rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

The current global financial crisis has not only shed doubts on the proper functioning of conventional “Western” banking, but has also increased the attention on Islamic banking.<sup>1</sup> Academics and policy makers alike point to the advantages of Shariah-compliant financial products, as the mismatch of short-term, on-sight demandable deposits contracts with long-term uncertain loan contracts is mitigated with equity elements. In addition, Sharia-compliant products are very attractive for segments of the population that demand financial services that are consistent with their religious beliefs. However, little academic evidence exists on the functioning of Islamic banks, as of yet.

This paper describes some of the most common Islamic banking products and links their structure to the theoretical literature on financial intermediation. Specifically, we discuss to which extent Islamic banking products affect the agency problems arising from information asymmetries between lender and borrower or investor and manager of funds. Second, we compare the business model, efficiency, asset quality and stability of Islamic banks and conventional banks, using an array of indicators constructed from balance sheet and income statement data. In separate regressions, we focus specifically on the relative performance of both bank groups during the recent crisis.

While there is a large practitioner literature on Islamic finance, in general, and specifically Islamic banking, there are few academic papers. Cihak and Hesse (2010) test for the stability of Islamic compared to conventional banks, while Errico and Farahbaksh (1998) and Solé (2007) discuss regulatory issues related to Islamic banking. This general dearth of academic work on Islamic finance stands in contrast with the increasing importance that Islamic banking has in many Muslim countries in Asia and in Africa. With this paper we hope to contribute to the emerging literature on this topic.

Sharia-compliant finance does not allow for the charging of interest payments (riba) as only goods and services are allowed to carry a price. On the other hand, Sharia-compliant

> 1 For just two examples, see Willem Buiter: <u>http://blogs.ft.com/maverecon/2009/07/islamic-finance-principles-torestore-policy-effectiveness/</u> and Jerry Caprio: http://blogs.williams.edu/financeeconomics/2009/07/20/narrow- <u>banks-or-islamic-banks-to-the-rescue/</u>

2
<!-- Start of picture text --> =<br>o- * oeaa 3anll- ~ te¥ a — : oe =°<br>TM, ~~. ae. Sena<br>-: v ‘i= 2=<~ ee‘5aerate. zw- a# M i« 5Sd?* Sn. : " ’ »<br>c en a : op<br>- & at a A ds<br>| 1 , .<br>Cumulative Mean Annual Losses<br>of GDP per capita growth ~s<br>(% points) (1994-2014) a<br>m@ <0.1<br>We 0.1-0.2<br>MB 0.2-04<br>m>o4<br><!-- End of picture text -->
# **The Economics of International Student and Scholar Mobility: Directions for Research**

# **Gnanaraj Chellaraj**<sup>**1**</sup>

**JEL Classification: F2, I2, J6**

## **Key Words: Skilled Immigration, International Students, Universities**

1 World Bank Washington DC. The author may be contacted at gchellaraj@worldbank.org This paper is the product of the Office of the Chief Economist, Eastern Europe and Central Asia Region (ECACE). An earlier version was presented as a Background Paper for “Critical Connections: Promoting Economic Growth and Resilience in Europe and Central Asia” by David Gould (2018). This paper benefited significantly from discussions with Aaditya Mattoo (DECTG) and Keith Maskus (University of Colorado). Comments and suggestions from David Gould (ECACE) and William Lincoln (Claremont-Mckenna College) are gratefully acknowledged. The author also wishes to thank Asli-Demirguc-Kunt (ECACE) and Hans Timmer (SARCE) for their support. Finally, the opinions expressed in this paper are the sole responsibility of the author and does not in any way reflect the views of the World Bank.
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

# **2.1.4 Institutional factors**

Institutional factors refer to sector-level legal and regulatory framework that influence and support the continuity of the reform process. An electricity act or law is generally recognised as the starting point and prerequisite for implementation of reform. In addition, most market-oriented reforms involve establishing independent regulatory agencies to oversee the sector and to protect the rights of consumers. Effective regulatory practice requires clarity of the regulator’s mandate and clarity of the rules defining her/his relationship with other bodies, such as competition commissions and relevant ministries. Regulators’ power and independence is often interpreted as an indicator of the decision-makers’ commitment to the reform process.

