rafmacalaba/datause-encoder-data huggingface dataset:
A rather different picture is observed when the analysis is detailed across urban and rural areas, particularly between large cities and other towns (Table 4. 10). Net enrolment rates improved during the school year 1998-99 for all education levels, except in rural areas for those aged between 11 and 18. Compared to increasing rates for urban areas, enrolment in rural areas slightly dropped, particularly for the 16-18 individuals (almost 3 points), although they recovered in the following year. Urban children are affected only in the 1999/2000 academic year, when enrolments for individuals aged 16-18 dropped almost 3 points, mainly in large cities.

**Table 4.10: Moldova: Enrolment rates 1997-2000**

|(percentofchildrenofspecif|icage groups atten|ding school)||
|---|---|---|---|
|**Largecities**|97/98|98/99|99/00|
|Preschool(6)|6.1|8.2|8.6|
|Primary(7-10)|93.2|94.2|95.4|
|LowerSec.(I1-15)|97.1|98.2|99.0|
|UpperSec. (16-18)|73.4|80.2|76.8|
|**Othertowns**||||
|Preschool(6)|17.1|5.4|14.3|
|Primary(7-10)|85.5|89.4|92.8|
|LowerSec.(11-15)|96.4|97.6|97.4|
|UpperSec.(16-18)|62.1|68.8|67.5|
|**Ruralareas**||||
|Preschool(6)|11.7|9.1|5.6|
|Primary(7-10)|92.6|94.7|95.4|
|LowerSec._(11-15)_|96.3|94.9|96.2|
|UpperSec.(16-18)|44.9|42.0|44.6|

Source: Moldova Household Budget Survey (1997-2000).

Is there a gender dimension the changes in enrolment rates around the Russian crisis? Table 4.11 shows that, first, females have higher enrolment rates across different age groups in 19997/98. The differences are particularly higher for Preschool and Upper Secondary. These differences, however, were reduced during the crisis period mainly because of the overall increase in enrolments among males and lack of improvement among females aged I I to 18. Enrolment among girls was not significantly changed during the period but it was not increasing at the boys' pace either.

Are these patterns the same for different socioeconomic groups? An examination of enrolment by consumption quintiles in Table 4.12 provides information about the groups that were affected by the worsening conditions after the Russian crisis. First, among children aged 7 to 10 years the overall increase in enrolment rates was not observed among the better off households while children from poorer households increased their chances by 2-4 percentage points (children from the top quintile actually decreased their 22
# **<u>Introduction</u>**

Over the last two decades access to safe water in developing countries has improved. Since 1990, 2.3 billion people have gained access to improved sources of drinking water and approximately 2 billion people gained access to improved sanitation (WHO/UNICEF, 2014; WHO/UNICEF, 2012). One important aspect about accessing water is its relation with hygienic practices, such as hand washing, which play an important role in the prevention of diseases (Liu et at., 2012; Bolt, 2005). <mark>In this sense, hand washing becomes a crucial preventative measure in the spread of illnesses, particularly among children. Studies in different parts of the world have shown that hand washing prevents infectious diseases, which are leading killers of young children (Center for Disease Control and Prevention, 2012; Luby et al., 2011; Talaat et al., 2011; Curtis and Cairncross, 2003). A study in Pakistan, for example, showed that intense hand washing among infants in low-income households decreased diarrhea and respiratory infections by 50 percent (Luby et al., 2004). Moreover, hygiene behaviors and hand washing have been associated with long-term outcomes of children, such as growth and biometric indicators that proxy for child health (Langford et al., 2011).</mark> Hand-washing practices have also been shown to reduce incidents of other diseases, particularly pneumonia (Rabie and Curtis, 2006), trachoma (Stocks et al., 2014), scabies, skin and eye infections (UNICEF 2014). <mark>This essential behavior improves the health and well-being of millions of children living in poverty, not only by reducing their chances of mortality, but also by favorably increasing their likelihood of future socioeconomic success (Eppig et al., 2010; Heckman and Carneiro, 2003). Economists often refer to these benefits as externalities. For instance, some studies have estimated the link between water, sanitation, health, cognitive and socio-emotional development, and their spillovers (Bhalotra and Venkataramani, 2012; Walker et al., 2007). Using state variation in diarrhea reduction, a study in Mexico evaluating the implementation of clean water found that early childhood infections drive cognitive and socio-emotional skills and academic achievement (Bhalotta et al. 2012). Another study conducted in rural India estimates two sources of externalities related to sanitation infrastructure, (1) when households move from open defecation to fixed-point or improved sanitation, and (2) the benefit to a neighbor from the spillover produced by neighbor's access to a fixed-point or</mark> 2
_Figure 5: Estimation Results - Additional Employment Probability (%) at the Time of Tracking Survey and Right after Completing Training_ <!-- Start of picture text --> Work experience<br>Years of schooling<br>High school (HS) grade<br>Interaction term between DTS and HS grade<br>DTS dummy<br>DTS dummy<br>TVI controlled<br>TVI not<br>controlled<br><!-- End of picture text --> <!-- Start of picture text --> 0.7<br><!-- End of picture text --> <!-- Start of picture text --> Employment at the survey<br><!-- End of picture text --> Source: _DTS Tracking Survey Data, 2015, World Bank; World Bank calculations._ Note: _Marginal effects on the employment probability, percentage point increase or decrease to the probability of being employed with one unit change to the listed controls are computed from a Probit estimation. The estimation controls for gender, education, years of work experience and squared term, maternal education level, TVI dummies, and enrollment year dummies. (See Table 1.2 for regression results in detail)_ 21
8

