rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3. ANALYTICAL FRAMEWORK**

## **3.1 Models and data**

The magnitude of SACU’s future demographic dividend depends on a range of economic variables that are not easily captured without consideration of the range of possible influencing factors in the global economy, their impact on the economies and households. LINKAGE, the recursive dynamic CGE model is well suited for this task.<sup>9</sup> It is supported by globally consistent data on production, consumption, investment, and trade from the GTAP Database V8.1 (Narayanan et al. 2012). LINKAGE is a multi-sectoral, multi-country and multi-agent dynamic recursive CGE model that assumes perfect competition, with equilibria in a given year being dependent on current year prices and quantities, and the previous year’s equilibria. Household demand behavior is modeled using the Constant Difference of Elasticities function, while production is assumed to be based on a multi-nested CES function. At the top of the multi-nested structure, an aggregate of intermediate inputs is combined with an aggregate value added under Leontief technology. Unskilled labor is substitutable for a skilled labor and capital composite, while skilled labor and capital are themselves complementary.

Output is produced by different production streams—differentiated by capital vintage. Each production stream has an identical production structure based on a multi-nested Constant Elasticity of Substitution functional form, but with different technological parameters and substitution elasticities. At the top of the nest, a value-added bundle is combined with an intermediate inputs bundle under the Leontief technology assumption. The intermediate inputs bundle is combined with different inputs, with an Armington assumption applied to specific inputs. That is, for a given type of intermediate input, there is substitutability between domestic and imported inputs, and then again between imported inputs from different source countries. The value added bundle is made up of unskilled labor being slightly substitutable with a capital and skilled labor bundle. Skilled labor and capital are substitutable.

LINKAGE also considers segmented labor markets in developing countries, i.e.
of durables in anticipation of prolonged lock down, and also purchases of health-related goods, like facemasks.<sup>19</sup> To isolate the response of firms to only the supply side shock, we exploit the time varying nature of COVID lockdowns across countries. Define the supply-side lockdown exposure of firm j in time t as: Lockdown Exposurej,t i,j,t0 × (Lockdown stringency in country i at time t) = ∑s i where si,j,t0 is the share of firm j’s import volume (measured in twenty-foot equivalent container units, or TEU) from country i in t0, the quarter December 2019 to February 2020, and lockdown stringency in country i at time t is the Oxford University lockdown stringency index (Hale et al., 2020). Time intervals are three-month quarters, so the stringency index in each time period is the average for the quarter. Lockdown exposure is zero for all firms prior to March 2020, and then varies across firms and quarters after the pandemic begins.

To isolate responses to this supply side shock we estimate the regression: Net Separation Rate = αj + αt + β1(Lockdown Exposurej,t) + β2(Lockdown Exposurej,t × Wj) + ε j,t where αj is a firm fixed effect and αt is a quarter fixed effect. The quarter fixed effects control for changes in demand in the US economy during the pandemic. Wj stands for alternative firm characteristics hypothesized to shape resilience, namely: (i) the share of import volume (again in TEU) of goods that are differentiated, rather than sold on an organized exchange or with reference prices, according to Rauch (1999); (ii) the number of suppliers to the firm at t0; and (iii) the total volume of imports at t0.

Results are reported in Table 1. Across all columns, and in contrast to the aggregate pattern in Figure 5, supply-side lockdown exposure increases net separations. This confirms that the
3 presents new and official survey information on bank regulations in over 125 countries,<sup>3</sup> makes comparisons with earlier surveys since 1999, and assesses the relationship between changes in bank regulations and banking system performance. To measure banking system performance, we use indicators of banking system depth, efficiency, and stability. In the analyses, we examine both the relationship between the level of banking system performance and indictors of bank regulation and supervision at various points in time over the last twelve year and we assess the relationship between changes in bank regulations and supervisory practices and banking system performance. Thus, we examine levels and changes to provide additional evidence on the linkages between banking system performance bank regulatory and supervisory policies.

