rafmacalaba/datause-encoder-data huggingface dataset:
# **4 Data**

The estimation of the labor mobility costs is conducted using Labor Force Surveys (LFS) from Brazil, Mexico and Argentina. A common feature of these surveys is their rotative panel structure that permits the construction of employment transition matrices. We restrict the sample to individuals between 15 and 65 years of age. The industries are aggregated into seven sectors: i) Agriculture, ii)Mining and Utilities, iii) Manufacturing, iv) Construction, v) Commerce, vi) Hotels and Restaurants, and vii) Other Services. Each sector is further divided into formal and informal. A worker is considered informal if she is not registered in the social security system. Additionally, there is a residual sector that captures individuals who are either unemployed or out of the labor force.

The “Encuesta Nacional de Ocupacion y Empleo” (ENOE) is used to compute the transition matrices for Mexico. The ENOE is a household survey that collects detailed information on labor force status, wages, occupational and demographic characteristics. It is collected quarterly since 2005, and it is representative at both the national and state levels. The sample size is around 120,260 households in each quarter. Each household is interviewed in five consecutive quarters. In each quarter, one fifth of the sample (e.g., households in their fifth interview) is replaced. We construct the transition matrix by looking at the first and fifth interview. We derive six year-on-year transition matrices, the first matrix reflecting transitions between 2005 and 2006 and the most recent between 2010 and 2011.

The “Pesquisa Mensal de Emprego” is the Brazilian LFS. The survey collects detailed information of Brazil’s labor market and it is representative of only six metropolitan (urban) regions. Each wave of the survey has approximately 120,000 individuals. The panel structure consist on each household being interviewed 12 times over an 18 months period. In each wave of the survey, we identify individuals in their first interview and we follow them a year later at the time of their fifth interview. We computed four year-on-year transitions matrices that cover the period between 2007 and 2011.

The “Encuesta Permante de Hogares” (EPH) is used to compute the transition matrices 23
Baker, B.B. and R.K. Moseley, 2007. Advancing Treeline and Retreating Glaciers: Implications for Conservation in Yunnan, PR China. Arctic, Antarctic, and Alpine Research 39:200– 209.

Bali, R., K.K. Agarwal, S.N. Ali, and P. Srivastava, 2011. Is the Recessional Pattern of Himalayan Glaciers Suggestive of Anthropogenically Induced Global Warming? Arabian Journal of Geosciences 4: 1087-1093.

Barnett, T.P., J.C. Adam, and D.P. Lettenmaier, 2005. Potential Impacts of a Warming Climate on Water Availability in Snow-dominated Regions. Nature 438: 303-309.

Berthier, E., Y. Arnaud, R. Kumar, S. Ahmad, P. Wagnon, and P. Chevallier, 2007. Remote Sensing Estimates of Glacier Mass Balances in the Himachal Pradesh (Western Himalaya, India). Remote Sensing of Environment 108: 327-338.

Bharti, R.R., I.D. Rai, B.S. Adhikari, and G.S. Rawat, 2011. Timberline Change Detection Using Topographic Map and Satellite Imagery: a Critique. Tropical Ecology 52:133–137.

Bhattacharya, S., C. Sharma, R.C. Dhiman, and A.P. Mitra, 2006. Climate Change and Malaria in India. Current Science 90: 369-375.

Bhutiyani, M.R., V.S. Kale, and N.J. Pawar, 2007. Long-term Trends in Maximum, Minimum and Mean Annual Air Temperatures Across the Northwestern Himalaya During the Twentieth Century. Climatic Change 85: 159-177.

Bhutiyani, M.R., V.S. Kale, and N.J. Pawar, 2010. Climate Change and the Precipitation Variations in the Northwestern Himalaya: 1866–2006. International Journal of Climatology 30: 535-548.

Biodiversity Action Plan (BAP), 2009. Biodiversity Action Plan. Ministry of Agriculture, Royal Government of Bhutan. pp. 124.