The main problem here is that it is very difficult to measure regulatory effectiveness as distinct from regulatory governance (see Domah, Pollitt and Stern, 2002). It is possible to develop a series of measures for the characteristics of the regulator, e. g. number of staff, age, pay scales, funding source and nominal independence, but these do not measure competence level or performance against regulatory objectives. Measures of regulatory effectiveness (or institutional quality) are extremely difficult to devise.

# **2.2 Empirical studies of reform**

As noted above, the actual consequences of reform are a matter for empirical testing rather than theoretical debate. It is possible to point to spectacular reform failures. However, the main issue is whether evidence from a small number of reforming countries yields useful general lessons. What is needed is a comprehensive characterisation (including institutional, policy and market structure) of the nature of reform and its effects within the context of a well framed model structure.

The structure-conduct-performance paradigm of industrial organisation can be adopted to represent the main components of the conceptual reform model outlined in Section 2 and Figure 1, and their inter-relationships in the form of a stylised set of simultaneous equations in (1)-(4). Broadly, the model encompasses most types of research question and hypothesis associated with electricity sector reform though the lag structure may be more complicated than in the simple form represented here.

|PI(t) =|_f_(REFT(t), MKST(t), INST(t), END(t))|(1)|
|---|---|---|
|REFT(t) =|_g_(PI(t-1), MKST(t), INST(t-1), END(t-1))|(2)|
|INST(t) =|_h_(PI(t-1), REFT(t-1), MKST(t-1), END(t-1))|(3)|
|MKST(t) =|_k_(PI(t-1), REFT(t-1), INST(t-1), END(t-1))|(4)|

|where:||
|---|---|
|PI(t) =|performance indicator in period t|
|REFT(t-1) =|reform type in previous period|
|MKST(t)=|market structure in period t|
|INST(t)=|institutional factors in period t|
|END(t) =|endowments in period t|

9
equation (2) below and the second stage is equivalent to the model presented in equation (1) above.<sup>16</sup> _Foreign Bank Presencei,j,k=_ α _0 +_ β _1 Parent Bank Characteristicsi,j +_ β _2 Home-Country Regulationsj+_ β _3 Affiliate Bank Characteristicsi +_ β _4 Host-Country Factorsk+ Home-Host Proximityj,k+_ ε _i,j,k (2)_ where _Foreign Bank Presence_ i,j,k is a dummy equal to 1 if parent bank i from home-country j has any kind of operations in host-country k, and 0 if it does not have a presence in the host-country. Most variables included in equation (2) have been defined above, with the following three exceptions. First, within the parent bank characteristics, _Parent Bank Worldwide Activity_ takes into account the number of countries worldwide where each parent bank has activities (data source: Bankers’ Almanac). Second, among the regulatory variables, _Host-Country (inward) Foreign Investment Regulation_ and _Home-Country (outward) Foreign Investment Regulation_ capture government controls on inward and outward direct investment in banking, respectively. Both variables are constructed as a five year average (2002-1998) from original IMF data.<sup>17</sup> Finally, _Home-Host Proximity_ is a matrix of variables that capture the degree of economic, cultural, and institutional affinity between the home and host countries. In particular, it includes separate dummies for whether the host and home-country share (i) a common language and (ii) a

> 16 An alternative approach would be to estimate a nested logit model where the first decision level is to enter a country or not and the second is whether to operate as a branch or as a subsidiary. The problem with this approach is that we could not study the impact of affiliate characteristics on the second level decision because these factors are not observed for cases when a bank has no operations in a given host. Despite of this limitation, results on the remaining variables, available upon request, yielded very similar findings to those reported here. 17 The original IMF data are dummies which equal 1 in those years when the countries had controls.

