rafmacalaba/datause-encoder-data huggingface dataset:
first principal component of three measures of cognitive ability available in IFLS: Ravens test, and two memory tests. In the second step, we take out the differences in ability due to age differences (i.e., the Flynn effect) by regressing the first principal component on age and age squared and retrieve the residual from this regression. Third, we calculate the percentile rank of an individual in the distribution of the residual as the measure of ability. We include this percentile ability measure as our indicator of ability heterogeneity across children.

A simple but plausible approach to understand the importance and direction of ability bias in the estimates in Table 2 is to control for children’s cognitive ability (and its squared) in the regressions and check the sensitivity of the estimated parameters of interest. Recall that the theory allows for both additive separable and interaction effects of ability in the education production function. We thus employ a flexible specification of the intergenerational persistence equation where ability and its squared are interacted with both father’s education and its squared.<sup>20</sup> The estimates from this exercise are reported in the appendix Table A1: none of the interaction terms is significant at the 5 percent level, and this is true in both the rural and urban areas.<sup>21</sup> In contrast, the direct effects of ability and its squared are statistically significant at the 5 percent level in rural areas, while ability has a linear significant effect in urban areas. The coefficient of ability squared is negative in rural areas, suggesting that high ability children face diminishing returns in rural areas, but not in the urban areas. The magnitudes of both the linear and quadratic coefficients are larger in rural areas which indicate a stronger role for cognitive ability of children in villages where the quality of schools is likely to be poor.

The estimates of the linear and quadratic CEFs for intergenerational mobility with ability and its squared as controls are reported in Table 3. The standard linear CEF estimates in the first two columns of Table 3 show that the inclusion of the ability controls

> 20The education production function with ability heterogeneity in Becker et al. (2015) yields an estimating equation where ability enters quadratically and also there is interaction of ability with father’s education. In their specification, there is no interaction of ability with the squared father’s education. Another difference is that when the interaction of ability with father’s education is not significant, their specification implies that the effect of ability is linear. In the more flexible specification adopted here, we can have quadratic effect of ability even when the interaction effects are zero.

> 21Only 1 interaction term is significant at the 10 percent level.

19
are advantages and disadvantages with both data collection approaches. High-quality surveys are clearly the long-term solution to the data missing challenge, but they are costly, in terms of both finances and time. Meanwhile, imputation-based estimates can fill in data gaps when actual survey data are not available (at least in the short term or where there is a need to obtain estimates going back in time), but they require certain levels of technical capacity.

# _Role of International Organizations_

Finally, we offer some further thoughts about the role of international organizations in tracking the SDGs. International organizations are the key actors that are actively engaged in all aspects of the process, from data collection, coordination, and standardization to analysis. Indeed, international organizations are assigned as the custodian agencies for reporting on the SDG targets in the areas of their specialty. In this capacity, they work with national statistical systems to develop methodologies for indicators to measure progress on the SDGs. The agencies also work with countries to compile data for SDG indicators, which they submit to the UN SDG database (which is reviewed in the previous section).

For example, FAO is responsible for indicators related to food and agriculture, and UNICEF for those regarding child welfare. Figure 6 illustrates the complex and multi-step process for tracking indicators related to SDG number 6 on water, based on a UN website dedicated to that goal. First, the custodian agency requests data from a country, or retrieves such data from public official sources. Second, the country sends the custodian agency the requested data. Third, the custodian agency validates the data in consultation with the country. Fourth, the country signs off 32
10 Figure 5: Sectoral decomposition of U.S. arm’s-length and intra-firm trade

- (A) Share of sector in intra-firm and arm’s-length exports

(B) Share of sector in intra-firm and arm’s-length imports <!-- Start of picture text --> Percent Oil and gas Food and bev Percent Oil and gas Food and bev<br>100 Textiles and app Chemicals Textiles and app Chemicals<br>90 Metal products Machinery 100 Metal products Machinery<br>Electronics Transport equip 90 Electronics Transport equip<br>80<br>80<br>70<br>70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Arm's length Intra-firm Arm's length Intra-firm<br><!-- End of picture text --> (C) Share of sector in intra-firm and arm’s-length trade

- (D) The share of intra-firm transactions in sectoral exports and imports

<!-- Start of picture text --> Percent Oil and gas Food and bev Percent Exports Imports<br>Textiles and app Chemicals 80<br>100<br>Metal products Machinery 70<br>90 Electronics Transport equip 60<br>80 50<br>40<br>70 30<br>60 20<br>10<br>50<br>0<br>40<br>30<br>20<br>10<br>0<br>Arm's length Intra-firm<br>app<br>Oil and gas Food and bev Textiles and Chemicals Metal products Machinery Electronics Transport equip<br><!-- End of picture text --> Source: U.S. Census Bureau.

