rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 1.9. Income per capita and Gini index for per capita incomes in HBS and RLMS, 1994-2015**

<!-- Start of picture text --> Panel A: Income from Different Sources Panel B: Gini using Different Data Sources<br>11 .5<br>Income per capita (HBS raw data)<br>Income per capita (HBS with corrected weights)<br>10.5<br>Income per capita (RLMS)<br>.45<br>10<br>9.5<br>.4<br>9<br>8.5<br>.35<br>8<br>7.5 .3<br>Log of income Gini coefficient<br>1994 1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 1994 1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

**Note:** Estimation results are obtained based on total household income per capita. We use the repeated cross sections for each year. Raw HBS microdata are available since 2003 (http://obdx.gks.ru/). HBS information on total monetary household income per capita collected in 4th quarter is used, RLMS refers to the 4th quarter of the same year. Corrected weights are calculated by Rosstat to adjust survey incomes to macroeconomic data. RLMS and HBS are deflated with December to December regional CPIs. RLMS incomes are weighted with population weights.

68
644|17.692|17.692|17.692|19.635|79.537|
|AR 95-CIs|[-0.059, -0.025]|[-0.039, 0.006]|[-0.145, -0.017]|[0.028, 0.147]|[-20.869, -6.287]|[-1.003, -0.256]|
|Dep. Mean|0.418|0.163|0.135|0.746|32.836|7.597|
|Other controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|7,768|3,687|3,687|3,687|3,687|6,392|
|Number of individuals|2,182|1,609|1,609|1,609|1,609|2,060|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Columns (1)-(4) and IV-FE model in Columns (5)(6). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F-test from Stock and Yogo (2005
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However, solvency is not the only consideration. Ability to pay does not necessarily imply willingness to repay. Creditworthiness (which depends on lenders' assessment of a country's ability and willingness to repay) therefore often imposes tighter constraints than solvency alone. Repayment requires not only a sufficiently high value of wealth to be able to repay, but also the generation of a surplus of traded goods production over traded goods consumption (net exports). This is likely to be much more burdensome in a country with most of its resources in non-traded goods sectors than in an outward-oriented country. But if it is more burdensome, a country might be more tempted not to repay, even if solvency requirements are met. Hence the importance of debt-export ratios in the assessment of creditworthiness.

Assessing the precise limits imposed by creditworthiness constraints is difficult for several reasons. First of all, while debt-export ratios are important, they are a biased estimate of the ratio of a country's debt to its output of tradable goods. Some domestically produced tradables are likely to be sold at home rather than exported. So the true measure lies somewhere between the debt-output ratio (which also counts non-tradables) and the debtexport ratio, which excludes tradable goods produced and sold at home. This chapter follows an approach pioneered by D. Cohen (1985, 1987). This approach chooses the ratio in between the debt-output (D/Y) and the debt-export (D/X) ratios in such a way that there are no incentives to overvalue or undervalue the exchange rate simply to mechanically improve creditworthiness indicators.

The precise way in which this ratio is derived is presented in Cohen (1987); it is influenced by the price elasticity of export demand and output supply. The outcome for Turkey places a 60 percent weight on debt-export ratio and a 40 percent weight on the ratio of debt to GNP. This construct is referred to as the debt-resource ratio, D/R.

ChhibberDisk/chhibb*r-Turkey Paper/ac:07-28-88: lc
) and TFPR (col. 3) in the years between 2008 and 2015. _Output NTM_ is the share of HS-10 digits products subject to at least one NTM within the corresponding five digit industry. For each five digit industry, _Inputs NTM_ is the weighted sum of output NTM in all five digit supplying industries. _Output tariff_ is the average tariff rate in the 5 digit industry of a plant. _Inputs tariff_ is the weighted average of the tariffs rates applied to the inputs used by a plant. The coefficients are obtained by the estimation of Equation 1. All specifications include plant fixed effects, 2-digit industry-year fixed effects and macro region-year fixed effect. The Herfindahl-Hirschman Index is computed for each 5-digit industry in the base year and interacted with year fixed effects. Standard errors are robust to heteroskedasticity and clustered at the industry level. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

19
|0.20|0.38|0.62|-0.50|-0.30|-0.13|0.28|0.49|0.67|-0.43|-0.20|-0.03|
|3 year|0.22|0.44|0.71|-0.57|-0.34|-0.14|0.31|0.55|0.77|-0.49|-0.23|-0.03|

Note: This table reports cumulative impulse response functions (in percentage point) of global inflation factor to positive one-standard deviation global shocks, based on the global FAVAR model that consists of oil price growth, and the global factors of inflation, output growth, and interest rates extracted from the dataset of G7 countries.

