rafmacalaba/datause-encoder-data huggingface dataset:
transitions that show negative values in four quarters. In rural areas the gender gap almost disappears with the quarterly trends being very similar. Both genders experience three negative spells and there is clearly more variations across quarters as compared to urban areas. In the latest 2009–2011 period females seem to outperform males. This is the only episode in the whole paper where females seem to do better than men.

**Figure 7 – Net Quarterly Upward Transitions between Economic Sectors (% of total transitions)**

<!-- Start of picture text --> Urban Rural<br>25.00 25.00<br>20.00 20.00<br>15.00 15.00<br>10.00 10.00<br>5.00 5.00<br>0.00 0.00<br>-5.00 -5.00<br>-10.00 -10.00<br>-15.00 -15.00<br>q1 q2 q3 q4 q1 q2 q3 q4 q1 q2 q3 q4 q1 q2 q3 q4 q1 q2 q3 q4 q1 q2 q3 q4 q1 q2 q3 q4 q1 q2 q3 q4<br>07/08 08/09 09/10 10/11 07/08 08/09 09/10 10/11<br>Male Females Male Females<br><!-- End of picture text --> Source: Authors’ estimations from LFSs 2007-2011.

P **ublic and private sectors** . If we consider the private and public sectors, the greatest transitions occur from unemployment and inactivity to the private sector. It is also evident that the private sector recruits mostly from inactivity. The second largest transition flow is from the private sector to inactivity indicating that most of the transitions under this categorization are between the private sector and inactivity with the two flows almost matching at times. These trends are very similar for all four groups considered except urban males for whom the largest transition flow is from unemployment to inactivity.

The public-private transitions are very few and they are roughly equivalent in both directions. There are almost no transitions from the public sector to unemployment indicating that the public sector rarely sheds labor. There are also few transitions from the public sector to inactivity (in working age) and these are likely to be early retirements. The public sector does not seem to recruit from either inactivity or unemployment meaning that all recruitment occurs from new labor market entrants exiting educational establishments.

Overall, transitions involving the public and private sectors are not many and they are particularly low for urban males while they are high for rural females. Transitions seem to decline for all groups during the four years considered. Upwards transitions dominate downward transitions with all upward transitions being well above 50%.

13
WTP surveys can certainly be used to generate a measure of the average households’ economic value for the proposed improvements in WSS and to examine the factors that affect these values. However, we show that beyond a general measure of economic benefits, WTP surveys often produce two other important pieces of operational information. First, they can be used to obtain a demand curve that maps the relationship between charge and the number of households who will take up the service at that particular charge. Second, they can make productive use of often collected information on various characteristics of households and their communities– e.g., income, assets, education, attitudes, location, and amenities. We show how regression modeling can be an important first step for incorporating this additional information into demand analysis.

Field implementation in and around three towns of Southwestern Sri Lanka shows that urban households value improved water services. The median WTP is at least twice the amount that the typical household is currently paying and three times higher than the implied monthly water charge. There are important cautions and conclusions regarding use of WTP data. First, in the design of tariffs, WTP data should be complemented by supply‐side information on costs and service area and by projected use levels (volumetric consumption and numbers of consumers). Second, they can be complemented by similar methods, such as conjoint analysis, to examine important dimensions of consumer preferences for service improvements other than price such as quantity, safety, and regularity (see Yang et al., 2005). Third, CV data are perhaps best viewed as a rough index of “genuine need” that can facilitate the design of subsidy policy. Fourth, the WTP data must be combined with a variety of socio‐economic characteristics to develop a defensible and complete picture of need and affordability.

Multivariate regression modeling shows that a complex reality of factors drive household demand for network connections as expressed in their demand for service improvements. While poverty and costs are undoubtedly key determinants of demand, we find that location, access to alternative sources (in this case, mainly access to private wells), and perceptions 26
of operators or service providers in the market. If a member state limits the number of licenses, such a limitation must be justified. Regarding the level of intervention, the Licensing Framework consists of three levels of intervention, ranging from individual licenses to class license (authorization or declaration) to open entry. Different telecommunications networks and services will be categorized according to the adapted market structure.

