rafmacalaba/datause-encoder-data huggingface dataset:
not be necessarily high except in Scenario 6c. This implies that the relationship between the carbon tax rate and corresponding economic impacts is non-linear. The carbon tax rate under Scenario 6c is lower than in some of the other scenarios because a sector with low emission intensity gets higher carbon tax rebates. Non-fossil electricity generation technologies (solar, wind, hydro, nuclear) get more output subsidies, enabling these technologies to substitute coalfired electricity generation in China. Note that the revenue recycling schemes considered while designing the scenarios here are also applicable to an ETS if the emission allowances are distributed through auctions.

We have taken two critical precautions while designing all carbon tax scenarios where tax revenues are recycled to the economy. First, the government revenue is kept neutral, meaning that only the revenue in excess of total government revenue in the baseline is recycled. This is a common principle in most existing carbon tax studies. The rationale behind this is that a carbon tax causes a contraction in the economy, and government revenues excluding carbon tax revenues will drop. It could adversely impact government budgets and public expenditures. Moreover, the governments eye on increasing government revenues when a new tax (here carbon tax) is introduced. Therefore, government revenues under the carbon tax should be at least equal to that in the absence of the carbon tax (i.e., baseline). Second, the objective of a carbon tax is to reduce GHG emissions. Recycling carbon tax revenues to fossil fuel industries through any channel (e.g., cutting corporate income taxes, cutting factor tax burden) will compromise this objective due to the rebound effect. Existing studies, such as Timilsina et al. (2021) have demonstrated it. Therefore, we have excluded fossil fuel industries and the thermal power generation industry from receiving any tax rebate under all carbon tax scenarios where tax revenues are recycled to the economy.

# **3.2 Incorporation of the scenarios into the CGE model**

# **_Incorporating Scenario 1 (NDC standards) and Scenario 2 (carbon tax with revenue_**

**_recycling like in the case of baseline)_ :** Modeling Scenarios 1 and 2 is straightforward and already captured in the base model. Under the scenario 1, total CO2 emission is estimated by setting the emission intensity at the NDC target level. The shadow carbon prices are

12
The following are middle-income countries among those that are not rich in natural resources: Cabo Verde, Côte d’Ivoire, Kenya, Lesotho, Mauritius, São Tomé and Príncipe, the Seychelles, and Eswatini. The list of low-income economies in the non-resource rich group includes the following: (a) Benin, The Gambia, Liberia, Madagascar, Malawi, Mozambique, Senegal, and Zimbabwe, all in Southern Africa and West Africa, and (b) Burundi, Eritrea, Ethiopia, Rwanda, Somalia, Sudan, Tanzania, and Uganda in East Africa.

Indicators of GVC linkages are analyzed at the group and country levels by benchmarking observations against those of external comparators comprising Bangladesh, Cambodia, Indonesia, and Vietnam. These countries have been chosen for the benchmark group for being comparable to the larger countries in the region in terms of population size and incomes per capita and for being in the process of industrializing in some sense. Their population sizes in 2017 ranged from 16 million for Cambodia to 96 million for Vietnam to 158 million and 261 million for Bangladesh and Indonesia, respectively. This is reasonably close to the range observed across Sub-Saharan Africa: from 16 million for Zambia to 83 million for the Democratic Republic of Congo to 105 million and 191 million for Ethiopia and Nigeria, respectively.<sup>5</sup> All four countries in the benchmark group are also middle- income economies and vary in purchasing power parity annual per capita incomes for 2017 from about US$4,000 for Bangladesh and Cambodia each to US$6,900 for Vietnam to US$12,400 for Indonesia. This compares with US$1,180 for Malawi to US$3,500 for Kenya to US$5,900 for Nigeria, and US$13,500 for South Africa. Thus, for example, Indonesia is reasonably comparable to Nigeria in terms of natural resource endowment and population size.

