rafmacalaba/datause-encoder-data huggingface dataset:
migrants as parametrically given. The formal statement of their decision problem is given in Appendix A.

By way of closing remarks, first, it is assumed that parents cannot borrow to finance schooling (Schaffner, 2004: 17). Second, the only form of investment is education, the costs of which are the children’s potential contributions to current output. Indeed, rural households in Ethiopia save little (Tengenu, 2003: 15) and cannot purchase formal insurance against risk (Dercon, 2004). Third, since all households are assumed to be identical, there is no trade in the services of labor and land.<sup>9</sup>

# 2.2 The urban sector

The number of children (per representative family) born in the urban sector (labelled 2) in period t − 1 that survive into young adulthood in period t is (1 − q2<sup>c</sup> ,t−1<sup>)N c</sup> 2,t−1<sup>.</sup> The total number of migrants arriving at the start of period t is Mt<sup>aN f</sup> 1,t−1<sup>.Hence,</sup> the total number of young adults who form unions in urban areas at this juncture is �1 − q2<sup>c</sup> ,t−1� N2<sup>c</sup> ,t−1<sup>N f</sup> 2,t−1<sup>+ M a</sup> t<sup>N f</sup> 1,t−1<sup>.GiventhedefinitionofFi,tasthenumberof</sup> surviving couples, we have The number of elders per urban household is, accordingly,

> 9Land is state-owned and distributed equally among farmers on a strict usufruct basis by local councils, though leasing is allowed (OPM, 2004: 3). Wage labor, whether in agriculture or otherwise, remains relatively rare, and many off-farm activities (such as selling home-made products) are closely linked to agricultural activities (Dercon, 2004: Sec. 5). Indeed, according to a survey conducted by the Ministry of Labor in 1996, 44% of rural households reported non-agricultural sources of income, but these sources accounted for only 10% of total income (Degefe and Nega, 2000: 179).

10
# **Table A4: Robustness test – Alternative measures of temperature**

_Panel A: Poverty_

|||De|pendent variabl|e:Povertyrate <br>Number of<br>|at $1.90<br>Dropping<br>||
|---|---|---|---|---|---|---|
||Log<br>temperature|Temperature<br>(<sup>o</sup>F)|Temperature<br>from CRU|days<br>temperature<br>above 28|subregions with<br>temperature<br>above 28|Temperature<br>shock|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Temperature|2.218***|0.434***|0.760***|0.070***|0.666***|0.337***|
||(0.481)|(0.064)|(0.118)|(0.025)|(0.111)|(0.114)|
|Controlling for<br>rainfall|Yes|Yes|Yes|Yes|Yes|Yes|
|Subnational FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of|||||||
|countries|134|134|134|134|134|134|
|Number of regions|1,594|1,594|1,594|1,594|1,594|1,594|
|Observations|5,090|5,090|5,059|4,209|4,856|5,090|

_Notes:_ Results of panel fixed effects model. Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. In Column (5), temperature shock is defined as the difference between actual temperature and long-term temperature being greater (less) than 2 (-2) standard deviation. *** p<0.01, ** p<0.05, * p<0.1 _Panel B: Inequality_

||||Dependent va|riable: Gini ind<br>Number of<br>|ex<br>Dropping<br>||
|---|---|---|---|---|---|---|
||Log<br>temperature|Temperature<br>(<sup>o</sup>F)|Temperature<br>from CRU|days<br>temperature<br>above 28|subregions with<br>temperature<br>above 28|Temperature<br>shock|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Temperature|1.018***|0.158***|0.248***|0.064***|0.302***|2.594***|
||(0.346)|(0.048)|(0.086)|(0.013)|(0.086)|(0.472)|
|Controlling for<br>rainfall|Yes|Yes|Yes|Yes|Yes|Yes|
|Subnational FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of|||||||
|countries|134|134|134|134|134|134|
|Number of regions|1,594|1,594|1,594|1,594|1,594|1,594|
|Observations|5,090|5,090|5,059|4,209|4,856|5,090|