# _Guidelines for numbering_

The ITU/EU study lists a number of general approaches for numbering and some key points that can be applied to any numbering scheme. A detailed listing of these points is beyond the scope of this study.

# _Guidelines for radio spectrum management_

The guidelines for radio spectrum management are divided in 17 sub-categories that include among others: interference issues, global and regional regulatory framework, role of regulators, radio spectrum coordination, economic principles of spectrum management, auctions, and spectrum pricing.

# _Guidelines for universal access and universal service_

The guidelines are listed in eight categories. Below we list some of the most important guidelines for each category:

# <u>Creating an enabling regulatory and policy environment</u>

- Governments must, at the highest level, identify ICT as a tool for socio-economic development. In doing so, governments should designate a national focal point (ministry, government department, other entity) for ICT development.

- National Regulatory Authorities (NRAs) must be established and given the statutory authority to play a key role in implementing universal access policies first through addressing the market efficiency gap (letting the market deliver universal access/service), and second through the true access gap. NRAs should be responsible for implementing policies directed towards assuring the best

21
|IR-Ahwaz & Shiraz Water Supplyand Sanitation Project|Bekele Debele Negewo|
|---|---|
|IR-Ahwaz & Shiraz Water Supplyand Sanitation Project|Bekele Debele Negewo|
|IR-Ahwaz & Shiraz Water Supplyand Sanitation Project|Bekele Debele Negewo|
|IR-Ahwaz & Shiraz Water Supplyand Sanitation Project|Bekele Debele Negewo|
|EmergencyPower SupplyProject|Joel J. Maweni|
|EmergencyPower SupplyProject|Joel J. Maweni|
|Provincial and Rural Infrastructure Project|Siele Silue|
|Provincial and Rural Infrastructure Project|Siele Silue|
|Provincial and Rural Infrastructure Project|Siele Silue|
|Provincial and Rural Infrastructure Project|Siele Silue|
|Provincial and Rural Infrastructure Project|Siele Silue|
|Provincial and Rural Infrastructure Project|Siele Silue|
|Provincial and Rural Infrastructure Project|Siele Silue|
|Provincial and Rural Infrastructure Project|Siele Silue|
|Provincial and Rural Infrastructure Project|Siele Silue|
|Provincial and Rural Infrastructure Project|Siele Silue|
|Provincial and Rural Infrastructure Project|Siele Silue|
|MULTICOUNTRY HIV/AIDS PROGRAM FOR THE AFRICA REGION|Anwar Bach-Baouab|
|MULTICOUNTRY HIV/AIDS PROGRAM FOR THE AFRICA REGION|Anwar Bach-Baouab|
|Programmatic Social Reform Loan Project|Evangeline Javier|
|Programmatic Social Reform Loan Project|Evangeline Javier|
|Grand Trunk Road Improvement Project|Rajesh Rohatgi|
|Grand Trunk Road Improvement Project|Rajesh Rohatgi|
|Grand Trunk Road Improvement Project|Rajesh Rohatgi|
|Grand Trunk Road Improvement Project|Rajesh Rohatgi|
|Grand Trunk Road Improvement Project|Rajesh Rohatgi|
|Grand Trunk Road Improvement Project|Rajesh Rohatgi|
|Grand Trunk Road Improvement Project|Rajesh Rohatgi|
|Grand Trunk Road Improvement Project|Rajesh Rohatgi|
|Grand Trunk Road Improvement Project|Rajesh Rohatgi|
|Grand Trunk Road Improvement Project|Rajesh Rohatgi|
|Grand Trunk Road Improvement Project|Rajesh Rohatgi|
|Economic and Public Sector CapacityBuildingProject|Huot Chea|
|Economic and Public Sector CapacityBuildingProject|Huot Chea|
|Economic and Public Sector CapacityBuildingProject|Huot Chea|
|Economic and Public Sector CapacityBuildingProject|Huot Chea|
|Economic and Public Sector CapacityBuildingProject|Huot Chea|
|Economic and Public Sector CapacityBuildingProject|Huot Chea|
|Economic and Public Sector CapacityBuildingProject|Huot Chea|
|Economic and Public Sector CapacityBuildingProject|Huot Chea|
|Economic and Public Sector CapacityBuildingProject|Huot Chea|
|Economic and Public Sector CapacityBuildingProject|Huot Chea|
|ANDHRA PRADESH AND TELANGANA MUNICIPAL DEVELOPMENT|PRRaghu Kesavan|
|ANDHRA PRADESH AND TELANGANA MUNICIPAL DEVELOPMENT|PRRaghu Kesavan|
|ANDHRA PRADESH AND TELANGANA MUNICIPAL DEVELOPMENT|PRRaghu Kesavan|
|ANDHRA PRADESH AND TELANGANA MUNICIPAL DEVELOPMENT|PRRaghu Kesavan|
|ANDHRA PRADESH AND TELANGANA MUNICIPAL DEVELOPMENT|PRRaghu Kesavan|
The different agricultural income components where not equally hard hit over the decade. The income from cultivation and farm enterprise where most severely affected; the share fell from 30.0 percent to 9.5 percent. The farm and agricultural labor income share dropped from 12.3 to 11.3 percent of total rural income. Also the share of income from self-consumption was reduced; from 8.7 to 3.1 percent during 1992-2002.

