rafmacalaba/datause-encoder-data huggingface dataset:
Implicit in our analysis is an East Asian social welfare function which treats persons irrespective of national borders. It is closely related to analyses of the global income distribution, which presumes a cosmopolitan social welfare function.<sup>3</sup> In opinion surveys, East Asia shows some of the highest levels of concern over inequality being too high (Ruggeri Laderchi et al., 2017). Similarly, an informal survey of policy makers in Asia shows a rising concern with inequality (Kanbur and Zhuang, 2012). This appears to be inconsistent with the within-country results presented in Figure 1, which showed a stable Gini index for the average country. However, as Alvaredo and Piketty (2014) argue in their analysis of inequality in the Middle East, inequality at the regional level could be important for explaining these growing concerns. This paper aims to provide the latest evidence on this regional inequality for East Asia.

Over the 25-year period, the East Asia and Pacific Gini index increased by approximately 4.2 points (or 10.8%), reaching 43.3 in 2012. On the one hand, the strong growth in average consumption in China had an equalizing effect on (between-country) East Asian inequality. On the other hand, the increasing region-wide inequality trend is explained mainly by rising within-country inequality in the two most populous countries, China and Indonesia. This is consistent with the trend in the population-weighted average Gini index in Figure 1b (dotted blue line), which captures national inequality for the average person in East Asia. In other words, while inequality has been approximately flat for the average country, it has increased strongly for the average person and, as this paper shows, for the region as a whole. Considering how growth was shared along the distribution, we find that the growth incidence was broadly pro-rich – in line with the observed increase in inequality. The pattern of growth was such that the richest 5% of East Asians received more than 20% of the gain in regional income, compared with less than 1% for the poorest 5%. In the most recent period, however, the upward trend in regional inequality appears to have reversed, as within-country inequality has stopped increasing.

The paper is structured as follows. First, we explain the construction of our database from household surveys. Second, we study the regional distribution, its inequality and the contributions of within- and between-country differences. Third, we examine both relative and absolute gains along the distribution using growth incidence curves. Fourth, we analyze relative movements of specific country groups in the regional distribution, and the resulting changes in the regional composition. The Appendix includes further methodological details, and robustness checks.

> 3 The literature on global inequality is reviewed in Milanovic (2005), Atkinson and Brandolini (2010), Anand and Segal (2015) and Milanovic (2016). Similar to our paper, Ravallion (2014a) estimates inequality in the developing world.

3
_23_ _WATER WHEN IT COUNTS_ in water allocations. It may be sufficient to remind farmers that crop requirements are not constant, allowing them to adjust the rules of thumb accordingly. But farmers may be misinformed of how their own water allocations actually relate to requirements. The literature on energy metering suggests a key role for individualized feedback to align users with allocations. Since information on individual metered information is expensive to collect and implement, we test whether individual reminders are needed to achieve the allocation gains identified in equation (6).

# **IV. Field Experiment**

Section III shows that efficiency-improving reallocations are possible given the type of practices observed in Section II.D. One hypothesis is that providing information on water requirements may lead to a redistribution of water from plots that over-watered in the early growth stages to plots that under-watered in the later growth stages. It is not clear what kind of information is crucial to achieve this redistribution process.

We designed and ran a field experiment to test the impact of providing information on water recommendations on water allocation. We implement two different feedback tools, one providing general watering recommendations across growth stages, and a second that adds individualized feedback on a farmer’s own actual water use to the general watering recommendations.

We randomly assign all households in our study area to one of two treatments, general recommendation (general feedback treatment) or general recommendation plus individualized feedback (individualized feedback treatment). The first round of feedback tools was distributed in December 2016. For the randomization, we used the latest household listing available from the July 2016 survey. We expect that improvements in allocation
A problem arises as weight and height depend on both age and gender (and other factors such as genetic variation) but it is possible to use physical measurements by comparing indicators with the distribution of the same indicator for a “healthy” reference group. In this way we use z-scores (standard deviation scores) which is the most common way of expressing anthropometric indices.<sup>16</sup> Table 6 presents some summary statistics.

The value of the mean of the haz z-score is –2.21, reflecting long-term cumulative inadequacies of health and/or nutrition.<sup>17</sup> There seems to be no wasting problem, the mean of whz is 0.23. Using the summary measure of nutritional status (waz) there is mild underweight, probably caused by long-term nutritional problems.

For the education outcome, we generated an index of school performance for children between ages 7 and 18, that is, children in primary and secondary school. The index is the ratio of years of education completed by an individual and the years of education this individual should have for his age.<sup>18</sup> The mean of this index for rural areas is 0.49 including children not attending school (approximately 45% of the sample).

