rafmacalaba/datause-encoder-data huggingface dataset:
# **ANNEX 1: Variables Description: Bulgarian Microdata, period 1993 - 1995**

Firms with missing observations were first deleted. Then we averaged all variables for the penod 93/95. The only variables which we first calculate as the first difference and then averaged were investment, **I,** which is obtained as a first difference of tangible fixed assets, and the first difference of sales, _DSAL._ All the variables are expressed in USD million.

Data available for Bulgaria in AMADEUS:

||**Balance sheet**|
|---|---|
|**Assets**|**Liabilities**|
|Tangible fixed assets|Shareholders funds|
|Fixed assets|Long term debt|
|Stocks|Non-current liabilities|
|Debtors|Current liabilities|
|Cash & cash equivalent|Loans|
|Total assets|Creditors|
|Working capital||
|Number of employees||
||**Profit and Loss account**|
|Sales||
|Gross profit||
|<br>P/L before tax||
|Taxation||
|Costs of employees||
|Depreciation||
|Interest paid||
|<br>Cash flow||
|1.<br>**The following variables hav**|**e been averaged over the period of observation:**|
|K<br>tangible assets,||
|**LTDEBT**long terrn debt;||
|LOAN short term loans;||
|**CREDIT**trade credits;||
|**WCAP**working capital;||
|<br>**NEMIP**number of employees;||
|<br>**PLBTAX**profit and loss before tax;||
|**TAX**<br>taxation;||
|**DEP**<br>depreciation;||
|**IPAY**interest paid;||
|**CF**<br>cash flow (equals before-tax|profits plus depreciation, DEP),|

2. **The following variables were obtained first as differences and then were they averaged for the period of observation:**

**I=d(K),** investment; D (SAL), first difference of sales

I(Investment) = d(Tangible fixed assets), IB =I+DEP, IBK=IB/K, DSAL = D(Sales); DSALI=DSAL/K, DCF=CF/K 23
_TA81E I_ CodT D **wuvEUAVIAL EELATIINSUP** FLOW **OF MNS IDENTITY**

# roernot finences cont'd

## **Central**

<!-- Start of picture text --> 3<br>I Ti ,<br>Tic Sector,<br>3<br>Y. Setor ^<br>k-i<br><!-- End of picture text -->

# **Central** CoveIvaet Finnces cont'd

- **Sg** - current balence Xotg flow of other lisbilities (net) of monetary sector to central goveriment

- Lpg flow of net lending by private sector to central governmont

- Lfg - floa of medi and long term external borrowing (not) (NLT)

- Lsff taflow of external short-term borrowing (net)

- l gross Investnt Lgp - flow of net lending by central govenMent to private sector.

sAere:

- n (t - 5) - correspond to the five min categories of indirect taxes: sales taxes on domestic anufactures, sales taxes on Iqports, datles

- on imports, dutles on exports, and other indirect taxes

- Sector - refers to activity shich generates the taxes

OthrR 1 other governnent revemues Subsg susidies Co central governmnt consumption - Tgp goverrnent transfers - Nga.Ngp interest on dmoestic borrowing Hof - Interest on NLT foreign debt lsgf Interest on short-term forefgn de't <!-- Start of picture text --> 3<br>E wt<br>k-i<br>CApr, _ _ . GDPfc,<br>3<br>I GDPfc 4",<br>k-i<br><!-- End of picture text --> where: CAPr - capital reverne I/ Includescapia ve.
<!-- Start of picture text --> 70% 25%<br>50% mgo, = 20% mg99%<br>| 95% see | 95%<br>50% 590% m90%<br>75% 10% n75%<br>40%<br>550% 5% 550%<br>30% m25% m25%<br>0%<br>010% 010%<br>20% 05% 5% 05%<br>1% 1%<br>10% -10%<br>2012 «-2013,—='—i«2014s (205 s206Ss«2017 2012,«2013,—'—i«a204s (2052062017<br><!-- End of picture text -->
data and methods, Section 3 gives the results and Section 4 concludes.

