rafmacalaba/datause-encoder-data huggingface dataset:
We also estimated the total or cumulative economic loss Nepal might have experienced due to the load shedding during the 2008-2016 period. Figure 3 presents actual GDP (measured in current price) and GDP the country would have realized if there were no load shedding. Our model estimates that Nepal has lost a total of US$11 billion value of GDP in nine years during the 20082016 period due to the electricity load shedding. This amount is almost equal to Nepal’s GDP in year 2008. If the annual GDP loss is expressed in 2016 price using the corresponding GDP deflator values, the total GDP loss over the 2008-2016 period amounts to US$14.5 billion.

# **Figure 3: Actual GDP and estimated GDP if there were no load shedding (Billion US$ current price)**

<!-- Start of picture text --> 24.0<br>22.0<br>20.0<br>18.0<br>16.0<br>14.0<br>12.0<br>10.0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Actual GDP Estimated GDP if there were no load shedding<br>GDP in current price (Billion US$)<br><!-- End of picture text --> Since the estimated impacts are based on a modeling exercise rather than an actual accounting exercise, the numbers should be interpreted as indicative. Note that the impacts of load shedding estimated here are relevant only in the context of not meeting electricity demand to the customers already connected to the national electricity grid. The country has been also suffering due to lack of electricity access in areas not served by the national grid or off-grid/distributed generation systems. Meaning that there was lack of access to electricity in the baseline as well. This analysis does not include economic loss of not providing electricity in those areas. Moreover, 11
# Abbreviations and Acronyms

|AFOLU|Agriculture, Forestry, and Other Land Use|
|---|---|
|AQUASTAT|global water information system of the FAO; developed by the FAO Land and Water Division|
|BRICS|association of five emerging countries, Brazil, Russia, India, China and South Africa|
|CGIAR|Global partnership of 15 organizations engaged in research dedicated to reducing rural<br>poverty, increasing food security, improving human health and nutrition, and ensuring<br>sustainable management of natural resources|
|CMIP5|Coupled Model Intercomparison Project Phase 5|
|CO2|Carbon Dioxide|
|CWATM|Community Water Model; open source hydrological model developed at IIASA|
|EFR|Environmental Flow Requirement|
|EPIC|Environmental Policy Integrated Climate Model; globally gridded crop model|
|FAO|Food and Agriculture Organization of the United Nations|
|GCM|General Circulation Model|
|GDP|Gross Domestic Product|
|GFDL/GFDL‐ESM2M|General Circulation Model of NOAA Geophysical Fluid Dynamics Laboratory|
|GGCM|Globally gridded crop model|
|GHG|Greenhouse gas|
|GLOBIOM|GLobal BIOsphere Management Model; developed at IIASA|
|HadGEM/Had GEM2‐ES|General Circulation Model of Met Office Hadley Centre|
|IIASA|International Institute for Applied Systems Analysis|
|IPSL/IPSL‐CM5A‐LR|General Circulation Model of Institut Pierre‐Simon Laplace|
|ISI‐MIP|Inter‐Sectoral Impact Model Intercomparison Project|
|LPJml|Lund‐Potsdam‐Jena managed land model; dynamic vegetation model developed at the<br>Potsdam Institute for Climate Impact Research|
|LUC|Land use change|
|LULUCF|land use, land use change, and forestry|
|MER|Market Exchange Rate|
|MESSAGE|Model for Energy Supply Strategy Alternatives and their General Environmental Impact;<br>energy model developed at IIASA|
|MIROC/MIROC‐ESM‐CH|General Circulation Model for Japan Agency for Marine‐Earth Science and Technology,<br>Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute<br>for Environmental Studies|
|NEPAD|New Partnership for Africa's Development|
|NOR/NorESM1‐M|General Circulation Model for Norwegian Climate Centre|
|PCR‐GLOBWB|PCRaster GLOBal Water Balance model; hydrological model developed at Utrecht University|
|RCP|Representative Concentration Pathway|
|SDG|Sustainable Development Goal|
|SSP|Shared Socioeconomic Pathway|
|USAID|United States Agency for International Development|
|WAE|Water application efficiency|
|WFaS|Water Futures and Solutions Project|

