rafmacalaba/datause-encoder-data huggingface dataset:
real GDP growth and annual real GDP good indicators of its economic growth and market size. Data for real GDP and per capita GDP are drawn from World Bank’s World Development Indicators. Real GDP data are measured in constant 2000 US dollars.

It is well substantiated that country-specific institutional and policy factors are important to MNC’s investment location preference. Easy access to market, favorable investment regulations as well as domestic political stability are all among the top factors controlling the direction of their investment flow. However, to correctly interpret the findings on the relevant determinants of FDI, one should bear in mind the distinction between two types of FDI identified by their respective motivations. Market-seeking or horizontal FDI, with a focus on the market in host country, chooses local production to avoid the associated costs of trade. It is the type that dominated the FDI flows in the early days and still plays important role in today’s global economy. Apart from that is the so called cost-minimizing or vertical FDI where investors pick up different locations for each link of production chain to reduce the overall costs. Hanson, Mataloni and Slaughter (2001) indicate that recent surge of FDI inflows, especially to the South, is mainly of vertical form. Similar conclusion is also found in Markusen and Maskus (1999). Distinction on the type of FDI will have different implications on its location determinants, e.g. market size, trade restrictions. In our analysis, trade restrictions are controlled for by including both import tariff rates and openness to trade. Import tariff rates are collected from IMF Government Finance Statistic Yearbook as well as national statistical agency websites and are measured as percentage of import tariff on total revenue from international trade and transactions. And openness to trade is calculated as the ratio of country’s trade turnover to its GDP.

14
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<mark>Larrick, R. P., Burson, K. A., & Soll, J. B. (2007). Social comparison and confidence: When thinking you’re better than average predicts overconfidence (and when it does not).</mark> _<mark>Organizational Behavior and Human Decision Processes</mark>_ <mark>,</mark> _<mark>102</mark>_ <mark>(1), 76‐94.</mark> <mark>Miller, D. T., & Ross, M. (1975). Self‐serving biases in the attribution of causality: Fact or fiction.</mark> _<mark>Psychological Bulletin</mark>_ <mark>,</mark> _<mark>82</mark>_ <mark>(2), 213‐225.</mark>

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<mark>Zenger, T. R. (1992). Why do employers only reward extreme performance? Examining the relationships among performance, pay, and turnover.</mark> _<mark>Administrative Science Quarterly</mark>_ <mark>, 198‐ 219.</mark> 14
# TAB:I- II (contlnued)

|MEXICO|I911|937|19|17119|t199|1991|1992|
|---|---|---|---|---|---|---|---|
|OVERALL CAPrrAL ACCOULNT<br>BALANCE||||||||
|*IlMItlImuofUSDoli|ism|1610|.4200|4140|0420|29520|24540|
|* %hfoDP|1.23%|1.15%|.2.4U%|_200%_|3116|720%|7.36%|
|GOvERW7CONsumPnoNI%G013|9PIEFa|379%|564%|345%|it.42%|901%|1010%|
|AVERAGE INFLATIONC%)|16.23|131.13|1141b|20.01|2665|_221_b|15.51|
|PWALEFPECIVEIIXCH.IRATEfClS'IW|30.53|30.71|10076|106.43|11239|12994|14343|
|CURRENTACCOUNTDIPICITI%OGDP)|0.54%|.2.34%|1.61%|3.11%|2.19%%|4-76%|700%|
|PIIILUPPINIS|1936|1937|19U|1939|19|1991|1992|
|OVERALL CAPITAL ACCOUNT BALANCE||||||||
|inmIIIlon orUS Dolls|ISO|336|1064|1752|2hS1|2792|2610|
|A%OrGDP|060%|1.16%|2.30%|_4.1M2_|6.03%|619%|**4.97%**|
|GOVERrTCONSUMFI1ON(%GDPI|795%|8.36%|3.99%|923%|10.17%|_to24%_|9.7t10|
|AVERAGEDNFLATIONRC)|0.75|3.79|37h|.<br>t.21|14.14|19.71|392|
|REALEFPECflVEEXCH.RATE<br>C,tSOI)|71.03|71,11|69.t3|74.93|7290|**71.03**|**79.87**|
|CURMNTACCOUNTDEPICITI%AC0DP)|-1.02%|'1.62%|.2.14%|243%|624%|3.54%|6.00%|
|TIIAILAND|1936|I237|alp|m999|190|1991|1991|
|OVERALL CAP1rAL ACCOUNr BALANCE||||||||
|**-InMIillonsaeUSDollm**|467|130l|4251|7523|10520|12225|9607|
|- %etGDP|1.12%|2.69%|7.12%|I0.37%|13.13%|13.10%|915%|
|GOVERITMCONSUNMW'ON"I9|1321%|11.76%|10.40%|9.35%|9.71%|9.,5%|970%|
|AVERAGFEINFLATION(%)|1.U4|2.47|3.16|5.36|5.93|570|4.14|
|REALEFFECTIVEEXCLRATE<br>(S4-10)|103.4|110.14|111.50|110.52|113.67|120.56|124.75|
|CURRENTACCOUNTDEFICTr(%GDb)|1.46%|0.33%|0.64%.|1.75%|6.77%|3.63%|5.32%|