# **2.  Data and Methods**

# **2.1 Data Sets**

# **Data set selection**

This study uses general household surveys and censuses rather than data sets that focus on disability, i.e., disability surveys. This stems from the objective to exploit data sets that are collected on a regular basis and may be potentially used to document and track human development for persons with disabilities. Of course, complementary work is needed to use disability-focused data sets, such as the Model Disability Survey developed by the World Bank and the World Health Organization (Cieza et al 2018).

We first examined nationally representative general household surveys and population censuses conducted in 136 LMICs (as defined by the World Bank) from 2009 to 2018 (Mitra et al 2021). The questionnaires of general household surveys and censuses were retrieved from online survey databases such as the International Household Survey Network (IHSN) Catalog, or the websites of individual National Statistical Offices. Questions were considered to be similar enough for inclusion in the study if they covered the four domains of functional difficulties recommended by the United Nations (2017, p. 207) for censuses. When functional difficulty questions followed a screener question like “Do you have a disability?” (e.g. Albania LSMS), we did not analyze the data as such screeners lead to under self-reporting.

From the data sets reviewed, this paper presents results for 21 LMICs with data sets using the Washington Group Short Set or similar questions where we could access general surveys or census data. We use 24 data sets as follows: eleven censuses (Dominican Republic, Indonesia, Kiribati, Mexico, Panama, Philippines, Rwanda, South Africa, Tanzania, Vanuatu, Vietnam), five Living Standards Measurement Studies (LSMS) (Ethiopia, Malawi, Nigeria, Tanzania, Uganda), four Household Income and Expenditure Surveys (HIES) (Bangladesh, Liberia, Namibia, Papua New Guinea), two Labor Force Surveys (LFS) (The Gambia and Rwanda), and two general surveys (Afghanistan and South Africa). Most data sets are publicly available and are accessible through websites such as those of LSMS or IPUMS International. Some data sets such as Kiribati and Vanuatu’s censuses are not publicly available and were obtained from the respective national statistics offices.

The data sets are described in Table 2.1. The data sets have three possible sample designs. Most follow a complex survey design in which case all estimates presented below are weighted. Kiribati and Vanuatu’s censuses have the entire population, while several population censuses have a random 10 percent sample, in which case no weighting was needed. Descriptive statistics are shown in Appendix 1 for the samples of individuals and households. Standard errors are not shown to keep the results legible but are available from the authors upon request.

4
knowledge by 69.9 percent, the relative probability of application given knowledge by 107 percent and the relative probablity of selection condional on knowledge and application by 38 percent.<sup>22</sup>

**Table 6: Determinants of EGPP Participation (Odds Ratio)**

||Participate in<br>EGPP|Knowledge of<br>EGPP|Application to<br>EGPP conditional<br>on knowledge|Selection into<br>EGPP conditional<br>on knowledge and<br>application|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|PAI|2.105***|1.699***|2.078***|1.382*|
||(0.18)|(0.23)|(0.23)|(0.24)|
|Minority|0.513|0.272**|1.591|0.84|
||(0.22)|(0.11)|(0.86)|(0.55)|
|Minority*PAI|1.846**|2.156***|0.811|1.483|
||(0.53)|(0.63)|(0.28)|(0.59)|
|Contact with UNO|1.151|1.358|1.062|0.919|
||(0.22)|(0.49)|(0.41)|(0.30)|
|Minority*contact with UNO|2.063|3.207|0.752|.|
||(2.51)|(3.86)|(0.83)|.|
|Powerful relative|2.001***|0.931|3.150***|1.587|
||(0.42)|(0.29)|(0.89)|(0.49)|
|Minority*powerful relative|1.138|1.04|.|0.599|
||(1.29)|(1.38)|.|(1.24)|
|Household characteristics|Yes|Yes|Yes|Yes|
|Asset ownership|Yes|Yes|Yes|Yes|
|Distance to facilities|Yes|Yes|Yes|Yes|
|Observations|3,191|3,191|2,382|1,919|

Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. PAI = Political access index.