3
# **Table A7.3. The Effect of PMT on Probability of Stunting Among Children 24 to 60 Months:  Using Binary Indicator for Program Exposure**

||Poole<br>|d Linear<br>|Linear<br>|Probability<br>|
|---|---|---|---|---|
||Prob|ability|w/Com|munityFEs|
|Year (2000=1)|0.012|0.005|-0.021|-0.024|
||[0.053]|[0.054]|[0.071]|[0.070]|
|Program since ‘98 (Yes=1)*Year|0.001|0.009|0.039|0.044|
||[0.052]|[0.053]|[0.072]|[0.072]|
|Gender (boy=1)|0.014|0.014|0.005|0.006|
||[0.016]|[0.016]|[0.016]|[0.016]|
|Age|-0.003***<br>|-0.003***<br>|-0.003***<br>|-0.003***<br>|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Mother education (years)|-0.006***|-0.006***|-0.005***|-0.005***|
||[0.002]|[0.002]|[0.002]|[0.002]|
|Father education (years)|0.001|0.001|0.001|0.001|
||[0.002]|[0.002]|[0.002]|[0.002]|
|Mother height (cm)|-0.019***|-0.018***|-0.018***|-0.018***|
||[0.002]|[0.002]|[0.002]|[0.002]|
|Father height (cm)|-0.011***|-0.011***|-0.011***|-0.011***|
||[0.002]|[0.002]|[0.002]|[0.002]|
|Head of HH (male=1)|-0.004|-0.006|0.010|0.009|
||[0.026]|[0.026]|[0.028]|[0.028]|
|Number of children 5-14 yo in HH|0.023***|0.021***|0.019**|0.019**|
||[0.007]|[0.008]|[0.008]|[0.008]|
|HH owns private toilet (Yes=1)|-0.040**|-0.037**|-0.041**|-0.041**|
||[0.017]|[0.017]|[0.020]|[0.020]|
|HH has sanitation (Yes=1)|-0.112***|-0.094***|-0.097***|-0.095***|
||[0.021]|[0.025]|[0.027]|[0.029]|
|Type of community (urban=1), C|-|-0.043*||0.015|
|||[0.023]||[0.038]|
|Distance to district capital (km), C|-|0.000||-0.000|
|||[0.000]||[0.001]|
|Distance to bus station (km), C|-|-0.000||-0.000|
|||[0.001]||[0.002]|
|Land w/ technical irrigation (%), C|-|-0.062||-0.027|
|||[0.045]||[0.070]|
|Comm. has asphalt road (Yes=1), C|-|<br>-0.020||<br>-0.073**|
|||[0.021]||[0.035]|
|C h Y=1 C||0029||0002|
|omm. as sewerage system (es),|-|.||.|
|||[0.018]||[0.029]|
|Comm. has piped water (Yes=1), C|-|<br>-0.000||<br>-0.003|
|||[0.019]||[0.030]|
|Constant|4.969***|4.879***|4.852***|4.926***|
||[0.319]|[0.321]|[0.347]|[0.354]|
|R-squared|0.11|0.11|0.21|0.22|

Notes: n is 3221. (C) indicates community-level variables. Robust standard errors are in parentheses. (*), (**) and (***) indicate significance respectively at 10%, 5%; and 1%.

42
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# III. DATA AND ESTIMATION