# 3.2. Rationale for selection of this group of comparators

Part of the rationale for the choice of the four countries as external comparators to countries in Africa is that it permits invoking evidence in the literature on drivers of the differences in the

> 5 Our population estimates come from the CEPII gravity dataset, except those for the Democratic Republic of Congo. Population estimates for the Democratic Republic of Congo are obtained from the World Development Indicators of the World Bank, which is also our source of estimates of income per capita.

9
skilled migrants and business investors. Many more people apply than the quota allows, so a ballot is used by the New Zealand Department of Labour (DoL) to randomly select from among the applicants. The probability of success in the ballot is approximately 10 percent. We evaluate the impact of individuals migrating to New Zealand via the PAC on household members remaining in Tonga (mainly parents, siblings, and nephews and nieces of the migrant applicant). We consider a wide range of impacts, including the impact on labor supply, income, durable assets, financial service usage, diet and physical and mental health.

Our results suggest that at least in the short run there may be some adverse consequences for those left behind when a subset of their household migrate to New Zealand. Income falls by approximately 20-25 percent, whether measured per capita or per adult equivalent, with a rise in net remittances not offsetting a large fall in labor earnings. Ownership of livestock, durables, and access to financial services is also lower for the remaining household members than for the control group. Diets change, with less fruit, vegetables and fats consumed and more rice and root crops. Beneficial health changes include falls in the body mass index and waist to hip ratio for working age adults.

We also use data from a sample of non-applicants, and from ballot losers in households which would entirely move if they had been successful in the PAC ballot, to examine the degree of selection of households into migration, and selection among households with a migrant as to which would partially move and which would move en masse. We find selection is important in both dimensions. Thus, the non-experimental estimation of migration impacts results in a biased assessment. In particular, using a sample of non-applicant households would lead one to conclude that emigration has made remaining household members wealthier, whereas the natural experiment shows the opposite result.

These results may have broader applicability since Tongan migrants to New Zealand under the PAC have characteristics that are quite typical of developing country migrants to the US, both in terms of their levels of education and the degree of educational self-selection relative to non-migrants (McKenzie, Gibson and Stillman, 2009). Moreover, although a stereotype is of a husband migrating alone and leaving a family behind in a developing country, a majority of married developing country immigrants in the US actually have their spouse present, similar to our setting.<sup>2</sup> Immigration policies in many countries worldwide (e.g. Australia, Canada, Ireland, United Kingdom, France, Italy) allow individuals moving on

> 2 Specifically, using the 5% public use sample of the 2000 U.S. Census, we find that 59% of married immigrants from developing countries who arrived in the U.S. in the last year had their spouses also present in the U.S. Even for Mexico, we find 46% of newly arrived married immigrants have migrated with their spouse.

3
International migration could make a significant contribution in this context by raising the likelihood that such regional cooperation bloc takes place and by raising the benefit obtained from it. This is likely to be particularly important – and the welfare impact particularly large – in cases where the heterogeneity in the endowments of the bloc’s member states is large.

Though an important international trade literature on South-North migration and on its impact in host and home countries exists, this is not the case for South-South migration. The classic trade and factor movement studies are Mundell (1957) and Markusen (1983). Mundell showed in a Heckscher-Ohlin model where wage differentials are caused by trade barriers that South-North migration and trade are substitutes. Markusen, on the other hand, showed in a model where wage differentials are caused by differences in technology that South-North migration and trade are complements.<sup>6</sup> A number of other studies have examined this relationship, including de Melo et al. (2001), de Melo (2007) and de Melo and Ivlevs (2008) who introduced political economy considerations in the analysis.

South-South migration may take place despite higher wages in the North because of constraints on financing South-North migration. As Ozden et al. (2009) have found, forty-seven percent of all migration from the South is to the South and over eighty percent of it is to a neighboring country. López and Schiff (1998) extended the Heckscher-Ohlin model by incorporating the migration cost and financing constraint features, and found that trade and migration are substitutes for skilled labor and complements for unskilled labor.

