rafmacalaba/datause-encoder-data huggingface dataset:
30 h* h - [E(Ft+1<sup>-Ft)/2X Var(F)] .................... (8)</sup> The first term in equation (8) is called the hedging component and this is, of course, the same as the risk-minimizing hedge ratio. Notice that if X-*w (i.e., infinite risk-aversion) the second term disappears and the optimal (utility-maximizing) hedge ratio is, in this case, the same as the risk-minimizing hedge ratio (i.e. h* = h*). The second term in equation (8) is called the speculat e component and this is inversely related to X and positiveJy related to the "bias" between the current and the expected futures price. The speculative component essentially captures the effect of hedging on expected returns.

We estimated ex-post optimal hedge ratios for FPA cotton using the July 1990 futures contract. We assumed that the hedge was placed in the first month of trading of the July contract in March 1989 and lifted in June 1990 before the expiration of the contract. Table 6 reports estimates of the optimal hedge ratio at different levels of risk aversion and the associated return and risk levels. It is clear from the table that for values of X between 10 and infinity, the optimal hedge ratio is essentially constant, implying that for these values of risk aversion, the speculative component is insignificant. This result is similar to Rolfo's (1980) result on optimal hedging for cocoa producing countries and Ouattara, Schroeder, and Sorenson's (1992) work on coffee hedging for Cote d'Ivoire. At values of X equal to or lesser than .10, the results
# **Internal Borders and Migration in India**<sup>∗</sup>

Zovanga L. Kone<sup>b</sup> , Maggie Y. Liu<sup>a,c</sup> , Aaditya Mattoo<sup>a</sup> , Çağlar Özden<sup>a</sup> and Siddharth Sharma<sup>a</sup>

> aWorld Bank Group

> bUniversity of Oxford

> cSmith College

**Keywords** : Internal migration; internal borders; immigration; emigration **JEL codes** : J6; O15; F22

∗We would like to thank the Data Dissemination Unit, Office of the Registrar General and Census Commissioner of India for preparing the data tables from the 2001 Census under a special administrative agreement with the World Bank. We are also grateful to Erhan Artuc, Sam Asher, Simone Bertoli, Bernard Hoekman, Chris Parsons, Mathis Wagner and participants at the 9th International Migration and Development Conference (June 2016) in Florence for comments, Professor Ravi Srivastava (JNU) for his valuable insights on internal migration in India, and Virgilio Galdo and Yue Li (Office of the Chief Economist, South Asia, World Bank) for sharing GIS shapefiles of India’s districts, and especially Simon Alder for generously sharing with us his data on travel time in India. We acknowledge the financial support from the Knowledge for Change Program, the Multi-Donor Trust Fund for Trade and Development, and the Strategic Research Program of the World Bank. The findings in this paper do not necessarily represent the views of the World Bank’s Board of Executive Directors or the governments they represent. Any errors or omissions are the authors’ responsibility.
**Table A4. Maternity Care by Quintile**

|**Quintile**|**Women who**<br>**delivered child in**<br>**last 5 years**<br>**(% of quintile)**|**% of group that**<br>**had a pregnancy**<br>**control**|**% of all pregnancy**<br>**controls**<br>**performed**|**% of group that**<br>**had medically**<br>**supervised**<br>**delivery**|**% of all medically**<br>**supervised**<br>**deliveries**|
|---|---|---|---|---|---|
||<br> <br>|**Socioeconomic status**<br>|**measured by consumpt **<br>|<br>**ion**<br>||
|1|11.2%|55.5%|**16.9%**|35.2%|**16.2%**|
|2|13.1%|64.2%|**20.2%**|42.8%|**20.4%**|
|3|12.6%|62.5%|**20.1%**|40.2%|**19.5%**|
|4|13.3%|67.2%|**22.9%**|44.9%|**23.1%**|
|5|11.0%|71.2%|**19.9%**|49.4%|**20.9%**|
|||**Socioeconomic status**<br>|**measured by asset ind**<br>|**ex**<br>||
|1|12.6%|45.2%|**15.5%**|23.9%|**12.4%**|
|2|11.6%|57.6%|**16.0%**|29.1%|**12.2%**|
|3|13.5%|58.6%|**20.1%**|35.3%|**18.3%**|
|4|12.2%|71.3%|**22.2%**|50.6%|**23.8%**|
|5|11.2%|91.7%|**26.2%**|77.2%|**33.3%**|
|**Total**|**12.2%**|**64.1%**|**100.0%**|**42.4%**|**100.0%**|