Note: U.S. exports and imports of goods, averages for 2002-14. Agricultural products, paper products, printing, non-metallic minerals, furniture, and miscellaneous manufactures have been omitted as they each account for less than 2 percent of total trade. Food and bev includes food and beverages. Textiles and app include textiles and apparel. Chemicals include chemicals and plastics. Electronics includes electronics and electrical equipment.

A.-C. Residual to 100 percent is the share of all other sectors in exports (A), imports (B), and trade (C). D. Residual to 100 percent is the share of arm’s-length transactions in U.S. exports and imports in each sector.
-89-

------------------------------------------------------------------------

__---_

|Persons<br>Engaged in Manufa<br>**-------------------------------------------------------------------------**|**TABLE-7.5**<br>cturing<br>and <br>|Total Popula<br> <br>|tion in 1975<br>**__--_**<br> <br>|
|---|---|---|---|
||Brazil|<br>Mexico|<br>USA|
|Food Products|482,434|<br>309,651|1,321,400|
|Beverages|52,080|<br>69.392|<br>203,800|
|Tobacco Products|23,965|<br>8,645|<br>66,200|
|Textiles and Wearing Apparel|507,593|<br>229,027|2,049,300|
|Footwear and Leather Products|129,231|<br>48,101<br>|<br>239,700<br>|
|Wood and Paper Products|524,402|<br>1<sup>64 </sup>595b|2,642,700|
|Chemical Products|177,920|<br>157,170|<br>9b3,100|
|Rubber and Plastic Products|120,866|<br>53,363|585,000|
|Stone, Clay and Glass Products|311,361|<br>100,714|<br>588,800|
|Metal products|429,539|206,509|2,505,800|
|Electrical Machinery|170,425|<br>114,382|1,523,600|
|Machinery and Transport Equipment|<br>595,580|<br>178,678|3,571,200|
|Other|146,260|<br>34,113|<br>893,200|
|Total Manufacturing|3,671,656c|1,674,340d|17,173,800e|
|Total Population|104,851,000|60,153,000 2|15,973,000|

Ratio of Total Engaged in Manufacturing to Population (percent) 3.50 2.78 7.95

- (a) employment in the footwear industry (95,358 employees) was reallocated from wearing apparel to footwear and leather.

- (b) includes 25,989 employees in petroleum refining which are not covered by the industrial census Resumen General, but taken from SPP, 1981.

- (c) excludes 152,682 employees in head offices and auxiliary activities.

- (d) excludes 69,448 employees in head offices and auxiliary units.

- (e) excludes 1,128,400 employees in administrative offices and auxiliaries.

- Source: Persons engaged: Brazil from IBGE, Censo Industrial (1981a) (see also table 2.1), Mexico from SPP, Resumen General (1979a) (see also table 2.4), USA from US Dept. of Commerce, Annual Survey of Manufactures 1975-76 (1979) (see also table 2.7); Population: Brazil from IBGE, Censo Demografico (1983), Mexico from Bank of Mexico, Indicadores Economicos (1986), USA from OECD, Labour Force Statistic (1987).
**_Energy demand models for policy_** **_<u>formulation</u>_**

Deimezis, N., 1995, ERASME: a short-term energy forecasting model for the European Community, in Lesourd et al. (1995) eds. Lesourd, J.B, J. Percebois and F. Valette, (eds.) 1995, Models for energy policy, Routledge, London.

- Denton, F. T., D. C. Mountain, and B. G. Spencer, 2000, A model of energy demand in the US commercial sector with declining rate schedule, Research Report 346, McMaster University, Canada (see http://socserv.mcmaster.ca/qsep/p/qsep346.PDF).

- Dhakal, S., 2003, Implications of transportation policies on energy and environment in Kathmandu Valley, Nepal, Energy Policy, 31(14), pp. 1493-1507.

