rafmacalaba/datause-encoder-data huggingface dataset:
Chaurey, R., 2017. Location-based tax incentives: Evidence from India. Journal of Public Economics, 156, pp.101-120.

- Chirinko, R.S. and Wilson, D.J., 2008. State investment tax incentives: A zero-sum game?. Journal of Public Economics, 92(12), pp.2362-2384.

- Diodato, D., Neffke, F. and O’Clery, N., 2018. Why do industries coagglomerate? How Marshallian externalities differ by industry and have evolved over time. Journal of Urban Economics, 106, pp.1-26.

- Duranton, G., Gobillon, L. and Overman, H.G., 2011. Assessing the effects of local taxation using microgeographic data. The economic journal, 121(555), pp.1017-1046.

- Duranton, G., and A. J. Venables. 2018. “Place-Based Policies for Development.” Policy Research Working Paper 8410, World Bank, Washington, DC.

- Ellison, G., Glaeser, E.L. and Kerr, W.R., 2010. What causes industry agglomeration? Evidence from coagglomeration patterns. American Economic Review, 100(3), pp.1195-1213.

- Faggio, G., Silva, O. and Strange, W.C., 2017. Heterogeneous agglomeration. Review of Economics and Statistics, 99(1), pp.80-94.

- Fox, W.F., 1986. Tax structure and the location of economic activity along state borders. National Tax Journal (1986-1998), 39(4), p.387.

- Giroud, X. and Rauh, J., 2019. State taxation and the reallocation of business activity: Evidence from establishment-level data. Journal of Political Economy, 127(3), pp.000-000.

- Glaeser, E. L., and J. D. Gottlieb. 2008. “The Economics of Place-Making Policies.” NBER Working Paper w14373, National Bureau of Economic Research, Cambridge, MA.

- Holmes, T.J., 1998. The effect of state policies on the location of manufacturing: Evidence from state borders. Journal of political Economy, 106(4), pp.667-705.

- Hsieh, C.T. and Klenow, P.J., 2009. Misallocation and manufacturing TFP in China and India. The Quarterly journal of economics, 124(4), pp.1403-1448.

- Jofre-Monseny, J. and Sole ́-Olle ́, A. 2012. “Which communities should be afraid of mobility? The effects of agglomeration economies on the sensitivity of employment location to local taxes.” Regional Science and Urban Economics, vol. 42, pp. 257–68.

- Kline, P., and E. Moretti. 2014a. “Local economic development, agglomeration economies, and the big push: 100 years of evidence from the Tennessee Valley Authority.” The Quarterly Journal of Economics, 129(1), pp.275-331.

- ______. 2014b. “People, Places, and Public Policy: Some Simple Welfare Economics of Local Economic Development Programs.” Annual Review of Economics 6 (1): 629–62.

- Li, Y., M. Rama, V. Galdo and M. F. Pinto. 2015. “A Spatial Database for South Asia.” Unpublished manuscript, World Bank, Washington, DC.

- Lu, Y., Wang, J. and Zhu, L., 2019. Place-Based Policies, Creation, and Agglomeration Economies: Evidence from China's Economic Zone Program. American Economic Journal: Economic Policy, 11(3), pp.325-60.

36
- 4 -

derived from large statistics reporting units, such as country, province and district, to a raster grid at a spatial resolution of 5 minutes of arc (approximately 9 km at the equator (normally referred to as 10km x10km pixel for simplicity). To infer likely production locations, the model uses a cross-entropy method (Shannon 1948). Given initial allocations, the crossentropy method minimizes the cross-entropy distance—entropy is referred to as a measurement of uncertainty of expected information—between different probability distributions of the variables in the analysis, under different spatial constraints. As a result, the SPAM allows the simulation of the most plausible agricultural production locations given all available data at different levels.

