rafmacalaba/data-use-mentions-extended huggingface dataset:
2 The Data Revolution in Justice 23 and earnings. Randomizing sentences is unethical, but randomizing cases to judges who are systematically harsher or more lenient than others generated the inference on the long-run impacts of sentences lengths. Thus, in countries where cases are randomly assigned, the random assignment itself can be used as an exogenous source of variation to evaluate the impact of judicial decisions. This method has become widely used since the credibility revolution.

For instance, Dobbie and Song (2014) investigate the causal effect of consumer bankruptcy–one of the largest social insurance programs in the United States–on debtors by exploiting the random assignment of bankruptcy filings to judges. The authors find that debt relief increases individuals’ earnings and employment and decreases mortality and foreclosure rates. In Norway, Dahl, Kostol, and Mogstad (2013) evaluate the existence and importance of family welfare cultures, where the receipt of a welfare program by one generation causes increased participation in the next generation. The authors exploit the random assignment of judges to applicants for disability insurance whose cases are initially denied, by comparing the implications of being assigned an appeal judge who is systematically more lenient than a harsher one. They find strong evidence for a causal link across generations, where the adult child’s participation increases if the parent also participated in the insurance program.

Another policy question that has been answered by leveraging the random assignment of cases to judges is the impact of pretrial detention on defendants. Arnold, Dobbie, and Yang (2017) use the detention tendencies of quasi-randomly assigned bail judges to evaluate the impact of pretrial detention of subsequent defendant outcomes. They find that pretrial detention–due to comparably harsher judges–decreases formal sector employment and the receipt of government benefits.

This “judge leniency” design may be applied to other characteristics of judicial decisions. For example, what is the impact of the speed of justice? These kinds of questions can be studied in administrative data where random case assignment exist. Judges predicted to be fast also tend to be fast
, which is sometimes measured as

> 3 Shared prosperity is an indicator of shared growth developed by the World Bank, measuring the income growth of the bottom 40 percent of the distribution in each country. It is often compared to the average growth of the country.

> 4 The definition of HFCE is broader than what is typically included in household surveys, encompassing items like spending of non-profit entities such as religious groups, NGOs and foundations. Previous literature (e.g. Ravallion, 2003, and Deaton, 2005) has referred to HFCE as private consumption expenditure (PCE), defined under earlier systems of national accounts.

> 5 Anand and Segal (2008), Ravallion (2003), Deaton (2005) use HFCE (or the older measure, PCE) to compare with consumption from surveys.

5
# 3.1 Data Description

The following are various variables that affect trade and have been used in gravity equations in the literature. Income and income per capita are real GDP and real GDP per capita in year t for country i. Distance is between country i and the United States in kilometers. Area of country i is measured in square kilometers. Border is a dummy variable which is 1 if the country shares a border with the US - in our case, only Mexico and Canada. WTO member is a dummy variable which is 1 if the country i was in the WTO in year t. Of the 173 countries in our sample, 142 and 152 were members in 1997 and 2001, respectively. Common language is dummy variable with value is 1 if country i has English as a commonly spoken language. Island is another dummy variable and is 1 if the country i is an island. The majority of the above data for these variables is provided by Rose [2003]<sup>8</sup> tough we added some missing observations from the World Development Indicators [2002] of the World Bank. Sample statistics are provided in Table 1a.

The trade flows, on the other hand, are from USITC, which provides extremely detailed data on various measures of bilateral trade of the United States with each trading partner under each preference program at the tariff line level<sup>9</sup> . The export value, Xijt, is the Customs Value of all exports from i to the United States in category j in year t (1997 or 2001).