This paper is structured as follows. Section II will very briefly review the structure of the survey and discuss some issues that arise in the responses to the three surveys. Next, Section III looks at the state of bank regulation around the world, and importantly how it has changed since 1999. At this point, we primarily focus on comparing Survey I and Survey IV. These comparisons and analyses will be updated and enhanced as our work continues. Section IV then asks whether the changes in bank regulation are contributing positively to banking sector development and to the efficiency and stability of banking systems around the world, comparing the results we obtained from Survey I. Section V concludes by drawing a few cautious, highly qualified lessons.

Based on our empirical analysis of what works best in bank regulation (BCL, 2006) and the subsequent changes that have taken place since the late 1990s in the regulatory environment, we see no basis for the view that countries around the world have primarily reformed for the better. Our earlier results showed no support for the view that tightening capital requirements or increasing supervisory powers had a positive impact on the financial sector, and in fact increased 3 See Barth, Caprio, and Levine, 2012, for much greater detail.
# **A1.2.2 Differential taxation and transaction costs**

As argued above, arbitrage is a powerful mechanism for ensuring that the riskreturn trade-off is equalised between different types of assets and across countries. However, even with complete arbitrage, prices are affected by differences in transaction costs and levels of taxation applied to various assets. Investors respond to post-tax returns while the companies themselves are concerned about pre-tax levels, thus variations in this spread affect the cost of capital. In several countries, steps are being taken to eliminate gross distortions in the tax system, for example, the recent reductions in the preferential treatment of house purchases in the UK. However, significant cross-country differences in transaction costs or taxation systems are likely to undermine the comparability of beta estimates unless corrections can be made for these distortions.

# **A1.2.3 Weighting of the index**

Another issue concerns the weights that should be applied to different assets in the market portfolio. A portfolio consisting of an equal investment in each security is unlikely to be optimal, as most investors would choose to put fewer funds into very risky assets. Thus, market capitalisation is generally used to weight each security, so that large companies form a greater proportion of the portfolio. However, when making international comparisons, there is no guarantee that the different national market indices are weighted in a consistent manner and calculation of a new index may be necessary.

# **A1.3 Choice of interval period**

Beta coefficients may be calculated from daily, weekly, monthly or even annual data, as long as a sufficient number of data points are used. Theoretically, the length of the interval over which returns are calculated should not affect the result. However, empirical studies have found the estimated value of beta to alter significantly and systematically 37
characteristics at the time of issuance (matu<br>effects for the ten loan rating categories establis<br>Brazil's CDI). Standard errors are doubled cluster|sions on our loan-level data<br>corresponds to the volume of<br>earmarked loan from a bank,<br>above the median and zero o<br>ship._MPft_is an indicator vari<br>median and zero otherwise.<br>rity, interest rate spread and a<br>hed by regulators, as well as<br>ed at the bank and time level.|for the sample of f<br>working capital lo<br>and zero otherwise.<br>therwise. Credit ris<br>able that equals on<br>Other controls incl<br>dummy variable i<br>fixed effects for th<br>|irms that at any<br>ans (in logs)._Ea_<br> _Riskft _is an indic<br>k is measured by<br>e if the market po<br>ude the length of<br>ndicating if the l<br>e type of loan (w|time in our sampl<br>_rmarkfbt_is an indi<br>ator variable that<br>the average ratin<br>wer in period_t_of<br>the relationship d<br>oan was collateral<br>hether the loan h|e period begin an<br>cator variable that e<br>equals one for firm<br>g of loans issued t<br>banks operating in<br>uration of each fir<br>ized). All regressio<br>as a fixed rate or a|earmarked credit<br>quals one for all<br>s with credit risk<br>o firms one year<br>firm_f_'s zip code<br>m-bank pair and<br>ns include fixed<br>rate indexed by|
**<u>Graph 4: Pre‐Conflict Labor Force Participation Rate and Projection</u>**

