rafmacalaba/datause-encoder-data huggingface dataset:
and likelihood of crop theft, for example. This finding would indeed render subjective assessments of soil quality significant predictors of agricultural productivity, but largely leaving true soil quality omitted.

Additional research is needed (and ongoing) to determine the effects of including these objectively measured soil properties in productivity analysis as opposed to, or in addition to, subjective assessments. Additionally, while a brief comparison of plot-level spectral analysis and AfSIS geospatial data was included to illustrate the ability of plot-level analysis to capture a greater degree of variation within small areas, further research in this arena would be valuable, including, for instance, a comparison of LASER results with the EthioSIS national soil map. Geospatial data on soil quality have been recently compared with subjective data by Kelly and Anderson (2016), who find a similar pattern in that farmers are often over-optimistic about the fertility of their soils with respect to the Harmonized World Soil Database. This line of work could be extended to include plot-level soil analysis and further validate the need for objective plot-level analysis.

Ethiopia is poised to benefit greatly from advancements in soil testing, particularly with the rollout of projects like EthioSIS combined with the upscaling of data collection efforts at the farm household level. The results of the LASER study, which bring subjective estimates of soil quality under scrutiny and point to the need for more direct, yet practical, soil measurements, show the potential value of the complementarities between platforms like EthioSIS, and household-level data collection, based on which accurate soil information can be made available as part of rich data sets on the socioeconomic condition and farming practices of farming units. Soil data collection through household and farm surveys may also provide a much needed vehicle to groundtruth remote sensing information and calibrate soil models. In this vein, fostering stronger linkages between national EthioSIS soil data and surveys like the Ethiopian Rural Socioeconomic Survey, a household panel survey supported by the LSMS-ISA, offers great opportunities from the research and operational perspectives.

Evidence from the Ethiopia LASER study suggests that subjective farmer assessments of soil quality poorly explain objective laboratory results and lack intra-household variation. Spectral analysis has been proven to near-perfectly predict key soil parameters as measured by conventional wet chemistry methods while providing highly detailed data that can be useful in policy aimed at increasing agricultural output, such as fertilizer input programs and identifying optimal crop selection, as well as agricultural productivity analysis. Improving agricultural statistics by reducing the uncertainties in soil quality assessment via objective measurement can enable better decision-making, both at micro and macro levels.

25
# **8. References**

Arellano, Manuel and Stephen Bond (1991). “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations,” Review of Economic Studies 58:277-297.

Arellano, Manuel and O. Bover (1995). “Another Look at the Instrumental-Variable Estimation of Error-Components Models,” Journal of Econometrics 68:29-51.

Barro, Robert and Xavier Sala-i-Martin (1995). Economic Growth. New York: McGrawHill, Inc.

Beck, Thorsten and Ross Levine (2004). “Stock Markets, Banks, and Growth: Correlation or causality,” Journal of Banking and Finance, 28, 2004, 423-442.

Impavido, Gregorio; Alberto R. Musalem; and Therry Tressel (2003). “The Impact of Contractual Savings Institutions on Securities Markets,” The World Bank, Policy Research Working Paper 2948.

King, Robert G. and Levine, Ross (1993a). "Finance and Growth: Schumpeter Might Be Right," Quarterly Journal of Economics, August, 108(3): 717-38.

King, Robert G. and Levine, Ross (1993b). "Finance, Entrepreneurship, and Growth: Theory and Evidence," Journal of Monetary Economics, 32(3), pp. 513-42.

Levine, Ross (1998). "The Legal Environment, Banks, and Long-Run Economic Growth," Journal of Money, Credit, and Banking, August, 30(3 pt.2), pp.596-613.

Levine, Ross and Sara Zervos (1998). "Stock Markets, Banks, and Economic Growth," American Economic Review, June, 88(3), pp. 537-58.

Levine, Ross (1999). "Law, Finance, and Economic Growth", Journal of Financial Intermediation, 8(1/2), pp. 36-67.

Levine, Ross; Norman Loayza; and Thorsten Beck (2000). “Financial intermediation and growth: Causality and causes,” Journal of Monetary Economics, 46:31-77.

