rafmacalaba/datause-encoder-data huggingface dataset:
could well be due to the change in methodologies. Whatever the reason, this appears to be one of the few categories in which Romania does not rank well. As in many other OECD and EU countries, this result appears to be driven by the existence of legal restrictions to entry in network and utilities sectors, such as rail, road and air transport infrastructure; electricity generation, transmission, distribution and supply; and gas production, transmission, distribution and supply; and telecommunications (see table A2.10 for details).

<!-- Start of picture text --> Figure 7. Barriers to Competition<br>a. Legal barriers (licenses and permit b. Antitrust exemptions<br>requirements)<br>2.5 1.5<br>2.0 1.2<br>2.0 1.8<br>1.6<br>1.0 0.9<br>1.5 1.4 1.4 1.4<br>1.1<br>1.0 0.5 0.4<br>0.6 0.6 0.3<br>0.5<br>0.0 0.0 0.0 0.0 0.0<br>0.0<br>0.0<br>Poland Bulgaria EU15 OECDHungary MICsRomania BulgariaCzech Rep.PolandRomania EU15 OECDHungary MICs<br>Czech Rep.<br>Slovak Republic<br>Slovak Republic<br><!-- End of picture text --> _Source:_ see Figure 3. _Note_ : Other MICs are Brazil, Mexico, and Turkey. Values refer to 2006 for Romania and Bulgaria, 2004 for Brazil, and 2003 for all other countries. Romania’s 2002 score was calculated using a different methodology so is not strictly comparable. For full data set see appendix I.

# _Regulatory and Administrative Opacity_

39. Romania scores well in terms of regulatory and administrative opacity (Annex table A1.3). It has made substantial progress in the simplification of licenses and permits, (Figure 8a) while maintaining its 2002 best practice positioning in terms of communication and simplification of rules and procedures (Figure 8b). The indicator captures aspects of the government’s communication strategy and its efforts to reduce and simplify the administrative burden of interacting with the government.

40. This reflects the substantial efforts made by Romania in streamlining licensing regimes since the previous PMR survey (OECD 2002a). Indeed, the simplification of licensing procedures has been a central theme of the country’s Action Plans for improving the business environment. Important milestones in the simplification process include: the reduction of the number of products and services requiring licensing; the removal of some barriers to free trade, the elimination of some import-export licenses; the simplification of licensing procedures for retailers through the elimination of ex-ante licenses, the use of voluntary assumption of responsibility statements, the strengthening of ex-post control and monitoring; the establishment of the one-stop shop and the elimination of some licenses; the adoption of the Silent Approval Law (2003), which establishes a maximum period of 30 days for the approval of a certain set of licenses and authorizations from the moment of the application.

14
23

# <u>Figure 3: Time path of per capita costs and benefits of the nutrition program</u>

<!-- Start of picture text --> $120<br>$100<br>$80<br>$60<br>$40<br>$20<br>$0<br>2010 2015 2020 2025 2030 2035 2040 2045 2050<br>Costs per capita Benefits per capita<br><!-- End of picture text --> The internal rate of return results are shown in Table 3 and Figure 4. The rate of return varies from 8.5% to 24%. The average is 16%. The East Asia & Pacific region has the highest rate of return (24%) reflecting the low per capita program cost, the high rate of return to education, the high initial GDP per capita, and the high GDP growth rate. Africa is the region with the lowest rate of return (15%) reflecting the high per capita program cost, the relatively low initial GDP per capita, and the relatively low GDP growth rate; these numbers are offset only partly by the relatively high rate of return to education in Africa. There are variations within regions, of course: India, for example, has a rate of return of 23% reflecting in part India’s low program cost and its high GDP growth rate.
<u>Geographical distribution of poverty. In Cali, as in Colombia in general, much of the</u> geographic discussion of poverty is framed along ‘ _strata’_ lines. These are definitions of socioeconomic groups, drawn up largely from census information and including mainly information on unsatisfied basic needs based on housing characteristics (material of the house, access to services etc.). Strata are defined at the _manzana_ -level which generally includes one complete block of houses (four sides). Differences in housing and basic service characteristics within a _manzana_ can hence not be picked up. Strata definitions in Colombia play an important role since subsidy programs are targeted according to such information and most public services are charged differentially according to strata as well.

