rafmacalaba/data-use-mentions-extended huggingface dataset:
on monthly maize prices observed in 72 markets across the country. We use deviations in 2012 from the mean price during the lean season January‐March, since this is the period prior to the harvest when households are most likely to need to purchase maize. We consider 2012 price shocks since we expect previous season prices to have an effect on producer decision‐making and well‐ being outcomes in 2013 without being caused by the weather shocks observed for 2013. Unlike rainfall, where very high and very low realizations are of interest due to their negative effects on well‐being, we construct the price shock as the difference in maize prices from the 9‐year average where those prices are higher, and set the shock to zero otherwise. Thus, we are capturing the consumer price shock in the lean season of 2012, which we expect to reduce consumption, and possibly income (Ecker & Qaim, 2011; Kaminski, Christiaensen, & Gilbert, 2014).

For health and off‐farm employment shocks, we relied on the spatial variation found in the IHPS survey responses. We proxy for illness shocks in the household by including a dummy for whether a household member had malaria in the two weeks prior to the interview. Perennial malaria transmission is intense in Malawi (Roca‐Feltrer, et al., 2012; WHO, 2014), and can have substantial effects on productivity and income (Gollin & Zimmermann, 2007; Asenso‐Okyere, Asante, Tarekegn, & Andam, 2009). The household economic impact we consider is whether the household received income from any off‐farm formal or informal employment (wages, ganyu, or self‐employment) in 2010 and lost access to that income in 2013. This loss of access to off‐farm income is included as a shock since off‐farm employment represents diversification of income, which is expected to contribute to a households’ ability to smooth consumption in periods when other sources of income are constrained (Morduch, 1995).

> 5 Flowering
the calibrated “frictionless norm” already includes frictions that may distort the real exchange rate. Further, the “norm” is calibrated as a typical value, which is not necessarily normative or frictionless. It is therefore difficult to interpret these measures as misalignment of the real exchange rate.

We propose an alternative methodology for assessing real exchange rate misalignment following the seminal work of Hsieh and Klenow [2009]. We propose to use firm-level or industry-level data to assess whether there are systematic dispersions in the marginal productivities of producers of tradable and and non-tradable goods. Such a dispersion would be indicative of a real exchange rate misalignment, as in the frictionless competitive equilibrium, marginal productivities are equated across all firms. We illustrate how this methodology can be useful not only in identifying and quantifying real exchange rate misalignment, but also in assessing its causes.

# **2 The real exchange rate, the terms of trade and the saving rate**

The starting point of the discussion on real exchange rate misalignments is that a devalued real exchange rate is tied to cheap exports and excessive saving. However, it turns out that the conditions under which these equivalences hold are quite subtle. Broadly, reducing domestic demand will result in a lower real exchange rate, and cheaper exports; however, a decline in domestic demand need not coincide with an increase in domestic savings. Supply shifters (such as subsidizing exports) may or may not result in a devalued real exchange rate, depending on the substitutability of inputs from the tradable to the non-tradable sector. When permanent, they will have no effect on savings.

To illustrate these interactions, consider an economy with three goods: an imported good (denoted with superscript _∗_ ), a domestically produced tradable good (denoted with superscript _T_ ), and a domestically produced non-tradable good (denoted with superscript _NT_ ). Consumers spend a constant fraction of their expenditures on each good; the expenditure shares are denoted _α_<sup>_∗_</sup> , _α_<sup>
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report should be considered only to be illustrative. Our data is as foilows.

The Mexican input-output data is given by a 72x72 matrix representing the year 1978.14 Since our current aim is to explain certain macroeconomic phenomena, we have aggregated this intermediate and final production to give a 7x7 matrix, the sectors of which are:

- (1) Agriculture

(1) Agriculture (4) Commerce (2) Manufacturing (5) Transportation (3) Petroleum (6) Communications and services (7) Imports For each of these sectors we have estimated shares of capital and labor in Cobb-Douglas production functions. We have not estimated the elasticities of government infrastructure, but have carried out simulations with alternative parameter values. The shares are: Table 5.1. Factor Shares in Private Production A/

|Sector<br>Share|of Capital<br>Share|of Labor|
|---|---|---|
|1|0.762|0.238|
|2|0.552|0.448|
|3|0.659|0.341|
|4|0.757|0.243|
|5|0.636<br>|0.364|
||n./aS|0.505|

A/ See Matriz de Insumo-Producto Afto 1978, (1983). Sector 7, imports, does not use inputs of capital and labor.

