rafmacalaba/datause-encoder-data huggingface dataset:
countries in the sample, except the Philippines and Taiwan, China. Yet there is considerable variation in the magnitude of OLCs across countries.

**Table 3:** Total employment, 2001-2011

|||**Short Run**<br>|<br>|||**Medium Ru**<br>|**n**<br>|
|---|---|---|---|---|---|---|---|
||**Obs.**|**OLC**|**S.E.**|**R2**|**Chi Sq**|**OLC**|**S.E.**|
|China|44|2.61<sup>***</sup>|0.53|0.94|697.74<sup>***</sup>|3.19<sup>***</sup>|1.49|
|Hong Kong SAR,<br>China|44|15.15<sup>***</sup>|1.72|0.95|929.63<sup>***</sup>|30.49<sup>***</sup>|2.54|
|Korea,Rep.|44|0.66|2.19|0.73|119.34<sup>***</sup>|11.22<sup>*</sup>|6.34|
|Malaysia|44|9.91<sup>***</sup>|3.09|0.37|32.83<sup>***</sup>|6.76<sup>***</sup>|1.35|
|Philippines|44|1.79|12.06|0.43|40.4<sup>***</sup>|38.90|35.40|
|Singapore|44|5.94<sup>*</sup>|2.36|0.75|135.6<sup>***</sup>|17.04<sup>***</sup>|1.84|
|Thailand|44|6.01<sup>***</sup>|1.00|0.96|1834.45<sup>***</sup>|12.92<sup>***</sup>|5.63|
|Taiwan,China|44|0.90|1.06|0.59|74.39<sup>***</sup>|0.63|2.74|
|*** p<0.01; ** p<0.|05; * p<|0.1||||||

A common explanation for such variation in the literature is that the magnitude of the OLC depends on labor market institutions, and in particular the ease of hiring and firing workers (e.g. Moosa 1997, Sögner and Stiassny 2002, Balakrishnan _et al._ 2009, and Cazes _et al._ 2011). Regulations that protect workers from being laid off will surely make adjustments in the labor market less smooth and more drawn out, as employers cannot recruit and dismiss workers as is strictly demanded by their business

**Figure 3:** Growth, employment generation and labor market rigidity: short run

<!-- Start of picture text --> 16<br>y = 9.1 - 0.1x<br>HKG<br>R2 = 0.57<br>14<br>12<br>10 MYS<br>8<br>6 SGP THA<br>4<br>CHN<br>2 PHL<br>KOR TWN<br>0<br>0 50 100<br>Difficulty: Hiring & Firing<br>OLC<br><!-- End of picture text --> _Source:_ See table 1 and Doing Business Report.

**Figure 4:** Growth, employment generation and labor market rigidity: medium run

<!-- Start of picture text --> 45<br>40<br>PHL<br>35<br>30 HKG<br>25<br>20 SGP<br>THA<br>15 y = 17.8 - 0.1x<br>R2 = 0.04<br>10 KOR (incl. PHL)<br>MYS y = 18.4 - 0.2x<br>5 R2 = 0.53<br>CHN TWN<br>(excl. PHL)<br>0<br>0 50 100 150<br>Difficulty: Hiring & Firing<br>OLC<br><!-- End of picture text --> _Source:_ See table 1 and Doing Business Report.

7
# **Figure B19: Rice**

<!-- Start of picture text --> Log scale<br>9<br>Log price Permanent component<br>8<br>7<br>6<br>5<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.0<br>Medium-term components Transitory components<br>0.5<br>0.0<br>-0.5<br>-1.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 60 —_
# Table 1