TABLE 12 <!-- Start of picture text --> Average Capital INloWS Trend in Government Consumption Change in the Real Exchange Rate<br>19891M (% of GDP) (cbange in percentage points of GDF Index<br>198692) (in % 1939-92) (1)<br>Thailand: 11.6%h Dccreasing: -335 + 13%<br>Malysia: 9.7% Decre;sing: -34 - 1%<br>Chile: 7.5% Decreasing -2.9 + 6%S<br>Philippincs: 53% Increasing: +1.8 + IMP)<br>Mexico: 52% lncreasing: 1.0 + 37W<br>Indonesia 4.1% Decraing: -1.4 + 5°h<br>Argentina: 14% Increasing: +4.5 (2) + 96% (3)<br>Kora: -12%h Increasing: +1.3 + 14%(4)<br>Notes:<br>(1) A positive number mens an appreciating RE.<br>(2) 987-92<br>(3) 1390-92 Bilaterl REER (CPI)<br>(4)5) 1933-92<br><!-- End of picture text -->
**9**

Asia, this holds across all continents, including Sub-Saharan Africa. It further appears that overall GDP growth in agriculture has been largely driven by growth in agricultural productivity, while growth in the non-agricultural sector has been largely fueled by population growth in nonagriculture especially in the developing countries (except Eastern Europe and Central Asia). Finally, while population growth has been the key contributor to overall growth in both sectors in SSA, agricultural productivity growth exceeded productivity growth in the non-agricultural sector.

**Table 2: Average agriculture and non-agricultural GDP, productivity and population**<sup>**1)**</sup> **growth rates, 1960-2003 by continent**

|**Average**<br>**annual**<br>**rates (%)**|**growth**|**Agric.**<br>**GDP**|**Labor**<br>**productivity**<br>**in agriculture**|**Population**<br>**in**<br>**agriculture**|**Non-**<br>**agric.**<br>**GDP**|**Labor**<br>**productivity**<br>**in non-**<br>**agriculture**|**Population**<br>**in non-**<br>**agriculture**|
|---|---|---|---|---|---|---|---|
|Sub-Saharan Africa||2.6|0.91|1.7|3.8|-0.64|4.5|
|South Asia||2.9|1.2|1.6|5.8|2.2|3.6|
|East Asia & Pacific||2.3|2.9|-0.5|5.7|2.7|2.9|
|Eastern Europe &|Central|0.81|3.4|-2.5|2.6|1.4|1.2|
|Asia||||||||
|Europe, others||1.5|4.6|-3|2.9|2|0.87|
|Latin<br>America|&<br>the|2|2.3|-0.24|3.3|0.48|2.8|
|Caribbean||||||||
|Middle East & North|Africa|4.4|4.3|0.21|4.7|0.26|4.4|
|North America||1.7|3.9|-2.1|3|1.8|1.2|
|Total||2.3|2.4|-0.05|3.9|0.74|3.1|

1) Figures for the total population in agriculture and non-agriculture were obtained from the FAO statistics. Source: Authors’ calculations based on World Bank (2005c) and FAO data.