14/ See Matriz de Insumo-Producto Ahlo 1978, (1983). We aggregated the matrix by simply adding corresponding rows and columns.
the effect of a liberalization is to lower the price of the input and, as in the case of tariffs, there need be no change in a. If allocated in less transparent ways, there likely would be.

# _Entry and Exit Decisions_

The own wage elasticity workers "face" is a combination of the reallocation decisions of continuing firms, as well as the entry/exit decisions of new or leaving firms. If restrictions on foreign investment are lifted and firms become more adept at comparing labor costs and moving from country to country, the exit elasticity of wages may grow significantly with liberalization. However, the labor demand relations estimated here and elsewhere cannot capture this latter effect, however important. Fajnzylber, Ribeiro and Maloney (2000) do attempt to measure this effect.

In sum, theory cannot be relied on to clinch the case that trade reforms have increased labor demand elasticities. We turn, therefore, to the empirical evidence.

## **Il. Data**

We work with comparable firm level data from Colombia (1977-1991), Mexico (1984-1990), and Chile (1979-95). The data sets to be used will be those prepared in the context of the World Bank funded project "Industrial Competition, Productivity, and Their Relation to Trade Regimes" (Roberts and Tybout, 1996).5 These data sets have several advantages that set them above any other work done to date.

1. They have broad micro-level coverage, including most manufacturing

## lower elasticity.

TIn the case of Chile, the original data set was updated to cover the period after 1986, using infornation 10
ESS data have some features important for the analysis of informality which are, to our best knowledge, not available in other multi-country datasets (in particular, in EU LFS). First, ESS questionnaires for rounds 2, 3, and 4 (implemented in years 2004-2005, 2006-2007, and 2008-2009, respectively), allow users to identify employees working without a contract. By contrast, LFS data (both the anonymized data sets disseminated by _Eurostat_ and, for most countries, also the original datasets) allow users only to distinguish between permanent and temporary contracts, while answer ̳no contract‘ is not offered to respondents (like it was in round 1 of ESS)<sup>2</sup> . Comparison of ESS data of rounds 2-4 with those of round 1 suggests strongly that if the answer ̳no contract‘ is not included, the proportion of employees who do not answer the question about type of contract (or choose answer ―Don‘t know‖) cannot, in general, be used as a proxy for proportion of informally employed dependent workers (see Table A2 in the Annex).

Second, in ESS data, a distinction can be made between self-employed persons with and without employees, and in the former case the number of employees is reported as well. This is important because in many studies which use data without direct information on contract, employees are ̳assigned‘ to informal sector if they work in enterprises with 5 or fewer workers. It would then make sense to apply the same criterion to employers, i.e. to consider an employer with 5 or fewer employees to be working in informal sector. However, LFS and most other internationally comparable datasets provide, at best, only information on ―number of persons working in the local unit‖ of respondent‘s main job; in case of employers this of course cannot be considered as a proxy for the total number of employees working for him. Third, ESS data are available not only for all EU countries,
signals and the target variable, in this case the poverty rate, could change over time.

**3. Spatial extrapolation.** This is the most ambitious potential application of CDR data. In countries or regions in which no recent survey data are available, such as conflict-affected areas or countries that have experienced severe political instability, estimates can be generated by using survey data and CDRs for a similar location and then applying this model to CDR data from the target location. This approach requires significant assumptions, but it may be useful in cases where no stronger data source exists. To date, research into the practical applications of spatial extrapolation has been limited.

# **_Evaluating the Model_**

One major liability of machine-learning models is a phenomenon known as “overfitting.” Training data always reflect both meaningful “signal” and random “noise.” Overfitting occurs when the model being fit has so many free parameters that it fits to both the signal and the noise. In such a case, the model will appear to be an excellent fit for the input data, with high R<sup>2</sup> and similar values, but it will perform poorly when applied to previously unobserved data.

