rafmacalaba/datause-encoder-data huggingface dataset:
Figure 2.4 <!-- Start of picture text --> GDS £ GCF - Regional Groups, 1999<br><!-- End of picture text --> <!-- Start of picture text --> 40<br>Ss<br>35 -......<br>30 - 28<br>25 _ .___<br>15 - _ . -_ 10~~~~~~~~~~~~~~~~~~~~~~2 t . g I , -_<br>20<br>ASEAN ASEAN+ 3 APEC NAFTA EMU<br>NOTE: Bnrnei Darussalam and Myanmar are not included,<br>Source: The World Bank, World Development Indicators 2001<br><!-- End of picture text -->

# An **Open and Globally** Integrated **Region**

Both trade and FDI have been significantly liberalized in what the World Bank refers to as the "third wave of integration (or globalization)" beginning in the 1980s and progressing since then by virtue of transportation and communications technology, declining tariffs and lower barriers to FDI. 1<sup>4 7</sup> The effect of this globalization on East Asia is evident in the data presented below.

Trade expansion is an indication of the level of openness of an economy measured by the ratio of its total trade (imports and exports) to GDP. (See Table 2.4.) 40
Figure 1: Data‐intensities using US Census software expenditures over labor by sector (2010) <!-- Start of picture text --> Non-Capitalized Software Expenditures over Labour (ISIC Rev 3.1)<br>2.7<br>2.2<br>1.8<br>1.6<br>1.5<br>1.4<br>1.2<br>0.8<br>0.7<br>0.5<br>0.3 0.3 0.3 0.3 0.3<br>Source: US Labor Statistics and US Census.<br>Figure 2: Data policy index by country and type (2019)<br>Data policy restrictiveness index for digital innovation<br>CHN VNM IDN THA MMR KHM KOR MYS SGP TWN PHL MNG LAO JPN HKG<br>CA CBDF DP IL INF IPR<br>Post & TelecomEducationInsuranceOther transportComputerFinanceCom. equipmentChemicalsCoke & PetroleumFood productsMotor vehiclesRenting machineryOther businessMedical & OpticsRecreation<br>3<br>2<br>1<br>(D/L) Data-intensity US Census & BLS<br>0<br>1<br>.8<br>.6<br>.4<br>Restrictiveness index (0-1)<br>.2<br>0<br><!-- End of picture text --> Source: US Labor Statistics and US Census.

Figure 2: Data policy index by country and type (2019) Source: Authors’ using Ferracane and van der Marel (2018). Note: Latest year taken for 2019. Abbreviations in each column are consistent with Table 1 which provides the type of restriction falling into each category. Intellectual Property Rights (IPR); Cross‐border data flows (CBDF); Domestic use and processing of data (DP); Intermediate liability (IL); Content access (CA); Infrastructure & Connectivity (INF).

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and poverty gap estimates when using a FH model. Molina and Rao (2010) though, using the same data sources as Molina and Morales (2009) but applying unit level models and EB prediction, obtain considerably larger gains. However, when the available census and survey are not from the same year, small area estimates based on unit level models may result in biased estimates. In such scenarios, FH models offer an alternative because these do not require census microdata. Alternatively, twofold models such as that of Torabi and Rao (2014) could also be considered to achieve larger gains as noted by Molina (2019).

Another potential solution is to use only aggregated covariates in the model for the household level welfare. This alternative is presented by Nguyen (2012) in an application for Vietnam. The author proposes a model where the dependent variable is household level logarithm of per capita expenditure from a recent survey, in this case the Vietnam Household Living Standard Survey from 2006, whereas all covariates are commune level means. These means are obtained from a dated (1999) census, although the author notes geographic information system data (GIS) could also be included into the set of covariates. Nguyen (2012) obtains ELL estimates for small areas under that model and compares the performance with that of typical ELL estimates obtained using unit level covariates from the Vietnam Household Living Standard Survey from 2006 and the 2006 Rural Agriculture and Fishery Census. The author finds provinces and districts hovering around the middle of the distribution suffer from considerable re-rankings across methods, but those at the top and the bottom are relatively stable.