**Table 3: Some Characteristics of Income Poverty, Cali, 1999 (percent)**

|||**Inc**<br>|**ome Quint**<br>|**ile**<br>|||
|---|---|---|---|---|---|---|
||**1**|**2**|**3**|**4**|**5**|**Total City**|
|labor market|||||||
|unemployment rate|35.9|22.4|18.4|11.8|5.8|17.1|
|- male|35.7|24.6|18.4|12.9|6.4|18.0|
|- female|36.1|18.8|18.5|10.0|5.0|15.9|
|- all 14-24 yrs olds|51.8|34.8|27.7|21.7|19.7|30.3|
|informal empl.: w/o contract|71.2|51.7|46.8|39.5|26.7|42.3|
|informal empl.: w/o social security|84.4|65.5|67.8|58.0|43.8|59.8|
|food security<br>|||||||
|Family member w/ hunger<sup>2</sup>|34.2|22.8|16.9|11.7|5.1|18.1|
|Had to reduce food intake<br>|60.6<br>|53.6<br>|42.7<br>|25.7<br>|12.9<br>|42.4<br>|
|Access to nutrition prog.<br>|4.4|3.7|3.1|1.9|0.5|2.7|
|health|||||||
|Sick in last 4 months|28.9|24.3|26.6|21.3|19.3|24.1|
|when used medical facility|||||||
|- Public health post|37.6|29.1|27.7|13.6|7.5|22.7|
|- Public hospital|21.1|16.1|16.9|8.8|8.9|14.2|
|Health insurance<br><sup>3</sup>|||||||
|- ISS<br>|13.8|19.6|26.3|27.2|29.4|23.3|
|- EPS<sup>4</sup><br>|13.2|22.9|20.2|36.4|41.4|26.8|
|- SISBEN<sup>5</sup><br>|22.7<br>|12..2<br>|10.9<br>|4.8<br>|0.8<br>|10.3<br>|
|- not affiliated<br>|46.0|41.2|39.2|26.9|17.6|34.2|
|geographical areas (distribution)<sup>6</sup>|||||||
|Strata 1 (low-low)|30.4|28.8|20.0|15.5|5.4|100|
|Strata 2 (low)|23.8|24.9|24.1|16.4|10.9|100|
|Strata 3 (medium-low)|14.5|15.9|22.1|25.7|21.8|100|
|Strata 4 (medium)|13.4|8.7|9.7|23.6|44.6|100|
|Strata 5 (medium-high)|5.6|5.3|3.5|14.5|71.2|100|
|Strata 6 (high)|5.6|3.5|7.1|18.5|65.2|100|

--------------------------------------------------------------------------------------------------------

1 Mean education years for the whole population 18 years and older.

- 2 in the past year.

3 ISS: Instituto de Seguridad Social de Colombia 4 EPS: Empresa Privada de Salud 5 SISBEN: Selección de Beneficiarios de Programas Sociales. SISBEN is not a program per se but a system that allows identification of the eligible population for subsidized social programs. 6 Distribution of strata by quintile, totaling to 100 percent per strata.

Source: Own calculations from EPSOC (1999).

14
contingent on school participation in Mexico and Brazil, but they do not have educational performance data to estimate effects on educational quality.<sup>4</sup> Research on the determination of educational quality in Latin America would be particularly welcome since Latin America is the region featuring the highest rates of return to education among the studies surveyed in Psacharopoulos and Patrinos (2004). Also, the returns to education typically increased in Latin America (including Argentina and Colombia) during the 1990s (cf. Pritchett 2004), which has been attributed to the economy-wide reforms that affected technological progress and created higher returns to education in the adaptation to the generated disequilibria (Behrman et al. 2000). While economy-wide reforms such as capital-market and trade liberalization, privatization and tax and labor-market reforms influence the profitability of educational investments, particularly during transitions to new equilibria, the same can be expected of the education system itself, which will lower the longrun returns to education if it does not succeed in producing basic skills to a satisfactory degree.

The remainder of the paper is structured as follows. Section 2 describes the database of the PIRLS 2001 international student achievement study and presents descriptive statistics on educational achievement and student and school characteristics in the selected Latin American and comparison countries. Section 3 derives the empirical model and its econometric implementation. Section 4 presents results on the relationship between student background and educational performance in the different countries. Section 5 estimates the relationship between school characteristics, both material and institutional, and educational performance. Section 6 summarizes the main findings and concludes.

# **2. The Database**

## **_2.1 The PIRLS 2001 International Student Achievement Test_**

The dataset used in the analysis is the PIRLS 2001 International Database. The PIRLS assessment was conducted in 2001 by the International Association for the Evaluation of

> 4 Cf. Hanushek (1995), Glewwe (2002), Pritchett (2004) and Glewwe and Kremer (2005) for reviews of research on the determinants of educational quality in developing countries. Velez et al. (1993) review the results of 18 early research reports on primary education in Latin American countries, mostly not published in refereed journals or books. While the evidence from other countries is informative, specific national evidence is indispensable because to some extent, the success of any service delivery depends on specific institutional circumstances that cannot be replicated elsewhere (cf. Pritchett and Woolcock 2004).