Due to the lack of data, most papers in the literature use the bilateral distance to measure transport and transaction costs. However, when we use distance as a proxy for all such costs, we are explicitly assuming that these are uniform across a wide range of products and that the cost of one kilometer is the same across countries. Our proxy Transport Costs is constructed as the natural log of the ratio of CIF value (
Table 2: Family characteristics

||All families|
|---|---|
|Number of Families in BF|6,582,657|
|Number of Families that left during the observation period|4,602,881|
|Number of families that came back to the program|630,549|
|Number of families that moved to diferent municipalities|457,079|
|Number of families that moved to a diferent municipality prior to exit|80,629|
|Average number of years in the program|3.8|
|% of families having one member in formal dependent employment at entry|28.02|
|% of families having one member in formal dependent employment at exit|35.48|
|% of families having head in formal dependent employment at entry|15.12|
|% of families having head in formal dependent employment at exit|21.98|

**Notes:** The table presents summary statistics for families registered in CadUnico in December 2012 for the first time and being in BF at the beginning of the first semester of 2013. Information on RAIS are obtained through a merge of CadUnico data, BF payroll information and administrative matched employer-employee data (RAIS).

characteristics of adults, as reported at the time of registration, and their probabilities to exit BF. This serves as useful descriptive evidence in support of the identification strategy for the effect of local employment changes on the probability of exit, which will be described in the next section. Following prior literature and Morgandi _et al._ (2023), we employ the Kaplan–Meier (KM) estimator and Cox regressions.<sup>15</sup> Table 3 shows that exit probabilities are correlated with some of the beneficiaries’ observable characteristics at entry.<sup>16</sup> The speed and probability of exit from the program increase with age and education levels of the adults, particularly those with tertiary diplomas. Exit is also higher
While we adopt the method of Shekar et al. (2015), it is not the only application of the FGT poverty indicators in non-monetary indicators. Nguyen and Wodon (2012, 2015) applied the same approach to the estimation of child marriage. Apart from estimating the incidence of child marriage (the share of girls marrying before age 18), they also estimated the “child marriage gap,” which accounts for how early a girl marries.

We intend to generalize the method for all of the aforementioned existing malnutrition indicators and produce the gap estimate for every country with data, using a standardized data set of growth Z-scores calculated from Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). We will also extend the calculation to the squared malnutrition gap, i.e., FGT(2). Foster, Greer, and Thorbecke (1984) showed that the FGT class of poverty indicators have a number of attractive axiomatic properties such as additive decomposability and subgroup consistency.

Analog to the poverty gap, the malnutrition gap is defined as the average shortfall of children’s Z-scores of an anthropometric measure from the reference line (counting zero shortfall for non-malnourished children) as a proportion of the reference line. It measures how far off a child is from the WHO growth standards. Taking stunting as an example, the national average stunting gap ( _Gap_ ) can be expressed as where N denotes the total number of children under 5 years of age in a given population, M denotes the number of stunted children, and z� denotes individual Z-scores of stunting and z� ��2 in this equation. Implicitly in this equation, the shortfall for non-stunted children is zero when z� ��2. Subsequently, the national average squared stunting gap ( _SqGap_ ) can be expressed as 5
Federalism serves as a laboratory for policy inventions (Oates, 1999). Looking at Switzerland, at the cantonal level, evaluating the impact of federalism on tax morale and the size of the shadow economy, Torgler, Schneider and Schaltegger (2010) provide evidence that a higher level of local autonomy is correlated with higher tax morale and a lower shadow economy.

_3. Evidence from Developing Countries: The Case of South Africa and Botswana_ Further insights that might be relevant for transition countries might be derived from comparative evidence in developing countries, under a relatively controlled environment. Cummings, Martinez-Vazquez, McKee and Torgler (2009) have compared Botswana and South Africa. Both countries have experienced strikingly different social histories, despite being neighbors. Thus, these countries offer a natural experiment for the investigation of the effects of tax morale stemming from perceptions of government. Analyses of data from surveys of public attitudes toward government show that perceptions of government fairness and efficacy are considerably higher in Botswana and self-reported tax compliance appears to be higher as well. Using controlled laboratory experiments Cummings et al. (2009) are able to confirm that these reported differences in tax attitudes can be explained by social norms. In the experiments, the same tax reporting and enforcement regimes were imposed in sessions conducted in both countries. Given the experimental control, the authors argue that observed differences in compliance behavior are the result of differing social norms. Similarities in the tax systems allow employment of the same jargon in each country, and since English is widely used in both countries, the authors were able to conduct the experiments in a common language. These facilitate comparisons of tax compliance behavior across these otherwise widely divergent countries. Their laboratory experiments were conducted in the fall of 1999 and the survey data were collected during 1999 – 2000.

