rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2:  Determinants of net revenues per hectare**

||Model 1|Model 2|
|---|---|---|
|Precipitation 1st Quarter|-0.56<br>|-1.620+<br>|
||(0.79)|(0.78)|
|Precipitation 2nd Quarter|2.539+|-1.499**|
||(1.30)|(0.59)|
|Precipitation 3<sup>rd</sup>Quarter|-2.869+|0.497|
||(1.55)|(0.66)|
|Precipitation 4th Quarter|-1.70|-2.499**|
||(1.02)|(0.95)|
|Precipitation 2nd Quarter Squared.|-0.014*||
||(0.00)||
|Precipitation 3rd Quarter Squared|0.035*||
||(0.01)||
|Average Annual Temperature|-21.104**|-27.858*|
||(9.90)|(7.46)|
|Size of Household||-3.714|
|||(5.97)|
|Male head of household||79.123**|
|(1: male head: 0: female head)||(32.18)|
|Age of the Household head||-0.416<br>|
|||(4.51)|
|Age of household head squared.||-0.003<br>|
|||(0.04)|
|Education of the Household head||19.093|
|||(14.10)|
|Education of Household head squared||-1.398+|
|||(0.79)|
|Farms practicing rainfed agriculture||54.291**|
|||(25.06)|
|Farms practicing rainfed and irrigated agriculture||68.079|
|||(62.67)|
|Household has land tenure||57.054**|
|(1: has tenure; 0: none)||(25.03)|
|Size of cropping area||-24.600*|
|||(7.52)|
|Size of cropping area squared||0.292*|
|||(0.10)|
|Farm cultivating single crop<br>||-122.951*<br>|
|(1: single crop; 0: multiple crops)||(21.56)|
|<br>Sell produce at Local market||<br>205.879*|
|||(17.36)|
|Sell produce at Urban market||241.611*|
|||(50.85)|
|Sell produce to Middleman||<br>266.735*|
|||(46.94)|
|Born in current location||-6.327|
|||(20.63)|
|Receive Subsidy||-7.838|

26
# Table 2: Livelihood Diversification Indices Summary

|Index<br>Type|Standardized<br>Across<br>Countries|Time<br>Period|Description|Pre-COVID-19 Ker|nel Density Graph|
|---|---|---|---|---|---|
|Fraction|Yes|Pre- and<br>Post-<br>COVID-<br>19|To generate this fraction index, we collapse multiple<br>income sources into seven broad income-generation<br>categories that are consistent across rounds and across<br>countries. These categories are: farm; wage; pension;<br>remittances; non-farm enterprise; income from<br>properties, investments and savings; and other. The<br>“other” income category varies across countries and<br>rounds but generally includes asset sales, income from<br>NGOs, and government assistance.|0<br>1<br>2<br>3<br>4<br>Density<br>.2<br>.4<br>.6<br>.8<br>1<br>Ethiopia<br>0<br>1<br>2<br>3<br>4<br>Density<br>0<br>.2<br>.4<br>.6<br>.8<br>1<br>Specialization Index<br>Nigeria|0<br>1<br>2<br>3<br>4<br>0<br>.2<br>.4<br>.6<br>.8<br>1<br>Specialization Index<br>Malawi|
||||Given the level of detail provided in the pre-COVID<br>|0<br>1<br>2<br>3<br>4<br>Density<br>Ethiopia|0<br>1<br>2<br>3<br>4<br>Malawi|
|HHI<br>|Yes<br>|Pre-<br>COVID-<br>19<br>|survey data, we are able to generate an HHI to capture<br>household income diversity more precisely. For this<br>index, we use the same 12 income categories used in<br>the simple fraction index but consider the amount<br>earned from each source.<br>|0<br>.2<br>.4<br>.6<br>.8<br>1<br>0<br>1<br>2<br>3<br>4<br>Density<br>0<br>.2<br>.4<br>.6<br>.8<br>1<br>Specialization Index<br>Nigeria<br>|0<br>.2<br>.4<br>.6<br>.8<br>1<br>Specialization Index<br>|

_Note_ : The table summarizes the two livelihood diversification indices used in the main analysis. Higher index values indicate more household specialization (less income diversification). Appendix A contains similar summary information regarding the other four indices we pre-specified.
- Duflo, E., Dupas, P., Kremer, M., March 2015. School governance, teacher incentives, and pupil-teacher ratios: Experimental evidence from Kenyan primary schools. Journal of Public Economics 123, 920–110.