Dhakal, S., 2006, Urban transportation and the environment in the Kathmandu Valley, Nepal, Integrating global carbon concerns into local air pollution management, Institute for Global Environmental Strategies, Japan. (See http://www.energycommunity.org/documents/iges_start_final_reprot.pdf). Dimitriopoulos, J., L. C. Hunt and G. Judge, 2005, Estimating underlying energy demand trends using annual data, Applied Economics Letters, 12(4), pp. 239-44.

- Donnelly, W. A., 1987, The econometric of energy demand: A survey of applications, Praeger, New York.

- DTI, 2000, Energy Paper 68, Energy Projections for the UK, Department of Trade and Industry, London.

Dubin, Jeffrey A and McFadden, Daniel L, 1984. An econometric analysis of residential electric appliance holdings and consumption, Econometrica, 52(2), pp. 345-62, March.

Dunkerley, J. and I. Hoch, 1987, Energy for transport in developing countries, Energy Journal, 8(3), pp. 57-72.

104
# **4. Estimation Results**

This study estimates the impact of electricity consumption on the value added using a cross-country data set that includes an unbalanced panel of annual observations of 126 countries during 1996-2014. It runs separate regressions for the three aggregate sectors of agriculture, manufacturing, and services, which have data for both electricity consumption and value added, as well as for such control variables as capital and labor. Estimating elasticities for individual sectors helps understand sector-specific patterns, as the sectors vary substantially in terms of intensity of consumed electricity and possibly its impact on growth. Although the data set includes more advanced economies than developing countries due to limited data availability, the latter are responsible for the most variations (see Figure 2). Finally, the study uses several control variables to account for time-variant observed characteristics that affect the value added, such as import and export shares in GDP, shares of respective sectoral value added in total, capital, and labor. All control variables are measured at the country level and are sector neutral, with the exception of sectoral share in the value added. Both export and import shares in GDP are important because they show the global competitiveness of a country, the openness of its economy, and its dependency on imports, reflecting a lack of countries’ productive capacities. Finally, capital and labor are the main components used in production, and they capture country-specific and time-varying effects, such as business cycle fluctuations.

The empirical strategy includes various econometric approaches to estimate the impact of electricity consumption on the value added with the two-step system panel GMM as a preferred estimator. In particular, the study applies the fixed-effects approach, two-step difference and system panel GMM estimation methods. The OLS estimators are prone to reverse causality and endogeneity bias, capturing only a correlation between the two variables despite an addition of several controls. The fixed-effects approach improves the OLS coefficients by removing country fixed effects that might generate omitted variable bias in the regression results. For example, a country with abundant oil resources would have a higher value added as well as electricity consumption, and omitting this variable would lead to a bias in the coefficients. However, excluding fixed effects from the regressions is not sufficient to address the bias in the estimators because omitted time-variant variables may affect both the dependent and independent variables, as well as the simultaneity bias.

Generally, an instrumental variable approach is applied to address the endogeneity and simultaneity bias, as it uses exogenous instruments or variables that are correlated with the explanatory endogenous variable but not with the error term. However, in the absence of proper instruments for the electricity 12
even more important than in other countries given the size and sensitivity of subsidies and the current political fragility. These aspects are all beyond the scope of this study but need to be tackled in preparing for subsidy reforms.

# **References**

Alleyne et al (2013) _Energy Subsidy Reform in Sub-Saharan Africa: Experiences and Lessons._ Washington DC: International Monetary Fund, 2013.

Araar, A. and P. Verme (2012) _Reforming Subsidies: A Toolkit for Policy Simulations._ World Bank Policy Research Working Paper No. 6148.

Araar and Verme (2014) “Indirect Effects of Energy Subsidy Reform in Tunisia and Morocco”, unpublished mimeo.

Bacon R. and M. Kojima (2006) _Phasing Out Subsidies—Recent Experiences with Fuel in Developing Countries._ World Bank Public Policy Notes No. 310, August 2006.

Chami, R. et al., _Libya beyond the Revolution: Challenges and Opportunities._ International Monetary Fund, 2012. (Available at http://www.imf.org/external/pubs/ft/dp/2012/1201mcd.pdf) Charap, Joshua, “Note on Subsidy Reform in Libya”, IMF Country Report No. 13/151, May 2013.

Clements et al (2013) _Energy Subsidy Reform: Lessons and Implications._ Washington DC: International Monetary Fund.

Clements et al (2013) _Case Studies on Energy Subsidy Reform: Lessons and Implications._ Washington DC: International Monetary Fund.

