rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 2B -Persistance of Equality of Opp**<br>Regression|**ortunity in**|**History**|Dependent|Variable|: Dissimilari|tyIndex -|Average M|athematic|s,Reading|and Scienc|e Outcom|es Level 2|- PISA 2009|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|OLS|
|Index of Knowledge Distribution (1858)|-0.258**<br>(0.07)|||||||||||||||
|Index of Knowledge Distribution (1868)||-0.253**<br>(0.07)||||||||||||||
|Index of Knowledge Distribution (1878)|||-0.246***<br>(0.06)|||||||||||||
|Index of Knowledge Distribution (1888)||||-0.235***<br>(0.06)||||||||||||
|Index of Knowledge Distribution (1898)|||||-0.243***<br>(0.06)|||||||||||
|Index of Knowledge Distribution (1908)||||||-0.292***<br>(0.06)||||||||||
|Index of Knowledge Distribution (1918)|||||||-0.305***<br>(0.06)|||||||||
|Index of Knowledge Distribution (1928)||||||||-0.302***<br>(0.06)||||||||
|Index of Knowledge Distribution (1938)|||||||||-0.297***<br>(0.06)|||||||
|Index of Knowledge Distribution (1948)||||||||||-0.207***<br>(0.05)||||||
|Index of Knowledge Distribution (1958)|||||||||||-0.254***<br>(0.05)|||||
|Index of Knowledge Distribution (1968)||||||||||||-0.234***<br>(0.05)||||
|Index of Knowledge Distribution (1978)|||||||||||||-0.258***<br>(0.05)|||
|Index of Knowledge Distribution (1988)||||||||||||||-0.275***<br>(0.06)||
|Index of Knowledge Distribution (1998)|||||||||||||||-0.266**<br>(0.08)|
|Number of Observations|24|26|28|29|29|33|34|39|38|41|44|45|51|51|59|
|R-squared|0.362|0.361|0.367|0.354|0.382|0.448|0.466|0.446|0.423|0.268|0.387|0.323|0.318|0.325|0.161|
|* p<0.05, ** p<0.01, *** p<0.001<br>*Average Mathematics, Reading and Science|Outcomes L|evel 2 - PIS|A 2009|||||||||||||

21
# **Appendix Table 8. Effect of preschool availability on female’s industry conditional on employment**

||(1)<br>Agricultu<br>re|(2)<br>Mining|(3)<br>Manufactu<br>ring|(4)<br>Utility|(5)<br>Construct<br>ion|(6)<br>Trade|(7)<br>Logistic|(8)<br>Finance|(9)<br>Social|
|---|---|---|---|---|---|---|---|---|---|
|**Panel A: Public**||||||||||
|Public * Eligible|-0.023|0.000|0.008|-0.001**|0.001|-0.014|0.002|0.001|0.014|
||(0.020)|(0.001)|(0.010)|(0.001)|(0.002)|(0.015)|(0.001)|(0.002)|(0.010)|
|Public Preschools|0.039|-0.002*|-0.030*|0.001|0.001|-0.001|-0.001*|-0.003|-0.008|
||(0.026)|(0.001)|(0.016)|(0.001)|(0.002)|(0.015)|(0.001)|(0.002)|(0.013)|
|Eligible Child|0.013***|-0.001*|-0.006*|-0.000|-0.001|0.001|-0.001|-0.001|0.002|
||(0.005)|(0.000)|(0.003)|(0.000)|(0.001)|(0.004)|(0.001)|(0.001)|(0.004)|
|FDR q-value|0.764|0.764|0.764|0.311|0.764|0.764|0.764|0.764|0.764|
|**Panel B: Private**||||||||||
|Private * Eligible|-0.002|0.000|0.001|0.000|0.000|-0.000|0.000|0.000|0.001|
||(0.001)|(0.000)|(0.001)|(0.000)|(0.000)|(0.001)|(0.000)|(0.000)|(0.001)|
|Private Preschools|-0.004*|-0.000|-0.001|-0.000|-0.000|0.004**|-0.000**|-0.000|0.001|
||(0.003)|(0.000)|(0.002)|(0.000)|(0.000)|(0.002)|(0.000)|(0.000)|(0.002)|
|Eligible Child|0.016*|-0.001*|-0.007|-0.000|-0.001|0.001|-0.001|-0.001|-0.001|
||(0.008)|(0.001)|(0.005)|(0.000)|(0.001)|(0.006)|(0.001)|(0.001)|(0.005)|
|FDR q-value|0.738|0.738|0.738|0.738|0.738|0.738|0.738|0.738|0.738|
|Observations|121,718|121,718|121,718|121,718|121,718|121,718|121,718|121,718|121,718|
|Mean|0.194|0.001|0.095|0.001|0.004|0.188|0.002|0.005|0.124|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data in-between PODES years. Each column regresses the dummy for being employed in a certain industry category indicated in column heading, conditional on being employed, on the density of public (Panel A) or private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3-6 in each district, a dummy for having a preschool-aged eligible child, and their interaction. Comparison group includes all mothers of children of the wrong ages and non-mothers. All regressions include mother’s age fixed effect, urban dummy, district, year, and individual fixed effects. Robust standard errors clustered at district level. Stars denote statistical significance at 1, 5, and 10 percent levels based on unadjusted p-values. FDR q-values for the interaction coefficient of preschool density and eligible child are computed over all 9 outcomes and are shown in the last row of each panel. FDR q-values indicate the probability of false positives among _significant_ tests. Number of female-year observations and means of dependent variables are indicated in the last two rows. See Table 10 for a list of top-3 occupations per category.