Levine, Ross (2004). “Finance and Growth: Theory and Evidence.” Forthcoming Handbook of Economic Growth.

Outreville, J.F. (1990). “The economic Significance of Insurance Markets in Developing Countries,” Journal of Risk and Insurance, 57(3):487-498.

Swiss Re (2005). “World Insurance in 2004: growing premiums and stronger balance sheets” No. 2.

17
A limitation of this study is that it does not consider the cost of enforcing PNMC since this policy is achieved mostly by enforcing the existing legislation, which makes cost estimation difficult due to lack of data. Since reducing deforestation is also costly, the economy-wide costs of meeting the voluntary GHG reduction target in Brazil would be much higher than those presented in this study.

# **APPENDIX**

**Table A1.** Elasticities Used in the Model

|#|Industry|||||||||
|---|---|---|---|---|---|---|---|---|---|
|i01|Other agriculture|0.3|0.3|0.6||0.3|<br>0.6|<br>2.0|3.0|
|i02|Sugar cane industry|0.3|0.3|0.6||0.3|0.6|2.0|3.0|
|i03|Soy beans industry|0.3|0.3|0.6||0.3|0.6|2.0|3.0|
|i04|Forest sector|0.3|0.3|0.6||0.3|0.6|2.0|3.0|
|i05|Livestock sector|0.3|0.3|0.6||0.3|0.6|2.0|3.0|
|i06|Food and beverage|0.2|0.2|0.6||0.3|0.6|2.0|3.0|
|i07|Crude oil & natural gas|0.2|0.2|0.6||0.3|0.5|0.8|3.0|
|i08|Metal & mineral mining|0.2|0.2|0.6||0.3|0.6|0.8|3.0|
|i09|Coal mining|0.2|0.2|0.6||0.3|0.6|0.8|3.0|
|i10|Textile & leather|0.3|0.3|0.6||0.3|0.6|0.8|3.0|
|i11|Wood production|0.3|0.3|0.5||0.3|0.6|0.8|3.0|
|i12|Pulp paper & furniture|0.3|0.3|0.6||0.3|0.5|0.8|3.0|
|i13|Petroleum refinery|0.3|0.3|0.5||0.3|0.3|0.8|3.0|
|i14|Biofuels sector|0.3|0.3|0.5||0.3|0.6|0.8|3.0|
|i15|Chemical industry|0.3|0.3|0.6||0.3|0.3|0.8|3.0|
|i16|Non metallic industry|0.2|0.2|0.5||0.3|0.3|0.8|3.0|
|i17|Metal industry|0.3|0.3|0.5||0.3|0.3|0.8|3.0|
|i18|Machinery equipment|0.3|0.3|0.5||0.3|0.2|0.8|3.0|
|i19|Other manufacturing|0.3|0.3|0.5||0.3|0.6|0.8|3.0|
|i20|Electricity generation||||||||1.0|
|ei01|electricity: hydro|0.0|0.0||0.5|0.1|0.0|0.0||
|ei02|electricity: nuclear|0.0|0.0||0.5|0.1|0.0|0.0||
|ei03|electricity: coal|0.0|0.0||0.5|0.1|0.0|0.0||
|ei04|electricity: gas|0.0|0.0||0.5|0.1|0.0|0.0||
|ei05|electricity: oil|0.0|0.0||0.5|0.1|0.0|0.0||
|ei06|electricity: wind|0.0|0.0||0.5|0.1|0.0|0.0||
|ei07|electricity: biomass|0.0|0.0||0.5|0.1|0.0|0.0||
|i21|Processed gas|0.2|0.2|0.5||0.3|0.1|0.1|3.0|
|i22|Construction sector|0.3|0.3|0.5||0.3|0.3|0.8|3.0|
|i23|Commercial sector|0.3|0.3|0.6||0.3|0.6|2.0|3.0|
|i24|Transportation sector|0.3|0.3|0.6||0.3|0.3|0.8|3.0|
|i25|Other service sector|0.2|0.2|0.6||0.3|0.3|2.0|3.0|
||Household||||||0.6|2.0||

For each sector _i_ , the Armington elasticity for aggregating imported and domestic goods is denoted by . For all other elasticity notations, please see Figure 1 for details. Sources: Paltsev et al. (2005); Timilsina and Shrestha (2007).