Nonetheless, it is quite possible that the trends observed in Table 2 simply reflect equilibrating movements of labor out of agriculture into non-agriculture in response to higher marginal products of labor (and thus wages) in non-agriculture (a phenomenon also known as “industrial pull”). This would lead to a convergence in sectoral labor productivity and is consistent with the observed faster (labor) productivity growth in agriculture. The faster agricultural labor productivity growth is in this view a result of labor movements out of the sector (consistent with labor surplus in agriculture) rather than a result of an increase in agricultural output.

Alternatively, if the observed agricultural labor productivity growth resulted from increased agricultural output, following investment and technological change in agriculture (or an increase in total factor productivity), productivity increases would free up labor in agriculture and induce it to
**Policy, Research, and External Affairs**

**Macroeconomic Adjustment and Growth** WPS 474

This paper -a product of the Macroeconomic Adjustment and Growth Division, Country Economllics Dcpartment is pail of a larger effort in PRE to analyle the impact of adjustment programis on **ilcome** distribution. Copies are available free from **thc** W'orld Bank, 1818 li Street NW, W'ashington DC 20433. Please contact Patricia Dixon, room 1NI11-025, extension 39175 (37 pages). Maasland reviews the different mcthods for struct a computable general equilibrium model measuring ho" adjustment affects the distribuwith whlich to generate quantitative estimates of tion of income and characterizes them as qualitathe impact of adjustment policics. Livc or quantitative - and gcneral equilibrium or partial equilibrium. Betwcen these extremes, other methodoloBetwcen these extremes, other methodolo- gies may be applicable depending on the availability of data and the particular focus of the reform program. Partial analysis may be retevant if a country faces special issues.

No single integrated model can answer all questions. Thc most practical approach for a particular country depends on the issues that the country faces -and available data and resourccs.

Maasland found that a study of **the** effects of macroeconomic policy on distribution will benefit from an analysis of microcconomic issues that address how the poor and other groups respond to the changed environment after adjustment. These responses can significantly affect the outcome of real incomes and poverty. The importance of thcsc feedback effects suegests that wherever possible. general equilibrium effects should bc considered.

In a data-poor country with no microsurveys or good macrodata, a more qualitative, partialequilibrium analysis will bc required.

If the country has a microsurvey, poverty profiles can be quantitative and more detailed.

In a data-rich country, macroeconomic and microcconiomic data can be combinied to conThe PRE NVorking Paper Series dioerminate, the findings of work under **way** in thc Bank's Policy. Research, and Extemal Affairs Complex. An objective of the scries is to get these findings out quickly, cven if presentations are less than fulh Pol ished. Thc findings, interprctations, and conclusions in these papers do not necessarily reprcsent official Bank policy.

Pr(luced **hv** thc PRE Dissemnination Center
paper: 1) country and region<sup>4</sup> , 2) year of financial closure and total life period of the project, 3) total investment (in both monetary value and physical capacity), 4) sector, subsector, segment, and technology (fuel) of each project.<sup>5</sup> The database includes observations for 105 developing countries from 16 years from 1993 to 2008.

