rafmacalaba/datause-encoder-data huggingface dataset:
_Figure 1. Study region in Eastern Uganda. Three images show Sentinel‐2 images and dates used in the study. Yellow polygons indicate outlines of plots where surveys/crop cuts were performed._

|**Name**<br>|**Equation**<br>**Table 2. Spectral Vegetatio**|**Equation using Sentinel‐2 bands**<br>**n indices(VIs) Used**|**Reference**|
|---|---|---|---|
|NDVI<br>(Normalized Difference Vegetation Index)|(RNIR– RRED) / (RNIR+ RRED)|(B8**–**B4) / (B8 + B4)|(Rouse et al., 1973)|
|GCVI<br>(Green Chlorophyll Vegetation Index)|(RNIR/ RGREEN) – 1|(B8/B3) ‐ 1|(Gitelson et al., 2003)|
|MTCI<br>(MERIS Terrestrial Chlorophyll Index)|(RNIR– R705) / (R705– RRED)|(B8‐B5) / (B5 – B4)|(Dash and Curran, 2004)|
|NDVI705<br>(Red‐Edge NDVI705)|(RNIR– R705) / (RNIR+ R705)|(B8 – B5) / (B8 + B5)|(Viña and Gitelson, 2005)|
|NDVI740<br>(Red‐Edge NDVI740)|(RNIR– R740) / (RNIR+ R740)|(B8 – B6) / (B8 + B6)|(Viña and Gitelson, 2005)|

11
the economic crisis, the healthcard evidently helped maintain public sector utilization rates among those who received it.

# Table 6 about here

# _Use of public sector outpatient care services_

Inpatient care utilization rates, historically quite low in Indonesia, remained relatively unchanged through the crisis years while outpatient care fell substantially both in the public and private sectors. I examine impact of the healthcard on changes in outpatient care use between 1997 and 2000. As before, both ATT and the unmatched difference-indifference results are presented.

Results in Table 6 suggest that the healthcard had a positive and statistically significant effect on the contact rate, or probability of receiving outpatient care from government facilities. The unmatched difference-in-difference estimate for healthcard owners and all others is not significantly different from zero, showing once again that the failure to match healthcard children with a comparable set of controls substantially underestimates program impact.

Estimates of the healthcard’s impact on the number of public sector outpatient contacts shown in Table 6 largely mirror those for outpatient contact rates above. The total number of visits declined for healthcard children and others between 1997 and 2000, but less so for the former as indicated by the positive ATT estimates.

39
_Table 1. Correlation between Inequality indicators and unfairness perceptions, LA 1997-2015_

