rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> ISL<br>ISL<br>USA<br>ISLGBRSWEUSADNK DNKDNKNOR NOR NOR<br>GBRFRADEUITASWEITAAUTBELCANNLDDEUFINGBRUSADEUFRACANBELAUTAUSIRLFRASWEBELJPNAUTFINNLDNLDIRL CHE CHECHE<br>ISRPRTPRTISRCYPISRGRCCYPESPHKGNZLGRCNZLESPIRLNZLGRCESPHKGFINCANITAAUS AUSJPNJPN<br>PRTKOR HKG<br>LBBDIZARRWASLEMOZGNBNERUGABT CAF Z G KINDHTIMRTS M SEN Z PAK VNIND CLKANICL M CBOL PNGEGY PHLHNDEGYID CHLKAPHL JKSYGPLMB CHNWDNJOR HL TUN SO SODOMJOR ID ECU MR HTHASLV PER GTMECUTHSWZTUNSWZDZSWZM JSS GTMD CBGRRPERTHAECU DV TUN COL IRNCOLDOM ON VCTDMA D FJIMAORROMBWAFJIJAM EJAM MUSFJI N BGRPANLVLVCRI IR DMAACRIZA N CTPANOARGMYS BRA MUSZABWATURURYAZAFMYSPANCROMZAFLCABRALVLTUURY A LCAURYTURZAFMUSUSMYSVENRUSCHLVENVENBWA R MEXTTORGCHLHRVCHLPOLLVAHUNSVKMEXGLTUTTOIMEXKNAPOLSYCKNAHUNSVKHRVSYCCZEHUNTTOKORCZEKORBHRBHRSGPBHRSGPSGPAREAREKWTKWT<br>0 20000 40000 60000 80000<br>GNI pc (constant 2005 US$)<br>1995 2000 2005<br>Figure 2a: Intangible capital is positively correlated with income<br>ZARBDILBRSLELBRMOZSLEGNBRWARWAGNBSLEMWIMWITJKUGAMOZMWIRWANERNERGNBNERZWENPLGMBGMBNPLGMBMLIUGANPLMOZBGDTGOMLITGOTJKTGOZWEZWEBGDGHAMLIGHABENBGDINDGHAZMBBENBENKENZMBHTIKENSDNHTIMRTKENHTIINDZMBMRTSENPAKPAKMRTVNMNICSENSDNPAKSENINDCHNCMRNICLKAMDALSOLSOCMRBOLPNGNICEGYBOLLSOLKAPHLBOLHNDCMRIDNHNDHNDIDNPHLCHNEGYLKAEGYIDNPHLSYRSYRSYRCHNJORTUNJORDOMIRNTHATHAPERGTMECUSWZSLVTUNDZASWZSWZSLVSLVJORPERGTMGTMDZABGRCOLPERECUTHADOMVCTTUNCOLCOLDOMDMAVCTFJIJAMBWAROMFJIJAMJAMMUSFJIBGPANCRIDMACRIPANMYSA BRA MUSBWAURYARGZAFTMYSCRIROMPANZAFBRALCAARGLVA R LTUURYLCA UR TURZAFMUSMYSVENRUSCHLVENGVENBWAMEXTTOCHLYHRVCHLPOLLVAHUNSVKMEXLTUTTOMEXKNAPOLSYCKNAHUNSVKHRVSYCCZEHUNTTOKORCZEKORPRTISRKOBHRPRTPRTBHRISRCYPISRGCYPESPHKGSGPNZLGRC R HKGNZLBHRESPSGPIRLCNZLGRCESPHKGFINSGPCANITAAUSAREGBRFRADEITASWEITABELAUTKWTCANNLDAUSDEJPNFINGBRUSAFRADEUJPNBELCANAAUSKWTIRLSWEFRABELJPNAFINISLGBRNLD U NLDSWEUSADNKIRLUSA U DNKTISLCHETDNKNORCHEISLCHENORNOR<br>ARE<br>ECU<br>BDIZAR MDA<br>IRN<br>CAF DZARUS<br>UGA<br>4 6 8 10 12<br>Ln(GNI pc)<br>1995 2000 2005<br>800000<br>600000<br>400000<br>200000<br>Intangible capital pc (constant 2005 US$)<br>0<br>1<br>.8<br>.6<br>.4<br>.2<br>Intangible Capital/Total Wealth<br>0<br><!-- End of picture text -->