## **III.** ROAD TRANSPORT TAXATION

26. _A road transpor_ **_t_** _ax_ has no clear-cut definition. If RUT is evaluated as tax on a sector, the evaluation might include whether tax matches the benefits from roadway expenditure; whether revenue collection is in step with growth in the potential transport tax base; whether the various types of RUT are having the desired allocative effects; and whether the right RUT "tax handlesw are being used to make administration efficient and fair. The following pages outline the use of each form of transport tax and charge. The discussion focuses on administrative procedures, experiences with evasion and avoidance, RUT revenue - ld and elasticity, the definition of rate and base, the revent-e split between central and local government, and problems with the structure and adminisLation of the tax.

# Defini Road T_port Taxation

27. In the eight case studies on which this paper is based, the scope of transport tax includes charges related to the ownership and operation of motor vehicles, and charges related to road use. By this general classification, RUT can be divided into the following categories:

- motor fuel tax;

- excise tax, sales tax, and import duty;

- annual licenses and vehicle registration charges;

- D vehicle tolls and tax on the movement of goods; 3 income tax on transport-related activities.

28. Within these categores, many types of levies are used. A road transport study in Indonesia, prepared by consultants in 1982, identified 22 types of RUT in the revenue structure (Table 2). Still, ambiguities persist about what counts as a tax on the transport sector. For example, should income tax on companies involved in the production and sale of motor vehicles and transport services be included? Some argue that this is a general tax on income earned by any business meeting the exemption test, and is not specific to any sector. A similar study of India, prepared as background for this report, does not count company income tax on road transport operators as a road user charge because it "is not a tax levied on inputs or outputs of die road transport industry". The Indonesia study, by contrast, does count corporate income tax on importers or domestic assemblers as transport tax.

29. Some studies view the separation of general taxes from those related to the use of roads as necessary. The Indonesia study separates transport tax into the broad categories of general revenue taxes and road user charges. The former are levied on the transport sector as part of national revenue policy, while the latter are differentially higher taxes levied on the transport sector. For example, the general rate of customs duty on
Table 1: Balance at Baseline by Treatment Assignment

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Control<br>Mean|Control<br>SD|Treatment<br>Diference|N|
|**Demographics**|||||
|Age|41.29|9.56|0.57|1122|
|Married|0.79|0.41|0.00|1122|
|Has children|0.90|0.31|-0.00|1122|
|Educ: less than high school|0.26|0.44|-0.01|1122|
|Educ: high school|0.47|0.50|0.00|1122|
|**Mobility**|||||
|a- Times Out Last Month|6.90|7.70|0.57|1122|
|b- Can go to training weekly|0.59|0.49|0.00|1122|
|c- Willing to operate in shop if rent is covered|0.56|0.50|0.00|1122|
|Mobility Index (avg. z-scores of a-c)|-0.02|0.69|0.02|1122|
|**Agrees with Statements**|||||
|If a mother works outside the home, children sufer|0.74|0.44|-0.00|1122|
|Society judges a mother that works outside the home|0.44|0.50|-0.01|1122|
|Better to work from home to conserve own value|0.34|0.48|-0.02|1122|
|**Digital Practices (personal profle)**|||||
|FB: personally uses it daily/weekly|0.85|0.36|0.01|1112|
|% of your acquaintances using FB daily/weekly|83.56|21.08|1.83|972|
|FB: profle shows her real name|0.64|0.48|-0.01|1122|
|FB: profle picture shows her face|0.11|0.31|0.01|963|
|**Digital Practices (business profle)**|||||
|Markets on FB|0.46|0.50|0.00|1119|
|Has FB business page|0.21|0.41|0.02|1122|
|Posts on FB groups|0.23|0.42|-0.00|982|
|FB page: willing to reveal name|0.67|0.47|-0.03|1122|
|FB page: willing to reveal picture|0.31|0.46|0.02|1054|
|**Business Characteristics**|||||
|Home-based|0.79|0.41|-0.00|1122|
|Product (partially) made|0.43|0.50|-0.01|1122|
|Product bought & traded|0.28|0.45|-0.01|1122|
|Product is a service|0.17|0.38|-0.02|1122|
|Business is registered|0.10|0.31|-0.01|1103|
|Has partners or paid help|0.37|0.48|0.02|1122|
|Currently ofers delivery|0.63|0.48|-0.01|1122|
|At least half of clients are friends/neighbors|0.68|0.47|-0.01|1122|
|Monthly proft (usd), 95p|141.46|152.87|-0.11|1122|
|F-test of joint signifcance (F-stat)||||.57|
|Observations|563|563|1122|1122|