||Pooling|all the d|ata|Averagin|gindicat|ors|Averaging|correlat|ions|
|---|---|---|---|---|---|---|---|---|---|
|_Correlation with..._|U. or V.U.|<br>V.U.|U.|U. or V.U.|V.U.|U.|U. or V.U.|V.U.|U.|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Gini coefficient|0.39|0.36|0.10|0.84|0.82|0.25|0.39|0.28|0.15|
||(0.07)|(0.07)|(0.09)|(0.10)|(0.16)|(0.35)|(0.07)|(0.07)|(0.09)|
|Theil index, GE(1)|0.39|0.38|0.05|0.85|0.82|0.27|0.33|0.20|0.18|
||(0.07)|(0.07)|(0.09)|(0.09)|(0.16)|(0.35)|(0.07)|(0.07)|(0.09)|
|Atkinson, A(0.5)|0.38|0.35|0.10|0.84|0.82|0.25|0.37|0.26|0.15|
||(0.07)|(0.07)|(0.09)|(0.10)|(0.16)|(0.35)|(0.07)|(0.07)|(0.09)|
|Atkinson, A(1)|0.36|0.30|0.13|0.84|0.82|0.25|0.38|0.28|0.13|
||(0.07)|(0.08)|(0.09)|(0.10)|(0.16)|(0.34)|(0.07)|(0.08)|(0.09)|
|Mean log deviation, GE(0)|0.35|0.29|0.14|0.84|0.82|0.25|0.38|0.28|0.13|
||(0.07)|(0.08)|(0.09)|(0.10)|(0.16)|(0.34)|(0.07)|(0.08)|(0.09)|
|Coefficient Variation|0.33|0.36|0.00|0.78|0.78|0.19|0.18|0.20|0.10|
||(0.08)|(0.08)|(0.09)|(0.13)|(0.16)|(0.36)|(0.08)|(0.08)|(0.09)|
|Ratio 75/25|0.29|0.15|0.24|0.80|0.78|0.26|0.36|0.29|0.12|
||(0.07)|(0.09)|(0.08)|(0.11)|(0.17)|(0.33)|(0.07)|(0.09)|(0.08)|
|Generalized entropy, GE(2)|0.29|0.35|-0.04|0.80|0.72|0.35|0.18|0.09|0.19|
||(0.05)|(0.08)|(0.08)|(0.11)|(0.18)|(0.34)|(0.05)|(0.08)|(0.08)|
|Ratio 90/10|0.23|0.10|0.21|0.81|0.79|0.25|0.30|0.30|0.07|
||(0.07)|(0.08)|(0.08)|(0.11)|(0.17)|(0.32)|(0.07)|(0.08)|(0.08)|
|Variance|-0.08|-0.01|-0.12|-0.25|0.10|-0.71|-0.06|0.04|-0.12|
||(0.07)|(0.08)|(0.08)|(0.39)|(0.43)|(0.14)|(0.07)|(0.08)|(0.08)|
|Absolute Gini|-0.21|-0.10|-0.18|-0.71|-0.46|-0.64|-0.18|-0.10|-0.22|
||(0.09)|(0.10)|(0.08)|(0.23)|(0.26)|(0.31)|(0.09)|(0.10)|(0.08)|
|Kolm, K(1)|-0.31|-0.18|-0.22|-0.80|-0.64|-0.50|-0.22|-0.16|-0.22|
||(0.09)|(0.10)|(0.08)|(0.12)|(0.18)|(0.37)|(0.09)|(0.10)|(0.08)|

Note: U. or V.U. = % Unfair or Very Unfair; V.U. = % Very Unfair; U. = % Unfair. Standard Errors are reported in parenthesis, and were calculated with bootstrap (500 iterations).

14
Table 1: Changes in Potential Crop Yields by 2100 (t / Ha)

|Model / Scenario|RCP 2.6F|RCP2.6NF|RCP 8.5F|RCP 8.5NF|
|---|---|---|---|---|
|GFDL-ESM2M|0,17|-0,16|0,46|-0,72|
|HadGEM2-ES|0,16|-0,18|-0,27|-1,30|
|IPSL-CM5A-LR|0,04|-0,29|-0,27|-1,30|
|MIROC-ESM-CHEM|-0,03|-0,34|-0,34|-1,32|
|NorESM1-M|0,20|-0,12|0,16|-0,92|

Notes. F: Fertilization Effect; NF: No Fertilization Effect. The results are normalized relative to _θT_<sup>_c_= 8</sup><sup>_._8</sup><sup>_._</sup> globally consistent, protocol-based, multimodel climate change assessment for major crops with explicit characterization of uncertainty. To quantify the range of uncertainty of climate change impact on potential crop yields we obtained results from the DSSAT crop simulation model, run globally on a 0.5<sup>_◦_</sup> grid and weighted by agricultural output of four major food crops (maize, soybeans, wheat and rice) under most optimistic (RCP2.6) and pessimistic (RCP8.5) Representative Concentration Pathways GHG forcing scenarios (Moss et al., 2008) and alternate assumptions on CO2 fertilization effects over the period between 1971 and 2099. To quantify uncertainty in temperature increases due to climate change we employ outputs for five global climate models (GCM): GFDLESM2M (Dunne et al., 2013), HadGEM2-ES (Collins et al., 2008), IPSL-CM5ALR (Dufresne et al., 2012), MIROC-ESM-CHEM (Watanabe et al., 2011), and NorESM1-M (Bentsen et al., 2012). For each of the simulations we fit a linear trend in order to parsimoniously characterize the evolution of crop yields in the face of climate change over the coming century.