A number of studies have revealed other important determinants of international (and internal) migration, including migrant networks (e.g., Beine et al. 2009) and individual, household, and community characteristics (e.g., Mora and Taylor, 2006, whose analysis of Mexico’s internal, international and inter-sectoral migration also accounts for the impact of networks). Migrant networks are also important for the trade-migration relationship (e.g., Rauch 1991 and Rauch and Trinidade 1992, who find that they are complements) and for the FDI-trade relationship (Javorcik et al., forthcoming, and Kugler and Rapoport 2007, who find that migrant networks have a positive impact on FDI). This paper provides a different motive for migration,

> 6 He also examined this issue in the case of factor market distortions, departure from constant returns to scale and perfect competition, and obtained the same result. Schiff (2006) showed that substitution can also obtain in his models.

4
34

# Table 6: Brazil Characteristics of Poor Urban Households by Region and Household Type, 1984

(percent)

|Characteristic|Northeast|Southeast|South|
|---|---|---|---|
|Incidenceof Poverty||||
|_All_households|33.2|11.1|12.4|
|All female-headed households (FHH)|35.7|14.3|14.9|
|)FHHw/children, no adults|62.3|36.5|37.4|
|CharacteristiesofPoorHouseholds||||
|Average Family Size||||
|All households|5.6|5.2|_5_|
|All FHH|4.5|4.1|3.9|
|FHH wlchildren, no adults|4|3.8|3.6|
|Average Number of Chbldren||||
|<br>All households|2.8|2.6|2.5|
|All FHH|2.1|1.9|1.9|
|FHH w/children, no adults|2.6|2.5|2.3|
|AverageNumber of Eaers||||
|All households|1.2|1|1|
|All FHH|0.9|0.8|0.7|
|FHH wlchildren, noadults|0.6|0.6|0.6|
|DependencyRatio||||
|<br>All households|1.3|1.3|1.3|
|All FHH|1.4|1.4|_1.5_|
|FHH w/children, no adults|2.6|2.5|2.3|
|Characterics<br>of Heads of Poor Households||||
|Mean Income (in minmum salaries)||||
|<br>All households|0.9|0.87|.088|
|All FHH|0.56|0.56|0.63|
|FHH w/children, no adults|0.63|0.67|0.73|
|Mean Education||||
|All households|2.6|3.6|3.3|
|All FHH|2.4|3|3.1|
|FHH w/children, no adults|2.5|3.5|3.6|
|Mean Age||||
|<br>All households|44.3|42.4|42.9|
|<br>All FHH|<br>48.3|<br>46.7|46.9|
|FHH w/children, no adults|41.7|38.9|39.8|
|MeanHours Worked Per Week||||
|All households|33|30.1|31.3|
|AllFHH|18.7|17.8|17.3|
|FHH w/children, no adults|22.6|25.1|23.7|

Soutc: au&oes wbdon
# **Figure 1.  Trends in Food Access and Violence**

<!-- Start of picture text --> Share of Respondents Using Food Coping Strategies<br>0.75<br>0.7<br>0.65<br>0.6<br>0.55<br>0.5<br>0.45<br>2016‐Q1 2016‐Q2 2016‐Q3 2016‐Q4 2017‐Q1 2017‐Q2 2017‐Q3 2017‐Q4 2018‐Q1 2018‐Q2 2018‐Q3<br>Reduce Meals Restrict Consumption Limit Portions<br>Conflict in Yemen<br>8000<br>6000<br>4000<br>2000<br>0<br>2016‐Q1 2016‐Q2 2016‐Q3 2016‐Q4 2017‐Q1 2017‐Q2 2017‐Q3 2017‐Q4 2018‐Q1 2018‐Q2 2018‐Q3<br>Fatalities Incidents<br><!-- End of picture text --> Sources: WFP mobile phone survey; ACLED conflict data base.