<!-- Start of picture text --> (a) 2018 Vintage (b) 2013 Vintage<br>%, labor force %, laborforce<br>participation rate (LFPR) %, growth participation rate (LFPR) %, growth<br>60.0 60.0<br>54.5 4.0 54.8 4.0<br>50.0 44.9 2.0 50.0 45.8 2.0<br>40.0<br>40.0 0.0<br>0.0 0.2<br>30.0 ‐1.3 ‐2.0<br>30.0 ‐1.5 ‐2.0<br>20.0 ‐4.0<br>20.0 ‐4.0<br>LFPR (WDI Archive, ILO‐modelled estimates, 2013), LHS<br>LFPR (WDI, ILO‐modelled estimates, 2018), LHS Growth in participation rate<br>Growth in participation rate Average growth, 2001‐2010<br>Average growth, 2001‐2010 Average growth, 2011‐2014<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text -->

**Human capital** growth, based on Penn World Tables (PWT) 9, which for Syria, uses Cohen, Soto and Leker (CSL) data for the average years of schooling of the population aged 25 and above (Cohen and Soto 2007; Cohen and Leker 2014), averaged 1.0% for the 10 years up to 2010.<sup>
spatial price adjustment for poverty analysis, it may be useful to exclude items that are not relevant to low-income households.<sup>12</sup> A few but growing number of countries have either officially reported or published as experiments subnational PPPs calculated based on the CPI price data. Those examples include the United States (Aten 2017), the United Kingdom (Office for National Statistics 2018), Germany (Weinand and von Auer 2019), and Italy (Biggeri and Tiziana 2014; Laureti and Rao 2018). An example from a developing country is Dikhanov, Palanyandy, and Capilit (2011) for the Philippines.

Finally, when price data (based on price surveys) are not available, unit values are often used as a proxy for price. As shown in Figure 1, about 40 percent of African countries currently rely on unit values for spatial price adjustment for their official poverty measurement. Unit values have advantages in several aspects, including their availability and geographic coverage. Nevertheless, the limited information about product quality and non-food goods and services is clearly a serious drawback for spatial price adjustment. An example is a substitution bias due to unobserved quality differences in unit values. Since Deaton (1986) established the use of unit values several decades ago, various adjustment methods have been devised to address concerns about the reliability of unit values (Gibson, n.d.). Recent research, however, has emphasized limitations in the use of unit values as a proxy for price data (Gibson and Kim 2019; McKelvey 2011). Thus, it would be better to avoid resorting to unit values for spatial price adjustment unless other price data are not available. In this paper, we focus on market price surveys and the CPI price data.

# **2.2. How should non-food items (particularly housing) be treated in measuring spatial differentials in the cost of living?**

_Treatment of non-food prices_ As people become richer, they tend to spend a lesser portion of their budget on
Figure 3: Percentage of Labor Force in Each Country with Good Job, _k ≥_<sup>1</sup> _/_ 2 <!-- Start of picture text --> Notes : Authors’ calculations using SEDLAC data. Sample size and years<br>covered differs by country as described in Table 1.<br>Arg Bol Bra Chl Col CriDom EcuGtm Hnd Mex Nic Pan Per Pry Slv Ury<br>.8<br>.6<br>.4<br>Percentage<br>.2<br>0<br><!-- End of picture text --> 18
theories, and offers new challenges to those seeking to understand financial integration and international corporate finance.

To conduct our study, we construct and analyze a unique dataset that includes information on major characteristics of 116,338 corporate bond issues in domestic and international markets conducted by 13,920 firms from 99 countries. Our study covers the period from 1991 to 2008, though all the results hold when we restrict the sample to the period from 1991 to 2006 to avoid any undue influence from the global financial crisis.