18
# **Figure 1: Allocation of labor across activities by gender and age.**

# **a) Migration rate by year, age and gender**

<!-- Start of picture text --> 1997 2000<br>2004 2006<br>16 20 25 30 35 40 45 50 55 60 65 70 75 16 20 25 30 35 40 45 50 55 60 65 70 75<br>Age<br> Men Women<br>.4<br>.3<br>.2<br>.1<br>0<br>.4<br>Miragtion Rate<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text -->

# **b) Share of labor force working in agriculture by year, age and gender**

<!-- Start of picture text --> 1997 2000<br>2004 2006<br>16 20 25 30 35 40 45 50 55 60 65 70 75 16 20 25 30 35 40 45 50 55 60 65 70 75<br>Age<br> Men Women<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>1<br>.8<br>Working in Agriculture .6<br>.4<br>.2<br>0<br><!-- End of picture text --> 25
# Source: Authors’ calculation using VAM and TRMM data

Figure A3: Rainfall and prices, Nigeria (North) and Senegal <!-- Start of picture text --> Nigeria Senegal<br>Rainy season Dry season<br>Rainy season Dry season Maize Maize_Imported<br>Maize Millet Millet Rice_Imported<br>Sorghum Sorghum<br>Source: Authors’ calculation using VAM and TRMM data<br>Figure A4: Rainfall and prices, Burkina Faso and Ghana (rainy season months)<br>Burkina Faso Ghana<br>Average monthly ra infall Cassava<br>Average monthly rainf all Maize Maize Plantain_Apentu<br>Millet Sorghum Rice_Local Yam<br>2000m72001m72002m72003m72004m72005m72006m72007m72008m72009m72010m72011m72012m7<br>2002m72003m72004m72005m72006m72007m72008m72009m72010m72011m72012m7<br>2008m7 2009m7 2010m7 2011m7 2012m7<br>2000m72001m72002m72003m72004m72005m72006m72007m72008m72009m72010m72011m72012m7<br>300 300 300<br>400<br>250<br>200 200 300<br>200 Rainfall(mm)<br>Rainfall(mm) 150<br>100 100 200<br>Average nominal price(XOF/KG)<br>Average nominal price(XOF/KG)<br>100<br>0 100<br>0 50<br>300 250 200<br>250<br>250 150<br>200 200<br>200 100<br>150 Rainfall(mm)<br>Rainfall(mm)<br>150<br>150 Average nominal price(XOF/KG) 50 Average nominal price(GHS/unit*)<br>100<br>100<br>100 0<br>50<br><!-- End of picture text -->

# Source: Authors’ calculation using VAM and TRMM data

Figure A4: Rainfall and prices, Burkina Faso and Ghana (rainy season months) Source: Authors’ calculation using VAM and TRMM data 27
Mexico to change the way it has administered these protection mechanisms so far. The changes in the staff and priorities that follow any change of government might gravely undermine originally serious institutions. The most effective way to prevent such institutions from degenerating is through a multilateral agreement that should impose strict discipline on all WTO members.

# **Bibliography**

Arriola, Carlos, editor, _Testimonios sobre el TLC_ **,** Ed. Diana, Grupo Editorial Miguel Ángel Porrúa, Mexico, 1994.

Arriola **,** Carlos (1994). _Documentos básicos del TLCAN_ , SECOFI, Mexico 1994.

Gabinete de Comercio Exterior (1986). _El Proceso de Adhesión de México al Acuerdo General sobre Aranceles Aduaneros y Comercio (GATT)_ , United Mexican States, August 7, 1986.

