rafmacalaba/datause-encoder-data huggingface dataset:
To construct our synthetic panel we use six rounds of cross-sectional data from the Household Income and Basic Amenities Survey (HIS) collected by the Department of Statistics Malaysia. The data are for the years 2004, 2007, 2009, 2012, 2014 and 2016. When Malaysia’s national poverty line was updated in 2020, the value of the line was only recalculated for the 2016 and 2019 surveys, and not for the earlier surveys. Hence, we employ our own poverty line, which corresponds to the income level of the 40th percentile in the first year of our analysis, 2004. This line is then kept constant in real terms for the subsequent years. It is close to the new official poverty line.<sup>5</sup> In addition, we study income mobility in a broader sense by drawing on the vulnerability analysis proposed by Dang and Lanjouw (2017). This allows us to define three income groups: the poor, the vulnerable (those non-poor who face a heightened risk of falling into poverty) and the economically secure. From these, upward mobility is defined as moving up one or two categories, downward mobility as moving down one or two groups. We note that this is a somewhat narrow definition of mobility that does not describe intergenerational mobility or mobility within the middle and higher reaches of the income distribution.

The main contribution of the present study is that it provides the first evidence on poverty and income dynamics in Malaysia based on official micro-level survey data. In comparison to synthetic panel studies carried out in other countries, our addition is that we are able to use six rounds of high-quality data, rather than only two or three. Moreover, each round has a large number of observations, which facilitates within- and between-group analyses. This means that we are able to present results on a variety of dimensions, with this paper focusing on chronic poverty, upward mobility and downward mobility, also taking into account differences between population groups.

We present three headline results. The first is that the likelihood of being chronically poor in Malaysia has decreased considerably over the 2004–2016 period. Nevertheless, as aggregate poverty in Malaysia has fallen, our evidence suggests that the remaining poor are increasingly likely to be poor in a structural sense – our second main finding. Third, after digging further into the structural nature of poverty, we observe that Malaysians in rural areas and in East Malaysia in particular experience a higher risk of chronic poverty. As a result, addressing chronic poverty might require more focus on lessdeveloped geographic areas, and less emphasis on existing ethnicity-based affirmative action policies.

> 5 Ravallion (2020b) proposes a weakly relative poverty line that would be around USD 12 or MYR 20 per person per day in 2016 prices when converted at PPP exchange rates. The line we use is around USD 10 (PPP) or MYR 17 per person per day. We chose the 40<sup>th</sup> percentile because this is in line with the policy concept of the bottom 40% of the income distribution that is used widely in Malaysia.

4
# **Appendix D: Estimates of Insurance ownership shares in Kenya, Tanzania, Uganda, Rwanda, SADC and COMESA**

All market share data come from Axco country reports.<sup>36</sup> Ownership data comes from Axco reports and company reports.

Given our focus on Kenya and Tanzania, we investigate these countries in detail and calculate market shares in insurance in Kenya and Tanzania for the regions in our model. This work is documented in Jafari (2014). Given that Uganda is a separate region in our model and we have the Axco country report, we also calculate the market share for Uganda in detail as explained below.

Given the relative size of South Africa in SADC, we calculate the market share by region in South Africa and take that as representative of SADC. For COMESA, we take an average of Zambia and Uganda as representative of COMESA. Zambia had a state monopoly in insurance prior to opening the market in 1992. Partly as a result of the late opening of the market, Zambia has a rather large domestic share of the insurance market. Uganda, which has been very open to foreign direct investment, has a much smaller domestic share. It is likely that neither market is representative of COMESA, so an average of the two is more appropriate. Without data on Rwanda, we take Uganda as representative of Rwanda, where Rwanda’s domestic share in Rwanda is assumed equal to Uganda’s domestic share in Uganda.

# **Tanzania**

In 2013 there were 23 non-life companies (including health insurers) registered and two composites, including one that is state-owned. There are also two registered life only companies. The market shares of these companies, based on written premiums, were obtained from the Axco company report for Tanzania. See Jafari (2014) for details.

Having been a monopolistic insurance system for more than 30 years, Tanzania has operated as a free market since 1998. There are two state-owned insurers: National Insurance Corporation of Tanzania (NIC) and Zanzibar Insurance Corporation (ZIC).

