rafmacalaba/datause-encoder-data huggingface dataset:
6 provinces, principally in Sumatra and Kalimantan, as well as several which are relatively densely populated and share some of the labour market characteristics of Java.<sup>9</sup> To assist analysis of labour market change, the Outer Island provinces (excluding Bali) have been assembled into five groups based on factors relevant to both labour supply and labour demand trends:

- (i) Relatively _resource abundant_ provinces in Sumatra, Kalimantan and eastern Indonesia (Aceh and Riau in Sumatra, East and Central Kalimantan, Irian Jaya and Maluku)

- (ii) Major _transmigration destination areas_ in southern Sumatra (South Sumatra, Jambi, Bengkulu and Lampung)

- (iii) Other _land abundant,_ mainly agricultural Outer Island provinces (West and South Kalimantan, Central and Southeast Sulawesi)

- (iv) The _out-migration_ provinces of Sumatra (North and West Sumatra)

- (v) _Densely populated and/or relatively poor_ Outer Island provinces (North and South Sulawesi, West and East Nusa Tenggara and East Timor)<sup>10</sup>

The grouping of provinces is to some extent arbitrary. The characteristics of several provinces overlap across these groups - or indeed almost any grouping one might attempt.

By way of background, a comparison is first made of the developments in these five Outer Island groups with those in Jakarta and the Java provinces and Bali over the period 1971-1990. We turn then more recent patterns of economic growth and labour market change

# **Economic Structure and Labour Markets 1971-90**

Although wage levels were lower, the more densely populated economy of Java-Bali was much more diversified than in most Outer Island regions when rapid economic growth began in the late 1960s. In general, the provinces of Sumatra, Kalimantan, Sulawesi and the eastern islands were similar to many other poorer Third World countries. The share of agriculture in regional gross domestic product (RGDP) was above 50 per cent in most regions. According to a range of other development

> 9 See especially Hill 1989.

10 East Timor is included in this group for the recent period but not for comparisons using the 1971 and 1980 census..
**Annex 4** - **Relative Importance of the 84-Country Sample in Developing World'**<sup>**8**</sup>

**GDP** of the 84-Country Sample as a share of GDP of GEP World

<!-- Start of picture text --> 10oo0% I- -.-.-.-..-..... _-.<br>90%<br>90% _<br>70%<br>80%<br>60%<br>40%<br>30%<br>20% -<br>10%-<br>0% --- I<br>1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1986 198e 1987 1988 1989 1990 1991<br>7 E __ 84 Developing Countries _______ C i Complement for GEP world_ _ __I<br><!-- End of picture text --> Stock of Flight Capital of the 84-Country Sample as a Share of Stock of Flight Capital of GEP World <!-- Start of picture text --> 100% - --<br>90% _<br>/0%<br>70%<br>D0%<br>D0%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1977 1978 1979 1980 1981 1982 1983 1994 1995 1988 1987 1988 1989 1990 1991<br>Z 84 Developing Countries Complement for GEP world<br><!-- End of picture text --> 1<sup>8</sup> GEP world refers to 151 countries as used in Global Economic Prospects, 1993. The World Bank, The GEP world figure of capital flight does not include data for all countries (outside the 84) for all years.
There is little, if any, _new_ information that congresspersons seek from lobbies in order to raise their re-election chances. And there is little, if any, new information that agriculture lobbies possess. At least in the agriculture case, then, we do not see a role for informational lobbying. Our experiment is different from Hansen’s (1991), who does find an important role for informational lobbying. He considers a longer period in history during which there were points in time when Congress sought new information from lobbies. At those threshold points, informational lobbying may have led to marked shifts in how Congress began to view agricultural policy. However, on a continuing basis over the 1991-2000 period we see little role for new information.

