rafmacalaba/datause-encoder-data huggingface dataset:
# **Global Corporate Debt during Crises: Implications of Switching Borrowing across Markets**

Juan J. Cortina Tatiana Didier Sergio L. Schmukler<sup>*</sup>

**JEL Classification Codes:** F30, F32, F34, F44, F60, F65, G00, G10, G32

**Keywords:** banking crises, capital raising, corporate bonds, debt maturity, domestic currency debt, firm financing, foreign currency debt, global financial crisis, security issuances, syndicated loans

> * An early version of this research was circulated as CEPR Discussion Paper DP13008 and World Bank Policy Research <u>Working Paper No. 7815</u> and won the Best Paper Award at the 2<sup>nd</sup> International Borsa Istanbul Finance and Economics Conference. We received very helpful comments from Tamon Asonuma, Max Bruche, Julián Caballero, Stijn Claessens, Asli Demirgüç-Kunt, Peter Eckley, Christian Eufinger, Yuko Hashimoto, Aart Kraay, María Soledad Martínez Pería, Colin Mayer, Marco Pagano, Andrea Polo, Andrea Presbitero, Alexander Popov, Luis Servén, and participants at presentations held at the ASSA Annual Meetings, Bocconi University, Borsa Istanbul, Central Bank of Argentina, Central Bank of Chile, CSEF-University of Naples Federico II, EBRD, FIW Research Conference, Georgetown University, Inter-American Development Bank, International Monetary Fund, LACEA-LAMES Annual Meetings, Stanford University, University of Buenos Aires, University of Cape Town, Washington Area International Finance Symposium, and World Bank. We are grateful to Marta Guasch for excellent research assistance. We received financial support from the CEPR RELTIF Programme and the World Bank Chile Research and Development Center, Knowledge for Change Program, Research Support Budget, and Strategic Research Program.

Email addresses: jcortinalorente@worldbank.org, tdidier@worldbank.org, <u>sschmukler@worldbank.org.</u>
it would imply that the purpose of the general question is to sum up. In this case assimilation is more likely than contrast effects.

Context or anchoring effects have been uncovered in the larger behavioral and experimental literature. The early work by Kahneman and Tversky (1981) reported that support of a policy can vary if it was presented as “lives lost” versus “lives gained.” Ariely et al., (2003) show that social security numbers of individuals create substantial anchoring effects on their willingness to pay for products that has no relation to their social security number. The effects of an uninformative anchor on a subject’s willingness to pay was also confirmed by Bergman et al., (2010). For a literature review of the robustness of anchoring effects across studies, see Furnham and Boo (2011). In contrast, studies have uncovered no influence of control questions in experimental games (Roux and Thoni, 2015). Using data on self-employed households in coastal India, Gine et al., (2014) find that the order of recall variables in the survey has no effect on self-reported earnings. Furthermore, some studies have indicated that a number of factors can weaken anchors. Implausible anchors may weaken anchoring effects (Sudgen et al., 2013). Anchoring effects may also be minimized by opposing anchors (Sitzia and Zizzo, 2011). Finally, there is some indication that anchoring effects are weaker than those reported in early studies (Maniadis et al., 2014).

To elucidate the likely channels that may influence the stability of perceptions, it is important to delve into the general structure of the WBES questionnaire. The WBES questionnaire evaluates 15 elements of the business environment (list provided in next section). Typically, the perception-based ratings of these elements appear at the end of each section. For instance, after exploring the incidence, frequency and losses due to crime experienced by firms, respondents are asked to rate to what extent crime is an obstacle to operations. Note that for certain business environment elements there are no related preceding experiencebased questions (e.g. political instability, functioning of courts).

8
changes experienced by developing countries. It is therefore perhaps reasonable to draw inferences based on the OECD experience, with the literature suggesting that larger relative cohort size will have a negative but relatively modest impact on youth employment prospects. A second point is that the relative size of youth cohorts, the measure that is the focus of study in most of this research, peaked in the 1970s and 1980s in many developing countries, and is beginning to decline even in the countries with the latest fertility decline. Combining the empirical evidence on the impact of cohort size with the demographic patterns, there would seem to be little reason to expect that developing countries will be experiencing a youth labor market crisis in the coming decades, at least as far as demographic pressure alone is concerned. On the contrary, the period of most rapid pressure on youth wages and employment has already been passed in most countries.

