rafmacalaba/data-use-mentions-extended huggingface dataset:
]<br>[0.255]|0.115<br>0.046<br>0.935|7.73<br>6.91<br>6.91|24,480<br>24,885<br>24,898|based on data from the Ethiopian Demographic and|ndex. The dependent variable is described at the top|ndardized value of the described outcome; and in col|dren, arguing with husband, refusing sex, burning foo<br> measure _I_FPE<br>_zy_<br>, two mother tongue instruction (MT|spectively, and the interactions for regions in which t|ear and zone fxed efects, zone-specifc linear trends,|n parentheses; _p_-values are shown in square brackets.|
|||�<br>Years of Schoolingizy|Mean of Dependent<br>(Pre-Reform Cohorts)|First Stage F-Statistic|N|_Source_: Author’s analysis|_Note_: BMI = body mass i|columns 4 and 5 it is a sta|permission, neglecting chil<br>education (FPE) intensity|and with script change, re|regressions include birth y|the zone level and shown i|

A.26
ation in coresidency rates compared to the IGRC estimates. Since coresidency rates can vary substantially across countries, over time, and across gender, the IGC estimates are likely to be more reliable for understanding the pattern and evolution of intergenerational mobility. The evidence shows that the IGRC estimates from coresident samples lead to the incorrect conclusion that intergenerational schooling persistence is virtually same in India and Bangladesh. In contrast, the IGC estimates from coresident samples yield the correct conclusion that persistence is higher in Bangladesh, and also provide a reliable estimate of the gap between the two countries. The evidence from both Bangladesh and India shows that coresidency rates are lower for girls, and thus the persistence estimates suffer from stronger downward bias, which may generate a false impression of lower gender gap.

The evidence and analysis in this paper thus provide a strong rationale for focusing on IGC as a measure of intergenerational mobility in the context of developing countries. Perhaps, the most important implication of our analysis is that a large number of good quality household surveys in developing countries that use coresidency to define household membership (for example, LSMS and HIES) are not worthless in analyzing the strength, pattern and evolution of intergenerational economic persistence. Much progress could be made with the imperfect data if the researchers move away from the current emphasis on IGRC and use IGC as the appropriate measure instead.

# **References**

Arrow, K, S. Bowles, S. Durlauf (2000). _Meritocracy and Economic Inequality_ , Princeton University Press.

Atkinson, A.B., A.K. Maynard, and C.G. Trinder (1983). _Parents and Children: Incomes in Two Generations._ London: Heinemann Educational Books.

Bardhan, P (2014), The State of Indian Economic Statistics: Data Quantity and Quality Issues, Mimeo, Berkeley, CA.

Bardhan, P (2005), “Theory and Empirics in Development Economics”, Economic and Political Weekly, August, 2005.

30
Newspaper reports will also be subject to biases. For example, newspapers may only report high profile violent conflict. At the same time, coverage may differ significantly across regions. Areas with high readership or local correspondent penetration may also yield regional over-reporting biases. The PODES for example suggests that local newspapers are only available in 55.9 percent of villages. While readership may not be a proxy of correspondent coverage, clearly urban areas may be subject to more intense scrutiny. However, in large part because of the use of national media, which often do not record localized conflicts, the resulting data massively underreports levels of conflict. While further work is being conducted by UNSFIR, UNDP, the World Bank and independent researchers using _local_ media sources, which appear to more accurately report conflict, the creation of a national dataset by such means is still a long way away.

> 7 The module was apparently initiated by the BPS Directorate of National Resilience ( _Ketahanan_ ) under Drs. Suharno.  The PODES is typically conducted in conjunction with the Population Census (e.g., 1990 and 2000), the Economic Census (1986 and 1996), and the Agricultural Census (1993 and 2003) (Kaiser and Molyneaux 2000).

12
and is overwhelmingly rural<sup>25</sup> . Poverty rates, at 23 percent in the median District, are only slightly higher than the national rate of 20 percent in 2013. The numbers also show the subdued level of political competition in presidential elections: the candidate

> 21These are small areas estimates produced by the World Bank, using data from the 2013 UNHS and 2014 Census data. For details on methodology see for example Ebers, Lanjow and Lanjow (2002).

