rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 1. Cyclical Component of the Returns to Schooling and the Unemployment Rate by Country**

<!-- Start of picture text --> Argentina Brazil<br>0.03 0.015<br>0.02 0.01<br>0.01<br>0 0.005<br>-0.01 0<br>-0.02<br>-0.03 -0.005<br>-0.04 -0.01<br>Unemployment Rate Unemployment Rate<br>Age and Agesq Age Dummies Age and Agesq Age Dummies<br>Linear (Age and Agesq) Linear (Age Dummies) Linear (Age and Agesq) Linear (Age Dummies)<br>Chile Colombia<br>0.02 0.1<br>0.015<br>0.01 0.05<br>0.005 0<br>0<br>-0.005 -0.05<br>-0.01 -0.1<br>-0.015<br>-0.02 -0.15<br>Unemployment Rate Unemployment Rate<br>Age and Agesq Age Dummies Age and Agesq Age Dummies<br>Linear (Age and Agesq) Linear (Age Dummies) Linear (Age and Agesq) Linear (Age Dummies)<br>Costa Rica Ecuador<br>0.04 0.025<br>0.03 0.02<br>0.02 0.0150.01<br>0.01 0.005<br>0 0<br>-0.01 -0.005<br>-0.01<br>-0.02 -0.015<br>-0.03 -0.02<br>Unemployment Rate Unemployment Rate<br>Age and Agesq Age Dummies Age and Agesq Age Dummies<br>Linear (Age and Agesq) Linear (Age Dummies) Linear (Age and Agesq) Linear (Age Dummies)<br>Honduras Mexico<br>0.04 0.008<br>0.03 0.006<br>0.02 0.0040.002<br>0.01 0<br>0 -0.002<br>-0.01 -0.004<br>-0.006<br>-0.02 -0.008<br>-0.03 -0.01<br>Unemployment Rate Unemployment Rate<br>Age and Agesq Age Dummies Age and Agesq Age Dummies<br>Linear (Age and Agesq) Linear (Age Dummies) Linear (Age and Agesq) Linear (Age Dummies)<br>Peru El Salvador<br>0.015 0.015<br>0.01 0.01<br>0.005<br>0.005<br>0<br>0<br>-0.005<br>-0.01 -0.005<br>-0.015 -0.01<br>Unemployment Rate Unemployment Rate<br>Age and Agesq Age Dummies Age and Agesq Age Dummies<br>Linear (Age and Agesq) Linear (Age Dummies) Linear (Age and Agesq) Linear (Age Dummies)<br>Uruguay Venezuela<br>0.04 0.13<br>0.03<br>0.02 0.08<br>0.01<br>0 0.03<br>-0.01<br>-0.02 -0.02<br>-0.03<br>-0.04 -0.07<br>Unemployment Rate Unemployment Rate<br>Age and Agesq Age Dummies Age and Agesq Age Dummies<br>Linear (Age and Agesq) Linear (Age Dummies) Linear (Age and Agesq) Linear (Age Dummies)<br>Source: Own calculations and ILO.<br>Cyclical Component (of the RTS) 0.07 0.09 0.11 0.13 0.15 0.17 0.19 0.21 Cyclical Component (of the RTS) 0.055 0.06 0.065 0.07 0.075 0.08 0.085 0.09 0.095 0.1<br>Cyclical Component (of the RTS) 0.04 0.05 0.06 0.07 0.08 0.09 0.1 Cyclical Component (of the RTS) 0.07 0.09 0.11 0.13 0.15 0.17 0.19 0.21 0.23<br>Cyclical Component (of the RTS) 0.03 0.04 0.05 0.06 0.07 0.08 0.09 Cyclical Component (of the RTS) 0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12 0.13 0.14 0.15<br>Cyclical Component (of the RTS) 0.03 0.035 0.04 0.045 0.05 0.055 0.06 0.065 Cyclical Component (of the RTS) 0.02 0.03 0.04 0.05 0.06 0.07 0.08<br>Cyclical Component (of the RTS) 0.065 0.07 0.075 0.08 0.085 0.09 0.095 Cyclical Component (of the RTS) 0.06 0.065 0.07 0.075 0.08 0.085<br>Cyclical Component (of the RTS) 0.06 0.08 0.1 0.12 0.14 0.16 0.18 Cyclical Component (of the RTS) 0.05 0.07 0.09 0.11 0.13 0.15 0.17 0.19<br><!-- End of picture text --> 23
# **Table 1: Asset Share of Banks in MENA, by Ownership and Other Categories, 2001-2008**