76
An additional bureaucratic constraint to return migration for many migrants concerns the portability of their social benefits, particularly retirement benefits. There are two issues here. The first is that migrants who work in multiple countries may not accrue enough years of work to become fully vested in the pension systems of either their home or their destination countries. The second is whether they retain eligibility to receive pension payments if they return to their home countries. OECD countries have many bilateral agreements, but coverage is very limited for migrants who move from one developing country to another (Avato et al, 2010). Pension portability can directly improve the well-being of return migrants, but we are unaware of literature which quantifies this, or which looks at its impact on the rate of return migration.

In addition to regulatory and bureaucratic barriers, informational barriers may prevent some migrants from returning. Information may be lacking about specific job opportunities at home, or about changes in conditions in the home country. A number of governments attempt to reduce job search frictions by making it easier for domestic firms to locate emigrant workers who may be interested in returning, and vice versa. For example, Jamaica’s returning residents program has a databank of migrants abroad that prospective employers can use (Thomas-Hope, 2004), Bulgaria runs an annual job fair to try to initiate direct contact between Bulgarian emigrants and leading companies in Bulgaria (TFMI, 2012), and Moldova has held job fairs in Italy to provide information about job opportunities back in Moldova.<sup>9</sup> It is unclear how successful any of these efforts have been in terms of increasing return migration or improving the jobs that return migrants get.

A more specialized information barrier is that facing refugees, who may not have migrated for work reasons, and who may wish to return to their home communities providing that peace and reconciliation efforts, or disaster recovery, has made the home community safe to return to. They may lack information as to the conditions on the ground, making them cautious about returning. The UNHCR and IOM programs for the reintegration of refugees organize “go-and-see” visits which are intended to overcome this constraint<sup>10</sup> . There does not appear to be evidence as to whether this leads to more return and better outcomes for these refugees.

> 9 <u>http://www.legal-in.eu/en/archive/100-job-fair-regarding-moldovan-labour-market-in-italy</u> [accessed 26 March, 2014].

> 10 <u>http://www.unhcr.org/pages/49c3646cfe.html</u> [accessed 26 March, 2014].

31
**Table 5: Average (trade weighted) applied tariffs levied on WTO non-agricultural products by scenario, %**

|**Regions**|**Scenarios**||||
|---|---|---|---|---|
||**Baseline**|**B**|**C**|**D**|
|Brazil|8.5|7.4|7.4|7.8|
|China|5.6|3.9|3.9|4.4|
|EU-27|1.8|1.0|1.0|1.0|
|India|12.9|11.7|11.7|12.0|
|Japan|1.3|0.7|0.7|0.7|
|United States of America|1.5|0.8|0.8|0.8|
|**World Bank Classification**|||||
|All countries|2.9|2.0|2.2|2.3|
|Low & Middle inc. countries|6.1|4.6|5.0|5.3|
|High income countries|1.6|1.0|1.0|1.0|
|LDCs|10.9|8.0|10.9|10.9|
|**WTO Classification**|||||
|Developed WTO|1.7|1.0|1.0|1.0|
|Developing WTO non LDCs|4.8|3.6|3.8|4.2|
|_Normal Developing WTO_|3.9|3.1|3.1|3.4|
|_RAM WTO_|9.5|7.1|9.5|9.5|
|_SVE WTO_<br>rce: Laborde and Martin (2011b).Detail|5.3<br>ed results available|3.9<br>athttp:|4.0<br>/gatt.ifpr|4.4<br>i.info/dda0/|

