# 1. Introduction

At the dawn of the new millennium, debates on language policy are more and more focused on the role of multilingualism and multiculturalism in the globalization process. The complexity of this issue lies mainly in the fact that the process of globalization at the cultural level produces contradictory behaviors. As Stromquist & Monkman (2000) explain:

> While the world is becoming smaller and more homogeneous at some levels, in a variety of ways local cultures are making efforts to retain their identity and, in some cases, even to rediscover it (Stromquist & Monkman, 2000, p. 7).

Hence, Cvetkovich & Kellner (1997) claim that:

> Although global forces can be oppressive and erode cultural traditions and identities they can also provide new material to rework one's identity and can empower people to revolt against traditional forms and styles to create new, more emancipatory ones (Cvetkovitch & Kellner, 1997, p. 10).

This paradox is very well captured by Pattanyak (1984) in his overview of the different positions in the current debate:

> The dominant monolingual orientation is cultivated in the developed world and consequently two languages are considered a nuisance, three languages uneconomic and many languages absurd. In multilingual countries, many languages are facts of life; any restriction in the choice of language is a nuisance; and one language is not only uneconomic, it is absurd (Pattanyak, 1984; quoted by Skutnabb-Kangas & Garcia, 1995, p. 221).

In the face of this complicated climate, where, on the one hand, the labor market is required to homogenize to its maximum its communication tools (i.e. languages of trade) and, on the other hand, the national political leaders are fighting for the preservation of the cultural and linguistic identity of their people, the education sector serves as the mediator between these two parties[1]. Hence, the sociology of learning in schools is built on the assumption that a polity targeting sustainable-development needs to focus on providing

<footnote>[1] Educationalists, such as Giddens, 1994, and Stromqvist & Monkman, 2000, are increasingly interested in the role of globalization in the re-conceptualization of knowledge.</footnote>
children with the knowledge, skills and values needed to make them become competent adult members of the society (Broadfoot, 1994). And so, by definition, the type of language-in-education policy (if any) adopted by a government reflects its ambition to educate a skilled and attractive labor force. As Marland (1977) highlighted in his advocacy for language across the curriculum (LAC):

> If a school devotes thought and time to assisting language development, learning in all areas will be helped; if attention is given to language in the content and skill subjects, language development will be assisted powerfully by the context and purpose of those subjects (Marland, 1977; quoted by Froese, 1994, p. 3205).

Building on the hypothesis that bilingual education programs favor cognitive learning, and thereby literacy (Jacob, 1994; Ogbu, 1994; Pease-Alvarez, 1994), and given the contradictory linguistic interpretations of globalization, the question of which languages to choose as part of a bilingual education policy confronts two main linguistics theorist groups, namely the ‘free-market’ theorists and the ‘green’ theorists. On the one hand, the ‘free-market’ theory of unfettered capitalism defines linguistic geo-strategy as a race for ‘market share’ ran by the governments representing the major international languages. On the other hand, the ‘green’ theory of ecological protection advocates for a linguistic geo-strategy of “protection of endangered languages undertaken by linguists and by those interested in linguistic human rights” (Kibbee, 2003, p. 47).

Although the emergence of this debate results from works in sociolinguistics and linguistics, this paper aims at showing that economics of education and economics of language contribute to this debate principally via their fundamental assumption that an optimal combination of languages exists for each labor market (Vaillancourt, 1982/1983, p. 167). This hypothesis, which supports the ‘free-market’ theory, has inspired Vaillancourt (1980) and Lacroix & Vaillancourt (1980, 1981) to elaborate a framework transforming this demand for language skills into a demand for individuals embodying language skills, thereby allowing them to make predictions on the relative earnings of Anglophone and Francophone salaried in Quebec. A similar framework was used by Boulet (1980) to examine the situation in Montreal. In total, more than two dozen studies have been conducted since 1970 based on this hypothesis and using either a 1/100 sample drawn from the 1971 Census of Canada or data from large-scale surveys. Vaillancourt (1982/1983)
highlights that “[a]ll studies make use of regression analysis, usually linking the logarithm of earnings to individual characteristics such as education and age, in addition to language skills” (p. 168). This method is derived from the ‘mincerian’ specifications of human capital. The principle is to add to his Mincer’s (1974) specification of the link between income and its determinants, one or several variables denoting linguistic competences (Grin, 1999, p. 30). The inherent hypothesis is that the higher the level of language competences, the higher the wages.