We now describe the exercises performed using the same matching methods as before. We want to assess the effects of participation in cotton production in other dimensions than monetary income. We estimate the effects on child nutrition and education of a switching from subsistence to cotton. Table 6 reports the results.

We estimate differences in outcomes for the sample of all infants and young children (0 to 59 months old), for the subsample of males, and for the subsample of females. Interestingly, we find statistically significant effects only in terms of stunting, or long-run nutritional gains. On

> 16A z-score is defined as the difference between the value for an individual and the median value of the reference population for the same age or height, divided by the standard deviation of the reference population. 17The WHO uses a z-score cut-off point of -2 to classify low weight-for-age, low height-for-age and low weight-forheight as moderate and severe undernutrition, and –3 to define severe undernutrition.

> 18Then, for an individual with no education the index takes a 0 and if she is in the grade that corresponds to her age the index takes 1.

24
# TABLE OF CONTENTS

# SUMMARY

T. COUNTRY CONTEXT.......cccsscscsesssesessesessacesscensesesssesecesenceeessessnesceesseetinesseseneesseeeseessesstssseresse Economic Growth qu... cesscssesssscssseessesssssescsecssseseeseeaeseesssecsseasccseessessecssesceaseesessesssetsesstatessee I Trends in poverty reduction and social indicators ...........ececesssesesesseseeeeseeneeseeteetsesststeteteeeenee I Structural and Governance Reforms<sup>...........ccccsscsseescescssecsceseesessncsaceecersrsaseesseeecesseessnesscsssaces4</sup> Challenges to sustained progress ..........ccccssssssescecsecceeessesesssseesesetsetscsscesssassecesetaseassacecetetsesnees 4 TI. PLANNING AND GOAL-SETTING IN VIETNAM.,.....c.cscscssesssssseseseseeseeeescesssesacerssesasaceeesetsseeeeseteeee The Socio-economic Development Strategy 0.0.0... cccsssessesssnecesseeteereeesseeeeaetseeseseeeeeeesees O The Comprehensive Poverty Reduction Strategy and the Vietnam Development Goals....... 5 The role of the Millennium Development Goals ............cccecssscsesseecertessssceseseeecsessetecesenesees D III. PROGRESS TOWARDS THE VIETNAM DEVELOPMENT GOALS.......c:csccssscsscssessesessssessescsessesere 12

|Vietnam Development Goals directlybasedonthe MDGS............cccccsssscsesseseseessessessssees 12|
|---|
|Goal One: Eradicate Poverty and Hunget............essscscssssseessessseceeeecseeeeseesseeseesesecseeeestteteee 12|
|Goal Two: Achieve BetterEducation for All... cessssecsessseeesesectseeseeeeseestersssesssesteteeee 1S|
|Goal Three: Reachgender equalityandempowerWOMEN .............::s:escssseseseecesateeresseseeeneee LY<br>|
|Goal Four: ReduceChild Mortality ..0........:ccscsssscscssssscssssetsessscsesesesesssssssseseessestecseserssessseees DD<br>|
|Goal Five: Improve maternal health .............cccccsscssscessecssecsseccesseeessesssecsseeeesssscstseesseecses DO|
|Goal Six: CombatHIV/AIDs, malariaandcommunicable diseases ..............cccccscceceeseeeeeees 30<br>|
|Goal Seven: EnsureEnvironmental Sustainability ........... cccccccseesesesecseeeeeeeeetssseeeeettetene OD|
|Vietnam Development Goals not directlybasedon theMDGS..............<br>cc eeeeseeeeteeteeeeeeees BO<br>|
|Goal eight: Reduce vulnerability 0.0.0...cscssescssesssessesscseccssecesesscesccseseseaesaceseasseestessetatere BO<br>|
|GoalNine: EnsureGoodGovernance forPoverty Reduction<sup>..............c:csccessessssccessseeereeeeee OT</sup>|
|Goal Ten: Eradicatepoverty andpreserve the culture and diversity ofethnic minorities.... 38|
|Goal Eleven: Ensuring pro-poor infrastructure development..............cccccssceseesseessecsseseeseesees OO|
|TV. CHALLENGES.........ccsssssesceccsscoeeesssceesassosessonscnssseseseceaecsccseeaecaeassetseesasssesencessssetssessessttseeetses 4Q<br>|
|Improving Equity 00...ceseccssssssssessssscsssscescassseaseccecsecsseecsssstsceseeassrsessessesscenssessccaseetesececeess 40<br>|
|Aligningresourcesto strategic targets...sssesessesessessesseessscesscessseasesssseteeseesssssccsetsseces FL<br>|
|Buildingcapacityandfillingdatagaps....cccscssssesssscessesesssscsscssscsesessesceecscesesessesrssaresees42|
2 percent, 89 percent and 74 percent of GDP respectively. Even 40 years after a pandemic shock, high-income countries remain 4 _._ 2 percent below steady state GDP, compared to 2 _._ 6 percent for middle-income countries and 0 _._ 8 percent for low-income countries. This is because the effect of human capital lost through disruptions to school and work persists until all the affected students and workers have left the labor force.