The main finding of this paper is that debt issues in domestic and international bond markets have different characteristics. In particular, international bond issues are larger, of shorter maturity, tend to be denominated in foreign currency, and are more likely to involve fixed interest rate contracts. These differences are not driven by differences between those firms that raise debt abroad and those that issue securities at home. Indeed, we find that the differences between bond issues at home and abroad remain after controlling for time-varying countryspecific factors and firm-level fixed effects, and when analyzing only those firms that actively issue debt both in domestic and international markets. In other words, issues conducted abroad by a given firm are different from those conducted in the domestic market by the same firm, suggesting that domestic and international markets specialize in bonds with different features. These findings hold for different cuts of the data and, importantly, for firms from developed countries, which are more financially integrated and have more developed domestic financial markets. Moreover, we find that issues abroad tend to entail lower yields than issues at home denominated in the same currency, after conditioning on different bond characteristics, countryyear dummies, and firm-level fixed effects, and when analyzing firms that issue debt both at 4
cropland experienced less change over time. For example, 15,081 Kebeles (95.9%) contained some cropland in 1996 and 15,045 (95.7%) contained some cropland in 2016—a 0.2 percentage point change.

When considering only areas near (within 5km of) an improved road, urban land and luminosity are also heavily concentrated. Table 1 shows that out of the 14,933 Kebeles near an improved road, 626 (4%) registered positive nighttime light values at baseline, and this value grew to 1,267 by 2013 (a 102% increase) and further grew to 6, 214 (a 390% increase from 2013); however, much of that increase may be due to differences in underlying nighttime lights data (2013 uses DMSP-OLS data and 2016 uses DMSP-OLS-like data, which better captures low values of nighttime lights). Urban area saw similar growth over time, from 520 Kebeles with some urban area to 1,035 (a 99% increase). While a small proportion of Kebeles near an improved road contain lit or urban cells, a high proportion of lit or urban cells are near an improved road.

**Table 1:** Summary Statistics of Outcome Variables Across Kebeles

|Variable||All||(Within|Treated A<br> 5km of Im|reas<br>proved Road)|C<br>(_>_5km|ontrol Ar<br>of Improv|eas<br>ed Road)|
|---|---|---|---|---|---|---|---|---|---|
||1996|2013|2016|1996|2013|2016|1996|2013|2016|
|**_Average across Kebeles_**||||||||||
|Avg. NTL|0.35|0.57|
’s international poverty estimates for China are instead based on an approximate distribution derived from grouped data, which cannot be used for poverty profiling. This study therefore utilizes household level data from the 2013 Chinese Household Income Project Survey (CHIPS), made available to the public by Beijing Normal University. The poverty rate for urban and rural China, derived from the 2013 HBS, is then applied to the CHIPS data to generate profiles of the extreme and moderate poor in China.

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_15_ diversion in favor of public transportation may result in an underestimate of demand. The behavior of bus services may also have a strong influence since they are quite an important feeder service. Increases in waiting time for bus services could hurt the demand for the commuter rail. All these assumptions can be quantified through relatively simple simulations and allow the regulator to better assess the risk and ... compensations if needed.

# _5.2_ **_Willingness for urban toll roads in France_**

Toll roads have been extensively developed in France, since the 60's. Their total length amounts to roughly 7000 km. But these toll roads are mainly intercity motorways for which traffic forecasting is a rather easy task as network effects are rather small in intercity roads; generally speaking there is just one competitive road and the main problem is to assess the split of the total traffic between the toll motorway and the competitive road. Furthermore, a long experience exists and traffic models for these situations are well calibrated and provide good fits with the reality. This is not the case for urban toll roads, which begin to appear in the largest agglomerations. There are few lessons from the past, and network effects are so large that it is not at all straightforward to estimate the sensitivity to competition or complementarity between routes. This is why the effects of the level of tolls on the traffic of the motorways have to be much more carefully studied than for other types of roads. Currently this is done in France by relying on rather large econometric models. One representative exemple is now in operation in Ile-de-France, the region which surrounds Paris and is almost full urbanized with about 11 Millions inhabitants.

The models used to forecast traffic on new infrastructures in this area are classically built on a four steps basis. Demand data are drawn from household surveys (16 000 households) updated every 5 years, and provide trip matrixes for several purposes of travel: job, non job, and for duty vehicles. The network supply is defined by nodes and
stabilizers by income group. The observed decomposition patterns are largely the same pattern as for the overall population with two exceptions. Lower income groups in Croatia and Poland show a more pronounced role of automatic stabilizers. While this is due to a higher contribution of SIC in Croatia, it is due to a higher contribution of taxes in Poland.