13
Policy ReseaRch WoRking PaPeR 4930

# **Abstract**

This paper studies how liberalization affects productivity growth using micro-level plant data. While previous studies have already shown the existence of a positive relationship between competition and economic performance, the novelty of this paper is that it analyzes not only the average impact of liberalization, but also goes “beyond the average” and shows how the liberalization can affect dissimilar plants in a different way. The author first develops a model which predicts that, while the impact of liberalization on productivity growth is positive “on average”, more advanced firms tend to benefit more. In fact, liberalization generates two competing effects: on one side it spurs more innovative efforts because of the increased entry threat by foreign competitors, on the other side, enhanced competition curtails expected profits and reduces the funds available to finance innovative activities. The pro-competitive effect is weaker for less advanced firms as for them it is harder to catch-up with the “technology frontier”. These predictions are then tested focusing on Mexican plants during the NAFTA liberalization. The results show that a 1 percent reduction in tariffs spurred productivity growth between 4 and 8 percent on average. However, for backward firms this effect is much weaker if not close to zero, otherwise for more advanced ones this effect is stronger with productivity growing between 11 and 13 percent. Consistent with the theoretical model the results are stronger in those sectors where the scope for innovative activities is more pronounced. These results are particularly important for policy makers because they suggest that while increasing competition may be good in spurring average productivity, it is also true that this effect does not hold for all type of firms, in particular more backward firms may need some complementary support policy to upgrade their capacities and keep up with the more competitive environment.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to understand the links between competition and innovation. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at Liacovone@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
expenditure distribution that would not be detected easily from a comparison of standard measures of inequality and polarization. Second and most important, the paper develops within the relative distribution framework a novel methodology to identify the drivers of distributional changes and quantify their impact on the welfare distribution; the main value added being it enables a very granular analysis of the distributional changes that an analysis based on standard inequality decompositions would not allow.

The paper is organized as follows. Section 2 discusses the data and presents the methodology. Section 3 provides the results. Section 4 concludes.

# **2 Data and methodology**

## **2.1 The Ghanaian household survey data**

The data used in this paper come from the Ghana Living Standard Survey (GLSS), a nation-wide survey conducted by the government-run Ghana Statistical Service that provides information for assessing the living conditions of Ghanaian households.

The GLSS has emerged as one of the most important tools for the welfare monitoring system in Ghana. It provides detailed information on approximately 200 variables, including several socioeconomic and demographic characteristics, and information on household consumption of purchased and home-produced goods as well as asset ownership. Each of the waves is organized into 4 modules, which are stored in the individual, the labor force, the household and the household expenditure files, for which survey questionnaires are readily available.

The Ghana Statistical Service has conducted six rounds of the GLSS since 1987, thereby providing over 20 years of comparable data. The second, third, fourth and fifth rounds were carried out, respectively, in 1988, 1991/92, 1998/99 and 2005/06. Recently, data for the sixth round of GLSS have also become available, so that the proposed case study paper will be one of the first studies using this data set. However, only the last four rounds, from 1991/92 (GLSS-3) to 2012/12 (GLSS-6), have been based on the same questionnaire and are therefore fully comparable.

The availability of comparable and extensive information represents a success on its own. Ghana is one of the few countries in Africa that has produced comparable, high-quality household data covering over two decades. This is an important achievement because the availability of such rich and comparable information beginning in 1991, as well as the quality improvements of the surveys over the years and the fact that they collect data on both the monetary and the non-monetary dimensions of welfare, permit the establishment of an accurate picture of inequality and polarization over time, including the drivers behind these phenomena.

3
we do not need to assume that the elasticity of substitution in the lower nest, between immigrant and native labor, is identical across education groups.

