rafmacalaba/datause-encoder-data huggingface dataset:
Latin American countries as investors sought higher returns. Such inflows have contributed to domestic expansions in these countries. A very large literature tried to quantify to what extent the world interest rate can explain fluctuations in small open economies using various methodologies. Lubik and Teo (2005), by using an estimated dynamic stochastic general equilibrium (DSGE) model for Australia, Canada, New Zealand, Chile and Mexico show that world interest rate shocks have substantial explanatory power for business cycle fluctuations in these countries, with their contribution ranging from 40 percent to 75 percent. Neumeyer and Perri (2005) parameterize a dynamic stochastic model of a small open economy, including various stochastic processes, one of which is the process for the world real interest rate. Using their simulations results of the model, one can quantify the contribution of the world interest rate shocks to the output fluctuations to be around 30 percent in a representative emerging economy. In a panel of emerging countries Uribe and Yue (2005) find a similar number. They show that innovations in the US interest rate account for about 20 percent of movements in aggregate activity of those countries. Blankenau, Kose and Yi (2001) using a different methodology find that world interest rate shocks can explain about onethird of the fluctuations in output of Canada.

Therefore, we consider the world interest rate in our evaluation. Figure 1.2(g) shows domestic GDP plotted together with the world real interest rate measured by the real 3-month US T-bill rate. We find a weak positive correlation with aggregate output, but negative correlation with agricultural output. Since it is possible that changes in the U.S. T-bill rate may also be associated with fluctuations in liquidity in the world markets, we also consider an alternative measure of the world interest rate as the real return on the S&P500.<sup>8</sup> Figure 1.2(h) plots that return together with GDP in Paraguay. The correlation between the two variables is positive, but somewhat weak, equal to 0.19.

**Figure 1.2: Business Cycle Fluctuations in Paraguay**

<!-- Start of picture text --> 1994q3 1999q1 2003q3 2008q1 2012q3<br>GDP (log) GDP Agri (log) 1994q3 1999q1 2003q3 2008q1 2012q3<br>GDP Non-Agri (log) GDP (log) Real int. rate<br>(a) (b)<br>.2 .3 .1<br>.1<br>.05<br>.2<br>0<br>0<br>-.1<br>GDP (log)<br>Real int. rate<br>.1<br>-.2 -.05<br>-.3<br>0 -.1<br><!-- End of picture text --> 8 Data is from the Global Financial Database.
(CPI).<sup>9</sup> To derive this index, TI collects data on corruption from a number of other sources,<sup>10</sup> standardizes them, and calculates the simple average by assigning equal weights. The assessment also combines data from a few past years to reduce abrupt variations in scoring.

The reliability of each figure is improved by including only countries that have been included into three polls at minimum. An indicator for the overall reliability of the index can be drawn from the high correlation between the sources (see Appendix A). Standardization of the index ensures that the inclusion of a source consisting of a certain subset of countries should not change the mean and standard deviation of this subset of countries in the CPI. The reason is that the aim of each source is to assess countries relative to each other, and not relative to countries not included in the source. This includes the idea that a country must not be punished for being compared with a subset of relatively uncorrupt countries, nor rewarded for being compared with a subset of relatively corrupt countries.

# **_Independent Variables:_**

# _1. The Role of the State in the Economy_

We use a variety of measures to capture different aspects of the state:

- _Size of Government_ : is proxied by public investment as a share of GDP, including investment by public enterprises, local authorities and consolidated central government.

- _Tax System_ : Efficiency and equality of tax system, tax rates, enforcement of tax regulations, and the tax system as an incentive mechanism are used. Our measure comes from the 1997 World Development Report, which examines the burden of tax regulations and high taxes. This information is used to measure the overall quality of tax management.

> 9 For details see http://www.transparency.org/surveys/index.html

> 10 Of these, five are for business executives of international or business companies, two by the relevant organization’s staff, one of employees of multinational forums and institutions, and one of embassies and chambers of commerce.