> 22Note that higher values of the UCE score are indicate of lower education performance, as described in the data Annex. We include 2011 UCE performance since those cannot be influenced by current politicians, who were elected in that year.

> 23The Herfindahl Index is most commonly used as measure of firm competition in a given market, but has been widely used to measure political competition . The Herfindahl Index is obtained, at the electoral area level, by the sum of the squares of candidates’ votes. Higher values are associated with a less competitive environment (the index ranges from 1/N to 1, where N is the number of candidates).

> 24We compute all the results at the electoral area level and then obtain the district-level average across all areas.

> 25According to the 2014 Census only 16 percent of the population in Uganda lived in urban areas.

17
to the United Nations (2018), the proportion of prisoners held in detention without being sentenced for a crime has remained almost constant between 2003–2005 and 2014–2016, and still affects almost one third of all prisoners. Thus, the importance of potential developments in this field may be particularly relevant all over the world to better identify those who should stay in prison–or at home–while expecting a trial.

In addition, clear predictions about the length of a trial or expected gains from it may be helpful to improve litigants’ decisions on their cases. In Mexico, the rate of settlement in mediation in specialized labor courts remains low because of overconfidence on the client-side and low-quality legal representation (Sadka, Seira, and Woodruff, 2017). Many plaintiffs spend more on legal fees than what they recover in court. Quality of legal aid and predictions about the case are both effective in correcting these erroneous perceptions regarding their entitlement and the importance of particular types of evidence. Sadka, Seira, and Woodruff (2017)find that providing information to litigants reduces the level of overconfidence of litigants, and nearly doubles the overall settlement rate. Nonetheless, the increase in settlement rates only occurs for the subset of cases for which the plaintiff is present to receive the information, as the information provided to lawyers does not have an effect. Thus, administrative data may also be able to improve litigants’ expectations of a case, identify the optimal path to resolve a specific case and–as in the case of Mexican labor courts–lead more litigants to settle, freeing up space in court proceedings.

Nonetheless, the implementation of machine learning tools in the judicial sector entails relevant risks that may not be overlooked. Inappropriate application of these tools can lead to decisions that may violate due process or that may discriminate based on race, gender or other characteristics. This may be of particularly high risk when the tools represent a black box with no clear explanation of how it works (Rudin and Ustun, 2017).
**Model 2**|**Model 3**|**Model 4**|
|---|---|---|---|---|---|
|**National**|37.5|41.0|48.7|61.0|26.6|
|**Urban**|25.4|34.6|34.2|38.7|14.9|
|**Rural**|43.4|44.1|55.8|71.9|32.4|

Sources: India National Sample Survey Office (NSSO) Surveys. Model 1: Final Model Model 2: District dummies*Time Trend Model 3: Expenditures at the Extensive Margin Model 4: Constant Coefficient Model Table 8: Preferred Model vs. Typical Line-Up Method Predictions for 2014-15

||**Model**|**Line-up**|
|---|---|---|
|National|14.6|11.0|
|Urban|10.0|7.1|
|Rural|16.8|12.9|

Source: India National Sample Surveys (NSSO) Surveys.

25
dummies indicating the dominant agricultural activity (rice, corn, rubber, or palm oil). Only one out of these ten characteristics (corn cultivation) is significantly correlated with turnover, as one would expect by chance. Appendix Table A.3 shows balance on whether a citizen’s contact information is provided by a village official or BPD member (columns 1–2) and the degree of connection between citizens and these officials (column 3). These balance checks support our analysis of the differential effects of turnovers with respect to social distance, discussed in Section 5. Finally, Appendix Table A.4 shows balance on citizens’ demographic characteristics (gender, age, disability, education, employment status, and monthly income).

