rafmacalaba/datause-encoder-data huggingface dataset:
**Table 9:  Truncation Bias in Absolute Mobility**

||**P**|**25**|**P**|**75**|
|---|---|---|---|---|
||**Rank-based**|**IGRC-based**|**Rank-based**|**IGRC-based**|
||||**BANGLADESH**||
|**All Children Sample**|||||
|**Father's Schooling**|**-0.7%**|**-22%**|**0.2%**|**1.90%**|
|**Mother's Schooling**|**-2.2%**|**4.7%**|**-2.4%**|**-8.1%**|
|**Average Schooling**|**-6%**|**-25.9%**|**-1.4%**|**-8.7%**|
|**Father-Sons**|**-12.9%**|**-3.9%**|**-5.7%**|**4.2%**|
|**Mother-Daughters**|**7.2%**|**-10.1%**|**2.5%**|**-10.4%**|
||||**INDIA**||
|**All Children Sample**|||||
|**Father's Schooling**|**1.60%**|**-25.5%**|**-3.8%**|**5.70%**|
|**Mother's Schooling**|**0.9%**|**-10.3%**|**1.2%**|**16.1%**|
|**Average Schooling**|**2.40%**|**-0.1%**|**-1%**|**5.70%**|
|**Father-Sons**|**-7.8%**|**-8.8%**|**-6.4%**|**-3.7%**|
|**Mother-Daughters**|**-4.5%**|**-7%**|**-0.3%**|**-9.3%**|

NOTES: (1) Rank-based absolute mobility is defined following Chetty et al. (2014). The IGRC-based absolute mobility is the expected rank of the predicted years of schooling using equation (5) in the actual schooling distribution of children. (2) The reported numbers are the percentage bias estimates as defined in Table 3.

**Figure 1: Child’s Education and his/her probability of non-residency in Bangladesh and India**

<!-- Start of picture text --> Figure 1a: Probability of non-residency in Bangladesh Figure 1b: Probability of non-residency in India<br>0 5 10 15 20 0 5 10 15 20<br>Child's Education Child's Education<br>Female Male Female Male<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>.9<br>.8<br>.7<br>.6<br>.5<br><!-- End of picture text -->
# **The Longer Students Were Out of School, the Less They Learned**

## <u>Harry Anthony Patrinos</u><sup>1</sup>

World Bank, 1818 H Street N.W., Washington DC, 20433, USA; hpatrinos@worldbank.org JEL classification: I10; I31; J13 Keywords: School closures; COVID-19 pandemic; learning loss

> 1 This is a working paper, and hence it represents research in progress. This paper represents the opinions of the author and should not be attributed to the World Bank Group. Any errors are the fault of the author. I benefited from discussions at seminars at Wesleyan University, University of Mississippi, University of Maryland, Carleton University, University of Arkansas, GLO conference, ICFES, Global Schools Forum and FHi360. I received excellent comments from George Psacharopoulos, Xiaoxue Zhao, Chris Sakellariou, Vicente Garcia Moreno, Kevin Macdonald, Gustavo Arcia, David Marshall, Karthika Radhakrishnan-Nair, and Noah Yarrow.
Where: MLV: estimate net monthly living wage CBpc: estimate minimum monthly consumption basket per capita (food +non-food component)<sup>14</sup> HH Size: Estimate average household size FT: Estimate the average number of full-time workers in the household

**This approximation needs to be extended to consider non-labor income sources and part-time vs fulltime work.** When extending this approach to include non-labor income sources and accounting for work intensity (the average amount of full-time vs. part-time employed household members), the approximation becomes:

MLVa= estimate net monthly living wage (adjusted) α = estimate labor income (as a share of overall household income), and FTE = number of full-time equivalent workers. An FTE of 1.0 is equivalent to a full-time worker, while an FTE of 0.5 signals half of a full-time worker (or a part-time worker).

**A target group must be chosen since the monthly living wage estimate varies significantly for households with different household compositions and across the income distribution.** The reference household used for our estimates is a “relative” poor household, defined as a household with disposable income lower than 60% of median household incomes. This assumes that the policy objective is to provide a minimum income so the “relative poor household” can afford a minimum consumption basket.