We have seen in the previous part of the report that tax compliance not only depends on enforcement effort, but also on the inhibitors that are inherent in the individual-government relationships. For the personal income tax (PIT), the selfassessment and audit
others, using our data on posterior probabilities and selecting their own 's from the copious data ii available from the WBES or other sources. ZZ

# **III.4 Selecting a Specific LCA Model and Evaluating Its Properties**

**III.4 Selecting a Specific LCA Model and Evaluating Its Properties** The implementation of step 1 of the 3-step process entails estimation of and the by cc maximizing the likelihood at (3). LCA is typical of latent-variable models in that implementation ff(∙| ∙) ππ requires making many detailed specification decisions. In most practical applications of LCA, the choice of how many classes ( _C_ ) to estimate is primary, with a relatively small number of options considered. However, relaxing local independence enormously increases the number of possible options when specifying the DGP. In this subsection, we provide an overview of the procedures we used to choose our favored model, relegating most details to Appendix C.<sup>53</sup>

Settling on the details of model specification moves through three stages. First, statistical measures of model-fit are used as criteria to choose a very small set of satisfactory models. Second, the results from those models are evaluated using more subjective criteria. For example, parsimony is important to avoid over-fitting and to facilitate meaningful interpretation, which usually means the use of a simpler model (with fewer parameters). Third, the researcher examines class homogeneity and separability. Homogeneity is the notion that the members of a specific class exhibit similar characteristics or, equivalently, that there are certain configurations of responses typifying each class. Separability captures whether each class looks quite different from all other classes, or, equivalently, that there are certain configurations of responses that distinguish each class from the others. In all three stages, statistical measures are used as model-selection criteria.

To begin the model selection process, three preliminary decisions were necessary. First, we chose to analyze the responses for suppliers and customers separately. We presume that firms

> 53 Detailed background
br>30%<br>25%<br>20% February<br>15% June<br>10%<br>-100 -50 Loss Percentage0 50 100 5%<br>February 0%<br>June 1 2 3 4 5<br>kernel = epanechnikov, bandwidth = 4.1864 Quintile<br>.04<br>.03<br>.02<br>.01<br>0 Percentage Lost<br><!-- End of picture text --> Source: Own computations using June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. Asset index weights are calculated using pre-earthquake assets.

# **5.3.Directly Affected Areas**

Table 15 presents descriptive statistics for households living in affected areas. The food consumption score is calculated based on the number of different food groups consumed by the household, to represent diversity, and the number of times a week they are consumed. Notably, we observe that the average food consumption score is 55.79, with a standard deviation of 19.75. A majority of households is above the limit food consumption thresholds (the limit consumption threshold being 42 and the critical threshold being 26).

40
A comparison of social returns to education to the discount rate used in social projects gives an indication of the social efficiency of the investment. Since the advent of human capital theory in economic thought, estimating the returns to investment in education has been a very popular subject among researchers (see Psacharopoulos and Patrinos 2018 for a recent review on the subject). We distinguish between private and social returns. We provide estimates using the Mincerian earnings function as well as the full-discounting method.