Various input data are taken into account in the cross-entropy procedure, such as sub-national crop production statistics, satellite data on land cover, maps of irrigated areas, biophysical crop suitability assessments, population density, secondary data on irrigation and rainfed production systems, cropping intensity, and crop prices. Specifically in the SPAM, we start with crop production statistics at the large administrative (geopolitical) units (Figure 1).<sup>3</sup> These are typically national or sub-national. Key information to determine where and how much agricultural land exists at the pixel level comes from the existing land cover imagery, which is divided into crop land and non-crop land. With this crop land combined with the crop suitability data based on local climate, terrain and soil conditions, the crop-specific land areas can be obtained (e.g., maize in the figure). Note that the SPAM model disaggregates crop areas and yields into four different management intensities: (i) irrigated; (ii) high-input rain-fed; (iii) low-input rain-fed; and (iv) subsistence. Together with all these data, the SPAM applies the cross entropy method to obtain the final estimation of each crop distribution.

For the current paper, the SPAM was updated for five member countries of the East Africa Community. The model was rerun for 42 crops around 2010 with the latest available crop statistics and other new spatial data included. In the previous SPAM, i.e., SPAM 2000, which

> 3 See You and Wood (2006), You _et al._ (2009), and You et al. (2014) for further details.
Having a connection with peers is important in explaining variance in Chile, but the relationship is less clear in Mexico. Chilean youth who do not have a group of friends with whom they spend time engage in a range of negative behaviors, including inactivity, school dropout, early labor force entry by women, and unsafe sexual behavior, earlier parenthood and less participation in activities (males and females) –, i.e. more social exclusion and worse behaviors go together. The Mexican data do not allow us to measure social exclusion directly, since it only tells us with whom Mexican youth spend their free time. Not spending free time with friends or a girlfriend/partner means greater school completion among Mexican males and participating in fewer in activities among Mexican females; it also means an older work age, safer sexual behavior, an older age at first parenthood and a healthier attitude among males and females. What this may be reflecting is that Mexican youth spend their free time (outside of school) with parents and siblings which, as discussed above, have a strong, positive correlation with good behaviors. Thus the composite variable that we call “social exclusion” may actually be another proxy for family connections among Mexican youth.

Trust in government institutions emerges as an explanatory factor of variance in Chilean behaviors, but it plays a much lesser role for explaining Mexican behaviors. Chilean males who trust in government institutions are older when they take their first job and have safer sexual behaviors while females are less likely to complete school. In Mexico, females that trust in government figures are more likely to be younger when first becoming a parent. Of course, this may be endogenous such that the trust is developed once these behaviors are established. This difference between Chile – where the variable emerges for several behaviors – and Mexico where it is virtually absent may reflect the difference in the access to, coverage of, and quality of institutions in the two countries. Or, it may reflect the different definitions of “government institutions” it the two surveys. In Chile, it is defined as higher level institutions – congress, city government, the judicial system, political parties – while in Mexico it is more personal – police, judges, and politicians.

School quality – which may be a proxy for school connectedness – is positively correlated with school- and labor market- related behaviors in Mexico (the variable is not 17
Table 4: The relationship between the employment probability of the non-migrants and the net in-migration rate. Probit analysis.

||Employment <br>(1)|probability<br>(2)|
|---|---|---|
|Net in-migration rate|-0.295***<br>(0.017)||
|In-migration rate||-0.253***<br>(0.016)|
|Out-migration rate||0.665***<br>(0.039)|
|Economic development in 2001|-0.100***<br>(0.002)|-0.095***<br>(0.002)|
|Male|0.231***|0.232***|
||(0.001)|(0.001)|
|Age|0.204***|0.204***|
||(0.001)|(0.001)|
|Age<sup>2</sup>|-0.009***|-0.009***|
||(0.000)|(0.000)|
|Education|-0.005***|-0.005***|
||(0.001)|(0.001)|
|Urban area|-0.076***<br>(0.002)|-0.088***<br>(0.002)|
|Pseudo-R<sup>2</sup>|0.237|0.237|
|Observations|530,827|530,827|

Notes: Source: Authors’ analysis based on data described in the text. The table reports marginal effects, based on a probit analysis. The unit of observation is the non-migrant. Robust standard errors are bootstrapped at the regional level. *, ** and *** indicate significance at the 10, 5 and 1% levels.