**Since there is no official measurement of the representative food basket as a relative poverty line is used in the EU, we use a Romanian food basket recently estimated by the EC under the European Reference budget network.** In 2015, the Romanian basket was estimated under the European Reference budget network.<sup>15</sup> The Romanian food basket indicates the monthly budget needed for an adequate food intake

> 14 This is equal to the poverty line estimate in countries measuring poverty using consumption, rather than income.

> 15 Reference budgets have been developed across the EU by _the Herman Deleeck Centre for Social Policy, University of Antwerp and DG Employment, Social Affairs and Inclusion,_ with the purpose of assessing income support – in particular minimum income schemes – in the EU. https://ec.europa.eu/social/main.jsp?catId=1092&intPageId=2312&langId=en. Further methodological details are available in Pop and Rat (2016) and Goedeme, Storms, and Bosch, (2015).

16
<!-- Start of picture text --> 7 = oO . a? 2 oO .<br>oe yatta ey z fFphe “2 ies tea et0it Az hes .<br>pee aor coee ~ > pee aor coee ~ ta<br>| Low | Low<br>(Metro boundaries (Metro boundaries<br>ME Non-metroMetro districtsdistricts: oee50 100 200Kilometers ME Non-metroMetro districtsdistricts oee50 100 200Kilometers<br><!-- End of picture text -->

<!-- Start of picture text --> ye<br>ee oy, o is aesisi oy, © is<br>CO Ramee. Be ee ee<br>Fy Roig! A Pee: ee PRs A zfs NEAR | Oi Rok. Jaren sos”, oa oa :<br>Populationoeoe in Agglomerations Agglomerations ' cq | | Populationwewe in Urban Clusters a Ber<br>Low Low<br>(Metro boundaries (Metro boundaries<br>Hi Metro districts 0 50 100 200 Hi Metro districts 0 50 100 200<br>GE Non-metro Non-metro districts: ee Kilometers ME Non-metro Non-metro districts ee Kilometers<br><!-- End of picture text -->

<!-- Start of picture text --> ye<br>ee oy, o is aesisi oy, © is<br>CO Ramee. Be ee ee<br>Fy Roig! A Pee: ee PRs A zfs NEAR | Oi Rok. Jaren sos”, oa oa :<br>Populationoeoe in Agglomerations Agglomerations ' cq | | Populationwewe in Urban Clusters a Ber<br>Low Low<br>(Metro boundaries (Metro boundaries<br>Hi Metro districts 0 50 100 200 Hi Metro districts 0 50 100 200<br>GE Non-metro Non-metro districts: ee Kilometers ME Non-metro Non-metro districts ee Kilometers<br><!-- End of picture text -->

<!-- Start of picture text --> 3 ad = .<br>a<br>i. Bernese Sal "ye<br>=i hg nh ete, —~ 4<br>Population in High Density Clusters = “ m ‘me<br>oe<br>Low<br>(Metro boundaries<br>HE Metro districts 0 50 100 200<br>GE Non-metro districts: ee Kilometers<br><!-- End of picture text --> <!-- Start of picture text --> +2 7% & .<br>&<br>i Se a RN oe<br>5 eh | eee he aa<br>< 2<br>[intensity of nighitime lights a<br>he<br>Low<br>(Metro boundaries<br>ME Metro districts 0 50 100 200<br>WE Non-metro districts ee Kilometers<br><!-- End of picture text -->

<!-- Start of picture text --> ae aS<br>ue Riera<br>Intensity of nighttime lights a<br>oe<br>Low<br>(Metro boundaries<br>HB Metro districts. a om 200.<br>lim Non-metza distrats —<br><!-- End of picture text -->
same information. Since we in MI find a long-run relationship between all the variables this is our preferred and also final selected model.

**Table 6 ECM for Manufacturing wages**

|Variable|Coefficient|Std. Error|t-value|t-prob|PartR<sup>2</sup>|Instab|
|---|---|---|---|---|---|---|
|CPI_1|-0.099|0.070|-1.425|0.170|0.092|0.09|
|-CPI|-0.245|0.176|-1.394|0.179|0.089|0.08|
|U_1|0.760|0.556|1.368|0.186|0.086|0.11|
|-U|-2.005|0.793|-2.528|0.02|0.242|0.15|
|Man-W 1|-0.168|0.078|-2.161|0.043|0.189|0.09|
|Constant|-0.684|0.360|-1.898|0.072|0.153|0.1|
|GDP_1|0.242|0.085|2.849|0.010|0.289|0.09|
|-GDP|0.165|0.061|2.735|0.013|0.272|0.1|
|i80-84|0.067|0.025|2.672|0.015|0.263|0.08|

