rafmacalaba/datause-encoder-data huggingface dataset:
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 score gap. Applying this trend, Vietnam would have benefited from a much higher gap if it had been a wealthier country, ceteris paribus. There is an “advantage” that Vietnamese children have in having more demanding parents, though perhaps Vietnamese teenagers may not always see it that way. Parents are demanding not only of their children, but apparently also of schools and generally parents appear to back up their demands by contributing on their own as volunteers. Interestingly, even though the individual coefficients of parent related variables are not statistically significant except for one variable, the variables appear to collectively influence the dummy coefficient up to one-tenth of a standard deviation of test scores. As Amy Chua [Chua, 2011] attested, parental attitudes and behav-

26
The GDB offers an overall country score, a pillar score, and a module score. The country scores are built on four pillar scores for governance, capability, availability, and use and impact. The module scores are available for the following thematic topics: (private) company information, land, political integrity, public finance, public procurement, climate action, health, and COVID19. The GDB assigns different weights to each of its indicators, depending on whether it is a primary indicator (more weight) or secondary indicator (less weight) or which pillar it belongs to (i.e., governance, capability, and availability pillars have more weight).

# _Other Tools_

The Ibrahim Index of African Governance Statistical Capacity (IIAG) is a regional assessment tool aiming at “measuring African governance performance”, where governance is defined as “the provision of the political, social, economic and environmental goods and services that every citizen has the right to expect from their state, and that a state has the responsibility to deliver to its citizens” (Mo Ibrahim Foundation, 2023). The IIAG consists of four main pillars of governance: Security and Rule of Law, Participation, Rights and Inclusion, Foundations for Economic Opportunity, and Human Development. These categories are comprised of 16 sub-categories and make up the Overall Governance score.

Besides the global and regional tools, there are several detailed self-assessment tools.<sup>1</sup> These include the European Snapshot Tool, the UN NQAF (National Quality Assurance Framework) self-check tool, and the Paris21 NSDS (National Strategies for the Development of Statistics) selfassessment tool. Unlike the tools mentioned above, these self-assessment tools do not offer explicit performance scores.

In summary, our brief review above suggests several key qualitative differences across the different tools. As their names suggest, the SPI (and SCI) aims to measure country statistical capacity, the other indexes focus more on aspects of statistical capacity such as data availability and open data standards (the ODIN and the ODB and GDB) or governance performance (the

> 1 The World Bank has also produced self-assessment tools related to NSO efficiency and data openness, including the Improving the Productivity of National Offices for Statistics (IPNOS) toolkit discussed in Medina Giopp _et al._ (2020) and the Open Data Readiness Assessment (ODRA). The IPNOS toolkit provides a tool for evaluating the efficiency of national statistical offices along three pillars: budget and cost-efficiency of production; quality of process, products, and user’s satisfaction; and institutional and organizational aspects and has been applied in Costa Rica, El Salvador, and the Seychelles. ODRA is a readiness assessment designed to give national statistical systems a roadmap for creating or expanding an open data program.

7
$31 (in 1995 dollars). Horton et al., surveying convenience samples at a small number of outdoor recreation sites in the U.K. and Italy in 1999, found much higher numbers, around $45 per household _per year_ , in each of the two countries, for a program to protect 5% of the Amazonian rainforest (and $60 for a 20% protection program). Apart from design issues—the latter study was limited in scope, with small and geographically concentrated samples that were not representative of the national populations—these differences could reflect preference differences between Europe and the United States. Alternatively or additionally, they could signal greater public attention to, and support of, rainforest issues during the period of the latter study.

Traditional SP elicitation involves for the most part random population surveys using contingent valuation (CV).<sup>2</sup> In particular, the two cited surveys by Kramer and Mercer and Horton et al. both utilized the CV technique. While we have more than 40 years of experience with CV, choice experiments (CE) have become widely applied to environmental valuation only over the last 10 years or so.<sup>3</sup> In this paper we present a “Delphi exercise” in which environmental valuation experts from several countries were asked to predict WTP for such preservation, on behalf of their respective national populations, in the event that a CV survey was done in their respective countries. Experts taking part in this exercise came from Europe (49 experts from 21 different countries); the U.S. and Canada (82 experts); Australia and New Zealand (16 experts); and Asia (70 experts from Bangladesh, Cambodia, China, India, Indonesia, Malaysia, Nepal, Pakistan, the Philippines, Sri Lanka, Thailand and Vietnam).

