rafmacalaba/datause-encoder-data huggingface dataset:
degrees of market power and hence a less competitive environment. We also include bank size, foreign bank ownership, the Legal Rights index and the log value of GDP per capita in all regressions to control for variations in the business environment and economic development. Our objective is to study the impact of market structure on financial stability. As discussed above, we include a quadratic term in the estimated equations to allow for a nonlinear relationship between measures of bank risk/loan risk and market structure in banking.

Table 3 shows our results using nonperforming loans to total loans as our proxy for loan portfolio risk. The coefficients of the linear terms are positive across all proxies of market power, while the coefficients of the quadratic terms are negative using HHI proxies of market structure. In order to evaluate the type of relationship between the degree of market power and NPLs, the inflection point of each quadratic function is calculated and compared with the distribution of the data. For example, in Model 1, the inflection point in Table 3 is -0.20, which is approximately the 4<sup>th</sup> percentile of the Lerner index distribution, implying that more that 96 percent of the data lies above the inflection point. Given that the coefficient of the Lerner quadratic term is positive, the relationship between the degree of market power and loan portfolio risk is significantly positive. Similarly, the results obtained for the HHI deposits show that the estimated function has a maximum (the sign of the quadratic term is negative) that occurs at the value of 0.25. With 99% of the data lying below the inflection point (the 99<sup>th</sup> percentile of the HHI deposits data for developed countries occurs at 0.228), a significant and positive relationship is established between the HHI deposits and NPLs. A comparable analysis of the results using the HHI loans also indicates a significant and positive association between market power and the ratio of nonperforming loans to total loans. In line with the “competitionstability” view of Boyd and De Nicolo (2005), the findings indicate that more market power is associated with riskier loan portfolios. The results are consistent across the three different proxies of market power.

Our main argument is that, even if market power in banking results in riskier loan portfolios, the bank’s overall risk needs not increase. Table 4 examines the impact of the degree of competitiveness on the overall bank risk, using the Z-index as an inverse proxy of such overall risk. A higher value for the Z-index may come from either higher earnings or more capital and 12
social protection scale‐up. More in‐depth analyses of the post‐disaster _financing gap_ have been performed using specific models, investigating the various mechanisms available to finance post‐disaster actions, including budget reallocation, domestic and international borrowing, and specific instruments such as catastrophe‐bonds, insurance contracts, or reserve funds (Cardenas et al., 2007; Hochrainer‐ Stigler et al., 2014). In a next phase, results from these more sophisticated models can be used to refine our estimates of the ability of each country to fund crisis management and provide post‐disaster support.

Our methodology to assess asset risk is a simplification of state‐of‐the‐art catastrophe modeling (Aerts et al., 2014; Michel‐Kerjan et al., 2013; UN‐ISDR, 2015), applicable with global data. As a result, our estimates for flood probability, exposure and asset vulnerability have to be considered with caution. Our simplifications are acceptable as we focus on how asset losses translate into welfare losses (socioeconomic resilience), not specifically on asset losses. The sensitivity analysis presented in the Online Technical Note concludes that our estimates of socioeconomic resilience and our ranking of policy options are largely independent of aggregate asset exposure and vulnerability. Our main results are thus robust to the simplicity of the hydrological modeling and to errors in exposure and asset vulnerability data.

We assess the resilience of 90 countries to floods, by calculating the ratio of expected asset losses to expected welfare losses. Resilience averages 61% across our sample, ranging from 25% to 86%. Resilience in Malawi is 53%, which means that $1 of asset losses in Malawi has the same impact on welfare as a reduction of Malawi’s national income by almost $2.<sup>6</sup> All 90 countries are represented in Map 1, and the list of countries with their risk and resilience information is in the Appendix below. Risk to wellbeing (expected welfare losses in percent of GDP) decreases rapidly with income per capita (Figure 5a), mostly due to better protection and lower asset vulnerability.

High‐income countries also tend to have higher resilience (Figure 5b), but resilience has a large variance even when GDP per capita is controlled for, especially at low income levels. Resilience varies across countries of similar wealth because welfare consequences depend on a multitude of factors, including pre‐existing inequality and safety nets to reduce the instantaneous impacts of a disaster. This finding suggests that all countries – regardless of their geography or income level – can act to reduce risk by increasing resilience.

