregion level. This allows us to test how well each method works, and it gives us a way to compare the methods.

We see from Table 2 that the spatial disaggregation method performed best in terms of both mean absolute deviation and root means square error. The cross‐entropy method performed second best, and not that much less successively, with a mean absolute deviation that is only 15 percent higher than that for the spatial disaggregation method, and a root mean square error that is only 10 percent higher. Both methods performed better than that of the traditional method of using rural population to map agricultural GDP. In fact, the traditional method had a mean absolute deviation that was 4 times higher than that of the spatial disaggregation method. The root mean square error was only 50 percent higher, meaning that it was much less likely to generate large outliers.

Figure 2 shows the complete distribution of actual and predicted values for municipios for the three methods. The spatial disaggregation method had several municipios with predicted values of 0 but actual values greater than 0. In fact, there are several large outliers using this method in which the predicted value was much higher than the actual. This may suggest that the somewhat ad hoc method for adjusting the fixed effects was less than optimal and an alternative method should be found. Generally, though, the spatial disaggregation method cluster near the diagonal line is narrower than that of the cross‐entropy spatial allocation method, which in turn is narrower than the one for the rural population density method.

_Table 2. Deviations of pixel agricultural GDP summed to municipio from actual value_

||**Mean absolute**<br>**deviation**|**Root mean**<br>**square error**|
|---|---|---|
|Rural population density|28,744|25,397|
|Cross‐entropy spatial allocation|8,249|18,347|
|Spatial disaggregation from regression on agricultural<br>production|7,214|16,673|

_Source: Authors._ Figure 3 shows the pixel‐level results produced by the three methods. The difference between the white and the orange should mostly be overlooked. It shows different assumptions that fixed certain municipios and their underlying pixels to be valued at 0 (the white areas). Ignoring a somewhat artificial contrast between the white and orange, the cross‐entropy method and the spatial disaggregation method appear to be very similar in most locations. The results using the population density are less smooth, looking more speckled, based simply on the nature of the underlying rural population data set.

# Conclusions

When there are enough data to allow high enough degrees of freedom to use a regression approach for all the key crops and contributors to agricultural GDP, the spatial disaggregation approach is superior. In principle, we could have used many more parameters – though the hedonic price interpretation would 13
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18
land or area under specific crops at the national or other administrative level). For the analysis of household level processes and outcomes, on the other hand, it is vital that the land area being measured can then be linked to other variables concerning the agricultural production, or welfare outcomes, or other variables of interest for the same household or holding.<sup>3</sup> The main types of surveys for which these measurements are relevant are agricultural sample surveys, agricultural censuses, multi-topic surveys that cover agriculture (such as most Living Standard Measurement Study (LSMS) surveys), and smaller scale household surveys carried out for research purposes.

This paper aims to provide some elements to inform the selection of measurement methods, based on empirical evidence gathered by the Living Standard Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) team of the World Bank in Ethiopia, Tanzania, and Nigeria during methodological fieldwork aimed at understanding the relationship between farmer estimates, GPS measurement and the traditional compass and rope method. This paper focuses on the analysis of area measurement data from the abovementioned studies. For more detailed guidance on implementation and practical considerations for area measurement in household surveys, refer to the complementary guidebook (Carletto et al., 2016).

# **II. Methods for land area measurement in surveys**

Achieving accurate area measures in a household survey setting with limited financial resources and a potentially strict fieldwork schedule is a challenging endeavor. Numerous considerations need to be balanced in designing the survey and selecting the appropriate method including the logistics of transportation to plots, the length of the full questionnaire and the likelihood of respondent and/or enumerator fatigue, and the security of teams and equipment. Three main methods are used in the context of agricultural statistics data collection for the measurement of land area: compass and rope, traditionally held as the ‘ gold standard ’ ; respondent self-reported land area; and, the most recent addition, GPS-based measurement. Each area measurement option has unique costs and benefits that need to be carefully assessed in view of the scale of the data collection of which they are a part, the intended use of the data, and the characteristics of both the plots to be measured and the respondents to the survey. Different methods also present different challenges in terms of their implementation: a potentially accurate method can become highly inaccurate if poorly implemented in the field, or it may simply not be feasible on a

> 3 Similarly, for plot level productivity analysis it is essential that input and output data can be combined at the plot level.