**Electoral Data Checks.** We further report several checks on the validity of the electoral data. Appendix Figure A.1 plots the raw turnout data and turnout winsorized at 100% against the vote share of the incumbent (panels a and b) and against our running variable, the margin of victory of the highest-ranked challenger (panel c and d).<sup>5</sup> There is no systematic evidence of turnout manipulation in favor of incumbents, as the few instances of excessive turnout are located on both sides of the RD threshold. We confirm this in Appendix Table A.1, where we estimate equation (1) using voter turnout and a dummy for turnout being greater than 100% as dependent variables. There is no evidence that turnovers are associated with differential turnout at the threshold (column 10), nor that they are associated with suspiciously high or low turnout (column 11). Turnovers also have a null effect on an alternative measure of electoral competition, a Herfindahl index of vote shares (column 12).

> 5Recall that 4% of villages in our sample, i.e. 21 out of 512 villages, report turnout over 100%.

9
# **1 Introduction**

Microdata from agricultural surveys is an essential ingredient for empirical analysis of the economic life of smallholder farmers. Particularly in Africa, farm surveys remain the backbone of agricultural statistics (Carletto et al., 2015). However, collecting accurate data in lowincome countries through agricultural surveys is challenging due to various factors, such as the complexity and seasonality of agricultural operations, illiteracy among respondents, and unfamiliarity with standard units of measurement. Overcoming these challenges requires the implementation of complex and costly survey operations. Therefore, the cost-effectiveness and design efficiency of farm surveys, as well as the use of statistical methods to overcome the analysis challenges of their data, remain active areas of methodological research to which this paper intends to contribute.

The analysis focuses on crop yields at the plot level. Generally, plot-level crop yield statistics obtained from agricultural surveys are calculated using either farmers’ reported harvest weight or enumerators’ measured crop-cut harvest weight. Research has shown that self-reported crop yields are prone to non-classical measurement errors and tend to be higher than objectively measured yields obtained through crop cutting (Abay et al., 2019, Desiere and Jolliffe, 2018, Gourlay et al., 2019, Yacoubou Djima and Kilic, 2024). Although enumerator-measured crop-cutting is widely considered the most precise method for quantifying agricultural output (Fermont and Benson, 2011), its implementation is more costly and time-intensive. As a result, this information is often available only for a subset of the survey, leading analysts to rely on statistical methods and imputation techniques to fill these data gaps.

With the recent advances and their off-the-shelves availability, machine learning (ML) algorithms have become go-to tools of analysts to perform prediction exercises so including them as part of an imputation exercise is a natural next step. Economics research has seen the increasing use of ML techniques in recent years, with proven effectiveness. According to Athey (2018), evaluation of ML’s early contributions to economics, ML techniques have been particularly successful when applied to prediction-based problems
.82|2,068|
|Child has clean nose|0.00|( 0.02)|0.94|0.83|2,068|
|Child does not have skin problems|0.00|( 0.02)|0.81|0.92|2,067|

Every row corresponds to a separate estimation (equation 3). The vitamin index is based on three variables: the child has received vitamin A, ferrous sulfate and deworming medicine during the last six months (yes/no questions). The hygiene index captures the hygienic condition of the child observed by the test administrator. The Attitudes index is calculated based on answers by the caregiver to the following questions: 1. Do you answer your child’s questions? 2. Do you think boys can play with dolls? 3. Do you think girls can play with cars? 4. Do you think children start talking by nature? 5. Does the child’s brain develop from gestation or when the child starts going to school? Each outcome variable is standardized using the mean and standard deviation for control households. The rows shows ITT estimates. All regressions include controls for the household-level treatment, the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, whether the leader household had access to electricity, child age, gender and test-administrator fixed effects as well as a binary indicator indicating the data was collected in 2016. Five villages without leaders were excluded from the estimation. The standard errors (in parentheses) are clustered at village level. p<0.1, p<0.05, p<0.01 67
changes. Although this paper does not conduct quantitative causal analyses to examine the effects of ETI on tax compliance, it provides a descriptive analysis by utilizing various sources including studies, materials, and information regarding Korea’s ETI introduction, statistical data provided by the National Tax Service (NTS), and the results of a perception survey on ETI conducted by the author for this paper.

