rafmacalaba/datause-encoder-data huggingface dataset:
CONTENTS

|GLOSSARY OF TERMS|
|---|
|INTRODUCTION AND BACKGROUND<br>1|
|OUTPUT AND PROFIT RESPONSE TO THE ETP<br>2|
|Output Trends<br>2<br> <br>|
|Profit and Cost Dynamics<br>6<br> <br>|
|Underlying Profitability<br>6|
|Other Income<br>7|
|Cost Dynamics<br>7|
|DEBT DYNAMICS: BANK LOANS AND INTERFIRM CREDIT<br>10|
|Bank Loans<br>10|
|Inter firm Credit<br>12|
|Bankruptcy Triggers<br>12|
|INCOMES POLICY AND FIRM-LEVEL WAGE SETTING AND EMPLOYMENT<br>13|
|Wage Setting<br>13|
|Employment<br>17|
|Effectiveness of Wage Policy<br>17|
|Inflation Control<br>17|
|Norm and Actual Real Wages Under Indexation<br>18|
|PPWW, Decapitalization and Dividends<br>18|
|Wages and Value Added<br>20|
|HARD CURRENCY EXPORT PERFORMANCE<br>20|
|Real Exchange Rate, Export Profitability and Volume<br>21|
|EVOLVING TAX BURDEN<br>23|
|Real Exchange Rate, Wages and the Budget<br>24|
|QUALITATIVE CHANGES IN FIRMS<br>24|
|Organization Structure<br>24|
|Disposal of Redundant Assets<br>25|
|Links with Customers and Suppliers<br>26|
|Links with Foreign Partners<br>27|
|Role of Managers<br>28|
|CONCLUDING REMARKS: MICROECONOMICS OF TRANSFORMATION<br>29|
|Microeconomics of the ETP<br>29|
|SOE Governance in Transition to Privatization<br>30|
|Factor Markets for Credit and Labor<br>30|
|Managerial Training<br>31|
|Tax Reform<br>31|
|Monopoly and Industrial Organization Policy<br>31|
|Annex I<br>Sample and Data Description<br>32|
|Annex 11<br>Firm Maximizing Behavior and Nominal Interest Rates<br>34|
|<br>Annex III<br>Restructuring and Liquidation Efforts<br>35|
|Annex IV<br>Wage Indexation during the ETP and Regression Results<br>37|
|Annex V<br>Sectoral Graphs<br>39|
|<br> <br><br>READING LIST<br>42|
national statistical agencies and corresponding national ministries.<sup>16</sup> To conduct robustness checks, we use two alternative measures of transport infrastructure: the length of the paved road network, collected from the same sources, and the combined total length of the road and railway network. The railway information is obtained from the World Bank‘s Railways Database and complemented with data from national sources.<sup>17</sup> As we shall impose constant returns to scale in the estimations (see below), the three variables underlying the index (phone lines, power generation capacity and the length of the road network) are measured in per-worker terms, and expressed in logs.<sup>18</sup> Their first principal component accounts for 82 percent of their overall variance and, as expected, it is highly correlated with each of the three individual variables.<sup>19</sup> More specifically, the correlation between the first principal component and main telephone lines per worker is 0.96, its correlation with power generation capacity is 0.97, and its correlation with the total length of the road network is 0.74. In addition, all three (log-standardized) variables enter the first principal component with approximately similar weights: where _z_ is the synthetic infrastructure index, _(Z1/L)_ is the number of main telephone lines (per 1,000 workers), _(Z2/L)_ is the power generation capacity (in GW per 1,000 workers), and _(Z3/L)_ represents the total length of the road network (in km per 1,000 workers).

Table 1 shows descriptive statistics for output, physical and human capital, and the various infrastructure indicators, for the cross-section corresponding to the year 2000. Output and the capital stock are expressed in PPP US dollars at international 2000 prices, while infrastructure variables are expressed in per worker terms.

> 16 One caveat regarding these data, as noted by Canning (1999), is that they may exhibit significant variations in quality. In particular, they do not reflect the width of the roads nor their condition. 17 The railways database can be found at http://go.worldbank.org/13EP3YJVV0

> 18 Before applying principal component analysis, the underlying variables are standardized in order to abstract from units of measurement.

> 19 As shown below, the individual infrastructure measures display stochastic trends, and hence we obtain the first principal component by computing the weights from the (stationary) first-differenced series.