FAO (2003) _Food Energy–Methods of Analysis and Conversion Factors,_ Food and Nutrition Paper No. 77 (ftp://ftp.fao.org/docrep/fao/006/y5022e/y5022e00.pdf) Guillaume et al (2011) _Iran—The Chronicles of the Subsidy Reform_ IMF Working paper No 11/167, Washington DC.

IMF (2013) Libya: Selected Issue, IMF Country Report No. 13/151, May 2013 Vagliasindi, Maria. 2012. _Implementing Energy Subsidy Reforms: An Overview of the Key Issues._ Policy Research Working Paper No. 6122. Washington DC: World Bank.

Vagliasindi, Maria. 2013. _Implementing Energy Subsidy Reforms: Evidence from Developing Countries._ Directions in Development. Washington DC: World Bank.

Vandewalle D (2011) _Libya: Post-War Challenges._ African Development Bank Economic Brief, September 2011.

Verme P., el-Massnaoui, K. and Araar, A. (2014) “Reforming Subsidies in Morocco” in _Economic Premise No 134_ Washington DC: World Bank PREM Network.

Wahby, E (2005) _<u>Libya: Economic Reforms Anger Citizens,</u>_ Carnegie Endowment, June 20, 2005.

35
as customs controls, quota licensing, pre-shipment inspections, and additional fees paid at customs, among many others.<sup>14</sup> Table 1 presents the list of importing countries included in our sample, together with their coverage ratios of border NTMs. There are a total of 49 importing countries with 96 exporting countries. The sample is largely determined by the availability of NTM data in the UNCTAD TRAINS database. There is a wide range of variations in terms of the coverage ratios across these di¤erent importing countries. We exploit this wide range of variations across countries to estimate the AVEs at the HS 6-digit level.

Trade discrepancies are constructed by matching import data with export data at the HS 6-digit level according to Equation (8) for both quantity and value, based on UNSD Comtrade HS 6-digit level import and export data. Since we do not observe the true quantity, Q; we replace it with the average value of import and export quantity and value. The quantity data were thoroughly examined, and we include only observations in which the importer and the exporter use the same quantity unit in their reporting, to make sure that the discrepancies are not due to di¤erences in the units of measurement in quantity. In addition, the quantity units remain the same within any given HS 6-digit product.<sup>15</sup>

# 4 Estimation of the Ad Valorem Equivalent of NTMs

In the theoretical model, the impacts of NTMs on trade discrepancies work through the ad valorem equivalent (AV E) of NTMs. To empirically test the hypothesis, we will need estimates of these AV Es; which vary by product and importer-exporter pair. There is no such detailed estimates available in o¢ cial published statistics. We will need to estimate these AV Es: The closest estimates are provided by Kee, Nicita and Olarreaga (2009), at

> 14Please refer to Ederington and Ruta (2016) for details.

> 15For the regression analysis, we always control for product ...xed e¤ects so that we do not rely on di¤erences in the quantity units across products to estimate the coe¢ cients of interest.

13
Table B4: Which villages were replaced? - Household Variables

||(1)|(2)|(3)|
|---|---|---|---|
|Variables(Y)|Treatment status|Same name|Matched name|
|HOUSEHOLD LEVEL VARIABLES||||
|12. Household size|0.1844|-0.1634|-0.1378|
||(0.1559)|(0.1615)|(0.1734)|
|13. Household members under 5|0.0214|-0.0711|0.0049|
||(0.0566)|(0.0592)|(0.0657)|
|14. Household members under 18|0.0925|-0.1835|-0.1770|
||(0.1279)|(0.1284)|(0.1360)|
|15. Main source of drinking water:||||
|15.1.Public tap|-0.0104|-0.0524|-0.1460|
||(0.0772)|(0.0887)|(0.1020)|
|15.2.Unprotected well|0.0195|0.0039|0.0428|
||(0.0545)|(0.0571)|(0.0612)|
|15.3.Unprotected spring|-0.0150|0.0361|0.0646|
||(0.0384)|(0.0392)|(0.0423)|
|16. Has any toilet|-0.0112|-0.0085|0.0096|
||(0.0232)|(0.0274)|(0.0300)|
|17. Has radio|0.0084|-0.0076|-0.0068|
||(0.0324)|(0.0348)|(0.0417)|
|18. Firewood is main fuel|-0.0214|-0.0181|-0.0318*|
||(0.0185)|(0.0183)|(0.0178)|
|19. Has no window|0.0050|-0.0365|-0.0619|
||(0.0656)|(0.0712)|(0.0766)|
|20. Number of separate rooms|0.0225<br>(0.1049)|-0.1434<br>(0.1118)|-0.1389<br>(0.1215)|
|21. Number of sleeping rooms|0.0020<br>(0.0509)|-0.0236<br>(0.0523)|-0.0265<br>(0.0532)|
|22. Number of sleeping spaces|-0.1641|-0.0582|-0.2794|
||(0.1900)|(0.2048)|(0.2172)|
|23. Asset index|0.0736|-0.0553|-0.1479|
||(0.1259)|(0.1417)|(0.1782)|

