rafmacalaba/data-use-mentions-extended huggingface dataset:
Environmental Performance Index (Wolf _et al._ , 2022), the OECD Better Life Index (Durand, 2015), the United Nations Human Development Index (UN, 2022), the World Bank Human Capital Index (Kraay, 2019), and the World Press Freedom Index (Reporters without Borders, 2023). The SPI has strongest correlation with three out of the six indexes, followed by the ODB (two indexes), and the ODB (one index). However, most of these correlation coefficients are not statistically significantly different from each other as shown by statistical tests and further multivariate regression results (Appendix A, Table S7 and Tables S10 to S21), suggesting the tools having rather similar relationships with these six development indexes.

# **Discussion**

We offer the first rigorous assessment of the various tools that are commonly used to assess country statistical capacity. These include the Global Data Barometer index (GDB), the Open Data Inventory index (ODIN), the Statistical Performance Indicators and Index (SPI), their predecessors (the ODB and the SCI), and some regional and self-assessment tools (including the IIAG). Each tool follows its own guiding principles in collecting data and generating its metrics. Compared to the SPI framework, the ODIN and the ODB focus more on data services and products, the IIAG and the SCI focus more on data products and infrastructure, and the GDB and all the three selfassessment tools focus more on data infrastructure. While the SPI (and SCI) obtain most of the data from curated international databases, the ODIN exclusively from NSO websites, the GDB (and ODB) mostly from expert surveys, the IIAG from a mix of different sources, and the three self-assessment tools from expert self-assessment tools. These different methods could offer their own strengths and weaknesses.

Except for the IIAG that focuses on Africa, all the remaining tools are global. The ODIN covers the most countries, to be followed by the SPI (and the SCI), the
again similar to the previous two subjects - with the student specific variables accounting for the biggest decline (19 points) , followed by school (13 points), parents (11.5 points) and teacher related variables (2 points). The final specification reduces the dummy to 88.54. Together with rotated variables, presented in Appendix Table A7, available on the online github repository, the lowest value for the Vietnam dummy is 80.09, a total reduction of 55 points explained, but still leaving a large portion unattributable through the Fryer-Levitt method.

# **3.4 Summarized insights from Fryer-Levitt**

We can see that across the three test subjects, the Vietnam dummy comes down by nearly 50 points, or half a standard deviation. We find that student, parent and school related variables appear to explain part of Vietnam’s superior performance in PISA.

**Students** : The student related variables reflect two policy elements that could be useful for other countries that seek to learn from Vietnam. The investment made by Vietnam in pre-schools appears to have long lasting effects, and indeed in Vietnam the government continues to invest deeply not only for universal pre-school, but also for early childhood care services even prior to pre-school. A policy lesson can also be derived from the effect of class repetition - cause and effect is difficult to extract in the case of repetition and test score performance, but one can see that repetition is much lower in Vietnam (Table 1) and the regression coefficient on a student being a repeater has a large negative value, even in the final specification with all other variables included. The other student related variables regarding being late for school and skipping classes perhaps do not have clear policy implications for other countries but help us understand a cultural effect regarding Vietnam.

**Parents** : As noted in the text, the household wealth/possessions, parents’ education levels and socio-economic indices reflect Vietnam’s per capita GDP and act against explanations of the test
|Received any transfers from...|||||||||
|<br>... other poor|0.31|0.46|4,045|0.34|0.47|2,687|0.389|-0.043|
|... middle class|0.64|0.48|4,045|0.68|0.47|2,687|0.204|-0.063|
|... upper class|0.19|0.39|4,045|0.25|0.43|2,687|0.015|-0.095|
|Gave any transfers to...|||||||||
|... other poor|0.22|0.41|4,045|0.22|0.41|2,687|0.976|-0.002|
|... middle class|0.28|0.45|4,045|0.29|0.45|2,687|0.807|-0.019|
|... upper class|0.02|0.15|4,045|0.04|0.20|2,687|0.053|-0.079|