_Notes:_ Results of panel fixed effects model. Robust standard errors in parentheses. Standard errors are clustered at the subnational level. Regressions are weighted by region population. In Column (5), temperature shock is defined as the difference between actual temperature and long-term temperature being greater (less) than 2 (-2) standard deviation. *** p<0.01, ** p<0.05, * p<0.1 51
**<u>Table 10: Level of Annuitization in the Second Pillar</u>**

||**Level of annuitization**<br>**(percent of balances)**|**Comments**|
|---|---|---|
|**Australia**|**Very low**<br>Probably less than 10%|Lump sums are allowed without restrictions and account for<br>55% of all payouts from superannuation funds. Retirees also<br>favorallocated and termannuities over life annuities.|
|**Chile**|**High**<br>About 70%|Lump sums are restricted. The share of annuitants in total<br>retirees exceeds 60% overall, and exceeds 70% excluding<br>disability and survivor pensioners.<br>PWholders accountfor most oftheremaining 30%.|
|**Denmark**|**High**<br>ATP: 100%<br>Occupational schemes:<br>50%|ATP:**mandatory annuitization,**except for very low balances.<br>Occupational schemes: annuitization depends on the terms of<br>collective labor agreements. Average level of annuitization is<br>**relatively high**, based on choices made during contribution<br>period: 50% of contributions are allocated to life annuities, 35%<br>to term annuities, and 15% to lump sums.|
|**Sweden**|**High**<br>PPM: 100%<br>Occupational schemes:<br>Low, probably less than<br>30%|PPM:**mandatory annuitization**<br>Occupational schemes: annuitization depends on the terms of<br>collective labor agreements. Lump sums are not permitted in<br>occupational plans, but extensive use is made of term annuities<br>for5 or 10 years.|
|**Switzerland**|**High**<br>Occupational schemes:<br>80%|Lump sums are allowed, but joint life annuities are favored by<br>the terms and conditions ofannuities.|

Sources: Andersen and Skjodt (2007); Brunner and Thorburn (2008); Buetler and Ruesch (2007); Palmer (2008); Rocha and Rudolph (2010); Rocha and Thorburn (2007); and Vittas (2008).

In Chile there are data on the numbers of retirees who opt for life annuities or phased withdrawals and estimates of the proportion of accumulated balances that are converted into life annuities. Including deferred annuities, 62 percent of pensioners selected life annuities in 2004. However, excluding disability and survivor pensioners, who are covered by group disability and term life insurance and have no choice in the matter, the share of retirees who selected annuitization rises to 71 percent. No data are published on the size of accumulated balances that are used for PWs and thus there are no published data on the share of accumulated balances that is annuitized. However, this proportion is also likely to be close to 70 percent.

Most annuities are joint life annuities, reflecting the regulation that forces married males to take this type of annuity. The share of annuities with guaranteed periods is large and most of these annuities are guaranteed for periods of 10-15 years and even longer. The strong demand for guaranteed annuities reveals a preference for bequests.

25
policymakers prepare to accommodate the next wave of rural migrants with China and India for example contemplating the development of super-cities (Henderson, 2010) and Africa also urbanizing rapidly, while finding itself already at high levels of urban primacy (Behrens and Bala, 2013). The lock-in of urbanization patterns, including through infrastructural lock-in, adds further urgency.

Building on Ravallion, Chen and Sangraula (2007) and Christiaensen and Todo (2013), this study further illustrates the differential effects on poverty reduction from metropolitization versus rural diversification and development of secondary towns by presenting stylized facts from a rather exceptional survey documenting nearly two decades of spatial and occupational change for a panel of respondents from Kagera, Tanzania. Cross-country analysis using country fixed effect panel estimation techniques applied to 206 poverty spells from 51 developing countries spread across five continents, spanning 1980-2004, is subsequently applied to examine whether broader empirical regularities can be uncovered.

Practically, in both the Tanzania case and the multi-country study, the population is classified into three groups according to their occupation and location: 1) those living in rural areas and employed in agriculture, 2) those living in mega cities and employed in industry and services, and 3) those living in rural areas and secondary cities and employed outside agriculture. The latter group will be referred to as the “middle”, reflecting its operational definition as the residual category between the total population and those employed in agriculture and those living in mega-cities. Hence the study differs conceptually from most of the literature, which typically applies either a spatial (rural-urban) or an occupational (agriculture-non-agriculture) dichotomy.

In Kagera, Tanzania, 1 out of 2 individuals interviewed in 1991/4 that exited poverty by 2010, did so through transition out of agriculture into the middle, 1 out of 3 while continuing as farmers, and only 1 out of 7 through migration to capitals or other big cities (Dar es Salaam, Kampala, and Mwanza). In addition, while unemployment rates were more than three times higher among migrants in the latter group than among those moving to the middle, their 6
Table C.2: Descriptive Statistics by Locations: Main Variables