In addition to the lack of alignment between the geographic distribution of violence and food access in the June 2018, this lack of alignment persists throughout the entire conflict. Figure 3 presents the geographic distribution of violence over time. One can see that the regions experiencing the most violence has evolved over time. Initially, regions along the front lines in the eastern and southern parts of the country experienced the most violence. However, this evolved over time, where governorates on the front lines in the west and the north began experiencing more severe conflict.

However, figure 4 presents the geographic distribution of food access over time. The regional distribution of food access has been relatively fixed‐ the regions that had poorer food access in 2016 still had poorer food access in 2018. Furthermore, the regions with the poorest access to food were not necessarily those that experienced the most violence. Specifically, regions in the north that were worse affected by the port blockages tended to have the worst access to food, regardless of whether the regions were on the front lines and experienced violence or not.

7
Table 2. Basic statistics, schools near concession versus far away schools A. CHARACTERISTICS OF SCHOOLS

|1 "Bf i" 1999|M<br>Schools near|Sdd<br>a concession|M<br>Schools far aw|Sdd<br>ayfrom|
|---|---|---|---|---|
|. eore concessons ()|ean|t.ev|ean|t.ev|
|Infrastructure|||||
|Number of computers|17.82|16.75|24.96|22.69|
|Number of classrooms<br>Number of bathrooms|21.40<br>1.45|10.58<br>0.78|22.24<br>1.67|12.93<br>0.87|
|Number of sport facilities|1.78|0.54|2.59|1.52|
|Teachers|||||
|Number of teachers|30.27|16.34|34.46|24.06|
|Number of admin. pers.|6.97|4.45|9.69|11.96|
|Number of physicians|0.06|0.24|0.45|1.10|
|Number of psychologists, etc|1.52|1.29|2.19|1.85|
|<br>% teachers with second. educ|0.07|0.18|0.06|0.12|
|% teachers with college educ|0.60|0.23|0.60|0.62|
|% teachers with graduat. educ|0.25|0.17|0.34|0.34|
|Ration students / teachers|24.92|3.24|26.16|28.78|
|Drop-out rates|0.09|0.07|0.07|0.06|
|2. “After concessions” (2003)|||||
|Localization|||||
|1|0.00|0.05|0.10|0.30|
|2|0.00|0.00|0.01|0.10|
|3|0.00|0.00|0.05|0.22|
|4|0.04|0.19|0.06|0.24|
|5|020|040|015|036|
|6|.<br>0.00|.<br>0.00|.<br>0.04|.<br>0.19|
|7<br>|0.15<br>|0.36<br>|0.30<br>|0.46<br>|
|8|0.45|0.50|0.12|0.32|
|9<br>Type of school|0.16|0.37|0.17|0.38|
|Male|0.00|0.00|0.07|0.26|
|Female<br>Coed|0.00<br>1.00|0.00<br>0.00|0.03<br>0.90|0.16<br>0.30|
|Te of secondar|||||
|yp y<br>Academic|0.80|0.40|0.63|0.48|
|Technical|006|024|029|045|
|<br>Both|.<br>0.14|.<br>0.35|.<br>0.07|.<br>0.26|
|Schedule|||||
|Complete|0.04|0.20|0.03|0.18|
|Morning|0.28|0.45|0.40|0.49|
|<br>Afternoon|0.07|0.25|0.14|0.35|
|Evening|0.61|0.49|0.43|0.49|
|<br>Nb f d|||||
|umer o stuents<br>Total school|2544.18|786.25|2954.60|1376.76|
|Taking exam<br>|136.97<br>|69.02<br>|122.15<br>|79.16<br>|
|In overage|35.52|35.30|47.28|45.15|
|<br>Reeatin rade|11493|8869|10330|7372|
|pg g<br>Ratio teacher/stud.|.<br>31.42|.<br>2.56|.<br>28.44|.<br>6.20|
|Year of education, teachers|16.49|0.73|16.07|2.28|
|Drop-out rates|||||
|Grade 1|0.035|0.04|0.040|0.05|
|Grade 2|0.030|0.03|0.031|0.04|
|Grade 3|0.030|0.03|0.026|0.04|
|<br>Grade 4|0.018|0.02|0.026|0.03|
|Grade 5<br>Gd 6|0.021<br>0054|0.02<br>006|0.020<br>0071|0.33<br>009|
|rae<br>Grade 7|.<br>0.029|.<br>0.03|.<br>0.040|.<br>0.04|
|Grade 8|0051|004|0038|004|
|<br>Grade 9|.<br>0.046|.<br>0.03|.<br>0.039|.<br>0.05|
|Grade 10|0040|003|0033|004|
|<br>Grade 11|.<br>0.012|.<br>0.02|.<br>0.012|.<br>0.02|
|Number of schools|23||416||