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|Defnition|household head.<br> level of the household<br>r the gender of household<br>= 1).<br>atus of the household′s<br>elling of the household.<br>members in a household<br>f male members in a<br>(in log).|
|---|---|
||he<br>n <br>fo<br>ale<br> St<br>dw<br>of<br> o<br>ld|
|Level)|Age of t<br>Educatio<br>head.<br>Dummy<br>head (m<br>Marital <br>head.<br>Type of<br>Number<br>(in log).<br>Number <br>househo|
|Source<br>old-Head|ALCS<br>ALCS<br>ALCS<br>ALCS<br>ALCS<br>ALCS<br>ALCS|
|Max<br>Househ|64<br>7<br>1<br>5<br>6<br>3.61<br>3.04|
|Min<br>sehold,|18<br>0<br>0<br>1<br>1<br>0.69<br>0|
|SD<br>ls (Hou|11.02<br>1.37<br>0.07<br>0.55<br>0.85<br>0.38<br>0.40|
|Mean<br>y Contro|39.15<br>0.74<br>0.99<br>1.09<br>1.41<br>2.06<br>1.49|
|Observations<br>Supplementar|13156<br>13156<br>13156<br>13156<br>13156<br>13156<br>13156|
|ariable|tus<br>elling<br>Size (log)<br>Household (log)|
|V|on<br><br>Sta<br>Dw<br>old<br>er|
||ti<br>r<br>al<br>of<br>h<br>p|
||ca<br>de<br>rit<br>e<br>use<br>les|
||Age<br>Edu<br>Gen<br>Ma<br>Typ<br>Ho<br>Ma|

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Lane, P. R. 2003a. “Business Cycles and Macroeconomic Policies in Emerging Markets Economies.” _International Finance_ 6(1): 89–108.

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Lorenzoni, Irene, Leiserowitz, Anthony, De Franca Doria, Migue, Poortinga, Wouter and Nick Pidgeon. 2006. “Cross-National Comparisons of Image Associations with “Global Warming” and “Climate Change” Among Laypeople in the United States of America and Great Britain” <u>Journal of Risk Research</u> 9: (3):265-281.

“Affect” is considered by social psychologists to be the positive or negative evaluation of an object, idea, or image. Similar to emotions, but not as “full blown”, affect has been shown to powerfully influence both information processing and decision-making. This paper in the area of risk perception and affect in the United States and Great Britain found that, “the terms “global warming” and “climate change”, and their associated images, evoked negative affective responses from most respondents. Personally relevant impacts, causes, and solutions to climate change, were rarely mentioned, indicating that climate change is psychologically distant for most individuals in both nations.” The authors also note that “personally relevant impacts, causes, and solutions to climate change, were rarely mentioned, indicating that climate change is psychologically distant for most individuals in both nations.” Meijnders, Anneloes Cees Midden and Henk Wilke 2001 “Role of Negative Emotions in Communication about CO2 Risks” Risk Analysis 21(5): 955-966.

The authors use an experiment regarding the risks of carbon dioxide emissions to support their claim that both emotion and information are factors influencing effective risk communication. In the experiment, inducing fear of carbon dioxide risks leads participants to more effectively process information about energy conservation, which in turn results in more favorable attitudes toward energy conservation. “Exposure to strong arguments resulted in more positive attitudes towards the energy-efficient light bulb than exposure to weak arguments in the moderate fear condition.” This positive role of the emotion of fear in information processing may be counter to ethnographic and interview work by Norgaard and work from focus groups by Immerwahr, although the latter focus on negative emotions as barriers to action, not barriers to information processing per se. The authors note that extreme fear can reduce information processing effectiveness, but do not characterize climate change in the realm of extreme fear as the impacts are in the future (a questionable assumption).

- Meijndes Anneloes, Cees Midden and Henk Wilke. 2001. “Communications ‘About Environmental Risks and Risk-Reducing Behavior: The Impact of Fear on Information Processing” Journal of Applied Social Psychology 31(4): 754-777.

Similar to the companion piece, this article addresses the role of emotions (specifically fear) in how people respond to information. Authors set up an experiment through which the emotions of fear and the strength of arguments 60
Second, a mechanism was needed to derive estimates of risk from the TRI data. Conceivably it would be possible to combine the TRI information on the quantity of particular chemical releases with the LRD data on quantity of inputs, thus developing a picture of cross-sectoral chemical input-output coefficients. While this might provide useful insight into the flow of specific chemicals within the economy, the wide range of environmental and health risks associated with different chemicals would restrict inter-sectoral comparisons of pollutant risk. A better alternative for the comparison of risks is provided by the multi-index categorization of toxic potency in the US EPA's HHED.