Table 5: The relationship between the net in-migration rate and the instruments. First stage of an IV probit analysis.

||Net in-migration rate|
|---|---|
|Weather-predicted value of the in-migration rate|0.976***<br>(0.005)|
|Weather-predicted value of the out-migration rate|-0.601***<br>(0.005)|
|Economic development in 2001|0.009***<br>(0.000)|
|SPI (yearly average between 1997 and 2002)|0.022***<br>(0.000)|
|Male|0.001***<br>(0.000)|
|Age|0.000***<br>(0.000)|
|Age<sup>2</sup>|-0.000***<br>(0.000)|
|Age<sup>2</sup>|0.008***<br>(0.000)|
|Urban area|0.019***<br>(0.000)|
|R<sup>2</sup>|0.378|
|Observations|530,827|

Notes: Source: Authors’ analysis based on data described in the text. The table reports marginal effects, based on the first stage of an IV probit analysis. The unit of observation is the non-migrant. Robust standard errors are bootstrapped at the regional level. *, ** and *** indicate significance at the 10, 5 and 1% levels.

45
|DALYs per 100 thousand cases of typhoid in persons<br>under 5 and over 5|190-820|
|---|---|
|DALYs per case of diarrheal and typhoid mortality in|32-34|
|children over 5 and under 5||

Table 4.2 presents the estimated health impacts from inadequate water, sanitation and hygiene, based on the parameters given in Table 4.1, including the assumption (from WHO) that 88 percent of diarrheal illness is attributable to water, sanitation and hygiene. The table also provides estimates of DALYs lost to waterborne diseases. About 60 percent of the DALYs are from diarrheal child mortality. Typhoid/paratyphoid deaths add another 20 percent of DALY.

**Table 4.2: Estimated Annual Health Impacts from Water,** **<u>Sanitation, Hygiene</u>**

||Ca|ses|Estimated Annual DALYS|% of Total<br>|
|---|---|---|---|---|
||||<br>|DALYS|
||Urban|Rural|Urban<br>Rural||
|Children (under the age of<br>5 years) – increased<br>mortality (Thousand)|41|198|1,384<br>6,714|87-93|
|Children (under the age of<br>5 years) – increased<br>morbidity (Thousand)|57,831|178,898|20<br>63|1|
|Population over 5 years of<br>age – increased morbidity<br>(Thousand)|149,836|344,183|177<br>406|11-6|
|Typhoid/paratyphoid<br>mortality (Thousand)|0.|57|19|0|
|Typhoid/paratyphoid<br>morbidity (Thousand)|1,1|50|8|0|

_Source:_ Staff estimates.

. The estimated costs associated with the impacts identified above are given in Table 4.3. The hypothetical value from which the estimates are based relies on the WHO methodology which uses conditions in developed countries as the benchmark. The incidence rates for these illnesses are close to zero in those countries (0.3 per person/year as in Fewtrell and Colford, 2004).

The total cost is Rs. 490 billion. The cost of mortality is based on the human capital approach (HCA) for children under 5. The cost of morbidity includes the cost of illness (medical treatment, medicines, and value of lost time) and value of lost DALYs estimated at GDP per capita. We used GDP per capita as a proxy for WTP for one additional year of life, expressed in DALYs.

**<u>Table 4.3 Estimated Health Impacts from Inadequate Water, Sanitation, Hygiene</u>**

||**E**|**stimated Annual**<br>**Rs. Bn.**|**Cost**|
|---|---|---|---|
||**Urban **|**Rural **|**Total **|
|**_Mortality_**||||
|Children under age 5 diarrheal mortality|50|227|277|
|Children under age 5 typhoid|||0.3|
|Persons over 5 typhoid|||0.5|
|**_Morbidity_**||||

17
# 6.3 Officials believe that more money is needed but are skeptical about effectiveness.

The gap between officials’ preference for education spending and their perception about current spending on education is also larger in the region than in other surveyed countries (Figure 7). Indonesia is an outlier in EAP, as official data perfectly match officials’ estimates and the preferred level of spending is only 2 percentage points higher.<sup>18</sup> In Lao PDR, the Philippines, and Vietnam, officials overestimate public spending substantially while still believing that more resources should be spent. The magnitude of the difference between actual spending and desired spending is about 12 percentage points, which is significantly larger than in non-EAP countries where this same gap is only about 9 percentage points. The average estimated level would require a 31 percent increase in spending, while the average desired level of spending would require a 79 percent increase in actual education spending in surveyed EAP countries.