# A. The data

The data are from the Egyptian Fertility<sup>Survey, conducted in 1980 as part</sup> of the World Fertility Survey programme<sup>and in collaboration</sup> with the World Bank. The executing agency for the<sup>survey was the Central Agency for Public</sup> Mobilisation and Statistics (CAPMAS). The<sup>principal findings of the survey are</sup> presented in the four-volume survey report<sup>(CAPMAS, 1983) and in a further</sup> volume of in-depth analyses (Hallouda, Farid,<sup>and Cochrane, 1988). The EFS</sup> documents that Egypt is characterized by relatively high infant and child mortality (the infant mortality rate is<sup>roughly 130 for the five years preceding</sup> the survey), high fertility (a TFR<sup>of 5.27 for the five years preceding the</sup> survey), and long durations of breastfeeding<sup>(an average age at weaning of 17</sup> months). About one-quarter (24%) of currently-married women of reproductive age reported using some method of contraception in the five years prior to the survey, with the contraceptive pill the most popular method. The percentage of contraceptive users in our sample (24%) is different<sup>than in the total sample</sup> (16%) because our sample contains births<sup>in the five years prior to survey while</sup> the percentage for the total sample is based<sup>on current status.</sup> A complete pregnancy history (live and<sup>non-live births) was obtained in</sup> the EFS. For each live birth, survival<sup>status at the survey was ascertained,</sup> and the age at death (in months) of those<sup>children who died. Duration of</sup> breastfeeding of the most recent two live births was<sup>also obtained. Thus, with</sup> the EFS data it is possible to determine,<sup>for recent births, the ages (in</sup> months) at death, weaning, and next<sup>pregnancy. (For all children, age at next</sup> pregnancy is calculated by subtracting nine months from the age at the birth of 13
where _Lhi_ is the subjective land value of parcel _i_ owned by household _h_ , and νhi is an error term. The interaction term of the tenure regime with ownership status is excluded because the estimation is done on a sub-sample of owners for the reason mentioned above. Given the subjective nature of reported land values, and the fact that household characteristics may affect the ability to make the most productive use of land, we also control for household characteristics and expect signs of the coefficients on the different variables to be consistent with what had been obtained earlier.

# **3. Context and descriptive evidence**

To provide the context for our empirical analysis, this section highlights the importance of tenure security for investment and increased agricultural productivity for poverty reduction in Uganda. It describes the historical events that have significantly reduced tenure security for a large number of occupants as well as the measures taken by the recently passed Land Act to restore such security. We note the vast gap between the desired institutional structure and actual levels of implementation and use our survey data to provide empirical evidence on household and parcel characteristics as well as awareness of different provisions of the Land Act in the population.

# **3.1 The broader relevance of land-related investment**

Although Uganda is a rather small country by African standards, it is characterized by a high level of variation in population density - which ranges from 65 per km<sup>2</sup> in the North to 226 per km<sup>2</sup> in the SouthWest and in physio-geographic conditions, which vary between nomadic semi-deserts in the North-East and the high elevations of the West and South-East. Population growth of 3.3% in the 1991-2002 period considerably increased land scarcity, with an increase in average population density from 64 persons per km<sup>2</sup> in 1980 to 124 in 2002 (Uganda Bureau of Statistics 2005). As more than 85% of the population live in rural areas and agriculture accounts for 77% of employment and 50% of total output (Belshaw _et al._ 1999), land has enormous socio-economic significance as a key productive asset and source of livelihood. Household surveys indicate that land makes up more than 50% of the average household’s asset endowment and that 74% of households use agricultural land for subsistence (Deininger and Okidi 2003).

Elimination of implicit and explicit taxation of the agricultural sector in the early 1990s, together with comparatively high levels of economic growth created favorable conditions for agricultural productivity growth (Blake _et al._ 2002). However, the extent of investment and associated diversification of the productive sector remained limited and productivity failed to increase as expected (Belshaw _et al._ 1999). As a result, most of the increments in production achieved were due to expansion of cultivated area at the expense of woodlots, wetlands and natural grazing areas (Place and Otsuka 2000, Pender 2004) that can not be sustained in the future. There is consensus that higher investment to enhance land productivity, 8
31 Faini, R. (1994). “Workers remittances and the real exchange rate: A quantitative framework.” _Journal of Population Economics_ , 7, 235-245.

Faini, R. (2003). “Is the brain drain an unmitigated blessing?” WIDER Discussion Paper 2003/64. Helsinki, Finland: World Institute for Development Economics Research.

Faini, R. (2006). “Remittances and the brain drain.” Centre for Economic Policy Research Working Paper 5720. London, England: Centre for Economic Policy Research.

Faini, R. (2007). “Remittances and the brain drain: Do more skilled migrants remit more?” _World Bank_ _Economic Review_ , 21, 177-191.

Fearon, J. and Laitin, D. (2003). “Additional tables for ‘Ethnicity, Insurgency and Civil War.’” Unpublished manuscript, Department of Political Science, Stanford University, Palo Alto, CA.

Foster, A. and Rosenzweig, M. (2001). “Imperfect commitment, altruism and the family: Evidence from transfer behavior in low-income rural areas.” _The Review of Economics and Statistics_ , 83, 389-407. Freund, C. and Spatafora, N. (2005). “Remittances: Transaction costs, determinants and informal flows.” World Bank Policy Research Working Paper 3704. Washington, DC: World Bank.