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**Table 3. The macroeconomic effects of a 5 percent climate-induced shock to capital stock with reconstruction financed by redirection of public investment**

||Year 0|Year 1|Year 2|Year 3|Year 4|Year 5|Year 10|Year 20|Year 30|
|---|---|---|---|---|---|---|---|---|---|
|**Economic Activity**||||||||||
|GDP|0.0|-1.9|-1.9|-1.9|-1.7|-1.7|-1.5|-1.4|-1.1|
|Private consumption|0.0|-2.1|-2.2|-2.1|-1.7|-1.6|-1.3|-1.3|-1.1|
|Government consumption|0.0|-0.4|-1.0|-1.4|-1.7|-1.8|-1.8|-1.8|-1.4|
|Total investment|0.0|-2.9|-2.8|-2.7|-2.5|-2.4|-1.9|-1.2|-0.8|
|Exports|0.0|-0.1|-0.2|-0.3|-0.4|-0.6|-0.8|-0.7|-0.4|
|Imports|0.0|-2.2|-2.1|-2.0|-1.7|-1.5|-0.7|-0.4|-0.4|
|Current account balance<br>(pp of GDP)|0.0|0.4|0.4|0.4|0.3|0.3|0.2|0.0|0.0|
|**Prices**||||||||||
|Producer prices|0.0|0.0|0.1|0.1|0.2|0.3|0.3|0.1|0.0|
|Consumer prices|0.0|-0.0|-0.0|0.0|0.0|0.0|-0.0|-0.1|-0.1|
|Real exchange rate|0.0|0.1|0.2|0.4|0.5|0.7|1.1|0.9|0.5|
|Import prices|0.0|-0.1|-0.1|-0.2|-0.3|-0.4|-0.8|-0.8|-0.5|
|Export prices|0.0|-0.0|0.0|0.1|0.1|0.2|0.2|0.1|0.0|
|**Fiscal**||||||||||
|Revenues|0.0|-1.9|-1.9|-1.8|-1.5|-1.4|-1.2|-1.3|-1.1|
|Expenditures|0.0|-0.7|-0.8|-0.9|-0.9|-0.9|-0.9|-1.2|-1.1|
|Deficit (pp of GDP)|0.0|-0.2|-0.2|-0.1|-0.1|-0.1|-0.0|-0.0|0.0|
|Debt (pp of GDP)|0.0|0.7|0.8|0.8|0.7|0.7|0.5|0.4|0.1|
|<br>**Supply Side**||||||||||
|Potential GDP|0.0|-3.0|-2.8|-2.6|-2.3|-2.1|-1.6|-1.3|-1.0|
|Capital stock|-5.0|-4.6|-4.3|-3.9|-3.5|-3.2|-2.7|-2.1|-1.6|

_Sources:_ MFMod, World Bank; Authors’ calculations. _Note:_ Table shows deviations as percent from a baseline scenario.

24
proximity) between location _g_ of individual/firm _i_ and the nearby main road or the interaction between this variable and the submarine Internet treatment indicator. _HistRoad_ is the shortest distance to the historical network built from _i,g,t_ Murdock’s 1959 map (see below for the rationale and details on this instrumental variable), and _Submarine ∗ Internet ∗ HistRoadi,g,t_ is its interaction with the treatment variable. As additional instruments, we use geographic characteristics ( _geographyi,g,t_ ) including terrain elevation and lightening intensity, which are clearly exogenous. _Xi,g,t_ is a vector of control variables including age, gender, education, and place of residence; we also control for year fixed effect, country fixed effect, and grid cell by connectivity fixed effect as in the second stage equation below.