State (private) banks are defined as banks in which the government (private sector) is a majority shareholder. Foreign (Domestic) banks are defined as banks in which foreign (domestic) entities are majority shareholders. Listed banks are defined as banks that are listed in a stock market. Shares are calculated as simple country averages. GCC countries are Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and UAE. Non GCC private-led banking systems include Egypt, Jordan, Lebanon, Morocco, Tunisia, and Yemen. Non-GCC state-led banking systems include Algeria, Libya, and Syria.

|**MENA **|**2001 **|**2002 **|**2003**|**2004 **|**2005**|**2006**|**2007 **|**2008**|
|---|---|---|---|---|---|---|---|---|
|State Banks|41%|39%|37%|38%|36%|34%|34%|33%|
|Private Banks|59%|61%|63%|62%|64%|66%|66%|67%|
|Private Domestic|42%|43%|44%|44%|46%|47%|47%|47%|
|Private Foreign|18%|18%|19%|18%|18%|20%|19%|20%|
|Private Foreign International|8%|8%|8%|8%|8%|9%|9%|10%|
|Private Foreign Regional|10%|10%|10%|10%|10%|10%|10%|10%|
|Listed Banks|56%|58%|64%|66%|70%|75%|76%|78%|
|Listed Private Banks|49%|50%|53%|53%|55%|60%|60%|61%|
|Listed State Banks|7%|8%|11%|13%|15%|15%|16%|17%|
|**GCC**|**2001 **|**2002 **|**2003**|**2004 **|**2005**|**2006**|**2007 **|**2008**|
|State Banks|28%|28%|28%|27%|28%|27%|28%|28%|
|Private Banks|72%|72%|72%|73%|72%|73%|72%|72%|
|Private Domestic|47%|46%|47%|49%|48%|51%|52%|52%|
|Private Foreign|26%|26%|25%|24%|23%|22%|20%|20%|
|Private Foreign International|12%|12%|12%|11%|10%|9%|8%|9%|
|Private Foreign Regional|13%|14%|13%|13%|13%|13%|12%|11%|
|Listed Banks|79%|79%|84%|85%|86%|89%|89%|90%|
|Listed Private Banks|66%|65%|67%|68%|67%|69%|68%|68%|
|Listed State Banks|13%|14%|17%|17%|20%|20%|21%|22%|
|**Non-GCC**|**2001 **|**2002 **|**2003**|**2004 **|**2005**|**2006**|**2007 **|**2008**|
|State Banks|56%|53%|50%|52%|47%|43%|43%|41%|
|Private Banks|44%|47%|50%|48%|53%|57%|57%|59%|
|Private Domestic|36%|38%|40%|38%|42%|41%|38%|39%|
|Private Foreign|8%|9%|10%|10%|11%|16%|19%|20%|
|Private Foreign International|3%|3%|4%|4%|5%|9%|12%|12%|
|Private Foreign Regional|5%|6%|6%|6%|6%|7%|7%|8%|
|Listed Banks|29%|31%|37%|41%|45%|52%|54%|56%|
|Listed Private Banks|29%|31%|33%|32%|38%|45%|46%|48%|
|Listed State Banks|0%|0%|4%|8%|7%|7%|7%|7%|

34
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Democratic Republic of Congo, where freight often sits for 16 days (World Bank 2010c).<sup>3</sup> In this corridor, transit time accounts for more than 30 percent of the total time required to move imports from ports to landlocked capitals. Along most other corridors, dwell times at ports account for between 40 and 70 percent of the total time, followed by lengthy administrative processes.

**Figure 2.3 Time required to import goods: Road versus rail routes**

# a. Total time (hours)

<!-- Start of picture text --> 1250<br>1000<br>750<br>500<br>250<br>0<br>road rail road rail road rail road rail road rail<br>Port Locomotive Interchange Transport Border Administrative<br>b. By step (% of total time)<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>road rail road rail road rail road rail road rail<br>Port Locomotive Interchange Transport Border Administrative<br>Lusaka Durban-<br>Durban-Gaborone Durban-Gaborone Durban-Harare Durban-Lusaka Durban-Lusaka Dar es Salaam- Lubumbashi<br>Lusaka Durban-<br>transport Lubumbashi<br>Durban-Harare Dar es Salaam-<br>along altnernative modes of<br>Shares of time to import goods<br>Durban-Gaborone Durban-Gaborone Durban-Harare Durban-Harare Durban-Lusaka Durban-Lusaka Dar es Salaam-Lusaka Durban-Lubumbashi<br>alternative modes of transport Dar es Salaam-Lusaka Durban-Lubumbashi<br>Total time (hours) to import goods via<br><!-- End of picture text -->

# b. By step (% of total time)

_Source:_ Based on data collected from World Bank 2009; AICD ports and railway databases; Nathan Associates 2010; Teravaninthorn and Raballand 2009; and World Bank 2010.

