rafmacalaba/data-use-mentions-extended huggingface dataset:
Moving on to Figure 3 c, we illustrate the censored headcount ratio of each indicator as a function of the spillover rate. This analysis allows us to see how each poverty indicator’s censored headcount ratio changes as the spillover rate varies. Indicators with the lowest and highest values for the censored headcount ratio show the least susceptibility to changes in the spillover rate, indicating that their censored headcount ratio remains relatively stable even as varies. In contrast, indicators with intermediate levels of the censored headcount ratio appear the most susceptible, showing greater rr<sup>cc</sup> fluctuations with changes in . rr<sup>cc</sup> Figure 3 d provides a more detailed view by plotting a bar chart for the changes in the censored headcount ratio for each indicator under different spillover rates (the indicators are ordered according to their CHR value in 2019 from lowest to highest). Indeed, three indicators with intermediate levels of CHR in 2019 (Schooling, Drinking Water, and Assets) display the largest change in CHR due to changes in the spillover rate. For lower spillover rates (in this case 0 and 0.3), Assets appears as the indicator with the largest decrease in CHR, whereas for higher spillover rates (in our case 0.7 and 1.0), Schooling becomes the indicator with the largest decrease in CHR. This result could be due to shifts in policy priorities driven by spillover effects. When higher spillover rates are present, resources and attention may be redistributed towards amplifying indicators, thereby changing the dynamics of multidimensional poverty within the country. This underscores the importance of spillover effects in the development of policy interventions aimed at reducing multidimensional poverty. By identifying which indicators are most responsive to these shifts, policy makers can better target their efforts to maximize the impact of resource allocation and intervention strategies, ensuring that critical areas receive the necessary support to reduce poverty effectively.

Altogether, this analysis, based on Ethiopia’s data, demonstrates the potential of integrating the Poverty Space
# **4.5. Accounting for Macroeconomic Risks in Aggregate PDs: The Systemic Component of Credit Risk**

Given the changes in NPLs in scenario S computed as described in Section 4.4, and the benchmark TTC PDs and LGDs taken from the QIS5 study and reported in Table 1, we can construct the probability of default for asset class _i_ under scenario S as the sum of the TTC PDs for that asset class and an additional factor that is scaled by the influence of the macroeconomic variables in the considered scenario. To illustrate this computation, let ∆ _NPL_<sup>S</sup> _t_ +1<sup>denote the change of NPLs in the future period under macroeconomic scenario</sup> S, where S = _{PIT_ , _Stress}_ as before. Also, define the TTC PD for asset class _i_ as _PDi_<sup>_TTC_</sup> . The probability of default in macroeconomic scenario S for asset class _i_ (denoted by _PDi_<sup>S)</sup> is then computed as The ∆ _NPL_<sup>S</sup> _t_ +1<sup>term captures changes in the aggregate NPLs of the banking sector due</sup> to changing macroeconomic conditions under the PIT and Stress scenarios. The _φ_ parameter expresses the degree of proportionality between the changes in NPLs and PDs. In one can consider values in the 0.6 _−_ 1.0 on empirical applications, range depending the definition and reporting standards for NPLs. For instance, if the NPLs are defined as _−_ 60 day overdue loans, one could consider a _φ_ parameter that is closer to 0.6. If NPLs are defined as category D and E classified loans, a value of _φ_ closer to 1 could be used. In other words, _φ_ calibrates how close or far a given NPL classification is from actual default 32
|(58)|(58)|(58)|
|Wald Chi2(6)|41.93|18.57|31.15|18.71|23.89|13.22|600.70|37.22|25.33|24.74|
|(Prob>Chi2)|(.00)|(.03)|(.00)|(.22)|(.00)|(.15)|(.00)|(.00)|(.00)|(.01)|

# Note:

1. * 0.01 significance level, ** 0.05 significance level, *** 0.10 significance level, and insignificant otherwise

2. EDU2-2: Inputs: orthogonalized public spending on education per capita, teachers per pupil Outputs: gross primary and secondary enrollments

- EDU2-2n: same inputs as EDU2-2, outputs: net primary and secondary enrollment EDU3-2: literacy of adult is added to EDU2-2 as input

- EDU3-2n: literacy of adult is added to EDU2-2n as input EDU3-3: literacy of youth is added to EDU3-2 as output EDU3-3bl: same inputs as in EDU3-2,

outputs: average years of school, first level complete, and second level complete (Barro-Lee education indicators) HEA2-2: Inputs: orthogonalized public spending on health per capita, literacy of adult

- Outputs: life expectancy at birth, and immunization DPT

HEA3-2: orthogonalized private spending on health per capita is added to HEA2-2 as input HEA3-2m: Immunization Measles is in place of DPT in HEA3-2 as output HEA3-3: Immunization Measles is added to HEA3-2 as output 36
University of Singapore‐Credit Research Initiative using a Merton (1974) model with stability adjustments suggested by Duan (2010) and considering macroeconomic and firm‐specific financial factors.<sup>8</sup> We cover monthly data of 1,786 listed financial institutions in 36 EMDEs.