Glytsos, N. (1997). “Remitting behavior of ‘temporary’ and ‘permanent’ migrants: The case of Greeks in Germany and Australia.” _Labour,_ 11, 409-435.

Higgins, M., Hysenbegasi, A., Pozo, S. (2004). “Exchange-rate uncertainty and workers’ remittances.” _Applied Financial Economics_ , 14, 403-411.

Ilahi, N. and Jafarey, S. (1999). “Guestworker migration, remittances and the extended family: Evidence from Pakistan.” _Journal of Development Economics_ , 58, 485-512.

International Monetary Fund (various). _Annual report of exchange arrangements and exchange restrictions._ International Monetary Fund, Washington, DC.

International Monetary Fund (various). _Balance of payments statistics yearbook_ . International Monetary Fund, Washington, DC.
Prefeasibility studies and assessments could deploy only simpler and more reliable econometrics (network econometrics focused on the concept of centrality, combined with reduced-form regressions). At a later stage, feasibility studies and project appraisals could introduce more efficient methods (network econometrics based on the integration of heterogeneous agents and structural GE econometrics) to think through possibly richer dynamics of interactions, as well as direct, indirect, and second-round effects due to the corridor intervention.

Finally, the appraisal econometrics available today still does not estimate the link between the potential for market access and the actual use of this access by firms (through trade and travel) and people (for travel for jobs, education, health care, and so on). The econometrics largely assumes the link exists and is on average the same in every context. This assumption could be problematic. For instance, if complementary factors are needed for firms and people to embrace the market access potential provided by transport connectivity and actually use it, this link cannot be assumed. It can be broken in many countries, or vary greatly across different economic actors and/or geographic units. Appraisal econometrics and research should aspire to shed more light on the empirics of this microeconomic mechanism—including by building on the existing anecdotal evidence (see, for example, Spotlights 1 and 2 in ADB, DFID, JICA, and WBG forthcoming).

17
Figure 1. Distribution of Privatizations by Region (percent) <!-- Start of picture text --> Transition Economies I<br>Developed Countries<br>Africa and Middle East<br>Asia<br>Central and South<br>America<br>0 10 20 30 40<br>* Sample * Original List<br><!-- End of picture text --> _Source:_ Data collected by authors. Original list based<sup>on1,500firms.Samplereflects 308firms.</sup> Figure 2. Accumulated Distribution of Privatizations by Year (percent) <!-- Start of picture text --> 100 -<br>90<br>80-<br>70-<br>60-<br>50 -<br>40-<br>30-<br>20-<br>1 0<br>0*<br>-Sample -Original List<br><!-- End of picture text --> _Source:_ Data collected by authors. Original list based on 1,500 firms.<sup>Sample reflects308 firms.</sup> 23
30 investing across borders: do fdi-specific regulations matter?

