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Micco, A., Panizza, U., Yañez, M., 2007. “Bank ownership and performance. Does politics matter?” _Journal of Banking & Finance 31_ , 219-241.

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Turk-Ariss, R., 2009. “Competitive behavior in Middle East and North Africa banking systems.” _The Quarterly Review of Economics and Finance_ 49, 693-710.

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18
determining outcomes—for example, the merit-based scholarship program in Kenya explicitly targeted the most able students. We return to these points in our discussion of the impacts of the CSP program in Cambodia.

The rest of the paper proceeds as follows. In section 2, we describe the CSP program and the data. Section 3 discusses our identification strategy. Section 4 presents our main results. We conclude in Section 5.

# **2. The CSP program and data**<sup>1</sup>

# A. <u>Scholarships in Cambodia</u>

Cambodia has a tradition of demand-side incentives intended to raise school enrollment and attendance rates at the secondary school level. Like many countries, it has a program of school meals, and several relatively small-scale programs that distribute bicycles, uniforms, and school materials to children in order to lower the travel and other direct costs associated with schooling. In addition, it has had a number of scholarship programs financed by non-governmental organizations (NGOs), international donors, and the government. These scholarship programs do not function as fee waivers. Rather, the families of children selected for a “scholarship” receive a small cash transfer, conditional on school enrollment, regular attendance, and satisfactory grade progress.

One recent such scholarship program was the Japan Fund for Poverty Reduction (JFPR) scholarship program, which operated in 93 lower secondary schools in Cambodia in the 2003-06 school years, with support from the Asian Development Bank and UNICEF. The JFPR offered scholarships to girls for the three years of lower secondary school.2 Recipients were selected on the basis of a proxy means test, although there was room for communities to “revise” the list of recipients, which considerably complicates identification of program effects. Filmer and Schady (2008) evaluate the impact of the JFPR using propensity-score matching with data on attendance from unannounced school visits, and triple-

> 1 This section draws on Filmer and Schady (2009a).

> 2 In 17 schools in 4 remote provinces ethnic minority boys and girls were eligible for scholarships under the JFPR program.

5
Fischer, G., M. Shah, and H. van Velthuizen, 2002. Climate Change and Agricultural Vulnerability. IIASA, International Institute for Applied Systems Analysis. Rema print, Vienna. pp. 152.

- Fowler, H.J., and D.R. Archer, 2005. Hydro-climatological Variability in the Upper Indus Basin and Implications for Water Resources. Regional Hydrological Impacts of Climatic Change—Impact Assessment and Decision Making 295: 131–138.

- Fujita, K., and T. Nuimura, 2011. Spatially heterogeneous wastage of Himalayan glaciers. Proceedings of the National Academy of Sciences 108: 14011-14014.

- Fujita, K., R. Suzuki, T. Nuimura, and A. Sakai, 2008. Performance of ASTER and SRTM DEMs, and their potential for assessing glacial lakes in the Lunana region, Bhutan Himalaya. Journal of Glaciology 54: 220-228.

- Fukushima, Y., O. Watanabe, and K. Higuchi, 1991. Estimation of Streamflow Change by Global Warming in a Glacier-covered High Mountain Area of the Nepal Himalaya. 20<sup>th</sup> General Assembly of the International Union of Geodesy and Geophysics, Vienna, Austria, Aug. 11-24. pp. 181-188.

- Ganjoo, R.K., 2010. Are secular movements in the glaciers of Ladakh Mountains, Ladakh (J & K State, India) a response to climate change? International Symposium Benefiting from Earth Observation, ICIMOD, Kathmandu, Oct. 4-6.

- Gao, X., Y. Shi, R. Song, F. Giorgi, Y. Wang, and D. Zhang, 2008. Reduction of Future Monsoon Precipitation over China: Comparison Between a High Resolution RCM Simulation and the Driving GCM. Meteorology and Atmospheric Physics 100: 73-86.

- Gao, X.J., Y. Shi, and F. Giorgi, 2011. A High Resolution Simulation of Climate Change over China. Science China Earth Sciences 54: 462-472.

- Gardelle, J., Y. Arnaud, and E. Berthier, 2010. Contrasted Evolution of Glacial Lakes Along the Hindu Kush Himalaya Mountain Range Between 1990 and 2009. Global and Planetary Change 75: 47-55.

Gautam, M.R., and K. Acharya, 2012. Streamflow Trends in Nepal. Hydrological Sciences Journal 57: 344-357.