# **Definitions of Variables**

Man-Wi1 is log manufacturing wages lagged once

- U_1 is log total unemployment rate lagged once

- -U is the first order difference of U

CPI_1 is the log of the consumer price index lagged once

- -CPI is the difference of CPI

GDP_1 is the log of GDP lagged once

- -GDP is the first order difference of GDP

- i80-84 is a dummy-variable taking on the value one for the years 1980-84, zero otherwise

# **Test Statistics**

- R<sup>2</sup> 0.718 F(8, 20) = 6.360 [0.000] a = 0.040 DW = 1.20 RSS = 0.031 for 9 variables and 29 observations

Variance instability test: 0.224 Joint instability test: 1.281 Infornation Criteria: SC = -5.790 HQ = -6.081 FPE =0.002 AR 1- 2F( 2, 18) = 2.700 [0.094] ARCH 1 F( 1, 18) = 0.251 [0.623] Norrmality Chi<sup>2</sup> (2) = 9.874 [0.007] Xi<sup>2</sup> F(16, 3) = 0.534 [0.825] RESET F( 1, 19) = 4.847 [0.040] 27
<!-- Start of picture text --> All Sectors<br>8<br>ig<br>2=<br>8<br>=:az<br>8<br><!-- End of picture text --> <!-- Start of picture text --> é"<br>o<br>1960 1980 2000 2020<br>year<br>—— High Income ——————_ Middle Income<br>—— Low Income<br><!-- End of picture text --> <!-- Start of picture text --> =a<br>=88<br>&<br>x= ©<br>8<br><!-- End of picture text --> <!-- Start of picture text --> 2.<br>>A<br>1960 1980 2000 2020<br>year<br>—_——— Low Income<br><!-- End of picture text --> <!-- Start of picture text --> Manufacturing<br>&<br>38<br>=<br>85<br>x=a<br>88<br><!-- End of picture text --> <!-- Start of picture text --> 38o<br>&<br>1960 1980 2000 2020<br>year<br>———— High Income “Middle Income<br>—— Low Income<br><!-- End of picture text --> <!-- Start of picture text --> 3<br>=<br>=go<br>g<br>a=<br>8<br><!-- End of picture text --> <!-- Start of picture text --> Be<br>ba<br>1960 1980 2000 2020<br>year<br>—— Low Income<br><!-- End of picture text -->
|Firmcharacteristics(2000)|Numberof<br>firms<br>(sample)a)|<br>Average<br>employment<br>(2000)|Changeine<br>2000-2<br>Absolute<br>(persons)|mployment,<br>001<br>Relative<br>(%)|
|---|---|---|---|---|
|Yes|1878|50.3|0.6|1.2|
|No|7698|6.2|0.3|4.7|
|_Exportorientation_|||||
|Exportingfirms|1753|46.4|0.7|1.5|
|Non-exportingfirms|7823|7.8|0.3|3.6|

Definitions of variables:

# Access to credit = liabilities to credit institutions.

Net investment = increase in the value of fixed tangible assets over one year. Capital-to-labor ratio = value of fixed tangible assets over employment. Productivity = sales over employment. Unit labor costs = wages and social security contributions over sales. Profitability = operating profit over sales a) Active at both January and December 2001.

Source: FINA, Author's calculations.

In this context a seemingly positive association between job creation and the finn wage level may seem surprising. However, given the earlier results, the positive relationship between wages and employment growth reflects the fact that higher productivity firms are able to award their workers higher wages. This is consistent with the concept of "efficiency wages", whereby as long as higher wages lead to a more efficient use of labor they increase rather than decrease profit. This is also consistent with the theory of monopsonistic competition in labor markets, which claims that the employer offering higher wages can fill their vacancies more easily and employs more workers (Bhaskar et al., 2002). However, if wages are misaligned with productivity, which translates into high unit labor cost then firm employment is negatively affected. Thus, descriptive data strongly reinforce the earlier finding that productivity improvements coupled with wage moderation are key factors behind faster employment growth.