Table 2: Regressions: OECD Source Countries Excluded

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Probit|Benchmark|Heterogeneity|Bias I|Bias II|
|FDI Procedures|-.049|.326**|.312*|.334**|.300*|
||(.096)|(.156)|(.158)|(.158)|(.156)|
|FDI Arbitration|-.150**|.246|.379**|.327*|.286|
||(.065)|(.196)|(.190)|(.184)|(.192)|
|FDI Openness|-.092|.072|.117|.106|.082|
||(.116)|(.125)|(.132)|(.126)|(.126)|
|Quality of Institutions|.274|-.130|-.233|-.238|-.078|
||(.171)|(.243)|(.257)|(.256)|(.238)|
|GDP|.668***|.939***|.669***|.659***|.974***|
||(.067)|(.082)|(.162)|(.149)|(.088)|
|GDP Per Capita|-.207*|.140|.210|.210|.105|
||(.117)|(.186)|(.192)|(.195)|(.182)|
|Distance|-1.832***|-1.945***|-1.283**|-1.222***|-2.026***|
||(.203)|(.190)|(.503)|(.424)|(.190)|
|Remoteness|3.823***|4.657***|3.309**|3.153***|4.702***|
||(.565)|(.723)|(1.302)|(1.177)|(.756)|
|Weighted Tariff|-.296**|-.489**|-.363|-.368|-.489**|
||(.150)|(.229)|(.225)|(.225)|(.224)|
|School (Mean Years)|.598***|.108|-.164|-.141|.170|
||(.187)|(.327)|(.394)|(.390)|(.332)|
|Contiguity|.217|2.297***|2.122**|2.143***|2.323***|
||(.609)|(.775)|(.819)|(.789)|(.787)|
|Colony|.494|1.246***|1.029***|1.018***|1.258***|
||(.386)|(.289)|(.339)|(.332)|(.301)|
|Language|.468**|.445|.228|.227|.415|
||(.234)|(.279)|(.324)|(.313)|(.283)|
|Bilateral Investment Treaty|.489***|.296||||
||(.151)|(.219)||||
|Inverse Mills Ratio<br>�<br>~~ˆ~~<br>η<sup>_~~∗~~_</sup><br>ij<br>�|||.306||.160|
||||(.693)||(.430)|
|Z1<br>�<br>~~ˆ~~<br>z<sup>_~~∗~~_</sup><br>ij<br>�|||1.204**|||
||||(.511)|||
|Z1*Z1|||-.116|||
||||(.094)|||
|H1<br>�<br>Φ<sup>−1</sup>( ˆpr)<br>�||||.448*<br>(.238)||
|No.|1220|622|622|622|622|
|Adj. R-sq.||.62|.63|.63|.62|

Note 1: robust standard errors, clustered by host country, reported in parenthesis Note 2: statistical significance indicated as * for p<0.1, ** for p<0.05, and *** for p<0.01
- 120 -

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Between 1996 and 2001, access to clean water was provided to approximately 9 million people increasing coverage from 60 per cent to 85 per cent of all households. In the same period, electricity provision increased from 50 per cent to 70 per cent. Between 1994 and 2003, 1.6 million houses were built for low-income families through a state subsidy scheme<sup>4</sup> . (GCIS 2003) A composite index relating to the provision of housing, water and sanitation, electricity and telephones has increased from 0.46 to 0.60 between 1995 and 2002 (full provision to all would be indicated by a score of 1.00). (GCIS 2003) Independent surveys<sup>5</sup> from 2003 indicated that public approval rates with regard to basic services stood at 75 per cent and housing at 64 per cent (up from 30 per cent in 1996). Between 1994 and 2002, housing as a key concern dropped from 46 per cent to 22 per cent.

Government spending has been substantially redistributive since 1994.

Of the total population of 43 million, the number of welfare grant recipients has increased from 2.6 million in 1994 to 6.8 million in 2003. The poorest quintile now receives two-thirds of its income from grants. A 2000 Gini coefficient of 0.59, when transfers to poor households are not taken into account, is transformed to 0.35 after these transfers. In 2003, independent surveys showed approval on welfare grants stood at 78 per cent. (GCIS 2003)

# 2.2. <u>Demographic and economic changes</u>

These improvements have taken place against a background of significant economic and demographic changes. Key trends have included significant urbanization, with the metropolitan areas and key cities expanding by between 21 per cent and 40 per cent between 1991 and 2001. There has been a large increase in the number of households accompanied by a decrease of average household size from 4.5 to 3.8 between 1996 and 2001. (Patel 2004) Currently, 40 per cent of the urban population are regarded as ‘poor’ ; 54 per cent of the unemployed are located in urban areas; of them 38 per cent live in the 9 largest cities and more than 25 per cent live in informal settlements. (Patel 2004) The change in the employment profile is of great significance. The net number of people in employment rose from 9.6 million to 11.2 million between 1995 and 2002. However, there was an even larger increase in the number of unemployed, from 1.9 million to 4.3 million during the same period. The increase in employment can be attributed to people from rural areas seeking work, especially women, entering the labor market. (GCIS 2003)

# 2.3. <u>Macro-economic environment</u>

Over the last decade the macro-economic environment has improved significantly.

Economic growth has been steady, albeit unspectacular, improving from negative per capita real growth in the decade prior to 1994, to an average of 3 per cent, equivalent to just over 1 per cent positive per capita growth since then. The country is currently experiencing the longest period of

> 4 49 per cent of recipients were women.

> 5 These figures are drawn from surveys by IDASA (Institute for Democracy in South Africa) quoted in GCIS (2003)

3