# **_Relative Cohort Size, Savings, and Public Expenditures_**

Another important area in which there has been attention to the economic implications of relative cohort size is the literature on dependency burden and savings rates. This literature has a long history, going back to the work of Coale and Hoover (1958) and Leff (1969), who argued that countries with high population growth rates suffered from low savings rates due to the high ratio of children and youth to the working-age population. These arguments have taken on a somewhat different form in a body of work that focuses on the rapid changes in dependency burdens that take place during the demographic transition. Bloom and Williamson (1998) argue that a rapid rise in the ratio of the working population to the non-working population in East Asia between 1965 and 1990 played an important role in driving the East Asian “economic miracle.” Bloom and Williamson argue that part of the effect of having faster growth in the working-age population is due simply to the faster growth of the labor force, increasing per capita output due 20
# Table 9: NIS Efficiency Static Negative Binomial with Pre-sample mean

**Annual Observations** 636 **Countries** 49 **Period** 1976-99

|**ln R&D**|0.366 ***|0.155 ***|0.314 ***|0.372 ***|0.301 ***|0.135 ***|0.188 ***|0.300 ***|
|---|---|---|---|---|---|---|---|---|
||(0.03)|(0.01)|(0.03)|(0.03)|(0.03)|(0.01)|(0.04)|(0.05)|
|**ln US trade**|0.262 ***|0.324 ***|0.280 ***|0.231 ***|0.316 ***|0.331 **|0.320 ***|0.288 ***|
||(0.03)|(0.04)|(0.04)|(0.02)|(0.03)|(0.03)|(0.03)|(0.05)|
|**Natural Resources**|-0.033 **|-0.061 **|-0.056 *|-0.075 ***|-0.004 *|-0.060 **|-0.060 **|-0.055 **|
||(0.02)|(0.03)|(0.03)|(0.02)|(0.00)|(0.03)|(0.03)|(0.03)|
|**ln GDPcap**|0.028 ***|0.024 ***|0.021 ***|0.013 **|0.018 **|0.011 **|0.008 **|0.006 **|
||(0.01)|(0.01)|(0.05)|(0.01)|(0.09)|(0.01)|(0.00)|(0.00)|
|**ln (Mean Patents)**|0.210 ***|0.174 ***|0.216 ***|0.229 ***|0.225 **|0.184 ***|0.207 ***|0.233 ***|
||(0.04)|(0.04)|(0.05)|(0.04)|(0.05)|(0.04)|(0.04)|(0.04)|
|**Quality**||0.097 ***<br>(0.03)||||0.115 ***<br>(0.03)|0.065 ***<br>(0.01)||
|**Colaboration**|||0.054 ***|||-0.049 *||0.025 ***|
||||(0.02)|||(0.03)||(0.00)|
|**Education**||||0.068 ***||0.053 ***|0.045 ***|0.050 ***|
|||||(0.02)||(0.02)|(0.01)|(0.00)|
|**IPR**|||||0.021 ***|0.014 **|0.015 ***|0.016 ***|
||||||(0.01)|(0.01)|(0.01)|(0.00)|

**Note: The dependent variable is the count of Patents. Robust standard errors were estimated in all the specifications. All specifications include time dummies.**

31
18 Table 6. Compars of Nondal 6nd Effective Rates of Tariff Pwction for Sdected P ocessd Commodifis in OECD Couies

||Eurowa<br>Co|st<br>mumnitv|bn||VUnited<br>S|<br>tates|All OEM|Counis|
|---|---|---|---|---|---|---|---|---|
|Processed Commodity|Nominal|Effective||Effective||Effective|N|Effective|
|Processed mea products|17.9|51.7|22.5|_59.6_|23|4.4|7.8|15.0|
|Preserved swe foods|12.4|26.5|10.7|23.2|1.1|2.5|1.7|3.7|
|Preserved fruits|16.6|40.8|21.8|31.6|20.3|72.5|17.6|43.4|
|Preserved vegelables|15.1|37.9|17.5|40.2|11.0|20.2|12.2|30.6|
|Wood manufacturs|4.2|9.2|1.2|1.3|4.7|10.3|3.4|7.4|
|Paper and paperbosd|6.0|5.5|3.6|13.7|3.8|0.7|2.5|4.3|
|Articles of paper|6.0|12.6|3.6|10.7|3.8|8.7|33|7.6|
|Coun fabrics|5.6|11.8|5.9|10.0|10.4|13.5|8.5|11.0|
|Wool fabrics|2.7|5.1|11.0|25.3|37.3|85.8|14.6|34.0|
|Leather manufactures|_5.5_|9.9|12A|18.6|9.2|17.5|7.6|13.7|
|Vegetable oils|6.1|50.6|6.2|49.6|0.7|0.0|4.5|36.1|
|Ferrcus mets|2.2|5.0|2.3|_5.1_|5.3|11.8|4.6|9.2|
|Nonferrous metals|2.0|5.0|4.2|10.5|3.0|7.5|3.1|7.8|