Lange et al. (2018) present an approach similar to Nguyen’s (2012) which the authors suggest as an alternative in cases when census and survey data are not from similar periods, though the same issues noted above for the ELL method would likely persist in a model using only arealevel covariates. Masaki et al. (2020) use a similar modeling approach to Nguyen’s (2012), but take measures to address some of the shortcomings of a standard ELL approach and obtain EB estimators of Molina and Rao (2010) which appear to be more precise. The authors conduct a design-based validation study using a wealth index constructed with principal component analysis using census data for Sri Lanka and Tanzania. Their results show the approach may hold promise. In the sections that follow, the different procedures discussed here are tested under model-based and design-based simulation experiments.

# **3 Model-based simulation experiments**

The simulation experiment described here is based on those conducted by Marhuenda et al. (2017) in which the true data generating process is a twofold nested error model. Such a model will better accommodate the usual applications of poverty mapping, where household surveys use two-stage sampling. In fact, the traditional ELL method includes random effects only at the cluster level but estimates are given for a higher level.

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The regions and samples of proposed projects considered via MCE and IPF are summarized in Table 3.

Table 3. Water Catchment Project Sample

|Region|Number of Projects in|
|---|---|
||IPF Sample|
|Biobio|14|
|Maule|13|
|O’Higgins|22|
|Valparaiso|12|

To compare IPF and MCE prioritization outcomes, projects were first assigned scores and ranked in regional groups, as in practice. They are also presented in ranked order as a pooled (all regions combined) group in the IPF analysis.

# Water Catchment Project Indicators

<mark>The selection of indicators used to construct the SEI reflects some of the key policy goals of the government with respect to developing water catchments. As discussed in Chapter 2, many of the intended benefits associated with improving water resources are strategic, environmental, and social – and these are often difficult to quantify and monetize. Therefore, economic analyses often result in calculated internal rates of return (IRRs) below the 6% threshold required for favorable recommendation. These low IRRs are likely due to undervaluation of some long-term benefits. Recognizing the importance of water resource development, however, the government</mark> implemented the Multi-Criteria Evaluation approach to assess these projects.

<mark>The IPF draws from the MCE’s components to select the sets of input indicators for the SEI and FEI. Data previously gathered for the MCE were simply re-organized to fit the format of the IPF approach, with three important changes. First, the selection of input variables required paring down the extended list of MCE indicators. The resulting IPF indicator set excluded some MCE variables either because (a) they brought redundant information to the analysis or (b) exhibited no variation across projects. Second, some indicators used in IPF drew directly on project data in natural units rather than the ordinal score (e.g., 0, 5, 10) assigned to projects in the MCE approach. Third, some values maintain their ordinal scores, but are transformed so that more positive scores are recorded with higher values (in the MCE, lower scores are attributed to better performance on sub-indicators). The resulting indicators are described in Table 4, and relevant transformations are described in Table 5.</mark> 25
**Figure 4 : Share of  Labor Income from Tradable Sector, 1996     Figure 5 : Share of  Labor Income from the Public Sector, 1996**

<!-- Start of picture text --> National National<br>Poverty Line Extreme Poverty Line<br>Extreme Poverty Line<br>Poverty Line<br>20000 80000 300000 1000000 20000 80000 300000 1000000<br>Adult-equivalent per-capita household expenditure, 1996... Adult-equivalent per-capita household expenditure, 1996...<br>Share of Tradable Labor Income Share of Public Sector Labor Income<br>density: Adult-equivalent per-capita household expenditure, 1996 density: Adult-equivalent per-capita household expenditure, 1996<br>Source: Authors' calculation based on EDAMS 1996 Source: Authors' calculation based on EDAMS 1996<br>.6 .6<br>.4 .4<br>.2 .2<br>Share of Tradable Labor Income<br>Share of Public Sector Labor Income<br>0 0<br><!-- End of picture text -->

