rafmacalaba/datause-encoder-data huggingface dataset:
Figure 1: Proportion of Land under Restrictions and Out-Migration: Population Census 2012 Figure 2: Proportion of Land under Restrictions and split households, 2002 (HIES) <!-- Start of picture text --> Out-Migration<br>0 .1 .2 .3 .4 .5<br>(mean) area_ldo<br>95% CI Fitted values<br>12.5<br>12<br>11.5<br>11<br>10.5<br><!-- End of picture text --> <!-- Start of picture text --> Split Households<br>0 .2 .4 .6<br>(mean) area_ldo<br>95% CI Fitted values<br>.1<br>.08<br>.06<br>.04<br>.02<br><!-- End of picture text --> Figure 3: Proportion of Land under Restrictions and share of female in total population, 2012 Figure 4: Proportion of Land under Restrictions and change in female share in total population for same age cohort (15-49 in 2002), 2002-2012 <!-- Start of picture text --> Female Share of Population Adult Female Share: Same Age Cohort<br>0 .1 .2 .3 .4 .5<br>0 .1 .2 .3 .4 .5 (mean) area_ldo<br>(mean) area_ldo<br>95% CI Fitted values<br>95% CI Fitted values<br>.04 .04<br>.03 .03<br>.02 .02<br>.01<br>.01<br>0<br>0<br><!-- End of picture text --> Figure 5: Proportion of Land under Restrictions and female share in labor force, 2002 (HIES) Figure 6: Proportion of Land under Restrictions and Farm Size, 2002 (HIES) <!-- Start of picture text --> Female Share in Labor Force Farm Size<br>0 .2 .4 .6 0 .2 .4 .6<br>(mean) area_ldo (mean) area_ldo<br>95% CI Fitted values 95% CI Fitted values<br>.08 4<br>.06 2<br>.04 0<br>.02 -2<br>0 -4<br>-.02 -6<br><!-- End of picture text -->
households during the PPAs could be addressed at either a policy level or during the development of the Province's Hunger Eradication and Poverty Reduction plans.

# **The Origins of the PPAs in Vietnam**

## **_World Bank plans a collaborative, participatory poverty assessment_**

Planning for the PPAs began well in advance of their implementation. The need for a new poverty assessment had been identified by the World Bank in its Country Assistance Strategy (CAS) in early 1998. The first poverty assessment, published by the World Bank in 1995, was based on household survey data collected in 1992/3. There was very limited use of qualitative infornation and little ownership of the analysis by either Govemment or the wider donor community. The CAS identified a need to do the second poverty assessment differently. Importantly, the CAS specified:

- That the work would be done in partnership with central and local Government, with NGOs and with other donors and that there would be _one_ poverty assessment which could serve the needs of all these groups;

- That the poverty assessment would incorporate qualitative information in a way that would build upon what was already becoming standard practice within the World Bank.

It was hoped that PPAs could be implemented in each of the seven regions of Vietnam, though this turned out to be overly ambitious given the timeframe and range of possible PPA partner agencies. UNDP and SIDA had provided support for the first Vietnam Living Standards Survey (VLSS1) and had agreed to support the second round of household survey data collection (VLSS2). These two donors agreed at this early stage that they would collaborate in producing _one single, shared_ poverty assessment, which would reinforce the policy messages emerging from the analysis.

## **_DFID initiates discussions and offers support to the World Bank_**

At the same time as the World Bank was drafting its CAS, DFID was seeking to strengthen their own understanding of poverty issues in Vietnam and, following the spirit of their recent White Paper on development assistance, was adamant that this should be done in partnership with others. This led to a joint agreement between DFID and the World Bank to work together on a poverty analysis which could influence poverty. DFID provided two poverty specialists, one quantitative and one qualitative, to act as a resource in this poverty analysis work. DFID also provided a trust fund to support the poverty work. The PPAs were therefore backed up by resources and funds, which were unusually supportive and unconstrained. This support was fundamental to allowing the PPA research to be embedded within a wider process at pre-implementation, implementation and post-implementation phases, which in turn generated widespread buy-in to the findings within the donor community and across Government.