Source: Laborde and Martin (2011b). Detailed results available at http://gatt.ifpri.info/dda0/

# **4 Why So Much Political Resistance?**

The formulas used in both the agricultural and non-agricultural negotiations were both highly progressive, in terms of cutting the highest tariffs by the most. From an economic point of view, these are outstandingly good approaches to tariff reform, because the economic costs of a tariff rise with the square of the rate. But, this economic virtue may have an associated downside. High tariffs are that way because there is strong political support for the beneficiaries of this high protection. If, as Falconer (2008) has suggested, the aggressive nature of tariff-cutting resulted in intense pressure for exceptions, excessive zeal in the tariff cutting formulas may have contributed to the problems created by the exceptions—including the loss of market access liberalization, reduced efficiency of tariff cutting, and a lack of transparency about the implications of the reform.

14
<!-- Start of picture text --> i=]8 °r=)r=)<br>“| Se ee e ~<br>H % ee e g<br>x8 ee 53<br>33i} eoe.@1c0 em ecu®e e e 8ga2a2<br>3PF. @ceNontet 6P|<br>3 e e ae 3<br>ee4°5 e ° ° iad wots. es<br>= e 8 @zAF @nam z55<br>[~Se @mus @ BEN ecivezusOR 3a<br>FS @ TUN _MAR econMDG ie<br>o @syc @coG o<br>0 50 100 150 200<br>Time to export (hours)<br>e Africa @ Asia<br>e America @ Europe<br>— >>> Fitted values<br><!-- End of picture text -->

<!-- Start of picture text --> °r=)r=)<br>~ i) e e<br>g | 2 e e @ ome<br>53 i ry e e e<br>2 e A e e Avs<br>8ga2a2 Ge ° ee . gp onwa e<br>P|6P| 8 gets@ @SEN ~<br>3 LA e. ~<br>es “8 ° Sr, ><br>z55 %e.. ° mee ~ @Nnam<br>3a eon EGYat @ze<br>ie @maR =@NDGecon Oru<br>o esyc 9006<br>0 100 200 300 400<br>Cost to export (documentary compliance)<br>e Africa @ Asia<br>e America @ Europe<br>- - => Fitted values<br><!-- End of picture text -->
<!-- Start of picture text --> 1<br>.8<br>.6<br>.4<br>.2<br>0<br>ETHIOPIA_2011-12 TANZANIA_2010-11<br>MALAWI_2010-11 UGANDA_2010-11<br>Agriculture Industry Services Unknown None<br>05-1415-2425-3435-4445-5455-6465-older05-1415-2425-3435-4445-5455-6465-older05-1415-2425-3435-4445-5455-6465-older05-1415-2425-3435-4445-5455-6465-older<br>Participation rate in sector<br><!-- End of picture text --> Figure 7: Primary sector participation patterns across age groups.

27
29 impact in the country of the trainees when they return? How useful are exchange programs between developed and developing countries? What should be done in order to take advantage of such programs so as to achieve a balance between academic quality, on the one hand, and relevance to the context of the trainee, on the other?

# TOWARDS A RLAR AGUD

Most of the issues that _ve_ have discussed throughout this paper represent important topics for research. The fact that we posed them as questions was intended, precisely, to emphasize their researchability and to convey the sense that it is necessary to seek answers through sound studies. The problem, of course, is that the number of issues is too large to constitute a workable research agenda. It is necessary, therefore, to establish priorities. In this last section of the paper we will briefly sketch what such priorities might be.

A first consideration in designing a research agenda on a topic such as hospital management is to strive for a balance between relevance to decision making and excellence in the strict adherence to the norms of scientific research (Frenk et.al. 1986). Within this broad guideline priorities must be defined on two aspects: the type of research and the topics to be researched.