# 5.2 Results

We present our OLS and IV estimates of the impact of immigration on natives of different educational attainment in Tables 7 and 8, respectively. Each column represents results for natives of one of five levels of educational attainment. All regressions include region by industry, industry by year, and region by year fixed effects. Panel A reports the impact on native employment, since our estimates lack precision we extend the years used for our analysis to 2003-10.<sup>37</sup> Panel B reports the effect of immigration on the log of native wages (using 2007-10 data). Panel C of Table 8 reports the implied structural parameters, and Panel D the native wage elasticity with respect to immigration. We also report the share of natives and immigrants in each educational category for the period 2003-10. 76 percent of the immigrant stock in Malaysia over this period had at most completed primary school education, as compared to 26 percent of Malaysians. Particularly few, 1.2 percent, had some type of vocational training (certificate / diploma), compared to 10 percent of Malaysians. They were also much less likely to have a university degree, 3.2 percent compared to 8.7 percent among Malaysian workers.

The results show highly heterogeneous impacts on immigration on natives with different educational attainment. Focusing on the IV results (Table 8) we find that natives with the same educational attainment as the bulk of immigrants (at most primary educa-

> 37Recall that the instrument is based on the average distribution of immigrants over the period 19902002.

41
Table 4.1: Summary Statistics of Variables in Mover/Stayer Models

||**Movers**<br>**toNE**|**Movers**<br>**toSE**|**Stayers**<br>**inNE**|**Stayers**<br>**inSE**|
|---|---|---|---|---|
|||Meanofvariable:|||
|Age|32.88|30.89|37.35|37.17|
|Famsize|3.73|3.84|4.11|3.75|
|Expir|21.46|18.99|25.64|23.56|
|||Percentagesshares:|||
|**Education:**|||||
|No|||||
|education|0.13|0.09|0.19|0.02|
|PrimaryI|0.32|0.35|0.26|0.27|
|PrimaryI|0.18|0.15|0.14|0.15|
|Secondary|0.29|0.35|0.34|0.43|
|University|0.08|0.06|0.07|0.13|
|Gender:|||||
|Male|0.75|0.64|0.62|0.62|
|Female|0.25|0.36|0.38|0.38|
|**Working**<br>Class:|||||
|Formal|0.26|0.59|0.39|0.56|
|Self|0.43|0.15|0.34|0.22|
|Informal|0.31|0.26|0.27|0.22|
|Sector:|||||
|Agriculture|0.26|0.06|0.21|0.10|
|Industry|0.22|0.31|0.19|0.25|
|Services|0.47|0.61|0.54|0.59|
|Public|||||
|Sector|0.05|0.02|0.06|0.06|
|**Location:**|||||
|Urban|0.69|0.93|0.75|0.88|
|Rural|0.31|0.07|0.25|0.12|

_Source:_ Authors' own calculation based on PNAD 1999.

22
covariance matrix adjusted for the clustering of the survey sample. We also estimate the distribution parameters σμ 2 and ση 2 . We shall denote the estimates with a hat (e.g. ˆ α ).

As with Elbers et al. (2003), we estimate left-hand-side variables in Eq(5)–Eq(10) for each census record repeated by a Monte-Carlo simulation. To allow for the error in the estimated regression coefficients, we draw regression coefficients from a multinomial normal distribution in each round of the simulation. We shall denote the drawn coefficients by superscript ( _r_ ) to specify the _r_ -th round of the simulation. In addition, we draw error terms for each census record. For example, the estimate of wage income ˆ ( _r_ ) **w** _hi_ for (census) household _h_ and individual _i_ in the _r_ -th round is calculated as follows: ˆ where **x** _hi_ comes from the census data set and μ _hi_ (<sup>_r_</sup> ) is drawn from the normal distribution with mean zero and variance σ ˆ μ 2 . Note that we know the employment status of each individual in the census and thus we observe **IW** _hi_ and **IS** _hi_ . However, ˆ _w_ , ( _r_ ) ˆ<sup>_s_</sup> , ( _r_ ) ˆ _n_ , ( _r_ ) ˆ<sup>_n_</sup> , (<sup>_r_</sup> ) we still need to draw _uhi_ , _uhi_ and _uhi_ for the later simulation. We can draw _uhi_ ˆ _w_ , ( _r_ ) ˆ<sup>_s_</sup> , (<sup>_r_</sup> ) from the Gumbel distribution. _uhi_ and _uhi_ must be drawn conditionally on the observed dummy variables for the employment status **IW** _hi_ , **IS** _hi_ as well as the drawn ˆ<sup>_n_</sup> , (<sup>_r_</sup> ) error term _uhi_ in order to be consistent with the observed employment status.

