rafmacalaba/datause-encoder-data huggingface dataset:
# **Assessing Real Exchange Rate Misalignments**<sup>*****</sup>

## **Megumi Kubota** **_The World Bank_**

**First Version:** November 2007 **This Version:** December 11, 2011

### **JEL Classification:** F31, F41

**Key Words:** Real Exchange Rate Misalignment, Undervaluation and Foreign Exchange Intervention

### **Sector Board:** EPOL

*Kubota: The World Bank, Office of the Chief Economist, Latin America and the Caribbean Region (LCRCE). E-mail: mkubota@worldbank.org. The author is deeply grateful to Michael Wickens, Peter N. Smith and Neil Rankin for their guidance, suggestions and a human sweetness in her endless way. She would like to express her sincere appreciation to César Calderón, Michael Funke, Eduardo LevyYeyati, Ioannis Litsios, Frank McGroarty, Enrique Mendoza and her VIVA external examiner Ronald MacDonald for their precious suggestions and comments. This research paper is developed from Chapter 3 and 6 of the author’s PhD thesis at University of York, ―Real Exchange Rate Misalignments‖ and a part of the University of York Discussion Paper No. 2009/25 in November, 2009. The earlier version of her dissertation was presented at the following workshops and conferences: special thanks to the participants of Research Student Workshop at the Department of Economics, York, WEF/ESRC workshop on Incentives and Governance in Global Finance at Warwick, the University of Sheffield Postgraduate Research Workshop in Economics at Sheffield, the 2008 LACEA/LAMES Annual Meetings at Rio de Janeiro, and Macroeconomic and Financial Linkages: Theory and Practice at Cambridge, INFINITI Conference 2010 at Dublin, Ireland (Trinity College) and 2010 Econometrics Society World Congress, Shanghai, China for their discussion and advice. She greatly acknowledges the support of the Department of Economics at University of York, ESRC, University of Sheffield, the Faculty of Economics at University of Cambridge, the Royal Economic Society Conference Grant and the Econometrics Society Travel Grant. The usual disclaimer applies and all errors are hers. The views expressed in this paper are those of the author, and do not necessarily reflect those of the World Bank or its Boards of Directors.
**Table 6: Disability and illness, by age and gender**

|**Age in**|**2000**|15-19<br>|20-24<br>|25-29<br>|30-34<br>|35-39|40-44<br>|45-49|50-54<br>|55-59|60-64|65-69|70-74|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Disabili**|**ty**|||||||||||||
|Males|Mean|0.0147|0.0144|0.0204|0.0323|0.0364|0.056|0.044|<br>0.0433|0.04899|0.0371|0.0355|0.0385|
||St. error|[0.0018]|[0.0026]|[0.0031]|[0.0038]|[0.0041]|[0.0063]|[0.0063]|[0.0069]|[0.0083]|[0.0081]|[0.0089]|[0.0117]|
||N|4082|2075|1980|2133|2036|1328|1044|<br>868|669|543|432|271|
|Female|s Mean|0.0083|0.0081|0.0132|0.0094|0.0064|0.01148|0.01432|0.01464|0.0151|0.0344|0.0313|0.0422|
||St. error|[0.0014]|[0.0019]|[0.0024]|[0.0020]|[0.0016]|[0.0024]|[0.0030]|[0.0036]|[0.0041]|[0.0068]|[0.0073]|[0.0109]|
||N|3700|2118|2193|2289|2244|1908|1524|<br>1109|858|713|568|336|
|**Illness**|**or in jury d**|**uring last 3**|**0 days**|||||||||||
|Males|Mean|0.0336|0.0385|0.0397|0.0443|0.051|0.052|0.0653|<br>0.0665|0.0899|0.06485|0.1275|0.1422|
||St. error|[0.0028]|[0.0042]|[0.0043]|[0.0044]|[0.0048]|[0.006]|[0.0076]|[0.0084]|[0.0110]|[0.0105]|[0.0160]|[0.0212]|
||N|4082|2075|1980|2133|2036|1328|1044|<br>868|669|543|432|271|
|Female|s Mean|0.0138|0.019|0.0237|0.0259|0.0174|0.0188|0.0354|<br>0.0313|0.0291|0.0531|0.0452|0.0514|
||St. error|[0.003]|[0.0051]|[0.0049]|[0.0057]|[0.0061]|[0.0074]|[0.0090]|[0.0101]|[0.0118]|[0.0153]|[0.0213]|[0.0268]|
||N|3700|2118|2193|2289|2244|1908|1524|<br>1109|858|713|568|336|
|**Disabili**|**ty from lan**|**dmine or w**|**eapon**|||||||||||
|Males|Mean|0.0016|0.0023|0.0104|0.0184|0.0205|0.0314|0.0223|<br>0.0198|0.023|0.0025|0.0039|0.0048|
||St. error|[0.0006]|[0.0010]|[0.0022]|[0.0029]|[0.0031]|[0.0047]|[0.0045]|[0.0047]|[0.0058]|[0.0021]|[0.003]|[0.0042]|
||N|4082|2075|1980|2133|2036|1328|1044|<br>868|669|543|432|271|