The important role of local politicians in facilitating the participation process in EGPP is also supported by the results we find on the probability of knowledge about EGPP among the religious minority. We find that households belonging to a minority religion are less likely to participate in EGPP, although the result is not statistically significant. However, minority households who have higher access to local

> 22 To ensure that our results are not sensitive to the design of the political access index we also estimate participation regressions with different measures of political access and present the results in the Appendix. For example we use a separate dummy for access to Union Parishad Chairman, Union Parishad member and UNO.  We also estimate these relationships using a single dummy for access to any of the three above mentioned individuals. In all the cases the coefficients on the political access dummy are found to be statistically sinificant at 5% level and the relative magintudes remain the same. We also estimate the disaggregated effect of PAI by estimating the effect of having one political connection as opposed to two. The results show that having two political connections has a larger effect on participation than having just one.

23
the Climate System: A Scientific Assessment.” _Journal of Geophysical Research: Atmospheres_ 118 (11): 5380–5552.

- Campbell, B. M., S. J. Vermeulen, J. J. Mangono, and R. Mabugu. 2003. "The energy transition in action: urban domestic fuel choices in a changing Zimbabwe." _Energy Policy_ 31 (6):553‐562.

- Christiansen, L. and R. Heltberg (2014). “Greening China's rural energy: new insights on the potential of smallholder biogas.” _Environment and Development Economics_ , 19, pp 8‐29. doi:10.1017/S1355770X13000375.

- Cooke, Priscilla, Gunnar Kohlin, and William F. Hyde. 2008. “Fuelwood, Forests and Community Management‐Evidence from Household Studies.” _Environment and Development Economics_ 13 (1): 103.

- Davis, M. 1998. "Rural household energy consumption: The effects of access to electricity—evidence from South Africa." _Energy Policy_ Vol. 26 (3):207‐217.

- Ekouevi, K., and V. Tuntivate. 2012. _Household Energy Access for Cooking and Heating: Lessons Learned and the Way Forward_ , _World Bank Studies_ . Washington, D.C.: The World Bank.

- Grieshop, Andrew P., Julian D. Marshall, and Milind Kandlikar. 2011. “Health and Climate Benefits of Cookstove Replacement Options.” _Energy Policy_ 39 (12): 7530–42.

- Heltberg, R. 2005. "Factors determining household fuel choice in Guatemala." _Environment and Development Economics_ Vol. 10 (3):337‐361.

- Heltberg, R. 2004. "Fuel switching: evidence from eight developing countries." _Energy Economics_ Vol. 26 (5):869‐887.

- IPCC. 2013. _Climate Change 2013: The Physical Basis Summary for Policymakers_ , Working Group 1 Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva

- IPCC. 2006. _Good Practice Guidelines for National Greenhouse Gas Inventories._ Switzerland: Intergovernmental panel on climate change.

- Jeuland, M.A. and S.K. Pattanayak.  2012.  “Benefits and costs of improved cookstoves:  Assessing the implications of variability in health, forest and climate impacts.” _Plos ONE_ 7(2): 1‐15.

- Johnson, Michael, Rufus Edwards, Claudio Alatorre Frenk, and Omar Masera. 2008. “In‐Field Greenhouse Gas Emissions from Cookstoves in Rural Mexican Households.” _Atmospheric Environment_ 42 (6): 1206–22.

- Krauth, Brian. 2011. “Bounding a Linear Causal Effect Using Relative Correlation Restrictions.” Simon Fraser University. http://summit.sfu.ca/item/10925.

- Leach, G. 1992 “The Energy Transition” _Energy Policy_ Vol. 20 (2): 116

- Lim, Stephen S, Theo Vos, Abraham D Flaxman, Goodarz Danaei, Kenji Shibuya, Heather Adair‐Rohani, Mohammad A AlMazroa, et al. 2012. “A Comparative

17
**Table 14. Services Trade Restrictions Index (STRI) and Ad valorem Equivalents (AVEs) by Sector and By Level of Development**

|||STRIs<br>Transition and|||A<br>Transition and|VEs*<br>|
|---|---|---|---|---|---|---|
|Sector|OECD and<br>other EU<br>countries|<br>other<br>developing<br>countries|Least<br>developed<br>countries|OECD and<br>other EU<br>countries|<br>other<br>developing<br>countries|Least<br>developed<br>countries|
|Accounting|0.37|0.44|0.42|29|35|33|
|Legal Services|0.39|0.59|0.58|31|46|46|
|Air Transport|0.35|0.32|0.21|15|44|17|
|Rail Transport|0.47|0.59|0.59|16|58|56|
|Road Transport|0.54|0.32|0.30|18|38|31|
|Banking|0.02|0.21|0.17|2|18|15|
|Insurance|0.18|0.32|0.35|14|27|31|
|Fixed Line|0.28|0.37|0.51|35|69|764|
|Mobile Line|0.26|0.27|0.35|1|1|4|
|Retail|0.09|0.21|0.19|1|3|3|
|Maritime Transport|0.29|0.30|0.29|9|40|31|