## **_Some PPA-related activities already underway_**

In the meantime, one of the agencies who would subsequently emerge as a PPA partner agency - ActionAid Vietnam - had already received a request from its provincial 4
# _Appendix Table 1: Characteristic balance at baseline: beneficiary households_

|Baseline survey variables|Control<br>Mean|Difference<br>(Treated-<br>Control)|Standardized<br>Mean<br>Difference|p-value|
|---|---|---|---|---|
|LN Proxy Means Test Score<br>|9.093|-0.014|0.072|0.494|
|Household composition:|||||
|Household size|5.828|0.004|0.001|0.973|
|Children 5 years old and below|1.063|0.069|-0.065|0.244|
|Children between 6 and 14 years old|1.718|-0.022|0.018|0.742|
|Primary occupation: Farming and|0685|0040||0335|
|livestock|.|.|-0.096|.|
|Educational attainment of the household head:|||||
|No grade completed|0.078|-0.002|0.002|0.913|
|Some elementary school|0.433|-0.006|0.000|0.856|
|Completed elementary school|0.214|0.014|-0.044|0.585|
|Some high school|0.132|-0.029|0.104|0.105|
|High school graduate|0.100|0.004|0.000|0.841|
|Some college|0.031|0.008|-0.036|0.444|
|College graduate|0.015|0.009|-0.057|0.206|
|School Attendance:|||||
|Children between 6 to 11 years|0.857|0.002|0.000|0.925|
|Children between 12 to 14 years|0.767|0.038|-0.094|0.216|
|Housing Amenities:|||||
|Strong roof materials|0.307|-0.041|0.111|0.164|
|Strong wall materials|0.191|-0.023|0.080|0.276|
|Light roof materials|0.478|0.050|-0.130|0.107|
|Light wall materials|0.445|0.017|-0.065|0.577|
|Owns a house and lot|0.345|-0.034|0.093|0.239|
|House has toilet|0.388|-0.018|0.013|0.632|
|Shares a water source|0.200|-0.023|0.066|0.463|
|Household Assets:|||||
|Electricity in house|0.407|0.021|-0.016|0.542|
|Owns a television|0.203|-0.004|0.030|0.879|
|Owns a video recorder|0.090|-0.020|0.091|0.241|
|Owns a Stereo/CD player|0.100|-0.006|0.015|0.755|
|Owns a refrigerator|0.012|-0.004|0.044|0.430|
|Has a telephone/cellphone|0.072|-0.013|0.064|0.292|
|Owns a motorcycle|0.025|0.002|-0.002|0.822|

note: *** p<0.01, ** p<0.05, * p<0.1.

Estimates include municipality fixed effects. Standard errors clustered at the village level; there are 130 villages. Source: 2008 Household Assessment Form 50
young people enter unemployment or leave unemployment at different rates than do adults at any one period of time.

Second, as proposed in Bosch and Maloney (2007), we decompose the transitions matrix into two separate components. The first component represents the transition probabilities independent of the rate at which different age groups leave any sector, and is called the _propensity matrix_ . The second is the rate of transition, and is referred to as the _rate of separation matrix_ . By decomposing the transition matrix into the propensity matrix and the rate of separation matrix, we can determine if movements to unemployment observed in the transition matrix are reflecting greater entry of certain age groups into unemployment or if the observed transitions are simply due to greater turnover by certain age groups in general.

Finally, elements of the propensity matrix allow us to estimate the duration of unemployment to determine whether the high unemployment rates among youth are due to longer unemployment spells.

All three methodologies take advantage of the panel nature of the three data sets utilized in this paper. In particular, we can follow the labor market status of young people at discrete moments in time over a one month (Brazil), three month (Mexico), and six month (Argentina) period.