Table 1 summarizes simulation results for four climate scenarios (RCP 2.6 and 8.5 with and without fertilization effects) in 2100, normalized relative to an assumed yield potential of 8.8 t / Ha. There is a significant heterogeneity in terms of both direction and magnitude of climate impacts on agricultural yields across global climate models when the CO2 fertilization effect is considered. For the most optimistic scenario, RCP2.6 with fertilization effects (RCP2.6F), four out of five GCMs predict an increase in potential yields, whereas one model (MIROC-ESM-CHEM), predicts a very small decline in potential yield. In a scenario of rapid increases in global temperature with CO2 fertilization effects (RCP8.5F) three out of five GCMs predict a decline in potential yields of comparable magnitude (between 0.27 and 0.34 t / Ha). Another GCM (NorESM1-M) predicts an increase in potential yields, but to a lesser extent as compared to scenario RCP2.6F. Only one climate model (GFDL-ESM2M) predicts an increase in potential yields, which is greater than in scenario RCP2.6F. These results are consistent with recent findings of Rosenzweig et al. (2014), who show the potential for negative climate impacts on major crops at higher levels of warming.

The predictions of global climate models become qualitatively more similar 18
# **Table of Contents**

|**Acknowledgements.................................................................................................................. 3**|
|---|
|**Introduction............................................................................................................................. 4**|
|**Section 1: Sources and Coverage of Remittance Statistics in Developing Countries ....... 6**|
|What Types of Transactions are Reported as Remittances?................................................. 9|
|Coverage of Informal Channels .......................................................................................... 11|
|**Section 2: Remittances Fees ................................................................................................. 15**|
|Cost of Remittance Transfers.............................................................................................. 15|
|Improving Information Available to Consumers................................................................ 18|
|Accounts in US Dollars or other International Currency.................................................... 19|
|Access to Central Banks’ Clearing and Settlement Systems .............................................. 20|
|**Section 3: Regulatory Issues................................................................................................. 22**|
|Oversight of Institutions Delivering Remittances............................................................... 22|
|Resolution of Consumer Complaints .................................................................................. 24|
|Taxation of Remittances ..................................................................................................... 24|
|**Section 4: Recent Initiatives in Developing Countries to Facilitate Remittances Flows**<br>**through Formal Channels .................................................................................................... 26**|
|Bilateral Dialogue and Cooperation Between Authorities.................................................. 26|
|Incentives for the Use of Formal Remittance Channels...................................................... 27|
|**Conclusions............................................................................................................................ 30**|
|Annex 1. World Bank Survey on Remittances- Questions for Central Banks ................... 36|
|Annex 2 – Country Data ..................................................................................................... 42|
|Annex 3. Recent Country Initiatives to Improve Statistical Data on Remittances............. 43|

2
# **Regional Roaming Project**

Another telecommunications integration project was initiated in 2003. ECOWAS Council mandated the Executive Secretariat to explore the feasibility of establishing a region-wide GSM roaming facility based on the use of one SIM card in all Member States. Currently it costs more to make calls to Member States within the region than to Europe and the United States. Although a number of GSM service providers have signed bilateral roaming arrangements these usually involve post paid customers, when more than 90% of the GSM subscribers are pre-paid customers.<sup>30</sup> A subsequent study on GSM roaming confirmed the technical and economic feasibility of cross-border connectivity but stressed the necessity of substantial investment in requisite infrastructure. To this end the report recommended that the embargo on the use of the Special Fund for Telecommunications (SFT) be lifted, so that member states could access it for the construction of uncompleted sections of the links<sup>31</sup> .