**_Measuring sovereign stress._** We measure financial stress in the sovereign public sector by using the sovereign EMBI spread for 36 EMDEs. We recognize that the sovereign EMBI spread tracks US‐dollar denominated sovereign debt instruments with minimum face value outstanding of US$500 million, but we consider the sovereign EMBI spread to be a reasonable proxy for the risk of sovereign debt instruments in local currency as well.

**_Quantile regressions to estimate nexus strength at increasing levels of sovereign stress._** To calculate the Nexus indicator, we estimate the following basic model:

Sov. EMBI spread�,� �α�β∗Median PD Financial Institutions�,� �γ∗D� �ε�,� (1) where subscripts _c_ and _t_ represent country _c_ and time _t_ , _Dc_ represents a set of country‐fixed effects, and ε�,� is the error term. β is our Nexus indicator with higher values suggesting a tighter nexus. For example, if β�100 , a 1 percentage point increase in the PD, is associated with a 100‐basis points increase in the EMBI spread. We conjecture that the Nexus strengthens at higher levels of sovereign stress. To estimate the Nexus indicator at different levels of sovereign stress, the coefficient β is estimated using quantile regressions which produces an estimated β for each percentile of the EMBI spread. Figure 7, Panel C shows the estimated _β_ s for 10<sup>th</sup> to 90<sup>th</sup> percentiles using an unbalanced panel of monthly information for 36 EMDEs between January 2000 and December 2017. Similarly, Figure 7, Panel D shows the estimated _β_ s for 10<sup
, Kampala, Uganda.

Uganda Bureau of Statistics (2016), The National Population and Housing Census 2014 – Main Report, Kampala, Uganda.

University of Essex . (2016), British Household Panel Survey, <u>https://www.iser.essex.ac.uk/bhps</u> <mark>Witoelar, Firman. (2011). T</mark> racking in Longitudinal Household Surveys. <mark>World Bank’s Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA). World Bank.</mark> World Bank . (2020), Basic Information Document - The Uganda National Panel Survey (UNPS) - - - 2015/2016. <u>http://surveys.worldbank.org/lsms/programs/integrated surveys agriculture</u> - <u>ISA/uganda#bootstrap panel.</u> Zardetto, D . (2015). ReGenesees: An advanced R system for calibration, estimation and sampling error assessment in complex sample surveys. Journal of Official Statistics, 31 (2), 177.

35
future we may be able to more accurately measure improvements in child outcomes if they have indeed improved.

This analysis has focused only on participants in the endline survey. The midline survey results are left to an appendix because of issues with attrition discussed in section 3. However, we follow the procedure in Lee (2009) to estimate upper and lower bounds to correct for the sample selection issues stemming from attrition. Table A3 estimates the upper and lower bounds on the treatment effects for the indices reported in the main tables. Estimates are reported separately for the information only versus control group and the information plus cash versus control group. The bounds for the most part cannot rule out the findings from the endline, which implies that the two survey waves can be consistent with each other.<sup>14</sup>

> 14Specific tables that mimic the exact main results for the midline survey waves are available on request, but are left out to save space.

14
First, the efficiency wage model with labor tumover captures well a number of features of LDC labor markets, and in particular the Mexican case we consider. The literature suggests that the self-employed informal sector comprises both workers rationed out of formal salaried jobs as well as a relatively prosperous "upper tier" that may prefer self-employment.<sup>4</sup> In other words, the literature is consistent with one of the central ideas of the efficiency wage model with labor turnover, namely that at each point in time workers are voluntarily leaving their formal-sector job for self-employment and simultaneously self-employed workers are unsuccessfully trying to reenter the formal sector. Moreover, neither minimum wages nor unions are credible explanations for the observed segmentation.<sup>5</sup> Finally, Constitutional proscriptions against firing suggest quittiing as the dominant mode ofjob separation.

Second, our data set has an important time dimension which allows us to estimate the quitting response of individual workers to changes in macroeconomic conditions. Given our final goal of quantitative macroeconomic analysis, this feature ofthe data set seems essential. In addition, the data on self-employed workers offer a measure of the benefits (payoffs) to not being employed in the formal sector that displays substantial variations over time. These variations in selfemployment benefits are important since they provide us with an additional test of the "quitting theory" which predicts that labor turnover is positively correlated with expected benefits to selfemployment and that the quit-rate function is symmetric: the benefits-elasticity of quitting, is equal to the negative ofthe wage-elasticity of quitting. Moreover, if the symmetry property ofthe quit-rate function is supported by the data on self-employed workers, we may use it as a working hypothesis (until refuting evidence is forthcoming) and apply it to unemployment. This opens the door for an assessment of the quantitative macroeconomic effects of changes in unemployment benefits without 4 Harris and Todaro (1970) offer the canonical statement of the dualistic (rationing) viewv and Fields (1990) discusses the "two-tier" view of the informal sector.