> 3 The baseline for transit times at the border of the Democratic Republic of Congo and Zambia was reported to be 16 days for mining freight.

15
TABLE 1: XOREA, CAPITAL ACCUMULATION ACCOUNT. 1984

|Billions<br>of won|Hous<br>Accun.|eholds<br>Finance|Busin<br>Accun.|ess<br>Finance|Gove<br>AcctIn.|rnment<br>Finance|Financial<br>Accux.|Institutions<br>Finance|
|---|---|---|---|---|---|---|---|---|
|Saving (net)||7237.0||1800.0||4539.0||474.4|
|Consumption of fixed capital||896.4||4890.4||143.7||139.7|
|<br>Capital<br>transfers<br>(net)<br>Gross fixed capital<br>formation|4562.9|122.5|12657.8|143.3|3179.8|-390.2|394.5|124.4|
|Increase in stocks|139.0||243.3||||||
|Purchases of land*|-1010.6||872.9||-16.2||153.9||
|Purchases of intangible<br>assets*|-- '.8||60**.**||0.3||21.1||
|Net Lending|4646.4||-703j.7||1128.6||169.0||

Source: Bank of Korea, Financial Statements Analysis, 1985.

TABLE 2: KOREA, CAPITAL FINANCE ACCOUNT. 1984

|Bitlions<br>of won|Hous<br>Assets|eholds<br>Liabs|Busin<br>Assets|ess<br>Liabs|Gove<br>Assets|rnmwent<br>Liabs|Financial<br>Assets|Institutions<br>Liabs|
|---|---|---|---|---|---|---|---|---|
|Net Lending||4285.7||-7533.1||1147.5||272.7|
|Gold|||||||0.2||
|Currency and transferable<br>deposits|42.8||-14.7||461.9||675.6|1165.7|
|Other deposits|2895.6||2591.7||486.7||885.3|6859.4|
|Life<br>insurance reserves**|1804.8|||||||1804.8|
|Short-term<br>securities<br>Long-term securities|162.5<br>839.7|%|-410.8<br>463.3|90.4<br>1440.6|9.3<br>66.1|-116.5|1427.9<br>901.8|1098.5<br>946.8|
|Stocks|887.4||584.1|1575.6|19.2||340.0|255.0|
|Loans by financial<br>institutions||3133.5||7171.0||104.2|12413.3|2004.5|
|Goverrmlent loans||17.4||-8.7|313.5|||304.8|
|Equities<br>other than stocks|730.8||10.7|872.0|210.4||-0.2|79.6|
|Trade credit||1327.6|2648.1|1320.4|||||
|Foreign exchange holdings|||||||596.2||
|Foreign claims and debts|1400.8||-171.2|-607.4||505.9|82.5|2436.4|
|miscellaneous|||471.3|1851.7|139.5|65.5|785.5|879.8|

Source: Bank of Korea, Financial Statements Analysis, 1985.

*(net)

**(and pension funds); Net equity of households.

Note: GNP=69400 billion won 5
14 a consistent set of values for reducing health risks. The results are summarized in Figure 2, which provides an overview of the distribution of net benefits of 54 final regulations. The left side of the figure shows the number of rules with net costs that fall in various categories. The right side of the figure shows the number of rules with net benefits that fall in various categories. The figure illustrates that average benefit for a rule with net benefits exceeds the average cost for a rule with net costs.

Several conclusions emerge from his analysis. First, using government agency data, it would appear that there is a present value of about US$280 billion (1994 dollars) in net benefits to government regulation in those areas since 1990. Yet over half the final rules would not pass a benefit-cost test, even when we use government agencies' numbers. Aggregate net benefits are positive because many of the rules that do pass have substantial benefits. Eliminating final rules that would not pass a benefit-cost test could increase the present value of net benefits by more than US$115 billion.