Our initial results indicated a hign rank correlation between pollution risk intensity and pollution output intensity (see section 4.4). Therefore, subsequent work focused solely on medium-specific pollution output intensities (see section 5.3). These intensities were calculated at varying degrees of sectoral disaggregation, and with a number of different denominators, so that pollution projections could be made using the manufacturing data which are readily available in many developing countries.

# 3.2.3. The Choice of a Denominator

The LRD provides a number of options for the measure of Manufacturing activity to be used as a denominator ir calculating pollutant intensity. Four of the most obvious are: implications of concentrating trace toxins from waste water into highly toxic solid waste for shipment to a landfill.

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Table 4: Baseline Difference-in-difference Estimation: West Manggarai (Restaurants)

|**Control vs. POS rec**|**ipient resta**|**urants**||
|---|---|---|---|
|**Dependent Variable:**|**(1)**|**(2)**|**(3)**|
|**Log (restaurant tax receipt)**|**RPOS**|**TMD**|**WS**|
|ATET (given RPOS vs. not)|0.216<br>(0.117)|||
|ATET (given TMD vs. not)||1.814***<br>(0.222)||
|ATET (given WS vs. not)|||0.582**<br>(0.295)|
|Constant|0.00870|0.00883|0.00899|
||(0.0259)|(0.0258)|(0.0297)|
|Observations|18,186|17,930|17,790|
|Adj. R<sup>2</sup>|0.0093|0.1028|0.0799|

Robust standard errors in parentheses *** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1 Table 5: Baseline Difference-in-difference Estimation: West Manggarai (Hotels)

**Control vs. POS recipient hotels**

|**Dependent Variable:**<br>**Log (hotel tax receipt)**|**(1)**<br>**WS**|
|---|---|
|ATET (given WS vs. not)|0.517*<br>(0.311)|
|Constant|0.002<br>(0.050)|
|Observations|10,800|
|Adj. R<sup>2</sup>|0.002|

Robust standard errors in parentheses *** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1 16
# **ANNEX B: DISPARITIES IN INCOME LEVELS AND HEALTH OUTCOMES**

**Figure B1: The Relationship Between Infant Mortality Rate, Under‐five Mortality Rate and Maternal Mortality Rate, and GDP per Capita, 2000‐2008** (Trendlines for each given year across provinces)

<!-- Start of picture text --> GDP per capita‐IMR Relationship 2000‐2008<br>60<br>50<br>2000<br>40<br>2001<br>2002<br>2003<br>30 2004<br>2005<br>2006<br>20 2007<br>2008<br>10<br>0<br>1000 10000 100000<br>Provincial GDP per capita (RMB, log scale)<br>GDP per capita‐MMR Relationship 2000‐2008<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>2000 2001 2002 2003 2004 2005 2007<br>3000 30000 2006 2008<br>Provincial GDP per capita (RMB, log scale)<br>GDP per capita‐U5M Relationship 2000‐2008<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>2008<br>2000 2001 2002 2004 2006 2007<br>0<br>2003 2005<br>3000 30000<br>Provincial GDP per capita (RMB, log scale)<br>Provincial IMR (per 1000 of live births)<br>Provincial MMR (per 100,000 of live births)<br>Provincial U5M (per 1000 of live births<br><!-- End of picture text --> _Source: DevInfo_ 37
# **before Dawn: Evidence from a Up Experimental Cross-Border Trader Training at the Democratic**

# **Republic of Congo–Rwanda Border**<sup>_∗_</sup>

Kevin Croke<sup>1</sup> , Mar ́ıa Elena Garc ́ıa Mora<sup>2</sup> , Markus Goldstein<sup>2</sup> , Edouard Mensah<sup>3</sup> , and Michael O’Sullivan<sup>2</sup> JEL Classification Codes: O17, F14, F15,D04, C70, J16 Keywords: trade; gender; randomized experiment; corruption; gender-based violence

> _∗_ This paper is a product of the World Bank Africa Gender Innovation Lab. The authors gratefully acknowledge the conceptual and design contributions of Harsha Thirumurthy and Sabrina Roshan, as well as the excellent research assistance of Christine Jonason. C ́elestin Bashige, Douglas Amuli, Maria Lange, Paul Brenton, and Shiho Nagaki provided guidance and expertise on the study design and implementation. We thank Nicholas Magnan, Eric Verhoogen, Mateusz Filpski, and Gregory Colson, as well as seminar participants from the PSIA Evidence to Policy Workshop for their helpful comments. Funding from the PSIA Multi-Donor Trust Fund, the World Bank Gender Action Plan, the Belgian-Netherlands Poverty Reduction Partnership, and the Umbrella Facility for Gender Equality is gratefully acknowledged. All usual disclaimers apply, particularly that the views expressed in this paper do not represent those of the World Bank and its members. Corresponding author: Michael O’Sullivan; World Bank, 1818 H St., NW, DC 20433; mosullivan@worldbank.org.
problems (Pariyo et al., 2019; De Weerdt et al., 2020). Our results are aligned with those of Siddique et al. (2021). The authors show that awareness and compliance with health guidelines during COVID was higher when households were approached via phone calls when compared to SMS. As in our paper, effects are also larger for women.