4
Mechler, R., J. Linnerooth‐Bayer and D. Peppiatt (2006), Microinsurance for Natural Disasters in Develop‐ ing Countries: Benefits, Limitations and Viability, ProVention Consortium, Geneva, http://www.proventionconsortium.org/themes/default/pdfs/Microinsurance_study_July06.pdf.

Meyer V. & Messner, F., 2005. National Flood Damage Evaluation Methods. A Review of Applied Methods in England, the Netherlands, the Czech Republic and Germany. Discussion Papers, Umweltforschungsze trum Leipzig–Halle, Department Ökonomie, Leipzig: 47 pp.

Norris, F. H. (2005), Range, Magnitude, and Duration of the Effects of Disasters on Mental Health: Review Update 2005, Dartmouth Medical School and National Center for PTSD, Hanover and Boston.

Noy, I., & Nualsri, A. (2007). What do exogenous shocks tell us about growth theories? University of Ha‐ waii Working Paper 07‐28.

Noy, I. (2009), The Macroeconomic Consequences of Disasters, Journal of Development Economics 88, 221–231.

Noy, Ilan, and Tam Vu, 2009. The Economics of Natural Disasters in Vietnam. University of Hawaii Work‐ ing paper 09‐03.

NZ Climate Change Office, 2003. Climate Change Case Study: Assessment of the impacts of sea level rise on floodplain management planning for the Avon river, Harris Consulting Ltd in conjunction with Christchurch City Council Okuyama, Y. (2004). Modeling spatial economic impacts of an earthquake: Input‐output approaches. Dis‐ aster Prevention and Management, 13 (4), 297–306.

Okuyama, Y., Hewings, G., & Sonis,M. (2004). Measuring the economic impacts of disasters: Interregional input‐output analysis using the sequential interindustry model. In Y. Okuyama & S. Chang (Eds.), Model‐ ing Spatial and Economic Impacts of Disasters. Heidelberg: Springer.

Pelling, M., A. Özerdem and S. Barakat (2002), “The Macro‐economic Impact of Disasters”, Progress in Development Studies 2, 283–305.

Raddatz C. (2009), The Wrath of God: Macroeconomic Costs of Natural Disasters,World Bank Policy Re‐ search Working Paper 5039.

Ranger N., S. Hallegatte, S. Bhattacharya, M. Bachu, S. Priya, K. Dhore, F. Rafique, P. Mathur, N. Naville, F. Henriet, C. Herweijer, S. Pohit, J. Corfee‐Morlot, 2011. A Preliminary Assessment of the Potential Impact of Climate Change on Flood Risk in Mumbai, Climatic Change, in press, doi:10.1007/s10584-010-9979-2 Rodriguez‐Oreggia, Eduardo, Alejandro de la Fuente, Rodolfo de la Torre, Hector Moreno, and Cristina Rodriguez (2009). The Impact of Natural Disasters on Human Development and Poverty at the Municipal Level in Mexico. Manuscript 25
# **Household Assets**