**_Djibouti does not have formal safety nets to protect the poor from real income losses. The poor are supported informally by remittances from relatives living abroad or from better-off relatives living in Djibouti._** Table 2 shows net remittances as a share of total household expenditure. While the poorest households are net recipients, receiving equivalent of 7 percent of their total consumption, the richest households tend to be net distributors of remittances as indicated by the negative sign. High wages and other fringe benefits currently form the bulk of current expenditures, limiting the fiscal space to redistribute income to the poor _._ Non-wage allowances (pensions, health and family allowances, and other stipends) benefit primarily the upper-middle class. Access to the social security benefits depends on formal sector employment (mostly public sector employment), which is reserved largely for the middle class. The bottom decile, which includes the indigent population, receives only 4 percent of total government cash transfers—that is, 60 percent less than an average person. This contrasts with the eighth decile, which receives twice the average, and the tenth decile, which receives about the average amount of transfers. In Djibouti, virtually no formal safety nets (government-financed cash or in-kind programs targeted to the poor) protect the poor other than those financed by international agencies directed to refugees and victims of natural disasters.

**Table 2 Djibouti: Net Remittances Share in Overall Expenditure, 1996**

|**Income Categories**|**All Djibouti**|**Rural**|
|---|---|---|
|All|3.9|8.9|
|poorest|7.7|9.0|
|2nd quartile|5.4|11.7|
|3rd quartile|2.8|7.1|
|richest|-0.4|1.6|

_Note: Shares are expressed as a percentage of total household expenditure. Source: Authors' calculation based on EDAM 1996_ 9
estimates. However, since the sample size is reduced, the power of the corresponding tests is reduced as well. As this paper considers unbiased estimators, the choice of the boundary rule is not critical.

Decomposing the observed time series involves applying a high-pass wavelet filter from Daubechies compactly supported wavelet family _{h_<sup> ̃</sup> _j,l}_ (Daubechies (1992)). This yields a set of wavelet coefficients The application of a low-pass scaling filter, _{g_ ̃ _j,l}_ , then, yields a set of scaling coefficients: where _t_ = 0 _,_ 1 _, ..., N −_ 1, “mod _N_ ” notation stands for circular convolution, _h_<sup> ̃</sup> _j,l_ and _g_ ̃ _j,l_ are the _j_ th level MODWT wavelet and scaling filters, and _Lj_ defines the length of the latter filters. There are _Lj −_ 1 wavelet and scaling coefficients affected by the boundary rule. This number increases as scale _j_ increases. These boundary coefficients do not have any meaningful interpretation, and result from the fact that the observed data are not an infinite sequence.

The _j_ th level wavelet coefficients capture the changes of the time series associated with unitless scale _τj_ = 2<sup>_j−_1</sup> . The MODWT uses approximate ideal band-pass filters, where the band is given on the interval of frequencies [1 _/_ 2<sup>_j_+1</sup> _,_ 1 _/_ 2<sup>_j_</sup> ) and 1 _≤ j ≤ J_ . In order to establish the association of the wavelet coefficients with the processes they are used for analyzing, we invert the frequency scale and produce equivalent periods of [2<sup>_j_</sup> _,_ 2<sup>_j_+1</sup> ]∆ _t_ for the scales 1 _≤ j ≤ J_ , where ∆ _t_ are the time units.

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and the opposite trend for the unorganized sector. Instead, the primary issues are found in local areas.<sup>21</sup> This decomposition provides an important background setting for the Duranton et al. (2014) study of district-level determinants and consequences of land and building misallocation.<sup>22</sup> To summarize, to the extent that one can compare the two using the frameworks of this study and Duranton et al. (2014), land and building access appears the more critical constraint for Indian manufacturers, a topic further elaborated upon in Duranton et al. (2014). This gap is present in raw levels and appears to be worsening with time. Similar to energy movements, the land and building challenges are principally occurring within local areas rather than through spatial dynamics across the country as a whole. This rank ordering does not provide an excuse for delaying further improvements with respect to energy access and efficiency, but it does provide some praise for the progress made and baseline for policy priorities.