-

- Gehrig Fasel, J., A. Guisan, and N.E. Zimmermann, 2007. Tree Line Shifts in the Swiss Alps: Climate Change or Land Abandonment? Journal of Vegetation Science 18:571–582.

33
Table 7: Global Policy Selectivity, Democracy, and Resources (1998–2015)

|Dependent Variable: CPIA,|log||||||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Lagged GPS|0.114<sup>_∗∗_</sup><br>(0.043)|0.152<sup>_∗∗∗_</sup><br>(0.050)|0.083<sup>_∗∗_</sup><br>(0.035)|0.102<sup>_∗_</sup><br>(0.054)|0.116<sup>_∗∗∗_</sup><br>(0.039)|0.136<sup>_∗∗∗_</sup><br>(0.044)|
|Lagged IDA recipient|-0.029<br>(0.057)|0.086<br>(0.141)|||||
|Lagged GPS* IDA recipient|-0.004<br>(0.015)|-0.024<br>(0.037)|||||
|Lagged Democracy (ANRR)|||-0.047<br>(0.053)|-0.070<br>(0.187)|||
|Lagged GPS*Democracy|||0.051<sup>_∗∗∗_</sup><br>(0.013)|0.052<br>(0.049)|||
|Rich in Natural Resources|||||-0.052<br>(0.100)|-0.044<br>(0.121)|
|Lagged GPS* Rich|||||-0.022<br>(0.027)|-0.009<br>(0.032)|
|Region-Year FE|No|Yes|No|Yes|No|Yes|
|GPS without outliers|Yes|Yes|Yes|Yes|Yes|Yes|
|N|2323|2323|2319|2319|2323|2323|
|R-squared|0.016|0.146|0.138|0.225|0.103|0.169|

Dependent variable is the log of the overall CPIA in recipient countries. GPS is the log of the share of policy-selective aid disbursed globally, where GPS is determined with removing outlier observations. IDA recipient is a recipient that is IDA eligible. Democracy measure taken from Acemoglu, Naidu, Restrepo, and Robinson (forthcoming) and updated in accordance with their methodology. A recipient is rich in natural resources if the initial value of total natural resources rent as a percent of GDP is above the 75th percentile. All regressions use recipientyear observations and include a constant term that is not reported. Regressions (2), (4), and (6) include region and region-year fixed effects that are not reported. Year level cluster-robust standard errors in (1), (3), and (5) and region-year level cluster-robust standard errors in (2), (4), and (6) reported in parenthesis. Significance levels : _∗_ : 10 percent _∗∗_ : 5 percent _∗∗∗_ : 1 percent generated by our theoretical model. Columns (1) and (2) investigate whether the incentive effect of the global share of policy-selective aid applies differently for recipient countries that received aid from IDA or not. The World Bank has a formal process that allocates more of its IDA aid to countries with higher CPIA ratings. We therefore would expect that IDA-eligible countries – simply by being eligible – are more likely to play the contest for aid. However, we do not find any evidence of a differential effect for IDA-eligible and non-eligible recipients for both our GPS measures no matter whether we control for region-year fixed effects or not.

Columns (3) and (4) in both tables investigate the role of democracy. The democracy measure 20
Armavia has the right to conduct air passenger transportation from Armenia, while the other air companies do not have such a right. They carry freight from Armenia, for example, "Air of Armenia", which operates flights to Europe, but has no right to carry passengers.

Companies engaged in cargo transportation are rather big - they have 4-5 large cargo aircraft IL-76.<sup>29</sup>

**Air Armenia** is possessing cargo airplanes and operating cargo flights from Armenia to the rest of the world. This company operates regular flights twice a week from Yerevan to Frankfurt (Hahn) and from Frankfurt to any other destination. Air Armenia is a private airline established in 2003 and based at Yerevan Zvartnots Airport in Armenia (EVN, UGEE).<sup>30</sup>

The most challenging was to obtain data on air transportation in Armenia. Through National Statistical Service of RA we got the total air passenger and cargo traffic data for 2009 in Armenia. (8,4 th. Tons of cargo freight and 1496,3 th. Man). Majority of armenian air companies do not have web-sites and did not provide any private information. Only the national carrier of Armenia Armavia publicly report information on its air traffic. The company flew 700,000 passengers, which is 47,64% out of volume of passenger transport in 2009.

