rafmacalaba/datause-encoder-data huggingface dataset:
**_35_**

_54._ Suggestions to the appropriate price index to use in computing<sup>and testing PPPs have</sup> "ranged from the price of the least traded commodity **--** labor<sup>**--**</sup> to<sup>anindexofnontraded</sup> goods only, to the broadest domestic index available, to an<sup>index comprisedonly</sup><sup>**of**traded</sup> goods, to individual prices of traded commodities."<sup>**2**Comparison ofunit laborcostsis</sup> beset with measurement problems. Among these are measurements<sup>reflectingdifferences</sup> in types and quality of labor; and adjustment for differences<sup>in productivity(tradedgoods</sup> sector only, nontraded goods sector only, or combination of<sup>both?).Price indicesbasedon</sup> internationally traded goods (export and import price indices)<sup>arelimitedtoasmallclass</sup> of commodities, relative to all goods and services, and are therefore<sup>subject tovariations</sup> that presumably would not be present in a broad-based price measure.<sup>Also,tradedand</sup> nontraded goods are not unvarying collections of commodities. There<sup>isneveradefinite</sup> group of commodities that can be exported. Changes in the structure<sup>oftheeconomyor</sup> changes in profitability may widen or restrict the group of<sup>traded/exportablegoods.Inthe</sup> same vein wholesale price indices (WPI) are also heavily weighted<sup>withtraded goods.The</sup> use of cost-of-living indices **(COL)** requires that the weighting<sup>patternbethesamefor</sup> each country's price level, that is, taking a common basket of goods with<sup>a standardsystem</sup> of weighting. The PPP theory essentially refers to the internal value of the currencies concerned, and variations in this value can be measured best<sup>**by**generalindex figures</sup> representing as far as possible the whole mass of commodities (and<sup>services),tradedand</sup> nontraded, marketed in the country. This is reflected in the **GDP** deflator<sup>whichisthe price</sup>

> **' Alan C. Shapiro, "What Does Purchasing Power Parity Mean?" Journal**<sup>**ofInternational Moneyand**</sup> **Finance, Vol. 2, No. 3 , December 1983; pp. 295-318.**
# **I. Introduction**

Equatorial Guinea<sup>1</sup> is a tiny country of 28,000 km<sup>2</sup> with a relatively small population estimated at about 506,000 inhabitants<sup>2</sup> and consists of several geographically distinct areas. Rio Muni, the mainland region, accounts for approximately 85 percent of the land area and 80 percent of the total population. The rest of the country includes the island of Bioko, which is the site of the capital city, Malabo, and five additional small islands in the Gulf of Guinea. Oil production began in 1992, with a production of 6,000 barrels a day, when an independent oil company, Walter International, started operations in the Alba field, situated 36 kilometers off the coast of Bioko; the larger Zafiro field, operated by Mobil Oil, came on stream in August 1996. Petroleum output average 83,000 barrels per day in 1988 representing more than 60 percent of GDP and reached a milestone of 400,000 barrels a day in 2005, about 90 percent of GDP.

Since Adam Smith and David Ricardo there has been a belief that natural resources are “a blessing”; therefore, countries richly endowed with natural resources have an advantage over countries that are not. Natural resource endowments have helped many countries (Norway, Malaysia, Botswana, Finland and Indonesia) to grow and diversify, in part by providing a basis for developing associated technologies and capital goods industries.

The abundance of natural resources also carries a paradox that has inspired innumerable studies of mineral-rich countries in the developing world. Since the 1970s they have consistently underperformed their mineral-poor counterparts on a variety of economic performance, good governance and income equality (Eifert, Gelb and Nils 2002); (Gelb1998); (Sachs and Warner 1997); (Auty 1998, 2001): this phenomenon has come to be known as the resource curse. The “resource curse” is the phenomena whereby a country with an export-driven, natural resources sector, generating large revenues for government, leads paradoxically to economic stagnation and political instability. There is considerable evidence that non-renewable natural resource revenues, especially windfall can, if not properly managed, adversely affect economic growth and poverty reduction.

The large-scale production of hydrocarbon in Equatorial Guinea has set the stage for a significant and dynamic structural transformation of its economy. The objective of this paper is to propose a medium and long-term strategy for managing the country’s rapidly rising oil wealth; ensure that the large oil endowment relatively to the size of the population is being translated into economic opportunities and benefices for the overall economy. We also argue in this paper that the ongoing Dutch Disease is a natural and necessary reallocation of resources within the Equatorial Guinea economy.