The “Delphi method” was developed by the RAND Corporation during the 1950s and 1960s, principally by Dalkey and Helmer (1963), and has by now a long background and tradition as a management decision tool.<sup>4</sup> The key elements are: (a) anonymous responses by

> 2See Mitchell and Carson (1989), Carson, Flores and Meade (2001), and Carson (2011), for presentations of the CV method. The latter reference contains a comprehensive bibliography. See Carson (1998) for a more specific discussion of CV in the context of rainforest valuation.

> 3 For a discussion of CE studies see Adamowicz et al (1998), Hanley, Wright and Adamowicz (1998), and Louviere, Hensher and Swait (2000). Among prominent, more specific and recent, applications to environmental and resource economics, are Bennett and Blamey (2001), Bateman et al. (2002), Holmes and Adamowicz (2004), Rolfe and Bennett (2006), and Bennett and Birol (2010). It may be argued, as do Carson and Louviere (2011), that CE in its environmental applications is best seen as a particular approach to doing contingent valuation.

> 4 See also Dalkey (1967, 1969), and Linstone and Turoff (1975), for overviews and further discussions of the Delphi method.

3
20

**Table 4.9: Rural/Urban Poverty Decomposition by Geographical Regions 1985-93, (Poverty Line=N395.41)**

||||**_RURAL_**||||
|---|---|---|---|---|---|---|
|||**1985/86**|||**1992/93**||
|**Region**|**P0**|**PI**|**P2**|**P0**|**P1**|**P2**|
|Northern|46.8|52.8|53.4|51.3|50.84|60.24|
|Middle Belt|19.2|21.7|23.6|20.6|22.06|23.35|
|Southern|34.1|25.4|22.9|28.1|17.10|16.41|
|_All Rural_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|
|||**1985/86**|_URBAN_||**1992/93**||
|**Region**|**P0**|**P1**|**P2**|**Po**|**P,**|**P2**|
|Northern|34.2|38.6|41.6|38.7|49.6|48.5|
|Middle|19.0|23.9|24.2|22.9|24.94|26.04|
|Southern|46.7|37.5|34.2|38.4|25.4|25.5|
|_All Urban_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|

Source: NCS, 1985/86 and 1992/93.

Tables 4.10 and 4.11 show the poverty incidence indicators by agroclimatic zones. In 1985, 54.9 percent was poor in rural north and 34.2 percent in urban north. In 1992, the corresponding figures are 44.2 percent and 40.3 percent respectively. The Southern region classified into Southwest and Southeast is very illuminating. Poverty in the cocoa producing rural Southern regions is the lowest. This comes out convincingly in Table 4.11, where rural Southwest contributes the least to national rural poverty with 17.1 percent in 1985 and 15.3 percent in 1992. Even in urban areas, the Southwest decline in poverty incidence is very pronounced. In terms of depth and severity of poverty, Southwest is performing well. Except for rural Southeast, where poverty severity has worsened over the seven year period, all indicators of agroclimatic zones show a general trend of improvement.

Table 4.12 shows the evolution in poverty measures, by state. Taking the head count (P0) first, the table shows that Gongola, Cross River and Sokoto are the only states that show an increase in head count index of poverty during 1985/86-1992/93. The sharpest decline in head count (26%) was for the state of Bendel, Cross Rivers, Sokota are the states indicating an increase in the depth (PI) of poverty. Six states however, show an increase in severity (P2 ) of poverty during the seven year period of this study. The highest increase in P2 (6.4 percentage points) occurred in Sokoto.
an alternative training modality aimed at strengthening the original mentoring program.<sup>3</sup> The API _Plus_ modality embeds all the features of the _Original_ modality, with two significant changes in the training module. First, it entails two weeks rather than one week of initial training. The extra week is focused on hands-on strategies to improve students’ reading and math competencies. Second, mentors attend an additional day during each one of the bimonthly meetings throughout the school year. The schedule of the extra day is organized around peer-to-peer sessions in which mentors share experiences and design common strategies to better address the most pressing issues. Among the large array of possible improvements in the design of the program, enhanced training was ex-ante scalable and yet a promising alternative from the perspective of the government. The _Plus_ modality entails a cost per child of US $332 as opposed to US $285 per child for the _Original_ modality. These cost figures are very much in line with another recent government-run intervention that targeted both children and parents through home visits in Colombia (Attanasio et al., 2022a).