Source: Compiled from Lad and Yeats (1987, Table ISA on page 119). Some data were changed due to recent tariff c bnge
translates into within-province inequality increasing from about twice higher than betweenprovince inequality in the early 2000s, to about three times higher than between-province inequality in the late 2010s. Thus, within-province inequality has become much more significant over time than between-province inequality.<sup>6</sup> There were significant differences in inequality levels within the provinces (Appendix A, Table A.1). Compared with the national average of 0.37, the Gini index was greater than 0.40 in Lao Cai, Dien Bien, and Lai Chau in the northern mountain region. In 2020, these provinces still had some of the remarkably high poverty rates (Lao Cai: 21.5%, Dien Bien 46% and Lai Chau: 36%). Inequality and poverty levels were similarly high in the central highlands region (e.g., Kom Tum and Gia Lai had Gini values of 0.41 and 0.39 and poverty rates of 17% and 27% respectively). On the other hand, inequality was lower in Mekong River delta with a Gini index of about 0.31 in Long An and Tien Giang (where poverty rates hover around 1%) and 0.28 in Hung Yen and Thai Binh in the Red river delta (where poverty rates range around 1%).

Finally, we also show in Table 1 the estimates of three Foster-Greer-Thorbecke _(_ FGT) indices of poverty (Foster _et al._ , 1984). The poverty rate (i.e., the headcount ratio) measures the incidence or the proportion of the poor in the population, the poverty gap measures the depth of poverty (i.e., the average income shortfall of the poor), whereas the poverty severity index (i.e., the squared poverty gap) takes into account inequality of the income distribution among the poor. All three measures are estimated using the national (expenditure-based) poverty line as well as the World Bank’s PPP $3.1 per day poverty line. In tandem with the rapid economic growth, we find that poverty rates declined significantly. Nationwide, the headcount poverty rate decreased from 29%

> 6 Interestingly, the world has witnessed rising within-country inequality in the past two decades as well, although within-country inequality still tends to be lower than between-country inequality (Gradin, 2021).

8
We matched forest cover data to village, town and subdistrict boundaries using geographic boundary data purchased from ML InfoMap. In remote parts of India, we received only settlement centroids rather than village boundaries. We generated Thiessen polygons for these villages; all results are robust to excluding this set of villages.

# **III.B Rural Roads**

We scraped village-level administrative data describing the construction of rural roads from the program’s online management portal.<sup>7</sup> For each road, the data provide the names of connected villages, the date when the contract for road construction was awarded, and the date of road completion. While data were reported at the sub-village (habitation) level, we aggregated the data to the village level to match our other data sources. We define a village as treated if any habitation in the village was provided with a new road. The data construction and scraping approach is described in detail in Asher and Novosad (2017). The dataset describes over 100,000 new roads built between 2001 and 2014; we limit our sample to areas with non-zero forest cover and no paved road in the baseline year, leaving approximately 65,000 new roads in the analysis sample.<sup>8</sup>

# **III.C Highways**

Construction dates and geocoordinates for the Golden Quadrilateral and North-South and East-West corridors were generously shared with us by Ghani, Goswami and Kerr (2016). We linked these to the village, town and subdistrict polygons described above by calculating straight line distances from polygon centroids to the nearest point on each highway.

# **III.D Population and Economic Censuses**

We matched all villages and towns from the 1991, 2001 and 2011 population censuses using a combination of incomplete keys provided by the Registrar General and a set of fuzzy matching

> 7The data is publicly available at http://omms.nic.in.

> 8 Results are robust to including upgrades and/or villages with no forest cover at baseline. These would be expected to attenuate non-zero treatment effects, thus their exclusion if anything biases us against finding zero

10
Figure 4. The Correlation between the Export Orientation Index and Productivity <!-- Start of picture text --> 11<br>9<br>7<br>0 .2 .4 .6 .8 1<br>Export Orientation Index<br>Log of Productivity<br><!-- End of picture text --> Source: Authors’ calculation using HIES 2016. Note: The line is the fitted line.