# **Figure 2a: Intangible capital is positively correlated with income**

**Figure 2b: Share of intangible capital in total wealth is positively correlated with income**

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subscribers are merely 244,455 coupled with 96,890 payphones countrywide, bringing the national tele-density to 32.2 only.<sup>16</sup> Mobile traffic is largely dominated by voice, logging about two billion voice minutes in the first quarter of 2010, of which 89 percent is in-network traffic, thanks to discounted in-network tariffs, which help explain multiple-SIM ownership strategies adopted by many subscribers. However, MSM usage has been growing fast as MNOs encourage use through campaigns and innovative services like missed call alerts, call me back, etc. that tend to be free. SMS usage is also still largely dominated by in-network usage.

Mobile internet access has grown thanks to increasing competition in data services among MNOs. The arrival of cheaper bandwidth via undersea cables coupled with increasing 3Gnetwork coverage is driving down the cost of data services. In addition, MNOs have partnered with social networking sites like Facebook to provide free mobile access.

Since 2007, Uganda has opened up the sector fully to competition. Currently, Uganda’s major mobile telephony providers are MTN Uganda, Orange Uganda, Airtel (formerly Zain), Uganda Telecom Limited and Warid Telecom. MTN is the dominant player with 41 percent market share in 2012, albeit it has been declining from 60 percent a few years ago. Airtel, Warid and Uganda Telecom each account roughly for 18 percent of the market and Orange has a 3 percent market share. In 2013 Warid was acquired by Airtel, threating the dominance of Warid.<sup>17</sup>

# **Regulatory environment**

There is no legislation governing mobile money services in Uganda. Bank of Uganda has put in place guidelines to be followed prior to the licensing of a mobile money service. These guidelines point out the need for a partnership between a telecommunication company and a financial institution before start up. The essence of the partnership is to ensure that Bank of Uganda can protect the monetary value of the mobile transactions through appropriate regulation

> 16 BuddeComm, Uganda Mobile Market (2011)

> 17 Wireless Intelligence.

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designs. Some of the key issues in such studies are extensions of the data collection themes in this paper. Other issues, both in regard to the economic content of techniques employed and of the ethical challenges in setting up such empirical economic studies are well developed elsewhere (Deaton, 2010; Ravallion, 2012; Barrett and Carter, 2010; Glennerster, this volume) and will be secondary focuses here.

# <u>Balancing Transparency and Confidentiality</u>

Participants in field trials and observational surveys have a recognized right that the information they provide will remain confidential. Field conditions, however, often put pressure on the best intentions of researchers. For example, it is difficult to arrange a one-on-one interview in a village setting. Moreover, by its very nature, it is impossible to do so in a focus group. A noted illustration of this challenge was reported by <mark>Nancy Scheper-Hughes who was run out of the village where she had conducted an award winning anthropological study after the study was perceived as overly negative and after observers deduced in which village she had worked and with whom she had interviewed despite her efforts to provide anonymity (Scheper-Hughes, 2000).</mark><sup>3</sup> <mark>This successful unraveling of attempted concealment of identities achieved long before the design of current search engines that make a concerted effort at re-identification likely to be successful.</mark> While generally this is a minor concern for data collection it brings out the practical complexity of standard, though slightly conflicting, principles of research ethics beyond the voluntary nature of participation: the more or less categorical admonition to ‘do no harm’ and the somewhat more utilitarian concern that the risk to participants in research be vanishingly small

> 3 This incident was highlighted in an essay challenging the goal of anonymity on the grounds it is “usually impossible to ensure” and “often undesirable to try to do so” (Walford, 2005).

6
survey conducted on households with ever-married women aged 15 to 49 and provides data on key indicators such as fertility, use of contraceptives, infant and child mortality, immunization, coverage of antenatal and delivery care, nutrition, and prevalence of anemia. In addition, height and weight measures were collected for children under six years of age and never-married youths and young adults aged 10 to19 in all households in the survey. The Egypt DHS included interviews with 15,573 ever-married women in 2000 and 16,527 in 2008. The paper specifically focuses on children in the early age group (ages 0-4) living in these households.