So, We define the share of airline in passenger carrying market by a company share in total passenger turnover, the same for the cargo carrying market. To derive the total market share we put weights to be equal 50% for passenger and 50% for cargo carrying market.

As the only Armavia has the right to conduct air passenger transportation from Armenia, we assume that the rest of market share (52,36%) refers to foreign air companies mentioned above. Unfortunately there is no public available information of passenger allocation among foreign companies. So we allocate equally 33,33% to CIS, EU, and ROW region in the same amount (8,73%), and rescale the total to be equal 100%.

In 2009 the leader of cargo flights Air Armenia served 4,6 th. tons or 54,76% of cargo carrying. Unfortunatly there is no any information conserning domestic cargo flights aircompanies. Several of them have started to operate in 2010, so we just allocated the rest of market shares. After rescaling the results for market share by regions (in percent) are as follows: ARM 70.43%, CIS 8.73%, EU 8.73%, ROW 12.12%.

# **VII. Railroad Transportation**

Since Armenia does not have a direct exit to the sea and located in mountains 1800 meters high above the sea level, the railway transportation has an important strategic value in maintenance with the reliable transport communication promoting economic and social development of the Republic.

> 29 http://www.newsarmenia.ru/exclusive/20100412/42230623.html

> 30 http://www.armenianairlines.com/, http://www.armats.com/eng/activity/statistics.htmStatistics

82
# Table 2: Correlations

||**Patents**|**ln R&D**|**UStrade**|**Natural**<br>**Res.**|**ln (Mean**<br>**Patents)**|**Quality**<br>**Acad.**<br>**Inst.**|**Coll.**<br>**Private**<br>**Sector**|**Educ.**|**IPR**|
|---|---|---|---|---|---|---|---|---|---|
|**Patents**|1.00|||||||||
|**ln R&D**|0.51|1.00||||||||
|**ln US trade**|0.47|0.69|1.00|||||||
|**Natural Resources**|-0.27|-0.16|-0.07|1.00||||||
|**ln (Mean Patents)**|0.27|0.66|0.35|0.01|1.00|||||
|**Quality of Academic Institutions**|0.32|0.59|0.34|0.08|0.90|1.00||||
|**Collaboration with the Private Sector**|0.27|0.51|0.34|0.27|0.64|0.71|1.00|||
|**Education**|0.30|0.63|0.30|-0.07|0.64|0.64|0.70|1.00||
|**IPR**|0.47|0.84|0.61|-0.07|0.69|0.61|0.60|0.64|1.00|

26
of the questions the survey was designed to be answered by the fi rm’s manager. The typical observation is based on a three-hour interview which often implied two visits to the fi rm to accommodate the manager’s time schedule. This resulted in a sample of 1641 fi rms with information on several characteristics of the fi rm, such as total employment, sales, value added, labor costs, capital stock, share of high educated workers, share of workers with training, age of the fi rm and share of foreign and public ownership. These fi rms are located across 306 cities (77 subregions) in Brazil. The survey also collects information on the number of labor inspections in each fi rm and whether it had to pay any fi nes or bribes. Of particular interest to us is information about the degree of labor informality in the fi rm. Even though every fi rm in our sample is legally registered there are some fi rms that employ informal workers.

The survey collects indirect information on the use of informal labor through the following question: “Given the constraints to hire workers and the additional costs that it entails, in your opinion, what is the percentage of the permanent employment that is informal/unregistered in a typical fi rm of this size and in this industry?”.<sup>17</sup> The question is phrased indirectly to avoid implicating the respondent in any wrongdoing. Throughout the paper we will assume the answer to be a good indicator of the fi rm’s own degree of labor informality. Still, many of our empirical results relating enforcement and fi rm performance do not depend on the use of this variable.

Even though similar questions have been used successfully in the study of corruption (Svensson, 2003), there can be doubts about the extent to which the answer to this question measures the degree of informality of the respondent fi rm. Several arguments support our use of this variable, although none of them is de fi nitive. First, fi rms are also asked to judge to what degree similar fi rms generally comply with labor law. We fi nd that 41% of the fi rms simultaneously claim that their competitors comply perfectly with labor law and state that similar fi rms hire a positive percentage of informal workers. Such responses would be inconsistent unless fi rms that were similar to the respondent fi rm were not its major competitors (which is unlikely), or if the answer to the question on informal employment

> 17The survey also collects information on the share of temporary workers. However, since in our data temporary employment is negligible we focus only on permanent workers.