Bolch, T., M. Buchroithner, T. Pieczonka, and A. Kunert, 2008a. Planimetric and Volumetric Glacier Changes in the Khumbu Himal, Nepal, Since 1962 Using Corona, Landsat TM and ASTER Data. Journal of Glaciology 54: 592-600.

Bolch, T., M.F. Buchroithner, J. Peters, M. Baessler, and S. Bajracharya, 2008b. Identification of Glacier Motion and Potentially Dangerous Glacial Lakes in the Mt. Everest region/Nepal Using Spaceborne Imagery. Natural Hazards and Earth System Sciences 8: 1329-1340.

Bolch, T., T. Pieczonka, and D.I. Benn, 2011. Multi-decadal Mass Loss of Glaciers in the Everest Area (Nepal Himalaya) Derived from Stereo Imagery. The Cryosphere 5: 349-358.

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45 Table 3. NRA dispersion across covered agricultural products in focus regions, 1960–2009 (percent)

||1960–69|1970–79|1980–89|1990–99|2000–09|
|---|---|---|---|---|---|
|Africa|31|33|36|29|36|
|Asia|45|45|59|57|69|
|Latin America|59|48|48|37|41|
|E. Europe-Central Asia|-|-|-|48|39|
|Western Europe|104|99|108|75|60|
|United States-Canada|24|23|67|35|31|
|Australia-New Zealand|36|35|18|13|6|
|Japan|55|119|159|149|124|
|**All focus countries**<br>**(unweighted average)**|**59**|**55**|**63**|**48**|**47**|

Source: Updated from Anderson, Cockburn, and Martin 2010, Table 2.4, using estimates in Anderson and Nelgen 2012b.

Note: For each region, dispersion is a simple average of the country-level annual standard deviations around a production-weighted mean of NRAs, across covered products, per country, per year.
65 years). To define our sample, we used two criteria: (i) we excluded age groups which may have been exposed to historical malaria that afflicted Sri Lanka before 1950; (ii) we focused on the rural sample. Note that the rural sample does not include any household that has moved to urban areas with all the members, but the split households where some members stay behind in the village are part of the sample. The number of adult females who were born after 1950 and are currently residing in rural areas is 10,850. The sample for the wage regressions are, however, smaller. Among females in our main sample (10,850), 42 percent are employed. About a third of those employed are self-employed. We have thus complete information on wages and other relevant variables for 2,918 females who were born after 1950 and live in rural areas. The dependent variable in the wage regression is deflated using the region specific consumer price index.

A key piece of information for our analysis is the amount of land under LDO restrictions in a DSD. We draw this information from the Agricultural Census of 1998. We estimated percentage of agricultural land under LDO leases (including permits and grants). The DSD identifiers in the HIES (2002) and Agricultural Census allow us to merge individual level data from HIES 2002 with data on percentage of land under LDO leases from Agricultural census. The geographic information including travel time from surveyed DSDs to major urban centers with population of 100 thousand or more are drawn from the Geographical Information System (GIS) database. The travel time is estimated using the existing road network and allowing different travel speed on different types of roads.

A critical variable for our instrumental variables analysis is the historical district level malaria prevalence rate. The data on historical malaria prevalence are taken from Newman (1965). The measure for malaria prevalence used in this paper is called Gabaldon’s endemicity index (see column 2 in Table 4, P.34, Newman, 1965). This index is based on the estimates of enlarged spleens in children due to malaria, and is a good indicator of the degree to which malaria is high and permanent in a district. However, we need a measure of malaria variations at the subdistrict level because the land restrictions vary at that level in the data. Also, we rely on district fixed effects in the IV regressions reported below in section 6 to control for unobserved land and labor 12
from the OECD do matter for the CEC. Second, regression analysis demonstrate that the problems with social security spending is largely due to loose eligibility criteria for early retirement, i.e. to a large number of early retirees. Third, non-parametric efficiency analysis reveals (< cost-inefficiency >> of transition governments. It seems that CEC the could enhance their efficiency by lowering and restructuring their government expenditure. The reform of future pension rights would contribute to this end. Fourth, it is recommended to introduce a countryspecific multipillar pension system . The transition from current pay-as-you-go to capitalisation system need to be financed by a combination of: (a) government debt, (b) state assets derived from privatisation, (c ) efficiency gains from expenditure management.