The HIECS, on the other hand, is a large scale and nationally representative household consumption, income and expenditure survey conducted by CAPMAS every five years since 1995 and every year since in 2008. The HIECS is the main (and the only official) source for poverty and inequality data in Egypt. The HIECS data are used to impute the per adult equivalent consumption expenditures at the household level. The imputed consumption expenditure is used as a proxy for wealth and hence a circumstance variable for children, along with those circumstance variables directly obtained from the DHS data. By combining information in the DHS and HIECS, the report analyzes the impact of circumstances on access to basic services and human development outcomes for children and documents their changes over time.

# **_Outcome Variables_**

A total of 18 indicators and three composite indicators have been identified from both data sources on four main outcome categories. The categories include: (i) access to healthcare (during pregnancy, birth and early postnatal period); (ii) nutrition (malnutrition, micronutrient intake); (iii) access to basic services (housing, clean water, sanitation); and (iv) school enrollment. Under the first category, the following variables are analyzed: (i) lack of antenatal care (proxied by incidence of mother not having any blood tests during pregnancy); (ii) birth not taking place at a health facility; (iii) birth not being assisted by a skilled health professional; (iv) child not having a postnatal checkup within two months after birth; and (v) immunizations within one year after birth.

7
# liV INTRODUCTION

Table **11** Long-Run and Stationary Population, Momentum, Doubling **Year,** and Year When Net Reproduction Rate Reaches **1** by Geographic Region and by Income Group

|||Populatio|n(mill|ions)|Station-<br>ary over|<br>Popu-|Year<br> <br>when||
|---|---|---|---|---|---|---|---|---|
|Region or<br>income group|1985|<br>2050|<br>2100|Station<br> <br>**ary**|1985<br>- popula-<br>tion|lation<br> <br>mo-<br>mentum|popula-<br>tion<br>doubles|<br>Year<br>when<br>NRR-I|
|World|4,844|10,035|11,330|11,514|<br>2.4|1.4|<br>2044|2060|
|Less developed (a)|3,666|<br>8,716|10,020|10,200|<br>2.8|1.5|<br>2028|2060|
|More developed (a)|1,179|<br>1,319|<br>1,310|<br>1,314|<br>1.1|1.1|<br>(c)|2030|
|Africa|559|2.275|<br>2,962|<br>3,049|<br>5.5|1.6|<br>2009|2060|
|East Airica|231|1,034|<br>1,383|<br>1,424|<br>6.2|1.6|<br>2008|2055|
|West Africa|204|873|<br>1,152|<br>1,190|<br>5.8|1.5|<br>2008|2060|
|North Africa|124|<br>368|<br>427|<br>435|<br>3.5|1.6|<br>2016|2050|
|America|667|1.146|<br>1,192|<br>1,201|<br>1.8|1.4|<br>**(C)**|2030|
|Latin America and Caribbean|<br>399|814|<br>864|<br>872|<br>2.2|1.6|<br>2043|2030|
|Northern America|268|332|<br>328|<br>329|<br>1.2|1.2|<br>(c)|2030|
|Asia|2,823|<br>5.728|<br>6,288|<br>6.374|<br>2.3|1.5|<br>2047|2045|
|East and Southeast Asia|1,650|<br>2,719|<br>2.822|<br>2,842|<br>1.7|1.4|<br>(c)|2030|
|South Asia|1.058|<br>2,614|<br>2.977|<br>3,034|<br>2.9|1.5|<br>2025|2040|
|Southwest Asia|115|<br>396|<br>490|<br>498|<br>4.3|1.6|<br>2013|2055|
|Europe and U.S.S.R.|771|843|<br>842|<br>846|<br>1.1|1.1|<br>(c)|2030|
|Oceania|25|42|44|<br>45|1.8|1.3|<br>(c)|2030|
|Income group (b)|||||||||
|Low|2,711|6,343|7,315|7,458|2.8|1.5|2030|2060|
|Lower-middle|580|1,416|1,609|1,632|2.8|1.6|2026|2055|
|Upper-middle|422|891|989|999|2.4|1.5|2040|2050|
|High|767|869|869|871|1.1|1.2|(c)|2030|
|Nonreporting<br>nornember|365|515|<br>548|<br>554|<br>1.5|1.2|<br>(c)|2030|

a. "More developed" comprises Europe, U.S.S.R., Northern America (United States and Canada), Australia, New Zealand;-and Japan. "Less developed" conprises the rest of the world.

b. Criteria for income groups are given in World Bank 1989. See listing In Exhibit 1. c. Population wilL not double with projected rates.
Nations Development Programme.