We also examine to what extent farmers’ crop mix is diversified. Diversification tends to be associated with risk-coping strategies <mark>(Di Falco and Chavas in Ethiopia, 2009; McCord et al. in Kenya, 2015); r</mark> esilience to climate shocks <mark>(Birthal and Hazrana in India, 2019; Lin, 2011; and M</mark> ulwa and Visser in Namibia, 2020); food security <mark>(Bellon et al. in Ghana, 2020; Waha et al. for 18 African countries, 2018); p</mark> overty reduction <mark>(Michler and Josephson in Ethiopia, 2017), and sustainable agriculture in Sri Lanka (D</mark> harmasiri 2008). On the other hand, some papers recognize the role of crop specialization in raising agricultural productivity <mark>(Huffman and Evenson in the United States, 2001; Latruffe et al. in Poland, 2005; Kurosaki in West Punjab, 2003). How diversification versus specialization would affect agricultural productivity appears to depend on the country and the context.</mark> Following common practice in the literature, we measure a farmer’s level of crop diversification using the Simpson Index of Diversification (SID)<sup>9</sup> : i where SIDi is the Simpson Index of Diversification for farmer _i_ and is the proportionate cultivated area dedicated to the _k_ th crop. A high value indicates that a farmer cultivates many iikk PP crops, in an evenly distributed way, which is consistent with the notion of high diversification. A zero score implies that the farmer cultivates only one crop, which is consistent with complete specialization. The average SID for female farmers is estimated to be 0.060 which is

> 9 SID is equal to one minus the Herfindahl index, which is widely used to measure industry concentration.

10
The GEIH has national coverage with the following levels of temporal and geographical disaggregation: (i) Monthly: for a group of 13 large capital cities and their metropolitan areas, 11 intermediate capital cities and for the national total. (ii) Quarterly: by capital city (large and intermediate) with its corresponding metropolitan area and for the total of the country by zone (head of the department, populated centers and dispersed rural areas). (iii) Biannual: by capital city, by large regions (Atlantic, Eastern, Central, Pacific and Bogota) and for headlands and populated centers and rural dispersed and for the national total per zone (headers and populated centers and rural dispersed). (iv) Yearly: by capital city with its metropolitan area, by large regions and area (headlands and populated centers and dispersed rural) and by departments.

# **3.2. The characteristics of the synthetic panel**

Following Dang and Lanjouw (2013) and Dang et al. (2014) it is important to verify that the distributions of the time-invariant variables for the two survey rounds are similar across different periods, since the proposed approach relies on the assumption that both surveys represent the same population and that income can be modeled based on such time invariant characteristics. **Table 1** shows the estimated poverty rates for Colombia during the 2008-2016 period using both samples: the full sample of observations available in the GEIH and the restricted sample which includes only household heads whose age goes from 25 to 60 years old in 2008 (i.e. household heads who were born between 1948 and 1973). The final sample includes 1,479,549 households with an average of 184,944 households per survey. The estimates in **Table 1** suggest that the restricted sample reproduces very closely the moderate official poverty estimates for Colombia during the period of analysis. The average difference in poverty rates associated with the full and the restricted sample is 0.40 percent points from 2008 to 2016. This suggests that our estimation sample reflects adequately the Colombian population’s poverty rates as measured in the unrestricted cross sections.

The second step is to assess whether the GEIH rounds are strictly comparable. Our findings suggest that the survey rounds do not appear to suffer from serious comparability issues, especially the potentially time invariant variables for the income model. We focus on household heads, which represent 28 percent of the population, to implement the synthetic-panel methodology. In addition, this paper uses the survey design of the GEIH to improve the precision of the estimates presented in this section. The same cohort of individuals is followed across time to implement the methodology proposed by Dang and Lanjouw (2016). The variables chosen to construct the synthetic panels are the following: birth year (cohort group), gender, 9
where _y_ is the household specific 12-month moving average inflation, _t_ indicates the survey year, _decile_ denotes the categorical decile indicators, _d_ indexes each decile (the first decile is set as the reference term) and is the error term. Standard errors for each _β_ are used to measure the statistical precision of decile-specific inflation rates. εε Our inflation calculations are adjusted to match the official TUIK numbers which include additional sources of information beyond HBS. TUIK uses HBS, Tourism and the Constitutional Population Expenditure surveys to compute the weights of the CPI basket. TUIK also draws from administrative data to refine the consumption weights for energy services such as electricity, natural gas and water. These adjustments create minor differences with respect to the weights derived exclusively from HBS. To ensure consistency between the two methodologies, the final CPI values were adjusted using the ratio of the HICE-based CPI rates to TUIK’s official CPI rates. More specifically, the official TUIK national CPI in a given year was divided by the calculated overall national CPI. The resulting coefficient was used to scale up the national and decile specific CPI values that we estimate using the HBS data.