With respect to the former, we believe that the order of priorities should begin with observational studies that document levels of hospital performance and correlate them with organization design and environmental variables. Apart from offering basic descriptiorns that are much needed, especially in developing countries, such studies would make it possible to operationalize and measure the constructs that we have proposed in our
<!-- Start of picture text --> Afghanistan<br>Bangladesh<br>Ethiopia<br>Gambia<br>Indonesia<br>Kiribati<br>Liberia<br>Malawi<br>Namibia<br>Nigeria<br>Papua New Guinea<br> Rwanda (LFS)<br>South Africa (Census)<br>South Africa (GHS)<br>Tanzania (Census)<br>Tanzania (LSMS)<br>Uganda<br>Vanuatu<br>Vietnam<br>0 0.2 0.4 0.6 0.8 1 1.2<br>No or some Difficulty At least a lot of difficulty<br><!-- End of picture text --> <!-- Start of picture text --> Afghanistan<br>Bangladesh<br>Ethiopia<br>Gambia<br> Indonesia<br>Kiribati<br>Liberia<br>Malawi<br>Namibia<br>Nigeria<br>Papua New Guinea<br> Rwanda (LFS)<br>South Africa (Census)<br>South Africa (GHS)<br>Tanzania (Census)<br>Tanzania (LSMS)<br>Uganda<br>Vanuatu<br>Vietnam<br>0 0.2 0.4 0.6 0.8 1 1.2<br>No Difficulty Some difficulty At least a lot of difficulty<br><!-- End of picture text --> 18
<!-- Start of picture text --> Central Government Balance Sheet<br>Assets Liabilities<br>Foreign Currency Assets Foreign Currency Liabilities<br>Deposits at Central Bank International Bonds<br>Official and Commercial Loans<br>Local Comremcy Assets Local Carvency Habiiltes<br>Deposits at Central Bank Government bonds & bills<br>Deposits at commercial banks<br>Onlending Payables<br>Receivables Other liabilities<br>Equity in Public Companies<br>Fixed assets Equity (net worth)<br><!-- End of picture text --> <!-- Start of picture text --> Central Bank Balance Sheet<br>Assets Liabilities<br>Foreign Currency Assets Foreign Currency Liabilities<br>International Reserves Central Government Deposit<br>Local Currency Assets Local Currency Liabilities<br>. . .<br>Claims on banks Currency in circulation<br>Other assets Central Government deposit<br>Bank deposits<br>Other liabilities<br>Fixed assets Equity<br><!-- End of picture text -->

# Consolidated Balance Sheet

- Assets Liabilities

- Foreign Currency Assets Foreign Currency Liabilities International Reserves International Bonds Official and Commercial Loans

- Local Currency Assets Local Currency Liabilities Claims on banks Currency in circulation Deposits at commercial banks Government bonds & bills Onlending Bank deposits Receivables Payables Equity in Public Companies Other liabilities Other Central Bank assets

- Fixed assets Equity (net worth)
<!-- Start of picture text --> Changes in TFP between 2001 and 2006<br>Food and Tobacco Textile and leather Wood and Paper<br>ao a oO<br>im : os,<br>* is if s t<br>f ol f a<br>a a a fa m4 oy<br>fo] = a| 4 ~ — fo] ° a ~~<br>o 2 4 6 9 o 5 10 16 20 o a] 1 14<br>Printing Coke, chemistry, plastics Non-metallic minerals<br>a5 finf 2= far].+<br>| =, P —<br>o i % a on os ¥ s<br>o f “ oa “ ee — o aaa (le —— -——<br>a 10 20 30 40 50 5 1 1.4 a 25 3 a 2 4 6 8<br>Metallurgy Machinery and equipment High-tech machinery<br>« 2 -|<br>. ba fos to é ‘<br>. aan 8 t ‘ 2 i<br>cS > a , “ t<br>o — © == °<br>a 2 4 6 a 10 20 30 a 10 20 30<br>Vehicles and transport Furniture and others<br>Mm -<br>7 fa“ ~ og moe<br>a ao Ey<br>ir i oe oa a<br>eyo4 ~ Bya4 —=<br>o5 10 16 o 10 20 20<br><!-- End of picture text -->
a public middle school teacher who works two after-school jobs and spends her nights bartending just so she can afford to stay in the classroom. _Laney fears she won’t make enough to pay her bills—and fears even more that she can’t give 100 percent to her students because she is so over-worked and exhausted. . . ._ _If teachers like Laney were appropriately compensated they would no longer need to work two and three jobs outside of the classroom. Instead of struggling to pay rent, they would be able to fully devote themselves to our nation’s children._ _“It makes me really upset to think I’m not giving them my best,”_ Laney says in the film. (Teacher Salary Project 2015) In Peru, a survey and study of teachers finds that they too argue that low pay inhibits performance: 77.5 per cent of the Peruvian teachers interviewed consider that they are “badly” or “very badly” paid. Very often, _they need to complement their income with other jobs, which results in less time available for lesson preparation and a focus on teaching. Better salaries could benefit the professionalisation of teaching and would allow teachers to focus more on their careers._ (van der Tuin and Verger 2013) In Ethiopia, teachers surveyed for a study also make this argument: The issues raised by the research were numerous, but _the most significant and most oftenmentioned causes of demotivation and low morale were: • inadequate salaries_ • low respect for and low status of teachers • poor management and leadership. These issues have a _significant impact on classroom performance, that is, teachers’ ability to deliver good quality education,_ as well as on levels of teacher retention.