Importantly, our experimental design provides a novel way to simultaneously measure the effects of different survey modes and control for components that can confound the effects. Our experimental design relies on a sample that was subject to the same sampling method, contact rate, incentives, survey length, survey structure and reminders. This advantage is important since by design it allows to overcome some of the shortcomings in the existing literature considering that each of these dimensions could on its own alter response rates, group composition and data quality beyond the true effect of the survey mode.<sup>6</sup> Second, our findings contribute to a rich literature that examines the effects of self-administration on data quality and reporting related to sensitive survey items, such as intimate-partner violence (Aguero and Frisancho, 2021; Cullen, 2020); contraceptive use (Greenleaf et al., 2020); and other health issues (Kays et al., 2012).<sup>7</sup> Initial research suggests that interviews conducted via text messaging on mobile devices essentially mimic self-administration in terms of higher reporting of sensitive behaviors and higher data quality in general (Brenner and DeLamater, 2014; Cocco and Tuzzi, 2013; Schober and Conrad, 2015). Interviews using text messages also remove the potential for interviewer effects on responses due to observable characteristics of the interviewers, on which there is ample research (Schaeffer et al., 2010). In theory, one can therefore hypothesize that survey designs involving interviewers and mobile phones will simultaneously reduce respondent burden (due to decreased questionnaire length) and increase the quality of reporting on sensitive survey items; we test this hypothesis with the present study. Specifically, we focus on dimensions that have not been considered so far (perceptions of physical-punishment effectiveness and mental health). Overall, our results suggest there are no significant differences in response quality between phone-based surveys

> 6In Table A6 in the Appendix we provide a summary of the literature and the design features in each study. 7Within this strand, a subset of studies analyzes response and measurement error from surveys in general (not only phone-based or message-based ones). See, for example, the works from Friedman et al. (2017); Gibson et al. (2015); De Weerdt et al. (2016).

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## **2.2 Economic and Urban Growth**

Along with the continued development of the Ethiopian road network, the various phases of RSDP coincided with rapid urban population and economic growth. Ethiopia’s economy has been one of the fastest-growing in the region, experiencing an average of 8.1% annual growth between 1996 and 2015, compared to a 4.6% annual growth rate for Sub-Saharan Africa. However, it has been challenged by high inflation rates between 2008 and 2010 that led to local currency depreciation, which in turn increased fuel and transportation costs (World Bank Group, 2012). Spurred by an average of 2.2 billion USD in official development assistance per year between 1996 and 2015, Ethiopia has focused its economic development strategy on education, energy, and roads projects—such as RSDP (World Bank Group, 2012). Ozlu et al. (2015) note that despite only 17.3% of Ethiopia’s population residing in urban areas in 2012, the urban population is one of the fastest-growing in the world. This growth rate was estimated to be around 3.8% per year during the 2010s. Schmidt et al. (2018) identified the drivers of this rapid urbanization as road infrastructure, secondary city development, and rural-to-urban migration.

Addis Ababa has been the main destination of this rural-to-urban migration, attracting 40% of the total rural-to-urban migration between 2008 and 2013 (Bundervoet, 2018). However, despite the high percentage of migration flowing to Addis Ababa, Bundervoet (2018) notes that smaller cities and towns were also the focus of rural migration. This demonstrates not only that major urban centers have rapidly urbanized during the RSDP period, but that population is also growing in smaller cities and rural townships.

# **3 Data**

This paper relies on three types of geospatial data for our treatment variables and the three outcomes of interest (local economic activity, urban land, and cropland). First, we rely on digitized road improvement data shared by the ERA to capture the evolution of RSDP. Second, we rely on nighttime lights to capture the evening use of light and electricity as a proxy for local economic activity. Third, we rely on annual landcover data from the European Space Agency (ESA) GlobCover program to capture the evolution of the surface area of urban land and cropland. The following four sections describe the data sources (Section 3.1); how data are aggregated to different units of analysis (Section 3.2); how units are separated into different groups of economic activity for exploring heterogeneity of results (Section 3.3); and, lastly, we present descriptive statistics for the area and period of interest (Section 3.4).

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