In January 2006, the ECOWAS Council of Ministers endorsed the recommendations of the study for the GSM roaming facility and approved the use of SFT to construct the identified gaps in inter-state links. At the same time, ECOWAS announced that two sub-groups had been set up by its technical branch on GSM Roaming to address the issues of interconnectivity and tariff harmonization that will facilitate the introduction of a region-wide roaming facility by the end of the year.

The ECOWAS Technical Group has also proposed some measures for implementation under a tripartite Memorandum of Understanding (MOU) involving member states, telecommunications regulators and GSM operators that will enhance the creation of an environment conducive to the goals of the roaming project. The measures include the adoption of laws that will promote the implementation of cross-border connectivity, the liberalization of international gateways to include mobile networks, the implementation of a fiscal incentive regime that will reduce tariffs on international calls and the operation of a regulatory framework that will support the full utilization of transmission capacity. The MOU calls for the introduction of appropriate incentives that would encourage member states to develop their transmission capacities and upgrade

> 30 Sources: www.panapress.com, www.sunnewsonline.com. 31 Source: www.businessdayonline.com.

24
impact on depletion including through crop selection, notably through the choice of irrigated crops. Using mainly evidence from the Republic of Yemen, as well as data from the MENA region more widely, we show that energy mis-pricing has been associated with accelerated depletion rates. Given the relative scarcity of water resources in this region, the consequences are significantly adverse. Further analysis, and data collection, will allow more precise identification of these costs not only in these two sectors, but also across a broader spectrum of sectors.

33
groups where the estimated effect was positive but not significant, the estimated effect of the program on 14 types of social relations was either negative or zero to two decimal places. As shown in the final panel of the table, averaging the effects of the program across all 14 of these types of relations produces a negative mean effect of 0.18 percent of a standard error and this estimate is highly significant statistically.

A clear picture emerges from these various network results: The program did not produce spillover effects for other sorts of social relationships. If anything the effect of the program was to reduce the number of these relationships among villagers. This estimated reduction in the number social relationship is interesting because it is consistent with the findings of Labonne and Chase (2011) who conjectured that that the CDD program they studied may have actually crowded out other social activities that would have naturally occurred. Perhaps the same phenomenon was occurring in this program.

# **7.3 Survey Measures**

Finally we turn to measures of from the household survey. We included questions to get at pro-sociality and community cohesion in the treated and control communities. We present these results in two separate tables. Table 6 offers the results from a series of questions that asked respondents about their own pro-social action over the last three years. Table 7 presents results from a series of questions that asked respondents to characterize the cohesion of their communities.

Table 6 is divided into upper and lower panels. In the upper panel we offer estimates of the effect of the program on responses to each of the questions about the respondents’ pro-social action over the last three years. The mean effect of the program calculated from _z_ -scores of the estimates across all 11 indicators is shown in the lower panel (column 12). All of the questions in this table had the form “ _In the last three years have you done_ : X?” where X is listed at the heading of each of the 11 columns in the upper panel of Table 6. The actions about which the survey asked are: (1) voting in an election, (2) joining a civic association, 28
Appendix Table 2. Naïve Probits: Marginal Probabilities for Likelihood of Employment, Nepal DHS, 1996-2006