# _Transition Matrices_

Transition matrices are generated using the panel data, where each cell denotes the probability of moving between an initial labor market state i to a final labor market state j. Each cell of the transition matrix is a simple probability where: Where pij is the probability that a person moved from some initial state i into a final state j for i=1, ..., K and j=1, ..., K. The term nij is the number of people who were in state i and moved to state j between periods t and t+1 and ni is the number of people who were in state i in period t and were potential candidates for moving to state j in period t+1. The transition matrix is denoted by:
Table 2 provides an overview of the relative importance of these three main categories of trade for major country groups. Industrialized countries dominate global exports in all three categories. In relative terms developing countries have the greatest export share for 'travel' (i.e., tourism) with 17 percent of total exports.

Table 2: Shares in Global Service Exports and Relative Specialization, 1980 and 1992

|Country group|Tra|vel|Tran|sport|All|Other|
|---|---|---|---|---|---|---|
||1980|<br>1992|<br>1980|1992|<br>1980|1992|
|Share in global trade:|||||||
|OECD members|71.6|<br>77.9|78.4|81.8|<br>80.2|<br>84.6|
|Developing countries|21.8|<br>17.3|<br>16.5|<br>12.5|15.3|<br>11.7|
|Relativespecialization:||||___|||
|OECDmembers|1.01|0.96|1.10|1.02|1.13|1.06|
|Developing countries|0.93|1.12|0.65|0.82|0.65|0.74|
|Small LDCs (I million people or less)|2.19|3.45|1.19|1.85|0.39|1.11|

Although in aggregate value terms global trade in services is dominated by the OECD, this does not imply that developing countries have little interest in services trade. To the contrary, many developing countries are relatively specialized in exporting services. Table 2 also provides some data on revealed comparative advantage (RCA) indices for selected country groups.' Small countries (defined as those with less than one million people) are the most specialized in exports of services. Moreover, their relative specialization increased significantly during the last decade. These countries have higher than average export intensities for all three services categories, but are clearly most highly specialized in travel, that is, tourism. The relative importance of travel receipts was about twice the world average in 1980, rising to over three times in 1992.

Cross-country data on the magnitude of barriers to trade in services do not exist. Because services are generally intangible and often nonstorable, barriers to trade do not take the form of import tariffs. Instead, trade barriers take the form of prohibitions, quantitative restrictions (QRs), and government regulation. QRs may limit the quantity and/or value of imports of specific products for a I These are defined as the ratio of exports of a product category to a country's total exports of goods and services, divided by the saine ratio for the world: RCA = [X,j/Y)j/fX1'/YJI, where _Xy,_ are exports of product i by country _j, Y1_ are total exports of goods and services by country j, and w stands for the "world:" the sum of all countries). The value of this index may range from zero to a very large number. If the index is greater than one this implies that the country is relatively specialized in the product concemed.

4
# **1 Introduction**

Demographic aging poses a major challenge to all industrialized economies and a large number of developing countries. According to UN projections, total world population will increase by 40% and median age on the planet will increase by 7.8 years within the next four decades. Compared to the recent history, these changes represent a significant slow-down in population growth and a considerable acceleration in aging.

Although an increase in the average age is a common trend around the world, the factors that lead to such changes vary across countries. In the short and medium term these be led events such as of wars may by transitory out-migration young population, or aging of past baby-boom generations. However, in the long-term, it can be traced back to decreases in fertility rates and increases in longevity, albeit at different magnitudes of importance in different economies.

Economic implications of demographic aging are complex, and they are not always well understood in public discussions. Some consequences are clearly unfavorable: aging, whether it is driven by a decrease in fertility or an increase in longevity, pushes the old-age dependency rate up, i.e. a larger group of elderly people relative to economically active population. This will, in turn, increase the pressure on financial balances of unfunded pensions. In contrast, some consequences of aging are perceived to be positive: if the decline in fertility outweighs the increase in longevity, total population would decrease. This outcome may be welcomed by some on the basis of environmental concerns. Finally, there are some ambiguous consequences. An example is the effect of aging on capital accumulation, a key determinant of growth, which we investigate in this paper.