**The macro data are collected from other central databases** , including i) the _World Development Indicators (WDI) 2009_ for macroeconomic variable and the _World Governance Indicators_ and Polity IV to select a few indicators of governance ii) the _Energy Balances and Statistics 2009_ of the International Energy Agency (IEA) to derive indicators of energy import dependence and per capita CO2 emissions and the electricity databases of the Energy Information Agency (EIA) of Department of Energy (DoE) of the US government (Annex 1) to countries‟ total installed capacity and their composition, total electricity generation, consumption, net import, and distribution losses. Finally, we also use REN21 to compile the list of countries adopting feed-in tariffs and for publicly available information on the list of countries with an autonomous electricity regulator.

**Fig. 6 PPP Investment in renewable-based energy, by region**

<!-- Start of picture text --> 3%<br>13%<br>27% EAP<br>5% ECA<br>LAC<br>MNA<br>SAR<br>16%<br>SSA<br>37%<br><!-- End of picture text --> _Source:_ World Bank/PPIAF PPI Database

**The key summary statistics show PPP investment highly concentrated in few regions.** As Figure 6 illustrates of all the PPP investment reported in the PPI data the large majority benefitted the LAC and EAP region (where investments are respectively just below 40% and above 30%). About 15% and 12% of investment in renewable took place respectively in the SAR and ECA region. Very little . investments have been recorded in the MNA and SSA regions

> 4 The six regions are East Asia Pacific (EAP), East Europe and Central Asia (ECA), Latin America (LAC), Middle East and North Africa (MNA), South Asia Region (SAR), and Sub-Sahara Africa (SSA).

> 5 The primary sector is energy, and the subsectors are electricity, natural gas, and other (road, telecom, or treatment plant). For the electricity subsector, the segment information tells whether it is a project for power generation, transmission, or distribution, or a combination of these. For power generation project, the technology (fuel) indicates what fuel (coal, oil, or natural gas etc.) or technology (hydro, wind, nuclear etc.) is used for the generation.

9
Farrell, J. and G. Saloner (1986), "Installed Base and Compatibility: Innovation, Product Preannouncements, and Predation," _American Economic Review_ 76: 940-955.

Gasmi, F., J.-J. Laffont and W. Sharkey (2000), "Competition, universal service and telecommunications policy in developing countries," _Information Economics and Policy_ 12(3): 221-48.

Gutierrez, L. and S. Berg (2000), "Telecommunications liberalization and regulatory governance: lessons from Latin America," _Telecommunications Policy_ 24: 865-884.

Katz, M.L. and C. Shapiro (1985), "Network Externalities, Competition, and Compatibility," _American Economic Review_ 75(3): 424-40.

Keler, W. (2001), "International technology Diffusion", NBER Working Paper No. 8573, October Kiiski, S. and M. Pohjola (2001), "Cross Country Diffusion of the Internet," United Nations, WIDER Discussion paper 11/2001.

Laffont, J.-J. and J. Tirole (2000), _Competition in Telecommunications, MIrr_ Press, Cambridge (MA).

Levy, B. and P.T. Spiller (1994), "The Institutional Foundations of Regulatory Commitment: A Comparative Analysis of Five Country Studies of Telecommunications Regulation", _Journal of Law, Economics and Organization,_ Vol. 10, N° 2.

Levy, B. and P.T. Spiller (1995), _The Institutional Foundations of Regulatory Commitment,_ Cambridge University Press, New York Levy, B. and P.T. Spiller (1996), _Regulations, Institutions, and Commitment: Comparative Studies of Telecommunications,_ Cambridge U Press, New York Norris, P (2000), "The Global Divide: Information Poverty and Internet Access Worldwide", available at http://www.ksg.harvard.edu/peonle/pnorris/acrobat/IPSA2000.pdf.

Rodriguez, F. and E. Wilson III (2000), "Are poor countries losing the information revolution", The World Bank, infoDev Working Paper, available at http://www.infodev.org/l ibrary/workin-.htm.

Roller, L.H. and L. Waverman (2001), "Telecommunications Infrastructure and Economic Development: A Simultaneous Approach", _The American Economic Review_ 91(4): 909-923.

Stoneman, P. (ed.) (1995), _Handbook of the Economics of Innovation and Technological Change,_ Blackwell, Oxford.