Investment in fixed capital evidently brings about job gains. Machines and equipment are not substituted for workers but instead enhance their productivity. Firms who do not invest often lay-off workers and close jobs. In contrast, firms which invest simultaneously create new jobs, on average 1.4 job per firm. This may seem a small number, but if aggregated across firms it translates into a much bigger _-38-_
We then employ two approaches to measuring the impacts of the RSE. The first is difference-in-differences estimation on this propensity-score screened sample, and the second is panel data estimation with fixed effects. Both give similar results in most cases, and they allow us to control for unobserved time-invariant characteristics of RSE households that may differ from those in households that did not participate in the RSE. Table 9 then summarizes some of the main impacts. Over a two-year period, households participating in the RSE are typically earning 35 percent more income. Some of this goes to an increase in income, while household savings and durable asset ownership increase. Subjective standard of living increases by approximately the same amount (in terms of standard deviations) as the increase in income.

**<mark>Table 9: Development Impacts of the RSE in Tonga and Vanuatu</mark>**

||Tonga|Vanuatu|
|---|---|---|
|Increase inper-capita income|34 to 38%|35 to 43%|
|Increase inper-capita expenditure|9 to 10%|28%|
|Increase in savings|122%|181%|
|Increase in subjective standard of living|0.45 s.d.|0.43-0.50 s.d.|
|Percentagepoint increase in dwellingimprovements|10 to 11|7 to 8|
|Percentagepoint increase in bank account use|10 to 14|17 to 18|
|Notes: s.d. denotes standard deviations|||
|Source: Gibson and McKenzie (forthcoming)|||

The median after-tax income earned in New Zealand reported by the seasonal migrants in our surveys is approximately NZ$12,000. This is in line with administrative data reported by Merwood (2012), and is several multiples of mean household income per capita of RSE households at baseline of NZ$1,400 in Tonga and NZ$2,500 in Vanuatu. One might then wonder why households did not experience even larger increases in per-capita incomes. In Gibson and McKenzie (forthcoming) we note several reasons why the per-capita gain in income to participating households is much less than would be suggested by just comparing incomes earned in New Zealand to those in the Pacific.

A first reason is that workers face costs in New Zealand, both from living expenses (including rent and health insurance) and from repaying their share of the airfare. From the NZ$12,000 in income, the average worker remitted or brought back with them an average of NZ$5,500. Second, when we consider per capita income, this gain in income has to be shared 22
**<<B>>** <u>Specification 2: Comparison of Outcomes in 2001 and 2006 for a Given Age at Interview</u>

In equation _(1)_ , I only use data from the postconflict DHS (2006) and exploit variations in exposure to conflict according to birth year cohort and district. However, there is a comparable survey for 2001, just before the conflict escalated, which allows me to estimate the impact of the conflict using an alternative identification strategy based on variation in conflict exposure by survey date and district. The idea is to exploit the fact that a child aged, for example, 10 years in 2001 will have experienced much less conflict during his lifetime than another child aged 10 years in 2006 in the same district, and the difference in conflict exposure between these two children will also vary across districts. Finding results similar to those obtained using the traditional identification strategy in equation _(1)_ would bolster confidence in the reliability of my estimates. More specifically, I estimate the following: where EDUCijs is the primary education completion dummy or the number of completed years of education of individual _i_ in district _j_ observed in survey year _s_ , θj represents district fixed effects, λs is a survey dummy equal to one for DHS 2006 and zero for DHS 2001, CONFjy is the number of conflict deaths per 1,000 inhabitants that occurred in district _j_ in year _y_ , and CONFEXPijs is the number of conflict deaths per 1,000 inhabitants in district _j_ that occurred between the individual’s birth year and survey year _s_ in which the individual is interviewed, which I calculate from yearly district death counts. When EDUCijs is the primary education indicator, the sample comprises 14
# **Determinants and consequences of land sales market participation:**

# **Panel evidence from India**

Klaus Deininger<sup>1</sup> , Songqing Jin<sup>1*</sup> , and Hari K. Nagarajan<sup>2</sup> 1 World Bank, Washington DC, USA 2 National Council for Applied Economic Research, New Delhi, India