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35 relatively closed economy with exports accounting for less than 4 percent of total manufacturing output and imports contributing to only 11 percent of domestic supply of manufacturing output. Total factor productivity growth was therefore modest during this period increasing on average by 1.3 percent per year and contributing to 12 percent of output growth.

A third study worth mentioning is the World Bank Audit Report (1988) evaluating SALs IV and V. This report addresses the issue of productivity in the public sector in Turkey during the 1980s. By examining the evolution of labor productivity, defined as real output per employee, during 1982-86, the Report concludes that "improvements in SEE [State Economic Enterprises] profitability are due to price increases, not to efficiency gains" (page 84). This conclusion clearly contrasts with the findings of this paper. A number of reasons explain the contrasting conclusions. First, the results of the above-mentioned report are based on labor productivity as opposed to total factor productivity. Second, and more importantly, the data used in the above report apparently refer to all SEEs whereas the data used here refer only to manufacturing S:Es. Thus both the real output and the number of employees in the present paper are only a fraction of those contained in the above report. However, for the sake of comparison I also calculated labor productivity as defined in the Bank report for both the private sector and the public sector. The results are reported in Tables 26 and 27. The numbers -learly show that after a historic low in 1979-80, labor productiv<sup>4</sup> ty rises sharply in both the private sector and in the public sector. More interestingly, in conformity with earlier results, labor productivity in the public sector rises faster and its level is above that in the private sector during the 1980s.

In summary, the findings in the present paper appear to confirm earlier findings that trade liberalization is associated with improvement
and general knowledge.<sup>9</sup> Each domain is scored from 1 (low) to 10 (high). For the purpose of measuring gender gaps in non-cognitive skills, we are particularly interested in the social competence and emotional maturity domains of the EDI. Data are available for 8,653 children who were age 8 and below.<sup>10</sup> Summary statistics for the raw EDI score in 2013 are shown in Table 1. On average, the EDI scores of these children (ages 6 to 8) show a slight female advantage with girls scoring higher than boys in all five domains.

# _Enrollment_

Information on enrollment history in preschool and primary school for each academic year between 2008 and 2013 was collected from the mother or main caregiver of the 10,858 children in our sample.<sup>11</sup> For each academic year, we asked whether a child was enrolled and coded as 1 if they were enrolled and as 0 if they were not enrolled. For each academic year, we also asked how many months a child was enrolled. The response here ranges from 0 to 10 months.<sup>12</sup> We use this information to compute whether or not a child was ever enrolled between 2008 and 2013 in preschools and primary schools and the cumulative months of enrollment in each between 2008 and 2013. Preschool is defined as enrollment in kindergarten and playgroups, which are the two most common types of center-based services for young children before primary school in Indonesia. The summary statistics of rate and duration of enrollment are shown in Table 1. On average, 80 percent of girls report ever having attended preschools compared to 76 percent of boys. On average girls enroll for 14.3 months compared to 13.2 months for boys. Since all children were observed in primary school, there are no differences in enrollment rates though there is a small difference in duration of enrollment – on average girls have enrolled in primary schools for 0.5 months more than boys.

# _Quality of preschool services_

The quality of preschool services in this paper is measured using the Early Childhood Environment Rating Scale (ECERS-R) (Harms, Clifford and Cryer, 2005). Two raters assessed

> 9 The EDI domains have been shown to be correlated with children’s readiness for school. 10 EDI data were not collected for 9 year olds due to ceiling effects. There was very little variation at age 9 with almost all children scoring at the maximum end of the EDI scales, which range from 1 (low) to 10 (high). 11 2008 was chosen because it was the year before the Indonesia ECED project launched in 2009.

> 12 The maximum number of months is 10 months since we follow the Indonesian academic calendar.