Even in a country such as Lao PDR, where financing was identified as a major constraint, officials commonly believe that additional money would not improve learning (Figure 17). There is considerable variation in the opinions of officials on whether additional resources for education will improve learning. If a system does not have the capacity to spend resources effectively, it would make sense that additional financing would not improve student learning.

_Figure 17: Share of officials who believe that more money will_ **_not_** _improve learning._ <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>PHL IDN VNM MNG LAO Non-EAP<br>Strongly agrees Agrees Does not know Disagrees Strongly disagrees<br>money will not improve learning<br>Share of officials believeing that more<br><!-- End of picture text --> Sources: CGD–World Bank survey, 2022. Per capita GDP is not statistically linked with the displayed variable.

Most officials in all the other EAP countries believe that additional resources would improve learning, in line with responses of surveyed officials from countries outside the region. In Indonesia however, only about half of officials believe more resources would be beneficial, while in Mongolia, the figure is about a third.

18 This is likely explained by the constitution, according to which public spending for education should match 20 percent of the total budget.

21
higher levels of education (64% and 9.6% having completed primary and secondary education, respectively, compared to averages of 57% and 7%).

Regarding measures of fragmentation, the mean self-reported distance between homestead and parcel is 0.17 hours, from 0.12 hours in the bottom to 0.21 hours in the top tercile, slightly higher than the computed minimum cost path of 0.11 hours (0.08 to 0.14). Total travel time from the homestead to visit all parcels and then return home is 0.82 hours overall, from 0.38 hours for the bottom tercile to 1.37 hours for the top one, an increase that is markedly higher in relative terms than the increase in cultivated (0.53 to 0.76 ha) or owned area (0.59 to 1.23 ha). Simpson and Shannon indexes peak at 0.7 and 1.4 (appendix figures 2 and 3). As can be seen in appendix figure 4, the association between holding size and number of parcels is weak, i.e., it seems that a desire to expand the size of cultivated area is not the primary motivation for increasing the number of parcels cultivated but that other reasons such as crop diversification could play a significant role. The mean parcel is 0.19 ha and 28% of households had experienced a crop shock during the period under concern.

# **3.2 Parcel distances between and within households**

To explore potential links between land market participation and fragmentation in more detail, table 2 compares key distances for land rental (panel A) and land purchase (panel B) between participants and nonparticipants and, more importantly, within the same household between owned and rented or purchased parcels. We report the mean walking time between parcels on the fastest path taking into account slopes from the DEM (cols. 1-2), between parcels and the homestead using the least-cost path based on the DEM (cols. 3-4), and walking time between parcels and the homestead as obtained from households’ responses in the survey (cols. 5-6).

Two results emerge from this exercise. First, while survey-based estimates of mean walking distance to owned parcels seem to be reasonably close to the ones based on the computed least-cost path, households seem to systematically overestimate the distance to rented parcels. As a result, a significant difference in homestead-parcel distances emerges between owners and renters-cum owners (rows 1 and 2, cols. 5-6) but this is not backed up by figures on the least cost path between homestead and parcels or all parcels computed based on of GIS/DEM information. Similarly, while rented parcels are more distant from the homestead based on owners’ estimates (12 vs. 23 min) and GIS/DEM information (10 vs. 16 min), there is no significant difference if the least cost path from rented to owned parcels and between owned parcels is considered. Land rental thus appears to increase homestead-parcel distances, but have no impact on mean distances to be covered between parcels and from a substantive point of view, inclusion of rented parcels in our analysis would not raise any issues. Second, differences are slightly more pronounced for purchased parcels; while there is no statistically significant difference in distance between pure owners and those who 10
## _2. Existing tools for SDG benchmarking_

derived from network theory and economic complexity. Section 4 presents an application to a country‐specific case. Section 5 concludes with areas for further development and research.

# 2. Existing tools for SDG benchmarking

Several initiatives to define measurement standards, identify statistical gaps, collect cross‐country comparable data, and monitor progress in SDGs have been launched. Some of these recent efforts are highlighted below.

- The Overseas Development Institute (ODI) produced scorecards to illustrate long‐ term projections for the achievement of the 2030 Agenda (Nicolai, Hoy, Berliner, & Aedy, 2015). Each scorecard classifies the effort needed in three groups: (i) Reform – if there is a need for better targeted solutions to accelerate progress; (ii) Revolution – significant action and innovation required; and (iii) Reversal – complete and total revision is compulsory. The scorecards produce a more complete and sharper understanding of the global challenge; however, they rely heavily on regional aggregates, thus limiting the application at the country level.