These studies have the advantage of coherently supporting the validity of the above framework to predict the relative returns to language skills, even when taking into account the level of knowledge of these languages (e.g., Boulet, & Castonguay, 1979; Chizwick & Miller, 1992; Fixman, 1990; Grenier & Vaillancourt, 1982; Grin, 1999; Sabourin, 1979; Veltman, Vaillancourt & Pes, 1980). On the other hand, their weakness lies in their use of datasets that are nationally designed and thereby not internationally comparative.

The purpose of this paper is therefore to go beyond this limitation by testing the ‘free-market’ theory on eight countries, using the database provided by the International Adult Literacy Survey (IALS), ran between 1994 and 1998. Three countries (Finland, Hungary and Norway) officially apply a bilingual education policy and five (Chile, Czech Republic, Denmark, Italy and Italian-speaking Switzerland) officially apply another type of language-in-education policy. Table I presents the language policy of all IALS countries covered by this study. Note that these countries have been selected among the twenty-one countries participating in the IALS, based on availability and reliability of data.

Among the linguistic factors influencing wages, the level of literacy in the working language (which is assumed to be the national official language) and the number of languages spoken are retained for this study. One of the objectives is to test the assumption that proficiency in the national language is more significant to immigrants (measured as not born in country of survey) than to native individuals. Further, the number of languages spoken serves to test the assumption that globalization requires skills in foreign languages (free-market theory).
Building the hypotheses mainly on results found by previous studies in the United States and in Canada in the past twenty-five years (with the exception of the works by Grin in Switzerland), this study attempts to test their generalizability at the international level. Interestingly, the studies conducted so far show rather mixed results. For instance, some find that a variable measuring English proficiency is not statistically significant in influencing hourly wages. In the U.S., such findings include the studies by Borjas (1984) using the 1976 Survey of Income and Education (SIE) for various Hispanic groups, Reimers (1983, 1985) for males and females in the SIE data set, and Gwartney & Long (1978) and Carliner (1980) using Census data. In Canada, Bloom & Grenier (1992), Chizwick & Miller (1992), Grenier (1987), Robinson (1988), Shapiro & Stelcner (1987) and Vaillancourt (1992) failed to find strong language effects on earnings outside Quebec (where the returns to bilingualism in French and English are generally positive), confirming thereby the findings from the U.S.

On the other hand, research by Grenier (1984), Kossoudji (1988), McManus et al. (1983), Rivera-Batiz (1990), Tainer (1988), have found significant positive effects of English language proficiency on earnings in the U.S. Moreover, in Canada, Christofides & Swidinsky (1998) have shown that, relative to the earnings of unilingual Anglophones, the returns to bilingualism have increased significantly between 1971 and 1991 in both Quebec and the Rest-of-Canada, which alters previous results. Further, Grin's (1999) study on the returns to proficiency in a foreign language (namely, English) in Switzerland confirms a significant effect on earnings.

Rivera-Batiz's (1990) and Grin's (1999) studies differ from the other studies in their use of test-based measurements of language proficiency, rather than self-assessed subjective measurements. The present study offers similar reliability for skills in the official national language(s) by using the test-based measurement of prose, document and quantitative literacy computed by the IALS. However, skills in foreign languages are based on self-assessment.

Because the aim of this study is to test the free-market theory, this paper addresses the following specific questions:
1) Does proficiency in the official national language(s) play a significant role on wages?

2) Does this result differ by gender and between native and non-native individuals to the country of survey?

3) Are language skills more rewarded in countries applying an official bilingual policy than in countries applying another type of language policy?