||Aggregate|Location 1|Location 2|Location 3|Location 4|
|---|---|---|---|---|---|
|Child Care Choices:||||||
|Use: =1 if main form of care is a day care center|0.30|0.34|0.30|0.24|0.31|
|Price: Hourly fee for a 2 year old attending full-time|4.38|2.82|4.32|4.53|5.80|
|Quality: Average overall ITERS score|4.29|4.33|4.13|4.08|4.54|
|Use: =1 if main form of care is non-relative care|0.06|0.03|0.05|0.06|0.08|
|Use: =1 if main form of care is relative care|0.19|0.23|0.16|0.25|0.12|
|Child’s Cognitive Score:||||||
|Bailey Short Form Mental Ability Score|127.94|126.94|128.29|126.42|129.87|
|Parents’ Labor Market:||||||
|Mothers||||||
|=1 if working full-time for pay|0.28|0.29|0.31|0.25|0.28|
|=1 if working part-time for pay|0.21|0.27|0.14|0.22|0.18|
|=1 if not working for pay|0.52|0.44|0.54|0.53|0.54|
|Wage: Gross hourly wage|17.97|13.03|18.49|16.87|24.40|
|Education||||||
|=1 if college completed or more|0.42|0.27|0.47|0.31|0.62|
|=1 if high school completed or more but no college|0.47|0.56|0.47|0.55|0.32|
|=1 if not completed high school|0.11|0.17|0.06|0.14|0.05|
|Fathers||||||
|=1 if working full-time for pay|1.00|1.00|1.00|1.00|1.00|
|=1 if working part-time for pay|0.00|0.00|0.00|0.00|0.00|
|=1 if not working for pay|0.00|0.00|0.00|0.00|0.00|
|Wage: Gross hourly wage|23.24|14.78|26.25|20.75|29.46|
|Education||||||
|=1 if college completed or more|0.51|0.32|0.55|0.40|0.69|
|=1 if high school completed or more but no college|0.42|0.52|0.42|0.51|0.25|
|=1 if not completed high school|0.09|0.17|0.03|0.09|0.05|
|Proportion Single Mothers||||||
|=1 if single-mother household|0.19|0.25|0.14|0.25|0.12|

Note: Table reports definitions and descriptive statistics of the estimation sample’s main variables. Sample extracted from the second wave of ECLS-B for children born in 2001.

# **C.2.3 Transfers**

As described in Section 4.1.2, we assign non-labor income equal to zero to two-parent households and equal to $41 a day to single mothers. This amount is calibrated to match the poverty line in 2003; the transfer is sufficient to allow single mothers to consume and purchase center-based care.<sup>43</sup> Of course, in reality two-parent households might also receive transfers, and transfers might not be homogeneous among households. We have chosen this simplified transfer rule because transfer amounts are not reported in our data, and the simplification reduces household-level heterogeneity thereby lowering computational burdens.

> 43We have tried alternative transfer amounts, including values extracted from the literature (Fang and Keane, 2004; Hoynes and Schanzenbach, 2018), values proportional to the minimum wage, and values proportional to the poverty line. Calibrating the transfer to the 2003 poverty line provides the best fit.

67
9

# _Quality of education_

The quality of education in Malaysia and Thailand is not up to the standard of the highest Asian performers in world rankings. This may lead to competitive challenges if the labor force is not being endowed with the appropriate technical and creative talent.

Both PISA and TIMSS international assessments show that compared to Indonesia and the Philippines, Malaysia and Thailand perform better in both math and reading. But secondary school students are still learning much less than their peers in wealthier countries like Hong Kong SAR, China; Macao; Japan; Korea; and Singapore (Figure 4). A Malaysian or Thai student on average scores between1.2-1.5 standard deviations below a Korean student in mathematics (TIMSS 2007). This underperformance can be a serious issue as nearly half of 15-year-old Thai students fail to achieve the level of functional literacy in PISA assessments. In other words, most of them are unable to locate information which may need to be inferred or perform other tests (World Bank 2012a).

**Figure 4: Learning Outcomes in East Asia over Time**

<!-- Start of picture text --> Mean Reading Score (PISA) Mean Math Score (TIMSS)<br>Korea, Rep.<br>Singapore<br>Korea, Rep.<br>Hong Kong SAR, China Hong Kong SAR, China<br>Singapore Japan<br>Japan<br>Macao SAR, China<br>Malaysia<br>Thailand Thailand<br>Indonesia<br>Indonesia<br>Philippines<br>2000 2003 2006 2009 1995 1999 2003 2007<br>Year Year<br>550 600<br>550<br>500<br>500<br>450<br>450<br>400<br>400<br>350 350<br><!-- End of picture text --> Source: OECD Programme for International Assessment (PISA) and Trends in International Mathematics and Science Study (TIMSS) The time trend in academic performance indicates that Malaysia and Thailand have not caught up with others in the region; indeed they may have fallen even further behind. Mean scores in both mathematics and reading from PISA and TIMSS declined between 1999 and 2007. Thailand improved slightly in 2009 but not enough to return to the level of 1999. The World Bank’s (2012c) analysis shows that the decline in Thai students’ performance is mostly explained by the deterioration of the quality of the education system rather than by changes in students’ backgrounds.