Unskilled workers with less than completed primary education receive an average hourly wage income of P$8.2. This compares to skilled workers with completed high school education who earn P$25.0 or more per hour. Hence, skills are key to obtaining a good job. Hourly wages in rural Mexico increase monotonically with completed levels of education (Figure 2.7). In 2003, a male worker with completed higher education received on average more than 300 percent higher wages than did male workers with no level of completed education. Real wages fell in the 1995-2003 period for the highly skilled. Workers with completed primary, lower and upper secondary, and tertiary education experienced a 3.5, 5.4, 13.1, and 9.9 percent wage reduction, respectively. The average male worker with no completed education experienced the largest hourly wage fall during 1995-2003; 17.2 percent (Figure 2.7).

**Figure 2.7:   Average Hourly Male Wages (2002 pesos), by Education Attainment in Rural Mexico, 1995-2003**

<!-- Start of picture text --> 30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>No eduation/Primary Incomplete Primary Complete<br>Lower Sec. Complete Upper Sec. Complete<br>Higher Education Technical<br>0.0<br>1995 1996 1998 1999 2000 2001 2002 2003<br>Source: ENE 1995-2003.<br><!-- End of picture text --> Education is key to poverty reduction. Increased educational attainment can improve the livelihoods of the poor and reduce the likelihood of becoming poor, as shown above. International evidence shows that more education is also a key factor in obtaining a higher income. Furthermore, education is associated with fertility: The more education a woman attains, the lower her fertility rate and, therefore, the lower the dependency ratio and the lower the likelihood of falling into poverty. Therefore, a clear message is that rural Mexicans need to be brought up the educational ladder to escape poverty. Moreover social networks are important (see Araujo, de Janvry and Sadoulet 2003). Both education and social networks will be better addressed in section 3.

**16**
We use a semi-log functional form for the relationship between calories and income and estimate it using OLS. The semi-log form has a number of desirable properties. First, the relationship between calories and log income is almost linear so using OLS for the estimation is reasonable (See Annex Figure 1). Second, the semi-log form also ensures that the elasticity is declining in income.

