rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> o RCA>1 Countries (weighted)<br>N= re be*<br>Fae Tf.<br>a 4<br>ge ‘ ~@<br>co ¢“ Nl c<br>ot 7 2<br>:= a al N=®<br>~ - | ° '<br>-_-<br>© ~~7 —_—- -<br>eo) 2 *<br>1995 2000 2005 2010 2015<br>year<br>—— Hearing-aid —---—- Synthetic hearing-aid<br>Difference (RHS)<br><!-- End of picture text -->

<!-- Start of picture text --> RCA<1 Countries (weighted)<br>pido > “ a nN<br>7 q<br>=> F \en a ‘<br>Ax® — = 7Z . oy of,<br>& | ; 2<br>£ Zl 3S<br>re)« “ - ¢<br>Le %<br>Va7~~<br>fo} +<br>1995 2000 2005 2010 2015<br>year<br>—— Hearing-aid -—---—- Synthetic hearing-aid<br>Difference (RHS)<br><!-- End of picture text -->

<!-- Start of picture text --> o RCA>1 Countries (weighted)<br>x rn eo4<br>rv ee \. N<br>e 4 \\ oO<br>so “ VL 'g<br>Sr 7 os<br>=oO - +o$<br>©~ / \ A sor < -- v : | *N=<br>21 4 1 Pet ba db,<br>1995 2000 2005 2010 2015<br>year<br>——— Hearing-aid —---—- Synthetic hearing-aid<br>[ p-value (RHS)<br><!-- End of picture text -->

<!-- Start of picture text --> RCA<1 Countries (weighted)<br>re} a7 2<br>a y aN \-sQNU- N<br>ca8xD Z Z oos3<br>& , $<br>£ it =o<br>re} fo- 7 -,<br>° __7 N<br>2<br>foe) , | o-<br>1995 2000 2005 2010 2015<br>year<br>——— Hearing-aid —---—- Synthetic hearing-aid<br>[ p-value (RHS)<br><!-- End of picture text -->
but population does not increase much in the same location, the location must be a hard place to move to. By contrast, if there is only a small decline in population in a low initial utility location, the place may be difficult to leave for any of the reasons mentioned above. By proxying initial utility in all locations within a country with the average country-wide subjective well-being (SWB) score, Desmet et al. (2018) assume that population changes in the location can be attributed either to differences in initial technology or fundamental amenities. In such a model, a situation of lower real wages in a location with similar population density to other parts of the country will be explained with much better fundamental amenities, when in fact people may have lower initial welfare and may not be able to leave the location due to institutional restrictions on their mobility or other reasons, including idiosyncratic preferences.

The assumption of uniform initial utility within countries is appropriate in smaller or advanced economies, where spatial convergence of welfare might hold. However, in many developing countries this may not be the case. Regional differences in welfare may be substantial, when proxied by differences in self-reported well-being (Burger, Hendriks, and Ianchovichina, 2021) or when proxied with differences in per capita consumption (Gollin, Kirchberger, and Lagakos, 2017). These differences in welfare might reflect higher mobility frictions in these developing economies (Bryan and Morten, 2019), differences in non-monetary amenities (Gollin, Kirchberger, and Lagakos, 2017), or idiosyncratic preferences for living in a certain place (Chauvin, Glaeser, Ma, and Tobio, 2017). In line with this evidence, one of the contributions of this paper is that we relax the assumption of uniform initial utility in the case of the Latin American countries. Using geo-coded data on subjective well-being, which we obtain from multiple survey rounds of the Gallup World Poll and Latinobarómetro, we compute the average subnational (level-1 administrative region) subjective wellbeing scores in each large Latin American country and assign the same initial utility only to locations within the same subnational region. This way domestic migration costs between any two locations in different subnational level-1 administrative regions could be different not only due to differences in initial technology or amenities, but also to different initial welfare levels. Then, a small change in population in a location with low initial utility relative to other parts of the country can be correctly attributed to migration frictions restricting the movement of people out of this location rather than with good fundamental amenities in the location.