5Bell
2004 and 2018, seven of 13 MENA countries, or more than half, conducted fewer than three surveys. This falls short of areas suitable for socio-economic comparison to MENA, such as Latin America and the Caribbean (LAC) and Europe and Central Asia (ECA). The gap between MENA, LAC, and ECA widens once the population size of each country is considered: 46 percent of MENA’s population live in data deprived countries compared to 5, 1, and 0 percent in LAC, EAP and ECA, respectively.

> 8 For a comprehensive assessment of available household surveys suitable to measure poverty, we use the number of poverty statistics reported in the World Development Indicators (WDI) database for the last 15 years, 2004 to 2018 (we use both poverty headcount ratio at $1.90 a day 2011 PPP and national poverty rates including noncomparable ones). In the MENA region, we manually added information about existing household budget surveys in the Islamic Republic of Iran for each year between 2004 to 2018, Jordan for 2017 , Libya for 2007, and Egypt for 2017,  countries for which we either have access to microdata or know can be, or were, used to measure monetary poverty but not included in the WDI.

7
text --> _Note: The human capital index H is characterized by the relationship between h and s as_ φ�s����φ� ∙s�� _for each country i in period t —where the returns to education are heterogeneous across countries. The second set of Mincerian returns are those estimated by Montenegro and Patrinos (2014). Source: The data has been collected from PWT 9.0 (Feenstra, Inklaar and Timmer 2015)._ The swing in the growth rate per capita of the region over the three different periods is also observed across all country groups, and these fluctuations tend to be sharper among resource rich countries. Growth per worker of oil rich countries decelerates from 1.5 percent per year in 1960-77 to -0.1 percent per year in 1978-96. In both periods, physical capital accumulation contributes positively to growth per worker while TFP growth plays a detrimental role in the growth of labor productivity. Growth per worker then accelerated to an annual average rate of 3.8 percent in 19962014. Factor accumulation explained half of the growth per worker achieved during this period (1.4 percent attributed to physical capital accumulation and 0.5 percent to human capital) while the remainder was attributed to TFP growth.

_page 7_
# **B – Price shock model**

# Step 4 – Identify the price shock

There are two main forms of subsidies reforms simulations that have slightly different implications for modeling. These two forms can be regarded as looking at the same problem from two different angles.

The first form is an increase in prices of the subsidized product. The government has an idea of the price increase it wants to apply to the subsidized product and wants to know what this implies for household welfare and government revenues. From a modeling perspective, this is a simple increase in the price of the subsidized product that can be modeled by changing the product price in the household expenditure data.

The second form of subsidies reform simulation is a budget cut that affects agents that manage the subsidized products. For example, the subsidized product may be produced by a public or private company that receives subsidies directly from the government so as to sell the product at regulated prices. In this case, we know the intended budget cut but not the corresponding increase in prices.

To model such change, we can follow one of at least two options. The first option is to spread the budget cut on households proportionally to the share of subsidized product consumed. In this case, households will bear the budget cut in a proportion that is equivalent to the share of subsidized product consumed over total household consumption of that product. This is what we described for the incidence analysis but it is unsatisfactory because amounts to an incidence evaluation where we cannot model elasticities. The second option is to simulate increases in prices that would result in an increase in revenues for the government equivalent to the budget cut. This is a more accurate approach because the final change in government revenues will result from simulations that can take into account price-quantity and crossprice elasticities and other factors that may affect demand. This is the approach followed in this manual.

# Step 5 – Define the assumptions

Before modeling changes in subsidies, the analyst needs to make a number of critical decisions on assumptions regarding
2 The Data Revolution in Justice 20 forefront of e-justice systems. Even so, electronic systems have also been implemented or are in the process of implementation in Latin America (Chile, Argentina, Uruguay, Peru, the Rep ́ublica Bolivariana de Venezuela, and some Mexican states, among others), Africa (Kenya, Rwanda, Lesotho, etc.) and Asia (Malaysia, Singapore, and many others). The increased availability of judicial data can be used to improve the functioning of judiciaries, define what works in justice reforms, and produce rigorous evidence on how the judiciary may affect development outcomes. That said, this paper does not establish standard performance indicators. Indicators have already been discussed on areas such as access to justice; expedition and timelines; equality, fairness and integrity; independence and accountability; and public trust and confidence (Reno et al., 1997). Moreover, we do not cover issues of transparency or accessibility of the data. The recommendations are targeted to everyone who may have access to judicial data. We also acknowledge that not all countries may have the institutional capacity or infrastructure to create data ecosystems, but even countries without electronic systems in place may benefit from understanding what may be possible were they to develop such data systems.