In the empirical section a reduced form approach is used to explore the association between lobbying spending and agricultural protection. This is the first evidence on such an association in the case of agriculture, and the nature of the exercise and the evidence is exploratory. The main message is evidence of a strong association. Whether the association is via informational channels or whether lobbying is payment for services are questions that are not directly addressed, for they require more theory-based investigation. Gawande and Hoekman (2004) undertake one such examination of the quid pro quo hypotheses. Perhaps, other second generation studies will use more formal structural models to investigate the relative importance of informational versus quid pro quo lobbying in the context of US agricultural policy.

# **4. Econometric Evidence**

# _A. Data_

Econometric evidence on the association between lobbying spending and protection in agriculture is presented in this section, using panel data for 44 commodities across 1985-2001. The USDA’s Production, Supply and Distribution database PSD Online (at <u>http://www.fas.usda.gov/psd/complete_files/default.asp) was the source for trade and</u> production data in order to construct the import penetration ratio. Time series data over 19852001 for farm products at the 4- and 5-digit Harmonized System (HS) level are available at 45
and 2017 school years in both CLRW treatment and control schools. Monitors attended reading instruction classes and assessed whether specific teaching practices and techniques promoted by the CLRW intervention were used by the teacher. Practices used by teachers during teaching and observed by the monitors were divided into 6 domains: phonemic awareness, phonics, reading of words, writing words and letters, forming of sentences and reading comprehension. For each of the practices within these domains, the observer rated whether the practice was fully, partially or not implemented. Classroom observations were conducted for all teachers in both 1st and 2nd grade of treatment and control schools. Estimates of effects described below account for unequal selection probabilities of schools and the small population of schools through a finite population correction.

The second source of data, the Tongan Early Grade Reading Assessment (TEGRA) was an adaptation of the Early Grade Reading Assessments initiated by the Research Triangle Institute (RTI International 2009) and adapted and applied to more than 100 languages in 65 countries (Dubeck & Gove 2015). The assessment was adapted to the Tongan language prior to its first implementation in 2009 and updated for subsequent rounds in 2014, 2016 and 2017. The assessment measured reading skills across 9 domains. Data were also collected on several measures of home support for reading.

The 2016 and 2017 rounds of TEGRA were used in this analysis to measure the impact of CLRW on students’ acquisition of reading skills during the school year. The survey was conducted in February and October of 2016, corresponding to the beginning and end of the 2016 school year, and in March and October 2017, corresponding to the beginning and end of the 2017 school year. All treatment and control schools were sampled. Within each school, all first and second grade classes were selected within which a random sample of students was drawn, stratified by gender and, for first grade, whether they had attended a CPBA previously. For the October 2017 round, third grade classes were also selected. For this analysis, standard errors were estimated to be robust for intra-cluster correlation within schools (the primary sampling unit) and adjusted for small population size both at the school stratum and student stratum levels. Sampling weights were calculated to account for unequal selection probabilities of schools and attrition of schools and students.

22
# **3.  Computations**

Stata operations begin with three datasets for Africa, Asia/Pacific and Latin America/Caribbean (LAC): (1) the regional Hansen grid-scale point files for 2001-2012, with each point identified by latitude, longitude, country and level-one administrative unit; (2) the Hansen point files for protected areas, with each point identified by latitude, longitude, WDPA ID number, and a unit entry that identifies it as inside the identified park; (3) the park boundary point files, with each point identified by latitude, longitude and WDPA ID number.

# **3.1 Boundary zones**

In the first computation step, we merge the point-level information in the two Hansen files by latitude and longitude. This enables separation of the merged file into points that are inside protected areas (identified by the parks file) and outside points (all the others). Then we use the Stata routine geonear to identify the point in the park boundary file that is closest to each point in the Hansen file. Geonear also computes the distance from the Hansen point to the closest boundary point. To keep the potentially-massive geometric calculations tractable, we use a looping routine that applies geonear sequentially to the point files for each level-one administrative unit.