13
Policy Research Working Paper 9810

# **Abstract**

Building on prior work that documented the impact of COVID-19 on firms in developing countries using the first wave of Business Pulse Surveys, this paper presents a new set of stylized facts on firm recovery, covering 65,000 observations in 38 countries. This paper suggests that: _One_ , since the outset of the pandemic, some aspects of business performance such as sales show signs of partial recovery. _Two_ , other aspects remain challenging, including persistently high uncertainty and financial fragility. _Three_ , recovery is heterogeneous across firms and more sensitive to firm-level attributes such as size, sector, and initial productivity than to country-level differences in the severity of the initial shock. In particular, larger and more productive firms are recovering faster, with implications for competition policy and allocative efficiency. _Four_ , the decline in jobs has been steeper during the initial shock than the expansion in employment during recovery, raising the risk of a “jobless” recovery pattern. _Five_ , the diffusion of digital technology and product innovation accelerated during the pandemic but did so unevenly, further widening gaps between small and large firms. _Six_ , businesses now have more access to policy support, but poorer countries continue to lag behind and appropriate targeting of firms remains a challenge.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at agrover1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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 for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 7** : Cross-section regression estimates (IV) of the effect of per capita income, trade, FDI net inflows on the occupational gender wage gap by occupational skill level and country income (standard errors in parentheses)

||**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|
|---|---|---|---|---|---|---|---|---|
||**Low an**|**d lower midd**|**le income co**|**untries**|**High an**|**d higher mid**|**dle income c**|**ountries**|
|Log GDP per capita|0.02|0.02|0.10|0.02|-0.03|-0.03|0.01|-0.01|
||(0.02)|(0.02)|(0.07)|(0.02)|(0.01)|(0.01)|(0.02)|(0.01)|
|Trade (% GDP)|||||||||
|* Low skill|-0.001<br>(0.001)||||-0.002<br>(0.0004)||||
|* High skill|0.0005||||-0.002||||
||(0.0006)||||(0.0004)||||
|Trade (% GDP, constant LCU)|||||||||
|<br>* Low skill||-0.001||||-0.002|||
|||(0.0005)||||(0.0004)|||
|* High skill||0.001||||-0.002|||
|||(0.001)||||(0.0003)|||
|Sector Trade (% sector output)|||||||||
|* Low skill|||0.001||||-0.001||
||||(0.001)||||(0.0005)||
|* High skill|||0.002||||-0.001||
||||(0.002)||||(0.001)||
|FDI net inflows (% of GDP)|||||||||
|<br>* Low skill||||-0.03||||-0.04|
|||||(0.01)||||(0.01)|
|* High skill||||0.01||||-0.03|
|||||(0.01)||||(0.01)|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Occupation dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Dummies Cyprus, Japan and|N/A|N/A|N/A|N/A|Yes|Yes|Yes|Yes|
|Korea|||||||||
|Dummy for (ex)Communist|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1766|1762|334|1764|7030|6842|2351|6903|
|Adjusted R<sup>2</sup>|0.27|0.27|0.12|0.25|0.60|0.61|0.45|0.54|

Note: standard errors are corrected for clustering within the same country/occupation observations. NA = not applicable.

42
18 raised, although it is not clear that the change is statistically significant. In the same areas there was a marked reduction in fertility. The fraction of the relevant cohort with no primary schooling fell by slightly more in the IADP areas than in the other areas (but since the initial fraction was smaller, the percentage reduction in non-education was much larger).

These data cannot be said to be very precise but they do provide supporting rather than contradictory evidence for the view that parents see increased human capital per child as a substitute for numbers of children and that their willingness to substitute human capital per child for children depends on the return to education. It is also suggested that the magnitude of the effect may be restricted by the costs of fertility control.

Complementing these studies are macroeconomic analyses. These pick up the inverse relationship between investing in human capital and investing in children and extend the analysis to the whole economy by integrating these decisions with the consumption/saving decision. The model presented by Barro and Becker (1989) is discussed in detail in sections 3 and 4 with the results of simulation experiments. The cross-country macroeconomic evidence is discussed after these simulations. Rosenzweig (1988) makes the point that macroeconomi **c** omparisons generally suggest much more powerful links than do survey-based results and the reason for this is not completely clear. The survey-based results may be missing effects which are external to the individual but internal to the country or the coefficients may be biassed due to measurement error. On the other hand the cross-country studies may well suffer from omitted variable bias, so that effects are being attributed to education rather than to some other variables.