The extent to which each of these countries can utilize the approaches outlined in this paper will depend on the extent to which e-judiciaries are integrated, the sources of data available, as well as the quality and accuracy of this data. Common concerns surrounding representativeness and exhaustivity of the data will be present in contexts where not every case is added to the database, which may impose a burden on those with lower digital access.

The main sources of data are automated registrars, which generate a database that includes every case in a specific court, division (for example, civil or criminal cases), and country. These databases tend to include at least the following information: type of case and tribunal, the stage of the case, the dates of filing
Using survey data in Nigeria, Abidoye and Cal`ı (2021) find that higher prices of consumed goods increase the risk of conflict by reducing consumers’ incomes (in line with the opportunity cost channel), while higher oil prices increase the risk of conflict in oil-producing areas (in line with the rapacity effect). Concerning less organized and violent forms of collective actions, a large body of empirical research finds that food prices can act as a trigger for urban riots and social unrest (Bush, 2010; Bellemare, 2015; Hendrix and Haggard, 2015). They generally assume that relative deprivation and grievances are the main explanatory channel, although it is difficult to provide empirical evidence of causality (Martin-Shields and Stojetz, 2019). Another explanatory mechanism is the breakdown of state authority and legitimacy when it fails to provide food security (Arezki and Brueckner, 2014; Buhaug et al., 2015).

7
a wide use of this classi fi cation system only started in the mid 1990s. The basic data set consists of an unbalanced panel of imports for 117 countries at the six digit level of the HS for the period 1988-2002. The number of countries obviously varies across products depending on the presence of import fl ows and on the availability of trade statistics at the HS level.

There are three factor endowments included in the regression: labor, capital stock and agriculture land. Data on labor force and agriculture land are from the World Development Indicators (WDI, 2003). Data on capital endowments are constructed using the perpetual inventory method based on real investment data in WDI (2003).

The estimation sample did not include tariff lines where the recorded trade value at the at the six digit level of the HS was below $50,000 per year. This eliminated less than 0.1 percent of imports in the sample, and it is necessary in order to avoid biasing our results with economically meaningless imports. The elasticities are constructed following Equation (11), where the import share is the sample average (i.e., we constrained the elasticities to be time invariant). We also purged the reported results from extreme values by dropping from the sample the top and bottom 0.5 percent of the estimates.

# 5 Empirical Results

To be precise, we estimate a total of 315,451 import demand elasticities at the six digit level of the HS for 117 countries. The simple average across all countries and goods is -1.67 and the standard deviation is 2.47 suggesting quite a bit of variance in the estimates. Figure 1 shows the Kernel density estimate of the distribution of all estimated elasticities. The vertical line to the left denotes the sample mean (-1.67), and the line to the right the sample median (-1.08).

All import demand elasticities are quite precisely estimated. The median t-statistics is around -11.96. Around 89 percent
result of bank crisis resolution in the twelve countries under consideration. If anything, there has been a decrease in financial depth in most of the countries. Financial intermediation in all three country groups remains considerably lower than in OECD countries, with financial intermediation in the CIS being the lowest.

# **b. Banking Sector Soundness**

Two measures of banking sector soundness - the ratio of non-performing loans to total loans (Table 3) and the capital to asset ratio (Table 17) - indicate that the results of bank crisis resolution in the twelve transition countries were generally positive, although improvements are still necessary.

With respect to the ratio of non-perforning loans to total loans, all the _CEE_ countries showed large improvements during the 1990s, ranging from a 20 percent reduction in the case of the _Czech Republic_ to an over 70 percent reduction in the case of _Hungary._ For the _Baltic_ countries, this ratio fell sharply in _Latvia_ and _Estonia,_ but in _Lithuania_ the initial improvements were subsequently reversed somewhat. Among the _CIS_ countries, all showed improvements except for _Ukraine,_ where the ratio actually worsened which is not surprising since there was no bank restructuring in _Ukraine._ Despite the generally positive trend, however, the bad loan problem remains critical in most countries. In fact, at the end of the 1990s, the share of nonperforming loans was close to or higher than 10 percent in all the sample countries except for Estonia, and was significantly higher than 10 percent in the _Czech Republic, MIacedonia, Lithuania_ and _Kazakhstan._ Data on the capital adequacy ratio suggest that the level of capitalization is satisfactory in those countries for which data is available. Even in countries where the authorities did not undertake major injections of capital, as in the _Baltics,_ the restructuring and downsizing operations have produced better capitalized banking systems. Among the _CEEs, Bulgaria_ had the highest capital adequacy ratio in 1998 of around 37 percent due to revaluation gains.

# **c. Banking Sector Efficiency**

The effects of bank