In a second stage, we examine the simultaneous causal impacts of the accessibility to major roads and the availability of Internet on employment status and other labor-market outcomes by estimating equation (2), which combines the two aforementioned strategies: with _Yi,g,t_ being individual _i_ ’s employment outcome (or firm _i_ ’s outcome). The variable _RoadProximityi,g,t_ is the proximity to the nearest major road. It is a continuous variable representing the negative distance to the nearest main road. The proximity to the nearest main road is measured by taking the length of the shortest straight line between the place of residence and the closest major road. However, given the fact that roads’ networks are probably not randomly built across cities in such direct routes, OLS estimates might be biased. Therefore, we use two instrumental variables, namely the land roughness and the distance obtained from exogenous historical theoretically built networks.

_Submarinei,g,t_ is a dichotomous variable taking the value one when a fiber node in a given area is connected to fast internet (submarine cables). The variable _Interneti,g,t_ takes the value one when the grid in which the individual _i_ is 16
of _Ecatepec de Morelos_ provides some basic information on schooling expectations in our setting. Among the 1,822 individuals aged 14-15, roughly 40 percent report secondary education as the highest schooling level that they expect to complete. Hence, there is a potentially very large fraction of high school entrants for whom the vocational track should be attractive. The size of the potential demand for the vocational track helps to contextualize the large effect of the cash transfers on track choices documented in Section 3.3.

# **4.2 Empirical Evidence**

High-ability students are more likely to attend university, and hence they foresee the higher labor market returns of an academic-oriented track. Those students are also more likely be less financially constrained due to higher access to merit-based scholarship at the upper secondary level.<sup>21</sup> We thus expect _Oportunidades_ to have a larger impact on high school track preferences among lower ability students. We use information on the cumulative Grade Point Average (GPA) in lower secondary and accordingly classify as high-ability and low-ability students whose GPA is above and below the median respectively. We allow the ITT effect to vary with the level of ability and we report the results in column 1 in Table 6. For those with a high GPA the effect of _Oportunidades_ on the probability of choosing the vocational track as first option is small and not statistically significant from zero, as opposed to a large and statistically significant effect (5.7 percentage points) for those with a GPA below the median.

Transportation costs may constrain school choices, especially in low income families. If _Oportunidades_ is changing preferences for high school tracks by relaxing the financial constraints, we do expect the effect on the probability of choosing the vocational track to be stronger among those applicants who live further away from a vocational option. We pull together the data on the entire set of preferences of all the individuals in our sample and we consider this as the universe of the high schools that students in our sample can potentially apply to. For each student in our sample, we compute the distance from its residence address to the closest vocational option and we use this as a proxy for the transportation cost that students have to sustain in order to attend a vocational

> 21For instance, students whose COMIPEMS score are high enough to enter _UNAM_ options are provided with direct access to university programs of the same institution - among the best ones in the country - and are automatically granted a scholarship that covers most of the school-related expenses both at secondary and tertiary level.

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# Annex 1 Table 1

|**# Rated**|**Cambodia**|**China**|**EAP**|**Indonesia**|<br>**Kiribati***|**Lao**<br>**PDR**|**Mongolia**|<br>**Papua**<br>**New**<br>**Guinea**|**Philippines**|**Samoa***|**Solomon**<br>**Islands**|**Thailand**|<br>**Timor-**<br>**Leste**|**Tonga**|<br>**Vietnam**|<br>**Total**<br>**Region**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|FY08-FY10 (# IEG Rated)|2|39|1|17||3|4|1|7|1||1|5|1|12|96|
|FY11-FY13 (# IEG Rated)|8|24|1|22|1|6|6|1|8|1|1|1|4|1|15|101|
|**Grand Total(#)**|**10**|**63**|**2**|**39**|**1**|**9**|**10**|**2**|**15**|**2**|**1**|**2**|**9**|**2**|**27**|**197**|
|FY08-FY10 (MU- %)|100%|8%|0%|41%||0%|0%|0%|57%|0%||0%|40%|0%|0%|21%|
|FY11-FY13 (MU- %)|25%|17%|0%|32%|100%|33%|33%|0%|75%|0%|0%|100%|100%|100%|27%|34%|
|**Grand Total(MU-%)**|**40%**|**11%**|**0%**|**36%**|**100%**|**22%**|**20%**|**0%**|**67%**|**0%**|**0%**|**50%**|**67%**|**50%**|**15%**|**27%**|
|Candor Gap Set #**|4|17|#N/A|19|1|5|4|#N/A|8|#N/A|#N/A|1|3|1|12|77|
|Candor Gap Set: % MU-|50.0|23.5|#N/A|36.8|100.0|20.0|25.0|#N/A|75.0|#N/A|#N/A|100.0|100.0|0.0|25.0|38|
|FY12-13 Sample #|6|12|1|11||4|5|1|6||1|1|4||10|62|
|FY12-13 Sample % MU-|33|33|0|64||50|40|0|83||0|100|100||30|48|
|Predicted MU- %|50%|24%|100%|37%|100%|20%|25%|100%|75%|100%|100%|100%|100%|0%|25%|38%|
|# IPFprojects inportfolio|9|108|1|34|4|20|13|10|14|8|5|1|4|4|52|298|
|Sample Size for Watch list†|5|25|1|13|4|4|3|10|11|8|5|1|4|0|13|112|