**_Source_ :** Cambodia, Demographic and Health Survey (2000).

41
# **Table 3 – Determinants of reservation wages in North Macedonia**

||||_Dependen_|_t variable: Lo_|_g of the reserv_|_ation wage_<br>|By education<br>|<br>|
|---|---|---|---|---|---|---|---|---|
|||||||Primary|Secondary|Tertiary|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Female**|<br>-0.071***|<br>-0.074***|<br>-0.074***|<br>-0.082***|<br>-0.080***|<br>-0.106***|<br>-0.082***|<br>-0.048***|
||(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|(0.013)|(0.008)|(0.015)|
|**Age**|-0.002***|-0.002***|-0.002***|-0.002***|-0.001***|-0.001*|-0.001***|-0.005***|
||0.000|0.000|0.000|0.000|0.000|(0.001)|0.000|(0.001)|
|**Married**|0.024***|0.027***|0.025***|0.024***|0.022***|<br>0.036**|0.015|<br>0.031|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.017)|(0.011)|(0.020)|
|**Number of children**|0.001|0.011***|0.012***|0.013***|0.013***|0.007|0.016***|0.011|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.007)|(0.004)|(0.013)|
|**Secondary education**|0.116***|0.100***|0.097***|0.086***|0.069**||||
||(0.007)|(0.008)|(0.008)|(0.009)|(0.033)||||
|**Tertiary education**|<br>0.414***|<br>0.384***|<br>0.380***|<br>0.352***|<br>0.327***||||
||(0.010)|(0.011)|(0.011)|(0.013)|(0.052)||||
|**Recipient of**||<br>0.022**|<br>0.023***|<br>0.022**|<br>0.021**|0.034**|0.026**|0.024|
|**unemployment benefits**||(0.009)|(0.009)|(0.009)|(0.009)|(0.016)|(0.012)|(0.022)|
|**Unemployment spell**||<br>-0.002***|<br>-0.002***|<br>-0.002***|<br>-0.002***|<br>-0.002***|<br>-0.002***|<br>0.000|
|<br>**duration**||0.000|0.000|0.000|0.000|(0.001)|(0.001)|(0.002)|
|**Log of household’s welfare**||0.048***|0.047***|0.045***|0.046***|0.005|0.054***|0.068***|
|||(0.008)|(0.009)|(0.009)|(0.009)|(0.020)|(0.011)|(0.021)|
|**f**<br>Remittances|||<br>0.047***|<br>0.046***|<br>0.044***|<br>0.022|<br>0.056***|<br>0.021|
|**f:**<br>**s o**|||(0.015)|(0.015)|(0.014)|(0.028)|(0.018)|(0.032)|
|**of**<br>**(Re**<br>**ion**<br>**)**<br>Social|||<br>-0.034***|<br>-0.034***|<br>-0.033***|<br>-0.060***|<br>-0.021|<br>0.032|
|**es**<br>**d**<br>**ns**<br>**ers**<br>assistance|||(0.011)|(0.011)|(0.011)|(0.016)|(0.015)|(0.048)|
|**urc**<br>**hoo**<br>**/Pe**<br>**th**<br>Capital|||<br>0.075***|<br>0.068***|<br>0.067***|<br>0.143***|<br>0.014|<br>-0.013|
|**So**<br>**eli**<br>**ges**<br>**o**<br> <br>income|||(0.020)|(0.020)|(0.020)|(0.031)|(0.022)|(0.051)|
|**liv**<br>**Wa**<br>Other|||<br>-0.035*|<br>-0.035*|<br>-0.032|<br>-0.123***|<br>0.031|<br>0.005|
|<br>income|||(0.020)|(0.020)|(0.020)|(0.030)|(0.022)|(0.076)|
|<br> <br>Humanities||||<br>0.008|<br>-0.011||<br>-0.014|<br>0.018|
|**of**<br>**es**<br>**ted**<br>**:**<br>**ral**<br>and arts||||(0.017)|(0.068)||(0.088)|(0.126)|
|**eld**<br>**udi**<br>**ple**<br>**Ref**<br>**ne**<br>Social||||<br>0.042***|<br>0.027||<br>0.016|<br>0.061|
|**Fi**<br>**st**<br>**om**<br>**(**<br>**Ge**<br>sciences||||(0.010)|(0.052)||(0.063)|(0.099)|
|**c**<br>STEM||||-0.01|-0.03||-0.075|0.075|