This modification allows us to assess more precisely the size of domestic migration frictions circa 2000. Our results suggest that in most Latin American countries the adjusted migration entry costs are 4
NEPAL

- 2. Projections NRR-1 by 2030

|**_Yow_**o|rp.latimt|OW.<br>ratio|Period|grth<br>rate|O|CD4|TFt|tM|s**O**<br>FFmLs|**In**|**x1000**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990|19102|84.0|1990-94|2.42<br>|38.1<br>|13.8<br>|;."<br>|53.1<br>|52.2<br>|118<br>|173<br>|
|1995|21562|82.4|1995-99<br>|2.24<br>|34.8<br>|12.4<br>|4.84<br>|54.9<br>|54.3<br>|106<br>|154<br>|
|2000|24119|77.5|2000-04|2.11<br>|32.3<br>|11.2<br>|4.33<br>|56.9<br>|56.6<br>|93<br>|133<br>|
|2005|26799|71.5|2005-09<br>|<br>1.95|29.8<br>|10.3<br>|3.82<br>|58.6<br>|58.5<br>|83<br>|117<br>|
|2010|29539|67.0<br>|2010-14<br>|1.73<br>|26.8<br>|9.5<br>|3.31<br>|60.3<br>|60.5<br>|73<br>|102<br>|
|2015|32216|62.5<br>|2014-19<br>|1.56<br>|24.3<br>|8.8<br>|2.94<br>|62.0<br>|62.6<br>|63<br>|86<br>|
|2020|34824<br>|56.1<br>|2020-24<br>|1.36<br>|21.8<br>|8.2<br>|2.62<br>|63.8<br>|64.8<br>|53<br>|71<br>|
|2025<br>|37272<br>|53.5<br>|2025-49<br>|1.00<br>|17.6<br>|7.7<br>|2.20<br>|68.3<br>|70.4<br>|31<br>|39<br>|
|2050|47810|47.3|2050-74|0.52<br>|14.2<br>|9.0<br>|2.10<br>|73.7<br>|77.2<br>|11<br>|14<br>|
|2075|54417|57.0<br>|2075-99<br>|<br>0.24<br>|12.9<br>|10.5<br>|2.08<br>|77.4<br>|81.8<br>|6<br>|8<br>|
|2100|5774|66.0<br>|2100-24<br>|0.14<br>|12.3<br>|10.9<br>|2.06<br>|80.3<br>|8S.8<br>|3<br>|4<br>|
|2125<br>2150|59761<br>6076|69.4<br>71.8|2125-49|0.07|12.0|11.3|2.06|81.3|87.4|2|4|

# 3. Age structure (percent): 1990 and<sup>2025</sup>

<!-- Start of picture text --> 75 +<br>70-74<br>65-69<br>60-64<br>55-59<br>50-54<br>4b-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5-9<br><!-- End of picture text --> <!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 8 lO<br><!-- End of picture text --> <!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 4. Comparison of Country with Region<sup>and Income Group, 1985-90</sup>

<!-- Start of picture text --> TFR e0 Growth rate<br>Region Incom group Country Region Income group Country Region Incom group<br><!-- End of picture text --> <!-- Start of picture text --> Country Region Incom group<br><!-- End of picture text --> 83
# Appendix A: Sample of Countries