_Notes:_ FY08-FY13 data are from IEG RAP report.

* Kiribati and Samoa were not in the FY12-FY13 set.

**The calculation of candor gap for this report used the number of projects reviewed in the 18-month period 07/01/2103-12/31/2014.

†Watch list sample size was calculated using the candor gap set.

16
-7 -

secondary and higher education rise. The trends are relatively flat, however: the share of primary education, for example, falls only from 45 percent to 31 percent as per capita GNP rises a hundred-fold from $200 to $20,000. Moreover, there is wide variation across countries in the distribution of spending, as indicated by the small R2 statistic for all the regressions (last two columns in the table). The result implies that countries have substantial leeway to set priorities for spending across levels of the education.

**Table 1: Simulations of the sub-sector share of public spending on education at selected per capita GNP, circa 1993 (%) al**

|Sbt|||Per ca|pita GNP,|1993 $|||||
|---|---|---|---|---|---|---|---|---|---|
|u-secor|200|400|800|1,500|3,000|10,000|20,000|(1)|(2)|
|Primary|45.1|43.0|40.8|38.9|36.8|33.1|31.0|0.21|0.02|
|Secondary|25.8|26.8|27.8|28.7|29.7|31.5|32.5|0.06|0.03|
|Higher|17.1|17.7|18.4|19.0|19.6|20.7|21.4|0.05|0.05|

a! Excludes data for countries where primary and secondary are combined as basic education.

Note: The R<sup>2</sup> corresponds, in column (1), to a regression of the form noted in footnote 5, based on data for the whole sample; and in column (2), to the same regression based on data for countries with a per capita income below $4,000 in 1993.

Consider now the allocation of spending by expenditure category. The available data pertain to spending on financial aid for students (in the form of grants, scholarships, bursaries and welfare services); and on pedagogical materials. Figure 2 (panel A) shows that the share of financial aid in total spending on education follows a U-shape, falling from about 10 percent in countries at $200 in per capita GNP, to about _5_ percent among countries at $3,000, and then rising to about 8 percent among countries at $20,000. The U-shape is even more pronounced if we exclude the data for primary education where hardly any spending is used to provide student financial aid. The relation between the share claimed by student aid and the per capita GNP is relatively loose, however, with a R<sup>2</sup> statistic of only 0.06.9 It means that country wealth have little influence on the pattern of allocation.

With regard to spending on textbooks and pedagogical materials, we focus on the pattern at the primary and secondary levels where these inputs are a key determinant of student achievement. The figure (panel B) shows that as countries grow richer this category claims an 9 The R2 statistic for separate regressions on secondary and higher education is, respectively, 0.23 and 0.38.
<!-- Start of picture text --> 0 5 10 15 20<br>costperGDP<br>80<br>60<br>40<br>migpercap<br>20<br>0<br><!-- End of picture text --> Table 4 then examines how robust the relationship seen in Figure 3 is to the inclusion of other determinants of migration which may also be correlated with passport costs.