Leycegui, Beatriz (2000). “Acordar para disentir: la solución de controversias en el Tratado de Libre Comercio de América del Norte,” in _¿Socios Naturales? Cinco años del Tratado de Libre Comercio de América del Norte_ , Leycegui and Fernández de Castro (coordinators), January 2000, ITAM and Grupo Editorial Miguel Ángel Porúa.

Miranda, Jorge (1995). “Análisis económico de la forma en que México ha venido aplicando las leyes de prácticas desleales de comercio internacional de 1987 a 1995,” in _Comercio a Golpes: Las prácticas desleales de comercio internacional bajo el TLCAN_ , Leycegui, Robson and Stein, coordinators, Grupo Editorial Miguel Ángel Porrúa, 1<sup>st</sup> Spanish edition, October 1997.

Ramos Tercero, Raúl (1995). “Prácticas Desleales de Comercio Internacional y Medidas de Emergencia”, _El Mercado de Valores_ **,** Issue 11, November 1995, Mexico.

Reyes de la Torre, Luz Elena (1995). “Metodología para evaluar el impacto de las importaciones en condiciones de dumping sobre la industria nacional: la experiencia mexicana”, in _Prácticas desleales de comercio internacional_ , UNAM, Mexico City.

45
Policy Research Working Paper 9501

# **Abstract**

Using country-to-country data, this paper documents a set of novel stylized facts about the rise of the South in global finance. The paper assembles comprehensive bilateral data on cross-border bank loans and deposits, portfolio investment in debt and equity, foreign direct investment, and international reserves. The main finding is that global financial integration with and especially within the South (countries outside the G7 and Western Europe) has grown faster than within the North. By 2018, the South accounted for 24 to 40 percent of international loans and deposits, portfolio investment, and foreign direct investment, an increase of roughly 10 percentage points since 2001. The growing importance of the South is reflected in the intensive and extensive margins, with fast growth in the number of bilateral links. Although China weighs heavily in these trends, international investment in the rest of the South has increased to a similar extent.

This paper is joint product of the Chief Economist Office, Equitable Growth, Finance and Institutions Vice Presidency and the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fbroner@crei.cat; tdidier@worldbank.org; sschmukler@worldbank.org; and Goetz.von.Peter@bis.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **_Sectoral convergence_**

Given the patterns of cross-country convergence are reflections of and shaped by countries’ sectoral composition, it is insightful to investigate sectoral convergence. Using data on more than 100 countries, Rodrik (2013) shows strong evidence of unconditional convergence in manufacturing labor productivity, as well as _σ_ -convergence for a sub-sample of countries. However, there is no aggregate convergence because of a rather small share of formal manufacturing employment and the slow pace of industrialization in low-income countries. In addition, Kinfemichael and Morshed (2019a), using 95 countries, find evidence of unconditional convergence for aggregate labor productivity, primarily driven by the fast convergence in services. Kinfemichael (2019) also reports similar results.

By contrast, Herrendorf et al. (2022), based on a new dataset for low-income countries during 1990-2018, uncover that cross-country differences in productivity gaps (with high-income countries) are larger in manufacturing than in the aggregate, and no observed tendency for productivity to converge in manufacturing. They also find a positive correlation between manufacturing labor productivity growth and aggregate and other sectors’ labor productivity growth, suggesting weak growth of manufacturing employment is not necessarily the force behind the lack of convergence in lowincome countries. The seemingly sharp contrast findings between Rodrik (2013) and Herrendorf et al. (2022) are partly attributed to the role and size of the informal sector in developing countries. Rodrik (2013)’s findings in support of unconditional convergence are based on data on larger establishments in the formal manufacturing sector.<sup>6</sup> When the sample coverage extends to small and informal establishments and workers, which are highly prevalent in developing countries, as in Herrendorf et al. (2022), the data does not reveal unconditional convergence in manufacturing.

An earlier study by Wong (2006) decomposes the pattern of _β_ -convergence into within- and between-sector contributions and show that both the within-sector growth and between-sector reallocations. Among the OECD countries during 1970-1990, Wong (2006) shows sizable contributions of productivity growth in agriculture and services to aggregate convergence, but limited role of labor reallocation and productivity growth in manufacturing. Relatedly, Dieppe and Matsuoka (2021) apply the same technique on a larger sample of advanced and developing countries and more recent years<sup>7</sup> and show that both the within-sector growth and between-sector reallocations

> 6 Rodrik (2013) clearly indicates the context in which to interpret the findings; they apply to formal manufacturing.