Notes: * p _≤_ 0.1, ** p _≤_ 0.05, *** p _≤_ 0.01. Column (3) is the coefficient from a regression of the outcome on treatment assignment, holding strata fixed effects. Continous variables are winsorized at the 95th percentile (noted by 95p).

13
- 68 -

# Table 3 (contnued)

3) Ru1_1 **eof** _a fWet'_ (nh*4ased FuIy Eletronc Read Pt Scme , a Marginal Cost ToU appled IJUnk-by-Unk vlo-yl the Bm Cme of No Toi

||Sravngp In<br>P°<br>Opting<br>Cost|Savg in Travel<br>Time|Welfar Ls of<br>Surplus|Tota|Benft|
|---|---|---|---|---|---|
|Peak Periods|7.6|162.6|-19.3||150.9|
|Interpak Perl|0|0|0||0|
|Off-peakPid|0|0|0||0|
|All Perod|7.6|162.6|-19.3||150.9|
||Annual<br>Anmnsl B<br>A aliz<br>Beanits|Toll Reveues,_R_<br>efits, B<br>,<br>ed Cost of ToU Colle<br>loss System Cost,_AN_|cdon, C<br>|152.1<br>150.9<br>>70.0<br><80.9||
||Dedt-Co|st Raio, WIC<br>||<2.2||
||Re3meC|<br>bti<br>Rad,, I/C||<2.2||

%gw: Ls and Ranoodi, 1990; Ra and Larsen, 1991; Lusen. MA=eu and Ranjd 1991; RaAd, 199, with correton; and disusions with the athors.
# **Changing contributions of different agricultural policy instruments to global reductions in trade and welfare**

## **Johanna Croser**

University of Adelaide johanna.croser@adelaide.edu.au and

**Kym Anderson** University of Adelaide kym.anderson@adelaide.edu.au

**Keywords:** Distorted incentives, agricultural price and trade policies, trade restrictiveness index

**JEL codes:** F13, F14, F15, N57, Q17, Q18

**Author contact:** Kym Anderson School of Economics University of Adelaide Adelaide SA 5005 Australia Phone +61 8303 4712 Fax +61 8223 1460 kym.anderson@adelaide.edu.au

This is a product of a World Bank research project on Distortions to Agricultural Incentives (see www.worldbank.org/agdistortions). The authors are grateful for collaboration on earlier papers with Peter Lloyd, and for funding from World Bank Trust Funds provided by the governments of the Netherlands (BNPP), the United Kingdom (DfID) and Ireland, as well as from the Australian Research Council. The views expressed are the authors’ alone and not necessarily those of the World Bank and its Executive Directors, nor the countries they represent, nor of the institutions providing the project research funds.
to many environmental refugees, those displaced by development projects/policies remain largely within their own country, and do not meet the legal requirements to be considered refugees. Surveys specifically targeting those who may be affected by such policies would be most beneficial. Moreover, in designing surveys to answer questions about all types of forced migration, it is important to include questions regarding where individuals lived previously and why they moved.

# IV. CONCLUSIONS

The study of migration and its importance as a driver and consequence of many socioeconomic processes has long been a key component in many countries’ development debates. However, the availability of data has been a major constraint hampering the formulation of proper policies in both destination and origin countries. A number of data sources exist, including censuses, various administrative data and surveys, but their thematic and geographic coverage remains inadequate and inconsistent.