- Maloney, W., and C. Molina. 2016. “Are Automation and Trade Polarizing Developing Country Labor Markets, Too?” Policy Research Working Paper 7922. Washington, DC, United States: World Bank.

- Maurizio, R. 2015. “Transitions to Formality and Declining Inequality: Argentina and Brazil in the 2000s.” _Development and Change_ , 46(5): 1047–79.

- Maurizio, R., and G. V ́azquez. 2015. “Desigualdad e informalidad en Am ́erica Latina: El Caso del Ecuador”. In: _Desigualdad e Informalidad: Un An ́alisis de Cinco Experiencias Latinoamericanas_ . Santiago, Chile: Comisi ́on Econ ́omica para Am ́erica Latina y el Caribe.

- Meyer, B. D., and N. Mittag. 2019. “Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness, and Holes in the Safety Net.” _American Economic Journal: Applied Economics_ 11(2): 176-204.

- McCaig, B. 2011. “Exporting Out of Poverty: Provincial Poverty in Vietnam and US Market Access.” _Journal of International Economics_ 85(1): 102–113.

- McCaig, B., and N. Pavcnik. 2015. “Informal Employment in a Growing and Globalizing LowIncome Country.” _American Economic Review: Papers and Proceedings_ 105(5): 545–50.

- ———. 2018. “Export Markets and Labor Allocation in a Low-Income Country.” _American Economic Review_ 108(7): 1899–1941.

- Menezes-Filho, N., and M. Muendler. 2011. “Labor Reallocation in Response to Trade Reform.” NBER Working Paper 17372. Cambridge, United States: National Bureau of Economic Research.

- Messina, J. 2017. “The Market for Skills: Beyond Supply and Demand.” In: M. Busso, J. Cristia, D. Hincapi ́e, J. Messina, and L. Ripani, editors. _Learning Better: Public Policy for Skills Development_ . Washington, DC, United States: Inter-American Development Bank.

- Meyer, B. D., and N. Mittag. 2019. “Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness, and Holes in the Safety Net.” _American Economic Journal: Applied Economics_ 11(2): 176-204.

- Messina, J., and J. Silva. 2018. _Wage Inequality in Latin America: Understanding the Past to Prepare for the Future_ . Washington, DC: World Bank.

- Pavcnik, N. 2002. “Trade Liberalization, Exit, and Productivity Improvements: Evidence from Chilean Plants.” _Review of Economic Studies_ 69(1): 245–76.

- ———. 2017. “The Impact of Trade on Inequality in Developing Countries.” In: _“Fostering a Dynamic Global Economy,”_ Jackson Hole Economic Policy Symposium. Kansas City, United States: Federal Reserve Bank of Kansas City.

- Pavcnik, N., A. Blom, P. Goldberg, and N. Schady. 2004. “Trade Liberalization and Industry

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- 6 -

match their long-term liabilities while extracting a higher return. Moreover, although the higher return on these instruments reflects in part a risk premium, it probably also reflects a liquidity premium that rewards investors for their much lower liquidity.

The search of annuity providers for higher risk-adjusted returns is also observed in other countries. For example, the Teacher’s Insurance and Annuity Association—College Retirement Fund (TIAA-CREF), the largest annuity provider in the US, holds primarily privately-issued fixed income instruments to match its fixed annuities, as shown in Table 3. These instruments are much less liquid than government bonds, and include very illiquid corporate bonds bought through private placements. This portfolio strategy has arguably allowed TIAA-CREF to extract high risk-adjusted returns and share them with its annuitants. The question is whether the same type of strategy has allowed Chilean providers to pay a high annuity rate, cover their costs, and generate profits.

**Figure 2**

<!-- Start of picture text --> Average Interest Rates on Annuities (RV-85), Central<br>Bank Bonds and Corporate Bonds (% p.a.), 1993-2004<br>10%<br>9%<br>8%<br>7%<br>6%<br>5%<br>4%<br>3%<br>2%<br>1993 1995 1997 1999 2001 2003<br>Annuity Rate PRC20 Corp. Bonds Adjusted Annuity Rate<br><!-- End of picture text --> Sources: SVS, Central Bank of Chile.