# **VII. Conclusions**

This paper has investigated a number of aspects about the levels and trends of electricity usage in Indian manufacturing. India’s manufacturing sector has undergone many spatial adjustments since 1989, and our work measures their impact for electricity consumption. On the whole, we can conclude that the spatial dynamics have not reduced India’s electricity consumption per unit of output. If anything, these dynamics have increased usage levels, and this is partly due to countervailing movements between the organized and unorganized sectors. Most of the aggregate declines in consumption levels have instead come from reductions within sites of existing activity. There has also been substantial convergence in usage levels for states and

> 21 It is noteworthy that the two key trends of spatial movement noted—the organized sector’s movement towards rural areas, the unorganized sector’s movement towards urban areas—are not do some obvious “deal” in terms of land and buildings. In both cases, the migration is towards places with at least as high cost shares as the places from which the migration is occurring, and the cost shares at the destination points are rising.

> 22 In terms of the other analyses undertaken in this paper, plant-level traits explain 54%-76% of the variation in land and building usage, which is an order of magnitude higher than what one can explain in terms of energy usage. Land and building consumption shares do not link to the economic development of states in that way that we observe for electricity consumption, but we do observe similar (in fact, stronger) convergence across locations with time. Industries do not display convergence respect to land and building consumption. There is no clear correlation between industry-level difference in rural-urban ratios and how important land and buildings are to industries. This contrasts with electricity, where the heaviest-using industries (e.g., basic metals, paper and paper products) tend to have the largest deviations between rural and urban usage rates.

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represents a 7.5% increase over the control group. We find no effect in Rwanda, with a point estimate close to zero with small standard errors. One interpretation of these differential effects rests on the legal rights provided by the programs, which we explore in Section 5.

For those impact evaluations with a second round of follow-up data, Table 4 shows that treatment effects are insignificant across the board. This raises the question of whether we are identifying a true increase in cumulative fertility, or merely a shortening of birth spacing with no total effect. To compare the relative empirical support for these two competing hypotheses, we test whether the first follow-up and second follow-up treatment effect coefficients are statistically different from each other and show the resulting p-values in the last row of the table. Except in the case of Ghana, we cannot reject the hypothesis of equality between the two-periods coefficient, suggesting a positive overall effect on fertility.

Table 5: Probability of having had an additional child at follow-up

||_Ethiopia DOT_|_Ethiopia_<br>_WALN_|_Togo_|_Rwanda_|
|---|---|---|---|---|
||Bu|siness training||Land titling|
||(1)|(2)|(3)|(4)|
|Treatment follow-up 1|0.061**|0.067|0.141**|0.008|
||(0.030)|(0.080)|(0.056)|(0.016)|
|Treatment follow-up 2|0.030<br>(0.036)|-0.068<br>(0.116)|||
|Observations|1,380|145|386|1,971|
|Control group mean|0.273|0.283|0.136|0.155|
|p-val|0.270|0.325|||
|Controls|YES|YES|YES|YES|
|Clustered SE|YES|NO|NO|YES|
|Strata fxed effect|NO|YES|YES|YES|
|Time lag b/w interven-<br>tion and follow-up 1|12 months|12 months|18 months|12 months|
|Time lag b/w interven-<br>tion and follow-up 2|24 months|48 months|||

Notes: This table describes the average treatment effect of each intervention on the probability that women had a child post-treatment, estimated with a linear probability model. Standard errors are clustered at the randomization level. The controls include: number of children at baseline, marital status, highest level of education completed, age at baseline, age square, a dummy equal to one if the woman is household head, and household size. If there is no baseline (as in Benin and Ghana), the variable number of children at baseline corresponds to the number of children born before the beginning of the treatment built from midline household roster. We include stratification fixed effects when required. We control for time when there are two periods. *, **, *** denote significance at the 10, 5 and 1 percent levels respectively.