Barro (1991) studies the economic factors which influence fertility rates. He finds from the cross-section of countries that an increase of 10% of the relevant age group attending primary school reduces the number of children bom to each woman by 0.13. An increase of the secondary school population by 10% of the cohort reduces the fertility rate by 0.26. The infant mortality rate (deaths up to the age of 4) is an explanatory variable, but the effect is not very different if the net fertility rate (number of children surviving to the age of 4) is studied instead. The level of GDP in 1960 is included as a regressor. This is not statistically significant unless dummies are included for Latin America and Africa; there is, however, no obvious reason why dummies should be introduced for these regions. When the latter dummies are present the effect of primary education on fertility rises from 0.13 to 0.16 children while that of secondary education falls from 0.26 to 0.24; it does not, therefore change markedly the conclusions to be drawn from this about the effect of education on fertility. Barro does not test the restriction that the coefficients on both types of education are equal and it is not possible to do this with the results as they are presented, but, in view of the t-statistics shown, it would probably be accepted.
ist

# (3.14) Cibb m (1-5i)eBBJ

Broadly speaking, there are four alternative ways to finance a buyback. First, a foreign country may provide the funds as a grant or gift. That is, the country uses "external financing". Second, the country can use its own reserves to buy back its debt. Third, old debt can be traded for new debt. Following general practice, we will call this new debt "exit bonds". This operation is sometimes called debt conversion. Finally, the country may exchange its debt for equity. This is the traditional debt-equity swap.

The user must therefore start by specifying the share of each financing method in the cost of the buyback. It is necessary also to make assumptions about the interest rate (iJbb), maturity (Mibb) and grace period (GJbb) applicable to exit bonds. Once this is done, the DM calculates the effects of the buyback on the stock of debt and on amortization and interest payments. That is, the DM computes series for AFJbb, AAPJbb and, AIPJbb#

# A buyback will reduce the stock of debt by the value of the retired debt minus the amount of exit bonds issued. Therefore: (3.15) AFJbb = EBJ - BBJ

In addition, amortization and interest payments must be reduced. The question is how much are they reduced. If all the loans given by a single creditor where homogeneous or, 49
other hand, increased transportation and communication technologies facilitated concentration of goods and services markets in specific urban centers, and thus pulled the population and labor force towards these centers (Kahraman et al., 2002). Table 4.9 shows that internal migration flows have consistently followed an east-to-west pattern over the last 30 years in Turkey, with Istanbul in the lead in terms of receiving in-migration. Istanbul, the largest metropolitan area in Turkey, has reached a population of 10,018,735 (2000 Census of Population) with a 3 percent annual population growth rate, higher than the Turkish average. Turkey’s second largest metropolitan area, Ankara, has over 4 million residents and also continues to attract migrants from the rest of the country. The population of Izmir, in the Aegean region, is about 3.4 million for the same census year, and also has shown a higher than average population growth rate of approximately 2.5 percent per annum. Similarly, Antalya, which is the center of a major tourism hub in Turkey, has received substantial migration flows in the past and, with a population of about 2.5 million, has grown roughly 3.6 percent. This is well-above the Turkish average. All metropolitan areas mentioned, i.e. Istanbul, Ankara, Izmir and Antalya, are located in the migrant-receiving western part of the country (see Table 4.9).