We randomly selected 230 schools in rural Chiapas from a set of schools that were not previously part of the API program. Assignment of the program was carried out using a randomized block design at the school level, with the strata represented by the deciles of the 2012 school-average in a national standardized achievement score in the Spanish test (see Appendix A.1). As a result, 60 schools were assigned to the API _Plus_ , 70 schools were assigned to the API _Original_ modality, and the remaining 100 schools were in the control group with no API intervention.

We rely on both administrative and survey data sources as well as qualitative interviews for the second field experiment. Most of these variables are shown in Table B-2, and they are balanced with respect to treatment assignment. The data collection took place by the end of the second school year after the inception of the API program in the evaluation sample. By that time, two schools out of the original 230 schools in the evaluation sample had closed, while the program could not be put in place in another four schools due to high political instability. Within the remaining 224 schools, one quarter of the community instructors reported eight or fewer months of tenure in the school, and only 56 out of the original 126 mentors were working in the same schools to which they had been originally assigned. All

> 3The _Original_ modality in the second experiment is meant to track the benchmark intervention with two minor differences. First, the ability to speak the main indigenous language in the community would become the most important criterion for the assignment of the mentors across program-eligible communities. Second, the supervisors of the mentors would receive a salary increase in exchange for a mandatory increase in the frequency of their visits to the targeted communities.

8
identifying assumption is that the effect of reforms in specific services sectors should be more pronounced in manufacturing sectors relying more heavily on those services inputs. The specification also controls for the level of import tariffs on output and inputs as well as for firm and year fixed effects.

The analysis is based on firm-level data from the Capitaline database, a commercially available database including balance sheets, profit and loss statements, and ownership information on large private and public firms operating in India. Firms included in the database account for 62 percent of India‟s total manufacturing output during the period covered by the analysis. Our data set forms an unbalanced panel covering 3,771 firms or 22,558 firm-year observations during the 1993-2005 period.

Our results suggest that policy reforms in services sectors had a significant impact on firms in the manufacturing sector. The aggregate effect of services liberalization was an increase in productivity of 11.7 percent for domestic firms and 13.2 percent for foreign firms for a one-standard-deviation increase in the liberalization index. When the individual services sectors are examined in the same specification, a one-standard-deviation change in the banking sector index corresponds to a 6.5 percent change in productivity for both domestic and foreign firms. A one-standard-deviation change in the telecommunications liberalization index corresponds to a 7.2 percent increase in productivity for domestic firms and a 9.8 percent increase in productivity for foreign firms. A similar change in the transport index leads to a 19 percent improvement in productivity of all firms. Only foreign firms appear to benefit from the insurance reform enjoying a productivity boost of 3.3 percent.

Our results are confirmed by an instrumental variables approach in which we instrument for reform in India using measures of services reform in two countries that can be viewed as India‟s competitors, namely China and Indonesia. The results are also robust to focusing on structural breaks in services reform instead of using the reform index. Finally, the results remain unchanged if we control for delicensing and lifting of restrictions on FDI inflows in a given manufacturing sector.

This paper proceeds as follows. Section II describes the related literature. Section III describes services liberalization in India between 1990 and 2005 and presents some evidence on its impact. Section IV describes the data and the construction of the liberalization index and reviews our estimation procedures. Section V interprets the results, and section VI concludes.

4
# **SI-4 Supplementary Tables**

## **Table SI-1:** Migration impacts of storms

||Impact on|out-migration|Impact on|net migration|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|1(Storm year)=1|-0.004|-0.008|0.103<sup>_∗∗∗_</sup>|0.111<sup>_∗∗∗_</sup>|
||(0.009)|(0.011)|(0.031)|(0.035)|
|1(Storm year), t-1=1||0.001||0.079<sup>_∗_</sup>|
|||(0.012)||(0.043)|
|1(Storm year), t-2=1||0.013||0.020|
|||(0.011)||(0.034)|
|1(Storm year), t-3=1||-0.006||0.020|
|||(0.017)||(0.034)|
|1(Storm year), t-4=1||-0.017||0.007|
|||(0.020)||(0.039)|
|1(Storm year), t-5=1||0.016||-0.007|
|||(0.018)||(0.040)|
|N|64,449|52,514|64,368|52,448|
|Σ<sup>5</sup><br>_i_=0<sup>_Stormt−i_</sup> <sup>= 1</sup>||-0.001||0.229|
|||(0.069)||(0.156)|
|Fixed Efects:|||||
|County|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|
|State_×_Year|Yes|Yes|Yes|Yes|