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22
**Table A.4. E-commerce development and various categories of household expenditure per capita by region (Extended Model 2)**

|VARIABLES|(1)<br>All|(2)<br>Urban|(3)<br>Rural|(4)<br>East|(5)<br>Central|(6)<br>West|
|---|---|---|---|---|---|---|
|Total household expenditure|0.245***|0.176***|0.722***|0.231***|0.234**|0.538***|
||(0.062)|(0.052)|(0.096)|(0.078)|(0.107)|(0.157)|
|Observations|9,502|4,402|5,100|3,808|3,018|3,163|
|Adjusted R square|0.341|0.314|0.383|0.336|0.325|0.338|
|Cosmetics and beauty|0.406***|0.327***|1.011***|0.419***|0.311*|1.259***|
||(0.125)|(0.118)|(0.248)|(0.155)|(0.181)|(0.302)|
|Observations|9,422|4,366|5,056|3,772|2,997|2,653|
|Adjusted R square|0.342|0.367|0.323|0.317|0.368|0.362|
|Food|0.289***|0.219***|0.764***|0.302***|0.233**|0.479*|
||(0.063)|(0.053)|(0.122)|(0.079)|(0.099)|(0.243)|
|Observations|9,398|4,346|5,052|3,751|2,998|2,649|
|Adjusted R square|0.326|0.321|0.341|0.333|0.302|0.360|
|Of which: Food at home|0.342***|0.279***|0.814***|0.338***|0.291**|0.453*|
||(0.067)|(0.059)|(0.126)|(0.086)|(0.108)|(0.257)|
|Observations|9,395|4,344|5,051|3,749|2,997|2,649|
|Adjusted R square|0.375|0.385|0.375|0.316|0.372|0.459|
|Of which: Dining out|0.349**|0.214|1.202***|0.234|0.464*|1.165**|
||(0.146)|(0.132)|(0.265)|(0.173)|(0.240)|(0.496)|
|Observations|9,503|4,402|5,101|3,808|3,019|2,676|
|Adjusted R square|0.329|0.321|0.359|0.308|0.331|0.359|
|Clothes|0.211**|0.144|0.738***|0.195|0.233|0.502*|
||(0.103)|(0.096)|(0.178)|(0.137)|(0.146)|(0.289)|
|Observations|9,297|4,285|5,012|3,704|2,951|2,642|
|Adjusted R square|0.321|0.341|0.312|0.312|0.349|0.304|
|Utilities|0.149***|0.141***|0.321**|0.190***|0.076*|0.486|
||(0.034)|(0.034)|(0.141)|(0.044)|(0.041)|(0.307)|
|Observations|9,469|4,376|5,093|3,787|3,012|2,670|
|Adjusted R square|0.298|0.284|0.313|0.311|0.292|0.309|
|Communications|0.190***|0.154***|0.547***|0.148**|0.233***|0.375**|
||(0.045)|(0.040)|(0.084)|(0.057)|(0.061)|(0.177)|
|Observations|9,382|4,338|5,044|3,734|2,996|2,652|
|Adjusted R square|0.341|0.364|0.334|0.303|0.397|0.324|
|Local transport|0.364***|0.297***|0.807***|0.300**|0.410***|0.281|
||(0.083)|(0.077)|(0.165)|(0.117)|(0.083)|(0.299)|
|Observations|9,335|4,308|5,027|3,708|2,979|2,648|
|Adjusted R square|0.299|0.307|0.296|0.285|0.315|0.303|
|Travel|0.704***|0.534***|1.609***|0.638**|0.657***|1.888***|
||(0.198)|(0.184)|(0.257)|(0.285)|(0.189)|(0.567)|
|Observations|9,478|4,382|5,096|3,794|3,014|2,670|
|Adjusted R square|<br>0.296|<br>0.291|<br>0.333|<br>0.286|<br>0.279|<br>0.350|
|Entertainment|0.528***|0.379**|1.158***|0.398*|0.687***|0.106|
||(0.151)|(0.147)|(0.348)|(0.212)|(0.186)|(0.287)|
|Observations|9,455|4,371|5,084|3,778|3,011|2,666|
|Adjusted R square|0.289|0.291|0.314|0.270|0.292|0.325|
|Automobiles|0.384***|0.247**|1.600***|0.291*|0.505**|0.979***|