**<u>Table 4.9. NUTS Level-1 Regions, net internal migration (‰)</u>**

|**1970–1975 **|**1975–1980 **|**1980–1985 **|**1985–1990**|**1995–2000**|
|---|---|---|---|---|
|**Istanbul**<br>127.46|67.27|56.53|99.86|46.1|
|**Western Marmara**<br>-5.89|-3.78|-1.18|3.08|26.1|
|**Aegean**<br>17.16|21.79|13.37|25.52|22.9|
|**Eastern Marmara**<br>18.99|38.52|27.26|41.95|15.9|
|**Western Anatolia**<br>40.45|9.59|5.65|8.75|15.9|
|**Mediterranean**<br>12.75|12.4|14.87|19.94|0.4|
|**Mid-Anatolian**<br>-25.1|-27.14|-23.9|-49.21|-24.9|
|**Western Black Sea**<br>-22.78|-18.95|-23.09|-46.54|-50.3|
|**Eastern Black Sea**<br>-35.94|-35.58|-36.94|-70.57|-26.1|
|**Northeastern Anatolia**<br>-35.69|-71.54|-58.27|-113.38|-49.8|
|**Mid-eastern Anatolia**<br>-27.95|-43.45|-32.62|-59.01|-33.4|
|**Southeastern Anatolia**<br>-30.81|-30.39|-20.36|-30.33|-36.2|

Net internal migration rates do not take into account the migration across provinces within the same region and it is measured as the ratio of net internal migration to mid-period population. _Source: TurkSTAT Website_ As large urban areas continue to act as major attraction forces in internal migration, they face the challenge of meeting water demands by the ever-growing urban population. According 44
consumption measures being statistically significant. Karshenas (2003) finds a corresponding difference of only approximately 5 percent using a slightly different assessment method.<sup>22</sup> While our estimates differ substantially from those of Deaton and Ravallion, we find this difference disappears when we compare our estimates over similar time periods. The Deaton analysis includes data from 1979-2000, while the Ravallion analysis covers 1985-1998. Our estimated overall difference between household survey means (pooling consumption and income) and HFCE for the 1980s and 1990s are 15 percent. These estimates are similar to Ravallion’s 17 percent (95% confidence interval of approximately 9 to 25 percent) and Deaton’s 12 percent (95% confidence interval of approximately 6 to 18 percent). One inference to be drawn from this analysis is that our estimated gaps are substantially larger from those estimated separately by Deaton and Ravallion, but this is not due to differences in methods or assumptions, but due primarily to our data including more recent observations. We estimate the overall average gap during the 2010 decade to be 75 percent greater in absolute magnitude relative to the average gap during the 1990s. For a more detailed comparison of our findings with those of Deaton (2005) and Ravallion (2003), see Appendix 1.

Across all sub-samples by geography and income groups, the gap is negative and statistically significant (Table 1). In Panel A of Table 1, we compare all survey measures (overall, consumption, income) with HFCE. There is a marked pattern of the ratio and country income levels, with the gap being largest among middle-income countries (23 percent for lower middle-income countries and 33 percent for upper middle-income countries). The gap is much smaller for low- and high-income countries, but still statistically significantly different from zero. The pattern across levels of economic development is shown in greater detail in Figure 3, which plots all gaps (and highlights the most recent observations) against economic development, as measured by GNI per capita. The relationship between the size of the gap and level of income forms a clear u-shape, indicating that, on average, the gap increases (gets more negative) from low income to middle-income country range, but then is diminishes for highincome countries. The lines show the results of locally weighted regressions of the latest observation for each country.

> 22 Karshenas (2003) estimates the gap using mean difference in the logs of the measures, at -.05.