Collecting migration data poses a number of technical and logistical challenges requiring special methods and techniques to properly sample and interview migrants. Furthermore, because migration is a highly selective decision, assessing the true impact of migration on the outcomes of choice remains particularly difficult and demanding in terms of data. The objective of this paper was to give readers of this volume a primer on the issues surrounding the collection of 42
A challenge for those interested in assessing how different households are impacted by trade policy is that tariff data are typically classified using the Harmonized System, whereas household survey classifications have historically been somewhat ad hoc. To render them compatible and comparable across countries, we aggregate goods in the household surveys to common 4- and 2-digit categories using separate expenditure, autoconsumption and income templates. We cover spending on, income derived from, and autoconsumption of 53 4-digit agricultural and food items. These include Staple Agriculture, such as corn and rice, and Non-Staple Agriculture, such as oils, cotton and tobacco. We also categorize spending on five classes of manufacturing items. In addition, we keep track of spending on five (non-tradeable) services and on four other expenditures. The fact that we have much more granular data on agricultural products than on manufacturing services reflects the nature and structure of the household surveys we are standardizing. Note that not all categories are populated in all surveys, which reflects both survey design and local consumption patterns (e.g., pork not being consumed in the majority of predominantly Muslim countries). The expenditure template is shown in Figure 1.

On the income side, we keep track of income derived from the sales of the same 53 food items we cover on the expenditure side. In addition, whenever the survey design allows it, we also split wage income by sector, defined roughly at the 1 digit level, and keep track of non-farm household enterprise sales across 10 sectors, as well as various types of transfers. The income template is shown in Figure 2. We also keep track of production for home consumption using the autoconsumption template, which is shown in Figure 3 and contains 53 agricultural products and a select few categories for other goods.

Since many of the surveys are subject to confidentiality agreements, we aggregate the households and offer statistics for each percentile of the household per capita real income distribution. The database thus has 5,400 observations (54 countries and 100 observations per country), but is based on an underlying data set of 521,639 households which are, in turn, representative of approximately 1.8 billion people. The HIT database is best suited for country-specific analysis. In order to facilitate cross-country comparisons, we converted incomes to their constant 2010 USD equivalent by setting the survey mean of real expenditure 3
Costinot, A., D. Donaldson, and I. Komunjer (2012). “What Goods Do Countries Trade? A Quantitative Exploration of Ricardo’s Ideas,” The Review of Economic Studies, vol. 79(2), pp. 581–608.

Costinot, A., A. Rodriguez-Clare (2014). “Trade Theory with Numbers: Quantifying the Consequences of Globalization,” Handbook of International Economics, Edited by G. Gopinath, E. Helpman and K. Rogoff, Chapter 4, pp. 197–262.

Deaton, A. (1989). “Rice Prices and Income Distribution in Thailand: a Non-parametric Analysis,” Economic Journal, 99 (Supplement), pp. 1–37.

Deaton, A. (1997). _The Analysis of Household Surveys - A Microeconometric Approach to Development Policy_ . Baltimore: Johns Hopkins Press.

Deaton, A. and O. Dupriez (2011). “Purchasing Power Parity Exchange Rates for the Global Poor” American Economic Journal: Applied Economics, 3 (2):137-66.

Faber, B. (2014). “Trade Liberalization, the Price of Quality, and Inequality: Evidence from Mexican Store Prices,” mimeo, Department of Economics, University of California at Berkeley.

Fajgelbaum, P. and A. Khandelwal (2016). “Measuring the Unequal Gains from Trade,” Quarterly Journal of Economics, vol. 131, pp. 1113–1180.

Galle, S., A. Rodriguez-Clare, and M. Yi (2017). “Slicing the Pie: Quantifying the Aggregate and Distributional Effects of Trade,” mimeo Berkeley University.

Nicita, A. (2009). “The Price Effect of Trade Liberalization: Measuring the Impacts on Household welfare,” Journal of Development Economics, vol. 89(1), pp. 19–27.

Nicita, A., M. Olarreaga, and G. Porto (2014). “Pro-Poor Trade Policy in Sub-Saharan Africa,” Journal of International Economics, Vol. 92(2), pp. 252–265.

Melitz, M. and S. Redding (2015). “New Trade Models, New Welfare Implications,” American Economic Review vol. 105(3), pp 1105–1146.

16
# **<u>Disaggregated effects: Comparing the decay function of the IV</u>**

For tables A21 to A28, regressions (1) to (4) show results with the regressor (Log or refugees at 50km) predicted by the Log of distance IV generated using the mean border distance. For regressions (5) to (8), the IV is generated using the square of the mean border distance while for regressions (9) to (12), the square root of the mean border distance is used. These represent the decay categories of the IV. Sampling weights are considered and Standard errors clustered at the cluster level in all regressions. Robust standard errors in parentheses. Significance level is: *** p<0.01, ** p<0.05, * p<0.1.