||_All W_|_omen_|_Women with Hu_|_sbands Migrated_|_Widow/Sep_|_/Div/HH Head_|
|---|---|---|---|---|---|---|
||_Employed_|_Self-Employed_|_Employed_|_Self-Employed_|_Employed_|_Self-Employed_|
|Interaction Terms (reference=mortality_|1996)<br>||||||
|Mortality_2001|0.477<sup>**</sup>|0.384<sup>***</sup>|0.604<sup>**</sup>|0.093|0.454|1.647<sup>***</sup>|
||(0.201)|(0.108)|(0.296)|(0.104)|(0.327)|(0.418)|
|Mortality_2006|0.045<sup>***</sup>|0.018|0.084<sup>***</sup>|-0.016|0.084<sup>**</sup>|0.080|
||(0.017)|(0.015)|(0.028)|(0.015)|(0.040)|(0.053)|
|Education (reference=no schooling)|||||||
|Some or all primary school|-0.024<sup>***</sup>|0.025<sup>***</sup>|-0.041<sup>**</sup>|0.034<sup>***</sup>|-0.015|0.079<sup>***</sup>|
||(0.009)|(0.008)|(0.017)|(0.009)|(0.031)|(0.020)|
|Some secondary school|-0.054<sup>***</sup>|0.013|-0.063<sup>**</sup>|0.021<sup>**</sup>|-0.034|-0.061|
||(0.013)|(0.012)|(0.029)|(0.010)|(0.041)|(0.061)|
|Completed secondary school+|-0.076<sup>***</sup>|-0.324<sup>***</sup>|-0.153<sup>***</sup>|-0.363<sup>***</sup>|-0.057|-0.046|
||(0.021)|(0.043)|(0.049)|(0.054)|(0.049)|(0.080)|
|Age (reference=age<=20)|||||||
|20<age<=35|0.109<sup>***</sup>|0.005|0.087<sup>***</sup>|0.005|0.107<sup>**</sup>|0.057|
||(0.016)|(0.011)|(0.027)|(0.012)|(0.048)|(0.034)|
|age>35|0.146<sup>***</sup>|0.039<sup>***</sup>|0.129<sup>***</sup>|0.016|0.107<sup>*</sup>|0.122<sup>***</sup>|
||(0.019)|(0.011)|(0.026)|(0.016)|(0.072)|(0.046)|
|Socioeconomic status|YES|YES|YES|YES|YES|YES|
|Household composition|YES|YES|YES|YES|YES|YES|
|Year and sub-region dummies|YES|YES|YES|YES|YES|YES|
|Pseudo R<sup>2</sup>|0.193|0.207|0.234|0.276|0.173|0.201|
|N|25,724|21,807|5,253|4,430|1,404|1,282|

Notes: Weighted to national level with weights provided by the Nepal DHS in each year. Standard errors, in parentheses, are clustered by regionyear. The notation<sup>***</sup> is p<0.01,<sup>**</sup> is p<0.05,<sup>*</sup> is p<0.10. In each regression the mortality-year interactions are presumed exogenous and we do not instrument for them. The Widow/Sep/Div/HH Head sub-sample excludes women whose husbands have migrated. Source: Authors‟ calculations based on MHP/NE/MI (2007), MH/NE/ORC (2002), and Pradhan _et al._ (1997).

43
**How Much of the Labor in African Agriculture Is Provided by Women?**

Amparo Palacios-Lopez<sup>†</sup> , Luc Christiaensen<sup>‡</sup> and Talip Kilic<sup>*1</sup>

**JEL Codes:** J16, J22, O13, Q12. **Keywords:** Gender, Labor, Agriculture, Sub-Saharan Africa.

1†Corresponding author: apalacioslopez@worldbank.org, Economist, Living Standards Measurement Study (LSMS), Surveys and Methods Group, Development Research Group, The World Bank.<sup>‡</sup> <u>lchristiaensen@worldbank.org, Lead</u> Economist, Jobs Cross-Cutting Solution Area, The World Bank.<sup>*</sup> <u>tkilic@worldbank.org, Senior Economist, Living</u> Standards Measurement Study (LSMS), Surveys and Methods Group, Development Research Group, The World Bank. This paper has been produced under the partnership project “Agriculture in Africa – Telling Facts from Myths”, which seeks to revisit common wisdom about African agriculture and its farmers’ livelihoods using the household survey data collected under the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative (http://www.worldbank.org/en/region/afr/brief/office-of-chief-economist-in-the-africa- <u>region-afrce). The authors gratefully acknowledged the comments from Isis Gaddis, Hazel Malapit, Mieke Meurs and</u> conference participants at the 4<sup>th</sup> International African Association for Agricultural Economists Conference in Hammamet (2013), the European Development Days (2013) and the Global Development Network (2014).
21