||Default|Probab|ility Mo|del|||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|_Panel A: Probit Estimation_|||||||||
|Log(Net Worth)|-0.011|-0.010|-0.010|-0.010|-0.009|-0.009|-0.008|-0.009|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Log(Value Added / Number of Workers)|-0.021<br>(0.001)|-0.020<br>(0.001)|-0.018<br>(0.001)|-0.018<br>(0.001)|-0.019<br>(0.001)|-0.019<br>(0.001)|-0.017<br>(0.001)|-0.017<br>(0.001)|
|Firm Age|-0.001|-0.001|-0.001|-0.001|-0.002|-0.002|-0.002|-0.002|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Log(Wage Bill)|-0.010<br>(0.001)|-0.009<br>(0.001)|-0.008<br>(0.001)|-0.008<br>(0.001)|-0.008<br>(0.001)|-0.008<br>(0.001)|-0.007<br>(0.001)|-0.007<br>(0.001)|
|Log(Annual Sales)|0.007<br>(0.001)|0.006<br>(0.001)|0.003<br>(0.001)|0.003<br>(0.001)|0.000<br>(0.001)|-0.000<br>(0.001)|-0.003<br>(0.001)|-0.003<br>(0.001)|
|Log(Debt Outstanding)|||||0.013<br>(0.001)|0.013<br>(0.001)|0.013<br>(0.001)|0.013<br>(0.001)|
|Spread _Ex Ante_|||||0.003|0.003|0.003|0.003|
||||||(0.000)|(0.000)|(0.000)|(0.000)|
|Dependent Variable Mean|0.088|0.088|0.088|0.088|0.088|0.088|0.088|0.088|
|Dependent Variable Std. Dev.|0.284|0.284|0.284|0.284|0.284|0.284|0.284|0.284|
|Number of frms|96,411|96,411|96,411|96,411|96,411|96,411|96,411|96,411|
|R<sup>2</sup>|0.051|0.061|0.064|0.073|0.094|0.103|0.104|0.111|
|Industry FE|No|No|Yes|Yes|No|No|Yes|Yes|
|Municipality FE|No|Yes|No|Yes|No|Yes|No|Yes|
|_Panel B: Predicted Default Probability_|||||||||
|With Previous Loans|0.088|0.088|0.088|0.088|0.089|0.089|0.089|0.089|
|Without Previous Loans|0.113|0.113|0.107|0.107|||||

This table reports probit estimations of the probability of a firm with previous loans defaulting on a loan on a set of firm-level characteristics (Panel A) and the resulting predicted default probabilities for firms with and without previous loans (Panel B) for the default model sample. The dependent variable is a dummy equal to one if the firm defaults on a loan during 2019 (has a payment past due over 90 days) and zero otherwise. All explanatory variables are calculated as of December 2018. Given that the data on firms’ net worth are not available for all firms, all specifications include an unreported dummy variable equal to one if the data for the firm’s net worth are missing and zero otherwise. Firms with previous loans are those that either have bank credit outstanding in December 2019 or receive a bank loan over the period 2012–2019. Conversely, firms without previous loans are those with no credit records in the banking system throughout the same period. Columns 1–4 include real characteristics, and Columns 5–8 add financial characteristics. Columns 1–8 include different sets of fixed effects (FE). The table uses the 2018–2019 sample of formal firms with previous loans. Standard errors, shown in parentheses, are clustered at the industry and municipality level.

43
(captured by the polity index) and commercial openness exert a positive and significant effect on the returns to schooling when considering them separately (Regression 2 and 3) and simultaneously (Regression 4).

|(1)<br>**Table 5: Effect of selected v**|(2)<br>**ariables on re**|(3)<br>**turns to educa**|(4)<br>**tion**|
|---|---|---|---|
|EU accession<br>0.986|0.821|0.941|0.737|
|(0.688)|(0.652)|(0.692)|(0.667)|
|Schooling<br>-0.146|-0.105|-0.277|-0.239|
|(0.243)|(0.236)|(0.278)|(0.267)|
|Polity index|0.161**||0.169**|
||(0.079)||(0.076)|
|Trade (% GDP)||0.013**<br>(0.005)|0.014***<br>(0.005)|
|1995<br>0.984***|0.952***|0.866**|0.881**|
|(0.356)|(0.349)|(0.370)|(0.364)|
|2000<br>1.316***|1.155**|1.145**|1.026**|
|(0.498)|(0.506)|(0.509)|(0.517)|
|2005<br>2.332***|2.142***|2.133***|1.974***|
|(0.463)|(0.457)|(0.464)|(0.457)|
|2010<br>2.826***|2.646***|2.559***|2.407***|
|(0.466)|(0.452)|(0.444)|(0.435)|
|Constant<br>7.562***|5.989***|7.895***|6.172***|
|(2.112)|(2.096)|(2.201)|(2.215)|
|Prob > chi2<br>0.000|0.000|0.000|0.000|
|Observations<br>158|155|155|152|
|Groups<br>40<br>Clustered standard errors at national level in par<br>*** p<0.01, ** p<0.05, * p<0.1<br>DiD = Difference-in-Difference<br>EU accession includes only the post-communist<br>the EU between 1994 and 1999, namely: Bulgari<br>Lithuania, Romania, Slovakia, and Slovenia.<br>1995, 2000, 2005, and 2010 are time controls.|39<br>enthesis.<br>countries that si<br>a, Czech Republi|40<br>gned their associa<br>c, Hungary, Polan|39<br>tion agreement to<br>d, Estonia, Latvia,|