This paper describes Korea’s motivation for ETI, the implementation process, the legal and regulatory regimes, the key issues associated with the introduction, the accomplishments and lessons learned, and future challenges for tax policy makers and tax authorities in developing countries that are contemplating ETI. Chapter 2 presents an overview of ETI including its concept, the process of e-voicing, and the experience of various countries with e-invoicing. Chapter 3 then reviews the “drivers” of ETI in Korea and the implementation process and challenges. Chapter 4 evaluates the effects of ETI on compliance cost reduction and combating tax fraud 5
In the first round, more than 14,000 individual respondents were selected to provide detailed accounts of their employment, current and historical—going back five years to 1988. Target respondents expanded further in subsequent rounds.<sup>5</sup> By the fifth round in 2014, more than 34,000 individuals were interviewed in detail. Combining the current and recall employment survey modules enables us to construct an annual employment history from 1988 to 2015 for individuals who were successfully tracked in all five waves.

IFLS also includes a module for ever married women ages 15-49, which includes detailed questions about all their pregnancy incidences—still in womb, resulting in livebirth, stillbirth, and miscarriage. In the first round, close to 5,000 women were interviewed. Tracking the same women over time allowed us to add in subsequent pregnancies that occurred after the first wave. For each live birth, respondents were interviewed at about the year of (or age at) childbirth. We can, therefore, complement our annual employment data with information on children’s age at each year, and thus their preschool eligibility. To implement the fixed effect model discussed in Section 4.3, we further restricted our sample to women who were found to be between ages 19 and 45 in at least two waves.<sup>6</sup> In the remaining text, we refer to this as our “constructed panel.” We define preschool access as the number of preschools per 1,000 preschool-aged children, aged 3-6, in each district (henceforth, referred to as preschool density). The number of preschools is obtained from pooling Village Census (PODES) cross-sections from 1990 to 2014. PODES is fielded, roughly, once every three years.<sup>7</sup> The population of preschool-aged children comes from the annual<sup>8</sup> National Socioeconomic Survey (SUSENAS) cross-sections of respective PODES years.

Following decentralization reform in 1999, regional governments have been entrusted
7.9%|Yes|
|Sales tax (alcoholic/non-alcoholic beverages)|0.9%|0.2%|Yes|
|Sales tax (luxury goods, cars and others)|0.1%|0.0%|No|
|Excise taxes|6.9%|1.4%|Yes|
|Foreign trade taxes|1.1%|0.2%|No|
|Others|1.5%|0.3%|No|
|Non-tax accounts|-0.1%|0.0%|No|
|Non-tax revenues|13.3%|2.8%|No|
|**Contributions to social security**|**6.8%**|**1.4%**||
|From employees|6.6%|1.4%|Yes|
|From employers|0.2%|0.0%|No|
|**Grants**|**0.2%**|**0.1%**|No|

_Source_ : Author’s elaboration based on Direccion de Presupuestos (2014) and the 2009-2015 data on annual tax revenue published by Chile’s Internal Revenue Service (Servicio de Impuestos Internos, SII).

# **3. METHODOLOGY, DATA AND ASSUMPTIONS**

The analysis follows the so-called CEQ methodology (Lustig and Higgins, 2013; Lustig, 2017b) to assess the distributional impact of taxes, transfers and subsidies across income groups in Chile in 2013 based on household-level data and administrative records on taxes and social spending. Basically, the methodology consists of defining income concepts first, and then allocating taxes, social contributions, subsidies and public social spending to individuals included in the household survey in a consistent and methodologically sound way, so that it is possible to compare incomes and incomebased measures of wellbeing before and after taxes and public transfers.

The methodology
clustered at the firm level to allow for potential autocorrelation in the choice of export destination at the level.