In the case of Cambodia, an NGO study (VSO 2008) cited by some international agencies recommends:

[i]ncreasing the salaries of teachers, school directors and staff of the provincial and district offices of education to a level appropriate to the cost of living and linked to inflation. _In every focus group conducted with teachers, the issue of pay emerged as the most powerful de-motivating factor. It is impossible to earn a living on a teacher’s salary in Cambodia. This basic need is going to remain the top priority over and above any other aspirations teachers have for the quality of their teaching practice until it is fulfilled._ It goes on to say that a reasonable salary would make the pressure to earn a living wage less intense, which should have a _positive effect on teachers’ commitment_ and practice. (VSO 2008 _)_ And in the case of the Caucasus and Central Asia, UNICEF (2011) finds that The need to rely on additional income from economic activities outside of school applies specifically to teachers in rural areas. . . . . _[T]eacher absences during harvesting season_ 5
Table 5. Impacts of Extremism on Mental Health

|Variables are indicators|Mental Health<br>Issues<br>Index<br>(1)|Felt Little<br>Interest<br>in Doing<br>Things<br>(2)|Felt<br>Depressed<br>or Hopeless<br>(3)|Had<br>Eating<br>Problems<br>(4)|Suicidal<br>Thoughts<br>(5)|Ask<br>Mental<br>Health<br>Assistance<br>(6)|
|---|---|---|---|---|---|---|
|_Panel A. Taliban Exposure_|||||||
|_ETi_|0.075**|0.039|0.099|0.129**|0.009|1.750***|
||(0.032)|(0.079)|(0.068)|(0.053)|(0.018)|(0.445)|
|FDR q-values|[0.035]|[0.286]|[0.125]|[0.035]|[0.286]|[0.001]|
|R-squared|0.031|0.025|0.033|0.039|0.031|0.406|
|_Panel B. Contested Territories Exposure_|||||||
|_ECi_|0.019|-0.000|-0.005|0.082***|0.002|0.476***|
||(0.012)|(0.031)|(0.026)|(0.020)|(0.007)|(0.137)|
|FDR q-values|[0.185]|[0.977]|[0.977]|[0.001]|[0.977]|[0.003]|
|R-squared|0.030|0.025|0.032|0.040|0.031|0.397|
|_Panel C. U.S.-Allies Exposure_|||||||
|_EAi_|-0.017|-0.002|-0.005|-0.058***|-0.002|-0.399***|
||(0.010)|(0.024)|(0.020)|(0.016)|(0.005)|(0.107)|
|FDR q-values|[0.102]|[0.849]|[0.849]|[0.001]|[0.849]|[0.001]|
|R-squared|0.030|0.025|0.032|0.040|0.031|0.402|
|Mean Dependent Variable|0.159|0.301|0.197|0.106|0.011|0.768|
|Observations|5,590|5,554|5,578|5,573|5,568|259|
|_Controls in All Panels_|||||||
|Individual Covariates|Yes|Yes|Yes|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Enterprise Property in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Contract in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Dependent variables: Mental Issues Index is constructed using the average of the variables in columns (ii) to (vi). The average is calculated with the number of variables available per individual, the index do not take into account the variables with missing values. See Appendix B for details of variable’s constructions. The Individual controls include: age, gender, employed, student status in Afghanistan, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