31
all registered establishments in India and abroad from April 2018 to October 2020. This data is collected by the tax authority’s _E-way Bill_ system to increase compliance for tax purposes. This is an advantage over standard VAT firm-to-firm trade datasets in developing countries, which suffer from severe under-reporting. By law, anyone dealing with the supply of goods and services whose transaction value exceeds Rs 50,000 (700 USD) must generate E-way bills. Transactions with values lower than 700 USD can also be registered, but it is not mandatory. The E-way bill is generated before transport (usually via truck, rail, air, or ship), and the vehicle driver must carry the bill with them, or the entire extent of goods can be confiscated. Our data is generated from these bills. This implies that our network is representative of relatively larger firms, but the threshold is sufficiently low that we are likely capturing small firms as well.

Each transaction reports a unique tax code identifier for both the selling and buying establishments, all the items contained within the transaction, the value of the whole transaction, the value of the items being traded up to 8-digit HS codes,<sup>15</sup> quantity of each item, unit, and mode of transportation. Each transaction also reports the ZIP code of both the selling and buying firms, which we use to merge with other district-level datasets.

Since the data report both value and quantity of traded items, we construct unit values for each transaction. We also calculate average unit values at the product-by-time-by-seller-by-buyer level, the number of transactions, and the total value of the goods transacted. This is the foundation of our firm-to-firm dataset used in the analysis.

**Lockdowns.** On March 25, 2020, India unexpectedly imposed strict lockdown policies nationwide. Districts were classified into _Red_ , _Orange_ , and _Green_ zones according to each district’s severity of Covid-19 cases. Each color corresponds to different lockdown degrees, where _Red_ was severe lockdown, _Orange_ was medium
# (OB.2) Evidence on Potential Coresidency Bias

## (OB.2.1) Are the Census Estimates of Mobility CEFs Substantially Biased?

Recent evidence shows that sample truncation due to nonrandomly missing children biases the estimated slope parameter (called IGRC in the literature) downward in a linear educational mobility model, see Emran et al. (2018) and Azam and Bhatt (2015). To understand whether the estimates from the census data are substantially biased, we take advantage of the fact that the IFLS collected information on the nonresident children. If the estimates from census data are substantially biased downward, the estimates of relative mobility would be substantially dierent from those based on IFLS. Table B.6 below presents the estimates of the linear (see columns 1 and 2) and quadratic (see columns 4 and 5) mobility models for the census and IFLS data (including IFLS-East). The evidence suggests that the census estimates do not suer from any substantial biases. For example, for years of schooling, the IGRC estimates are 0.489 (census) and 0.522 (IFLS) for girls, and 0.433 (census) and 0.455 (IFLS) for boys. Thus the estimated intergenerational persistence in the census data is smaller in magnitude which is consistent with the ndings of Emran et al. (2018), but the magnitude of the downward bias is small. The estimates for primary completion similarly suggest small to moderate bias.<sup>1</sup> The evidence on the quadratic mobility model (for years of schooling and primary completion) also suggests that, in general, the census estimates are moderately biased downward, except for the case of sons' years of schooling for which the biases are somewhat larger.

## (OB.2.2) Inverse Probability Weighting for Correction of Coresidency Bias

Given the evidence that the estimates of the mobility models using census are in general close to the IFLS estimates, it seems unlikely
# **7 Trend analysis**

This section presents results for which QFDs are estimated under current performance where data are available for two or more years. Multiyear trend analysis helps to check the robustness of the results— for example, to see if the reference year could have been an outlier—and identify trends within the data collected. In addition, where the AICD data are available, it is informative to compare results and assess longer-term trends. Although methods used are not strictly comparable between the two studies, significant changes are likely to indicate changes in QFD patterns.

## 7.1 Robustness of results in reference year

Multiyear data are available for 31 countries, and three or more years of data available for 23 countries. Detailed results are provided in annex 8. Data are sufficient to estimate the QFD under current performance. Conducting analysis under benchmark performance and breakdown of hidden costs for all years is beyond the scope of the paper.

As illustrated in annex 8, in general QFD estimates are relatively steady from year to year. For countries with three or more years of data, the pattern is generally consistent (improving, worsening, or remaining steady). Most of the countries with low QFDs are also improving over time, while most of the countries with high QFDs are steadily high. These patterns of change are broadly consistent whether QFDs are measured relative to GDP or revenue (Kenya and Tanzania are slightly different, reflecting different growth rates in GDP and utility revenue).

Several significant increases in QFD are noteworthy:

- In **Botswana** , the QFD increased significantly in 2012, following the commissioning of Morupule B coal plant, increasing capex. Further, major capacity constraints with the new plant forced the government of Botswana to rent short-term emergency power, increasing overall costs significantly and driving up the QFD.