There are several reasons for the _asymmetric_ impact of a _symmetric_ shock on countries of different income levels. One is that returns to education do not vary much across income levels but there is significantly less schooling to begin with in middle and low income economies: the average years of schooling in each group is 13, 8 and 6 years, respectively. The negative effect of the employment shock is also higher in high-income economies, due to the presence of higher returns to experience there.

The disruption to schooling is also likely a greater influence on long-term outcomes than the disruption to experience – as the interruption to work affects mostly lowskilled workers, whereas the interruptions to schooling are more widespread, and as the returns to education are several times larger than the returns to experience.

We then use our framework to pinpoint carefully _which pandemic scenario most accurately reflects the COVID-19 shock_ . We achieve this through a thorough analysis of several variables that vary across income levels and that affect the shock’s severity – such as data on school closures, access to and effectiveness of distance learning, and evidence on the extent of disruption to employment to different types of workers.

While high-income countries are more vulnerable to a symmetrical shock due to the relative importance of human capital, the impact of COVID-19 on the high- and middle-income countries turns out to be similar. The reason is that middle-income countries are hit much harder both in terms of employment loss and school closures. This contrasts with the policy literature on COVID-induced schooling disruptions that argues that the impact disproportionately falls on low income economies.<sup>1</sup> However, the relatively lower impact does not imply that the overall effects on low-income economies should be discounted. Given the challenging context in many low-income economies, they are still heavily and durably impacted by the pandemic shock.

The welfare impact is equivalent to 2 _._ 5 percent of steady state income in perpetuity 1For example, Brookings (2020a) states that the “students in low-income countries and those in subSaharan Africa will be the most negatively affected. [...] The learning gap between rich and poor will likely grow during the pandemic, not just between high- and low-income countries [...].”
**Table 3: Industry Labor Demand for Blue Collar Workers in Colombia (1980-91) (standard errors in parenthesis)**

||Ln L t-I|Ln WBt|Ln WBt-l|Ln WWt|Ln WWt-I|Ln Industry<br>Value<br>Added|Constant|Wald<br>Test: p-<br>value(b)|L.R.<br>Elast.(a)|Sargan<br>Test|Autoco.<br>Test<br>(2nd o.)|No. Obs<br>(Plants)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Base<br>Specification(c)|0.215(*)<br> <br>(0.080)|-0.424(*)<br>(0.028)|0.037<br>(0.035)|0.074(*)<br>(0.010)|0.011<br>(0.009)|0.054(*)<br>(0.013)|-0.005<br>(0.009)|--|-0.493|0.311|0.312|22992<br>(1916)|
|Time Variant<br>Specification|0.159<br>(0.316)|-0.453(*)<br>(0.053)|-0.051<br>(0.094)|0.118(*)<br>(0.024)|-0.001<br>(0.018)|0.01 1(*)<br>(0.023)|0.004<br>(0.018)|--|-0.599|0.386|0.929|22992<br>(1916)|
|Variable* T81|-0.149<br>(0.302)|0.090<br>(0.060)|0.064<br>(0.100)|-0.058(***)<br>(0.032)|<br>0.011<br>(0.028)|0.010<br>(0.039)|-0.013<br>(0.019)|0.094|-0.353||||
|Variable* T82|-0.021|-0.065|0.058|-0.030|0.036|-0.011|0.024|0.488|-0.593||||
||(0.369)|(0.089)|(0.124)|(0.037)|(0.028)|(0.035)|(0.020)||||||
|Variable* T83|0.032<br>(0.573)|-0.051<br>(0.098)|0.129<br>(0.250)|-0.087(**)<br>(0.037)|0.018<br>(0.041)|0.060<br>(0.038)|-0.013<br>(0.022)|0.154|-0.525||||
|Variable* T84|-0.176<br>(0.334)|0.011<br>(0.064)|0.031<br>(0.108)|-0.066(***)<br>_(0.035)_|<br>0.058(**)<br>(0.027)|0.092(*)<br>(0.035)|-0.013<br>(0.019)|0.008|-0.454||||
|Variable* T85|0.830<br>(0.665)|-0.199(**)<br>(0.098)|0.346<br>(0.273)|0.004<br>(0.052)|-0.017<br>(0.051)|0.215(*)<br>(0.062)|-0.081(*)<br>(0.020)|0.025|-32.194||||
|Variable* T86|-0.482|0.049|-0.157|-0.058|0.017|0.020|-0.033|0.702|-0.463||||
||(0.524)|(0.089)|(0.266)|(0.057)|(0.080)|(0.068)|(0.028)||||||
|Variable* T87|0.625<br>(0.817)|0.150<br>(0.095)|0.355<br>(0.329)|-0.107(***)<br>(0.057)|<br>-0.023<br>(0.075)|-0.019<br>(0.049)|0.020<br>(0.022)|0.281|0.005||||
|Variable* T88|-0.036|0.137(***)|0.094|-0.052|0.048|-0.007|-0.023|0.058|-0.311||||
||(0.359)|(0.081)|(0.114)|(0.040)|(0.035)|(0.050)|(0.020)||||||
|Variable* T89|0.366|0.200(***)|0.170|-0.058|-0.028|-0.050|-0.007|0.569|-0.281||||
||(0.532)|(0.108)|(0.194)|(0.043)|_(0.050)_|(0.069)|(0.020)||||||
|Variable* T90|0.091|0.156|0.062|-0.067|0.034|0.081|-0.019|0.214|-0.382||||
||(0.444)|(0.106)|(0.130)|(0.045)|(0.040)|(0.077)|(0.021)||||||
|Variable* T91|-0.040<br>(0.334)|0.058<br>(0.078)|-0.017<br>(0.103)|-0.035<br>(0.044)|0.034<br>(0.042)|0.086<br>(0.054)|-0.032<br>(0.020)|0.432|-0.525||||
|Wald Test: p-<br>value(d)|0.732|0.049|0.698|0.505|0.667|0.001|0.000||||||