There are reasons, however, _not_ to take the agency numbers at face value. Both theory and empirical evidence suggest that agencies are likely to overstate substantially the aggregate numbers for net benefits. Agencies with a single objective (e.g., protecting the environment improving safety in the workplace) have an incentive to overstate the benefits of their program relative to the costs so that they can better meet the demands of interest groups.

Another measure of the impact of regulations is how many lives a regulation is likely to save. Interestingly, a review of several final and proposed regulations reveals the amount spent for each premature death that would be avoided because of the existence of the regulation varies over eight orders of magnitude - from roughly US$100,000 to over US$5 trillion (1990 dollars) (Morrall, 1986)! This suggests that regulations could be developed that would prevent many more premature deaths while still saving consumers' money. Recent studies have attempted to quantify potential gains in both the United States and abroad. Reallocating the current U.S. investment in 185 life-saving interventions could avert an additional 60,000 deaths, or twice that of the status quo (Tengs and Graham, 1996). In addition, reallocating recent domestic regulatory expenditures of about US$8 billion (1994 dollars) could save more than 100 million additional life-years in developing countries (Hahn, 1996).
ering all countries and sectors worldwide since 2003. Data include the name of the country in which the firm engaging in greenfield FDI is headquartered, the year of investment, the recipient sector, the function (nature) of the project, the type of project (new, expansion, co-location), and the capital investment (capital expenditures) associated with the FDI project.<sup>13</sup> There is no minimum investment size for a project to be included but the equity stake of the foreign investor cannot be lower than 10%. Data are collated through daily searches of Financial Times newswires and internal information sources, other media sources, project data received from industry organizations and investment agencies, and data purchased from market research and publication companies. Each project is crossreferenced against multiple sources, with the main focus on direct company sources. _FDI Markets_ is the primary source of greenfield FDI data for various international organizations (UNCTAD, World Banks), consultancies (the Economist Intelligence Unit), major corporations and over 100 governments.

Contrary to balance of payments FDI our FDI data are available for a large number of countries and sectors, and are not distorted by “round-tripping” and “trans-shipping” phenomena.<sup>14</sup> In addition, the _FDI Markets_ database, by providing us with information on the number of projects and average size of projects, will allow us to investigate the effects of the global financial crisis on the extensive and intensive margins of outward FDI.

One drawback of this database is that it does not include data on cross-border mergers and acqui-

> 13Data on capital investment are based on the investment the company is making at the time of the project announcement or opening. The data include estimates for capital investment (derived from algorithms) when a company does not release the information. These estimates may introduce measurement error in our dependent variable, generating larger variances in our estimators.

> 14“Round-tripping” refers to the situation where different treatments of foreign and domestic investors encourage the latter to channel their funds into special purpose entities (SPEs) abroad in order to subsequently repatriate them in the form of incentive-eligible FDI. With “trans-shipping”, funds channeled into SPEs in offshore financial centres are redirected to other countries, leading to strong divergences between the source country of the FDI and the ultimate beneficiary owner. The _FDI Markets_ database reports the ultimate parent company. Nevertheless, despite the different nature of the _FDI Markets_ FDI data and the balance of payments outward FDI data compiled by UNCTAD, the coefficient of correlation between theses two variables at the country-level is 0.66, significant at the 1% level.

7
_-5-_ constant over time.' Equation (2) describes welfare changes as a function of changes in household endowments. This assumes that initial conditions do not matter, and in the case of household welfare, this is not a tenable assumption. In fact, households' response to a changing economic environment is very much a function of the level of endowment prior to the changes, and the then prevalent behavior with respect to income generation. For example, whether the head of household works in government or is a farner will affect the household's flexibility to change the allocation of time use of household members. The amount of physical assets affects the degree to which income drops can be smoothened over time by borrowing or by selling assets. Initial conditions can easily be entered in the difference equation.

The results in the next two sections will show the estimation of equations (2) and (3). The dependent variable in these equations can take on any positive or negative value, and is bound from below only by the highest per capita expenditure figure in the survey results, since this determines the largest possible decline. Hence, equation (3) can be estimated by OLS methods.