**Source:** Author’s analysis based on original survey data. **Notes:** The sample includes observations from the baseline survey and is limited to ultra-poor households. Columns 1,2,3 (4,5,6) give the mean, strandard deviation and number of observations respectively for the relevant variable in treatment (control) communities. Column 7 reports the p-value for the test of equality of treatment and control samples’ means with the standard errors clustered at the branch office level (unit of randomization); column 8
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-  29  -

a 19-percent increase in total EU-destined exports), and their share rose from 24 percent to 32 percent. But it fell to 28 percent in 1994. Since the share of unskilled labor intensive products in EU-oriented exports of Czech Republic, Hungary and Poland had also been contracting since 199293, there was nothing unusual about it.<sup>15</sup> However, while 1992-93 witnessed what turned out to be historically peak levels of the shares of unskilled labor intensive products in EU-oriented exports from these countries, Bulgaria’s pattern has been diverging. The 1994-96 witnessed a major realignment in the export growth pattern, with skilled labor and capital intensive products emerging as major levers of Bulgaria’s export expansion, turned out not to be sustainable (Figure 4).<sup>16</sup>

**Figure 4:** _Factor intensities of Bulgaria EU-oriented exports in 1991-2003(in percent)_

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Bulgaria’s pattern in terms of export intensities has become a complete reversal of patterns observed in Hungary or Poland as well as in other transition economies in that period. This was a repetition of the patterns observed during the initial stages, albeit on a much larger scale, as the share of unskilled labor intensive products rapidly expanded to levels well above the earlier peak in 1993 and their expansion extended over a much longer period of time. The share of both skilled labor and capital intensive products in Bulgaria’s EU exports after increases in 1994-95 has
>Married|0.88|0.85|0.02**|5038|
||Blood pressure measured in|||||
||past year|0.50|0.45|0.50|5038|

Data from Baseline survey. a The tests are based on ordinary least square regressions with standard errors clustered at the district level. Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 32
|**Table 4: Correlation of Vote**<br>**_(Barangay_**|**Buying with Ho**<br>**_clustered stand_**<br>(1)|**usehold Reported**<br>**_ard errors in paren_**<br>(2)|**Access to Heal**<br>**_theses)_**<br>(3)|**th Services**<br>(4)|
|---|---|---|---|---|
||Births with<br>Trained<br>Assistance<br>|Received<br>Prenatal Folic<br>Acid|<br>Received<br>Prenatal Iron<br>|<br>Children<br>Receiving Iron|
|Vote Buying<br>(% respondents reporting it in<br>bgy)|-0.35<sup>~~***~~</sup><br>(0.12)<br>|-0.01<br>(0.10)|-0.20<sup>~~*~~</sup><br>(0.10)<br>|0.02<br>(0.09)|
|Mayor’s family reported as major<br>employer or landowner|-0.12<sup>~~***~~</sup><br>(0.04)|-0.06<br>(0.05)|-0.15<sup>~~***~~</sup><br>(0.04)|0.03<br>(0.04)|
|Indicator variable = 1 if no clan<br>dominates
19 welfare contributions of those industries’ policies as well. Together these results suggest much of the welfare cost of protection globally could be removed if just a few agricultural markets were liberalized.

# **Conclusions and areas for further research**

The following are the key messages that emerge from our analysis:

- The answer to the question in the title of the paper is yes, in the sense that full global liberalization of goods and services trade would raise net farm income in all six developing country regions, and more than it would raise non-agricultural value added;

- Global liberalization would not raise net farm incomes in each and every developing country, however, with our results suggesting that Bangladesh, India, the Philippines and Russia would be among the exceptions – but only because of their own high agricultural protection rates in the GTAP database;

- With that Version 6 GTAP database, our results are not inconsistent with those of Jensen, Robinson and Tarp (2002) in that for several large developing countries (Bangladesh, China, India, Indonesia), own-country policies help rather than harm agricultural relative to non-agricultural value added, with the harm from own non-agricultural policies being more than offset by help from own agricultural and food policies; and