## 2. The empirical model

This study applies the following empirical human capital model, estimated separately for men and women, and native and non-native individuals in each country of the sample:

$$\log W_{ijk} = \beta' X_{ijk} + U_{ijk},$$

where $W_{ijk}$ is the estimate of personal income from only wages, salary or self-employment in the year of the survey received by individual $i$ of gender $j$, and place of birth $k$ (i.e. in or not in country of survey); $\beta$ is a vector of coefficients to be estimated; $X_{ijk}$ is a vector of human capital and demographic characteristics affecting wages; and $U_{ijk}$ is a stochastic disturbance.

In order to determine the role played by language proficiency on earnings, three human capital equations have been computed. The first one is a ‘standard’ human capital equation, where vector $X_{ijk}$ includes two key explanatory variables. First of all, years of schooling, $a7$, as an indication of the impact of academic skills on earnings. Moreover, years of on-the-job experience, proxied by the variable *exper*, measured as age minus years of schooling minus six[2], to incorporate the effect of non-academic skills on wages.

The second human capital equation adds to the previous the scores received by individuals in literacy, as measured by the IALS. The IALS defines three domains of literacy:

<footnote>[2] On average, compulsory education starts at the age of six at the international level, including in the countries of this sample. It is therefore commonly admitted to define *exper* as age minus years of schooling minus *six* (Rivera-Batiz, 1990).</footnote>
a) *Prose Literacy* – the knowledge and skills needed to understand and use information from texts including editorials, news stories, poems, and fiction;

b) *Document literacy* – the knowledge and skills required to locate and use information contained in various formats, including job applications, payroll forms, transportation schedules, maps, tables, and graphics; and

c) *Quantitative literacy* – the knowledge and skills required to apply arithmetic operations, either alone or sequentially, to numbers embedded in printed materials, such as balancing a checkbook, calculating a tip, completing an order form, or determining the amount of interest on a loan from an advertisement (Statistics Canada, 2002, p. 15; italic in original).

For each of these three scales (prose, document and quantitative), individuals are assigned scores, ranging from 0 to 500, according to how well they perform on a number of tasks of varying difficulty. The scale scores are in turn grouped into five empirically determined literacy levels, each of them implying an ability to cope with a particular subset of reading tasks. Variables *prose*, *doc* and *quant* provide the average score for prose literacy, document literacy and quantitative literacy respectively (see Table II for a definition of each level and score range).

Finally, the third human capital equation adds skills in two languages, proxied by the dummy variable *lang2*, which equals 1 if the person can conduct a conversation in a foreign language in addition to the national official language, and zero otherwise. Although this variable is based on self-assessment, and can therefore not be considered as an evidence of bilingual proficiency, it helps measuring the significance given by the labor market to language competences beyond the official national language.

Only individuals for whom non-zero wages are observed are retained for the analysis. This implies a non-random selection of cases, which biases the error term $U_{ij}$. This selectivity bias problem could be solved with the two-stage sample selection bias correction procedure postulated by Heckman (1979).
### 3. Results

The results for the countries of our sample are grouped by type of national language policy (bilingual and other types). Tables III-A and III-B depict the sample means for bilingual and non-bilingual countries respectively for the variables included in the analysis. Tables IV-A and IV-B show the results for native men and women in bilingual and non-bilingual countries respectively. Whenever possible, results have been computed for individuals born in another country than the country of survey to look for eventual differences of results with the individuals born in the country of survey.

For comparison purposes, equation (1) presents the estimated coefficients when all variables on language skills are excluded from the wage equation. Equation (2) then shows the results when the variables in prose, document and quantitative literacy in the official national language are included. Finally, equation (3) presents the results including skills in two languages (see annex). Note that cases with negative adjusted R² are not presented in these tables.

Countries applying bilingual (or multilingual) policies are assumed to value skills in two languages more than countries applying other types of language policies. This implies that the significance of *lang2* on wages is expected to be higher in bilingual countries. Conversely, proficiency in the official national language is expected to have a higher significance in countries valorizing their unique official language.