- In **Malawi** , the QFD increased in 2013, in part due to the commissioning of a new 50-MW hydropower plant, and in part due to large increases in distribution
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 in other cases. The causal effect of faster case resolution can be studied by linking to long-term outcomes where the data infrastructure permits.

Overall, experimentation brings the opportunity to implement rigorous causal evidence to the legal realm. Whether it is via RCTs or leveraging the random assignment of cases to judges, “clinical trials” can bring important lessons on why rule of law matters for development outcomes, and perhaps more importantly, on what is the impact of specific policies and reforms on the litigants’ well-being.

# **2.C Machine Learning Applications**

In this section, we discuss avenues to leverage machine learning to improve judicial decision-making and increase the effectiveness of justice. Moreover, we propose a multistep approach to leverage administrative data to analyze disparities in judicial decisions. We also explore the potential of machine learning and natural language processing (NLP) techniques to process large quantities of text to improve the knowledge and efficiency of justice systems. These techniques currently allow researchers and policy makers to analyze texts to an extent that was not humanly possible before. Such opportunity has arisen not only due to the increase in the large volumes of data available, but also thanks to the recent development of computational tools that make it possible to process and analyze such large and complex data.

## **Applications of Administrative Data**

Even though the adoption of machine learning tools in the justice system is in its early stages, the preliminary results showcase the potential of these methods while also highlighting the risks that
**Figure 7 Migration to high-income countries is more responsive to demographic divergence from middleincome countries, but not from low-income countries**

_Sources_ : Original estimation based on demographic data from UN DESA 2022 and migration data from Özden et al. 2011 and UN DESA 2020. _Note_ : The figure shows the relationship between demographic divergence and migration from low-income countries (in blue) and middleincome countries (in green) to high-income destinations. The lines represent the estimation of β from the equation: ln (Mo,d,t) = βGAP_ELDo,d,t + δod + γt + εo,d,t , where Mo,d,t is the number of migrants from origin country o in destination country d in year t per origin country population; GAP_ELD is the gap in the population share of the elderly (ages 65+) between destination and origin countries; δ represents corridor (origin-destination pair) fixed effects; and γ represents year fixed effects. The estimation is done separately for lowincome countries and middle-income countries. The scatterplot shows the residuals of ln (M) and GAP_ELD grouped into a hundred bins after the corridor and year fixed effects are taken out. The residuals are scaled by their respective mean values.

However, migration fueled by demographic transition also depends crucially on the income levels of origin and destination countries. For instance, as seen in panel a of figure 3, migration from low-income countries to other low-income countries with similar demographic profiles falls with increased development. Furthermore, as seen in Figure 7, migration to high-income destinations is more responsive to demographic divergence, measured by the gap in elderly shares of the population between destination and origin countries, from middle-income countries but not from low-income countries. This suggests that while demographic divergence can be a strong impetus for migration, the level of development matters for actual migration. This could be driven by the high costs of migration to high-income countries or other pertinent barriers in these corridors.

# 3.3 Heterogeneity by population size

A common cause for concern is
in 2000.<sup>5</sup> We also include a broad range of fixed effects, including country-product and countryyear dummies. We exploit the extensive country coverage of our data to disaggregate quality upgrading into that of developed and developing economies. Moreover, the richness of our trade data, with 5,646 unique products, enables us to examine which products are being upgraded within each country. Specifically, we examine quality upgrading for new export varieties, more complex products (following Levchenko, 2007) and initially low-quality exports. Finally, we use novel information on the type of robots being adopted to examine whether quality gains are specific to the diffusion of advanced robots or are more broad-based.

We find that robot diffusion leads to increases in the quality of exported products. In aggregate the strongest quality gains accrue to developing economies, with more muted quality growth for developed economies and only evident for the first 5 years of our sample. Within each country, we find robust evidence that robots lead to quality upgrading of initially low-quality exports, furthest from the quality frontier, and this is true of both advanced and developing economies. Since developing economies in general tend to produce lower-quality exports, this reconciles the stronger aggregate quality upgrading we observe for developing countries. The paper also finds some evidence of differences in quality upgrading by types of robots, adoption of more basic robots leads to quality upgrading of developing economies, whereas some sophisticated robot applications lead to stronger quality upgrading in advanced economies. For instance, in terms of cutting machines, we find mechanical cutting robots matter more for quality upgrading in emerging economies and laser cutting robots more for high income economies. Finally, the strength of our instrumental variable suggests that the technological choices of foreign suppliers can be an important factor in driving robot adoption in their international supply chains.