**<u>Table 2: Portfolio of Life Insurance Companies (in % of Total), 1991-2004</u>**<sup>**1**</sup>

||**1991**|**1995**|**2000**|**2003**|**2004**|
|---|---|---|---|---|---|
|**Government Sector**|38.3|40.3|28.7|17.6|17.1|
|**Financial Sector**|23.0|28.4|45.1|37.6|32.9|
|Mortgage Bonds|13.9|18.6|24.2|18.8|14.7|
|Mortgage-Backed Securities|3.0|6.0|10.1|10.1|9.2|
|**Company Sector**|29.0|22.1|15.3|33.4|37.8|
|Shares|8.9|10.2|3.4|2.9|3.4|
|Bonds|20.1|10.7|10.7|29.3|33.3|
|**Real Estate**|7.8|7.7|7.4|7.3|7.4|
|**Other Assets**|2.0|1.5|3.6|4.1|4.8|
|**Total**|**100.0**|**100.0**|**100.0**|**100.0**|**100.0**|

Source: SVS
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Nonetheless, the plant-level econometric studies have significant shortcomings too. One common problem is a failure to disentangle causality. For example, contemporaneous correlation between exports and efficiency tells us nothing about what caused what. Even studies that use lagged exports to predict current efficiency may miss the knowledge transmission mechanism, as Westphal (2001) has emphasized. A second problem is that productivity is almost always poorly measured.<sup>3</sup> Manufactured products are quite heterogeneous, even within narrowly defined industries, so there is no single measure of output that can be compared across firms. Real revenue is typically used as a stand-in for physical product, but this variable responds to product-specific price adjustments as well as fluctuations in physical volume. Productivity measures consequently confound productive efficiency and market power. Further, when technology diffusion leads to product innovation rather than process innovation, these productivity measures may miss the effect entirely. Finally, like the cross-country regressions, studies in this literature usually focus on a single conduit for international technology diffusion and ignore the others, opening the door to omitted variable bias.

# B. **The contribution of this paper**

This paper is an econometric study based on plant-level data, so it falls squarely in the third category mentioned above. But we attempt to improve on existing methodologies in two respects. First, we abandon the standard approach to measuring productivity in favor of an alternative approach that treats the observed plant-level data

> 3 Few studies in the other literatures do better, but Pack (1987) is an exception. He uses detailed information on machines and workers at a sample of plants to calculate high quality productivity indices.

4
port-of-call data to offer a sketch of freight shipping in the Caribbean. We compare the observed supply of JA-compliant shipping services to the characteristics of the overall freight hauling fleets calling in PR and the Caribbean. We use the NBIC (2024) data to verify our conclusions about the JA fleet and to provide a better representation of barge traffic on the U.S.-PR route.<sup>18</sup> Panel A of Table 1 reports data for the Caribbean on three relevant measures of freight supply for six different types of freight-hauling maritime vessels. The statistics reported are the number of vessels calling, the number of port calls, and the DWT associated with vessels making port calls. These data illustrate the importance of self-propelled, non-containerized freight vessels in the overall Caribbean shipping market. Collectively, tankers, bulk, liquid and general cargo vessels account for 82 percent of the vessels calling in the Caribbean in 2019, 62 percent of the port calls and approximately 64 percent of the DWT. Container ships are an important part of Caribbean shipping, but collectively these other ship types arrive with greater frequency, and provide much more capacity.

Panel B of Table 1 reports the same statistics from the LLI data for vessels arriving in PR. The composition of freight vessel supply to PR is broadly similar to that of the Caribbean as a whole. Container ships and barges play a larger role (especially with respect to port calls), but the other vessel types collectively retain sizable shares of reported freight vessel traffic.

In order to distinguish JA-compliant ships from others serving PR, we use the LLI data to identify vessels that were U.S.-flagged and U.S.-owned.<sup>19</sup> Only 11

> 18The NBIC data do not report DWT, though this information is available for most vessels via public web sites like https://www.vesselfinder.com. Our data summary uses DWT data collected by Colin Grabow, who used a collection of public web sites to match DWT data to the names of JA vessels in the NBIC. We cross-validated this information with the NBIC and the sources of DWT data he provided. We thank Mr. Grabow for bringing the NBIC data to our attention, for providing the results of his DWT calculations, and for highlighting the rare examples of JA vessels granted exemptions from the U.S. build requirement.

> 19Because the law allows exceptions to the U.S.-build requirement, we focus on flag-of-registry

12
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