Since the economic and political transformations required to join the EU takes time to build up, which may not show in the exploratory regression analysis (Table 5) and could explain its contradiction with the inferences from the graphical analysis (Figure 5), we adopted a similar strategy to that used for the transition analysis, namely a DiD method with an RE model, to find if the staggered EU accession process effectively caused an increase in the returns to education of those post-socialist countries that joined the EU. The tests that support the selection of an RE model are shown in Annex 4.

16
Why Do Firms Pay Bribes? Evidence on the Demand and Supply Sides of Corruption in Developing Countries Bernard Gauthier<sup>_∗_</sup> Jonathan Goyette<sup>_†_</sup> Wilfried Kouamé<sup>_‡_</sup>

**Keywords:** Corruption, Bribery, Procurement, Tax evasion. **JEL codes:** D7, H26, H32, O1

> _∗_ HEC Montréal

> _†_ Université de Sherbrooke & GREDI

> _‡_ World Bank. Corresponding author: wkouame@worldbank.org – Authors are grateful to Bob Rijkers, participants in the Birmingham Seminars, the Oxford University CSAE Conference and the Canadian Economics Association Annual Conference for their insightful comments.
# **Table 4: Panel B – Nested Logit Regressions**

Log Odds ratio between Owning with and without a Mortgage

|**Country**|**lage**|**female**|**hhsize**|**lnincome**|**hhnodep**|**hhdep**|**house**|**secedu**|**teredu**|**fulltime**|
|---|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_|||||||||||
|Czech Republic|0.89 ***|-0.07|0.21 ***|-0.73 ***|0.14|-0.68 ***|4.36 ***||4.56|0.32 *|
|Estonia|2.73 ***|-0.12|0.21 ***|-0.90 ***|0.92 ***|-0.32|-0.40 ***||2.62|-0.83|
|Hungary|1.03 ***|0.10|0.03|-0.33 *|0.08|-0.48 ***|0.25 ***|-2.58 ***|-1.40|-0.12|
|Latvia|1.56 *|0.03|0.07|-0.63|0.33 *|-0.02|1.04 ***|4.19 **|-1.77|-0.05|
|Lithuania|2.77 ***|0.17|0.80 ***|-0.37|0.02|-1.00 ***|-0.01||3.01|-6.10|
|Poland|1.59 ***|0.17|0.45 ***|-0.24|-0.08|-1.07 ***|0.58 ***|5.64 ***|1.29|-0.93 **|
|Slovak Republic|1.38|-0.13|0.23 ***|-0.99 *|0.04|-0.50 ***|3.20 ***|-1.39|0.25|0.20|
|_EU-15 and Cyprus_|||||||||||
|Austria|1.49 **|0.04|0.13 **|-0.65 **|0.00|-0.92 ***|0.45 ***|-3.68 ***|-1.93|-0.81 **|
|Belgium|2.96 ***|-0.04|0.09|-1.13 ***|0.71 ***|-0.68 ***|0.04|-10.03 ***|-14.60 ***|-0.53 *|
|Cyprus|3.54 ***|0.20|0.24 ***|-1.14 ***|0.04|-0.76 ***|-0.11|-9.24 ***|-4.76 *|-0.32|
|Denmark|-3.61|0.22 ***|-0.12|-1.21|-0.18|0.12|-1.19 ***|-22.41 ***|-24.05 ***|-0.45|
|Finland|2.66 ***|0.04|0.05|-1.01 ***|0.73 ***|-0.19|-0.16 *|-2.57|-9.60 ***|-0.29 *|
|France|1.52 ***|-0.03|-0.10 **|-0.49 ***|0.35 ***|-0.26|0.30 ***|-4.16 **|-9.64 ***|-0.73 ***|
|Greece|1.37 ***|-0.29|0.16 **|-0.19|-0.09|-0.97 *|0.41 ***|6.42 **|6.93 ***|-1.25 ***|
|Ireland|1.79 ***|0.13|0.19 ***|-0.45 ***|0.24|-1.17 ***|0.36|-0.30|-8.24 ***|-1.13 ***|
|Italy|2.15 ***|0.14 **|0.26 ***|-0.57 ***|0.18 *|-0.75 ***|0.41 ***|-0.12|-3.60 ***|-0.93 ***|
|Luxembourg|2.84 ***|0.39 *|0.24 **|-1.08 ***|0.03|-1.39 ***|0.92 ***|-13.38 ***|-20.45 ***|-1.22 ***|
|Netherlands|-1.72 ***|0.00|-0.05|-1.96 ***|0.11|0.04|-0.70 ***|-16.76 ***|-14.74 ***|-0.49|
|Portugal|1.16 ***|0.16|0.25 ***|-0.47 ***|0.15|-1.07 ***|1.35 ***|1.47|-3.38|-0.83 ***|
|Spain|2.30 ***|0.18 **|0.49 ***|-0.85 ***|0.07|-1.17 ***|0.16 **|-4.37 ***|-6.95 ***|-0.65 ***|
|Sweden|2.09 ***|-0.36 *|-0.38 **|-1.16 ***|0.50 *|0.23|2.01 ***|-5.54 *|-4.01|-0.55|
|United Kingdom|1.99 ***|-0.32 ***|0.15 **|-0.72 ***|0.04|-1.06 ***|-0.18||-13.00 ***|-0.83 ***|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