- Other countries’ policies have the opposite effect, of depressing agricultural

value added in developing countries, with high-income country policies
]|[0.335]|||
|Concentration 3|||-0.129<br>[0.142]||
|Herfindahl index||||-0.033<br>[0.148]|
|Log firm size|0.086***|0.088***|0.087***|0.087***|
||[0.004]|[0.004]|[0.004]|[0.004]|
|Manufacturing|0.028**|0.028**|0.031***|0.032***|
||[0.011]|[0.011]|[0.011]|[0.011]|
|Exporter|0.032***|0.028***|0.032***|0.031***|
||[0.008]|[0.010]|[0.008]|[0.008]|
|Foreign-owned|-0.080***|-0.092***|-0.083***|-0.081***|
||[0.011]|[0.012]|[0.011]|[0.011]|
|Government-owned|-0.136***|-0.164***|-0.137***|-0.141***|
||[0.031]|[0.035]|[0.031]|[0.032]|
|Log firm age|0.013***|0.016***|0.013***|0.012***|
||[0.004]|
(3.43)|(3.88)|
|Ave max (in m)|10.52|13.26|25.12|31.78|
||(6.65)|(12.26)|(23.58)|(16.83)|
|Ave TFA (in m2)|78.65|140.75|354.58|707.37|
||(111.24)|(325.67)|(1,118.25)|(1,209.62)|
|Ave TFA/City Area|0.79|0.78|1.19|1.11|
||(0.18)|(0.26)|(0.44)|(0.38)|

Source: Analysis based on DLR WSF 3D data set Note: These are average heights for 90m x 90 m blocks, further averaged across the city’s extent

# 3. Stylized facts

In this section, we provide stylized facts on the magnitude and spatial distribution of built-up area growth and vertical layering in cities. As we have data on heights and can compute floor area, we provide a new measure of floor space adjusted density.

# 3.1 The growth of urban built-up area is slower than has been thought

Analysis of the satellite data show that between 1990 and 2015 the world’s total urban built-up area increased by 66,000 km<sup>2</sup> , or the size of Sri Lanka. Urban built-up area increased from 226,768 km<sup>2</sup> to 294,550 km<sup>2</sup> , a growth of 30 percent. In developing countries, urban built-up area increased from 117,977 km<sup>2</sup> to 166,231 km<sup>2</sup> over the same time period. That is
including among educated youth who aspire to white-collar jobs. In a recent survey among unemployed youth in Bhutan, 50.3 percent of respondents said they would prefer to work for the government and another 32.4 percent aspired for a job in a state-owned enterprise (MoLHR and UNDP, 2014). Similar data from the 2014 edition of the Bhutan Labour Force Survey confirm that the majority of unemployed persons would like to work in the public sector. Highly educated persons are particularly keen to find a job in the public sector and the perceived superior salary, good benefit packages and high job security of public sector jobs are cited among the main reasons for why work in the public sector is so attractive.

- 5 -
continuation as some enrolled youth will be absent the day of the ENLACE application and others may have repeated a grade/grades and thus not be in the ENLACE application. In our estimations, our objective will be the comparison of levels to derive impact. Still it is noteworthy in Table 3 that enrollment levels for indigenous continuation rates are even lower both for females and males than for the overall sample of children in medium to very poor areas.<sup>11</sup> Table 3: Enrollment Rates in 7th Grade, 9th Grade and 12th Grade

||Primary schools in<br>High Marginali|6th Graders in 200<br>Medium to Very<br>zed Areas|8<br>Indigenous Pr|imarySchools|
|---|---|---|---|---|
||Male|Female|Male|Female|
|7th grade|59.13%|63.79%|45.56%|44.92%|
|9th grade|59.22%|62.97%|50.16%|49.27%|
|12thgrade|29.31%|32.43%|24.95%|24.96%|

Source: Author’s calculations using ENLACE databases.

Table 4 shows the distribution of sixth grade students who enroll in seventh grade and in which types of schools. The majority of indigenous children enroll in tele-secondary schools with about 20% enrolling in technical secondary schools. Only about 10% enroll in general secondary schools. The proportions in tele-secondary schools are somewhat lower for the overall sample of youth in marginalized areas at about 59% for boys and 46% for girls. Girls have a correspondingly higher probability of enrolling in a general school within our sample of schools located within marginalized areas.