- The SDG Index and Dashboards (Sachs, Schmidt‐Traub, Duran‐Delacre, & Teksoz, 2017) address country‐level objectives by ranking countries based on their overall SDG achievements. Presently, 39 indicators, equally weighted, are included in the index. An additional SDG Dashboard proposal was constructed as a tool to visually present data for each country and goal. Its purpose is to highlight one’s most pressing constraints to identify priorities in which an evaluation of policy is urgent. The initiative started with data for OECD countries.

- Le Blanc's (2015) study explores the extent to which the structure of the proposed goals and associated targets necessitate better integration across sectors through the analysis of the target statements. This study constructs a network built on the interpretation of the wording for targets as it has been stipulated in each goal. As such, the resulting network can be thought of as a “political mapping,” i.e., a reflection of the consensus among stakeholders as far as how they perceive the connection between the SDGs.

- The Country Development Diagnostics Framework (Gable et al., 2015) is used to examine country‐specific progress conducive to the achievement of the SDGs. The analysis relies on cross‐country data; however, the outcomes are specific to each country considering its capacity level, proxied by income per capita. The framework is implemented as follows:

- _Benchmarking recent outcomes_ for SDG target indicators inclusive of the factors (including policies) that influence them – how well is a country doing compared to other countries at similar levels of per‐capita incomes?

- _Projecting 2030 outcomes_ for selected indicators (when the association between GNI per capita and indicators is deemed sufficiently strong) – under current trends and given expected growth, what are likely achievements for SDG indicators by 2030?

- _Assessing options for accelerated progress_ – what can a country do to increase financial space destined to fulfill the SDG agenda?

3
# 2.4. PIRLS (2016)

The newest round of PIRLS includes 50 countries. It was designed to be comparable to prior rounds. Inclusion of PIRLS is straightforward and we use the same parameters as used in prior databases using the microdata. Overall, through the inclusion of recent rounds of PASEC, SACMEQ, and PIRLS as well as the new inclusion of EGRA, we are able to improve the quality of, and include recent data for, over 75 countries since Altinok, Angrist and Patrinos (2018).

# 3. Methodology

# 3.1. Linking Methodology

We build on a literature linking learning assessments starting with Barro and Lee in 2001. Our methodological approach in this paper extends and improves on the one detailed in Altinok, Angrist and Patrinos (2018) to build the most current database covering the largest number of countries to date. Our Harmonized Learning Outcomes (HLO) database includes 164 countries/territories from 2000-2017.

Various methodologies can be used to make assessments comparable. We do not attempt to equate scores. Rather, we link scores to achieve comparability (Kolen and Brennan, 2014; AERA, APA, NCME, 1999; Holland and Dorans, 2006; Linn, 1993; Mislevy, 1992). This distinction is important, since we do not claim tests or test scores are equivalent across tests. Rather, we aim to compare scores on a similar scale.

We do not link using Item Response Theory (IRT) – the technique used to generate scores for each respective international and regional assessment. IRT models the probability a given pupil answers a given test item correctly as a function of pupil and item-specific characteristics. While this methodology is used within each of the international and regional tests we use, to use it across ISATs and RSATs would require overlap in test items. This is not true for a significant enough set of tests and time intervals to create a globally comparable panel dataset. Moreover, even when there is overlap, for IRT to be reliable there must be large item-specific overlap. When this overlap is small, standard maximum likelihood estimates will reflect both true variance and measurement error, overstating the variance in the test score distribution. Das and Zajonc (2010) elaborate on the various challenges of estimating IRT parameters with limited item-specific overlap.

It is possible to empirically test the conditions under which IRT produces reliable estimates by examining differential item functioning (DIF). Sandefur (2016) equates SACMEQ and TIMSS with IRT methods. Sandefur (2016) measures the DIF as the distance between the item-characteristic curve (ICC) for the reference population and actual responses for the focal group, an approach first proposed by Raju (1988). The resulting DIF is high, casting doubt on the IRT approach in a context with limited item overlap, which is the case for most RSATs we aim to include in our database.

9