> 7 Their results are based on sectoral data for 91 countries over 1995-2018, including a longer horizon (between 1975 and 2018) for a sub-sample of 60 countries.

6
To address these needs, we work with the unique, nationally-representative household survey data from Malawi and Ethiopia that exhibit near-negligible rates of missingness in GPS-based plot areas and use these datasets to gauge the validity and accuracy of an MI-based approach to predict missing GPS-based land areas among plots that would otherwise be deemed “distant” in a typical survey operation. In doing so, we test the typically-untestable assumptions of MI and identify the acceptable rates of missingness beyond which these assumptions are less likely to hold, specifically for the reliable estimations of cultivated area and agricultural productivity.

The use of actual data collected as part of large-scale household surveys that have adopted GPS-based area measurement is key to the operational relevance of our research. As such, we provide operational recommendations that can enable survey practitioners, including agricultural and development economists involved in primary data collection, to collect the minimum-required data for model-based imputation applications. In addition, we make available the constructed datasets and syntax files to replicate our analyses; enable future MI applications to address similar missing information problems; and catalyze further research for deriving alternative acceptable rates of missingness that can drive the design and implementation of future survey efforts that may have different analytical objectives than those that we work with.

Our headline finding is that in Malawi, the multiply-imputed distribution of plot-level agricultural productivity is statistically indistinguishable from the _true_ distribution in each of the 50 imputed datasets with up to 82 percent missingness in GPS-based plot areas that are more than 1 kilometer away from the associated dwelling. The comparable figure in Ethiopia is 56 percent. If one sets the distance threshold at 500 meters, the tolerate rates of missing GPS-based areas among distant plots stand at 45 percent in Malawi, and 36 percent in Ethiopia. If one focuses on plot area as the outcome variable of interest, as opposed to productivity, the estimated tolerable rates of missingness present an even more optimistic outlook regarding the promise of MI, irrespective of the country and/or the distance threshold in question.

5
Policy Research Working Paper 7258

# **Abstract**

Providing protection against the financial risk of high outof-pocket health spending is one of the main goals of the Philippines’ health strategy. Yet, as this paper shows using eight household surveys, health spending increased by 150 percent (real) from 2000 to 2012, with the sharpest increases occurring in recent years. The main driver of health spending is medicines, accounting for almost two-thirds of total health spending, and as much as three-quarters among the poor. The incidence of catastrophic payments has trebled since 2000, from 2.5 to 7.7 percent. The percentage of people impoverished by health spending has also increased and, in 2012, out-of-pocket spending on health added 1.5 percentage points to the poverty rate. In light of these findings, recent policies to enhance financial risk protection—such as the expansion of government-subsidized health insurance for the poor, a deepening of the benefit package, and provider payment reform aimed at cost-containment—are to be applauded. Between 2008 and 2013, self-reported health insurance coverage increased across all quintiles and its distribution became more pro-poor. To speed progress toward financial protection goals, possible quick wins could include issuing health insurance cards for the poor to increase awareness of coverage and introducing a fixed copayment for non-poor members. Over the medium term, complementary investments in supply-side readiness are essential. Finally, an in-depth analysis of the pharmaceutical sector would help to shed light on why medicines continue to place such a large financial burden on households.