Notes: GMM estimates with first-differenced data. All variables are assumed exogenous except for lagged employment. Instruments are based on the second and furiher lags of employment and capital stocks. (*) Significant at the 1% level. (**) Significant at the 5% level. (***) Significant at the 10% level. (a) Best point estimates calculated as [(Ln WBt + Ln WBt-1 )/ Ln Lt-l ]. (b) Wald Test of Joint Significance of the variables interacted with a given year dummy. (c) Time dummies were included but are here omitted (d) Wald Test of Joint Significance of year dummies interacted with a given variable.
The World Ban. nas played a major<sup>role in encouraging countries to</sup> implement structural adjustment<sup>policies. Its Structural Adjustment Lending</sup> (SAT.) programs are designed to "increase<sup>efficiency economy-wide through</sup> changes in pricing and trade<sup>policies, in the size and structure of government</sup> expenditure, and in the extent<sup>of the government's controls on productive</sup> activity" [Nicholas (1989,<sup>p.vii)]. Sectoral Adjustment Lending (SECAL)</sup> programs are designed to "support<sup>more narrowly focused programs of reform and</sup> restructuring, ranging from<sup>major changes in macroeconomic policy in a sector</sup> such as trade cr agriculture<sup>to the establishment of an appropriate framework</sup> for investments in a sector<sup>such as energy or elucation" (Nicholas (1989, p.</sup> vii)]. The World Bank has made<sup>a large number of these loans since 1980, with</sup> 52 countries being recipients<sup>of one or more of either of these. [Nicholas</sup> (1989, pp. 42-45)]. I will<sup>refer to the two together as Adjustment Lending</sup> (AL) programs.

There is some controversy as<sup>to the impact of AL programs. Nicholas</sup> (1989) provides the World Bank<sup>view: although there have been some</sup> difficulties in implementation<sup>and oversight, they have on the whole been</sup> effective instruments for<sup>structural adjustment. World Bank (1988, 1990)</sup> provide statistics that support<sup>that view. Critics include Berg and</sup> Batchelder (1985) and Sachs (1986)<sup>who suggest that these programs have much</sup> less of an impact than the World<sup>Bank claims for them. Mosley (1987) takes</sup> the agnostic middle ground<sup>in saying:</sup> .by the end of 1985 **countries**<sup>that</sup><sup>**had adhered to SAL conditions**</sup> had done better than those<sup>that had not, andI...]</sup> not **all of the difference was due to underlying**<sup>**disparities in the capabilities**</sup> **of their economies. But this**<sup>**encouraging short-term result**cannot</sup> **be used to make a definitive assessment**<sup>**of a medium-term reform**</sup> program; some of the success<sup>[ ... ]may have been due</sup><sup>**to luck**</sup> rather than judgement. (Mosley<sup>(1987, p. 29); emphasis added]</sup>
The available data do not allow distinguishing “time since arrival” for native born, i.e. whether the native born are second or third generation immigrants or whether their ancestors arrived in the US a long time ago. Asian and Hispanic natives are probably more likely to be second or third generation, and therefore more comparable to foreign born, while Black and White natives are more likely to come from families established for a longer time in the US.