9
.

25

|

.

|

## Bibliography

# Ausch, Sandor. 1961. “Bilateralism and Multilateralism in CMEA." Institute ofEconomics, Hungarian Academy of Sciences, Studies No. 1.

~—-. 1969. "Possibilities of Developing Market Relations in the Cooperation of the CMEA Countries.” In Foeldi and Kiss (1969).

Ausch, Sandor, and F. Bartha. 1969. “Theoretical Problems of CMEA Intertrade Prices.” In Foeldi and Kiss (1969).

-——, Ausch, Sandor. 1972. Theory and Practice of CMEA Cooperation. Budapest: Akademiai Kiado.

Borner, S., and A. Taylor, (editors). 1987. Structural Change, Economic Interdependence, and World Development. New York: St. Martin’s Press.

Centre for Economic Policy Research. 1990. Monitoring European Integration: The Impact ofEastern Europe. London: CEPR.

Clement, Hermann. 1988. “Transfer-Rubel und Hartwaerungsverrechnung im Intra-RGW und im Ost-West Handel." Osteuropa-Institut Muenchen. Mimeo.

Crane, K., and D. Skoller. 1988. Specialization Agreements in the Council for Mutual Economic Asistance. Santa Monica, CA: RAND Corporation.

Csaba, Laszlo. 1985. “Three studies on the CMEA.” Hungarian Scientific Council for World Economy.

Dawisha, Karen. 1988. Eastern Europe. Gorbachev and Reform; The Great Challenge. New York: Cambridge University Press.

Drabek, Zdebek. 1988. "The East European Response to the Debt Crisis: A Trade Diversion or a Statistical Aberration?” Comparative Economic Studies XXX(1/Spring): 29-58.

Economic Commission for Europe (ECE). 1989. Economic Survey ofEurope in 1988-89. ECC.

New York:

-~---, 1990a. Economic Survey of Europe in 1989-90. New York.

——. 1990b. Economic Bulletin for Europe 42 (November).

Economist Intelligence Unit (EIU). 1984. “North/South Countertrade." Special Report No. 174 London.

Foeldi, T., and T. Kiss. 1969. Socialist World Market Prices. Budapest: Akademiai Kiado.

Gastrisch, Hubert, and Kirkari. 1985. “Finnish-Sovict Economic Relations." Vienna Institute for Comparative Economic Studies, No. 109.

Hewett, Ed. 1988. “Reforming the Soviet Economy; Equality vs. Efficiency." Institution, Washington, D.C.