As a second step, we explore whether the third-country effect of RTAs is stronger for deep trade agreements by estimating the following equation: where _Exp Other Depthij,t−_ 1 = max _k_ = _j {Exporting Dummyik,t−_ 1 _× Depthjk,t}_ captures the depth of the deepest RTA that country _j_ has with a country to which firm _i_ exported in _t_ -1. We exploit the richness in the content of RTAs to construct measures of depth and investigate different aspects of third-country RTAs that generate stronger spillover effects for firms that already exported to one of the member countries. The first indicator is the aggregate depth, measured as the additive number of provisions that are strictly legally enforceable. This aggregate depth measure is normalized between 0 and 1 so that ( _β_ 1 + _β_ 2) captures the impact of prior export experience of firm _i_ to a country, to which firm _i_ exported in _t_ -1 and has the deepest RTA with country _j_ , on its export participation and entry to country _j_ .

Going beyond the aggregate depth measure, we further investigate whether certain provisions are particularly important in driving the spillover effect of RTAs. Specifically, we separately estimate the effects of RTA depth based on provisions that are nondiscriminatory (or most favored nation-MFN) and preferential. This distinction is interesting and important for our analysis, since we focus on third-country effects of RTAs. This means that _Depth_ in equation (3) becomes a vector of MFN-depth and preferential-depth, measured as the additive number of MFN and preferential provisions respectively, again normalized between 0 and 1.<sup>20</sup> As above, the interaction variables are constructed such that they capture the maximum MFN and preferential depth of country _j_ ’s RTAs among the set of countries that firm _i_ exported to in _t_ -1 . We expect RTAs that are “deep” in MFN-provisions to generate larger spillovers to
## 2.2.2 Poverty line used in HNAP analysis

Another source of poverty over-estimation in the HNAP report’s methodology is related to the definition of the poverty line. The poverty line used in HNAP analysis is $1.90, converted in local currency using the 1USD: 3200 SYP exchange rate prevailing at the time of the survey. This approach implies a poverty line of 6,080 SYP per person per day.

Despite the fact that the report uses the “extreme poverty” terminology – the approach deviates substantially from the one that underlines extreme poverty estimation. The $1.90 extreme poverty line in fact is defined in 2011 PPPs terms.<sup>16</sup> Used as a benchmark to compare poverty across low income countries and to obtain global poverty estimates, the $1.90 value is obtained as an average of LIC national poverty lines, all expressed in PPP values. The correct approach for using the $1.90 international poverty line corresponding to extreme poverty should have been to first use 2011 PPP value<sup>17</sup> to convert 1.90 US dollars in local currency (SYP) and then express it in 2021 prices using CPI inflation over the 2011-2021 period. Following the correct approach leads to a 2021 value of the $1.90 2011 PPP extreme poverty line of 1,537,153 SYP per person per day, which would be approximately one-fourth the value of the poverty line used in the HNAP report. The incorrect use of the international poverty line implies that the 98 percent extreme poverty estimation provided in the HNAP report is grossly over-estimated.

# 3 Poverty estimation using the HNAP 2022 Demographic and WASH Household Survey

The analysis of data from the HNAP 2021 Demographic and WASH survey revealed severe limitations in the design of both the household income and expenditure module which limit the possibility to use these data for poverty measurement.<sup>18</sup> In particular, both income and expenditure modules fail to capture the
objective of this study is to propose a structural framework to estimate equilibrium credit, which is anchored in the long-run transaction demand for credit by the real economy, and accounts for the effects of economic and financial development on equilibrium credit. The proposed framework consists of two stages. First, we estimate country specific credit demand functions and conduct cross-country poolability tests on the income and price elasticities of credit. This step is implemented using the Mean Group (MG) and Pooled Mean Group (PMG) estimation methods of Pesaran and Smith (1995) and Pesaran _et al._ (1999) and quarterly panel data for high- and middle-income countries over the period 1980 _−_ 2010.

Second, we model the cross-country variation in the income and price elasticities of credit, as well as the speed of adjustment of credit to its long-run equilibrium, by regressing the country specific coefficients on a set of relevant development indicators. To 4 As an example, consider the countries of the US and Croatia. Taking the credit-to-GDP ratio as the dependent variable to model credit demand would imply that the use of credit in economic transactions in these economies is the same. This seems hard to rationalise. One would clearly expect that US consumers and businesses use credit much more frequently in their transactions than consumers and businesses in Croatia.

4