11
## **CGE Modeling of Market Access in Services**

Elisabeth Christen<sup>1</sup><sup>_,_2</sup> Joseph Francois<sup>1</sup><sup>_,_3</sup> Bernard Hoekman<sup>3</sup><sup>_,_4</sup>

# **1 Introduction**

Services play an increasingly important role in world trade. According to the WTO, the global value of cross-border services exports in 2007 was $3 trillion, or about 20 percent of world trade in goods and services. Because services are less tradable than goods, this number significantly understates the importance of services in world trade. In practice, much services trade is indirect, in that services are embodied in traded goods, or are sold to foreign consumers through direct investment in affiliates. If account is taken of sales of services by foreign affiliates of multinational firms, then the value of trade in services rises substantially. Data for 15 OECD countries puts the value of such sales at some $1.5 trillion in 2007 (WTO, 2009). If account is taken of the direct and indirect value added content – that is, if trade is measured in terms of the value that is added by processing of imported components into final products for export as opposed to measuring trade flows on the basis of the gross value of goods crossing the border, the share of services in world trade rises to almost 50 percent (Escaith, 2008; Francois, Manchin, and Tomberger 2012).

While the expanding economic importance of services has not gone unnoticed, services have been downplayed in the economic growth and development literature, and have only recently been highlighted in the multi-sector computable general equilibrium (CGE) literature. The trigger for greater attention was the emergence of services on the international policy agenda in the 1980s, in particular the effort that was launched in the Uruguay Round to negotiate multilateral rules on policies affecting trade and investment in services. One result was to mobilize the first analytical contributions to the trade literature (e.g., Deardorff, 1985). This initial effort suggested that in contrast to the presumption that services were mostly of export interest to high-income countries, many economies had a potential interest in liberalizing trade in services. Thus, for example, many of the poorest developing countries have a revealed comparative advantage in ser1Johannes Kepler Universit ̈at, 2Universit ̈at Innsbruck, 3CEPR, 4World Bank. We are grateful to Peter Dixon for tremendous patience, and to both him and David Tarr for very constructive and helpful comments on earlier versions of this paper. All views expressed in this paper are strictly those of the authors, and do not reflect the official views or positions of any institution with which they may be affiliated, or of anyone at those institutions. This is a ”working draft” version of a handbook chapter. This version date: June 2012.

1
7

# **Table 2. Elasticities of Motor Vehicle Ownership with Respect to Per capita Income, Population, and Population Density**

||El<br>|asticity Estima|tes<br>||
|---|---|---|---|---|
|Type of Elasticity Estimates|Per capita<br>income|Population|Population<br>density|R-squared|
|**National motor vehicle fleet**|||||
|Constant|0.9 *|1.0*|-0.1 *|0.94|
|Varying: at the mean of low-income group|1.3 *|0.9|-0.1 *|0.95|
|Varying: at the mean of high-income group|0.9 *|0.9|-0.1 *|0.95|
|First difference|1.0 *|0.5|n.a.|_0.55_|
|**National passenger cars**|||||
|Constant|1.0 *|1.0*|-0.1|0.93|
|Varying: at the mean of low-income group|_1.5_*|0.8*|-0.1 *|0.94|
|Varying: at the mean of high-income group|0.9*|0.9*|-0.2 *|0.94|
|First difference|1.1*|0.3|n.a.|0.61|
|**National commercial vehicles**|||||
|Constant|0.7*|1.0*|-0.2 *|0.88|
|Varying: at the mean of low-income group|1.0*|1.2|-0.1|0.89|
|Varying: at the mean of high-income group|0.3*|1.2|0.0|0.89|
|First difference|1.1*|0.9|n.a.|0.26|
|**Urban motor vehicle fleet**|||||
|Constant|0.7 - 0.8*|0.8 - 1.0*|-0.4*|0.90|
|Varying: at the mean of low-income group|0.6|n.a.|n.a.|0.90|
|Varying: at the mean of high-income group|0.7|n.a.|n.a.|0.90|
|First difference|1.0*|1.0*|0.1|0.49|

n.a. Not applicable.

* Statistically significant at the 0.05 level.

virtually identical at both the national and urban level--suggesting that the two specifications fit the data equally well.