·· 35
(2022), and it is implementable in an open source Python package.<sup>1</sup> The crucial intuition though is simply that we use a subset of high quality expert annotations to train a small bespoke model for each code. These models rely only on the annotated training data, unlike the pre-trained LLMs which are trained on huge quantities of text from a huge range of contexts.

Rather than asking LLMs to directly annotate text, another potential use for them is for data augmentation in combination with a supervised model, such as those described above. Data augmentation is a common technique in machine learning to generate more variation in a training set while preserving the important signals. For example, when training a model on a labelled dataset of images of animals, one might generate extra variation in the training data by rotating the images by 90 degrees or transforming them into a mirror image of themselves. The idea is to generate more training observations where the noise in the data is different but the signals are the same. A good example of this from the natural language processing literature is back-translation, where text is translated into a different language and then back into the original, so that the exact phrasing and style of the text is different but the meaning is the same (Edunov et al., 2018). Using LLMs for data augmentation has been found to increasing prediction performance in some contexts, so we follow the approach set out in Dai et al. (2023) as an additional experiment here. The example prompts and further details on the augmentation are shown in Appendix A.1.

We thus test two different versions of iQual: first, training supervised models on the human annotations without the use of LLMs, and second, training the model on data augmented by the LLMs to generate more variation in the text while preserving the meaning.

# **4 Results**

We assess the performance of LLMs in our annotation tasks along two dimensions. Firstly, we assess how accurate of the LLMs predictions relative to our expert human annotations, finding that performance is poor relative to our simpler supervised models. Secondly, and more importantly, we investigate whether the annotations provided by LLMs or iQual introduce bias. We here mean bias in the technical sense that the prediction errors which the models make are not random.

> 1https://github.com/worldbank/iQual

6
# Table of Contents

|I.|Introduction|I|
|---|---|---|
|II.|Basic Features of Inflation|3|
|III.|The Classical Hyperinflations|7|
||The Origins of the Hyperinflations<br>Stopping the Classical Hyperinflations|7<br>12|
|IV.|Origins<br>of the New Hyperinflations in Argentina, Brazil, and Peru|16|
||i.<br>The Background of High Inflation<br>ii.<br>Direct Origir.s of the Recent Hyperinflations|16<br>24|
|V'.|Programs to Stop Three Big Inflations|31|
||i.<br>Basic Features of the Programs|31|
||ii.<br>Classic Stabilization Programs with Non-Classic Outcomes|33|
||iii.<br>Why Has Inflation Been So Persistent?|39|
||iv.<br>Was There A Change of Regime?|41|
|VI.|Final Remarks|46|
|Refer|nces|49|

|References|
|---|
These micro-data sets are complemented by administrative data on transfers and taxes from the Ministry of Treasury and Finance (MoTF), as well as the Turkish tax code and laws to understand the design features of each specific fiscal intervention.

# _Empirical Approach_

There are largely three steps involved in the empirical approach of a CEQ. First, assess the coverage of the study. Second, measure taxes and transfers in the survey. Third, construct poverty and inequality measures for distributional analysis. While these are all important steps on its own, the second step is, by far, the most complex and labor-intensive component of the exercise.