> 1I am grateful to Rafael Munoz Moreno for his comments and suggestions; nevertheless, I am responsible for all errors or misunderstandings the document might contain. 2World Development Indicator Report (WDI) - The World Bank 2005.
Sadoulet conclude that the marginal effect of the transfer amount is high—in the transition from 6<sup>th</sup> to 7<sup>th</sup> grade, when dropout rates in the absence of PROGRESA transfers are high, every US $10 result in an additional 1.42 percentage points in enrollment. (The mean per student transfer is $169.) However, de Janvry and Sadoulet impose linearity in the relationship, so the model does not allow for declining marginal impacts; moreover, the cap in transfer amount is a function of family size and composition, and these characteristics may themselves not be orthogonal with school enrollment decisions, independent of the transfer. Todd and Wolpin (2006) also analyze PROGRESA, but use a structural dynamic model that incorporates fertility and schooling choices to simulate various counterfactual policy alternatives, and allow for varying marginal impacts. After estimating the structural parameters of their model using data from the experimental control group, they simulate halving and doubling the transfer amounts. They find that the impact of the program on mean years of schooling completed is linearly increasing in the amount of the transfer up to the actual transfer amount, and increasing but at a slightly diminishing rate thereafter (up to their simulated doubling of the transfer). Bourguignon, Ferreira, and Leite (2003) analyze the Bolsa Escola CCT program in Brazil. They simulate the effects of alternative transfer amounts allowing for nonlinear effects, and find that doubling, and then quadrupling, the transfer amount leads to successively smaller impacts on the probability of attending school. Despite these earlier contributions, however, there is to date no study that analyzes a program that deliberately altered the transfer amounts for comparable households with the explicit aim of estimating whether households that received larger transfers in fact were more likely to send their children to school than those which received smaller ones.

In this paper, we use data from Cambodia, where a program known as the CESSP Scholarship Program (CSP) made cash transfers of different magnitudes to observationally very similar households, based on an index of the likelihood of dropping out of school.<sup>1</sup> The program design lends itself to an identification strategy based on regression discontinuity. The intuition is that we compare the school enrollment of children “just” above and just below the cutoff for receiving a “large” versus a small transfer—US $60 versus US$45 per year, conditional on school enrollment—and then compare those just

> 1 CESSP stands for Cambodia Education Sector Support Project.

3
# **Figure 1: Size Distributions**

<!-- Start of picture text --> Size Distribution - Bangladesh Size Distribution - Ethiopia<br>-2 0 2 4 6 -4 -2 0 2 4<br>Ln L Ln L<br>Female Firms Male Firms Female Firms Male Firms<br>Size Distribution - Indonesia Size Distribution - Sri Lanka<br>-4 -2 0 2 4 6 0 2 4 6<br>Ln L Ln L<br>Female Firms Male Firms Female Firms Male Firms<br>.6<br>.4<br>.2<br>0<br>.6 1<br>.8<br>.4<br>.6<br>.4<br>.2<br>.2<br>0 0<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> Figure 1 Caption: Note:

- Kernel density estimates, estimated using the epanechnikov kernel,

- L measured in full time equivalent workers

-the measure of labor in Sri Lanka is discrete, which explains why the size distribution appears bimodal.

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# 1. Introduction

Cross-border trade plays a pivotal role in academic research on international development and the complexities of policy design. This importance stems from its ability to provide insight into economic interactions, dependencies, and the dynamics of global markets. The estimation of such cross-border activities primarily hinges on trade data, which provides a quantitative assessment of goods exchanged between countries. Accurate data informs import tariff setting, guides negotiations for trade agreements, and steers monetary decisions that impact interest rates and currency valuation. Furthermore, it influences industrial subsidies, infrastructure investments, environmental regulations, and even national security considerations. Given its importance, the increasing number of studies highlighting discrepancies in reported trade flows is concerning (Gaulie & Zignago, 2010; Carrère & Grigoriou, 2015; Fortanier, 2016; Shaar, 2019; Chen et al., 2022). Reporting discrepancies are often overlooked in the design of empirical studies of international trade, compromising the validity or robustness of the obtained results (Linsi & Mügge, 2019; Linsi, Burgoon, & Mügge, 2023). This is especially important because statistical results guiding policy interventions have been shown to be sensitive to changes in the underlying data (Bahmani-Oskooee, Hegerty & Harvey, 2013; Farhad et al., 2023). Consequently, any inaccuracy or inconsistency in trade data can distort or compromise the validity of research outcomes and the formulation of policy recommendations.