> 36 Axco country reports are available at: https://www.axco.co.uk/. Accessed on January 9, 2014.

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It is estimated that a 5.0 percent per year GDP growth rate would predict a reduction in national stunting prevalence of around 0.9 percentage point per year. Another paper (Marie T Ruel, Harold Alderman et al. 2013) noted “a 10.0% increase in GDP per capita predicts a 5.9% reduction in stunting” (p382), when economic growth was combined with other nutrition related interventions.

Despite the conceptual argument highlighting the strong linkages between economic growth and child stunting rate reduction, the empirical research has challenged this view. Vollmer et Al. (Vollmer, Harttgen et al. 2014) conclude that “the contribution of economic growth to the reduction in early childhood undernutrition in developing countries is very small, if it exists at all.” This perspective was based on 121 Demographic and Health Surveys from 36 low-income and middle-income countries. Subramanyam et al. (Subramanyam, Kawachi et al. 2011) concluded that there was no consistent evidence that economic growth had contributed to reducing undernutrition in India using three waves of household level data.

Another puzzling matter regarding nutrition in PNG is the relatively high stunting rate even for the richest quintile in PNG. As expected, the poorest quintile has the highest stunting rate at 55 percent. However, the stunting rate among the richest quintile is also quite high (36 percent), and similarly high among the third and fourth quintiles. In terms of underweight and wasting, the fourth quintile has even worse nutritional outcomes compared with the third quintile. Assuming income is less of a constraint for the richer quintiles, what are the other factors driving the stunting rate among the better-off quintiles in PNG?

Figure 2. The Stunting, Underweight and Wasting Across Wealth Quintile in PNG <!-- Start of picture text --> 0.55<br>0.52<br>0.42 0.44<br>0.36<br><!-- End of picture text --> Unfortunately, the research remains quite limited in PNG. The previous nutritional studies in PNG have reported low dietary intake of protein in the traditional diet (Oomen HAPC 1971). Low energy and protein levels were also reported among children who lived in Lufa (Eastern Highlands Province) and Kaul (Madang Province, (Ferro-Luzzi, Norgan et al. 1975). One study indicated moderate iodine deficiency among a cohort of 350 school children (6–12 years old) in the Southern 4
15 outside treatment than rural residents at any given consumption level. ii) Primary health centers: The 1978 results indicate that, for rural areas, primary health centers were predominantly used by middle expenditure households. The poor used these facilities relatively little (many going without treatment), while the rich tended to use other facilities more intensively (such as private doctors). In urban areas, the pattern differs between Java, where use is also the highest for the middle expenditure group (22%), and the Outer Islands, where the poorest are the most common users (27%) and the middle the least common (10%). By 1987, we see that use of primary health centers has increased for most subgroups. There are two exceptions. It has dropped for the wealthiest groups in the urban areas of both Java and the Outer Islands; and likewise for the middle expenditure group in rural areas, whose use surpassed those of others in 1978. In the urban areas of both Java and the Outer Islands, the use of primary health centers declines with consumption expenditures though only very mildly between the bottom and middle groups. In marked contrast, usage of rural primary health centers appears relatively equal across all expenditure groups. The upper 20% are just as likely (if not slightly more likely) to use them as the lower 40%. Based on these data for 1987, and those featured in Tables 5a and b, one could not co.iclude niow that subsidizing primary health care in rural areas is inherently pro-poor; the benefits will be quite uniformly distributed. However, the benefits of subsidized primary health care will tend to be more pro-poor in urban areas.

iii) Private Doctors: Unlike public health centers, visits to doctors increase markedly with expenditure levels in both rural and urban areas. Use is also higher in urban areas at any consumption level. The rate of use of private doctors has augmented for 5 out of 6 subgroups in the Outer Islands but shows a more mixed picture in Java having increased or decreased for half of all subgroups there. This may reflect the relatively lower availability of cheaper yet acceptable alternatives in the Outer Islands.

iv) Hosvitals: Hospital treatment also increases with urban residence
matrix results, and finally, examine the performance of specific subsamples, such as men and women, and urban and rural.