**13**
<br>(2011) (2010)<br>Percentage points<br><!-- End of picture text --> _Source: Authors’ calculation based on UNHS 2016/17; CEQ Institute database; and World Bank (2018a). Note for b): The year for which the analysis was conducted is indicated in parenthesis. The graph is ranked from the smallest to the largest poverty reduction effect, which is the change in the Poverty Headcount from market income plus pensions to consumable income. Except for Kenya, where the effect is the change from market income_ 23
a smoking mother), and logs of monthly temperature and humidity. PM 2.5 refers to average PM2 _._ 5 in weeks _t −_ 1 and _t −_ 2, where _t_ is the period of 7 days prior to interview. Note that reference period for morbidity questions is weeks _t_ and _t −_ 1. Panel A uses an indicator of PM 2.5 being above the U.S. standard, while Panel uses log of PM 2.5.

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direction|32|91%|31|89%|56|95%|
|<br>Headcount and gap<br>moving in different<br>direction|2|6%|4|11%|3|5%|

_Source: Authors’ calculations using DHS and MICS._

# _4.2: Comparison of Severity for Countries with Similar Headcounts_

In addition to understanding changes in malnutrition over time, it would also be useful to examine how the malnutrition gap and gap-squared vary for countries with similar headcount rates. If the malnutrition gap and gap-squared measures are substantially different for countries that have similar headcount rates, it would indicate that the gap measures are capturing information regarding severity that is not captured in the headcount measures. This is why we compare the gap and gap-squared measures for countries that have similar headcount rates, i.e. headcount rates within one percentage point of another country.

12
**13.3**|**10.7**|**16.4**|
|**The mixed-skilled group**|**7.4**|**4.9**|**15.4**|**9.8**|**5.5**|**98.1**|**6.1**|**3.9**|**7.9**|
|The agricultural group|5.1|2.7|9.0|6.4|3.6|15.2|5.0|2.3|15.9|
|The low-skilled group|5.2|3.9|11.7|5.6|4.0|75.9|3.8|2.8|4.7|

Source: Own calculations based on ENEU survey In 1999, teachers in the public education system earned higher mean hourly labor earnings than their counterparts in the private sector and in other occupations. This pattern changes at the tertiary level, where private school teachers earned twice the labor earnings of public school teachers. There is not a significant difference in basic public teachers’ mean hourly labor earnings in urban and rural areas. Basic public teachers’ hourly labor earnings are significantly above those earned by _the mixed-skilled_ group, _the agricultural_ group or _the low-skilled_ group. Teachers in the basic public school level earned three times more than the earnings of other workers. Alternately, adjusted real hourly salaries of primary teachers in public schools were higher than the adjusted real hourly salaries of those in the private schools.

16
Figure 1: Changes in average ad valorem trucking costs in Canada.

<!-- Start of picture text --> 1990 1995 2000 2005 2010<br>Year<br>.038<br>.036<br>.034<br>Mean transportation cost (500 km, truck)<br>.032<br><!-- End of picture text --> first decreasing – due, essentially, to reductions in labor costs at constant fuel prices – and then increasing – due, essentially, to increasing fuel prices at constant labor costs. They range from about 3.8% of the value of the shipment in the early nineties, to about 3.2% in the mid-nineties, with an average value of 3.4% (see Table 3). These figures are fairly close to the average ad valorem rates of 5% reported by Glaeser and Kohlhase (2004, p.206) using 2002 us data. As in their case, there is significant cross-industry variation in ad valorem trucking rates in our data. Table 11 in the Supplementary Appendix S.3 provides additional information on the highest and lowest ad valorem transport costs from 1992 to 2008. The highest ad valorem transport costs are for industries with low value to weight ratios (e.g., Cement Manufacturing and Breweries), with an average ad valorem transport cost across the top 10 industries in 2008 of 14%. The lowest are in industries with high value to weight ratios (e.g., Computer and Peripheral Equipment Manufacturing, and Medical Equipment and Supplies Manufacturing), with an average ad valorem transport cost in 2008 across the bottom 10 industries of 0.4%.

# **4 Empirical approach**

We now discuss the empirical model that we estimate and the different identification concerns that we need to address.

## **.1 Model 4**

The patterns documented in Section 3.1 reveal that there is a clear trend toward the
Upward mobility offered by agglomeration economies could be offset and hindered by climatic and environmental stressors. Therefore, we hypothesize that _large or dense cities that are more exposed to climatic and environmental shocks do not offer residents a higher chance to become or stay nonpoor, compared to cities of smaller size_ .