One of the difficulties in measuring the effects of technology diffusion is the likely presence of endogeneity bias. Technology is not typically randomly adopted but done so with an expected return on that investment. This paper uses
(i.e., featuring clearer land ownership) has less wasteland and faster post-war population recovery. The data on wasteland cover 332 counties on their share of wasteland in the Taiping provinces in 1915. We have data on 151 Early Taiping counties, 95 Late Taiping counties, and 86 control counties (see Panel A, Table 4).

The cross-sectional regression results of the determinants of the percentage of wasteland in 1915 are in panel B of Table 4. The control variables include the distance to the Yangzi River, to the coastline, the dummy for being the prefecture capital seat, the dummy for being the most important county in 1820 (“ _Zuiyao_ ”), and the dummy for being a trade center in 1915.<sup>44</sup> We also control for provincial fixed effects to hold constant province-specific features. The results show that the wasteland ratio in the Early Taiping areas is 3.6 percentage points higher than that in the non-Taiping counties, while the wasteland ratio in the Late Taiping areas is slightly

> 44 We do not control for the distance to the Grand Canal because it did not pass our sample counties here.

29
in the developing world, the potential of highly low-cost socio-emotional interventions stands as highly policy relevant. We aim to fill this gap.

We focus on Peru, a middle-income country that in recent years benefited from sustained economic growth along with substantial poverty reduction (monetary poverty reduced from 54% to 21% to between 2000 and 2015 according to official statistics), but where schooling achievement remains very low and highly unequal –by 2018, only 14% and 16% of students in the second grade of secondary level (equivalent to the eighth-grade) achieve a satisfactory performance in _Mathematics_ and _Reading Comprehension_ tests, respectively.<sup>11</sup> Within this context, we evaluate the impact of a growth-mindset intervention in Peru. The intervention, called ‘ _¡Expande tu Mente!_ ’, adapted existing tools (Yeager et al, 2016 and 2019; Paunesku et al., 2015) to a context in which online interventions are not feasible. The intervention, implemented with support from the Ministry of Education, consists of a 90-minute school session led by tutor teachers. To measure the impact of the sessions, 800 public schools were sampled in three regions in Peru. Two equally powered samples were created: a _metropolitan_ sample and a _regional_ sample. Within each sample, half of the institutions were randomly selected to participate in ‘ _¡Expande tu Mente!_ ’.

Packages were sent by courier and implementation monitored by phone. Tutor teachers from grades seven and eight (the first two years of the secondary level) were asked to deliver the sessions to all their students. Approximately 60% of all eligible schools delivered the sessions. We used information from the students’ census evaluation from 2015 and 2016 to test the academic impact of ‘ _¡Expande tu Mente!_ ’ sessions approximately 2 and 14 months after the intervention took place (respectively). Results after 2 months show that being assigned to the sessions improved _Mathematics_ test scores by 5% of a standard deviation (the point estimate for
TABLE 6: Impact of leaders’ exposure by distance to the closest leader’s dwelling

||(1)<br>ECD full sample|(2)<br>ECDyoung|(3)<br>ECD Old|
|---|---|---|---|
|**_Panel A: Leader treatment interacted with distance from closest leader_**||||
|ITT Leader|-0.168<sup>_∗∗∗_</sup><br>(0.05)|-0.126<sup>_∗_</sup><br>(0.07)|-0.195<sup>_∗∗∗_</sup><br>(0.07)|
|Distance from closest leader|-0.009<sup>_∗∗∗_</sup>|-0.017<sup>_∗∗∗_</sup>|-0.006|
||(0.00)|(0.00)|(0.00)|
|ITT leader X distance from closest leader|0.012<sup>_∗∗∗_</sup><br>(0.00)|0.022<sup>_∗∗∗_</sup><br>(0.01)|0.010<sup>_∗_</sup><br>(0.01)|
|**_Panel B: Leader treatment interacted with household treatment status_**||||
|ITT|-0.046|0.036|-0.112<sup>_∗_</sup>|
||(0.06)|(0.13)|(0.06)|
|ITT Leader|-0.151<sup>_∗∗
for Impact program, which is funded by the United Kingdom’s Foreign, Commonwealth & Development Office. Computational reproducibility verified by DIME Analytics. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. All potential errors are our own. The list of authors is in alphabetical order. The trial was registered on the AEA RCT registry (AEARCTR-0004043 Version 2.0).

> 1 Department of Psychology, University of Zurich.

> 2 Department of Economics, Trinity College Dublin.

> 3 Development Impact Evaluation Department, The World Bank.

> 4 Department of Psychology, University of Illinois at Urbana-Champaign.

> 5 Department of Regulation, Environment and Market, Paris School of Economics.

_∗_ Corresponding authors: martina.kirchberger@tcd.ie, cnewman@tcd.ie.