After completing the calculation loops, we combine the level-one results into regional Hansen point files, where each point registers the annual grid cell Hansen pixel count for 2001-2012 and identification by country, level-one administrative unit, inside/outside status (relative to park boundaries), the park ID number of the closest park boundary point, the distance to that point, and the characteristics of the park (e.g., name, designated status, establishment year). The database offers complete flexibility for computation of outside/inside deforestation ratios, since the 5
technical and economic challenges need to be overcome in order for wind ultimately to displace significant fractions of fossil fuel electricity capacity in developing and developed countries. This could be accomplished through both further advances in the technology at a larger scale of deployment, and increases in the demand for wind technology as a consequence of a larger global market for CO2 mitigation investments.

# References

Amin, A.L. 1999. “Institutional Barriers to Commercialization of Wind Power in India: The Case of Gujarat.” Working Paper No. 7. Center for International Climate and Environmental Research, Oslo, Norway.

Anderson, D. and S. Winne. 2007. “Energy system change and external effects in climate change mitigation.” _Journal of Economics and Development_ 12: 351-357.

Archer, C.L. and M.Z. Jacobson. 2005. “Evaluation of global wind power.” _Journal of_ _Geophysical Research_ 110: D12110, doi:10.1029/2004JD005462.

Ayres, R.U. 2008. “Sustainability economics: where do we stand?” _Ecological Economics_ 67: 281-310.

Beck, F. and E. Martinot. 2004. “Renewable Energy Policies and Barriers.” In _Encyclopedia of Energy_ , ed. Cutler J. Cleveland. Academic Press/Elsevier Science.

California Public Utilities Commission (CUPC). 2008.

= <u>http://www.sourcewatch.org/index.php?title Comparative_electrical_generation_costs</u> , Downloaded on 16 January 2009.

Commission of the European Communities (CEC), 2008. Proposal for a Directive of the European Parliament and of the Council on the Promotion of the Use of Energy from Renewable Sources, Brussels, January 23, 2008. Available at http://ec.europa.eu/energy/climate_actions/doc/2008_res_directive_en.pdf DeCarolis, J.F. and D.W. Keith. 2005. “The Costs of Wind's Variability: Is There a Threshold?” _The Electricity Journal_ 18: 69-77.

Earth Policy Institute (EPI). 2008. “Eco-Economy Indicators.” Retrieved from <u>http://www.earth-policy.org/Indicators/Wind/2008_data.htm#fig2, May 29, 2008.</u> Energy Information Administration (EIA). 2008. “International Energy Outlook 2008.” Washington, DC.

28
Teachers Association Resolution 1-90 1990, DECS Region XI Office District of Davao Oriental).<sup>13</sup> Another, equally or even more plausible, explanation is that national legislators feared that devolution of teachers, who also served as the board of election inspectors and were responsible for protecting ballots and votes, would be subject to political control and manipulation by local politicians, many of whom are national legislators’ political rivals.

**Table 1: Comparative Features/Highlights of HB 31046 & Senate Bill 155 (Proposed Local Government Code) on the Powers, Functions and Responsibilities of LGUs**