5
# **Figure 3: The MMI -predicted production deviations from average versus historical total Malawi national maize production (MT)**

<!-- Start of picture text --> The Malawi Maize Index, 1962-2009<br>140.00 3,500,000<br>120.00 3,000,000<br>100.00 2,500,000<br>80.00 2,000,000<br>60.00 1,500,000<br>40.00 1,000,000<br>Malawi Maize Index, MMI<br>20.00 500,000<br>National Summer Smallholder Maize Production (Normalized to 2008 Area Planted)<br>0.00 -<br>1962 1967 1972 1977 1982 1987 1992 1997 2002 2007<br>Harvest Year<br>MMI Value<br>National Maize Production (MT)<br><!-- End of picture text -->

# _The Contract Structure_

The annual premium price and the expected (annual average) payout for such a contract would vary with the trigger level (Table 2). When establishing a price for a weather risk management instrument, market providers will take into consideration their own risk appetite, business imperatives, cost of risk and operational costs. While there are a variety of methodologies for pricing, in general the pricing for all contracts will contain an element of expected loss, plus some loading or risk margin that corresponds to a capital reserve charge required to underwrite the risk at a target level for the business, as well as administrative costs. Therefore in general the premium charge for a contract can be broken down as follows:

# _Premium = Expected Loss + Risk Margin + Administrative Costs_

Expected loss is the average, or expected payout of the contract in any given season. In some years no payouts happen and in others payouts in excess of the expected loss can occur and the risk-taker must be compensated, through the risk margin, for this uncertainty and the internal provisions that must be made in order to honor these potentially large payouts. The values of the expected loss and the risk margin must be established from historical weather data. The approach for determining the risk loading over the expected loss differs from risk-taker to risk-taker and many use a combination of methods to determine the risk margin included. A sensible pricing methodology uses a risk measure such as the Value-at-Risk (VaR) of the contract to determine the risk margin. A VaR calculation is aimed at determining the loss that will not be exceeded at some specified level of confidence often set at 99 percent, i.e. the maximum loss that will occur with a 1% probability or less. It targets the potential extreme negative deviations in payouts for 11
face and does not use specific gendered language in the tax system. However, it arises when a tax policy or administration interacts with social arrangements, economic conditions, or behavioral differences between genders, inadvertently leading to a gender-biased tax system (see Barnett and Grown, 2004). Therefore, addressing implicit gender bias is an important area that necessitates further investigation and correction in order to realize the Sustainable Development Goal of gender equality and women’s empowerment. Some examples of implicit gender bias in tax systems are higher taxation on goods and services used primarily by women, higher tax rates on secondincome earners, and lower capital taxation (see OECD, 2022). Given that gender biases in the tax systems, whether explicit or implicit, runs counter to the fundamental principle of taxation (i.e., fairness), it is important to identify and address them.

The tax burden literature underscores the difficulty in precisely identifying how the profit tax burden is distributed. There is a notion that it often impacts employees, leading to a probable decrease in real wages. This trend is attributed to the mobility of capital, allowing it to relocate from countries with higher profit tax burdens (see Smith, 2015). However, this assumption relies on the presence of near-perfect factor markets for capital where it can easily cross borders while labor movement is restricted. In practice, gender-based disparities in tax burden can be traced to variations in tax compliance, divergent enforcement measures within tax administration, and tax structures that disproportionately affect one gender, among other factors. These discrepancies may stem from differences in income reporting, disparities in deductions claimed, and variances in eligibility for tax credits, all of which may be influenced by gender-related factors. Furthermore, tax administration practices, such as the frequency of audits and collection efforts, may inadvertently impact one gender more than the other. Recognizing and rectifying these gender-based inequities in tax burden is imperative for advancing both tax fairness and gender equality.

3
# **1. Introduction**

World prices of staple foods have increased since 2006, with particularly dramatic increases in cereal prices during 2007–08. As shown in Figure 1, world wheat prices more than doubled between March 2007 and March 2008, rising from $200 to $440 per ton. Rice prices on international markets nearly tripled in the four months from January to April 2008, rising to more than $900 per ton. Maize prices increased from $150 per ton to nearly $300 per ton between July 2007 and June 2008 (World Bank 2008a). Prices of oilseeds also increased rapidly over this period. The impact on consumers in developing countries has been profound, with an estimated 100 million people pushed into poverty (Ivanic and Martin 2008), and an even larger impact on the estimated 1.4 billion people who were already living on less than $1 per day before the food price shock (Dessus et al. 2008).

The spike in world food prices is attributable to several factors, most prominently increased diversion of food grain and oilseeds to biofuels production, the weak US dollar, increased food production costs from higher energy and fertilizer prices, and adverse policy responses to the initial shock, which pushed prices even higher (Mitchell 2008). Although prices have subsided from their peaks in the first half of 2008, they still remain considerably above their levels from early 2006, and are projected to remain more than 50% higher (in US$ terms) than their 2003 levels (World Bank 2008a).

In this context, low- and middle-income countries are trying to understand better how higher food prices affect their citizens, especially the poor and vulnerable. Uganda is no exception. Higher food prices could lead to a slowdown, or even a partial reversal, of the significant progress Uganda has made in reducing absolute poverty over the past 15–20 years. Moreover, the impacts within Uganda are likely to be distributed very unevenly. In general, the worst impact is suffered in areas with the steepest price increases and by those for whom market purchases of staple foods represent a large share of total consumption. Subsistence-oriented rural households that produce most of their staple food needs are partially buffered from the food price shocks, as are those whose main staples are foods that have not increased in price. Finally, rural households that produce marketable surpluses may potentially benefit from higher crop prices, although that benefit may be partially offset by higher production costs.

2