Due to data availability, the sample of countries differs by social indicator. The largest sample is given by the HDI indicator and includes Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, The Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei Darussalam, Bulgaria, Burkina Faso, Burundi, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Dem. Rep., Congo, Rep., Costa Rica, Côte d'Ivoire, Croatia, Cyprus, Czech Republic, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, Arab Rep., El Salvador, Equatorial Guinea, Eritrea, Estonia, Ethiopia, Fiji, Finland, France, Gabon, Gambia, The, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hong Kong SAR, China, Hungary, Iceland, India, Indonesia, Iran, Islamic Rep., Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Korea, Rep., Kuwait, Kyrgyz Republic, Lao PDR, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, North Macedonia, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Micronesia, Fed. Sts., Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russian Federation, Rwanda, Samoa, São Tomé and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovak Republic, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Sudan, Suriname, Eswatini, Sweden, Switzerland, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Uganda, Ukraine, United Arab Emirates, United Kingdom, United States, Uruguay, Uzbekistan, Vanuatu, Venezuela, RB, Vietnam, Yemen, Rep., Zambia, and Zimbabwe.

# Appendix B: Poverty Data

Most of the data for monetary poverty comes from PovcalNet, an interactive computational tool that allows users to replicate the calculations made by World Bank researchers when estimating the extent of absolute poverty in the world.

The PovcalNet data for LAC are taken from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC). SEDLAC was developed by the Center for Distributional, Labor, and Social Studies (CEDLAS) of the Universidad Nacional de La Plata (Argentina), in partnership with the Latin America team of the World Bank’s Poverty and Equity Group. Methodological and technical revisions to the SEDLAC project began in 2015 to better align its data with the household surveys 29
- **(i)** What is the threshold of the share of foreign private investor financing that triggers an increase in the marginal impact of higher expected public debt on government bond yields?

- **(ii)** What is the threshold of public debt that triggers an increase in the marginal impact of a higher share of foreign private investor financing on government bond yields?

Ebeke and Lu (2015) and Brzoza-Brzezina and Kotlowski (2018) investigate only one type of threshold, whereas this paper investigates two types of threshold values simultaneously.<sup>6</sup> The remainder of this paper is organized as follows. Section 2 examines non-linear models: the IT, PSTR and GPSTR to explore threshold values, based on the data for 11 advanced economies and 14 emerging markets. Section 3 presents the baseline estimation results, the model evaluation, and comparison with other studies. Section 4 concludes.

# **2. Empirical strategy and data**

Local currency bonds can be held by both domestic and foreign investors, whereas foreign currency bonds are assumed to be held by only foreign investors. To estimate the threshold of the composition for government bond holdings (foreign vs. domestic), we employ local currency bond yields because they reflect the behavior of both domestic and foreign investors.<sup>7</sup> We examine three models, namely, the (i) Interaction term model (IT), (ii) Panel Smooth Transition Regression (PSTR), and (iii) Generalized Panel Smooth Transition Regression (GPSTR) models.

The (i) IT model is a simple specification with an interaction term between the share of foreign private investors and the expected public debt level. The (ii) PSTR model, developed by González et al. (2017) and Fok et al. (2005), includes a standard logistic function that exhibits point symmetry with one slope parameter and one location parameter. Brzoza-Brzezina and Kotlowski (2018) is relevant to our empirical strategy because they employ the PSTR model to examine a nonlinear relationship between a country’s risk premium and net

> 6These two previous studies use actual debt data, whereas this paper uses a wide range of forecast data to exclude any effects from current economic conditions on interest rates as well as to consider the forward-looking behavior of financial markets.

> 7Ebeke and Lu (2015) and Brzoza-Brzezina and Kotlowski (2018), who also use local currency bonds in emerging markets, control for exchange rate risk whereas Moore et al. (2013) controls for it indirectly by estimating the share of foreign investors that is affected by exchange rate risk. We include exchange rate risk in the baseline estimation but exclude it as a robustness check. Amstad et al. (2020), using the average credit ratings provided by Standard and Poor’s, Moody’s and Fitch find a slow and steady convergence of sovereign risk in local and foreign currency due to higher availability of foreign currency via FX reserves and smaller dependence on foreign currency borrowing (decline of original sin) over the past 20 years.