Table 4: Secondary School Attended in 7<sup>th</sup> Grade

||6th<br>Primary Schools in<br>
countries’ consumption is still the best indicator for measuring the welfare of many households whose revenues are from the informal sector and very seasonal (Clementi et al. 2020).

# 3.2 Empirical Estimations

Figure 1 illustrates this latter point using data from Povcalnet. High-income countries, although they still collect consumption data, tend to measure their official inequality figures with income; official inequality measured on consumption are practically nonexistent. Official consumption-based measures start to appear among the upper-middle income countries, although less numerous than those income-based, and eventually become the majority within the low-middle income group (figure 1, panel b). Among lowincome countries, inequality is predominantly measured with consumption. Finally, looking at the different regions of the developing world, only in Latin America and the Caribbean do we have a predominance of inequality measured with income, while in other regions only the most advanced 9
for cohort 2). However, there is no impact on the likelihood that program school students sought out extra school coaching, handed in homework, were called on by the teacher in class, or did chores at home (Panel A). In the case of chores, the estimated zero impact suggests there was minimal cost of the program in terms of lost work effort at home, suggesting any increased study effort may have come out of children’s leisure time. In terms of educational inputs such as the number of new textbooks

> 19 We thank Joel Sobel for this point.

> 20 These results are for all teachers in the schools.  It is difficult to distinguish between teacher attendance in grade 6 versus other grades, since the same teacher often gives a class (i.e., mathematics) in several different grades in a given year, and the data were recorded on a teacher by teacher basis rather than by grade and subject, unfortunately.

24
# **4. Crisis impacts on social mobility, life satisfaction and perceptions of government performance**

# **4.1. Social mobility**

Respondents in LiTS II were asked to “imagine a 10-step ladder where on the bottom, the first step, stand the poorest 10 percent of people in [their] country and on the highest step, the 10th, stand the richest 10 percent of people in [the] country”. They were then asked to state on which step of the 10 their household rests today, where it stood four years before and where it is likely to be in four years’ time. The stated position<sup>11</sup> can be interpreted as a subjective ranking of a household's social standing: it is (weakly) correlated to the measured consumption aggregate but, similar to class, it is better predicted by education and asset ownership.<sup>12</sup> Such subjective views of relative social standing are important because relative incomes are known to play a larger role in satisfaction with life or happiness than absolute incomes.<sup>13</sup> We find that respondents who place themselves at a higher point on the ladder have significantly greater life satisfaction than those who report lower positions. And social mobility matters for life satisfaction: those who think that they have slipped down the ladder over the previous few years are significantly less satisfied with life.

In all transition subregions, households on the lower steps of the ladder report small improvements in their position (compared to four years before), whereas those in the middle and at the top of the ladder are more likely to report large falls. The higher a household considered itself to be four years before, the larger its perceived drop down the social ladder. This pattern may be explained by the fact that those at the top have more to lose, as those at the bottom cannot fall much in relative terms. However, it is also consistent with preliminary
|saved from the|costs of redistributing|
|Boys|-7.1|-2.5||non-poor are fully|the PSNP transfer.|
|<br>Girls|-6.8|-2.7||redistributed to||
|Rural|-6.8|-3.8||poor children.||
|Urban|-8.1|-0.8||||
|**_Simulation 4:_**|**_PSNP transfers_**|**_are doubled a_**|**_nd retargeted_**||No behavioral changes|
|_(effects in % ch_|_ange)_|||ETB 3.1 billion|and no administrative|
|All children|-8.8|-3.1|||costs of increasing the|
|Boys|-8.5|-3.0|||amount and|
|<br>Girls|-9.1|-3.4|||redistribution of the|
|Rural|-8.7|-4.5|||PSNP transfer.|
|Urban|-9.4|-1.0||||

**_Notes:_** _All simulation results are reported in percentage change. Monetary poverty and inequality indices are computed at the final income. Effects in % change of multidimensional poverty headcount are estimated only in Simulation 1 as education indicators directly enter the multidimensional child poverty index. However, this cannot be done for Simulations 2-4 since they are not directly linked to the index._

Doubling the amount of PSNP transfers to existing beneficiaries ( _Simulation 2_ ) reduces monetary poverty by 1.9% and inequality by 0.9%, with higher effects for girls than boys. Given that PSNP is currently rural-oriented, the effects of doubling its transfers are also more pronounced among children in rural areas than in urban areas. The overall
reflect the extent to which FDI can substitute for direct exporting activity, or offer possible diversification benefits. In contrast, acquisitions in other emerging countries tend to be associated more with considerations of factor price differentials. Finally, the ease of financial access—both in the home and host economies—appears to facilitate M&A transactions, a result consistent with the notion that limitations to trade flows may be overcome by substituting capital flows for goods exports.