Table 5 presents impacts on women’s probability to have given birth to at least one child between the intervention and the follow-up survey. We include all surveys where a baseline was conducted (i.e., we exclude Benin and Ghana). Women assigned to receive DOT are 18
to these biases. Second, we excluded workers in the agriculture sector and workers in the armed forces (major group 0).<sup>59</sup>

# Application #1: Estimation of employment levels

If green employment is defined as working in an occupation that performs at least one green task (meaning a positive GTI), then about 2.3 percent of workers have a green job in Indonesia. When using the broad dictionary, the share of green employment rises to 15 percent, meaning that the greening of the economy could push these workers to perform green tasks if greener technologies were applied in Indonesia (Figure 4.1 panel a).<sup>60</sup> Restricting the analysis now to the narrow definition of green jobs, the majority of jobs are relatively skilled ones: almost two-thirds of green jobs are in occupational group 7:Craft and related trade workers; 29 percent of jobs are high-skilled, either professionals or technicians but not managerial occupations (occupational groups 2 and 3, respectively). Managers, clerical support workers, service and sales representatives (group 1), and elementary occupations (group 9) only represent 6 percent of green jobs.

**Figure 4.1 Green employment, based on text analysis applied to ISCO-08 taxonomy**

# **a) Employment, by GTI**

**b) Share of green employment (narrow definition), by occupation major group**

<!-- Start of picture text --> 2.3<br>17.4 Managers<br>Professionals 11<br>Technicians and associate professionals 18<br>97.7 Clerical support workers<br>82.6<br>Service and sales workers 4<br>Skilled agricultural, forestry, and fishery<br>workers<br>Craft and related trades workers 61<br>Plant and machine operators, and<br>Narrow Broad assemblers 4<br>Elementary occupations<br>Zero GTI GTI<br><!-- End of picture text --> _Source_ : GTI and Sakernas 2017 Green employment is significantly larger when using the O*NET taxonomy due to the need to rely on crosswalks to transform occupations from O*NET SOC 8-digit to ISCO 4-digit. Given the lack of 59 Agriculture represents about a third of the employment in Indonesia, and hence it is an important sector to describe. However, the description of the green terms and the task statements were assessed not to be granular enough to produce a meaningful classification.

60 Notice that an alternative way of computing green employment would be to use the GTI to weight employment. This way of computing employment would assume that workers split their time equally across tasks. Since this is a strong assumption, we prefer not to use this measure.

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development of the stock market appears to be more associated with non-monetary financial depth than with monetary depth. Table 2 shows that the Pearson Correlation Coefficient is significantly positive at 0.1% level foi 2MY, DCPY and GNPCAP, but not for M1Y (The Coefficient is significantly positive at 3% for M1Y). This is consistent with the supposition that the development of the stock market is accompanied by financial deepening. It is also worth noting that the correlation is the strongest between MCY and DCPY, which means that countries with more developed stock markets have financial systems that issue more credit to the private sector as a share of GDP than countries with less well developed stock markets. This implies that there may be a complementary rather than substitution relationship between stock market development and financial intermediary growth. That is, a more developed st3ck market would allow firms to increase borrowing from financial intermediaries.

B. Correlations in the period 1978-1990 The same exercise as in the last sub-section is applied to the period 1978 to 1990, but to a smaller sample size (19), based on data from the IFC's Composite Index4.

This second data se. confirms the two patterns previously identified: the variations in the level of the stock market development across the countries are much larger than the variations in the level of financial depth across the countries; and the correlation of MCY with DCPY is the strongest among all the variables. These results are shown in Tables 3 and 4.

On the whole, the correlations in the period of 1978-1990 are weaker than those in the period 1984-1990. The weaker correlations for the longer period may reflect the fact that

> 4 The data series is much shorter for Zimbabwe and is excluded from our analysis even though it is included in the Composite Index.

_5_