8
**Table 4. Value-Added by Sector as a percent of the Value-Added of the Regional Market**

|Sector|Central|Far East|North|Siberia|South|Urals|Moscow|St.<br>Petersb.|Tumen|North-<br>west|
|---|---|---|---|---|---|---|---|---|---|---|
|MAR|0.6|0.6|0.7|0.7|0.5|0.8|0.3|0.6|0.7|0.6|
|AGR|14.8|7.6|3.7|8.7|15.8|8.7|1.2|3.9|0.9|10.0|
|AIR|0.5|0.6|0.5|0.5|0.6|0.6|1.2|0.5|0.3|0.4|
|CHM|1.9|0.1|0.8|1.1|1.2|2.0|0.6|0.7|0.0|2.8|
|CNM|1.4|0.5|0.4|0.5|0.8|0.7|0.7|0.8|0.1|0.4|
|COL|0.0|2.3|1.6|3.3|0.2|0.1|||||
|CON|10.2|3.1|3.0|5.6|13.3|8.5|11.2|9.7|2.5|7.6|
|CRU||1.9|8.3|1.0|4.8|5.5|||29.4|0.7|
|ELE|3.6|3.4|3.5|3.6|2.9|3.5|3.5|3.5|2.0|3.9|
|FIN|5.8|7.2|7.0|6.2|5.6|6.0|9.3|6.8|4.9|5.9|
|FME|2.5|0.2|0.8|0.9|0.5|1.9|0.2|0.4||7.5|
|FOO|3.3|4.0|1.8|1.8|3.0|1.6|2.7|5.8|0.1|3.1|
|GAS||0.1|0.2|0.0|0.2|0.2|||5.4|0.0|
|HEA|5.7|7.6|7.2|6.0|5.7|5.7|10.2|7.1|4.2|5.9|
|HOU|3.0|3.8|3.3|3.0|3.1|2.9|5.5|3.7|1.4|3.0|
|MWO|5.2|4.9|1.3|2.0|2.3|5.7|3.7|6.2|0.3|2.4|
|NFM|0.3|3.5|4.6|11.6|0.6|2.6|0.6|1.4||0.1|
|OIL|1.6|2.1|0.8|1.8|1.3|2.2|0.4|2.4|0.4||
|OTH|1.0|0.9|1.1|0.9|0.6|1.0|0.5|1.1|0.8|1.2|
|OTI|1.8|0.2|0.3|0.3|0.5|0.5|0.8|0.8|0.0|0.6|
|PIP|0.2|0.2|0.2|0.2|0.2|0.3|0.1|0.2|0.5|0.1|
|PST|0.4|0.4|0.4|0.4|0.4|0.4|0.6|0.4|0.3|0.4|
|RLW|3.1|3.0|3.1|3.1|2.6|3.0|2.8|3.1|1.7|4.1|
|SCI|1.2|1.4|1.2|1.1|1.0|1.3|1.3|1.4|1.0|1.0|
|TMS|1.0|1.2|1.1|1.0|1.0|1.1|1.7|1.2|0.8|1.0|
|TPP|0.9|1.9|7.5|1.3|0.3|0.7|0.5|2.4|0.1|2.8|
|TRD|27.5|34.2|33.0|30.8|28.3|30.1|36.8|33.3|40.2|31.9|
|TRK|1.4|1.5|1.5|1.4|1.4|1.4|1.9|1.5|1.3|1.5|
|TRO|0.6|0.7|0.7|0.6|0.6|0.6|0.9|0.7|0.6|0.6|
|CLI|0.5|0.7|0.6|0.5|0.6|0.5|1.0|0.7|0.2|0.5|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|

See Table 1 for sector codes.

Source: Regions of Russia, Roskomstat.

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In this setup, years of schooling are being adjusted by the number of years that it would take the numeraire country _n_ (given the average learning profile, now also defined in terms of shares who reach the benchmark) to get the same share of their students to the benchmark level as country _c_ does.

The advantage of this approach is that it is independent of the units in which the assessment is reported. Changing those units would lead to a concomitant change in the value set for the benchmark, and the share of test-takers above and below that benchmark would remain unchanged.