20
(remember that we use the same age cohort in estimating the synthetic panel, so that mean birthyear should remain the same if the composition of households does not change). A possible explanation is that there are more deaths among the older part of the sample. We also investigated the sensitivity of round 1 coefficients to the age cohort on which the model is estimated. Fortunately, our estimators of the coefficients are not much affected by the specification of the age cohort. This implies that the round 1 predictions for round 2 households are robust to the difference in population characteristics.

We use a vulnerability line to study income mobility in more detail. The line is set based on the synthetic panel for the first interval in the dataset (2004–2007) and kept constant afterwards. It is derived from a vulnerability index of 0.4, implying that all non-poor household in 2004 that face a probability of 0.4 or more of falling into poverty in 2007 are considered vulnerable.<sup>21</sup> In our data, this conditional probability is attained at an income level of MYR 703 per capita per month (about MYR 23 per day, or USD 14 at 2016 PPP levels) at the upper bound of mobility. This means that in any round all households with per capita income between MYR 527 and MYR 703 are considered vulnerable. Households with incomes above MYR 703 are labeled economically secure.

# 5. Population-level results

This section presents findings from applying the synthetic panel methodology to Malaysia’s official household survey data. In interpreting these results, it is important to recall that, although the poverty line is defined in relative terms in 2004, it is kept constant in real terms after that. That means we do not adjust it for greater general affluence in Malaysian society over time. This also holds for the vulnerability line. Moreover, all comparisons are based on households of heads aged 25–55 in round 1 and adjusted accordingly for round 2.<sup>22</sup> We analyze joint probabilities and conditional probabilities, both of which are relevant quantities. The first give a clear image of how mobile society as whole is, and allow us to draw conclusions on, for example, what percentage of Malaysians was poor both in 2007 and 2009. The latter enable us to zoom in on a specific group and ask, for instance, given that an individual was poor

> 21 The values that the researcher may select for this index are limited empirically by the poverty line and the average conditional probability of all non-poor to fall into poverty. In the upper bound scenario, this range for the index was approximately 0.27 to 0.41. This means that, on average, no non-poor household faces a probability of more than 41 percent to fall into poverty, and all non-poor households face a probability of at least 27 percent to do so.

> 22 For example, the estimates for the year pair 2014 – 2016 are based on a sample of households with heads of ages between 25 – 55 years in 2014 and between 27 – 57 years in 2016.

15
# **Table A3: Panel A – Multinomial Regressions, 2005**

Log Odds ratio between Renters and Mortgage holders (Marginal Effects)