Table 4 compares the percentages of NEETs in 2010 and 2018 derived from the cross-section data sets, the actual panel data tracking the same individuals in four survey rounds—notably 2010– 2011 and 2017–2018—and the synthetic panel based on cross-sectional data in 2010 and 2018. For 2010, there are no separate estimates of NEET rates based on the synthetic panel, because they are obtained using the share of individuals in the 2018 sample estimated from those who were NEET in 2010 and 2018, then adding the share of individuals falling into the NEET condition over the considered period.

**Table 4: NEET Percentage Rates in 2010 and 2018: Comparison of Cross-Section, Actual Panel and Synthetic Panel Lower- and Upper-bound Estimates**

|**Year**|**Lower-bound estimate**|**Cross-section**|**Actual panel**|**Upper-bound estimate**|
|---|---|---|---|---|
|**_2010_**|-|31.41<br>(30.96; 31.86)|31.82<br>(31.14; 32.51)|-|
|**_2018_**|46.66|46.66<br>(46.20; 47.12)|46.92<br>(46.33; 47.52)|46.66|

_Notes:_ results are restricted to the sample of individuals aged 15 to 24 in 2010 and aged 23 to 32 in 2018. “Lower” is the upper bound and “Upper” is the lower bound. Synthetic panel upper-bound estimates are based on 50 replications. Individual-level sampling weights are applied; 95 percent confidence intervals are given in parentheses.

Two key elements emerge from this comparison. First, NEET rates for both 2010 and 2018 calculated on the cross-section and the actual panel data almost overlap. Second, the upper- and lower-bound estimates of the synthetic panel for 2018 are remarkably close to the NEET rates observed in both the cross-section and the actual panel. This represents an encouraging result that gives a positive first indication of the quality of the synthetic panel.

The next step is represented by the analysis of the transitions in and out of the NEET condition based on the 2010/2018 synthetic panel. For this purpose, a transition matrix was created. The rows in table 5 indicate the proportion of individuals in the 2018 sample estimated to be in one of four groupings: a. In NEET both in 2010 and 2018 (NEET, NEET); b. Moved out of NEET during that time period (NEET, non-NEET); c. Fell into the NEET condition (non-NEET, NEET); or d. Were never in NEET between 2010 and 2018 (non-NEET, non-NEET). These four options are exhaustive, and therefore each column adds up to 100 percent.

21
WPS3966

# **On the Financial Sustainability of Earnings-Related Pension Schemes with “Pay-As-You-Go” Financing and**

# **the Role of Government Indexed Bonds**

## **David A. Robalino**<sup>**1**</sup>

## **András Bodor**<sup>**2**</sup>

## **_Abstract_**

In this paper we reconsider the idea of an earnings-related pension system with reserves invested in indexed government bonds as a mechanism to both ensure financial sustainability and improve security. The paper starts by reviewing the characterization of the sustainable rate of return of an earnings-related pension system with pay-as-you-go financing. We show that current proxies for the sustainable rate, including the Swedish “gyroscope”, are not stable and propose an alternative measure that depends on the growth of the buffer-stock and the pay-asyou-go asset. Using a simple one-sector macroeconomic model that embeds a notional account pension system we then show how GDP indexed government bonds, if combined with the right measure for the sustainable rate of return on contributions, could be used to generate a sustainable and secure earnings-related pension system, without becoming a fiscal burden. The proposal is particularly attractive for countries considering reforms to earnings-related systems that have accumulated a large implicit pension debt. In this case, the government bonds allow the financing of this debt in a transparent way. The proposed mechanism can also facilitate the transition to a fully-funded pension system when the government bonds are allowed to be traded.

## World Bank Policy Research Working Paper 3966, July 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ The authors thank Robert Palacios, Edward Whitehouse, Michal Rutkowski, Alberto Musalem, and Robert Holzmann, for useful discussions and insights on the subject, and to Oleksiy Sluchinsky and Anca Mataoanu for commenting on early drafts of the paper.