16
van de Stadt, H., Kapteyn, A., van de Geer, S. (1985) The relativity of utility: evidence from panel data. The Review of Economics and Statistics 67(2), 179– 187.

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Van Praag, B.M.S. and Frijters, P. (1999) The measurement of welfare and well-being: The Leiden approach, in Kahneman, D., Diener, E. and Schwarz, N. (Eds), Foundations of Hedonic Psychology: Scientific Perspectives on Enjoyment and Suffering, Chapter 21, New York, Russell Sage Foundation Veblen, T. (1898) The Theory of the Leisure Class, MacMillan, New York Veblen, T. (1899) The Theory of the Leisure Class. London: Macmillan, George Allen and Unwin.

Veblen, T. (1909) The limitations of marginal utility. Journal of Political Economy 17, 620–636.

Vendrik, M.C.M. and Woltjer, G.B. (2006) Happiness and Loss Aversion: when social participation dominates comparison. METEOR Research Memorandum RM/06/027. Faculty of Economics and Business Administration, Maastricht University Vendrik, M.C.M. and Woltjer, G.B. (2007) Happiness and loss aversion: Is utility concave or convex in relative income? Journal of Public Economics 91 (2007), 1423-1448 Vendrik, Maarten C. M. (1998) Unstable Bandwagon and Habit Effects on Labor Supply, Journal of Economic Behavior and Organization, 36(2): 235–55.

Verme, P. (2010) Happiness, Deprivation and the Alter Ego, in The Measurement of Individual Well-being and Group Inequalities: Essays in Memory of Z. M. Berrebi, Deutsch, J. and Silber, J. (Eds.), Routledge Verme, P. (2011) Life Satisfaction and Income Inequality, Review of Income and Wealth, Vol. 57 (1), 111-127 31
# **Table A2:** Factors of Production

Labor Types L PROF Scientific, technical, professional and related workers. LADMN Administrative, executive and managerial workers. LCLER CLerical and related workers. LRETL Sales workers. LSRVC Service workers. L AGRC Agricultural, animal husbandry and forestry workers, fisherman and hunters. L PROD Non-agricultural production & related workers, transport equipment operators & laborers. L OTHR Workers not classifiable by occupation. Capital Types K RENT Rent. K_AGRC Operating surplus in agriculture. K PROD Operating surplus in non-agriculture. K_TRAD Operating surplus in trade. K SERV Operating surplus in services.

Manufacturing Industry Statistics which indicates the number of people in each manufacturing activity.

In Table A2b, we list the percent of value-added by capital type. Some of these types are not very useful for our purposes, since they largely identify factors that are specific to certain groups of sectors. Thus, we aggregate K_RENT, K_PROD, K_TRAD and K_SERV into one homogeneous capital factor which can move across sectors. The factor K_AGRC is free to move across the agricultural sectors in which it is used in the base year, but not to any other sectors.

Some explanation may be required for the fact that the Turkish input-output table classifies 91 percent of value-added in agriculture to capital. The reason is that the returns to individuals who operate their own farms are classfied in the Turkish input-output table as a return to the owner as a capitalist, rather than to the owner as a worker. We chose not to alter the input-output data. But since we have classified both capital and labor in agriculture as sector A-6
WPS3936 _The Impact of Privatization on the Performance of the Infrastructure Sector: The Case of Electricity Distribution in Latin American Countries_ _Luis ANDRES,_<sup>_1_</sup> _Vivien FOSTER, and José Luis GUASCH_ _The World Bank_