> 6 Our measure of socioeconomic resilience does not include the fact that a reduction of national income by $2 has a larger impact in a low‐income country than in a high‐income country. While including this fact would be straightforward, it would imply to make inter‐country welfare comparisons, which is not required in our analysis.

11
stringency of the harmonized standards and the restrictiveness of the rules of origin. We contribute to this literature by identifying through a firm-level analysis regulatory convergence in deep trade agreements (and, specifically, the presence of SPS and TBT provisions in RTAs) as a source of spillover effect.

The paper is organized as follows. The next section describes the data and provides some descriptive statistics. Section 3 presents the empirical specification and results for the firm-level analysis. Section 4 explores the differential effects for regulation-intensive products. Section 5 provides robustness analysis and Section 6 concludes.

# **2 Data and descriptive statistics**

To identify the impact of trade-related institutions in destination markets on Costa Rican firms, we combine data on regional trade agreements, the universe of export transactions at the firm level, and product-level information on regulation sensitivity. First, we use the World Bank Deep Agreements Database to capture the depth of trade agreements (Hofmann et al., 2017). The data set provides information on the detailed content of 279 RTAs, signed by 180 countries between 1957 and 2015. Following Mattoo et al. (2017), we measure the depth of an agreement as the count of strictly enforceable provisions included in the RTA.<sup>8</sup> Other dimensions of the content of RTAs used in the analysis include the presence of regulatory provisions and the preferential or nondiscriminatory nature of provisions. Appendix A.1 provides the details. In a robustness test, we use an alternative measure of RTA depth from Mario Larch’s Regional Trade Agreements Database (Egger and Larch, 2008) which categorizes RTAs into partial scope agreements, free trade agreements, and customs unions.

For exporter transactions, we use the Exporter Dynamics Database described in Fernandes, Freund, and Pierola (2016). The underlying data for Costa Rica cover the universe of exporting firms over 1998–2012 and provides information on export value and quantity at the firm-product-destination-year level. The data cover 14,253 firms exporting to 211 destinations. We limit our analysis to destinations with population greater than 5 million, which reduces the sample to 12,918 firms and 115 destinations. Costa Rican data provide a good environment for our analysis due to the extensive time coverage and diversified export profiles in terms of products and destinations.<sup>9</sup> The data also exhibit high rates of firm turnover, providing sufficient dynamics to

> 8The legal enforceability of each provision is assessed based on the presence of dispute settlement or legal language. For more information on the database and coding, see Hofmann et al. (2017). The database is publicly available at https://datacatalog.worldbank.org/dataset/ content-deep-trade-agreements.

> 9Costa Rican firms export an average of 7.9 HS4-products, with a median of 2. In terms of destinations, the average is 3 and median is 1. See Appendix Table A.2 for their top exported products ranging across various industries.

4
following statement: Citizens should be allowed to live _and register_ wherever they like.” Similar levels of support were recorded for this question through August 2019, rising to a maximum of 91 percent of the population who support free movement within the country in February 2019 (figure 14 - right).

These facts are important context in the ongoing discussion of whether to adopt reforms to the propiska system in Uzbekistan. But in the broader context, this degree of popularity is perhaps not very surprising. The Soviet propiska system was thought to be very unpopular even before Uzbekistan’s independence (Kerblay, 1983), and in a similar circumstance, reform of the _ho khau_ system was also very popular in Vietnam (Demombynes and Vu, 2016).

**Figure 14: Views on Free Movement in Uzbekistan**

<!-- Start of picture text --> Baseline: “Citizens should be allowed to “Citizens should be allowed to live and<br>move to wherever they want” register wherever they like”<br>100% 100%<br>90% 95%<br>80%<br>90%<br>70%<br>60% 85%<br>50% 80%<br>40% 75%<br>30%<br>70%<br>20%<br>65%<br>10%<br>0% 60%<br>55%<br>50%<br>Agree Disagree Refuse total urban rural<br><!-- End of picture text --> _Source: L2CU, Author’s Calculations._ Most citizens also support constructing more urban housing. More than 80 percent of respondents would prefer to see more urban housing construction, and only 4 percent would like to see less.<sup>26</sup> Like with free moment, faster construction is popular in urban and rural areas alike. In addition, about 93 percent of residents believe that individuals should be allowed to own urban land, and 77 percent believe that agricultural land should be privately owned. At the time of this writing, there is no private

> 26 The remaining 15 percent would prefer to see about the same amount.