The analysis illustrated in figures 3 to 10 suggests that the main result of this paper - the reversal of the traditional advantage of native vs. foreign born ranking among Blacks – is not driven by differences in citizenship, ability to speak English, age at arrival or time since arrival in the composition of the pool of immigrants. The next section focuses on neighborhood effects as potential factors explaining that result.

# **V. Neighborhood effects**

The regressions used in the analysis so far only include regional indicators for the nine main regions in the US census. This is only a crude way to control for local conditions and neighborhood effects. Labor market outcomes might be affected by local economic conditions and by the composition of the neighborhoods. For example, Cutler and Glaeser (1997) document the negative impact of segregation among Blacks and Card and Rothstein (2007) show that the Black-White Test Score Gap is larger in highly segregated neighborhoods.

I first control for the racial composition of the Public Use Micro Data Area (PUMA) in which the household is located. PUMAs are the smallest level of geographical area available in the Public Use Micro Data of the Census. They generally follow the boundaries of county groups, single counties, or census-defined "places" if these areas exceed 200,000 residents; they are divided into as many PUMAs of 100,000+ residents as possible. None of the PUMAs cross state lines.

11
- 23 -

Table 1. Indicators of Crises: A Review of the Literature

|Study|Sample and<br>Frequency|Country<br>Coverage|Indicators|Comments|
|---|---|---|---|---|
|**Edin and**<br>**Vredin**(1993)|1978-1989,<br>monthly|Denmark, Finland,<br>Sweden and Norway.<br>16 devaluations,<br>defined as shifts in the<br>entire target zone.|(1) money<br>(2) output<br>(3) foreign interest rate<br>(4) foreign price level<br>(5) real exchange rate<br>(6) international reserves/imports<br>(7) trade balance|Estimate the one-step ahead<br>probability of devaluation, and<br>the expected size of the<br>devaluation (measured as the<br>change in the central parity of the<br>target zone) conditional on a<br>devaluation taking place.|
|**Edwards**<br>(1989)|1962-82,<br>pooled<br>quarterly data<br>and annual|39 devaluations;<br>24 developing<br>countries with a fixed<br>exchange rate for at<br>least 10 years serve as<br>the control group. The<br>devaluations were at<br>least 15 percent with<br>respect to the<br>U.S. dollar after having<br>fixed the rate at least<br>two years.|(1) central bank foreign assets/base<br>money<br>(2) net foreign assets/MI<br>(3) domestic credit to public<br>sector/total credit<br>(4) bilateral real exchange rate<br>(5) parallel market premium<br>(6) growth of credit<br>(7) growth of credit to the public<br>sector<br>(8) public sector credit growth/GDP<br>(9) fiscal deficit/GDP<br>(10) current account/GDP<br>(11) the terms of trade<br>(12) errors and omissions plus short-<br>term capital<br>(13) exchange controls|The focus is on understanding the<br>causes of devaluations. (1)-(5)<br>are used to estimate the<br>probability of devaluation and<br>(6)-(12) are used to describe the<br>stylized facts of the 3 years<br>preceding the devaluation.|
|**Edwards and**<br>**Montiel (1989)**|1962-1982,<br>annual|20 devaluations of at<br>least 15 percent with<br>respect to the<br>U.S. dollar after having<br>a fixed rate at least two<br>years.|Same as (3)-(I 1) in Edwards (1989),<br>and manufacturing real wages.|The stylized facts of 3, 1, and 0<br>years before the devaluation are<br>described.|
|**Edwards and**<br>**Santaella**<br>(1993)|1954-1971,<br>annual|48 devaluations (26<br>under an IMF program)|Same as (2)-(10) in Edwards (1989).<br>(14) number of official exchange rates<br>_(15)_political unpopularity<br>(16) democracy<br>(17) political violence<br>(18) Ideology (how leftist)<br>(19) number of coups<br>(20) relative GDP per capita|The evolution of (2)-(10) and<br>(14) is compared for devaluers<br>and nondevaluers. Some of these<br>variables and (15)-(20) are used<br>to estimate the probability of<br>entering into an IMF program.|

Note: Additional details on the individual countries included in the larger cross-country studies are available in the original studies.