<mark>Evaluating the reliability and accuracy of trade data typically relies on the principle of dual recording of bilateral transactions. Ideally, trade transactions between countries should be recorded by both the reporting country and its trading partner, resulting in dual records.</mark> However, empirical trade data often lacks this dual recording, with many transactions having no corresponding mirror (Carrère & Grigoriou, 2015; Shaar, 2019; Chen et al., 2022). Furthermore, mirror records often report conflicting information regarding the value or quantity of the transaction, resulting in a reporting discrepancy. The absence of mirror records and the prevalence of reporting discrepancies complicates the evaluation of data reliability. This has propelled numerous studies to explore the potential drivers of discrepancies (Ferrantino & Wang 2008; Carrère & Grigoriou, 2015; Demir & Javorcik, 2020; Kee & Nicita, 2022), construct measures for reporting reliability (Shaar, 2019, Farhad et al., 2023), and develop reconciliation procedures for estimating trade flows (Gehlhar, 1996; Gaulie & Zignago, 2010, Fortanier, 2016). Yet, resolving these trade data quality challenges remains difficult due to the multitude of factors contributing to the observed discrepancies.

3
|Sudan 1993|census|1.6|0.7|impairment types|
|---|---|---|---|---|
|Swaziland 1986|census|2.2|0.9|impairment types|
|Syria 1970|census|1.0|||
|Syria 1981|census|1.0|0.7||
|Syria 1993|survey|0.8|0.6|long-term condition that affects<br>normal activities|
|Thailand 1986|survey|0.7|0.5||
|Thailand 1990|census|0.3|0.7|impairment types|
|Togo 1970|census|0.6|0.1||
|Tunisia 1984|census|0.9|0.3||
|Tunisia 1989|survey|0.9|||
|Tunisia 1994|census|1.2|0.6||
|Turkey 1985|census|1.4|0.6||
|Uganda 1991|census|1.2|0.7|impairment types|
|Yemen 1994|census|0.5|0.1||
|Zambia 1980|census|1.6|0.7||
|Zambia1990|census|0.9|0.6|impairment types|

_Note_ : The UN warns against simple comparisons across countries since the data and definitions are not strictly <u>comparable</u>

|**Annex Table 4. Percent distribution of types of disabilities among those with a disability**|
|---|

||Visual|Hearing<br>and/or<br>Speech|Physical||Mental|Multiple/<br>Other|Total|
|---|---|---|---|---|---|---|---|
|Burundi 2000|||100||||100|
|Cambodia 1999|12|7|55||7|19|100|
|Cambodia 2000|||100||||100|
|Indonesia 2003|16|22|40||23||100|
|Jamaica 1998||||100|||100|
|Jamaica 2000|27|21|14||23|15|100|
|Mongolia 2000|63|37|||||100|
|Myanmar 2000|28|72|||||100|
|Romania 1995|15|7|47||27|4|100|
|SierraLeone2000|31|33|36||||100|

20
<!-- Start of picture text --> @ 140<br>a<br>°=® 120 100 — DevelopingHigh incomecountries countries<br>o 80<br>&<br>a 60<br>c<br>2 40 High income countries<br>S 20<br>ie} Developing countries<br>a4 0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017<br>ee Vobile cellular subscriptions<br>esssss= Fixed broadband subscriptions<br><!-- End of picture text -->
# **Mission Impossible?**

# **Exploring the Promise of MI for Predicting Missing GPS-Based Land Area Measures in Household Surveys**

Talip Kilic<sup>†</sup> , Ismael Yacoubou Djima<sup>†</sup> , and Calogero Carletto<sup>†1</sup>

**JEL Codes:** C53, C83, Q12, Q15.

**Keywords:** Global Positioning System, Land Area Measurement, Productivity Analysis, Missing Data, Multiple Imputation, Household Surveys.