Valletti, T.M. and A. Estache (1999), "The Theory of Access Pricing: An Overview for Infrastructure Regulators," Policy Research Working Paper 2097, The World Bank.

38
<!-- Start of picture text --> rigoe 3\Weather<br>proft-W*ealth R~atios an Wlass~e<br>\Va0I8bi'tY'<br>by Wealth'Cls<br>0.34<br>C<br>^ _ C _ I<br>0.29\<br>0.24<br>0.1 4 - - - --- - - -<br>0.09<br>jO SOf onse Standar7d D9vat9fl (nWekS)<br>-<br>2Ot~~~' Pern.- O t erCrt. c' 60th' pecdfl.C8thpece<br><!-- End of picture text -->
times less expensive per question asked.<sup>11</sup> Cost per question ranges from 10 cents in Malawi to 25 cents in Tanzania for in-person interviews, and from 3 cents in Uganda to 6 cents in Malawi for phone surveys (Table 3).

One should note, however, that the scope of data collection in the in-person surveys is far more extensive than that of the HFPS surveys, so these figures should be considered as illustrative of the cost differential with acknowledgment that the surveys are not comparable in terms of data collected. A truly meaningful comparison needs to qualitatively discount these numbers by also looking at the information that can be collected, and the discussion in earlier sections showed how some key objectives of agricultural sample surveys cannot be fulfilled via phone.

_Table 3. Comparison of in-person and phone surveys (US$)_

||**_By country_**|||
|---|---|---|---|
|_Country_<br>_Survey_|_Mode: phone vs._<br>_in-person_|_Cost per_<br>_interview_|_Cost per_<br>_question_|
|Burkina<br>EHCVM-1 2018/19|In-person|230|0.20|
|<br>HFPS|Phone|9|0.05|
|Ethioia<br>ESS4 2018/19|In-person|245|0.17|
|p<br>HFPS|Phone|10|0.04|
|Mli<br>IHPS 2019|In-person|269|0.10|
|aaw<br>HFPS|Phone|12|0.06|
|Nieria<br>GHS-Panel 2018/19|In-person|351|0.21|
|g<br>HFPS|Phone|12|0.05|
|Tanzania<br>NPS 2021<br>HFPS|In-person<br>Phone|409<br>8|0.25<br>0.04|
|Uanda<br>UHIS 2021/22|In-person|383|0.19|
|g<br>HFPS|phone|9|0.03|
||**_Overall_**|||
||_Min_|_Max_|_Mean_|
|_Costper interview_||||
|In-person|230|409|314|
|HFPS(phone)|8|12|10|
|_Costperquestionper interview_||||
|In-person|0.10|0.25|0.19|
|HFPS(phone)|0.03|0.06|0.05|

_Source: Authors’ calculation based on personal communication with World Bank staff supporting the implementation of the surveys._ The above comparisons are based on surveys conducted via CATI, but phone survey costs can vary significantly by approach. Based on IPA’s review of phone surveys using 27 studies in 18 countries, on a cost per interview basis, IVR is the cheapest mode ($4.86 on average), followed by automated SMS ($7.75), and CATI ($11.97) (Glazerman et al., 2020). Henderson and Rosenbaum (2020) add to the comparison a manual SMS survey in Nepal, where interviewers instead of an automated system were used to send text messages to respondents, with a cost per interview higher than CATI at $17.41 per 11 The calculations in Table 3 are based on the number of questions in the questionnaires, not on the questions actually asked. The total number of questions actually asked to respondents will always be lower, as not all questions will be applicable to all households.