44
# **Characterization of network type and condition**

The International Roughness Index IRI (m/km) was used to define the differences in road condition by network as follows:

|**Paved Roa**<br>|**d IRI(m/km) by**<br>|**Network & Con**<br>|**dition**<br>|
|---|---|---|---|
|Road Condition|Primary7m|Secondary6m|Tertiary5m|
|Good|2|3|4|
|Fair|5|6|7|
|Poor|8|9|10|

|**Unpaved R**<br>|**oad IRI(m/km) **<br>|**by Network & Co**<br>|**ndition**<br>|
|---|---|---|---|
|Road Condition|Primary7m|Secondary6m|Tertiary5m|
|Good|6|8|10|
|Fair|12|13|14|
|Poor|16|18|20|

Finally, using these parameters above, a final cost per ton-km for each road type is estimated ($/ton/km):

||**Paved FL**<br>|**AT**<br>||
|---|---|---|---|
|**Road Condition**|**Primary **|**Secondary **|**Tertiary**|
|**Good**|0.1174|0.1192|0.1237|
|**Fair**|0.1226|0.1264|0.1293|
|**Poor**|0.1286|0.1299|0.1349|
|**P**<br>|**aved ROL**<br>|**LING**<br>||
|**Road Condition **|**Primary **|**Secondary **|**Tertiary**|
|**Good**|0.1190|0.1191|0.1231|
|**Fair**|0.1241|0.1268|0.1302|
|**Poor**|0.1305|0.1315|0.1367|
|**Pave**<br>|**d MOUNT**<br>|**AINOUS**<br>||
|**Road Condition **|**Primary **|**Secondary **|**Tertiary**|
|**Good**|0.1283|0.1292|0.1312|
|**Fair**|0.1333|0.1318|0.1382|
|**Poor**|0.1410|0.1391|0.1449|

43
Swift, J. (1997a). _Regulating the Railway in the Public interest._ Office of the Rail Regulator. Railway Study Association. Sessional Meeting at the London School of Economics, 11 _June._ Swift, J. (1997b). _Regulation of the Railways in Great Britain. 1993-1997 to 2000+._ Office of the Rail Regulator. Address to the European Union Delegation of the French National Assembly and the Senate. Paris, 13 March.

Talley, W. K. (1988). _Transport Carrier Costing._ Gordon and Breach Science Publishers.

Tenenbaum, B. (1996), "Regulation: What the Prime Minister Needs to Know", _The Electricity Journal,_ March, pp. 28-36.

The World Bank (1996), "Argentina Transport Privatization and Regulation: The Next Wave of Challenges", Infrastructure Division, Report No. 14469-AR, The World Bank, Washington, D.C.

Thompson, L.S. (1993). "The Evolution of Railroad Regulation in the United States". In Jose C.Carbajo (ed.), _Regulatory Reform in Transport: Some Recient Experiences._ The World Bank. Washington D.C.

Thompson, L.S. and K-J. Budin (1997), "Global Trend to Railway Concessions Delivering Positive Results", _Public Policy for the Private Sector,_ 134, The World Bank. Washington D.C.

Thompson. L.S. and J.M. Fraser (1996), "Financial success hinges on productivity", _Database, Rail Business Report 1995,_ The World Bank, Washington.

Waters II, W.G. (1985). "Rail Cost Analysis". In Button, K. J. and D. E. Pittfield (eds.). _International Railway Economics._ Gower, Aldershot.

- 66 -
# **Table A.10. The effect of measurement error, RLMS 1994-2017**

||**Pooled Ordered Logit**|**BUC**|
|---|---|---|
|Lo of household income|0.558***|0.372***|
|g|_(0.010)_|_(0.070)_|
|L f hhld i|-0.318***|-0.127***|
|og o ouseo sze|_(0.022)_|_(0.032)_|
|Children*Lo of hosehold size|0.050***|0.021**|
|g u|_(0.008)_|_(0.010)_|
|_Number of observations_|242,768|733,754|
|_Log pseudolikelihood_|-413,485|-271,759|

**Note:** Robust standard errors are in parentheses, controlling for two-way clustering (i.e., at the individual for the POL model and at the household-wave level for the BUC models). All regressions include the same controls as in Table 1.

45