This paper provides the first estimate of the returns to education in Azerbaijan since 1995 (Newell and Reilly 1999). Newell and Reilly (1999) estimated a high 13.8 private return to tertiary education. Using the same data, a very low return of 3.7 percent overall, and 5.5, 4.2 and 4.0 percent for primary, secondary and tertiary education, as well as 3.8 and 4.5 percent for males and females, was estimated by Peet et al. (2015). Montenegro and Patrinos (2014) obtain a 7.2 percent return overall return, but 19.8, 2.2 and 8.1 for primary, secondary and tertiary education.

There is a sizeable overall gender wage gap in Azerbaijan’s workforce (Pastore et al. 2016). Using a unique database from a survey of young people ages 15-29 years shows that new labor market entrants begin with little or no gender differences in earnings, but a wage gap gradually emerges over time closer to the childbearing years. The gender wage gap grows from virtually zero, or even a small, positive gap in favor of women, until age 20 years, to about 20 percent two years later and even more than 30 percent at age 29 years.

4
# **7 Conclusions**

Child care markets are at the center of the trade-off facing parents as they allocate time between the labor market, child care, and leisure. By supplying labor in the market, parents receive income yet lose a portion of the time they would otherwise devote to caring for children. Therefore, access to high-quality child care options, which can effectively substitute for parents’ time, is crucial to family-friendly policies, child development, and labor supply. Any policy seeking to expand child care access should recognize that center-based quality and price are market equilibrium outcomes, which will adjust in response to policies.

We model the child care market and estimate its structural parameters, endogenizing both demand and supply to evaluate policies in an equilibrium context. We model parental choices on labor supply, child care, and time allocation and providers’ decisions on entry, quality, and price. We model two- and single-parent households. In addition to exclusive parental care, we allow parents to choose between care provided outside the home by center-based providers or relatives (e.g., grandparents), and home-based care provided by non-relatives (e.g., nannies). In equilibrium, the market features multiple levels of quality as well as horizontal differentiation and price variation within and across qualities.

We estimate the model by Simulated Method of Moments on a sample of two-year old children from the 2003 Early Childhood Longitudinal Study (ECLS-B). We obtain precise point estimates that provide a good fit of the data and are comparable with previous literature. Parameter estimates imply that households value consumption less than child development and show that mother’s time has a strong positive impact (increasing in education) on child development. They also show a high degree of substitutability among time with various care providers. We allow multiple parameters to vary by household location, finding that both wage offers and costs of care are increasing in the location’s size (children’s population) and (average) income yet not at the
were interviewed during both the IHPS 2013 and the FIAS 2015, 299 households were also interviewed during the IHPS 2016.

# **3.1. Household Aid Receipt and Livelihood Outcomes**

Both the IHPS and FIAS collected information on whether the household received food or cash aid from a variety of sources. We use a dichotomous variable equal to 1 if the household received aid from any government or NGO source. Both surveys also elicited comprehensive information on household food and non-food consumption, which allow us to construct our dependent variables, namely total non-food expenditures per capita in natural logarithms, the total value of food consumption per capita in natural logarithms; total value of food purchases per capita; total calories consumed per capita in natural logarithms; and food consumption score. We hypothesize that shocks will reduce these outcomes. With respect to food aid, we hypothesize that the total value of food consumption, calories per capita, food consumption score and school participation rates will increase with aid receipt. Food purchases may either increase or decrease, in part because some aid is distributed in cash.

# **3.2. Crop Production and Extreme Rainfall Events**

While our goal in this paper is to evaluate the impacts of extreme rainfall events on welfare outcomes, it is instructive to look at impacts on plot-level maize yields that are computed from the survey data.<sup>6,7</sup> McCarthy et al. (2021) systematically evaluated the explanatory performance of a wide range of drought and flood shock measures from different data sources using IHPS and FIAS sample data. That work finds that the most robust measure for flood shocks is a dichotomous measure based on an index of

> 6 Maize yield estimates provided by the agricultural extension services at the extension planning area level are also used in the MVAC model for targeting purposes, as detailed in the subsequent section.