At the national level, the piecewise linear regression specification (Appendix Table 6, Eq. 1) produces a lower income elasticity estimate for high-income countries than for developing countries, and the effect is significant. Yet that equation's adjusted R-square statistic of 0.95 is also virtually identical to the 0.94 of the linear specification. At the urban level, the piecewise linear regression specification (Appendix Table 7, Eq. 1) is not significant, and its R-square is similar to the regular linear regression.

The conclusion of this analysis is that income elasticities of motor vehicle ownership at the national level are only weakly nonlinear, and population and population density elasticities are essentially linear. At the urban level income elasticities of motor vehicle ownership are linear. Overall, linearity is the most parsimonious specification consistent with the data.

These results for income elasticities are summarized graphically in figures 2a and
**Table 2: Comparison across groups at baseline**

||_All_|_Mean_<br>_Control_|_Mean_<br>_Basic_<br>_training_|_Mean_<br>_Enhanced_<br>_training_|_Diff (Basic_<br>_- Control)_|_Diff_<br>_(Enhanced -_<br>_Control)_|_N_|
|---|---|---|---|---|---|---|---|
|**Personal character**|**istics**|||||||
|Age|43.10|43.21|42.76|43.36|-0.45|0.15|699|
||(0.35)|(0.59)|(0.58)|(0.65)|(0.83)|(0.88)||
|Educ: O level complete<br>|0.40|0.43|0.41|0.36|-0.02|-0.07|817|
|(share)|(0.02)|(0.03)|(0.03)|(0.03)|(0.04)|(0.04)||
|Percentage married|0.58|0.57|0.59|0.58|0.02|0.01|817|
|(share)|(0.02)|(0.03)|(0.03)|(0.03)|(0.04)|(0.04)||
|Head of household|0.36|0.39|0.34|0.34|-0.05|-0.05|817|
|(share)|(0.02)|(0.03)|(0.03)|(0.03)|(0.04)|(0.04)||
|Has at least 1 child under|0.13|0.13|0.13|0.11|0.00|-0.02|817|
|5 (share)|(0.01)|(0.02)|(0.02)|(0.02)|(0.03)|(0.03)||
|Access to clean water|0.82|0.83|0.82|0.80|-0.01|-0.03|817|
|(share)|(0.01)|(0.02)|(0.02)|(0.02)|(0.03)|(0.03)||
|Access to electricity|0.78|0.81|0.77|0.75|-0.04|-0.07|817|
|(share)|(0.01)|(0.02)|(0.03)|(0.03)|(0.03)|(0.04)||
|Total value of hh assets|10,500,000|18,000,000|7,202,417|5,611,235|-10800000|-12400000|817|
||(3906244)|(10900000)|(1465729)|(714551)|(11000000)|(10900000)||
|**Business outcomes,**|**characteristi**|**cs and practi**|**ces**|||||
|Revenues in a typical month|659,548|701,430|623,898|649,498|-77532|-51932|795|
||(36973)|(63651)|(69867)|(57374)|(94513)|(85693)||
|Revenues in previous month|863,418|885,121|987,667|704,895|102546|-180226|799|
||(96011)|(180530)|(203260)|(70444)|(271853)|(193805)||
|Profit in previous month|306,850|302,657|376,903|237,932|74246|-64725|798|
||(34032)|(40890)|(89554)|(27234)|(98443)|(49131)||
|Monthly earnings|620,913|487,813|858,242|520,358|370430|32546|811|
||(150593)|(56459)|(446080)|(64280)|(449613)|(85552)||
|Number of additional|1.31|1.41|1.26|1.26|-0.14|-0.15|817|
|workers|(0.08)|(0.13)|(0.16)|(0.12)|(0.20)|(0.17)||
|Has a budget (share)|0.51|0.54|0.49|0.48|-0.05|-0.06|797|
||(0.02)|(0.03)|(0.03)|(0.03)|(0.04)|(0.04)||
|Has an accounting system|0.67|0.69|0.62|0.69|-0.07|0.00|815|
|(share)|(0.02)|(0.03)|(0.03)|(0.03)|(0.04)|(0.04)||
|Has a business plan (share)|0.16|0.15|0.15|0.20|0.01|0.05|795|
||(0.01)|(0.02)|(0.02)|(0.03)|(0.03)|(0.03)||
|Pays herself a wage (share)|0.33|0.32|0.33|0.34|0.01|0.02|799|
||(0.02)|(0.03)|(0.03)|(0.03)|(0.04)|(0.04)||

Notes: Includes all baseline observations, robust standard errors in parentheses 21