37
which includes the pre-announced inflation target, displays some autocorrelation, it outperforms the model that excludes this variable in forecasting exercises.

In this paper, I employ a specification for the traditional Phillips curve that links inflation to inflation expectations, some measure of disequilibria and a variable reflecting changes in imported prices since Turkey is a relatively open economy. Under the assumption that expectations are formed adaptively and that the relationship is linear, one can employ lagged inflation as a proxy for inflation expectations and obtain the following specification: where πt is the inflation rate at time t, L is the lag operator, gapt is the output gap at time t, and st is the nominal exchange rate.

# **4. In-Sample Estimation Results of the Three Models for Turkey**

This section summarizes the estimation results for the three models of inflation and compares their in-sample performance. Each model is estimated using monthly data covering the period 1990.01- 2002.12.<sup>18</sup> Throughout the investigation, I include two dummy variables, one for 1994.4 and the other for 2001.4, to account for the sharp increases in inflation. As Perron (1989) indicates, it is important to include dummy variables, which allow the coefficient of intercept or trends to shift in response to large shocks. Appendix 1 presents the variables involved in the investigation and Appendix 2 depicts their evolution over the sample period.

19
# **Table A6. Descriptive Statistics** **_Using Program-Level Data from the WBSCPS and Administrative Sources_**

|**Variable**|**Mean**|**Std.**<br>**Dev.**|**Min.**|**Median**|**Max.**|**Obs.**|
|---|---|---|---|---|---|---|
|**Panel A. Programquality determinants**|||||||
|_Infrastructure_|||||||
|Maintenance of largest lab: every year|0.57|0.49|0|1|1|<br>2,103|
|Has enough equipment or tools for practice|0.72|0.45|0|1|1|<br>2,103|
|Number of workshops or labs available for practice|6.12|6.15|0|4|65|<br>2,031|
|>30% classes can be taken online|0.15|0.36|0|0|1|<br>2,103|
|Program offers at least one online class|0.35|0.48|0|0|1|<br>2,103|
|_Training and curriculum_|||||||
|Teaches numerical competencies|0.80|0.40|0|1|1|<br>2,103|
|Teaches a foreign language|0.60|0.49|0|1|1|<br>2,103|
|Teaches persistence in complex tasks|0.76|0.42|0|1|1|<br>2,103|
|Promotes work under hardship or pressure|0.84|0.36|0|1|1|<br>2,103|
|Offers credits for longer degrees|0.89|0.31<br>|0|1|1|<br>2,103|
|Curriculum is fixed|0.70|0.46|0|1|1|<br>2,103|
|Time assigned to practical training (%)|46.45|16.56|4|50|80|<br>2,072|
|Graduation requirement|||||||
|Test|0.40|0.49|0|0|1|<br>2,103|
|Thesis or research project|0.37|0.48|0|0|1|<br>2,103|
|Second language|0.11|0.31|0|0|1|<br>2,103|
|Internships outside institution are mandatory|0.58|0.49|0|1|1|<br>2,103|
|Mandatory internships during the program+|0.16|0.37|0|0|1|<br>2,103|
|<br>Mandatory internships at the end of the program+|0.22|0.41|0|0|1|<br>2,103|
|<br>Remediation support|||||||
|<br>Remediation classes before starting the program|0.51|0.50|0|1|1|<br>2,103|
|<br>Remediation classes during the program|0.57|0.50|0|1|1|<br>2,103|
|Non-class-based remediation|0.43|0.50|0|0|1|<br>2,103|
|Years since last update to curriculum|2.91|2.69|0|2|25|<br>1,946|
|Important reasons to update program:|||||||
|Government standards+|0.70|0.46|0|1|1|<br>2,103|
|Employment outcomes or employers' requests+|0.87|0.34|0|1|1|<br>2,103|
|HEI perception of labor market+|0.83|0.38|0|1|1|<br>2,103|
|Enrollment trends+|0.56|0.50|0|1|1|<br>2,103|
|Student feedback+|0.78|0.41|0|1|1|<br>2,103|
|More than once per year:|||||||
|Analyze student performance to solve problems|0.86|0.35|0|1|1|<br>2,103|
|Collect student satisfaction data|0.69|0.46|0|1|1|<br>2,103|
|_Costs_|||||||
|Annual tuition (2019 PPP USD)|2,244|1,762|0|2,367|25,515|<br>2,103|
|<br>HEI has received funding from||||||<br>|
|<br>Private sector|0.20|0.40|0|0|1|<br>2,103|
|Government|0.34|0.47|0|0|1|<br> <br>2,103|
|Funding options used by some students|||||||
|<br>Loans by banks|0.41|0.49|0|0|1|<br>2,103|