We present the results of this regression in Table 1. We carry out the regression with and without country fixed effects. The outcomes for the two regressions are reported under specification (1) and (2) respectively. We essentially restrict attention to within country variation in the calorie demand and income when we estimate the fixed effect model so in this case it seems that not taking into account differential dietary preferences across countries would have led us to underestimate the effect of income on calorie consumption. So the estimates for _θ_ and _δ_ obtained from the second specification are our preferred estimates and we use the same values for the baseline estimates of the undernourished population.

|**2.2 Estimating undernutriti**<br>**Table 1: The Calorie-Income Rela**|**on**<br>**tionship**<br>Calories|Calories|
|---|---|---|
|VARIABLES|(1)|(2)|
|Coefficient on Per Capita GDP(δ)|267.6***<br>(22.72)|330.0***<br>(13.96)|
|Constant (θ)|409.0***|201.2***|
||(4.75)|(2.48)|
|Country Fixed Effects|No|Yes|
|Observations|742|742|
|R-squared|0.419|0.407|
|t-statistics in parentheses|||
|*** p<0.01, ** p<0.05, * p<0.1|||

# **2.2 Estimating Undernourishment**

After estimating the calorie-income relationship, we require caloric requirements that define the threshold for undernourishment by region. These numbers derived from FAO
**Graph 2**

Share of SEE Investment In Total Public Investment. l9BO-90 <!-- Start of picture text --> 70<br>60 .\ ...............................................................................................................................<br>35.<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989E 1990 T<br>E: Estimate T: Target<br><!-- End of picture text --> <!-- Start of picture text --> Source: State Planning Organization.<br><!-- End of picture text --> Graph 3 <!-- Start of picture text --> Public/Private Sector Terms of<br>Trade Vl, (1987:100)<br>110<br>105 ..................... ............. I........................................ .. .....<br>100<br>Preslintial.<br>95 . ........ .. ectlons c..................c.........ti.<br>elections Local Policy pf sl prce increases0lasg<br>Peril eatlentry election in pubilc set torannounce<br>90<br>1 5 9 1 5 9 1 5 9 1 5<br>1 87 99 1 8 99 go<br>average (1107-51901 99.9 percent<br>Source: State Institute of Statistics.<br><!-- End of picture text --> 14
-19-

Table 8. Selection bias test using World Bank project presence

||Bank presence as<br>independent<br>variable|Truncating data if<br>Bank presence<br><=0.05|Truncating data if<br>Bank presence<br>>0.12|Truncating data if<br>Bank presence<br><=0.05, > 0.12|
|---|---|---|---|---|
|Specification|(1)|(2)|(3)|(4)|
|Intercept|47.5|34.2|34.6|37.3|
|Parallel<br>rate<br>premia|-0.045<br>(4.8)***|-0.053<br>(3.9)***|-0.049<br>(6.1)***|_-0.056_<br>(4.3)***|
|World Bank<br>project presence|-80.55<br>(3 9)***|-|||
|Capital/labor<br>ratio (log)|-3.20<br>(4.3)***|-2.34<br>(2.5)**|-2.18<br>(3.1)***|-2.65<br>(2.6)**|
|Years<br>of<br>education|0.26<br>(0.8)|0.33<br>(0.9)|0.15<br>(0.5)|0.30<br>(0.8)|
|Project<br>complexity|-3.35<br>(2.5)**|-4.09<br>(2.3)**|-2.71<br>(1.9)*|-3.42<br>(1.8)*|
|Terms of trade|0.04|-0.02|-0.01|0.01|
|improvement|(0.5)|(0.2)|(0. 1)|(0.1)|
|GDP growth|0.25<br>(1.2)|0.30<br>(I.0)|0.30<br>(1.4)|0.23<br>(0.8)|
|Log likelihood|-2512|-1495|-2432|-1409|
|No.<br>of<br>observations|656|395|631|370|

___ Notes: Dependent variable is reestimated economic rate of return (ERR) for public and private projects Numbers in parentheses are t-statistics Significance levels: *** = 99 percent; * = 95 percent; * = 90 percent. Source: Authors' calculations.