*For the four transport sectors, the ad valorem equivalent (AVE) or the tariff equivalent (TE) we have calculated has the domestic price in the denominator. For other sectors, the tariff equivalent contains the unrestricted shipping margins in the denominator. To make the conversion, if TE is the ad valorem equivalent with restricted country shipping margins in the denominator and TE* is the ad valorem equivalent with unrestricted country shipping margins in the denominator, then TE* may be derived from TE as TE* = TE/[1-TE].

54
<!-- Start of picture text --> 2 0 0 2 00 0 2 0 0 0 0 1 5 0 0 0 0<br>L an d s iz e in h a<br>Farm fixe d e ffec t R a yo n f ixe d effec t<br>95% co nf. in terv al<br>.6<br>.4<br>.2<br>0<br>F i xe d e ff ec t<br>-.2<br>-.4<br><!-- End of picture text -->

**Figure 3.1: Farm and rayon fixed effects by farm size, 2001-2006**

<!-- Start of picture text --> 2 0 0 2 00 0 2 00 00 1 5 0 0 0 0<br>L an d s iz e in h a<br>Farm fixe d e ffec t R a yo n f ixe d effec t<br>95% co nf. in terv al<br>.3<br>.2<br>.1<br>F i xe d e ff ec t<br>0<br>-.1<br><!-- End of picture text -->

**Figure 3.2: Farm and rayon fixed effects by farm size, 2007-2011**

30
# **Changing Wage Structure in India in the Post‐ Reform Era:  1993‐2011**

By Hanan G. Jacoby<sup></sup> and Basab Dasgupta<sup>†</sup> JEL codes: J21, J23, J24, J31 Keywords: Labor Supply, Labor Demand, Rural Wages in India

>  DECRG, World Bank, 1818 H St. NW, Washington DC 20433 (hjacoby@worldbank.org).  We thank Rinku Murgai and Ambar Narayan for helpful suggestions.

> † GWADR, World Bank (bdasgupta@worldbank.org)
reduction in poverty has occurred in the regions that have had the greatest proportion of irrigated land. There are evidences from East Asia, Pacific, North Africa, and Middle East. This is in contrast to Africa where only around 3% of cropland is irrigated and the region has experienced very little reduction in poverty in the 1990s (Lipton _et. al._ , 2003). As a production input in agriculture, irrigation water is an important socio-economic ‘‘good’’, with a positive role in poverty alleviation but it can also become a socio-economic ‘‘bad’’ when it leads to problems such as waterborne diseases (malaria, schistosomiasis), and land degradation including water-logging and salinity, water pollution and associated destruction of living beings and natural ecosystems, i.e. negative externalities associated with irrigation (Hussain and Hanjra, 2004).

Table 1 : Poverty Incidence and irrigation in developing regions.

|$|1–a–day pove<br>Incidence<br>(millions)|rty (1998)<br> <br>Total<br>Population<br>(%)|Change in<br>Incidence<br>1997-98 (%)|% Irrigated area<br>per ha cultivated<br>area(arable+<br>permanent<br>cropland)<br>1999|
|---|---|---|---|---|
|East Asia and Pacific|278|15|-33|20|
|Latin America h Asia|78|16|22|12|
|Sub-Saharan Africa and the<br>Caribbean|5|0.04|-44|27|
|North Africa and the middle East|522|39|10|6|
|Sub –Saharan Africa|291|44|34|3|