67
intergenerational insurance objective, ensuring a replacement rate of at least 40% of lifetime earnings for average workers, irrespective of the investment performance of the funded pillars of the system.

The earnings-related part of the pension is based on revalued (actualized) average lifetime annual earnings, while the flat rate part is proportionately adjusted to the length of a person's career. Actualizaton of lifetime earnings is based on the wage index.

The Swiss social pension system is redistributive in favor of low income workers. In theory, it can achieve a replacement rate of over 100% for workers earning less than 25% of average earnings. The replacement rate falls to 40% for workers with average earnings and to 20% for workers with twice average earnings. In practice, replacement rates are affected by the revaluation factors used by the social security system and by the length of a worker's career.

The Swiss "first pillar" is a pay-as-you-go system, financed with a total contribution rate of 8.4%, equally divided between employers and employees. In addition, the state makes a contribution from general revenue to cover 20% of pension payments. Social pensions used to be indexed to prices, but since 1980 pensions in payment are indexed to the arithmetic mean of the wage and consumer price indices. Adjustment is made every two years or when the index increases cumulatively by over 8%.

Because it is based on actualized lifetime earnings and is adjusted for the length of a person's career, the Swiss social pension system avoids the problems that bedevil so many developing countries. These provisions weaken the incentive to misreport (understate or overstate) earnings during a worker's career. They also discourage moral hazard since workers will not be entitled to the minimum pension by participating in the scheme only for a minimum vesting period. OECD estimates indicate that the internal rate of return in the Swiss system is higher for low than for high income workers, which is in line with the intentions of the designers of the system (OECD 1988). The social pension system does not appear to suffer from extensive capricious or perverse redistribution.

5
affordability became a concern throughout the country, it is mainly households in rural areas who are able to adjust their heating mix to mitigate price increases (Figure 17). Even though observed gaps are small, differences in access to gas and electricity connections do put rural households at a disadvantage; a monetary measure of poverty would capture very differently price increases for gas and electricity and their potential implications for welfare. Higher prices could be associated with higher consumption spending, which in certain circumstances could be measured as a reduction in monetary poverty, since if the rise in prices is not recognized, higher spending could be interpreted as an increase in household energy consumption.

**Figure 16. Deprivations for the Housing Dimension, 2010–15, Percent of Population**

<!-- Start of picture text --> 60%<br>50%<br>Adequate housing<br>40%<br>Healthy heating<br>30%<br>Hot running water<br>20%<br>Subjective housing<br>conditions<br>10%<br>0%<br>2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> _Source:_ ILCS data.

19
estimates. Specifically, for each aggregate indicator and year, we examine whether the equally weighted alternative estimate falls inside or outside of the 90 percent confidence interval around the baseline WGI estimate. In 2023, the share of countries for which the equally weighted alternative estimate is significantly different from the baseline WGI estimate is below 5 percent for four of the six aggregate WGI measures. For two of the aggregate indicators, the share is somewhat higher: 24 percent for Political Stability and Absence of Violence/Terrorism, and 17 percent for Rule of Law. A similar pattern can be seen averaging across all 25 years in the WGI dataset. For four of the six aggregate indicators, the share of significant differences between the baseline and equally weighted alternative is 3 percent or less, while for Political Stability and Absence of Violence/Terrorism and for Rule of Law, the proportions are slightly higher at 16 and 11 percent respectively.

In summary, the baseline WGI estimates by design assign greater weight to data sources that are more correlated with each other. Conditional on the identifying assumptions of the UCM, this makes sense – data sources that are more correlated have more favorable signal-to-noise ratios and should therefore receive greater weight. It is, however, possible that some of the observed correlation between data sources reflects correlated perceptions errors. In this case, the default WGI methodology would assign “too much” weight to data sources that make these correlated errors. However, the fact that the baseline WGI estimates are not very different from a simple equally weighted alternative suggests that this concern is not very significant.