# _Abstract_

This paper analyzes the impact of privatization on the performance of 116 electric utilities in ten Latin American countries. The analysis makes a number of contributions to the literature on changes in infrastructure ownership. First, this is the first systemic analysis of the impact of privatization on the distribution of electricity sector. Second, it constructs an unbalanced panel data set of key indicators for each country. Third, it includes a broaderthan in past studies- range of indicators, such as output, employment, productivity, efficiency, quality, coverage, and prices, offering a fuller picture of the effects of privatization on consumers. Fourth, this research covers a longer period of time, and evaluates three stages-before, transition and after-allowing for the identification of the short- and long-run effects of privatization, as opposed to previous analyses’ short time series data that do not identify long-run outcomes. Finally, the counterfactual is considered through the analysis in trends. Two different methodologies are applied. The first methodology uses means and medians from each period and tests the significance of the changes between periods. The second methodology consists of an econometric model that captures firm fixed effects, firm-specific time trends and heteroscedasticity corrections. When needed, we used firm-specific time trends to better understand the outcomes. The results suggest that changes in ownership generate significant improvements in labor productivity, efficiency, and product/service quality, and that most of those changes occur in the transition period. Improvements in the post transition period-beyond two years after the change in ownership- are much more modest.

# World Bank Policy Research Working Paper 3936, June 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1We are grateful for the helpful comments received from Florencia Borrescio Higa, Carolina Czastkiewicz, Austan Goolsbee, Antonio Estache, Sam Peltzman, Carola Pessino, Chad Syverson and Alejandro Zentner.

_1 of 28_
Policy Research Working Paper 9824

# **Abstract**

During protracted displacement, women and girls often face serious gender-specific challenges and vulnerabilities, including adverse norms and institutional barriers. Yet, quantitative evidence on gendered drivers and differentials during protracted displacement remains scarce. Using survey data from 18,533 displaced and non-displaced individuals in El Fasher, Sudan, this paper documents that livelihood outcomes are significantly shaped by strong and complex intersectionality between long-term displacement and gender. Being female and being a long-term displaced person have separate negative impacts on work and welfare. Therefore, being a long-term displaced woman is particularly challenging: internally displaced women work more than non–internally displaced women but are poorer, on average. For men, there is no such difference in employment between the internally displaced and non–internally displaced. These outcomes are the result of the ‘double burden of female displacement’: women are disadvantaged by norms and institutions both at their destination (due to being a displaced person) and their place of origin (due to their gender). The double burden is strongest for older displaced women. In contrast, protracted displacement can be an opportunity for younger displaced women. Future policies should address the challenges stemming from the intersectionality of gender and displacement and develop targeted programs.

This paper is a product of the Gender Global Theme. 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 brueck@isdc.org and stojetz@isdc.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
# variables obtained from the previous double orthogonalization step.

This approach provides us with multiple layers of robustness to ensure the plausibility of the unconfoundedness assumption. First, weather shocks are generally assumed to be random, and this is why they are often used as instrumental variables (e.g., Kleemans & Magruder, 2018; Miguel et al., 2004).<sup>15</sup> Second, the SPEI is normalized with respect to normal local conditions, thus filtering out heterogeneity due to different local averages. On top of this, the double orthogonalization approach we adopt before treatment effect estimation eliminates any residual source of confounding effects and omitted variable bias. Lastly, all the regression forests are cluster-robust since we explicitly account for household clusters at all estimation steps.

After applying orthogonalization, we proceed with the estimation of CATEs. Specifically, based on the current knowledge and scientific evidence about the key transmission channels of the climate-migration nexus (Cattaneo et al., 2019; Letta et al., 2023), the following vector _X_ of observable characteristics enters the CATE estimation model:

- Household asset index<sup>16</sup>

- Lagged GS-SPEI (reversed)

- _In situ_ adaptive capacity score<sup>17</sup>

- Annual total consumption per capita (logged)

- Share of adult members with tertiary education

All the conditioning factors are measured at baseline, i.e., in the preceding wave of the panel, so they are rigorously exogenous with respect to the treatment. The asset index (which provides an indication of household's wealth relative to others in the survey) and consumption account for

> 15 In the climate panel econometrics literature, it is common practice to simply regress the outcome of interest on the set of weather variables plus a full set of unit fixed effects and time dummies or trends (Dell et al., 2014; Hsiang, 2016).

> 16 The construction of the asset index follows the approach of Aiken et al. (2023) and is based on principal-component factor analysis. See Table A2 in the Appendix for the list of items included. To ease interpretability, the estimated index has been rescaled to be in the range 0-100.