5
**(UN)BUNDLING PUBLIC-PRIVATE PARTNERSHIP CONTRACTS IN THE WATER SECTOR:**

**COMPETITION IN AUCTIONS AND ECONOMIES OF SCALE IN OPERATION**

Atsushi Iimi<sup>¶</sup> Finance, Economics and Urban Finance (FEU) The World Bank 1818 H Street N.W. Washington D.C. 20433 Tel: 202-473-4698 Fax: 202-522-3481 E-mail: aiimi@worldbank.org

> ¶ The paper greatly benefits from the database which has been developed through funding from the PublicPrivate Infrastructure Advisory Facility (PPIAF). I am most grateful to my colleagues, Katharina Gassner and Nataliya Pushak, for their efforts toward collecting the relevant data which are used in the paper. I also thank Antonio Estache, Laszlo Lovei, Nataliya Pushak for their insightful comments on an earlier version of this paper.
worker. In the following, we describe the identification of deceased workers and incumbent workers in the administrative data. We also provide evidence that, when comparing peer groups with the same pre-death racial composition of employees, peer group characteristics do not predict whether a white or non-white worker dies. In addition, we verify that the death of a non-white worker, compared to the death of a white worker, leads to a lasting drop in the nonwhite share of the peer group. For these reasons, we can treat the race of the deceased worker as a conditionally exogenous and significant shock to the peer group’s racial composition and can estimate its effect on incumbent workers’ retention at the firm.

# **3.1 Sample Construction**

**Unexpected worker deaths.** We identify worker deaths based on the employer notifications in the RAIS data. When employers report contracts that were terminated because the employee died, they distinguish between deaths due to a work accident, deaths while commuting to work, and all other deaths. We exclude work accident and commuting deaths in order to avoid concerns that safety conditions at and on the way to work may affect both the likelihood of worker deaths and the retention decisions of workers. Moreover, to rule out spurious death notifications, we drop deaths of individuals for whom we observe an employment contract at least 30 days the date of death (less than 3% of all deaths).

In order to identify separations that are likely unexpected for coworkers, we focus on deaths of workers younger than 65 who have a full-time (with at least 30 hours) and permanent job. Moreover, we only include individuals with a minimum tenure of three months at the time of death, as a large share of contracts are terminated in the first three months when firms can dismiss workers without cost. We also exploit employer reports on workers’ sick leave periods and drop deaths of individuals who had any sick leave in the two years before the death (37% of all deaths).<sup>8</sup> While we do not observe the exact cause of death, this restriction allows us to exclude deaths associated with prolonged illness, such as cancer, and instead focus on unexpected deaths, such as those caused by accidents, strokes, or homicide. We consider deaths between 2009 and 2018, given that sick leave information started to be reported in 2007 and that we seek to follow incumbents for three years after the death.

**Incumbent workers.** We define the deceased worker’s peer group as all incumbent workers at the time of death who work full-time at the same establishment and in the same 4-digit occu-

> 8All formal employees in Brazil have a right to 15 days of sick leave during which the employer continues to pay wages. After that, employees can receive sick leave benefits from the _Instituto Nacional do Seguro Social_ (INSS) if they have contributed to the INSS for a minimum of 12 months. As every employee is entitled to the sick leave period offered by their employer, we can track all instances of employees taking sick leave.