The Brookings
# _4.2. Productivity Convergence: The Role of Capital and Intermediate Imports_

Table 4 reports the result of the productivity convergence effect of capital and intermediate import. Panel A reports the results for total capital import. the OLS estimates as reported in columns 1 and 2 turn out positive and statistically significant at the 1% significance level, suggesting a positive association between imported capital and productivity convergence across countries in Africa. We report the second-stage IV estimation results in columns 3 and 4, while the first stage IV results are reported in Panel A of Table A5 in the appendix. For the latter, the coefficients of the external instruments all meet the _a priori_ expectation. The respective first-stage F-statistics are also considerably higher than the 10 rule of thumb. Concerning the second-stage IV estimation results, they are consistent with those of OLS in suggesting that foreign capital import is a significant predictor of productivity convergence across African countries. This result holds at all conventional significance level and is robust to the choice of external IV.

Panel B and C of Table 4 reports the results when we distinguish the productivity convergence effect of capital imports from the Global North and Global South. The OLS estimated coefficient of capital import from the North and South are both significantly positive at the 1 percent significance level, as reported in the respective columns 1 and 2 of Panels B and C. This indicates a productivity convergence premium across African countries that source capital from abroad. In columns 3 and 4 of Panel B and C, we report the results of the second-stage IV estimation. The variables enter the regression with a positive coefficient and are highly statistically significant. Panels B and C of Table A5 in the appendix reports the first-stage IV estimation. The results show that the IVs are relevant instrument. Hence, the IV results corroborate those of the OLS results with the results being qualitatively similar. Quantitatively however, the IV estimates, which is our preferred estimate, clearly shows that capital import from the Global North exerts a bigger impact on productivity convergence as indicated by the size of the estimated coefficient.

Next, Table 5 reports the results on the effect of intermediate import on productivity convergence. Panel A shows the results for the total intermediate import. In the OLS estimation, the variable enters the regression with a positive coefficient and is highly statistically significant (see columns 1 and 2). For the IV estimation, however, although the coefficient remains positive, 21
**Figure 5: RDD estimates of Grade 4 teacher subject knowledge test score gap associated with teachers hired pre- and post-FPE (by varying bandwidth around cutoff year). Pooled sample (Kenya, Madagascar, Mozambique, Tanzania, Togo, Uganda)**

<!-- Start of picture text --> Language Math<br><!-- End of picture text --> Note: Model includes country fixed effects. Black lines with solid symbols show estimates are statistically significant at the 10% level. Gray lines with open symbols show estimates that are not statistically significantly different from 0 at the 10% level. Bandwidth is the number of years of teacher start-dates on each side of the FPE cutoff year that are included in the sample.

29
We augment equations (13) and (14) for South Africa and control for the effects of large policy changes to isolate the cyclical stance of the government – such as national plans that explicitly state what fiscal policy ought to do. These represent outlier cases of fiscal policy and would have an impact if any of the β� measures are significant: ������ We construct DUM� using the various policy documents from 1991 onwards. We assign a value of 1 when the document outlines a path of government that supports higher spending. This helps us to approach the narrative of policy and its relation to government outcomes and disentangle policies that were expansionary vs. contractionary (the assumption that contractionary policies do not alter the results). Annex 3 provides further information on the construction of the dummies.

Turning to the estimates of equations (15) and (16), we show that:

- Each of the policy measures (∑���� β�DUM�������) are positive and consequently lead to higher expenditures or a larger structural deficit.

- The coefficients on the trend is the cycle variable, however, is positive in all controls, indicating fiscal policy in South Africa has not really been countercyclical. This can be attributed by several factors such as correct intentions by the fiscal authorities, but missing either important turning points of the business cycle, perceiving long-cycles as the trend, poor choices on spending which are difficult to reverse such as wages or weak policy coordination where funds are disbursed and allocated but not spent on time. We discuss some of these factors in the next section.

It should be noted that our results are in line with Cespedes and Velasco (2013) who analyze the fiscal stance for commodity exporters. They study whether policy has been less procyclical or countercyclical in the recent commodity boom episode compared to previous episodes. While they find that for most countries the elasticity of overall balance to the cyclical component of commodity prices has increased, for South Africa it has become negative, implying procyclical fiscal policy during the 2006-2009 period as opposed to the 1973-1990 commodity price boom.