> 1† Development Data Group, World Bank. The senior authorship is shared between Talip Kilic and Ismael Yacoubou Djima, who is the corresponding author. The email addresses for the authors are <u>tkilic@worldbank.org, iyacouboudjima@worldbank.org, and gcarletto@worldbank.org. The authors thank (in alphabetical order)</u> Tomoki Fujii and Alberto Zezza for their comments on the earlier versions of this paper, and Heather Moylan for her insights on the field operations of the Malawi Integrated Household Survey. This work was supported by the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative, funded by the Bill and Melinda Gates Foundation, and the Minding the (Agricultural) Data Gap Methodological Research program, funded by UKAid.
The third group is composed of countries that have services policies that are of lower quality than they would appear to be on the basis of GATS commitments. This group includes Armenia, Georgia, the Kyrgyz Republic, Moldova, and the FYR Macedonia. These countries have no or little chance of joining the EU in the near future. With the exception of Macedonia, they are geographically or culturally distant from the EU, have small markets, and were not GATT members in 1994. For these countries the GATS appears to have been either a failure – in not helping to promote improvements in services policies – or irrelevant in the sense that commitments were made that governments either did not intend to implement or could implement without resulting in a significant change in actual policies.

There are a number of possible explanations for the observed disconnect between relatively extensive GATS commitments of non-EU accession countries and limited progress on actual reform. One possibility is that the intention was simply to “join the WTO club” rather than to pursue or reinforce domestic reforms. While this might be part of the explanation, a comparison of rankings based on the apparent depth of GATS commitments and the average level at which the countries concerned bound their goods tariffs reveals that the (non-EU candidate) countries with high levels of GATS commitments tend to have the lowest average level of bound tariffs (Table 7). This suggests there was a desire to “use” the WTO as a commitment and signaling device.

**<u>Table 7: GATS commitment index and average level of GATT tariff binding</u>**

|Rank|Country|Ranked by<br>average bound<br>tariff|Country|Ranked by GATS<br>commitmentindex|
|---|---|---|---|---|
|1|Moldova|5.5|Moldova|78.4|
|2|Croatia|5.8|Kyrgyz Republic|59.3|
|3|Georgia|7.2|Georgia|53.5|
|4|Kyrgyz Republic|7.4|Croatia|49.3|
|5|Armenia|8.5|Armenia|41.9|
|6|Bulgaria|24.2|Bulgaria|26.4|
|7|Romania|40.4|Romania|25|

Note: The average tariff is the most recent simple average of all tariff bindings. The countries selected exclude those that joined the EU. Data on tariff bindings were not available for Macedonia. Sources: Based on UNCTAD TRAINS and WTO IDB database Another explanation is that in practice the political economy resistance to reform proved too great. In the latter case it may be that the GATS was too weak to help governments overcome such resistance, assuming they desired to, or too weak to help constituencies in these countries to 17
explained by an increasing urbanization rate. Even if the demand for housing is projected to be flat post2015, it is still increasing at a low rate.

**Figure 3.3:** Annual Housing Demand Evolution in SSA, 1965-2050 (projection)

<!-- Start of picture text --> 6,000<br> 5,000<br> 4,000<br> 3,000<br> 2,000<br> 1,000<br> -<br>1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045<br><!-- End of picture text --> _Source_ : Authors’ calculation using the World Bank database on housing finance launched by Badev et al., 2014: “Housing finance across countries: New data and analysis,” WPS6756.

Many initiatives have been taken in Africa to support the development of the mortgage market. For example, the recent creation of national and regional mortgage refinancing institutions in Nigeria (2013), Tanzania (2011), and the WAEMU (West African Economic and Monetary Union) sub-region (2010) will improve mortgage loan granting. There potentially may be a way to improve household access to long-term liquidity through developing mortgage markets, and at a reasonable cost. Unfortunately, as shown in figures 3.4 and 3.5 below, interest rates are too high in almost all African countries, which undermines mortgage market accessibility.

**Figure 3.4:** Mortgage Market Access and Housing Finance Policy in SSA.

**Figure 3.5:** Mortgage Penetration and Housing Finance Policy in SSA.

<!-- Start of picture text --> 70 8<br>CAP MAS SOU<br>60 SOU 7 DJI CHATOG MAL<br>50 SEN 6 11.708 MAU<br>MAR<br>40 ANG<br>GUB 5<br>30 NAM LIB<br>BOT 4<br>20 SUD SWA GHA<br>ANG 3<br>10 COMETH SIE GAM MAD 2 BTS CMRUGA<br>0 MAINGRKENCAR ZAM<br>-10 0 10 20 30 40 50 1 14.5BENGABBUR BDICDRCONRWA GUI MAD<br>0<br>-20<br>Mortgage rate (%) 0 10 20 30 40 50<br>Mortgage policy (%)<br>Regression Mortgage access = f( Mortgage policy) Mortgage penetration=f(mortgage policy)<br>Linear (Regression Mortgage access = f( Mortgage policy)) Linear (Mortgage penetration=f(mortgage policy))<br>mortgage market (%) Mortgage penetration (%)<br>Proportion of population with access to<br><!-- End of picture text --> _Source_ : Authors’ calculation using the World Bank database on housing finance launched by Badev et al., 2014: “Housing finance across countries: New data and analysis,” WPS6756.