Studying the effect of aging on capital accumulation is particularly difficult when a large number of discretionary policy choices can affect the outcomes. In this paper, we use a two period overlapping generation model to show that the effect of aging on capital accumulation and welfare depend on: i) the type of aging, i.e. decreasing fertility or increasing longevity, ii) the type of unfunded social security system, i.e. defined contribution (DC), defined benefit (DB), or defined annuities (DA), and finally, iii) the regulation . of the retirement age, i.e. mandatory retirement vs. _laissez-faire_ To fix these ideas, we set up an economic environment where each individual lives two periods. The first period of her life has a unitary length, while the second one has a variable In the the individual works and earns a longevity. first period, wage equal to her marginal productivity net of social security contributions if any. This income is then devoted to consumption in the first period and saving for future consumption. In the second period, she works for a fraction of her remaining lifetime, where the work 2
distortions, revenue productivity would be the same for all firms in a sector and across all sectors.

This implies that the greater the dispersion of TFPR, the greater the degree of misallocation. Table 2 presents three measures of dispersion of TFPR.<sup>6</sup>

**Table 2. Measures of Dispersion of TFPR, 1998–2013**

||**_1998_**|**_2003_**|**_2008_**|**_2013_**|
|---|---|---|---|---|
|Standard deviation|0.95|0.98|1.08|1.11|
|p75–p25|1.23|1.25|1.39|1.39|
|p90–p10|2.39|2.44|2.73|2.82|

_Source:_ Author’s computations based on Economic Census data. _Note_ : _pX_ refers to the _X_ th percentile on the TFPR distribution. TFPR refers to the Total Revenue Productivity Two facts follow from the analysis of TFPR. First, there is large dispersion in revenue productivity across firms, implying substantial misallocation of resources. For example, in 2013, a firm in the 75th percentile of the revenue productivity distribution was 39 percent more productive than one in the 25th percentile.<sup>7</sup> Second, over the 15 years considered, this dispersion has persisted (in fact, increased slightly), indicating persistence in the misallocation of capital and labor.

# **Misallocation Has Specific Patterns Across Firms**

Misallocation in Mexico shows specific patterns. To identify these patterns, revenue productivity is compared across firm types. Table 3 shows the results of OLS regressions of the _log_ of firm _i_ TFPR in sector _s_ over the _log_ of the average TFPR of all firms in that sector. Regressions include

> 6 Computations are done at the six-digit sector level and include firms of all sizes in manufacturing, services, and commerce. There are 672 sectors in 1998, 679 in 2003, 687 in 2008, and 691 in 2013. Comparisons are only made for firms within the same sector. The numbers in the table are averages across all sectors.

> 7 The difference between firms in the 90th and 10th percentile is 182 percent. This compares with a difference of 92 percent in the manufacturing sector of the United States (Syverson 2004). Syverson’s computations are carried out at the four-digit level, and one would expect smaller differences at the six-digit level. The Inter-American Development Bank (IDB 2010) compares the dispersion of TFPR between manufacturing firms in the United States and Mexico, and finds that dispersion is substantially higher in Mexico. Busso, Madrigal, and Pagés (2013) find that dispersion is also higher in Mexico compared with other Latin American countries.

10
Quality determinants explain much of the variance in academic outcomes (determined mostly within the institution) and of formal employment (since institutions may vary in their ability to place students). However, they explain little of the variance in wages, which are monetary outcomes and therefore sensitive to the national context. Field of study accounts for a substantive share of explained outcome variation (6 to 21 percent, depending on the outcome), consistent with the documented cross-field variation in dropout rates, net returns, and SCP value added (Ferreyra et al. 2017, Dinarte et al. 2022, Ferreyra et al. 2021).

Among quality determinant categories, T&C accounts for the largest share of explained variation of ETG (25%), formal employment (22%) and wages (8%), and LPS accounts for the largest share of explained variation of dropout rates (18 percent). Costs, in turn, explain a nonnegligible share (13 percent) of the explained variation of academic outcomes but nothing of labor market outcomes. Student and program characteristics account for very little of the explained outcome variation but HEI characteristics account for more, particularly in the case of the academic outcomes (10 percent for dropout rates and 5 percent for ETG).