|**Migration cycle**|**Migrant**|**Migrant’s family**<br>**Sending community**|**Receiving community**|
|---|---|---|---|
|-<br>Integration policies for<br>migrants|Invest in education<br>and skills.||Many programs, little<br>empirical evidence.|
|**Return**||||
|-<br>Policies to remove regulatory,<br>bureaucratic and<br>informational barriers||No rigorous evidence||
|-<br>Policies changing financial<br>and other incentives for return||No rigorous evidence||
|-<br>Policies to make return<br>migrants more productive and<br>adjust reintegration easier||No rigorous evidence||

Source: McKenzie and Yang (2015); Verwimp and Maystadt (2015).
|**Table 15. Forms of income and Mea**|**ns of Conver**|**ting Checks**|
|---|---|---|
||**Banked**|**Unbanked**|
|**Way in which most income was received**|||
|**Cash**|**11.5%**|**7.8%**|
|**Check**|**32.0%**|**85.6%**|
|**Checking account deposit**|**13.0%**|**1.4%**|
|**Savings account deposit**|**41.3%**|**4.9%**|
|**Other**|**2.2%**|**0.3%**|
|**Most common way to convert checks among**<br>**those receiving check**|||
|**Deposit in some account**|**19.4%**|**16.8%**|
|**Cash check in a bank**|**77.4%**|**80.0%**|
|**Other**|**3.2%**|**3.2%**|
|**Source: ENDESFI**|||

In Mexico, unlike in the U.S, there are few practical alternatives for people to cash checks other than to go to a bank. Banks in Mexico commonly cash checks for people who do not have deposit accounts and they do not charge a fee for this service. As shown in Table 15, 80% of the unbanked cash their income checks in a bank, or make a deposit into a relative’s or friend’s account.

Table 16 presents data on the means that people use to pay bills. As indicated in the table, both unbanked and banked individuals pay the overwhelming proportion of their bills in cash. The only notable exceptions are for payments of purchases of appliances and furniture and for purchases from department stores. For such purchases, about 7.8% to 10.3% of the banked use credit cards.

41
10 The EBA results for GOV show that regardles of the conditioning set of information, the partial correlation between GOV and growth is always negative but never significant at the 0.05 level. This may reflect optimal fiscal policy or that COV is poorly measured.

Further examining the link between fiscal policy and growth, Barro (1991, p. 430) argues that "expenditures on *ducatiou and defense are more like public investmen **t** hen public consumption," and therefore he constructs the variable government consumption expenditures minus education and defense expenditures divided by GDP, over the 1970-85 period, and calls this variable HSGVXDXE. This variable is also fragile. When LLY, TRD, and STPI are included the coefficient on HSGVXDXE is insignificant.8 In another attempt to link government actions and growth, many studies examine the role of government fiscal surpluses and deficits. Government deficits are frequently considered bad for growth, or, sometimes, deficits are viewed as indicative of structural problems associated with poor growth. We use two measures of fiscal financing to investigate these claims. Firet, we study the ratio of the central government surplus to GDP (SURY). The EBA shows that only with particular combinations of explanatory variables does this variable have a significant partial correlation with growth. SURY does not enter significantly in the base regression, but the LBA finds that when PI, STPI, LLY are included the coefficient on SURY becomes significant. In our sample, we found two outliers for the variable SURY as shown in Graph I (Bolivia and Israel). Removal of these countries did not change the EBA results. Running the entire EBA after first omitting outliers on all variables does not change this paper's conclusions. SURY, however, does not accurately measure the size of the entire public sector deficit and may, therefore, be both incomplete and inconsistent across countries. For example, in many countries, the