> 1 Senior Economist at the World Bank. 2 Ph. D. Candidate in Economics at Georgetown University and Consultant with the World Bank.
Table 2: ICT Abundance: Values & Quantities

|||A. % $ICT/|$NICT||B. %|Units of ICTp|er Unit of N|ICT|
|---|---|---|---|---|---|---|---|---|
||1993-2011|1993-2000|2001-05|2006-11|1993-2011|1993-2000|2001-05|2006-11|
|Log Real GDP/L|0.870***|1.331***|0.759***|0.279|0.680***|0.499***|0.764***|0.876**|
||(0.239)|(0.286)|(0.259)|(0.281)|(0.174)|(0.0832)|(0.181)|(0.365)|
|t=1993|5.105***|5.261***|||1.034***|0.972***|||
||(0.508)|(0.498)|||(0.113)|(0.0938)|||
|t=1994|4.723***|4.866***|||1.048***|0.992***|||
||(0.403)|(0.394)|||(0.104)|(0.0850)|||
|t=1995|4.471***|4.598***|||1.082***|1.032***|||
||(0.339)|(0.331)|||(0.0978)|(0.0810)|||
|t=1996|4.380***|4.495***|||1.163***|1.117***|||
||(0.303)|(0.296)|||(0.0966)|(0.0822)|||
|t=1997|4.334***|4.440***|||1.297***|1.255***|||
||(0.288)|(0.283)|||(0.102)|(0.0896)|||
|t=1998|4.372***|4.479***|||1.526***|1.484***|||
||(0.282)|(0.276)|||(0.113)|(0.104)|||
|t=1999|4.536***|4.633***|||1.750***|1.712***|||
||(0.286)|(0.280)|||(0.125)|(0.119)|||
|t=2000|4.707***|4.780***|||1.977***|1.948***|||
||(0.294)|(0.287)|||(0.139)|(0.138)|||
|t=2001|4.508***||4.492***||2.157***||2.169***||
||(0.266)||(0.268)||(0.144)||(0.145)||
|t=2002|4.393***||4.380***||2.374***||2.384***||
||(0.260)||(0.260)||(0.158)||(0.158)||
|t=2003|4.170***||4.160***||2.573***||2.581***||
||(0.245)||(0.243)||(0.169)||(0.169)||
|t=2004|3.936***||3.931***||2.883***||2.886***||
||(0.225)||(0.223)||(0.184)||(0.184)||
|t=2005|3.761***||3.764***||3.316***||3.314***||
||(0.219)||(0.217)||(0.212)||(0.211)||
|t=2006|3.752***|||3.792***|3.757***|||3.744***|
||(0.223)|||(0.219)|(0.239)|||(0.241)|
|t=2007|3.719***|||3.784***|4.222***|||4.201***|
||(0.217)|||(0.212)|(0.257)|||(0.262)|
|t=2008|3.694***|||3.778***|4.626***|||4.598***|
||(0.223)|||(0.215)|(0.281)|||(0.289)|
|t=2009|3.967***|||4.035***|4.732***|||4.710***|
||(0.227)|||(0.218)|(0.281)|||(0.286)|
|t=2010|3.966***|||4.060***|4.952***|||4.921***|
||(0.232)|||(0.226)|(0.298)|||(0.306)|
|t=2011|3.943***|||4.053***|5.303***|||5.267***|
||(0.231)|||(0.226)|(0.327)|||(0.334)|
|Obs.|1292|544|340|408|1292|544|340|408|
|F-Stat.|32.3|37.7|74.1|67.8|22.7|28.3|51.0|49.9|

_Notes:_ The table reports coefficient estimates from linear regressions of our ICT abundance measures on log GDP per captia and time effects. Panel A reports regressions for the ratio of ICT values relative to NICT values and panel B reports units of ICT per unit of NICT. In these regressions we re-center log real GDP per capita so that it is zero for the median income country in our sample, which ensures that the time effects reflect the time specific average for the median income country. We note that the point estimates for the slopes are virtually unaffected by this normalization but give the time effects a clear-cut interpretation. Standard errors are clustered on country and reported in parentheses below each coefficient. Significance levels are indicated by * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, and *** _p <_ 0 _._ 01.