**Table 3: The fiscal stance of the South African government**

|||Model 1<br>∆ln�G�)|Model 2<br>∆ln�G�)|Model 3<br>∆ln�G�)|Model 4<br>∆ln�G�)|Model<br>5<br>∆��SBB�)|Model<br>6<br>∆��SBB�)|Model<br>7<br>∆��SBB�)|Model<br>8<br>∆��SBB�)|
|---|---|---|---|---|---|---|---|---|---|
||CITT|0.27**<br>(0.11)||0.24**<br>(0.10)||0.01<br>(0.02)||0.03<br>(0.02)||
||YGAP||0.85<br>(0.81)||0.69<br>(0.73)||0.94<br>(13.09)||1.18<br>(13.81)|
|�<br>�<br>���|β�DUM�<br>������<br>|||0.13|0.10|||1.45|0.13|
||adj R<sup>�</sup>|0.28|0.01|0.44|0.22|-0.03|-0.07|-0.02|-0.16|
|F-Stat|(P-value)|0.02|0.31|0.03|0.16|0.57|0.94|0.48|0.48|

Source: Authors’ calculations. Notes: Standard errors in parentheses.

15
- 26 -

The first conclusion from a comparison of Tables 15 and 16 is that Chilean annuitants have got a better deal than annuitants in other countries, especially considering that Chilean annuities are indexed. Buyers of indexed annuities in the UK get a much lower annuity value of 86 percent of the premium, and pay a charge of about 5 percent of the premium to obtain inflation protection. The cost of inflation protection in the US is even higher, amounting to more than 20 percent of the premium. This result is in part explained by the large supply of indexed instruments in the Chilean case – unlike their British and American counterparts, Chilean providers have access not only to indexed Government bonds, but also to other higher yield instruments indexed to inflation, and that allows them to hedge inflation risk while obtaining more attractive returns.

**Table 15: Money's Worth Ratios in Chile, March 2004** **<u>Computed with Cohort Annuitant Tables and Alternative Discount Rates</u>**

||**Risk-Free Rate**|**Corporate Bond Rate**|
|---|---|---|
|All cases|1.064|0.904|
|- maximum|1.276|1.146|
|- minimum|0.876|0.740|
|Male, Age 55|1.049|0.897|
|Male, Age 65|1.086|0.955|
|Female, Age 55|1.076|0.905|
|Female, Age 65|1.105|0.971|
|Joint (65-60)|1.078|0.892|

**Table 16:  Money’s Worth Ratios in Selected Countries** **<u>Computed with Cohort Annuitant Table and Risk-Free Rate</u>**

||Australia<br>(James)|Canada<br>(James)|Switzerl.<br>(James)|UK<sup>1</sup><br>(Cannon)|UK<br>(James)|UK<sup>2</sup><br>(Brown)|US<sup>3</sup><br>(Brown)|
|---|---|---|---|---|---|---|---|
|**Nominal Annuities**||||||||
|Male, Age 55|-|-|-|-|-|0.921|0.934|
|Male, Age 65|1.013|0.981|1.046|-|0.977|0.908|0.927|
|Female, Age 55|-|-|-|-|-|0.928|0.937|
|Female, Age 65|1.002|0.976|1.036|-|0.979|0.907|0.927|
|Joint|0.988|0.980|0.985|0.981|0.987|-|0.929|
|**Indexed Annuities**||||||||
|Male, Age 55|-|-|-|-|-|0.867|-|
|Male, Age 65|-|-|-|-|0.887|0.854|0.822|
|Female, Age 55|-|-|-|-|-|0.876|-|
|Female, Age 65|-|-|-|-|0.877|0.857|0.782|
|Joint|-|-|-|-|0.880|-|-|

Notes: (1) Cannon and Tonks’ estimate is the overall average; (2) For males 60 and 65 and females 60 and 65; (3) MWR for indexed annuities in the US relate to annuities offered by Irish Life of North America (ILONA), which have never been sold.

Sources: Brown et al (2002), James et al (2003), Cannon and Tonk (2004) Workers who retire early get lower MWRs in Chile. As mentioned before, this result is explained by the higher reinvestment and mortality risks associated with annuities with longer expected duration. The opposite result in the UK and the US cannot be easily interpreted. Longer expected duration also explains the lower MWRs of joint annuities in Chile, and it is noteworthy that joint annuities have similar or lower MWRs in other