|||<br>**Provinc**<br>**Mass**|<br>**e level inter**<br>**media treat**|<br>**vention**<br>**ment**|||**Commun**<br>|**ity treatmen**<br>|**t sample**<br>|**District level i**|**nterventio**<br>|**n**<br>**School**<br>|**component**<br>|**sample**<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Contr|ol mean|Treatm|ent mean||Contr|ol mean|Treatm|ent mean||Contr|ol mean|Treatm|ent mean||
||N|Avg.|N|Avg.|Difference|N|Avg.|N|Avg.|Difference|N|Avg.|N|Avg.|Difference|
|**Average HHs non labor**||||||||||||||||
|**income**|312|110.37|240|106.01|4.36|312|110.37|205|106.13|4.24|315|111.45|191|129.89|-18.44|
|**HHs Assets**||||||||||||||||
|Radio, CD, cassette|706|0.813|717|0.815|-0.002|706|0.813|762|0.745|**0.068***|682|0.812|705|0.75|**0.062***|
|TV|707|0.532|717|0.513|0.019|707|0.532|762|0.552|-0.02|682|0.595|705|0.609|-0.014|
|VCR|707|0.248|717|0.197|0.051|707|0.248|762|0.241|0.007|682|0.224|705|0.254|-0.03|
|Computer|707|0.02|717|0.015|0.005|707|0.02|762|0.012|0.008|682|0.025|705|0.016|0.009|
|Bicycle|707|0.184|716|0.196|-0.012|707|0.184|762|0.213|-0.029|682|0.217|705|0.24|-0.023|
|M otorbike|707|0.025|717|0.032|-0.007|707|0.025|762|0.039|-0.014|682|0.032|705|0.035|-0.003|
|Car or Tractor|707|0.014|717|0.018|-0.004|707|0.014|762|0.021|-0.007|682|0.015|705|0.007|0.008|
|Refrigerator|707|0.107|717|0.075|0.032|707|0.107|762|0.079|0.028|682|0.087|705|0.118|-0.031|
|Gas Stove|707|0.383|717|0.329|0.054|707|0.383|762|0.419|-0.036|682|0.346|705|0.445|-0.099|
|Other type of stove|706|0.153|717|0.079|**0.074****|706|0.153|762|0.083|**0.07****|682|0.15|705|0.092|**0.058***|
|Blender|707|0.209|717|0.17|0.039|707|0.209|762|0.22|-0.011|682|0.214|705|0.248|-0.034|
|Toaster|707|0.004|717|0.013|-0.009|707|0.004|762|0.007|-0.003|682|0.007|705|0.01|-0.003|
|M icrowave|707|0.011|717|0.007|0.004|707|0.011|762|0.005|0.006|682|0.01|705|0.013|-0.003|
|Washing machine|707|0.006|717|0.003|0.003|707|0.006|762|0.007|-0.001|682|0.009|705|0.011|-0.002|
|Water boiler|707|0.028|717|0.018|0.01|707|0.028|762|0.016|0.012|682|0.018|705|0.026|-0.008|
|Other houses/properties<br>M achinery, equipment for|707|0.109|717|0.035|**0.074***|707|0.109|762|0.171|-0.062|682|0.104|704|0.182|-0.078|
|family business|707|0.023|717|0.031|-0.008|707|0.023|761|0.021|0.002|682|0.015|703|0.02|-0.005|
|**Percentage of HHs having**||||||||||||||||
|**other piece of land**|707|0.475|716|0.369|0.106|707|0.475|763|0.383|0.092|684|0.477|705|0.43|0.047|
|**Percentage of HHs having**||||||||||||||||
|**farm equipment**|707|0.201|716|0.26|-0.059|707|0.201|763|0.215|-0.014|684|0.2|705|0.214|-0.014|
|**Percentage of HHs having**||||||||||||||||
|<br>**animals**|707|0.754|717|0.826|-0.072|707|0.754|763|0.742|0.012|684|0.775|705|0.729|0.046|
|**Average number of**||||||||||||||||
|**livestock owned**|707|2.337|717|2.787|-4.5|707|2.337|763|1.992|0.345|684|2.401|705|2.009|0.392|

41
# **3 Data and Methodology**

## **3.1 Data sources and description**

The dataset used for this paper was compiled from a variety of sources, the full details of which are described in the technical annex. Here, we limit our discussion to the main variables of interest.

The main dependent variable that we consider is quarterly total foreign bank claims for BISreporting banks with lending to developing countries. The claims detail the foreign exposure, on a worldwide consolidated basis with inter-office positions netted out, of up to 5,615 BISreporting international banks with foreign exposure to as many as 138 developing economies per quarter. This coverage within the foreign claims universe is nearly 100 percent, and the foreign claims include cross-border claims in all currencies, along with local claims of foreign affiliates in both foreign currencies and local currency. In our robustness checks, we also consider the exposures of just the 150 U.S. banks with foreign exposure, along with those of the 4,488 BIS-reporting banks based in Europe.<sup>8</sup> Overall, claims appear to have responded with some lag from the start of the crisis in 2007Q3 (peaks were attained about year later), and troughed at the start of 2009 (Figure 2).

<!-- Start of picture text --> $ Trillions<br>4.0<br>3.5 Foreign claims, other Foreign claims, US banks<br>Foreign claims, EU banks<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>2004Q1 2005Q1 2006Q1 2007Q1 2008Q1 2009Q1 2010Q1 2011Q1<br><!-- End of picture text --> Figure 2: Total cross-border foreign claims by BIS-reporting international banks to developing countries, disaggregated by exposure into EU, U.S., and other. Aggregate foreign claims rose steadily from the beginning of the sample period and peaked around 2008Q2, before assuming troughs in 2009Q1. Claims at the end of the sample period exceed the pre-crisis peaks.

Our main independent variables seek to capture the different channels of interest. To capture liquidity effects, our benchmark analysis relies on the Libor-OIS spread.<sup>9</sup> Since the spread represents the difference between (risky) interbank borrowing costs and a (low-risk)

> 8European countries with BIS-reporting banks are primarily, although neither exclusively nor exhaustively, members of the euro area: they comprise the EU-15 countries and Norway.