# Explanatory Variables

The main set of explanatory variables is the household's endowment of human and physical capital. The human capital of the household is embodied in its members and hence 1' This need not be the case, since R; includes e.g., location, and this can of course change over time. However, the CILSS panel did not trace households that moved, but replaced them with the new household in the same dwelling. These households are not part of the panel in this study.
that embarked on a fast-growth transformation process like Russia, ensuring equitable growth would require special attention on the poorer population groups that may lag (further) behind.<sup>4</sup> Second, we examine Russia’s welfare mobility over different time windows of varying lengths. In particular, we study a 20-year time period, from 1994 to 2015, and we further divide this period into short-run and medium-run periods. The former include four periods 1994-98, 19982004, 2004-09, and 2009-15, while the latter include two periods 1994-2004 and 2004-15. The reason for this division is twofold: first, the major financial crisis in 1998 forms a natural dividing line for pre- and post-crisis periods, and second, we want to analyze time periods of roughly equal lengths for better comparability.<sup>5</sup> This detailed dissection of the time periods offers more granular analysis than previous studies, and can uncover new insights on the dynamics resulting from the country’s economic growth. To our knowledge, no other study on Russia has offered such a detailed temporal breakdown as we attempt here.

Furthermore, studies of mobility and inequality have primarily focused on analysis of shortterm mobility. An operational obstacle to the type of welfare dynamics analysis we provide here is the prerequisite of panel survey data, which track income or consumption of the same households (or individuals) over time. Fortunately, we can exploit multiple rounds of panel data from the Russia Longitudinal Monitoring Surveys that span over two decades from 1994 to 2015. Very few, if any, transitional countries can offer the type of long-running, nationally representative panel household survey data that Russia does.<sup>6</sup>

> 4 But we return to discuss top income earners in the robustness checks section.

> 5 A major technical issue with any (long-running) panel survey is attrition over time; analyzing panels of varying lengths can help provide better comparison and more robust results. We also experiment with other ways to divide the time periods and discuss results in the robustness check section.

> 6 As an example, the China Health and Nutrition Survey (CHNS) collects panel data and was implemented as early as 1989, but does not offer nationally representative data. A more recent panel survey, the China Family Panel Study (CFPS) provides more coverage but was started only in 2010. Alternatively, statistical techniques have recently been developed that allow the construction of synthetic panels from repeated cross sections (Dang _et al._ , 2011; Dang and

4
|Taxi|1,199,430|1,153,366 (-4)|1,094,420 (-9)|1,189,090 (-<br>0.9)|
|---|---|---|---|---|
|Bus|359,829|454,684 (26)|323,021 (-10)|356,248 (-1)|
|Tram|59,972|57,003 (-5)|33,843 (-44)|55,955 (-7)|
|**Traffic Load(car-equivalent)**|1,301,277|1,270,095 (-2)|1,394,939(7)|1,311,514(0.8)|
|**Traffic load-to-capacity**|11.89|11.6 (-2)|4.4(-63)|10.8(-9)|
|**Fuel consumption(1,000 lit/year)**|649,129|<br>642,312(-1)|584,102(-10)|632,772(-3)|
|**Bus waiting time**|11.5|<br>8.1 (-30)|11.5(0)|11.5(0)|
|**Bus occupancy**|106|45.1 (-57)|98(-8)|105.6(-0.8)|
|**Travel time**|**(one-way minu**|**tes)**|||
|Private Car|23|22.5 (-2)|11 (-52)|21.1 (-8)|
|Motorcycle|23|22.5 (-2)|11 (-52)|21.1 (-8)|
|Taxi|26|25.5 (-2)|15 (-42)|24.2 (-7)|
|Bus|43.5|39.4 (-9)|26.8 (-38)|40.8 (-6)|
|Tram|26|26 (0)|26(0)|26 (0)|
|**Expected utility/MUI(MAD)**|886,951|888,243(0.15)|901,191(2)|888,713(0.2)|
|**Social welfare(MAD)**|887,828|888,652 (0.09)|895,493(0.9)|887,950(0.01)|
|**Social welfare increase as a % income**||0.9|8.5|0.13|
|**Fuel tax revenue(MAD)**|714|706 (-1)|642(-10)|696(-3)|
|**Park tax revenue(MAD)**|453|438 (-3)|508(12)|459(1.3)|
|**Annualized Road cost(MAD)**|0|<br>0|6,525|1,624|
|**B**|**us (MAD)**||||
|Annual Profit|-63|-505 (-704)|-71 (-13)|-63 (-0.2)|
|Annual Revenue|208|265 (27)|187 (-10)|206 (-1)|
|Annual Cost|271|770 (184)|258 (-5)|269 (-0.7)|
|**Ta**|**xi (MAD)**||||
|Annual Profit|0|0|0|0|
|Annual Revenue|1,418|1,360 (-4)|1,241 (-12)|1,394(-2)|
|Annual Cost|1,418|1,360 (-4)|1,241(-12)|1,394(-2)|
|Fare(per trip)|7.09|7.07 (-0.2)|6.8(-4)|7.03(-0.8)|
|Total Taxi|15,000|<br>14,424 (-4)|13,687(-9)|14,871(-0.9)|
|**Tr**|<br>**am (MAD)**||||
|Annual Profit|-227|-230 (-1)|-252 (-11)|-231 (2)|
|Annual Revenue|57|54 (-5)|32 (-44)|53 (-7)|
|Annual Cost|284|284 (0)|284 (0)|284(0)|