Source: Poverty figures from World Bank (2000, 2001), Irrigated land from FAO Statistical Database Lipton et al (2003), Hussain and Hanjia (2003) and Hussain and Hanjra (2004) laid out a conceptual framework for analyzing the transmission mechanism between irrigation and poverty. Irrigation has direct impacts on poverty via increases in output levels and employment and further second round effects on poverty via output, employment and prices. Irrigated land usually encourages farmers to adopt or increase their use of fertilizers, pesticides, improved seeds and other agricultural inputs and provides the stimulus for further research into improved plants and technology that lead to increased output, employment and 21
# **Table 6(a): Subjective welfare regressions for Tajikistan**

||(1)|Pre-vi|(2)<br>gnette||(3)|Post-v|(4)<br>ignette||
|---|---|---|---|---|---|---|---|---|
||Ordered p|robit|CHOP|IT|Ordered p|robit|CHO|PIT|
||Coeff.|s.e.|Coeff.|s.e.|Coeff.|s.e.|Coeff.|s.e.|
|log per capita real consumption|0.729***|0.042|0.731***|0.039|0.650***|0.041|0.650***|0.038|
|_Household Head Demographics_|||||||||
|female headed household|-0.155***|0.052|-0.069|0.051|-0.176***|0.052|-0.110**|0.049|
|age of hh head|-0.001|0.002|0.001|0.002|-0.002|0.002|0.000|0.0018|
|<br>_Ethnicity (Reference: Tajik)_|||||||||
|<br>Uzbek|0.020|0.046|0.031|0.048|0.064|0.045|0.056|0.0468|
|Russian|-0.334**|0.158|-0.148|0.159|-0.410**|0.161|-0.186|0.1544|
|Other|-0.690***|0.158|-0.481***|0.137|-0.647***|0.158|-0.396***|0.132|
|_Education (Reference: No Education)_|||||||||
|Primary|0.065|0.117|-0.138|0.115|-0.015|0.113|-0.259**|0.111|
|Basic|-0.134|0.109|-0.150|0.108|-0.193*|0.108|-0.271***|0.105|
|General Secondary|0.006|0.106|-0.037|0.105|-0.098|0.104|-0.184*|0.101|
|<br>Special Seconday|0.120|0.113|0.084|0.112|-0.032|0.111|-0.128|0.108|
|<br>Technical Secondary|0.077|0.116|0.056|0.114|-0.079|0.113|-0.125|0.1103|
|<br>Higher Education|0.283**|0.114|0.262**|0.112|0.180|0.111|0.103|0.1078|
|<br>Graduate School|0.542|0.375|0.784*|0.443|0.516|0.400|0.601|0.4281|
|_Household Characteristics_|||||||||
|log household size|0.072***|0.009|0.492***|0.048|0.064***|0.009|0.457***|0.047|
|no. of elderly (65+)|-0.001|0.043|-0.031|0.041|-0.028|0.042|-0.036|0.0397|
|any migrant(s) in the hhs|0.045|0.043|-0.004|0.042|0.068|0.043|0.012|0.0405|
|<br>_Employment Characteristics of Househol_|_d Head (Refer_|_ence for_|_Occupation:_|_Unemploy_|_ed)_||||
|No. of employed|0.036**|0.017|0.014|0.017|0.010|0.018|-0.005|0.0168|
|<br>Agriculture / Fishing / Forestry|-0.002|0.063|0.005|0.062|0.062|0.062|0.087|0.0598|
|<br>Manufacture / Mining|-0.033|0.094|0.013|0.095|0.053|0.092|0.096|0.092|
|Services|0.351***|0.130|0.355**|0.147|0.442***|0.119|0.367**|0.143|
|Construction|-0.072|0.080|-0.085|0.080|-0.032|0.077|-0.066|0.0778|
|Public Administration / Education /<br>Health|0.175**|0.069|0.134**|0.066|0.092|0.066|0.089|0.0643|
|Sales and Services|0.303***|0.064|0.249***|0.061|0.236***|0.062|0.200***|0.059|
|Other|-0.167|0.107|-0.041|0.106|-0.142|0.107|-0.033|0.1029|
We explore two policy shocks associated with an attempt to streamline SPS measures in Guatemala. The first is the unrealistic case of a total elimination of the costs of complying with SPS measures for the selected products (i.e. AVE=0). The second is the case where efforts to streamline SPS measures reduce the AVE by half. Given that we abstract from the impact on production (the change in income distribution), we only consider the impact on poverty for urban households. The underlying assumption is that the price change brought about by the NTM reduction decreases prices but leaves income distribution unchanged because urban households are employed in economic activities outside the agricultural sector. Finally, since we are not allowing the non-tradable prices to adjust, we center the discussion on the impact of our policy scenarios on extreme poverty rates, although we also report the results for general poverty rates. Table 9 compares the proportion of the population living in poverty now (2011) and after streamlining SPS measures.