# **3. Methodology**

## **3.1 Data**

We selected Chile, Colombia, and Indonesia as the cases for this study to demonstrate the application of analytical approaches with and without panel datasets. Analyzing these countries also merits the test of the approaches in countries where poverty is measured by income (Chile and Colombia) and consumption expenditures (Indonesia). The setting of Indonesia—its rapid urbanization and heterogenous urban and climatic characteristics across subnational regions—is particularly suitable to our analysis. Highly urbanized countries like Chile and Colombia have useful density variations to explore as well.

To answer our research question and verify our hypotheses, we combined household surveys with climatic datasets. For Chile and Colombia, we constructed synthetic panel datasets out of repeated cross-sectional household surveys. Flood risk is estimated as a key climate factor for each town. For Indonesia, we relied on panel household surveys (IFLS), combined with two climate indicators: SPEI and the flood risk index.

## **Synthetic panel data for Chile and Colombia**

Following Dang et al. (2014) and Dang and Lanjouw (2013), we applied the synthetic panel method to the household surveys of Chile ( _Encuesta de Caracterización Sociooeconómica Nacional_ [CASEN]) 2011 and 2015 and Colombia ( _Gran Encuesta Integrada de Hogares_ [GEIH]) 2008 and 2010.<sup>3</sup> This method essentially exploits the time-invariant variables in the cross-sectional surveys and some cohort-based assumptions about the error terms to construct the synthetic panels. The methodology is described in detail in Annex B. Recent applications and further validations of the synthetic panel methods have been implemented using household survey data from various countries in Sub-Saharan Africa, East Asia
redrew the boundaries for those two places, and the segmentation was done again following the process previously described.

The last challenge with using this segmentation method was that buildings demarcated on OpenStreetMaps do not typically correlate directly with population figures due to factors such as the lack of information on building heights and data being collected through public reporting. Subsequently, a ground-truthing component was designed in order to validate the mauza segmentation. The ground validation exercises yielded several key findings, notably:

- The segmentation of EAs was relatively less accurate in densely populated urban regions as opposed to rural mauzas with scattered settlements.

- The number of households found within these segments varied, with some segments having more households than expected and some having fewer than expected.

Field protocols were designed in order to address these concerns. The protocols involved combining GPS coordinates and also training enumerators in the use of Google Maps to allow for systematic field-based adjustment.

# _Drawing the host sample_

Once the strata were defined, the first stage of selection was done at the mauza level by strata. The selection process used probability proportional to the population size of the mauza. As Table 4 shows, the sample for host communities was divided equally between the two strata, so 33 mauzas were selected in each stratum. Within each selected mauza, three segments were designated with equal probability of selection. Mauzas were allowed to be selected more than once.<sup>34</sup> If this occurred, then the number of selected segments within the mauza increased accordingly. For example, if a mauza was selected twice in the sample, six instead of three segments would be selected from the mauza, each segment containing 100-150 households.

The second-stage selection was made using the enumeration areas created for the selected mauzas. All the segments within a mauza had the same probability of being selected. The field team listed all households within the boundaries of the selected segment, subject to field protocols where necessary.

With the listing data available, the third stage was to select 13 households in each of
# Table 8: Analysis of classroom quality, teacher, and structural characteristics on children’s <u>communication and general knowledge</u>