> 7 Maize harvest is computed in dried grain- and kilogram-equivalent terms. In doing that, harvests that are reported in non-standard measurement units are converted
et al., 2021; Michaels et al., 2012) and the sorting of higher-ability individuals into these areas (Combes et al., 2008), monetary poverty is not necessarily lower in urban areas once the higher cost of living is considered<sup>3</sup> . Our newly created dataset confirms that for the 16 Sub-Saharan African countries we consider, the cost of living is higher in urban areas, particularly in denser urban areas. Urban poverty can be as prevalent and severe as rural poverty when the negative effects of density, such as crowding and congestion externalities, outweigh its benefits (Lucci et al., 2018; Marx et al., 2013). Additionally, it is an empirical

> 1Target 1.1 aims to ‘eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a day’. The latest extreme poverty line is $2.15 per day in 2017 Purchasing Power Parity (PPP) terms (World Bank, 2022).

> 2The preliminary findings from 7 countries are reported in Combes et al. (2022).

> 3For example, African cities have a high cost of living relative to their country’s GDP level (Nakamura et al., 2019).

2
What teachers believe (teacher mindsets). These are discussed further in Section 3 and Annex 1.

The unit of observation is the teacher and the outcome of interest is average student learning gains between baseline and follow-up (approximately 8 months) for the teacher. We construct estimates of student learning gains using the matched teacher-student database and student assessment data from baseline and followup. Student learning gains linked to a particular teacher are calculated as the percentage correct score in the follow-up student assessment minus the percentage correct score in the baseline student assessment, averaged across their students. We then model student learning gains using our ML algorithms on a host of student, school and teacher level covariates. We conduct the analysis separately for Math and Kiswahili.

<mark>The estimation of student learning gains for a teacher can be seen as analogous to the estimation of Teacher Value Added (TVA), however there are some differences between our estimation of student learning gains and the way in which TVA is often estimated in the standard education economics literature. In this literature, TVA is estimated as the teacher fixed effect from a regression of student follow-up test scores on student level covariates including lagged test scores (see Koedel et al. 2015 for a comprehensive review)—this is often referred to as step 1. This teacher fixed effect is then regressed on a host of teacher and school level covariates to find out teacher characteristics that best predict TVA (Rockoff 2004, Chetty et al. 2014a, Koedel et al. 2015)—referred to as step 2. We choose the student learning gain approach over the standard TVA approach to avoid imposing a linear functional form in either step 1 or step 2. This allows</mark>

> 6 For Grade 2, the Math portion of the test had 12 questions while the Kiswahili portion had 16 questions. For Grade 3, The Math portion had 17 questions while the Kiswahili portion had 15 questions (to ensure uniformity in comparison, we only chose those questions that were repeated in baseline and follow-up
Table A.4: No role for perceived rank in th impact of IOp on investment behavior

|**Dep var:**Binary = 1 if subject chooses to|(1)<br>invest|(2)|(3)|
|---|---|---|---|
||All|Without rank feedback|With rank feedback|
|Perceived pre-investment earnings rank|0.024|-0.266|0.115|
||[0.833]|[0.204]|[0.390]|
|IOp treatment|7.468|7.938|6.268|
||[0.377]|[0.578]|[0.559]|
|IOp X Perceived rank|-0.033|0.171|-0.097|
||[0.807]|[0.510]|[0.543]|
|Constant|1.090|3.234**|0.399|
||[0.143]|[0.023]|[0.651]|
|Observations|499|164|335|
|Pseudo R-squared|0.0001|0.009|0.002|

**Dep var:** Portion of earnings invested, conditional on being _>_ 0

||All<br>Without rank feedback|With rank feedback|
|---|---|---|
|Perceived pre-investment earnings rank|0.333<br>0.917|1.55e-14|
||[0.743]<br>[0.612]|[1.00]|
|IOp treatment|7.468<br>7.938|6.268|
||[0.377]<br>[0.578]|[0.559]|