15
# **Bank Regulation and Supervision Ten Years after the**

# **Global Financial Crisis**

**Deniz Anginer, Ata Can Bertay, Robert Cull** , **Asli Demirgüç-Kunt, Davide S. Mare**<sup>1</sup>

_JEL Classifications: G18, G21, G28, E58_ _Keywords_ : Banking Regulation, Banking Supervision, Financial Policy, Bank Capital, Basel Capital Requirements 1 Deniz Anginer is with Simon Fraser University; Ata Can Bertay is with Sabanci University; Robert Cull, Asli Demirgüç-Kunt, and Davide S. Mare are with the World Bank. This paper’s findings, interpretations and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, their Executive Directors, or the countries they represent. The authors acknowledge the help of Jim Barth, Gerard Caprio, Ross Levine, and Amin Mohseni-Cheraghlou for the revision of the BRSS questionnaire. The survey benefited from inputs of Krishnamurti Damodaran. Peter Bourke helped with the setting up of the online survey platform. Nan Zhou and Jinjing Liu provided excellent research support for the analyses in the paper. The survey would have not been possible without the many country officials answering the questionnaire, as well as the support of Rudy V. Araujo and the Asociación de Supervisores Bancarios de las Américas (ASBA), and the financial sector experts with the World Bank, including Alphonsus Nji T Achomuma, Noro Aina Andriamihaja, Alejandro S Alvarez de la Campa, Ruben Barreto, Karlis Bauze, Mengistu Bessir Achew, Mazen Bouri, Julian Casal, Pietro Calice, Youjin Choi, Andrea Mario Dall'Olio, Brinda Devi Dabysing, Aurora Ferrari, Faris H. Hadad-Zervos, Norman Loayza, Martin Melecky, Cedric Mousset, Aaron Nathan Levine, Daniel Ortiz del Salto, Alexander Pankov, Sabin Raj Shrestha, Mehnaz S. Safavian, James Seward, Francesco Strobbe, John Vivian, and Lin Yang. The authors may be contacted at <u>danginer@sfu.ca, ata.bertay@sabanciuniv.edu, rcull@worldbank.org, ademirguckunt@worldbank.org, and dmare@worldbank.org. This research was supported by a grant</u> from the Knowledge for Change Program at the World Bank. All errors and omissions are ours.
this measure would probably only make sense in the context of a much broader package of reforms changing their markets shares, roles, mandates, and governance structures.<sup>20</sup>

# **References**

Andrianova, S., Demetriades, P., and Shortland, A., 2010. “Is Government Ownership of Banks Really Harmful to Growth?”. Discussion Papers of DIW Berlin 987, DIW Berlin, German Institute for Economic Research.

Anzoategui, D., Martinez Peria, M., and Rocha, R., 2010. “Bank Competition in the Middle East and Northern Africa Region” Policy Research Working Paper 5363. The World Bank.

Barth, J., Caprio, G., and Levine, R., 2008. “The Regulation and Supervision of Banks Around the World - A New Database” World Bank Policy Research Working Paper Number 2588.

Barth, J., Caprio, G., and Levine, R., 2006. _Rethinking Bank Regulation: Till Angels Govern,_ Cambridge University Press.

Ben Khediri, K., and Ben-Khediri, H., 2009. “Determinants of Islamic Bank Profitability in the MENA Region” _International Journal of Monetary Economics and Finance_ 2(3-4), 409 – 426.

Bennaceur, S., Goaied, M., 2001. “The Determinants of Commercial Bank Interest Margin and Profitability: Evidence from Tunisia” _Frontiers in Finance and Economics_ 5, 106-130.

Clarke, G., Cull, R., and Megginson, W., 2005. _Journal of Banking and Finance_ , Special Issue on Bank Privatization.

<mark>Cole, S., 2008. “Fixing Market Failures or Fixing Elections?”</mark> _<mark>American Economic Journal: Applied Economics</mark>_ <mark>1(1), 219-50.</mark> De La Campa, A., 2010. “Increasing Access to Credit through Reforming Secured Transactions in the MENA Region” unpublished manuscript (available at MENA Finance Flagship website: http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/MENAEXT/EXTMNAREGTOPPOVRED/0,, contentMDK:22734614~pagePK:34004173~piPK:34003707~theSitePK:497110,00.html) Demirgüç-Kunt, A., and H. Huizinga. 2000. “Determinants of Commercial Bank Interest Margins and Profitability: Some International Evidence” _World Bank Economic Review_ 13(2), 379-408.

Dinc, S., 2005. “Politicians and Banks: Political Influences on Government-Owned Banks in Emerging Countries” _Journal of Financial Economics_ , 77(2), 453-79.

International Finance Corporation (IFC), 2010, G20 Report: Scaling up SME Access to Finance. IFC, Washington DC.

> 20 We included an interactive dummy for public and listed banks, and the results indicated that public listed banks perform better than non-listed ones.  However, this result probably lacks statistical power due to very small sample of public listed banks and the short period of listing, and could simply reflect a selection bias – the public banks that were listed were the best performing.

28