# B. Econometric specifications with truncated samples.

We carried out a second statistical technique to test for possible biases arising from the Bank's project selection process. Most projects in this data set, in addition to receiving reestimated ERRs, received expected rates of return (AERR) before project implementation. Projects with high AERRs indicate investments where World Bank and borrower country staff anticipated, to some degree, a 'skimming' within the country; projects with low AERR's, by contrast, indicate an anticipated role as lender of last resort.

We truncated the project sample to exclude outlier observations: those with very low AERRs or with very high AERRs. We found (table 9) that the relationship between policies and reestimated ERRs was not found to be statistically different in the truncated samples. This suggests that 'skimming' in some countries and 'lending-as-a-last-resort' in others do not bias the policy parameters.<sup>29</sup> 29 We owe this suggestion to Eduardo Engel.
- 44 -

# Tabl S _Revme A _ime_ to Submiol Authorties

|_____~~~~~~~Ri<br>Trover tax<br>(er<br>VA)|Hungar<br>(1991)|Polan<br>(I991)|-_lonkR.<br>Romania<br>(19"l)<br>0|Czech R.<br>(1991)<br>|Slovak<br>(1991)<br>|_~<br>a. asiml<br>(195)<br>|_<br>--<br>_Vi_etam)<br>(1991)<br>|u<br>-<br>guia<br>(1991)|__<br>11,1,inse<br>(19)|
|---|---|---|---|---|---|---|---|---|---|
|<br> <br> <br>CTruoqteincoA<br>tax|0|X|<br>0|L0<br>|N.I.<br>|100%<br>|100%<br>|O_/fi_|20%|
|<br> <br>Peuionl<br>ome itax|<br>25%I/|<br>30%/|<br>0|N1<br>|N.I.<br>|IOO%<br>|100%<br>|50X/<br>|_<br>_60%|
|<br>Tx on Ppetty of|<br>0|_<br>100%|<br>_95%2_<br>_/_|_N.I._<br>|N.l.<br>|100%<br>|100%<br>|70%<br>|100%<br>|
|<br>Lcally Owned SOEs<br>Exciwes|<br>-I<br>0|<br>0|<br> <br>0|SO<br>|IO<br>_<br>|tO<br>_<br>|0<br>|_V-._|100%|
|Wage tax|<br>0|30%I/|<br>0|N.I.<br>|N.L<br>|0<br>|0<br>||61.1%|
|<br>Small and Medium ErAJ|<br>NA|<br>50%|<br>0%|0<br>|13%<br>|0<br>|0<br>|0||
|<br>Collves<br>bnometax<br>Local<br>Taxe:|..|||N.L|N.L|100%|N.L|N.L|_NA_<br>||
|<br> <br>- real es<br>tax|100%|100%|100%|_<br>||||||
|<br>- autolroad tax|<br>100%|<br>IOOX|<br>100%|100%<br>NL|100%<br>|N.L<br>|NJL<br>|100%<br>|100%<br>|
|<br>_-_s1ma busisa|<br>100%|<br>100%|<br>|.<br>|N.L<br>|N.l.<br>|Nl.<br>|N1|100%|
|<br>-agriculturaltx|<br>0%|<br>1OX%|_N_<br>100%|_.A.L_<br>|N<br>|N.L<br>|NX.<br>|N.L|_N.A._|
|<br>-pontax|<br>100%|<br>NA|<br>NA|N.<br>|100%<br>|100%<br>|100%<br>|100%|NA.|
|<br>Natuml Reaauce Tax|<br>NA|..<br>NA|..<br>NA|NAL<br>|N.A.<br>|N.A.<br>|N.A.<br>|_NA._|N.A.|
|<br>Odiet|..<br>touim &|..<br>i|..<br>|N.A.<br>|NA.<br>|100%<br>|N.A.<br>|NA.|100%|
||<br>misc.|ms.|Mile.|_NA._|N.A.|N.A.|N.A.|NA.|21 misc.<br>taxes|