Non-cognitive skills as well as cognitive skills determine an individual’s success (Heckman, Stixrud and Urzua 2006). As mentioned earlier, internationally comparable data on noncognitive skills do not yet exist. As a substitute, we present information collected by the World
# **Table A.2:  Determinants of Height  and Years of Schooling in 2004**

||Depende<br>(1)<br>Ln height|nt Variable<br>(2)<br>Years of Schooling|
|---|---|---|
|Mother died|-0.012*|-1.085**|
||(0.006)|(0.435)|
|Father died|-0.0002|-0.185|
||(0.0002)|(0.306)|
|Number of observations|718|718|

_Notes_ : Weighted 2SLS estimates with community and age fixed effects. Weights are the inverse of the predicted probability of inclusion in sample. Logit model predictions with specification as in table A.1. Standard errors in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1%. All other independent variables as in table 4 (coefficients not reported).

23
Time-use data confirms women are overburdened by household and family responsibilities, including care for children and the elderly.<sup>31</sup> Time spent on childcare and elderly care (as a share of total hours of activity in a regular weekday) is three times as much for women than men (Table 5). The pattern is independent of geographic location and much more significant in households with children, especially young children.<sup>32</sup>

# Table 5. Time Use Survey Responses, Armenia

QUESTION: HOW MANY HOURS DID YOU SPEND IN A TYPICAL WEEKDAY/WEEKEND ON THE FOLLOWING ACTIVITIES?

|||Elderly||Work and<br>commuting<br>|||Domestic<br>|||
|---|---|---|---|---|---|---|---|---|---|
||Sleeping|care|Childcare|to work|Studying|Leisure|tasks|Transport|Others|
|Location||||||||||
|Female|31.3|1.9|9.8|14.4|4.8|17.7|12.6|4.9|0.4|
|respondent||||||||||
|Armenia||||||||||
|Male|32.7|1.3|3.5|26.8|3.1|19.8|3.2|4.7|0.4|
|respondent||||||||||
|Armenia||||||||||

Women with tertiary education spend more time at work than women with no tertiary education while men spend close to 27 percent of their time in employment. Adding up time spent on elderly care, childcare, and domestic tasks – all of which contain elements of social reproduction – reveals that women (nationally) spend three times as much on those three tasks as men. Even the group of women spending the most time working – those with tertiary education or living in the capital Yerevan – spend three to four times as much on these activities as their men counterparts. The groups of women who spend the least time working – those in households with children, with or without elderly dependents – also spend three to four times as much on these activities as their men counterparts.

> 31. The survey for “Assessing Gender Disparities in Time Allocation and Household Responsibilities in Armenia” was conducted in 2022, by the World Bank, in partnership with CRRC Armenia. It aimed to complement results from the pilot study for the Engendered CEQ analysis and address current

> knowledge gaps on the decisions and perceptions of adults in Armenia related to (1) time use, (2) gender roles and responsibilities within households, (3) childcare access, (4) access to elderly care, and (5) opinions and preferences on gender disparities.

> 32. Men also consistently spend more time on leisure activities than women; the gap in leisure hours is also larger in households with young children.

21
**Table 4: Sales tax (percent) on energy commodities**

|Coal|0.0|
|---|---|
|Refined petroleum|15.4|
|Electricity & gas|0.1|
|_Source:_SAF SAM 2003||

**_Modeling energy policies._** The framework is modified from the standard specifications of a CGE model widely applied to developing countries by Löfgren, Harris, and Robinson (2001) and Dervis, de Melo, and Robinson (1982).<sup>16</sup> Different versions of the CGE model for South Africa have been used to analyze a value added tax (Go, Kearney, Robinson, and Thierfelder, 2005), oil price shocks (Essama-Nssah, Go, Kearney, Korman, Robinson, and Thierfelder, 2008), and a wage subsidy (Go, Kearney, Korman, Robinson, and Thierfelder, 2009).

Following common modeling practice, production in each sector has a nested structure (Figure 3), combining different levels of inputs, each level being depicted by imperfect substitution through a constant elasticity of substitution (CES) function or by zero substitution through a Leontief technology of fixed coefficients. Output by activity consists of value added and intermediate inputs. Except for energy inputs, intermediate inputs are held in fixed shares to output (Leontief technology), each input a composite of imported and domestic goods. Value added is a CES combination of labor and a capitalenergy composite. Three types of labor – skilled, semi-skilled, and unskilled – are aggregated into a single labor input.