_Step 1_ : The first step in the approach is to examine the HBS questionnaire, assess what information is being collected about the interaction of the household with the economy and its different markets, evaluate how questions are asked, and inventory which fiscal interventions can be plausibly studied. This determines the coverage of the study.

As a result, it was assessed that the following taxes and transfers can be analyzed using the HBS questionnaire, and subsequently captured in the study:

- _Direct taxes:_ Payroll Income Tax, Personal Income Tax, (including Agricultural Income Tax, Rent Income Tax from Workplace, Interest Income Tax, Private Pension Income Tax(Individual Retirement System Income Tax)), Motor Vehicle Tax, and Residence Property Tax.

- _Indirect taxes_ : VAT and Special Consumption Taxes.

- _Direct transfers:_ Total Direct Transfers, Old Age Transfer, Family Transfers, Disability Benefit, Home Care Benefit, Health Premium Fee Waiver, Disability Contributory Benefit, Scholarship Transfers, Unemployment Transfers, Near Cash Transfers, Sickness Transfer, Agricultural Support Transfer, and AGI.

- _In-kind transfers_ : health and education services.

_Step 2:_ The second step in the approach is to measure taxes and transfers for households in the HBS. Using laws, codes, program rules, plausible assumptions, and household survey responses we calculate how much each household is paying in taxes, and how much is receiving in transfers. For some interventions, the task is more straightforward, such as when the survey has a specific question to ask if a household is a beneficiary of a certain transfer program. Other interventions 14
The indicators that were extracted from Cargo iQ are based on a pair of milestones: NFD and DLV, i.e., timestamp of the notification of document’s readiness and final handing over of the physical shipment to the freight forwarder. These milestones have the highest compliance levels. The time elapsed between events NFD and DLV was computed for each e-AWB recorded in the system at destination country given the validity of the time difference (both timestamps have to exist and the time difference between them has to be positive). Destination country aggregates presented as part of the aviation pillar of natural unit indicators were constructed from these time differences derived for each e-AWB. Country-level aggregates computed were means, medians, and absolute square deviations.

Figure 3 shows the country coverage of the final subset of the Cargo iQ dataset. As was the case with the survey-based LPI, low-income countries have the least coverage (only a quarter of LICs are available as part of aviation statistics). Geographical coverage is lower overall for Cargo iQ in comparison with UPU. East Asia & Pacific, South Asia and Sub-Saharan Africa all have about 35-40 percent of countries covered. Overall, slightly less than a half of the World Bank member countries have records sourced from Cargo iQ information on aviation supply chains and logistics. For a more detailed overview of Cargo iQ data, refer to Appendix 3.

_Figure 3: Country coverage of Cargo iQ dataset by WB region and income group_ Figure 3 Cargo IQ country coverage <!-- Start of picture text --> 66.7%<br>58.6% 57.1%<br>60%<br>41.5%<br>39.5%<br>37.5%<br>40% 35.4%<br>20%<br>0%<br>East Asia & Pacific Europe & Central Latin America & Middle East & North America South Asia Sub-Saharan Africa<br>Asia Caribbean North Africa<br>80%<br>60.5%<br>60%<br>42.6%<br>38.9%<br>40%<br>25.0%<br>20%<br>0%<br>High income Low income Lower middle income Upper middle income<br><!-- End of picture text --> 8
*30

holdings also increase with the size of the plots owned. Returns to land holdings deteriorated from 1993 to 1999 for households with less than 0.4 hectares per capita, while they improved for those with more land. The changes in returns decreased poverty incidence among the latter group by 2 percentage points, while it increased poverty among the former by 0.82 percentage points. Paternostro, Razafindravonona and Stifel (2001) hypothesize that this difference follows from an extensification of land use by smallholders in the face of demographic pressures forcing small farmers to expand their fields into less productive and more fragile areas.