Soume: Misc. BRD domaents. See biligaphy. Noe: PN in **1992** _jI_ and hared according to a Reduced fom 100% (1990); **85X** 50% (cexral (1991).; **_-_** 15% (ocal) traio.; 3/ Unot 1M tax **_I** eform.; To be inrodud in 1993.; I/Alo coaidad as property income tax.; **2/** 100% of **ech** tax **acnus** if Tobe abo to die le le of in 992.; of in 992.; in 992.; **goveetneutownigth** 11 To be usneftmned istoa mmr effective **end** by provinciafy owned SOEs _accrues_ to **the** pvincial budgd.; **_I/_** 40% **from** unkiipal enterprises and 10% from **stente** pries.; NJL: No hfomation.; N-.: **_Not_** aplicable.

le of in 992.; of in 992.; in 992.; **goveetneutownigth** 11 To be usneftmned istoa mmr effective **end** eprie; eg. VAT **pdd**
DEATON, A. & ZAIDI, S. 2002. _Guidelines for constructing consumption aggregates for welfare analysis_ , World Bank Publications.

- FILMER, D. & PRITCHETT, L. H. 2001. Estimating wealth effects without expenditure data— or tears: An application to educational enrollments in states of india*. _Demography,_ 38 **,** 115-132.

- GALLANI, S., KRISHNAN, R. & WOOLDRIDGE, J. 2015. Applications of Fractional Response Model to the Study of Bounded Dependent Variables in Accounting Research. _Harvard Business School Accounting & Management Unit Working Paper_ .

- GREEN WILLIAM, H. 2000. Econometric analysis. _Forth Edition, Prentice Hall International, Inc, New York University_ .

- HAGGBLADE, S., HAZELL, P. & REARDON, T. 2010. The rural non-farm economy: Prospects for growth and poverty reduction. _World Development,_ 38 **,** 1429-1441.

- HOLDEN, S., SHIFERAW, B. & PENDER, J. 2004. Non-farm income, household welfare, and sustainable land management in a less-favoured area in the Ethiopian highlands. _Food Policy,_ 29 **,** 369-392.

KOENKER, R. 2005. _Quantile regression_ , Cambridge university press.

- LANJOUW, J. O. & LANJOUW, P. 2001. The rural non ‐ farm sector: issues and evidence from developing countries. _Agricultural economics,_ 26 **,** 1-23.

- LANJOUW, P., QUIZON, J. & SPARROW, R. 2001. Non-agricultural earnings in peri-urban areas of Tanzania: evidence from household survey data. _Food policy,_ 26 **,** 385-403.

- LANJOUW, P. & SHARIFF, A. 2004. Rural non-farm employment in India: Access, incomes and poverty impact. _Economic and Political Weekly_ **,** 4429-4446.

- LIANG, K.-Y. & ZEGER, S. L. 1986. Longitudinal data analysis using generalized linear models. _Biometrika_ **,** 13-22.

MATSUMOTO, T., KIJIMA, Y. & YAMANO, T. 2006. The role of local nonfarm activities and migration in reducing poverty: evidence from Ethiopia, Kenya, and Uganda. _Agricultural Economics,_ 35 **,** 449-458.

- NAGLER, P. & NAUDE, W. 2014. Non-farm enterprises in rural Africa: new empirical evidence. _World Bank Policy Research Working Paper_ .

- OSENI, G. & WINTERS, P. 2009. Rural nonfarm activities and agricultural crop production in Nigeria. _Agricultural Economics,_ 40 **,** 189-201.

23
<!-- Start of picture text --> PUBLIC DEFICrT AND CURRENT A/C DEFICIT<br>8-<br>6- -f<br>Li.<br>;g)- ,1 /y / eV 3<br>i,-| I 41'S<br>0*<br>70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88<br>YEAR<br><!-- End of picture text -->

# **+-** DG/GDP CAD/GDP

36