Each sector potentially has three types of energy inputs, coal, petroleum, and electricity & gas, which are combined in an Armington (or CES) aggregation function into a single composite energy input.<sup>17</sup> In the model, the composite energy is treated as a primary input that is imperfectly substitutable with capital, implying that energy technology is to some extent embodied or associated with invested capital. The level of each energy input use is somewhat responsive to the relative costs of alternative energy inputs and to the net cost of production as reflected by the returns to capital. In the simulations below, we test the sensitivity of the results to alternative values of the elasticity of substitution between capital and energy and among energy inputs. We also examine the extreme case where energy inputs have zero substitution with other production inputs, suggesting no responsiveness to price changes in the production structure, specifying them as fixed input requirement per unit of output much like the Leontief input structure of other intermediate goods, following standard CGE specifications found in Löfgren et al (2001).

> 16 The CGE model equations are solved simultaneously with the computer software package GAMS and the solver PATH.

> 17 See McDonald, Robinson, and Thierfelder (2008) for a similar specification of production functions with energy inputs in a global model. The model adaptations used in this analysis are based on the model used in that paper.

11
**Table A.14. Results for non-parametric scales, RLMS 1994-2017**

|**Parameter**|**Coefficient**|
|---|---|
|a1k1<br>a2k0|1.422***<br>_(0.233)_<br>1.243***<br>_(0.105)_|
|a2k1<br>a2k2|1.374***<br>_(0.146)_<br>1.259***<br>_(0.161)_|
|a3k0|1.454***<br>_(0.143)_|
|a3k1|1.704***<br>_(0.187)_|
|a4k0|1.731***<br>_(0.198)_|
|a4k1|1.836***<br>_(0.241)_|
|a5k0<br>a5k1|1.968***<br>_(0.312)_<br>1.836***<br>_(0.326)_|
|_Number of observations_|74,627|
|_Log pseudolikelihood_|-237,360|

**Note:** Robust standard errors are in parentheses, controlling for two-way clustering. All regressions include the same controls as in Table 1.

49
49

# **Economic Growth**

There are several projections of year-over-year economic growth for Ho Chi Minh City and for Vietnam as a whole. A study by PricewaterhouseCoopers (PWC) ranks urban cities around the world in terms of GDP and projected annual growth in GDP. It ranks Ha Noi and Ho Chi Minh City first with a projected 7% annual real growth from 2008 to 2025 (Hawksworth J. _et al._ 2009). In a related study, PWC estimated a 6% annual growth at purchasing power parity between 2007 and 2050. A study by the Asia Development Bank (2010) of Ho Chi Minh City’s ability to adapt to climate change estimates Ho Chi Minh City’s growth at 8.7% between 2011 and 2025, and, 8% between 2026 and 2050. The city has also set its own targets for growth – 12% annually between 2010 and 2015 (Voice of Vietnam 2011). We use these estimates to inform the range of economic growth for Ho Chi Minh City, from a low of 6% to a high of 12% annually.

# **Distribution of Economic Growth**

The literature does not offer projections of how the city’s economic growth will be distributed across districts. We have developed two boundary cases of this distribution informed by information about the recent distribution of wealth. This information is presented in Table A.6.

One boundary case represents an equitable distribution of future wealth. It describes the case where wealth is distributed uniformly across all areas of the city, i.e. a district accrues Ho Chi Minh City’s future wealth proportional to its geographic size. The third column of Table A.6 lists the percent of future wealth each district receives using this rule.

A middle case represents parity with today’s distribution of wealth. In this case, the distribution of future wealth is consistent with a district’s recent contribution to the city’s wealth. Thus, District 1 contributes 6.9% to Ho Chi Minh City’s wealth (as indicated by its contribution to Ho Chi Minh City’s tax revenues), and so would receive 6.9% of future wealth.

However, it may be important to examine the case in which wealthy areas become disproportionately still wealthier, reflecting a growing gap between rich and poor areas. The other boundary case represents an increasingly inequitable distribution of future wealth. In this case, a district accrues Ho Chi Minh City’s future wealth proportional to its _wealth per unit area_ . That is, District 1 accounts for 6.9% of Ho Chi Minh City’s wealth today but only 0.24% of the city’s area. Therefore, its wealth relative factor, 6.9/0.38 is 18.12. We calculate this for each district ( Table A.6, Column 4) and then normalize ( Table A.6, Column 5). This normalized wealth factor is the percent of Ho Chi Minh City’s future wealth that each district would accrue under an inequitable future. District 1 would account for 28% of Ho Chi Minh City’s wealth. The distribution in different futures ranges between these boundary cases.