|**Country**|**lage**|**female**|**hhsize**|**lnincome**|**hhnodep**|**hhdep**|**house**|**secedu**|**teredu**|**fulltime**|
|---|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_|||||||||||
|Czech Republic|-0.2534 ***|0.0095|0.0030|-0.1868 ***|-0.0250|-0.0235|-0.2708 ***||-0.4032 ***|0.0243|
|Estonia|-0.1701 ***|-0.0028|-0.0246 ***|-0.0159|-0.0720 ***|-0.0554 ***|0.0155||0.0795|-0.0900 ***|
|Hungary|-0.2323 ***|-0.0176 *|-0.0095|-0.0376|-0.0234 *|0.0005|-0.1278 ***|-0.3171|-0.0637|-0.0080|
|Latvia|-0.2000 ***|0.0382 **|0.0081|-0.0311|-0.0544 **|-0.0540 *|-0.1626 ***|-0.0868|-0.2184|-0.0593|
|Lithuania|-0.2716 ***|-0.0346 ***|-0.0165 **|-0.0342 ***|-0.0507 ***|0.0012|0.0108||-0.1377|-0.0292|
|Poland|-0.2704 ***|0.0138|-0.0200 ***|-0.0523 **|0.0453 **|0.0478 **|-0.6144 ***|-0.3846|-0.3613 *|0.0134|
|Slovak Republic|-0.3695 ***|-0.0550 ***|-0.0244 ***||-0.2369 ***|-0.0962 ***|-0.0645 ***|-0.3431||-0.0798 *|
|Slovenia|-0.1418 ***|-0.0112|-0.0377 ***|-0.0168|-0.0500 ***|0.0890 ***|-0.1792 ***|0.7582 ***|0.9552 ***|0.0622 *|
|_EU-15 and Cyprus_|||||||||||
|Austria|-0.2042 ***|0.0067|-0.0387 ***|-0.1687 ***|-0.0113|0.0130|-0.4904 ***||0.2252|0.0314|
|Belgium|-0.5392 ***|0.0462 **|-0.0204|-0.2190 ***|-0.0159|0.0664|-0.4962 ***|0.4386|-0.2780|-0.1352 ***|
|Cyprus|-0.0998|-0.0159|-0.0079|-0.1792 ***|0.0083|-0.0881 **|-0.2166 ***|0.3999|0.4291|-0.0691|
|Denmark|1.1392|-0.1038 ***|0.0620 ***|0.5896 **|-0.0034|-0.1880 ***|0.5353 ***|0.9999 ***|0.9996 ***|0.2028 ***|
|Finland|-0.5927 ***|0.0043|-0.0113|-0.2502 ***|0.0196|0.0719 *|-0.3478 ***|0.5301|0.2103|-0.2002 ***|
|France|-0.4434 ***|0.0310 *|-0.0291 **|-0.1053 **|-0.0669 **|0.0179|-0.5021 ***|0.9539 ***|0.9332 ***|-0.1116 ***|
|Greece|-0.3803 ***|-0.0141|-0.0722 ***|0.0030|-0.0385|0.0665 *|-0.1556 ***|0.8484 ***|0.4796|0.0014|
|Ireland|-0.5264 ***|0.0259|0.0029|-0.0659 **|-0.0025|0.0586 *|-0.6633 ***|0.7508 *|0.5408|-0.2043 ***|
|Italy|-0.3474 ***|-0.0143|-0.0151 **|-0.0809 ***|-0.0658 ***|0.0164|-0.1335 ***|-0.1101|-0.2359 ***|0.0290 *|
|Luxembourg|-0.2086 *|-0.0808 **|-0.0074|-0.1946 **|0.0032|0.0085|-0.5284 ***|0.8019 *|-0.2943|-0.0383|
|Netherlands|0.3940 ***|-0.0684 ***|0.0290 **|0.2696 ***|-0.0064|-0.0431|0.3462 ***|0.8541 ***|-0.0612|0.2457 ***|
|Portugal|-0.2499 ***|0.0152|0.0051|-0.1297 ***|-0.0866 ***|-0.1205 ***|-0.0337|-0.1770|-0.0277|-0.0241|
|Spain|-0.2461 ***|-0.0024|-0.0186 ***|-0.0769 ***|-0.0318 *|0.0004|-0.0476 ***|-0.1912|-0.1055|-0.0309|
|Sweden|-0.4169 **|0.0465 **|0.0097|-0.1165 *|-0.045|-0.0411|-0.5563 ***|-0.7319|-0.5136|-0.1386 ***|
|United Kingdom|-0.7063 ***|0.0202|0.0006|-0.1273 ***|-0.0351 *|0.0831 ***|-0.4215 ***|-0.6757 ***||-0.3818 ***|

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

27