||||**Local Government Code**||
|---|---|---|---|---|
|**Issues**|**HB 31046**|**SB 155**|**of 1991**<br>**(RA 7160)**|**Comments**|
|IRA|Gross internal revenue<br>collection; 25% of the<br>collection from the IR<br>taxes computed on the<br>basis of the gross<br>collection of the second<br>fiscal year preceding<br>the current fiscalyear.|35% of net IR collection<br>based on the second<br>fiscal year preceding<br>the current fiscal year<br>Sec. 247 (1)|40% of internal revenue<br>collections of the third<br>fiscal year preceding the<br>current fiscal year (Sec.<br>284 (c)).|The Aquino<br>administration<br>initially<br>proposed 20%<br>unconditional<br>and 5% subject<br>to revenue<br>effort.|
|IRA shares|25% to Prov<br>35% to Mun<br>25% to Cities<br>15% to Brgys|20% to Prov;<br>35% to Mun;<br>30% to Cities<br>15% to Brgys.|23% to Prov;<br>34% to Mun;<br>23% to Cities;<br>20% to Brgys(Sec. 285)|The final figures<br>are higher for<br>barangays.|
|IRA formula|Population 60%<br>Land area 20%<br>Equal sharing 20%|Population 35%;<br>Land area 35%;<br>Equal sharing 30%<br>(Sec 223)|Population 50%;<br>Land Area 25%;<br>Equal Sharing 25%<br>(Sec. 285)||
|IRA additional<br>incentives|An additional 5%<br>incentive for collection<br>efficiency among the<br>LGUs.||No additional incentives<br>in the final draft.||
|Elementary<br>school system||Sec. 27-B, Devolved the<br>elementary school<br>system|Elementary school<br>system was deleted and<br>only the school-building<br>program was devolved.|Amended in<br>Senate<br>deliberations<br>but dropped in<br>the final law.|

This brief and selective sketch of the politics of the LGC passage implies that: (a) the decentralization reform should be understood as an extension of the democratization process following the fall of the centralizing Marcos regime;

> 13 There were allegations of lobbying from the Education Secretary to remove the education system as one of the government services to be devolved and against other provisions pertaining to the education system in the proposed local government code as supported by the PPSTA (1990).

8
Barro and Lee (2000) statistics. In contrast, skilled workers - the sum of professional, technical, administrative, managerial, and skilled production workers - represent more than 60% of total workers in all but the pharmaceuticals industry. There seems to be a very important distinction between an educated workforce, which is lacking in all industries, and a skilled workforce, which is available in all industries. The difference in summary statistics for occupation-based versus education-based measures of skills is explained can be explained by skilled production workers. Production workers are a large fraction of the workforce in many firms and are likely to be proficient at what they do by virtue of experience or ability but they do not necessarily – most often don’t – have a large number of formal years of education.

# **_2) Integration into World Markets_**

Firms can be internationally integrated by virtue of ownership or by selling their products in foreign markets. Panel B of Appendix Table 3 shows the summary statistics for foreign ownership and exports. Foreign-owned firms represent about 10% of our sample. However, the leather/footwear industry has a much smaller proportion (4.2%) of foreign-owned firms. Export orientation varies significantly across industries. While virtually all firms in the ready-made garments industry and 91.7% of the firms in the leather/footwear industry are exporters, only 26% of firms in the pharmaceuticals industry export. In the ready-made garments and leather/footwear industries, most of the firms are majority exporters, i.e., they sell more than 50% of their output in export markets. In contrast, in the pharmaceuticals industry there are no majority exporters. Finally, firms in the garments and footwear/leather industries have on average a much longer experience in export markets than firms in other industries.<sup>28</sup>

# **_3) Technology_**

Technological progress is often measured by TFP growth for lack of more direct measures. In this paper, however, we rely on observable measures of technology. Measures such as the percentage of computerized machinery and the percentage of new

> 28 We use detailed information to construct the export experience variable. The survey asks the year since when a firm has exported, whether and in which year the firm has interrupted exports and if the firm interrupted exports, in which year exports restarted.