17 This score is also built via principal-component factor analysis; see Table A2 in the Appendix for the list of items included. To ease interpretability, the estimated index has been rescaled to be in the range 0-100.

18
Validation efforts by Novella and Thiaw (2013) found that low reporting rates for some GTS stations degrades model performance in those regions. Other findings are a general tendency to underestimate rainfall, which is enhanced in areas of high relief or complex topography.

Data and technical documentation are available for download from `https://www.cpc.ncep. noaa.gov/products/international/data.shtml` .

# **A.1.2 Climate Hazards group InfraRed Precipitation with Station data (CHIRPS)**

Like ARC2, the CHIRPS rainfall data set builds on established techniques for merging gauge and remote sensing data. Produced by the Climate Hazards Group at University of California, Santa Barbara this data set is designed for monitoring of drought and environmental change at a global level. To minimize latency, there are two products, a preliminary version with two day lag, and final output available at three weeks. Outputs are available at time-steps from six hours to three months. As inputs, CHIRPS makes use of a monthly climatology CHPclim, Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis version 7 (TMPA 3B42 v7) and global Thermal Infrared Cold Cloud Duration (TIR CCD) from two NOAA archives. The remote sensing data are then merged with gauge data from five public archives, including the Global Historical Climatology Network (GHCN) and GTS, several private sources, and meteorological agencies. While targeted gauge data collection efforts resulted in a greater number of input stations for years prior to 2010, the number of stations going forward is more limited, particularly in Sub-Saharan Africa. Detailed metadata by country is available and may be a useful reference to determine if coverage for a region of interest is sufficient for the analysis.

Validation for select countries found that the climatology input CHPclim outperformed other climatology data sets in data sparse regions and complex terrain (Funk et al., 2015). Furthermore, in an assessment of wet season statistics CHIRPS showed less bias than other rainfall sources and good correspondence with Global Precipitation Climatology Centre (GPCC) estimates.

Data and technical documentation are available for download from `https://data.chc.ucsb. edu/products/CHIRPS-2.0/` .

# **A.1.3 CPC Global Unified Gauge-Based Analysis of Daily Precipitation and Temperature**

NOAA’s Climate Prediction Center (CPC) Unified Gauge-based (CPC-U) data sets for daily temperature and precipitation do not incorporate remote sensing data in the estimation of weather variables. Instead, an optimal interpolation (OI) technique is used on gauge data for precipitation, and Shepard’s algorithm for temperature. CPC-U provides systematic global data sets for validation and climate monitoring. GTS is a primary input data source, with some national collections, but density is most sparse over Africa.

101
The three-stone tripod is the traditional injera baking technology in rural Ethiopia and in our study sites. It is, unfortunately, difficult to reliably monitor three-stone tripod cooking using SUMs, because the stones are often moved, and fires are less controlled than with an enclosed stove. Inference about exclusive use of the stove and potential rebound effects is, therefore, imperfect. Considering that households will in any case need to use three-stone tripods to cook most noninjera foods and we are analyzing frequency of stove use and not fuel savings, injera cooking rebound effects would be difficult to detect and not terribly relevant for this paper. There is a possibility that because of reduced fuelwood consumption per meal, cooks may cook more or pay less attention to fuelwood use; both these results would be scale effects and as they are equally probable for all stoves, neither would generate biased estimates across treatments.<sup>5</sup>

# _3.2. Sampling and fieldwork_

Our sampling frame is based on a random sample of 110 villages in local jurisdictions called kebeles _,_<sup>6</sup> from a 2012 study by the Ethiopian Development Research Institute (EDRI). We eliminate 29 of the 110 sites, because they were part of our pilot survey, _injera_ is not typically baked in these villages or the normal three-stone tripod technology is not used.

We then selected 36 villages using stratified proportional random sampling from the 81 villages that were left. Stratification was by regional state (Amhara, Oromia and Southern Nations, Nationalities and Peoples), with forest area used as the state weight. Forest area is used, because most fuelwood comes from forests and it also proxies well for total regional state population and land area. In total, 20% of villages are from Amhara, 50% from Oromia and 30% from SNNP regional states.

> 5 The Mirt injera stove has a 50 cm burner, which cannot accommodate normal pots.  The way the stove is designed therefore makes it virtually impossible to shift to other uses (a potential rebound effect).

> 6 _Kebele_ translates as peasant association and is made up of several villages.  It is the smallest official jurisdiction in Ethiopia. We have only one village per _kebele_ and therefore we use village and _kebele_ interchangeably.

11