If adding only ground level roads at the periphery, it is optimal to increase baseline road capacity almost threefold. This reduces travel times by 52% for cars and motorcycles, by 42% for taxi and 38% for buses. Trips by car and motorcycle increase by 12% and 13% respectively as switching in favor of the low occupancy modes occurs. Fuel consumption decreases by 10% mainly because speeds are improved as congestion is alleviated. Bus occupancy decreases by 8% reducing the disutility from standing in the bus. Expected utility improves by 2% and social welfare increases by 8.5% of income which dwarfs many times over the benefits of the demand-side policies. The number of taxis operating decreases by 9%. The bus operations deficit gets worse by 23
- Goetzmann, W. N., Li, L., and Rouwenhorst, K. G., 2005. Long-Term Global Market Correlations. _Journal of Business_ 78 (1), 1–38.

- Goldstein, M., 1998. The Asian Financial Crisis: Causes, Cures, and Systemic Implications. Policy Analyses in International Economics 55. Institute for International Economics.

- Goodhart, C.A., 2008. The Background to the 2007 Financial Crisis. _International Economics and Economic Policy_ 4, 331-346.

- Hamao, Y., Masulis, R. W.,  and Ng, V.,  1990. Correlations in Price Changes and Volatility Across International Stock Markets. _Review of Financial Studies_ 3, 281–308.

- Kaminsky, G. L., Lyons, R., and Schmukler, S., 2001. Mutual Fund Investment in Emerging Markets: An Overview. _World Bank Economic Review_ 15(2), 315-340.

- Kaminsky, G. L., and Reinhart, C. M., 2000. On Crises, Contagion, and Confusion. _Journal of International Economics_ 51 (1), 145–168.

- Kim, W., and Wei, S. J., 2002. Foreign Portfolio Investors Before and During a Crisis. _Journal of International Economics_ 56 (1), 77–96.

- King, M. A., and Wadhwani, S., 1990. Transmission of Volatility between Stock Markets. _Review of Financial Studies_ 3 (1), 5–33.

- Kizys, R., and Pierdzioch, C., 2009. Changes in the International Comovement of Stock Returns and Asymmetric Macroeconomic Shocks. _International Financial Markets, Institutions, and Money_ 19, 289–305.

- Kotlikoff, L., 2010. _Jimmy Stewart is Dead – Ending the World's Ongoing Financial Plague with Limited Purpose Banking_ . John Wiley and Sons.

- Levine, R., 2005. Finance and Growth: Theory and Evidence. In Handbook of Economic Growth <u>vol 1.: Aghion, P. and Durlauf, S. (eds.), Elsevier.</u>

- Longin, F., and Solnik, B., 1995. Is the Correlation in International Equity Returns Constant: 1970–1990? _Journal of International Money and Finance_ 14, 3–26.

- Masson, P., 1998. Contagion: Monsoonal Effects, Spillovers, and Jumps Between Multiple Equilibria. IMF Working Paper 98/142.

- Obstfeld, M., Shambaugh, J. C., and Taylor, A. M., 2009. Financial Instability, Reserves, and Central Bank Swap Lines in the Panic of 2008. NBER Working Paper 14826.

- Quinn, D., and Voth, H. J. , 2008. A Century of Global Equity Market Correlations. _American Economic Review_ 98 (2), 535–540.

- Reinhart, C., and Rogoff, K. , 2008. Is the 2007 US Sub-Prime Financial Crisis So Different? An International Historical Comparison. _American Economic Review_ 98 (2), 339–344.

21