This paper is a product of the Health Nutrition and Population Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at cbredenkamp@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
are to be expected regardless of how efficient a utility is, any difference between this and a utility’s actual, total losses can be referred to as “inefficient losses.” A higher level of losses implies higher costs. In terms of O&M, higher losses are linked to the use of more energy and chemical products. On the capital side, higher losses imply greater investment to cover the additional production needed to serve customers. We estimate the incremental cost associated with the difference between the 15 percent assumed in the model firm estimate and the level reported in IBNET with the following equation: I I + ∆C I (11) II II II Where I is the total cost differential associated with higher losses for utility ∆CCllCCI = ∆OO&MMllCCI CCIICCllCCI _s_ ; I is the O&M cost differential associated with higher losses for utility II _s_ ; and llCCI ∆C ∆CC III is the CAPEX differential associated with higher losses for utility _s_ . llCCI ∆OO&MM II CCIICCllCCI I + C (12) II II II II <u>(</u> llCCI <u>)</u> ∆C I∆OO&MM= = (IICC ) ∗(TTCCTT∗C I −TTQQTT)I � (13) ) II TTCCTT<sup>ss</sup> −TTIITT<sup>rr</sup> llCCI Where CCIICC are the reported electricity costs for utility (1−TTCCTT<sup>ss</sup> ∗�KK<sup>II,PPrrCCdd,II</sup> CCIIQQrrII I _s_ ; C are the reported chemical costs for TT<sup>II,WWppI</sup> utility _s_ ; are the reported total losses for utility _s_ ; are the efficient total losses from the II Chilean sector; IICC<sup>II</sup> is the estimated asset base for water production of size _m_ for utility _s_ in the IBNET database (calculated using weighted average of the unit water production asset base TTCCTT<sup>II</sup> TTQQTT for small, medium, and large utilities); and KK<sup>II,PPrrCCdd,II</sup> C I I are the reported water customers of utility _s_ in the IBNET database.<sup>rr</sup> CCIIQQrrII I TT<sup>II,WWppI</sup> The estimated asset base for water production is calculated using the weighted average water production asset base per customer from the Chilean model firms of similar size: 3,572, 2,165, and 952 USD per customer for small, medium, and large utilities respectively.

The model firm developed by SISS in Chile defines an efficient level of employees per customer for each firm. The efficient ratio of employees per customer<sup>21</sup> we adopt is based on a weighted average of our sample of 15 Chilean model firms by size: 5.2, 4.4, and 2.4 employees per 1,000 customers for small, medium, and large utilities respectively. In general, most WSS providers, particularly in developing countries, have staff numbers that are substantially higher.

Overstaffing implies higher O&M costs. To account for this cost differential, we estimate the incremental cost associated with the difference between (i) the efficient ratio of employees per customer and (ii) the level reported for each utility in IBNET, using the following equation:

> 21 A generally accepted benchmark for staff efficiency is 5 employees for every 1,000 customers, although we refrain from using this, since it does not reflect economies of scale and thus may result in the illusion that large companies have efficient staffing levels.

11
.9means that much of the observed variability<sup>in regression coefficienti is</sup> sampling variance and as a result unexplainable by school factors. Sufficient variability across schools on both the<sup>mean achievement and the SES</sup> differentiation effect does exist, however, to proceed.<sup>(The Chi-square chart</sup> indicates that all but the Portuguese<sup>social class differenti&vion</sup> effect estimated parameter variances are significantly different from zero).

Table 2: The _HIM_ unconditional modeil

||Gamma|||
|---|---|---|---|
|Estimated Effects|coefficients|Standard<br>||
|||error<br>t-stat|p-value|
|School mean Achievement||<br>||
|Mean mathematics|14.29|.75<br>19.04<br> <br>|.000<br>|
|Mean Portuguese|17.36|.41<br>42.39|.000|
|Social Class Differentiatio|n<br>|<br>||
|Mean mathematics|.29<br>|.15<br>1.99<br> <br>|.046<br>|
|Mean Portuguese|.31|.12<br>2.54|.011|
|The Chi Square Table||||
|Parameter|Estimated<br>D/F<br>|Chi-square|p-value|
||value|||
|Mean Achievement||||
|Mathematics|34.28<br>61<br>|4028.2<br>|.000<br>|
|Portuguese|9.87<br>61|1424.0|.000|
|SES Differentiation||||
|Mathematics|0.39<br>61<br>|90.41<br>|.009<br>|
|Portuguese|0.16<br>61|68.84|.229|
|<br>Reliability of School-Level<br>|<br>Random Effect|||
|Mean Achievement||||
|Mathematics-<br>.982||||
|Portuguese-<br>.948||||
|SES Differentiation||||
|Mathematics-<br>.223||||
|Portuguese-<br>.125||||