22
contributions, but also those made by companies and organizations, both governmental and nongovernmental.<sup>5</sup>

|TABLEII-1 TOTAL REVENUES OF THECOLO|MBIANGOVERNM<br>Fiscal acco|ENT2017<br>unts|Portion<br>fiscal ac<br>anal<br>In|of the<br>counts<br>yzed|Totals in<br>the<br>household<br>survey|Ratio<br>between<br>survey<br>total and|
|---|---|---|---|---|---|---|
||In billions<br>of Pesos|% GDP|<br>billions<br>of Pesos|% GDP|In billions<br>of Pesos|external<br>statistics %|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Total Revenue**|**205,056**|**22.3%**|**106,852**|**11.6%**|**63,637**|**31.0%**|
|**Taxes**|**152,621**|**16.6%**|**56,937**|**6.2%**|**22,392**|**14.7%**|
|**Income Tax**|**56,655**|**6.2%**|**9,273**|**1.0%**|**2,881**|**5.1%**|
|Fees|15,071|1.6%|||||
|Deductions|41,584|4.5%|9,273|1.0%|2,881|6.9%|
|Deductions for persons|9,273|1.0%|9,273|1.0%|2,881|31.1%|
|Deductions for companies|32,311|3.5%|||||
|**VAT**|**37,517**|**4.1%**|**37,517**|**4.1%**|**16,080**|**42.9%**|
|Internal VAT|37,517|4.1%|37,517|4.1%|16,080|42.9%|
|**National Fuels Tax**|**1,619**|**0.2%**|||||
|**National consumption tax**|**2,108**|**0.2%**|**2,108**|**0.2%**|**1,705**|**80.9%**|
|**Equity tax**|**3,975**|**0.4%**|||||
|**Property tax**|**7,298**|**0.8%**|**7,298**|**0.8%**|**1,240**|**17.0%**|
|**Vehicles tax**|**741**|**0.1%**|**741**|**0.1%**|**486**|**65.6%**|
|**Other taxes***|**42,708**|**4.6%**|||||
|**Social security contributions****|**49,915**|**5.4%**|**49,915**|**5.4%**|**41,245**|**82.6%**|
|**Other income**|**2,520**|**0.3%**|||||

**Source:** Ministry of Finance and Public Credit, National Statistical Office, Directorate of National Taxes and Customs, Internal calculations based on the 2017 ENPH.

**Note:*** 0.4% of GDP was deducted from the other taxes, which is equivalent to the external collection (different from external VAT) of the general government of the nation. The corresponding amount for local governments could not be discounted due to a lack of information. ** The social security contributions amounts are the contributions of the general government to social security, which appear in the medium-term fiscal framework of the Ministry of Finance and Public Credit

# **_a) Direct taxes and contributions to the social security system_**

The personal income tax in Colombia taxes the income of individuals, once it is cleaned to account for certain exemptions and deductions under law. Law 1819 of 2016 established an income type system for the filing and payment of personal income tax. Thus, income is classified according to its origin, with different deductions and exemptions applied accordingly. Consequently, income is divided into labor, pension, capital, non-labor, and dividend income. According to the Central National Government figures, revenue from income tax during 2017 was $ 56.655 billion.

> 5 For more information on the reasons why the survey differs from the administrative data, see annex I and II in Nuñez et al. (2019).

5
Poverty Mapping in the Age of Machine Learning Paul Corral,<sup>_∗_</sup> Heath Henderson,<sup>_†_</sup> and Sandra Segovia<sup>_‡_§</sup>

**Key words:** Small area estimation, Poverty mapping, Machine learning, Satellite imagery **JEL classification:** C13, C55, C87, C15

> _∗_ The World Bank Group - Poverty and Equity Global Practice (pcorralrodas@worldbank.org)

> _†_ Department of Economics and Finance, Drake University, United States of America

> _‡_ The World Bank Group

> §The authors acknowledge financial support from the World Bank. We thank Roy van der Weide and Isabel Molina for comments. Additionally, we thank Benu Bidani, Carlos Rodriguez-Castelan, and Johan Mistiaen for providing support and space to pursue this work. Finally, we thank the Global Solutions Group on Data for Policy. Any error or omission is the authors’ responsibility alone.