**Table 9. Poverty Impact of Streamlining SPS Measures in Guatemala**

**<u>(Urban Poverty Rates).</u>**

|**Poverty**|**Initial**<br>**Poverty**|**Poverty Rate**<br>**Ex-Post**|**s**|
|---|---|---|---|
|**Line**|**Rate**|**Total**<br>**Pa**|**rtial**|
|Extreme|5.07|4.59|4.91|
|Total|34.97|32.66|33.98|

Note: This table shows the poverty impact of total and partial elimination of SPS measures applied to beef, bread and pastries, chicken meat, and dairy products in Guatemala.

Source: Authors’ computation using the 2011 Guatemalan HHS.

The main finding is that an effort to streamline NTMs in Guatemala would likely reduce poverty. Specifically, working towards rationalizing the processes related to SPS measures affecting beef, bread and pastries, chicken meat, and dairy products would reduce domestic prices and, thus, make the basic consumption basket more affordable. Our computations suggest that streamlining the key SPS measures affecting these products by, for example, reducing the cost and time required to obtain sanitary registries, could reduce the urban poverty rate from 5.07% to 4.91%. This would mean lifting approximately 20 thousand people out of extreme poverty in urban areas in Guatemala. Figure 8 depicts the left shift in the poverty line due to a scenario of total elimination of the cost of complying with SPS measures in our products. The dotted line is the new extreme poverty line under total reduction of price-controls in Guatemala.

# **Figure 8: Poverty Effect of Streamlining SPS Measures in Guatemala Shift in Urban Poverty Lines**

26
Although some progress has been made in enhancing regional cooperation and crossborder electricity trade in the MENA region, much more work is needed to fully realize the benefits of regional electricity trade through creating a regional electricity market or regional power pool. Studies analyzing the impacts of enhanced regional electricity cooperation for the MENA region are limited. The few available studies include ESMAP (2010), El-Katiri, (2011) and CESI (2014). ESMAP (2010b) focusses on Mashreq economies -- Egypt, Iraq, Jordan, the Syrian Arab Republic, Lebanon and the West Bank & Gaza. This study does not study regional trade in the rest of the MENA countries. El-Katiri (2011) does not analyze the impacts of enhanced cross-border electricity trading; it only discusses the current status and historical perspectives the regional trade. Moreover, the discussion is limited to the context of Gulf Cooperation Council (GCC) countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates), it does not cover the rest of the MENA countries.

CESI (2014) uses a power system expansion technique to analyze enhancing of crossborder electricity trading in the MENA region for 2015-2030 under multiple scenarios. The study has, however, several limitations. For example, it does not account for benefits of exploiting renewable energy resources in different parts of the region, it offers only limited information on reserve sharing and it has limited representation of the transmission networks. Moreover, the change in technologies and fuel prices and the rapid drops in costs, particularly of renewable energy technologies, warrant a fresh study to estimate the benefits of regional electricity trade in MENA under alternative scenarios.

Outside the MENA region, Timilsina and Toman (2016) estimate the benefits of a hypothetical regional power pool in the South Asia region. Timilsina and Toman (2018) further extend the study to assess the complementarity between regional trade and carbon pricing. Timilsina (2018) further explains how regional electricity trade in South Asia would help exploit the hydropower resources which have been mostly unexploited in Nepal and Afghanistan.

The main contribution of this paper is to analyze the interactions between three policies – subsidy removal, cross-border electricity trade, and carbon emission constraining in the power sector. While there are some studies dealing with fuel subsidies and GHG mitigation in the power sector, our study is the first one to investigate the interactions between these three policies covering the entire MENA region with 18 economies. The study uses a long-term electricity planning model 3