It is straightforward to impute household non-wage income using Eq(6). By Eq(10), we ˆ ( _r_ ) get an estimate of the per capita household income **H** _h_ . We can then obtain aggregate welfare measures such as the FGT measure of poverty, see Foster et al. (1984). Letting _H_ be the set of households in province _p_ and _z_ be the poverty line, the head count _p_ ( _r_ ) poverty rate _Pp_ in province _p_ for the _r_ -th simulation can be written as follows: 15
7a and 7b illustrate banks’ perceptions of the role of regulations concerning documentation requirements for lending to SMEs, while Figures 8a and 8b show banks’ impression of how prudential regulations affect their involvement with SMEs. Close to two third of all banks, regardless of country of operation (Figure 7a) or bank ownership type (Figure 7b) indicate that they perceive documentation requirements for lending to SMEs to be “appropriate and beneficial”. Similarly, most banks consider prudential regulations to have a positive or inconsequential effect on SME financing (Figure 8a and 8b). This is an interesting finding, given that recent country studies (Adasme, Majnoni and Uribe, 2006) have found that capital requirements can bias against SME lending, as SME loans are more risky overall but have less tail risk than large enterprise loans.

Access to credit history information appears to be particularly important for banks in developing countries (Figure 9a). More than two thirds of developing country banks respond that the existence of a credit bureau in their country facilitates SME lending. On the other hand, only 44 percent of banks from developed countries rate the existence of a credit bureau as beneficial for SME lending. Similarly, access to credit information seems more relevant for foreign relative to domestic banks (Figure 9b). These differences might reflect the fact that SME opaqueness (e.g., lack of verifiable financial statements for the firm) and perhaps informality are greater issues for banks operating in developing countries and for foreign banks. The latter might have less first-hand experience with local borrowers and, hence, might have to rely more extensively on credit bureaus.<sup>5</sup> In summary, banks are attracted to the SME segment due to its perceived high profitability and good prospects. Relative to private domestic and foreign banks, government banks’ involvement with SMEs is in part driven by thinning margins in other segments. While banks in developed countries see competition as a major obstacle, banks in developing countries point mainly to macroeconomic conditions. Banks’ reaction to government support programs is positive overall and prudential regulations are not perceived as a hurdle. Access to information

> 5 The differences found for banks in developed and developing countries are also consistent with aggregate results from Djankov, McLiesh and Shleifer (2007) who find that the quality of credit information sharing is more important for financial deepening in developing than in developed countries.

9
associated with completion of the last year within a level for Filipino men. The results are reasonably robust to partial controls for ability, such as inclusion of measures of parental education, and within-sibling estimates. These findings have important possible implications for the decisions taken by private individuals as well as governments. For example, at reasonable discount rates, a very high degree of convexity in the earnings function could make further education unattractive for all but those who expect to complete the highest years of schooling.<sup>24</sup> Returns to education are not static, in the Philippines or elsewhere, and the shape of the earnings function may well change over time.<sup>25</sup> Additional research on a developing country with numerous cross-sections of data would allow for distinctions to be made between the age, cohort, and year effects which underlie changes in the relative shape of the earnings function.

> 24 One puzzling question which this paper does not address is how the high education levels and the reasonably high overall returns to education are consistent with the declining productivity and very low levels of growth found in the Philippines in the last two decades (for a general discussion, see Pritchett 1996).

> **25** Indeed, because 1998 was a year in which the Philippine economy was affected by the East Asian crisis, it might not be a representative year to study the relationship between wages and education-even though the effects of the crisis were nowhere near in magnitude to those felt by many of the Philippines' neighbors: The year-on-year change in real GDP per capita for the third quarter of 1998, corresponding to the period for the wage data in our sample, was only -2.2%.

24