15
|Table A4. Information about interv|ention a|nd abou|tpoliti|cal disc|ussions||C in|C in|C in<br>control|
|---|---|---|---|---|---|---|---|---|---|
||N. Obs|All|T|T1|T2|C in T|<br>T1|<br>T2|<br>clusters|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Panel A: Information about visit||||||||||
|Received visit before elections (1=Yes)|2,637|0.71|1.00|1.00|1.00|0.00|0.00|0.00|0.00|
|Neighbors joined during visit (1=Yes)|1,867|0.11|0.11|0.08|0.15|--|--|--|--|
|Issues raised during visit||||||||||
|Importance of voting|1,867|0.64|0.64|0.98|0.19|--|--|--|--|
|Importance of voting in accordance to own preferences|1,867|0.06|0.06|0.02|0.12|--|--|--|--|
|Both|1,867|0.30|0.30|0.01|0.69|--|--|--|--|
|Discussed visit with neighbors(1=Yes)|1,867|0.41|0.41|0.35|0.50|--|--|--|--|
|Panel B: Information about political discussions||||||||||
|Discuss political issues with women in neighborhood (1=Yes)|2,637|0.44|0.55|0.50|0.62|0.40|0.30|0.51|0.18|
|Issues raised during conversations||||||||||
|Party/Candidate Positions|847|0.61|0.62|0.61|0.63|0.59|0.58|0.61|0.59|
|Importance of voting|847|0.86|0.85|0.85|0.86|0.86|0.89|0.83|0.88|
|Importance of voting in accordance to own preferences<br>Notes: Data come from follow-up survey. Column (1) refers to th<br>data for treated women. Columns (6)-(8) report data for control<br>women in control clusters.|847<br>e numbe<br>women i|0.52<br>r of ob<br>n treat|0.53<br>servati<br>ed clus|0.53<br>ons for<br>ters. Co|0.53<br>column<br>lumn (9|0.47<br>(2). Co<br>) repor|0.49<br>lumns<br>ts data|0.45<br>(3) -(5<br>for co|0.51<br>) report<br>ntrol|
Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

# **B. Key indicators for the analysis of human mobility**

The analysis of patterns of human mobility during COVID-19 in The Gambia is based on a set of mobility indicators, which is calculated based on CDR aggregates. The indicators capture changes in population movements during the baseline, under COVID-19 and post-intervention periods, and results can be updated continuously as additional CDR data becomes available. We used the first two weeks of March to obtain indicators about routine baseline mobility. It could ideally be computed for a period of four weeks before the initial COVID-19 cases were announced, which was March 17, if the data before March were available.

The standardized indicators were proposed by the World Bank COVID-19 Mobility Task Force and build on a framework developed by Flowminder to support MNOs in producing basic indicators from telecom data (see Flowminder COVID-19 Resources - Mobility indicators). Methodologies for computing indicators were designed with the considerations that it is not excessively computationally intensive to produce even in resource scarce settings, fully anonymous and contain no information about individual subscribers, ensuring that the privacy of subscribers is maintained at all times, and robust to sparse tower distribution and to infrequent phone usage, both of which are common in low-and middleincome countries. It is composed of eleven key indicators that provide proxies for population, location of residence, distances traveled, and daily mobility across regions at different geographic and time levels. It also includes proxies to scale other indicators and as the input of epidemiological modeling. For this project, we selected four out of the eleven indicators. Table 1 summarizes the descriptions of the selected indicators based on information on the World Bank COVID-19 Mobility Task Force repository on GitHub.<sup>4</sup> <mark>Table 1.</mark> Summary of key indicators for the analysis

|Indicator ID|Frequency|Definitions|Use-case|
|---|---|---|---|
|Indicator 3 - Count<br>of unique<br>subscribers|Day|Represents the number of<br>unique IDs active (call, SMS,<br>and data communication) within<br>a day and region.|Proxy for population<br>count.|
|Indicator 6 -<br>Number of<br>residents|Week|Defines the modal location of<br>the last observation on each day<br>of the week, where the user is<br>most frequently in the evenings<br>or at night.|Proxy for home<br>location, flexible on a<br>weekly basis to capture<br>short-term relocation.|
|Indicator 7 - Mean<br>and Standard|Day|Captures movements across<br>regions and within a region|Proxy for daily distance<br>traveled.|
|Deviation of||where distances are a function of||
|distance traveled||the cell-towers density.||

> 4 Retrieved August 15, 2020 from Github, _<u>covid-mobile-data/cdr-aggregation</u>_ <u>.</u>

**9** | P a g e