# **_Distribution,_** _poverty and shocks_

Poverty estimates provide a snapshot of the standard of living at a certain point in time and reflect both policy reforms as well as temporary external shocks such as droughts. When evaluating the evolution of poverty it is thus important to control for the effect of external shocks on comparative poverty figures. Controlling for all other factors, the Ethiopian panel analysis estimated that household income growth was reduced by about a fifth because of rainfall shortage (Dercon, 2001). The role of rainfall variations in influencing household income growth was also an important feature of the Zimbabwean and Madagascar experience.

That poverty increased sharply in Zimbabwe during the 1990s is without question (Alwang and Mills, 2001). The decline in economic well-being (and increase in poverty) is evident from the leftward shift in the distribution of real household consumption (Figure 5). The change occurred mainly in the vicinity of the poverty line (Z$30 per month)-a sharp increase in the numbers of people consuming just below, and a parallel decline in the numbers just above the poverty line. What is less clear is whether poverty increased because of the droughts that afflicted the country in 1991/92 and again in 1994/95, or because of the Economic Structural Adjustment Program (launched in 1991) which was being implemented at the same time.

Alwang and Mills (2001) apply non-parametric methods to simulate what the 1995 distribution would have been if the 1990 rainfall patterns had applied that year. This exercise confrmns that the drought led to an increase in poverty during the early 1990s, but it also indicates that the drought alone cannot fully explain the deterioration in economic well-being (Figure 6 Panel A). As discussed before, actual changes in household location, assets and individual characteristics (notably the levels of educational attainment) would actually, other things constant, have _raised_ consumption levels and reduced poverty (Figure 6 Panel B).
The literature on micro factors has mainly focused on the optimal design of incomplete contracts under the changing state of the world (Tirole, 1999), but the empirical studies are only a few. An interesting hypothesis was raised by Woodhouse (2006), which discusses the obsolescing bargaining power of foreign investors and describes the situation where an investor holds the upper hand in negotiations with a host government _ex ante_ but it loses the bargaining power as the contract ages since exit from a committed investment is costly due to high sunk costs (Vernon, 1971). In these cases, host governments have leverage to break contracts, because the MNC’s asset immobility makes the firm’s threat of exit no longer credible. In general, the obsolescing bargain has been applied most often to investments in oil, natural resources, and metals, as well as infrastructure investments and other sites.

Given that the empirical facts have still not been fully investigated, this paper contributes to the literature by testing several theoretical hypotheses posed by previous papers in the area of political economics. The study highlights contract designs and economic cycles that could significantly affect host government’s incentive to breach contracts or re-negotiate contractual terms. This paper also offers analysis on cross-country heterogeneity about the effects of micro and macro triggers on breach of contract. Following a recent theoretical literature on state capacity and development, it looks at how the causal relationship between the probability of contract breach and potential triggers differs by the level of state fragility, inclusiveness of political institutions, and natural resource dependency (Besley and Persson 2009, 2010).

A unique contract-level data set is constructed covering a universe of contractual disputes using two main data sets (World Bank’s public-private infrastructure data and UNCTAD’s dispute data) to address these questions. The new data set has contractual details and improves measurement of the strength of property at the project level, which allows me to test the relative importance of the variety of micro vs. macro triggers. The cross-section of about 5,237 contracts is covered from 1985 to 2012, building a large sample of both publicly and privately procured contracts. The contract-level data are aggregated to establish a cross-country panel covering 150 countries from 1985 to 2012, which allows me to conduct international historical comparisons of the event of contract breaches.

There are two key empirical findings. First, contract-level analysis supports the obsolescing bargain hypothesis; the hazard rate of contract breach exponentially increases as a project matures. The hazard rate is significantly lower if projects are implemented in politically stable and democratic countries, which is consistent with previous findings in the case of outright expropriation and nationalization (see Eden, Kraay and Qian (2012) and World Bank (2012)). Furthermore, as the stake of the private sector in PPP projects gets larger, BOC risk gets elevated, but contracts can be designed to mitigate BOC risk by working in 3