_β_ 1 estimated from LS regression _Yi_ = _β_ 0 + _β_ 1 _Xi_ + _✏i_ , with standard errors in parentheses. Column (1) is analogous to the randomisation checks, presented in Tables 1 and 2 in the main body of the paper. In column (1), _Xi_ is an indicator variable =1 if village _i_ is in treatment group, =0 otherwise. In column (2), _Xi_ is an indicator variable =1 if village _i_ has same name in village lists 1 to 4, =0 otherwise. In column (3), _Xi_ is an indicator variable =1 if village _i_ has same name in village lists 1 to 4 or if the name of village _i_ was changed but can be matched, =0 otherwise. Observations are clustered at village level. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

16
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vulnerable to general wholesale funding shocks while high reliance on foreign funding specifically implies exposure to more volatile cross-border financing flows and sensitivity to foreign shocks. Taken together, our findings therefore suggest that funding model differences with the rest of the world were at the heart of ECA’s post-crisis credit growth contraction, and that this contraction was not simply due to the high prevalence of foreign bank ownership in the region.

Our findings provide yet another illustration of the potential downside risks of financial opening or financial integration in the absence of adequate regulatory and supervisory frameworks. The easy access to parent funding and direct wholesale borrowings abroad exposed many ECA countries to substantial funding risks which materialized when cross-border flows were interrupted by the global crisis and parent bank health deteriorated. The sudden slowdown of the pace of funding contributed in turn to a sharp slowdown of credit and GDP growth, as well as an accumulation of non-performing loans in many countries (Raiffeisen (2013) and Unicredit (2013)).<sup>11</sup> As discussed, the Vienna Initiative prevented a severe withdrawal of parent funding and a more dramatic slowdown of credit in the participating countries, but all the countries that were overextended before the crisis have been adjusting their funding structures, both the countries that relied more on parent funding and those that relied more on direct borrowings abroad. This ongoing adjustment is reflected in the decline in loan-to-deposit ratios and the share of foreign liabilities to total liabilities. This adjustment was necessary to curb the excess of the pre-crisis period, and reflect both market and regulatory pressures. The central question being faced by regulators in the region is the extent to which this adjustment will continue to take place.

Recent market research by one of the major international banks operating in the region suggests that European banks will continue adjusting their loan-to-deposit ratios and levels of foreign funding and may eventually move towards a “Spanish” or

> 11 The credit boom exposed ECA countries to other risks as well, including credit, interest rate, and exchange rate risks in their consumer, mortgage and SME portfolios.

29
# **Rice Prices and Poverty in Liberia**<sup>1</sup>

Clarence Tsimpo and Quentin Wodon JEL codes: I32, D1, Q12 Key words: Food prices, poverty, Liberia

> 1 This paper and the broader research project it is part of have benefitted from discussions with and/or comments from among others Douglas Addison, Harold Alderman, Antonella Bassani, Shanta Devarajan, Hinh Dinh, Wilfried Engelke, Louise Fox, Delfin Go, Ana Revenga, Sudhir Shetty, Kenneth Simler, Linda Van Gelder, Jan Walliser, Vera Wilhelm, and Hassan Zaman. All potential mistakes or omissions remain obviously ours.  The paper was presented at a workshop organized by Liberia’s core PRSP team in Monrovia on December 10-11, 2007.  The analysis is based on the 2007 CWIQ (Core Welfare Questionnaire Indicator) data collected by Liberia Institute of Statistics and Geo-Information Services under the leadership of Dr Liberty.  The survey benefited from generous support from UNDP and DFID.  At the World Bank, Ghislaine Delaine and Emmanuel Fiadzo also provided extensive support for the implementation of the survey.  Rebecca Simson was especially helpful in giving us with access to previous work on the topic of food production and food security in Liberia.