> * Corresponding author, email sjin@worldbank.org. 1818 H St. NW, Washington DC 20433, Tel 202 473 6394, Fax 202 522 1151. Financial support from the collaborative DFID-World Bank program on land policies and rural development is gratefully acknowledged. The views expressed in this paper are those of the authors and do not necessarily reflect those of the World Bank, NCAER, or DFID.
(VECM) framework, for which, in the case of a set of k variables, we may have cointegrating relationships denoted r, such that 0 ≤ r ≤ k − 1. This gives us a k-dimensional VAR: where m denotes lag length, a μ set of deterministic components and δ a Gaussian error term. Reparameterization provides the VECM specification: The existence of r cointegrating relationships amounts to the hypothesis that: where Π is p x p, and α,β are p x r matrices of full rank. H1 (r) is thus the hypothesis of reduced rank of Π. Where r > 1, issues of identification arise.<sup>35</sup> Estimation is by VECM cointegration.

# 5 Empirical and Estimation Results

As discussed in the theoretical background to the paper, we investigate two separate productivity impacts of infrastructure. The first follows Bogeti ́c and Sonogo (2005) in estimating the impact of infrastructure on output per worker, in effect allowing infrastructure to enter the aggregate production function of the economy directly. The second allows for an indirect impact of infrastructure on productivity, through TFP growth, within an endogenous growth framework.

## 5.1 The Impact of Infrastructure on Output per Employee

In this section we follow Bogeti ́c and Sanogo (2005) in exploring the impact of infrastructure on output per worker.

An empirical model of the general specification given by equation (5) is provided by:<sup>36</sup> where all variables are as defined as before, Ii,t is provided by a vector of infrastructure measures as defined in the data section of the paper, and Xit denotes a vector of additional relevant variables. Here we incorporate a range of additional variables that may be relevant to labor productivity, including the net export ratio of the industry as an indicator of the openness of the sector, denoted NXi,t, industry concentration in liu of scale effects, denoted GINIit, and the skills ratio of the labor force, denoted SKRATi,t.

### 5.1.1 Results in the Absence of Instrumentation

Estimation is by means of the PMG panel estimator for South African manufacturing sectors. Results are reported in Tables 2A and 2B.

For all specifications estimation results confirm not only adjustment to equilibrium, but a rapid adjustment (see the ECM -parameters, which correspond to the φ-parameters of equation 8). Moreover, in general, the Hausman tests (denoted h-tests) confirm the legitimacy of the PMG estimator

> 35 See Wickens (1996), Johansen and Juselius (1990, 1992), Pesaran and Shin (1995a, 1995b), Pesaran, Shin and Smith (1996).

> 36 Bogeti ́c and Sanogo (2005) employ the ratio of cumulative investment to employment instead of the capital labour ratio. Given the availability of capital stock data for South Africa, we employ the direct measure of capital intensity.

12
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

# **_IV.A Projected system dependency rate_**

The system dependency rate – a ratio of the total number of beneficiaries to the total number of active members – is largely determined by assumptions on coverage, retirement rates and mortality rates outlined in APPENDIX A. We argue that the use of country population mortality assumptions for civil servants may not be appropriate given the fact that in many countries, civil servants live on average longer than the general population. However, we could not confirm this with Ugandan demographers due to the lack of population specific mortality tables in the country. In order to allow for such assumption of higher longevity among civil servants, we constructed two baseline scenarios and called them “variant 1” and “variant 2”. Essentially, “variant 2” makes allowance for longer life expectancy of civil servant populations while “variant 1” uses the population mortality table.

Figure 9 compares the system dependency rates projected using general population civil servant mortality assumptions. In “variant 2”, longer life expectancy for system participants in retirement causes an increase in dependency ratios by increasing the number of beneficiaries in each year.

**Figure 9: Projections of civil servant dependency rates* under two alternative mortality assumptions**

<!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2003 2007 2011 2015 2019 2023 2027 2031 2035 2039 2043 2047 2051 2055 2059 2063 2067 2071 2075<br>Year<br>v ariant 1 v ariant 2<br>Dependency rate<br><!-- End of picture text -->

**Notes:** * Ratio of the total number of beneficiaries in all categories to the total number of active employees. **Source** : WB calculations based on PROST