11
Ky R,, <!-- Start of picture text --> R,,<br><!-- End of picture text -->

# R,

<!-- Start of picture text --> AXR.KwKw = Lxeky bseSplureRpArs bseSplureRpArsSplureRpArs<br><!-- End of picture text --> AXR.KwKw = Lxeky bseSplureRpArs bseSplureRpArsSplureRpArs Vw,v,v' ov ev", <!-- Start of picture text --> Vw,v,v' ov ev",<br><!-- End of picture text --> et Fay fe Sur <!-- Start of picture text --> Ky<br><!-- End of picture text --> Ky Hay Fy ACTIT, cs FipSpr rag ag ag al = Th vv Ww, vv vv pe", <!-- Start of picture text --> Hay<br>Fy ACTIT,<br>cs FipSpr rag ag ag al<br>= Th vv Ww, vv vv pe",<br><!-- End of picture text -->
Talent Allocation and Post-Reform Growth in Central America Rishabh Sinha<sup>§</sup> The World Bank

# **JEL classification:** O47; J24; J71

**Keywords:** Economic growth; misallocation; gender; labor market discrimination; barriers to human capital accumulation; preferences and norms; Central America; Costa Rica; El Salvador; Panama

> § 1818 H St NW, Washington, DC 20433, USA. Email: rishabhsinha@worldbank.org. Tel: +1-(202) 458 0770. Thanks to Charles Jones for his valuable comments and suggestions. Luis Diego provided excellent research assistance. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Most recent version available here.
|**Table 7.1: Probability of Fal**|**ling into P**|**overty, 20**|**01**|
|---|---|---|---|
|**Probit Regression**|Number of|obs. =|44.094|
|**Dependent Variable: P0**|**dy/dx**|**T**|**P>|t|**|
|Age|-0,005|-4,42|0,000|
|age_2<br>|0,000|2,96|0,003|
|female*<br>|0,054|10,44|0,000|
|black and mulatto*|0,063|18,52|0,000|
|rural*|0,038|5,43|0,000|
|family size|0,029|6,35|0,000|
|family size_2|-0,005|-11,61|0,000|
|family members under the age of 5|0,119|28,16|0,000|
|family members between age 5 and 15|0,101|24,85|0,000|
|family members between age 15 and 24|0,036|11,38|0,000|
|family members over the age of 65|-0,081|-6,86|0,000|
|Primary education (1-4 grade)*|-0,046|-10,09|0,000|
|lower secondary education (5-8 grade)*|-0,086|-19,55|0,000|
|upper secondary education(9-12 grade)*|-0,110|-24,71|0,000|
|Tertiary education (>12 grade)*|-0,115|-15,63|0,000|
|signed work card*|-0,123|-31,81|0,000|
|employed in agriculture*|0,103|7,81|0,000|
|employed in services*|0,005|0,47|0,637|
|Employed in industry*|0,009|0,89|0,374|
|Employed in the social sector*|0,009|0,73|0,466|
|Employed in “other” sector*|-0,036|-2,18|0,03|
|Bahia*|-0,001|-0,02|0,987|
|Bahia age|0,000|-0,01|0,991|
|Bahia age_2|0,000|-0,06|0,955|
|Bahia female *|0,028|1,84|0,065|
|Bahia black and mulatto*|-0,037|-3,36|0,001|
|Bahia rural*|<br>-0,025|<br>-1,82|<br>0,069|
|Bahia family size|0,044|4,08|0,000|
|Bahia family size_2|<br>-0,004|<br>-2,75|<br>0,006|
|Bahia family members under the age of 5|0,012|0,91|0,36|
|Bahia family members between age 5-15|<br>-0,001|<br>-0,06|<br>0,955|
|Bahia family members between age 15-24|0,004|0,40|0,692|
|Bahia family members over the age of 65|-0,019|-0,74|0,459|
|Bahia primary education*|0,024|1,79|0,073|
|Bahia lower secondary education*|0,042|2,05|0,04|
|Bahia upper secondary education*|-0,020|-1,20|0,229|
|Bahia tertiary education*|-0,064|-1,85|0,065|
|Bahia signed work card*|0,024|2,13|0,033|
|Bahia employed in agriculture*|-0,003|-0,11|0,91|
|Bahia employed in services*|<br>-0,011|<br>-0,50|<br>0,619|
|<br>Bahia employed in industry*|<br>-0,028|<br>-1,32|<br>0,188|
|Bahia employed in the social sector*|<br>-0,029|<br>-1,06|<br>0,291|
|Bahia employed in the “other” sector*|-0,030|-0,59|0,553|
|(*) dF/dx is for discrete change of dummy v<br>t and P>|t| are the test of the underlying coef<br>_Source_: Author’s calculations based on PNAD 2001.|ariable from 0<br>ficient being|to 1;<br>equal to 0.||

43