We also fit our empirical model of M&A deals to a set of growth assumptions for emerging and advanced economies to obtain a projection of outbound cross-border M&A by emerging market firms for the period 2010–25. Under plausible scenarios of relative growth rates, we find that M&A activity is expected to recover from the crisis-induced decline, and grow at an average of 8.2 percent annually over the period. This respectable rate nevertheless represents a moderation in the rate of growth relative to the past, where—for the decade leading up to the crisis (1998–08)—average annual growth was significantly higher, at 14.3 percent.

Our findings corroborate, and extend, the existing literature in several ways. Existing empirical papers on FDI have tended to focus on testing one theoretical framework against another (Braconier, Norb ̈ack & Urban 2005; Brainard 1997; Head & Ries 2008; Helpman, Melitz & Yeaple 2004). In contrast, we adopt an agnostic view on the different competing theories and seek instead to test a fairly eclectic set of potential hypotheses. Like several papers in the literature (Anand & Delios 2002; Blonigen & Piger 2011; Carr, Markusen & Maskus 2001; Makino, Lau & Yeh 2002), we indeed find that, depending on the circumstance, different theoretical motivations may drive FDI. Our innovation is to frame these distinct cases in terms of emerging versus advanced economy target acquisitions.

While a small number of papers have relied on cross-border M&A data (di Giovanni 2005; M ́eon
more suitable to investigate the consequences of the bias in land measurement on the IR, since key farm input and output data are only available in the survey at this level, not allowing the calculation of plot-specific productivity measures.

To be able to run the analysis maintaining a consistent sample throughout, we therefore restricted our sample further to 5,767 plots for which we have complete information on both type of measures for all the plots cultivated by the household. In the farm level analysis, for which we also drop 42 households with negative farm profits (equivalent to 1.5 percent of the sample), we end up with a sample of 2,860 rural households for which we have both non-zero land area measures for the entire households‟ landholdings.

Table 1 reports key summary statistics for the plots for which we have information on both types of area measurements, along with the mean and standard deviation (in acres) of plot size measured through GPS, self-reporting, and the absolute and relative difference between the two measures. For simplicity of presentation, we will take the GPS measure as the benchmark and talk of farmers over- (or under-) reporting plot size whenever the area self-reported is larger (smaller) than the area measured by GPS. We do acknowledge however that the GPS measure may also be subject to a certain degree of inaccuracy (Keita and Carfagna, 2009).

> 3 A Garmin 12XL GPS device was used by the survey teams to collect plot area data.

5
00)_|_(0.00)_|
|Prefers beneft to malaria (count)|0.69|0.54|0.79|0.64|-0.16|-0.15|
||_(0.05)_|_(0.05)_|_(0.05)_|_(0.05)_|_(0.03)_|_(0.03)_|
|Prefers beneft to road (count)|0.69|0.65|0.74|0.65|-0.04|-0.10|
||_(0.06)_|_(0.06)_|_(0.06)_|_(0.05)_|_(0.67)_|_(0.15)_|
|Governments should control prices|0.94|0.91|0.94|0.90|-0.03|-0.03|
||_(0.01)_|_(0.01)_|_(0.02)_|_(0.01)_|_(0.11)_|_(0.09)_|
|Governments should provide employment|0.95|0.96|0.96|0.95|0.00|-0.02|
||_(0.01)_|_(0.01)_|_(0.01)_|_(0.01)_|_(0.67)_|_(0.08)_|

_Note:_ This table presents group averages and differences in means for households matched across the two waves of the panel data. Columns (1)-(4) present average and s.d.