8
**Table 5**

**Parameter estimates for aggregate yield indices**

||**All**|**Big-4**|**Other**|**AE**|**EMDE**|
|---|---|---|---|---|---|
|**A. Base model**||||||
|_Constant_|4332.75<sup>***</sup>|4691.98<sup>***</sup>|4020.06<sup>***</sup>|7401.90<sup>***</sup>|3526.23<sup>***</sup>|
||(41.08)|(47.48)|(50.27)|(131.02)|(34.60)|
|_Trend_|108.98<sup>***</sup>|158.05<sup>***</sup>|59.12<sup>***</sup>|175.71<sup>***</sup>|102.05<sup>***</sup>|
||(1.33)|(1.62)|(1.41)|(4.20)|(1.43)|
|_PP test_<br>|-5.47<sup>***</sup>|-6.14<sup>***</sup>|-3.81<sup>***</sup>|-7.33<sup>***</sup>|-4.28<sup>***</sup>|
|_AIC_<br>|803.07|836.07|795.83|955.25|796.18|
|**B. With**TTT<sup>2</sup>||||||
|_Constant_|4367.41<sup>***</sup>|4705.00<sup>***</sup>|4077.56<sup>***</sup>|7548.26<sup>***</sup>|3688.04<sup>***</sup>|
||(68.35)|(55.38)|(97.15)|(196.21)|(59.54)|
|_Trend_<br>|105.67<sup>***</sup>|156.81<sup>***</sup>|53.64<sup>***</sup>|161.77<sup>***</sup>|86.64<sup>***</sup>|
||(5.65)|(5.85)|(6.85)|(16.22)|(5.00)|
|TeT<br>2|0.05|0.02|0.09|0.22|0.25<sup>***</sup>|
||(0.09)|(0.10)|(0.10)|(0.27)|(0.08)|
|_PP test_|-5.52<sup>***</sup>|-6.15<sup>***</sup>|-3.93<sup>***</sup>|-7.49<sup>***</sup>|-5.01<sup>***</sup>|
|_AIC_|804.60|838.03|796.37|956.56|785.64|
|**C. With a structur**|**al break in 1993**|||||
|_Constant_|4366.77<sup>***</sup>|4679.28<sup>***</sup>|4091.72<sup>***</sup>|7574.11<sup>***</sup>|3640.02<sup>***</sup>|
||(55.31)|(45.70)|(82.58)|(166.17)|(41.18)|
|_Trend_before_|106.85<sup>***</sup>|158.84<sup>***</sup>|54.64<sup>***</sup>|164.95<sup>***</sup>|94.94<sup>***</sup>|
||(2.80)|(2.82)|(3.60)|(8.07)|(2.41)|
|_Trend_after_|4.47|-1.67|9.42|22.64|14.96<sup>***</sup>|
||(5.40)|(6.47)|(5.85)|(16.58)|(4.77)|
|_PP test_|-5.54<sup>***</sup>|-6.15<sup>***</sup>|-4.07<sup>***</sup>|-7.67<sup>***</sup>|-4.93<sup>***</sup>|
|_AIC_|804.24|838.00|793.57|955.48|786.86|

**Notes** : Yield is measured as KCal per hectare. “All” includes 144 commodities. “Big-4” is the sum of maize, wheat, rice, and soybeans. “Other” includes the remaining 140 commodities. AE (Advanced Economies) includes North America, Northern Europe, Western Europe, and Southern Europe; EMDE (Emerging Markets and Developing Economies) includes LAC, SEAO, EECA, and MENA regions based on FAO classifications. Numbers in parenthesis refer to Newey-West standard errors that account for heteroskedasticity and autocorrelation. PP test refers to the Philips-Perron stationarity test, with the null hypothesis that the series under consideration contains a unit root. AIC refers to Akaike Information Criteria. Asterisks denote significance at 10 (<sup>*</sup> ), 5 (<sup>**</sup> ), and 1 (<sup>***</sup> ) percent levels.

_— 22 —_
- 17 -

# Table 6: RESULTS OF HOUSING INVESTMENT REGRESSION

|Independent<br>Variables|Dependent<br>HINV|Variable<br>NSHINV|
|---|---|---|
|CONSTANT|4.138<br>(2.946)|5558.048**<br>(1364.906)|
|HHPER<br>|-0.01|-10.938|
|10.938|(0.03)|(13.778)|
|AGEINDEX|-0.002|-3.624*|
||(0.004)|(2.039)|
|NEWHHDEX|-0.001<br>(0.008)|-29.353**<br>(3.929)|
|Adjusted R2|0.00|0.69|

Standard Errors in parentheses

* = significant at 0.90 level ** = significant at 0e99 level

concerning housing shortages and incremental demand are not being translated into increased investment. Indeed the fact that the R2 statistic for HINV is equal to zero is a confirmation<sup>of the basic</sup> observation that housing investment is occurring in a way that is less than rational.