Finally, household-specific inflation rates for 2020 were computed using TUIK’s price data for 2020 together with budget shares drawn from the HBS-2019. The HBS survey for 2020 was not carried out due to the restrictions imposed to contain the spread of Covid-19. However, a comparison of budget shares for the 12 COICOP goods and services groups across deciles in the 2018 and 2019 HBS surveys show that the composition of consumption bundles remained fairly stable between the two years. Out of the 12 groups, 10 changed from one year to the other by less than 1 percentage point and the other two varied by around 3 percentage points. Unfortunately, the analysis of real incomes and the effects of decile-specific inflation rates on poverty could not be carried out due to the lack of HBS data in 2020.

Total household disposable income is readily available in the SILC survey. In order to convert nominal income into real values, decile-specific inflation rates from the HBS were used for each of the income groups. Labor income, in turn, is defined as income earned from employment related activities including employee wages and business owner earnings. The units of analysis for the examination of labor income are individuals who work full-time and do not engage in other part11
Heterogeneous impacts of SPS and TBT regulations: Firm-Level Evidence from Deep Trade Agreements Ana Margarida Fernandes<sup>1</sup> , Kevin Lefebvre<sup>2</sup> , and Nadia Rocha<sup>∗1</sup>

# **JEL Classification** : F13, F14, F15, Q17, O13, L15.

**Keywords** : non-tariff measures, product standards, preferential trade agreements, exporter dynamics, firm heterogeneity.

> ∗World Bank, 1818 H Street, Washington DC, USA. Ana Margarida Fernandes, Email: afernandes@worldbank.org ; Kevin Lefebvre, Email: kevin.lefebvre@cepii; Nadia Rocha, Email: nrocha@worldbank.org. We thank Bishakha Barman and Natalia Camelo for excellent research assistance. We are also grateful to Scott Baier, José De Sousa, Anne-Célia Disdier, Lionel Fontagné, Roberta Piermartini and participants at the CESifo Area Conference on the Global Economy and the World Bank webinar on the economics of deep trade agreements for comments. This paper has benefited from support from the Umbrella Facility for Trade trust fund financed by the governments of the Netherlands, Norway, Sweden, Switzerland and the United Kingdom. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank of Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the countries they represent. All errors are our responsibility.
# TAXATION OF FINANCIAL ASSETS IN DEVELOPING COUNTRIES

by

# Christophe Chamley*

# TABLE OF CONTENTS

|I.|Introduction.............<br>0<br>....................<br>I|
|---|---|
|II.|Fiscal<br>Instruments...............<br>**_.._**<br>**_._**<br>..............<br>3|
||The Base* *. .<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>......... 3<br>Exnlicit Instruments**.............**<br>**_.4....... . ........._**<br>4<br>Implicit Instruments.<br>**......**<br>**........**<br>5|
|III.|Impact of Taxation on Financial Deepening**....................**<br>**#.**8<br>III.1 Effects of Taxes and Regulations<br>**......**<br>**8**<br>III.2Tax**Rates............**<br>9<br>III.3 Empirical Evidence**........****_#..... o......_**<br>**..**<br>**..**<br>**9**|
|IV.|<br>Measurements of Revenue ...<br>*.<br>........................... .. ..... 22<br>IV.1Revenues and Tax Rates .....<br>...*.....,................<br>22<br>IV.2 Methodology<br>...........................................<br>23<br>IV.3 Examples .............<br>................................<br>29|
|V.|Efficiency Cost of Taxation<br>.............................<br>....... 33<br>V.1 Efficiency Cost in a Static Economy (or on an<br>exogenous balanced growth path) ......................... 36<br>Taxation ...................................**......**...<br>36<br>Interest Ceilings and Allocations in Credit Markets .... 42<br>Imperfect Markets**............ .**<br>........................<br>..... 43|
||V.2 Impact on Long-Run Growth .................<br>.............<br>45|
|VI.|Conclusion: Financial Liberalization<br>and Recovery .... ........<br>51|
|Append|ix 1: Computation of Seignorage Revenues<br>**.**<br>**........**<br>**....**53|
|Append|ix 2: Interest Ceilings and Stabilization<br>...... 57|
|Refere|nces...<br>***.....**o**_......._**<br>**........................**<br>61|

*Very useful comments by Sheetal Chand, Javad Khalilzadeh-Shirazi and Bela Balassa are greatly acknowledged. This paper is forthcoming in Javad Khalilzadeh-Shirazi and Anwar Shah, Eds., Tax Policy in DeveloDing Countries, The World Bank, Washington, D.C.