Notes: All columns report the results of a fixed effects specification with the IHS transformation of the number of out-migrants from a county as the outcome in columns 1-2 and the IHS transformation of net migration as the outcome in columns 3-4. Net migration is defined as in-migration minus out-migration so that negative net migration indicates a declining population. Counties are defined as exposed to a storm if at least one hurricane resulted in a flood warning during the year or the county experienced wind speeds of at least 21 m/s - the speed at which structural damage begins to occur - during a hurricane in the year. 1(Storm year), t-# indicates that the county experienced a storm # years previously.<sup>_∗_</sup> p=0.1,<sup>_∗∗_</sup> p=0.05,<sup>_∗∗∗_</sup> p=0.01.

29
In contrast, lingering uncertainty about the direction of U.S. policy could dampen activity and investment abroad. A sustained 10 percent increase in U.S. economic policy uncertainty could, after one year, reduce U.S. output growth by about 0.15 percentage point and EMDE output growth by 0.2 percentage point.

_How important is the global economy for the United States?_ Because of its size and reach, the United States is at the center of global trade and financial networks. U.S. multinational corporations and their affiliates abroad are deeply integrated into global supply chains. Financial linkages between the U.S. and the rest of the world, including emerging market economies, have grown rapidly, widening the potential for spillovers in either direction. These two-way channels imply that, important as the U.S. economy is for the global economy, the U.S. economy is in turn affected by developments in the rest of the world.

In a highly integrated global economy, cross-border linkages translate into significant cyclical spillovers. These spillovers have material implications for all countries, irrespective of their size. Understanding these linkages and associated spillovers remains a fertile area of future research.

# **7 References**

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26
|Table 3<br>Compensation function estimation|
|---|
|Non linear least squares|
|Coefficient<br>Std. Err.|
|w0<br>0,816***<br>0,016|
|w1<br>1,24E-07***<br>6,36E-09|
|Delta<br>2|
|Number of obs<br>6198|
|R-squared<br>0.832|
|Adj R-squared<br>0.844|
|Note: * Parameter delta taken calibrated to 2.<br>significant at 10%; ** significant at 5%; *** significant at 1%|

**Table 4**

Summary statistics: actual, desired and frictionless factor levels

|**Mean values**<br>Variable<br>Observations|Mean|Std. Dev.|
|---|---|---|
|DW<br>5512|37|75|
|W<br>5512|32|63|
|DB<br>5512|115|208|
|B<br>5512|105|168|
|DK<br>5512|275333|788947|
|K<br>5512|249379|657317|
|**Pariwise Correlations:**|||
|FW|DW|W|
|FW<br>1.00|||
|DW<br>0.75|1.00||
|W<br>0.72|0.85|1.00|
|FB|DB|B|
|FB<br>1.00|||
|DB<br>0.66|1.00||
|B<br>0.65|0.88|1.00|
|FK|DK|K|
|FK<br>1.00|||
|DK<br>0.66|1.00||
|K<br>0.63<br>Note: K = actual capital, FK = frictionle|0.82<br>ss capital, DK=|1.00<br>desired capital|

Note: K = actual capital, FK = frictionless capital, DK= desired capital Idem with B, FB, DB and W,FW,DK for blue and white collar 21
- Mendelsohn R, Sanghi A & Dinar A, 2001. The effect of development on the climate sensitivity of agriculture. _Environment and Development Economics_ 6: 85–101.

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- Siebert, S et al., 2005. The digital global map of irrigated areas, V3 (April 2005). Institute of Physical Geography, University of Frankfurt & Land and Water Development Division, Food and Agriculture Organization of the United Nations, Rome, Italy.

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- USGS (United States Geological Survey), 2004. Global 30 Arc Second Elevation Data, USGS National Mapping Division, EROS Data Centre.

- http://edcdaac.usgs.gov/gtopo30/gtopo30.asp

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