# B. Net Migration and Housing Market Disequilibrium

If housing market disequilibrium affects the labor market through compensatory differentials for higher housing<sup>costs that are</sup> incorporated into the wage rate, then this not only affects the existing labor pool, but also the ability of establishments to attract labor from other regions. Labor migration is thus an intermediate<sup>link in the</sup> relationship between the housing and labor markets.<sup>We use several</sup> simple models of migration to examine this relationship. In each<sup>of the</sup> models tested, migration is treated as a function<sup>of the wage level in</sup> the region and of alternative measures of recent changes in the housing shortage in a region.
# **Table 5: Financial Rules of Thumb**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Financial Rules of<br>Thumb Composite<br>Score|Q1: Productive<br>Loans are Better<br>than<br>Consumption<br>Loans|Q2: Separate<br>Business and<br>Household<br>Accounts|Q3: Never<br>Borrow Beyond<br>Means|
|Autonomous Trainer Selection (T1)|0.010**|0.005*<br>|0.016|0.008|
||(0.004)|(0.003)<br>|(0.010)|(0.007)|
|Fixed Trainer Selection (T2)|-0.001|0.003<br>|-0.006|0.000|
||(0.005)|(0.003)<br>|(0.013)|(0.007)|
|R-squared|0.046|0.965<br>|0.006|0.722|
|N|7885|7885<br>|7885|7885|
|Dependent Variable Mean in Control Group|<br>0.606|0.502<br>|0.889|0.427|
|F-test(p-value): T1 = T2|0.041|0.411<br>|0.092|0.345|

This table presents analysis for financial rules of thumb using household survey data across two endline rounds. All regressions control for survey round dummies as well as stratification controls. Standard errors, clustered at the SACCO level, are reported in parentheses. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
if an HIPC passes Decision Point of the initiative in September 2001, the debt relief dummy equals one in 2001 and the years that follow. I also analyze the impacts of MDRI and the original HIPC Initiative as sensitivity tests (Section 4.2).

The mother fixed effect is given by _αm_ and _βay_ is a mother’s-birth-cohort by child’s-birth-year dummy which together gives the differences in mortality within mothers over time and subsequently differences between mothers within birth cohorts across countries. This controls for the fact that the effect of a mother’s age on infant mortality is likely to change over time due to cross-country improvements in the survival of babies. In some estimations, _βay_ is substituted for a simple birth-year fixed effect, _βy_ . In order to compare the within-mother results with the results from a within-country model, the mother fixed effect, _αm_ , is replaced by a country fixed effect, _αc_ . I also take compositional changes among the mothers in different countries into account by including dummy variables for the following mother-specific socioeconomic characteristics: poverty, educational status, and residency in some within-country regressions.

The vector of exogenous controls, **ximacy** , includes a girl dummy, multiple-birth dummy, birth-order dummies (one for each birth order starting from two and upward, birth order one is the reference, with one dummy for birth order ten and higher), and dummies for which quarter of the year the birth takes place (quarter one is the reference). Country-specific linear time trends are included to account for trend differences in cross-country mortality.

To enable distributional analyses of reaching the different stages of HIPC, I interact _Debt Relie fcy_ with the following child- and mother-specific characteristics: girl infant and the socioeconomic status of mothers: poverty, educational status, and residency. I estimate the following specification, In all regressions, standard errors are clustered at the country level to account for within-country correlations of _εimacy_ . This allows the standard errors to be correlated across siblings since siblings are assumed to be in the same country. The consistency of the empirical specification relies on _εimacy_ being uncorrelated with the debt relief variables over time and other countries where mothers of the same cohort have children in the same year.

9