Figure 4.1 illustrates, again for the 35 countries TIMSS in Figure 2.1, how implementing _LAYS_ using the share of Grade 8 students who reach the “low” benchmark on the TIMSS mathematics assessment compares to the _LAYS_ using the average TIMSS score (shown in the dark dots).<sup>13</sup> Note that an implication of this approach is that it “counts” improvements only at the lower end of the distribution of learning. If a country were to improve average learning levels but this improvement were to come from, say, students in the middle of the distribution, then this would not increase its _LAYS_ value.

Panel A shows how R�� (the ratio that adjusts years of schooling for learning) is affected by this change in metric. Points below the 45-degree line are countries where the change leads to increasing the amount by which years of schooling are changed by the _LAYS_ adjustment. The _LAYS_ adjustment is exacerbated in countries that are already doing poorly. Countries where learning is poor on average (and where in addition it is highly unequally distributed) have especially large adjustments under the low benchmark approach. For example, in Morocco R��� is 0.20 lower than R�� (which is 0.61), and in South Africa and Saudi Arabia it is 0.23 and 0.25

> to start earlier, as described above, would be similar in this case.

> 13 The TIMSS “low” benchmark is set at score of 400. Students who have reached this benchmark have some basic mathematical knowledge such as adding or subtracting whole numbers, recognizing familiar geometric shapes, and reading simple graphs and tables (Mullis and others 2016). Singapore remains the best performer on this measure, with 99 percent of students reaching the benchmark (the same as in the Republic of Korea).  The median percentage of test-takers who reach the benchmark across the 35 countries is 85 percent, with the lowest performers at 34 percent (Saudi Arabia and South Africa).

17
Table 8: Two factor model: Do social components equalize farm size?

|||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|---|
|Regressor<br><br>|Coefcient||Ethnicity|Kinship|Ethnicity|Kinship|
|(_r−_<br>_r_)<br>_Nv_|_α_1|0.065***<br>(0.021)|||0.089***<br>(0.029)|0.152***<br>(0.037)|
|(<br>_r__~~−~~rj_)<br>_Nv_<br><br>|_α_2|0.065***<br>(0.021)|||0.089***<br>(0.029)|0.152***<br>(0.037)|
|(_ri−_<br>_rk_)<br>_Nv_<br>_Dij_<br><br>|_α_3||0.037*** <br>(0.013)|0.045***<br>(0.015)|-0.026*<br>(0.013)|-0.090***<br>(0.023)|
|(<br>_rk−rj_)<br>_Nv_<br>_Dij_|_α_4||0.037*** <br>(0.013)|0.045***<br>(0.015)|-0.026*<br>(0.013)|-0.090***<br>(0.023)|
|Constant|_α_0|0|0|0|0|0|
|Observations||54,144|54,144|54,144|54,144|54,144|

Note: The dependent variable is the amount of land transferred from _i_ to _j_ measured in hectares. Standard errors clustered by village are shown in parentheses.*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Source: Author’s own calculations on the Gambia Networks Data 2009.

The first set of results is presented in Table 8, which mirrors Table 6. Results are qualitatively similar, but point estimates are larger – albeit still well below 1. As before, equalization within villages trumps equalization within ethnic or kinship groups: point estimates _α_ 3 and _α_ 4 become negative once village level regressors<sup>_<u>ri</u>_</sup> _N_<sup>_−_</sup> _vrv_ and _<u>rvN−vrj</u>_ are included. In results not shown here to save space, the winsorization exercise conducted on the one factor model is repeated. Results are qualitatively identical, albeit with larger point estimates. Table 9 mirrors Table 7, with the inclusion of direct kinship or neighbor links between household pairs. Results are again similar to those presented earlier.

To conclude, results regarding factor ratio equalization are quite similar to those of land equalization: land transfers on average go in the direction of factor ratio equalization but fall far short of achieving it in full. We do not, however, find that this failure is driven by a propensity to equalize factor ratios primarily within ethnic or kinship groups, or between directly related households. There is, however, some evidence that neighbors share land to even out their factor ratio differences over and above what is explained by village-wide equalization.