||D<br>no|V: Communi|cation & Gen|eral (Raw sco<br>w/ teacher|re)|
|---|---|---|---|---|---|
||<br>controls|w/ controls|w/ teacher|<br> <br>& structure|IV|
||(1)|(2)|(3)|(4)|(5)|
|**Observed classroom quality**||||||
|ECERS-R|0.151**|0.073|0.085|0.080|0.120|
||(0.062)|(0.064)|(0.062)|(0.062)|(0.075)|
|**Teacher characteristics**||||||
|% of teachers w/ sr. secondary edu.|||0.935**|0.926**|0.908*|
||||(0.465)|(0.470)|(0.476)|
|% of teachers w/ post secondary edu.|||1.206**<br>(0.473)|1.196**<br>(0.477)|1.183**<br>(0.484)|
|Mean years of teaching|||<br>-0.044***|<br>-0.043***|<br>-0.045***|
||||(0.011)|(0.012)|(0.
−0.26±0.56 % when compared with the corresponding TCCON data. Per the ESA’s recommendation, we use only pixels with quality values greater than 0.5. The data have been downloaded from the ESA’s AWS repository managed by MEEO (Meteorological Environmental Earth Observation).<sup>14,15</sup> We filter the bias-corrected XCH4 data for local concentration anomalies, or differences between observed and background CH4 at each point.<sup>16</sup> We calculate background CH4 using the methodology of Hakkarainen et al. (2019), which isolates local anomalies from the three background components described above. We compute the daily median XCH4 for each 10degree latitude band and linearly interpolate the result to each S5P observation with 1-degree resolution. Following Hakkarainen, we use the median as the representative value because it is not skewed by extreme observations. We subtract this background value to compute the local anomaly for each observation. Then we compute monthly mean values of concentration anomalies for each 5-km grid cell in our database.

For this exercise, we overlay the 5-km grid with the sampling polygons for our high-priority GGUs. Numerous technical and atmospheric factors produce highly-varied sample counts. To ensure robust estimates, we confine our exercise to GGUs with at least 20 observations in each year.

Table 6 provides summary statistics on sample counts for the 775 GGUs that meet this condition. The median GGU has 2,033 S5P observations for the period 2019 – 2022, while the leastrepresented GGU has 92 observations and the most-represented has 77,011.

**Table 6:  Statistics for GGUs with minimum yearly S5P sample of 20**

GGU S5P Observations, 2019 - 2022 GGUs Min P10 P25 P50 P75 P90 Max 775 92 464 980 2,033 3,861 8,123 77,011

# **5.  Priority Area Monitoring – Illustrations**

This exercise aims to use methane data from the Sentinel-5P platform to track changes in emissions-intensive areas identified by EDGAR. In the previous section
|
|Rain|-0.341|||-0.315|||-0.656|||
||(0.627)|||(0.304)|||(0.915)|||
|R<sup>2</sup>|0.699|0.354|0.295|0.609|0.288|0.184|0.678|0.340|0.264|
|F|11.67<sup>***</sup>|7.14<sup>**</sup>|5.44<sup>**</sup>|7.48<sup>**</sup>|5.27<sup>**</sup>|2.94|10.55<sup>**</sup>|6.70<sup>**</sup>|4.67<sup>**</sup>|

Notes: DHS=Demographic and Health Survey. Weighted to national level with weights provided by the Nepal DHS in each year. Standard errors in parentheses. The notation<sup>***</sup> is p<0.01,<sup>**</sup> is p<0.05,<sup>*</sup> is p<0.10. Model (1) includes each regressor measured as of 1994 as linear variables; Model (2) includes only forested in 1994 as a linear variable; and Model (3) includes only forested in 1994 as a binary variable. F-statistics reported in the table are the partial F-statistic values for the “Forested” variable. All regressions have 15 observations at the sub-region level. Source: Authors‟ calculations based on MHP/NE/MI (2007), MH/NE/ORC (2002)
EMBI+ (r=33.6%) Debt/GDP (r=43.2%) Reserves/GDP (r=46.4%)<br>Latent factor Latent factor Latent factor Latent factor<br>20092010201120122013201420152016201720182019 20092010201120122013201420152016201720182019 20092010201120122013201420152016201720182019 20092010201120122013201420152016201720182019<br>20092010201120122013201420152016201720182019 20092010201120122013201420152016201720182019 20092010201120122013201420152016201720182019 20092010201120122013201420152016201720182019<br><!-- End of picture text --> to incorporate such data. However, remote-sensing data requires considerable geospatial preprocessing, domain-specific expertise as well as significant computational resources.

# **5.3 Lack of regional focus**

At the outset of this study, we selected the largest possible set of countries to identify general relationships between natural capital and government yields. This meant, however, that countryor region-specific characteristics had to be abstracted from for the sake of generality. There is little doubt that narrowing the scope to a region, e.g. Latin America and Caribbean or Southeast Asia, would allow us to account for relevant variables such as local crop types, resource dependencies or the effect of the El Niño–Southern Oscillation. Comparison of such regional studies with this extended set of countries would produce interesting insights and lead to relevant takeaways for local governments and policy makers.

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