# **Individual-level regressions**

**Brazil.** Despite having student and peer administrative data in addition to survey-level data, we explain relatively little (12-15 percent) of the variation in graduation and labor market outcomes (Table 4). Nonetheless, the role of student and peer administrative variables is considerable for labor market outcomes: together they account for 30-40 percent of their explained variation (Table 4 and Figure A2). In contrast, they explain little of the variation in graduation, much of which is explained by field of study (24 percent) and HEI characteristics (32 percent), consistent with program-level findings. As in the latter, field of study explains much variation in graduation, while the geographic unit (state, in this case) accounts for most (26-37%) of the variation in labor market

33
**Table 2. Scope** **<u>and coverage of Standards-Related Measures</u>**

|||||**M**|**easures **|**includ**|**ed**|||**Separate**<br>|**provision on **<br>**Protection**<br>**of**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Agreement**|**Services **|**SPS**|**S**|**TR**|**A,T,C**|**CAP**|**AP**|**M**|**MRAs**|**Labeling**|**Environment**|
|_Customs Unions_||||||||||||
|EU|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|MERCOSUR|Yes|Yes|Yes|Yes|No|Yes|No|Yes|Yes|No|No|
|Andean Community|Yes|Yes|Yes|Yes|Yes|Yes|No|Yes|Yes|No|Yes|
|CACM<br>a|Yes|No|Yes|Yes|No|Yes|No|Yes|Yes|No|No|
|CARICOM|Yes|No|Yes|Yes|No|Yes|No|Yes|Yes|No|No|
|_Free trade agreements_||||||||||||
|NAFTA|Yes|No|Yes|Yes|No|Yes|Yes|No|Yes|No|No|
|Group of Three|Yes|No|Yes|Yes|No|Yes|Yes|Yes|Yes|Yes|No|
|Bolivia-Mexico|Yes|No|Yes|Yes|No|Yes|Yes|Yes|Yes|Yes|No|
|Chile-Mexico<br>a|Yes|No|Yes|Yes|No|Yes|Yes|No|Yes|No|No|
|Costa Rica-Mexico|Yes|No|Yes|Yes|No|Yes|Yes|Yes|Yes|Yes|No|
|Mexico-Nicaragua|Yes|No|Yes|Yes|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Mexico-Northern Triangle|Yes|No|Yes|Yes|No|Yes|Yes|Yes|Yes|Yes|No|
|Canada-Chile|No|No|No|No|No|No|No|No|No|No|No|
|Central America-Chile|Yes|No|Yes|Yes|No|Yes|Yes|Yes|Yes|No|No|
|Central America-Dominican Rep.|Yes|No|Yes|Yes|No|Yes|No|Yes|Yes|Yes|Yes|
|CARICOM-Dominican Rep.|Yes|No|Yes|Yes|No|Yes|No|Yes|No|Yes|Yes|

Notes: S=Standards; SPS=Sanitary, phytosanitary measures; TR=Technical Regulations; CAP=Conformity Assessment Procedures; A=Accreditation; C=Certification; T=Testing; M=Metrology; AP= Approval Procedures; MRAs=mutual recognition agreements. a. Measures include authorization procedures. Source: Based on Kotschwar (2001).

# **3 RTA APPROACHES TO UPGRADING AND HARMONISING STANDARDS AND CONFORMITY ASSESSMENT.**

This section examines the potential positive and negative aspects of the RTA approach, the different types of RTA approaches, the general features of an RTA approach and its main challenges.

# **3.1 POTENTIAL POSITIVE EFFECTS OF RTA UPGRADING AND HARMONIZATION**

A regional approach to upgrading and harmonizing standards and conformity assessment within the context of an RTA can provide stepping stones to greater participation in the global market. An RTA approach could help overcome the following gaps:

- **Economies of scale:** The upgrade and harmonization of standards and of conformity assessment at the regional level reduces trade barriers allowing member countries to fragment their production, become more competitive, and reach higher economies of scale in production.  Xiaoyang-Chen and Mattoo (2004) analyze the impact of regional standards harmonization in developed RTAs (EC, EFTA, NAFTA, and CER<sup>8</sup> ) and find that it can have a significant positive impact on trade within the region and with third countries.  They find that implementing a

> 8 CER is the Closer Economic Relations agreement between Australia and New Zealand.

14