29
- **_Experiment_** with different financial structures and approaches involving multiple World Bank institutions to better tailor them to client needs, especially in challenging contexts. IEG’s evaluation shows that projects with domestic investor participation, MDB involvement, and World Bank–IFC–MIGA collaboration have better PCM project outcomes than others, especially in the energy sector (World Bank, 2020). The World Bank’s IDA-IFC-MIGA Private Sector Window (PSW) is one innovative financial mechanism to support investment in IDA-only and Fragile and Conflict-affected Situations (FCS) countries. Since its inception in 2018-2019, its use has grown steadily, providing cumulative funding of $3.8 billion and demonstrating the viability of PCM approaches in the most challenging contexts.<sup>21</sup>

- **_Promote_** successful World Bank (and other MDB) efforts to mobilize private capital widely, while also learning from and being transparent about less successful efforts. Because the PCM efforts of MDBs cannot generate trillions of dollars of private sector investment on their own, demonstration effects could be crucial to enable the private sector to undertake such projects without MDB involvement. Promoting PCM experiences could also help generate greater academic interest in the topic, producing valuable research insights and rigorous tests for additionality. In this regard, the World Bank Private Sector Investment Lab, and its initial sectoral focus on renewable energy and energy infrastructure, is a welcome innovation for promoting interest in and housing knowledge on PCM.<sup>22</sup>

- **_Share_** relevant data and experiences with PCM. This, too, would help encourage more academic research on these topics. Again, the Private Sector Investment Lab would appear to be a natural repository for such data and knowledge. As noted above, because MDB participation in syndicated lending and public-private partnerships is well documented in global datasets, there is emerging empirical research in those areas. In contrast, to our knowledge, systematic information on MDB guarantees is not as readily available.

- **_Dive_** deeply to study the effects of a subset of projects. The monitoring and evaluation frameworks should be built into these projects while they are being designed. Attention should be paid to the types of data that must be collected to demonstrate the effects of projects on gender and poverty outcomes, which will often require representative household surveys. To identify the causal effects of such projects, approaches such as staggered program roll-outs and randomized controlled trials (RCTs) could be considered.

- **_Design_** upstream policy interventions for sector reform and to improve the enabling environment to complement PCM investments. This should be done intentionally and in

> 21 See https://ida.worldbank.org/en/financing/ida-private-sector-window/what-is-ida-private-sector-window

> 22 See https://www.worldbank.org/en/news/press-release/2023/06/22/world-bank-group-intensifies-focus-on-private- <u>sector-launches-effort-to-scale-investment-in-emerging-markets</u>

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39

# **Appendix 2: Sônia Rocha's (1993) Spatially Disaggregated Per Capita Poverty Lines**

|**PNAD Regions**|**Value(in Sept. 2004 Reais)**|
|---|---|
|Region I<br>Metropolis of Rio de Janeiro|234.59|
|Urban|145.44|
|Rural|105.57|
|Region II<br>Metropolis of Sao Paulo|249.96|
|Urban|157.48|
|Rural|99.98|
|Region III<br>Metropolis of Curitiba|200.91|
|Metropolis of Porto Alegre|139.48|
|Urban|127.65|
|Rural|85.10|
|Region IV<br>Metropolis of Belo Horizonte|192.79|
|Urban|129.16|
|Rural|75.18|
|Region V<br>Metropolis of Fortaleza|146.58|
|Metropolis of Recife|195.14|
|Metropolis of Salvador|224.02|
|Urban|132.00|
|Rural|79.21|
|Region VI<br>Brasilia|239.83|
|Region VII<br>Metropolis of Belem|135.91|
|Urban|120.96|
|Rural<sup>1</sup>|89.01|
|Region VIII<br>Goiania|227.91|
|Urban|173.20|
|Rural<sup>1</sup>|89.01|

_Note:_<sup>1</sup> The rural poverty line in Regions VII and VIII is the unweighted average of all other rural poverty lines.

_Source:_ Table XIII in Rocha (1993), inflated to 2004 R$ using the INPC deflator in Appendix 1.

39