Column 1 shows that the negative relationship seen in Figure 3 is significant, although passport costs only explain a small fraction of the variation in migration. Column 2 adds log population, log national income per capita, and government effectiveness as controls. The addition of these controls causes the coefficient on passport costs to fall in magnitude, but it remains significant. One potential concern is that the relationship in Figure 3 appears nonlinear, and one may wonder whether all the results are being driven by countries with extremely high passport costs. We explore this nonlinearity in two ways. Column 3 drops countries in which passport costs are higher than 20 percent of per capita annual income, and shows that the magnitude of the coefficient on passport costs actually increases in absolute value. Columns 4 and 5 add a quadratic term in passport costs as a percentage of per capita annual income. Both specifications show evidence for

- 12 -
Ladics and colleagues (2018) show most low-inference tools used in low- and middle-income countries rarely provide explanations or examples on the content they measure. For example, observers are asked to code whether the teacher provides “feedback” to students without an example or explanation of what “feedback” entails. In addition to ambiguous content, there is rarely accuracy criteria for observers to follow, and inter-rater reliability estimates are rarely conducted on data from the field.

High-inference tools, on the other hand, present different challenges as they were not designed for use in low- and middle-income countries. Evidence from the Measures of Effective Teaching (MET) study in the U.S. indicates that to obtain a 77% passage rate on the reliability exam (after two attempts), highly educated and experienced observers are needed to code high-inference tools. On average, all observers who participated in the study held a bachelor’s degree, two-thirds held a master’s degree, and about 7% held a Ph.D. Moreover, over three-fourths of observers had six or more years of teaching experience (Kane & Staiger, 2012).<sup>5</sup> Wolf and colleagues (2018) and Bruns and colleagues (2016) allude to the complexity of these tools and discuss potential difficulties in using them as a regular monitoring tool in low- and middle-income countries. To become certified on these tools, observers must undergo an extensive training to reliably code the nuances of high levels of practice. However, in most low- and middle-income countries, observers will never face those high levels of practice, as evidenced from the CLASS applications in Chile and Afghanistan (Leyva et al., 2015; Molina et al., 2018b). The use of U.S. videos for highinference tool training is also problematic, as it does not adequately prepare observers for the challenges they will face while coding in low- and middle-income countries (Wolf et al., 2018).

To contextualize a tool for use in low- and middle-income countries, the _Teach_ team designed a tool that is easy to follow, is concisely written, and includes specific examples. The length of the observation was determined based on rigorous evidence that shows that precise scores could be achieved in a 45-minute observation but also from numerous 15-minute observations (Ho & Kane, 2013). The observation form and electronic data collection application were created to minimize mistakes in data entry. In addition, the

> 5 As part of a different study (Trako et al., 2018) that used the CLASS for coding Tanzanian classrooms and recruited Tanzanian expats and foreigners who had previously lived in Tanzania to take the CLASS training, all of which had at least a master’s degree. Of the eight participants, only five passed the CLASS reliability exam.

12
than the WASP(-1) for the concurrent regressions. Interestingly, the effects were stronger for regressions on GDP and industrial value added at a one year lag. In both cases the WASP(+1) was significant at a 95% confidence level. Regression coefficients were consistently negative, implying a negative impact of excess rainfall. The greater lagged effect may imply that the excess rainfall was associated with flooding that impacted infrastructure. The damage to infrastructure then resulted in reduced output in terms of GDP and industrial value added, both of which may be expected to be more dependent on infrastructure than agriculture. This offers the tentative evidence of a flood effect on these variables in a multi-country analysis. Evidence from case studies of individual flood impacts and of single countries is consistent with this finding. However, a better flood index is needed to explore this effect with more confidence.

In sum, this study has found evidence that climate has a statistically significant impact on economic growth of the countries of the world, and precipitation variability, as characterized by the WASP indices, is the most significant effect. These represent new findings with important implications for how we conceive the relationship between climate and economic growth and how we prioritize adaptation activities.

# **_Acknowledgements_**

The authors acknowledgement the encouragement, contributions and comments of Claudia Sadoff, David Grey, Nagaraja Harshadeep, Marianne Fay and Alexander Lotsch. The views expressed in this paper are solely those of the authors, as are any errors. This research was supported by the Earth Institute of Columbia University, the Bank-Netherlands Water Partnership Program, and the 2010 World Development Report.

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