> 9We utilize a spread because rates alone—for example, the Libor overnight rate—are a reflection of not only liquidity availability but also the cost of capital, which is netted out when we take the spread between two rates of equivalent tenor.

8
behavior, calling for further theoretical and empirical research in this area.<sup>26</sup> This paper contributes to the emerging body of literature that studies the effects of informational asymmetries on intra-household transfers. Our results strongly support the view that asymmetric information is an important determinant of remittance behavior. Specifically, our contribution is three-fold. First, we provide direct empirical evidence of informational asymmetries by collecting and contrasting household members’ reports of foreign earnings that are observable by only one party. Second, we show that the discrepancy in foreign earning reports between migrants and remittance recipients is highly correlated with remittance behavior. Third, we offer a new approach towards modeling the exchange motive for remittances with asymmetric information about overseas earnings that is consistent with our empirical results.

Our survey of Indian households with husbands working in Qatar reveals significant informational asymmetries within transnational households. Empirical analysis demonstrates the presence of a number of patterns in reporting and remittance behavior that cannot be explained by individual biases. A striking feature of these patterns is the difference in the behavior of lowand high-income migrants, where the latter tend to underreport their earnings more and, accordingly, remit less as a share of their income. To explain the observed patterns in migrants’ behavior, which is inconsistent with existing remittance models, we adapt the classical model of costly state verification to the problem of remittance contracting under an exchange motive. The theoretical results produced by the model closely match our empirical findings. The optimal remittance contract, offered by the recipient who can observe the migrant’s earnings only at some cost, prescribes a threshold for remittances such that, if not met, verification is initiated.

> 26. Relatedly, Kinnan (2014) finds asymmetric information an important factor for insurance provision in Thai villages by showing that it is the predictions of the insurance model with hidden income that receive the most empirical support.

32
Other results verify that men and urban area residents are more likely to survive the difficult first year in self-employment. A positive concave relationship exists with age, but the maximum occurs at a younger age compared to the first stage equation. Education increases the probability of survival ceteris paribus, and so do previous selfemployment experience and the existence of another self-employed member in the household.

With respect to the past employment origins of the “survivors”, the results indicate that the informal sector is likely to act as an “incubator” of formal entrepreneurial ventures in the early years of transition. Informal sector employees are more likely to become self-employed than formal sector employees, and further more likely to make it through their first year.

# **6. Conclusions**

We examine the factors affecting the transition to self-employment and the viability of transitions in the short-run in BiH, employing a longitudinal household survey for the years 2001-2004. This is an interesting case study, since in 2000-2001 several regulatory changes concerning the financial and business environment, as well as the labor market, were implemented. Although the aggregate proportion of the selfemployed labor force did not increase over the sample period, examining transitions into self-employment indicated that even after controlling for the role of individual and social characteristics, financing constraints played an important role in promoting entry into self-employment and its success. Specifically, wealthier households are more likely to become entrepreneurs and survive the early period in business, emphasizing the importance of internal finance. Variables indicating financial institution customer affiliation, in terms of loans from formal financial institutions, or even informal sources that one needs to repay, have insignificant impact on self-employment transitions, but a positive effect on sustainable self-employment. Particularly an existing prior relationship with a bank significantly increases the chances of survival for new entrepreneurs, suggesting that while banks rarely finance entry decisions, they are still instrumental in survival.

20
||Log({To|tal SalesAm|ount)|Log(To|talSalesC|ount)|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|TreatxPre|-0,003<br>|-0.003<br>|-0,003<br>|0.007**<br>|0.007**<br>|0.007**<br>|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|—_(0.003)|
|TreatxPost|0.029***|o.020***|O.078***|0.030***|0.030***|0.096***|
||(0.006)|(0.006)|(0.005)|(0.006)|(0.006)|(0.005)|
|Treatx PostxSmallMerchant|0.081***|0.049***||0.106***|0.066***||
||(0.007)|(0.007)||(0.006)|(0.006)||
|Treatx Post xSmallMerchant<br>x SmallTran.Size||0.195***|||0.239***||
|||(0.014)|||(0.014)||
|TreatxPostxYoungMerchant|||0.030**<br>|||0.023<br>|
||||(0.015)|||(0.016)|
|Constant|8.988***|8.988***|§.988***|5.631***|5.631***|5.631***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Fixed Effects.|||Merchant, y|ear-month|||
|Observations|417,024|417,024|417,024|417,024|417,024|417,024|
|R-squared|0.944|0.944|0.944|0.938|0.938|0.938|