_Source_ : Authors’ calculation using the Global Financial Inclusion database (FINDEX) launched by Demirguc-Kunt, Asli and Leora Klapper (2012), “Measuring financial inclusion: the global Findex Database,” World Bank Policy Research Working Paper 6025.

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**Table 4: Impact on Business Practices**

||Finance &<br>Accounting<br>Traditi|Marketing &<br>Sales<br>onal Business|Operations & <br>HR<br>Practices|Verified<br> Traditional<br>Practices|Verified<br>Digital<br>Marketing|Overall<br>Index|
|---|---|---|---|---|---|---|
|**Panel A: Impacts in First Follow-up Sur**|**vey (One-yea**|**r after interve**|**ntion started)**||||
|Assigned to Insourcing|0.094***|0.074**|0.028|0.109***|0.079***|0.066***|
||(0.033)|(0.033)|(0.020)|(0.029)|(0.024)|(0.020)|
|Assigned to Outsourcing|0.064*|0.068**|0.005|0.099***|0.099***|0.058***|
||(0.033)|(0.031)|(0.020)|(0.028)|(0.025)|(0.019)|
|Assigned to Training|-0.002|-0.030|-0.013|0.017|0.011|-0.007|
||(0.034)|(0.034)|(0.021)|(0.026)|(0.024)|(0.020)|
|Assigned to Consulting|0.075**|0.019|0.019|0.083***|0.051**|0.042**|
||(0.033)|(0.033)|(0.021)|(0.029)|(0.023)|(0.020)|
|Mean of Control Group|0.631|0.588|0.481|0.225|0.221|0.473|
|Sample Size|670|670|670|670|670|670|
|P-value: all treatments zero|0.003|0.004|0.265|0.000|0.000|0.000|
|P-value: all treatments equal|0.012|0.004|0.187|0.002|0.004|0.001|
|<br>**Panel B: Impacts in Second Follow-up S**|**urvey (Two-**|**years after int**|**ervention start**|**ed)**|||
|Assigned to Insourcing|0.043|0.083**|0.032|0.117***|0.079***|0.059**|
||(0.038)|(0.038)|(0.024)|(0.028)|(0.024)|(0.026)|
|Assigned to Outsourcing|0.060|0.099***|0.046**|0.100***|0.083***|0.073***|
||(0.037)|(0.036)|(0.023)|(0.027)|(0.024)|(0.025)|
|Assigned to Training|-0.006|-0.016|0.009|0.039|0.008|0.002|
||(0.040)|(0.037)|(0.024)|(0.026)|(0.024)|(0.025)|
|Assigned to Consulting|0.093**|0.070*|0.052**|0.122***|0.044*|0.065***|
||(0.038)|(0.036)|(0.023)|(0.029)|(0.024)|(0.025)|
|Mean of Control Group|0.635|0.551|0.430|0.174|0.219|0.451|
|Sample Size|678|678|678|678|678|678|
|P-value: all treatments zero|0.041|0.002|0.104|0.000|0.000|0.002|
|P-value: all treatments equal|0.062|0.005|0.252|0.010|0.006|0.010|
|**P-value: equality of 1 & 2year effects**|0.485|0.780|0.596|0.620|0.944|0.789|

# Notes:

Regressions control for stratification batch fixed effects and baseline value of outcome. Robust standard errors in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent levels. **Finance & Accounting** is the proportion of 10 practices in this area that the firm uses; **Marketing & Sales** is the proportion of 9 practices in this area that the firm uses; **Operations &HR** is the proportion of 11 operations and human resources practices the firm uses; **Verified Traditional practices** is the proportion of 10 of these first 30 traditional practices that we could physically verify as implemented; **Verified Digital Marketing** is the proportion of 11 practices in this area that the firm uses; and **Overall index** is the proportion of all 41 practices that the firm uses. Equality of 1 & 2 year effects tests the joint hypothesis that the 1 year and 2 year treatment effects are equal for the different treatments over time, but not necessarily equal across treatments.

39