# **<u>Initial education level</u>**

**Table A21: Second stage: Effects of refugee presence on household welfare disaggregated by initial education level, with analysis at cluster level and the distance -** **<u>weighted refugee index at 50km from the clusters (Panel 2009 2012)</u>**

|||||L<br>|og of welfare<br>|(consumption<br>|aggregate per a<br>|dult equivalen<br>|t)<br>||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
||||||Panel|A (Didn’t comp|lete primary ed|ucation)|||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (mean border distance)|0.09736*|0.09596*|0.12497|0.11212|||||||||
||(0.05594)|(0.05540)|(0.07754)|(0.06946)|||||||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.10036*|0.09922*|0.14989*|0.13691*|||||
||||||(0.05612)|(0.05570)|(0.08256)|(0.07514)|||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.10672*<br>(0.06146)|0.10509*<br>(0.06077)|0.11914<br>(0.08002)|0.10799<br>(0.07226)|
|Observations|3,224|3,224|2,616|2,614|3,224|3,224|2,616|2,614|3,224|3,224|2,616|2,614|
|Kleibergen-Paap rk Wald F|12.54|12.55|8.298|8.177|11.83|11.83|7.698|7.619|10.95|10.97|7.384|7.274|
|Root MSE|0.544|0.543|0.509|0.372|0.544|0.544|0.511|0.374|0.544|0.544|0.508|0.372|
|Log of refugees (50km) predicted by Log of<br>Distance IV (mean border distance)|0.02190|0.01969|0.00903|0.03060|Pan|el B (Complete|d primary educ|ation)|||||
||(0.04250)|(0.04211)|(0.04793)|(0.04568)|||||||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.02474|0.02117|0.00889|0.03215|||||
||||||(0.04613)|(0.04560)|(0.05252)|(0.04982)|||||

33
**Table A18: Firm-level regression including PCM variable – likelihood of exit**

|Year, industry and location controls<br>Clustered at sector (4-digit NACE) and year|**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
||**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|**exit=**�<sup>1 if yes</sup><br>0 if no|
|**surge/slump (surge=1, slump=0)**|0.0101***|-0.00354*|0.00406|-0.00405|
||(0.00330)|(0.00213)|(0.00277)|(0.00421)|
|**ln(value added per worker)**|-0.00551***|-0.00522***|-0.00506***|-0.00534***|
||(0.000344)|(0.000330)|(0.000428)|(0.000450)|
|**ln(current assets)**|0.000713*<br>(0.000364)|0.000176<br>(0.000313)|0.000136<br>(0.000414)|0.000734<br>(0.000476)|
|**PCMsector**|-0.106***|-0.105***|-0.157***|-0.152***|
||(0.0214)|(0.0216)|(0.0227)|(0.0229)|
|**small (15-99 employees)**|-0.00401***<br>(0.00121)|-0.00341***<br>(0.00122)|-0.0190***<br>(0.00166)|-0.0204***<br>(0.00165)|
|**medium (100-249 employees)**|-0.00930***<br>(0.00182)|-0.00841***<br>(0.00182)|-0.0214***<br>(0.00254)|-0.0245***<br>(0.00253)|
|**large (250+ employees)**|-0.0169***<br>(0.00201)|-0.0163***<br>(0.00201)|-0.0303***<br>(0.00279)|-0.0352***<br>(0.00276)|
|**age**|-0.000479***<br>(3.32e-05)|-0.000373***<br>(3.20e-05)|-0.000819***<br>(4.49e-05)|-0.000629***<br>(4.11e-05)|
|**surge/slump × ln(value added per worker)**|-0.00203***<br>(0.000598)|-0.00299***<br>(0.000537)|-0.00434***<br>(0.000713)|-0.00386***<br>(0.000790)|
|**surge/slump × ln(current assets)**|-0.00164***<br>(0.000504)|||-0.00239***<br>(0.000854)|
|**surge/slump × age**||0.0153***||0.0363***|
|||(0.00148)||(0.00228)|
|**surge/slump × small**|||0.00178<br>(0.00238)|0.0126***<br>(0.00267)|
|**surge/slump × medium**|||-0.00362|0.0149***|
||||(0.00357)|(0.00432)|
|**surge/slump × large**|||0.00111<br>(0.00342)|0.0252***<br>(0.00465)|
|**Constant**|0.0640***|0.0653***|0.0963***|0.0930***|
||(0.00503)|(0.00485)|(0.00522)|(0.00534)|
|**Observations**|357,252|357,252|256,544|256,544|
|**R-squared**|0.016|0.016|0.020|0.022|

Note: Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Note: Microenterprises is the omitted category and absorbed by the constant. Source: Author’s calculations using RUSLANA 33