Of course, the equally weighted version of the WGI discussed in this subsection is just one of infinitely-many possible weighting schemes, and different weighting schemes will naturally lead to different results. For the convenience of WGI users interested in exploring alternative weighting schemes, the WGI website contains a consolidated dataset containing all six aggregate WGI indicators together with all the data from the individual data sources on which the aggregate indicators are based. This dataset can readily be used to construct alternatively weighted versions of the WGI reflecting users’ specific needs and compare them with the baseline WGI estimates and can be downloaded from <u>www.govindicators.org. This includes the extreme decision to assign zero weight to a source by</u> excluding it altogether.

# 5.3 Trends in Global Averages

In our baseline estimates of the WGI, we assume that the global distribution of governance has zero mean and unit standard deviation in every period. This assumption rules out the possibility of trends in global averages of governance. This also means that we can only interpret countries’ positions 25
|**Tabl**<br>**Code**|**e B2: Political Stability & Absence of Violence/Terrorism**<br>**Concept Measured**|
|---|---|
|**Repre**<br>|**sentative Sources**<br>|
|**BRI**|Fractionalization of political spectrum and the power of these factions.|
||Fractionalization by language, ethnic and/or religious groups and the power of these factions.|
||Restrictive (coercive) measures required to retain power.|
||Organization and strength of forces for a radical government.|
||<sup>Societal conflict involving demonstrations, strikes, and street violence.</sup>|
||Inst ability as perceived by non-const itutional changes, assassinations, and guerrilla wars.|
|**DRI**|Military Coup Risk|
||Major Insurgency/Rebellion|
||Political Terrorism|
||Political Assassination|
||Civil War|
||Major Urban Riot|
|**EIU**|Armed conflict|
||Violent demonstrations|
||Social Unrest|
||International tensions|
|**GAD**|Autonomy and Separatism|
||Civil Unrest|
||State of Emergency / Martial Law|
||Active Terrorist Groups in the Last Two Years|
|**GCS**|_Country terrorist threat_: Does the threat of terrorism in the country impose significant costs on firms?|
|**HUM**|Frequency of political killings|
||Frequency of disappearances|
||Frequency of torture|
|**IJT**|Security Risk Rating|
|**IPD**|Conflicts of ethnic, religious, regional nature ...|
||Violent actions by underground political organisations|
||Violent social conflicts|
||External public security|
|**PRS**|_Internal Conflict_**:**Asse sses political violence and its influence on governance.|
||_External conflict:_The external conflict measure is an assessm ent both of the risk to the incumbent government and to inward invest ment.|
||_Government Stability._Measures the government’s ability to carry out its declared programs, and its ability to stay in office.|
||_Ethnic tensions_: This component measures the degree of tension within a country attributable to racial, nationality, or language divisions.|
|**PTS**|Political Terror Scale|
|**WMO**|_Civil unrest_ How widespread political unrest is, and how great a threat it poses to investors. Demonstrations in themselves may not be<br>cause for concern, but they will cause major disruption if they esca late into severe violence. At the extreme, this fact or would amount to<br>civil war.|
||_Terrorism_ Whether the country suffers from a sustained terrorist threat, and from how many sources. The degree of localization of the<br>threat is assess ed, and whether the active groups are likely to target or affect businesses.|
|**Non-r**|**epresentative Sources**|
|**AEO**|Civil Tensions|
|**WCY**|Risk of political instability|

74
WHY PAPER MILLS CLEAN UP: DETERMINANTS OF POLLUTION ABATEMENT IN FOUR ASIAN COUNTRIES by Raymond S. Hartman* Mainul Huq David Wheeler (*) The authors are respectively Vice President, Charles River Associates; Consultant, PRDEI; and Principal Economist, PRDEI. Our thanks to the managers and staffs of our survey plants in Bangladesh, India, Indonesia and Thailand. This work would not have been possible without their generous donation of time and information. Funding for this study was provided by the World Bank's Research Support Budget under the study "Enterprise Ownership and Pollution" (RPO 677-44).