- 37 -
Table 7: Asset class comparison across 200 years Panel A: _Full sample_ , 1815-2016

||Real total<br>returns<br>(yearly|<br>Nominal<br>total<br>returns|Real total<br>returns<br>(yearly|<br>SD<br>(nom.,<br>arithm|Be<br>U<br>|nchma<br>K/US**b**<br> <br>|rk:<br>**ills**<br> <br>|B<br>UK<br>|enchma<br>/US**bo**<br> <br>|rk:<br>**nds**<br> <br>|
|---|---|---|---|---|---|---|---|---|---|---|
||,<br>arithm.)|<br>(yearly,<br>_arithm._)|,<br>_geom._)|.<br>return)|Excess<br>return|<br>Sharpe<br>ratio|<br>Adj.<br>Sharpe<br>ratio|Excess<br>return|<br>Sharpe<br>ratio|<br>Adj.<br>Sharpe<br>ratio|
|External sovereign bonds<br>(globalportfolio)|6.85|7.99|5.78|15.03<br>|<br>4.75|0.35|0.38|4.29|0.32|0.35|
|US equities (S&P 500)|8.49|9.22|6.81|18.78|6.15|0.32|0.38|4.24|0.23|0.26|
|UK equities (FTSE)|5.47|6.48|4.55|14.04|3.07|0.25|0.28|2.53|0.21|0.23|
|US corporate bonds<br>(S&P AAA,since 1900)|-1.09|1.05|-1.41|7.89|-1.59|-0.28|-6.99|-2.82|-0.65|<br>-12.43|
|Domestic sovereign bonds<br>(16 adv. countries, since 1870)|3.15|4.40|2.56|10.73|1.64|0.18|0.29|0.21|0.02|0.04|
|Equity portfolio<br>(16 adv. countries, since 1870)|8.25|9.65|6.95|16.41|6.75|0.42|0.46|5.31|0.29|0.36|
|US Treasuries|4.25|4.87|3.84|9.44|1.29|0.21|0.19||||
|UK government bonds|2.94*|3.88|2.39|10.98|0.55|0.07|0.08||||
|US bills (3-m, since 1835)|2.05**|3.27|1.90|5.37|||||||
|UK bills (3-month)|2.39***|3.45|2.21|6.24|||||||

Panel B: _Modern sample_ , 1995-2016

||Real<br>total|Nominal<br>total|Real<br>total|SD<br>(nom.,<br>|Ben<br>UK|chmar<br>/US **bi**|k:<br>**lls**|B<br>UK|enchma<br>/US **bo**|rk:<br>**nds**|
|---|---|---|---|---|---|---|---|---|---|---|
||returns<br>(yearly,<br>_arithm_.)|returns<br>(yearly,<br>_arithm._)|returns<br>(yearly,<br>_geom._)|<br>arithm.<br>return)|Excess<br>return<br>|Sharpe<br>ratio|<br>Adj.<br>Sharpe<br>ratio|Excess<br>return|Sharpe<br>ratio|<br>Adj.<br>Sharpe<br>ratio|
|Sovereign bonds (in USD or<br>GBP, global portfolio)|9.89|12.4|8.96|14.29|9.52|0.66|0.68|6.26|0.33|0.45|
|US equities (S&P 500)|8.40|10.85|6.76|18.13|7.96|0.44|0.58|4.93|0.22|0.36|
|UK equities (FTSE)|6.38|8.50|5.15|15.57|4.78|0.30|0.47|1.71|0.09|0.17|
|US corporate bonds<br>(S&P AAA)|3.29|5.64|3.82|5.82|2.85|0.52|0.69|-0.18|-0.04|-0.04|
|Domestic sovereign bonds<br>(16 advanced countries)|5.23|7.55|4.70|10.95|4.69|0.42|0.57|1.54|0.15|0.19|
|Equity portfolio<br>(16 advanced countries)|9.17|11.50|6.71|21.72|8.63|0.39|0.51|5.48|0.20|0.32|
|US Treasuries|3.47|5.81|3.14|8.48|3.03|0.37|0.60||||
|UK government bonds|4.66|6.76|4.33|8.51|3.07|0.38|0.50||||
|US bills (3-month)|0.44|2.72|0.42|2.10|||||||
|UK bills (3-month)|1.59*|3.61|1.56|2.76|||||||

_Note:_ The table shows average yearly arithmetic and geometric returns of our global portfolio of external sovereign bonds as well as for other asset classes / portfolios. See text for sources and further details. ***, **, and * indicate statistical significance at the 1%, 5% and 10% level (t-test for difference in returns compared to our sov. bond portfolio).

40