A general observation is the reduced role of other COVID-19-related policy changes in the bottom quintiles which seem to play an even more minor role for severely affected than the overall population. The only exception is Ireland, where other COVID-19-related policies significantly cushion decreases in market incomes. As shown by Figure A7, in selected countries social assistance benefits, among automatic stabilizers, had an important role in cushioning the negative income shock of the crisis on the poorest quintile for the most severely affected: this occurred to a larger extent in the Netherlands, Finland and Greece, but also in Italy, France, Austria, and Cyprus. This result can be explained by the fact that, in recent years, some of these countries (especially the Netherlands, Greece, Italy, and, to some extent France) have significantly improved generosity and coverage of their means-tested social assistance programs, especially of Guaranteed Minimum Income (GMI) schemes.

# **5 Conclusion**

This paper seeks to analyze the effectiveness of tax-benefit systems in mitigating the effect of the COVID-19 pandemic on household income in the EU. In the absence of actual data containing information on household income during the pandemic, we use the tax-benefit microsimulation model EUROMOD to nowcast the distribution of household disposable income during the first year of the pandemic in each EU country, based on information on labor market transition shares observed in the EU-LFS and administrative data. EUROMOD is further used to decompose the changes in the income distribution and to quantify the effects of: (i) earnings losses due to COVID19, (ii) automatic stabilizers, (iii) monetary compensation schemes introduced during the pandemic; and (iv) COVID-19-specific reforms to taxes and benefits implemented by
_4. What of the following options best describe the reason why this establishment did not receive any national or local government measures issued in response to the crisis?_ Menu of options: I was not aware; Too difficult to apply; I am not eligible; I have applied but not received it; Other.

> 6This is measured using country-level Google Mobility Data.

> 7 See World Bank, _Map of SME-Support Measures in Response to COVID-19_ . Data and visualizations available here: https://bit.ly/2SelF96. Policy responses are classified in eight different categories: business advice; business climate; business cost support; debt finance support; demand support measures; employment cost support; other finance support; and tax relief.

> 8Primarily through rate reductions, credits, waivers, and/or deferrals of VAT, payroll, social security, and land taxes (58% of all tax support measures); similar benefits on corporate taxes have been used to a lesser degree (30%).

4
major seed markets; for example, the leading maize seed company reports that its market share is being eroded by smaller firms undercutting its price. Overall, the large number of companies in Turkey's seed industry favors competitive markets despite some tendency toward over-regulation (eg, continuing compulsory variety registration).

Seed Imports and Exports: Reforms lowering barriers to varietal introduction and to seed imports led to more imports for some crops, such as maize and sunflower. Soon, however, production expanded to take care of demand and then pushed into export markets as well (see Figures 3.1 and 3.2). From 1988 maize seed exports exceed imports; for sunflowers, exports exceed imports from 1990. Once reforms allowed seed technology to enter, Turkey has been able to exploit its comparative advantage in seeds based on climate, scientific skills, and low cost labor. Table 3.4 shows Turkey's exports and imports for all seeds. Chile, another early reformer, shows the same progression from maize seed imports, to local production, and then to exports.

Maize and sunflower seed imports in the early 1980s assisted introduction of new varieties, allowing farmers to adopt them before locally produced seed was available. During the 1960s, imports of Mexican wheat seed played a similar role, facilitating rapid expansion of area planted to new varieties. A recent study of maize yields for 50 countries over 25 years found strong correlation between yield increases and volume of seed imports (Pray and Echeverria 1992, pp 372373).

Considering the importance of imported seeds to introduce new varieties, Government efforts to discourage seed imports (see Section 2.1) may be more effective in slowing technology transfer than in protecting domestic seed producers. In the early 1 990s, Turkey has been importing seeds for _-15-_