First of all, the results presented in this paper show that the three human capital equations estimated in this study provide a sufficient percentage of explanation of variations in wages only for Finnish men and women born in Finland (between 12.2 and 13.8 percent), Norwegian men born in Norway (between 11.2 and 12.7 percent), Danish men born in Denmark (between 12.5 and 13.3 percent) and women leaving in Italy and born abroad (up to 21.9 percent). For all other cases, the low level of adjusted R² highlights the need to refine the estimated equations. However, previous empirical studies applying the same Mincerian approach did not obtain higher explanation degrees, which allows us nevertheless to treat our results as valid.
In the two countries applying a national policy of bilingualism (Finland and Norway) linguistic skills, both in terms of literacy skills in the national official language and skills in a second language, have a significant effect on wages, as demonstrated by the increasing adjusted R² when incorporating the linguistic variables. However, the weights estimated for *pros*, *doc* and *quant* are very small compared to the weights estimated for *lang2*. This shows that for individuals born in the country of residency and work, although they can influence the type of work and thereby the income range one is eligible for, literacy skills in the national official language are not a requirement for wage improvements. On the other hand, the weight estimated for second language skills goes pass the weights estimated for educational level and professional experience in both countries. It is however worth noticing that although Swedish is the second official language of Finland, 55 percent of the Finnish population has English as second language (41.7 percent speaks Swedish as second language). The same applies to Norway, with English being spoken by 93 percent of Bokmål-speaking population as the second language.

Moreover, looking at the results for the six countries applying a national language policy other than bilingualism (Chile, Czech Republic, Denmark, Hungary, Italy and Italian-speaking Switzerland), it appears that despite the explicative weakness of the model, the inclusion of language skill variables also improves the adjusted R². It is interesting to see that skills in a second language are as praised by the labor market in non-bilingual countries as they are in bilingual countries and that the role played by literacy in the national official language varies strongly between countries and even between types of literacy skills. Furthermore, second language skills are more valued in women's wages than in men's wages in all countries of our sample except Switzerland (Italian-speaking part). These differences could be explained by the distribution of gender by type of professional occupation.

When examining the nature of the second language spoken by the individuals of our sample it is striking to see that English comes first in Chile (58 percent) and Denmark (79 percent), and second in Hungary after German (29 percent versus 52 percent) and in Italy after French (35 percent against 44 percent). The only exception to this trend in favor of English as common communication means are the Czech Republic, where 41 percent of the population still speaks Russian as second language before German (20 percent) -
English comes only in third position with 14 percent -, and the Italian-speaking Switzerland with 58 percent speaking the regional dialect, 24 percent speaking German, and 13 percent French[3] (only 1.5 percent for English). From this distribution of languages it is clear that the choice of the second language is more highly correlated to economic factors than the choice of the first language, which is still very much correlated to socio-cultural and historical factors. This finding for second languages supports the free-market theory, which states that the choice of languages should be ruled by competitiveness.

Finally, although one of the objectives of this study was to compare results for men and women according to their place of birth (assuming that immigrants would be included in the individuals not born in the country of survey), lack of valid data for individuals born outside the country of survey in all countries of our sample - except for Norway and the Italian-speaking Switzerland - hinders us from drawing any conclusions from that angle (see Tables III-A and III-B for sample means by gender and place of birth). In the case of Norway, none of the three human capital equations tested have the capacity to explain more than 0.5 percent of the variations in wages for men born abroad, and in the Swiss case, the inclusion of the linguistic variables diminishes the explanatory level of the model, implying that the sources of variations of wages of non-natives should be sought among other factors.

## 4. Conclusions

Rare study on economic returns to language skills conducted at the international level, this paper has made use of a test-based measure of literacy skills in national official language and self-assessment measure of competences in a second language to estimate the role played by language skills in explaining earnings in eight countries. This paper thereby contrasts with the previous literature in this field, which has used non-comparative national datasets.

This analysis demonstrates the existence of commonalities between countries in terms of language skills valuing, which go beyond the type of language policy applied at the

<footnote>[3] Together with Italian and Romansh, German and French are the official languages of Switzerland.</footnote>
national level. In each of the eight countries compared, skills in a second language are estimated to be a major factor constraining positively wage opportunities.

The initial objective of this empirical study was to test the free-market theory according to which ‘competitive’ bilingualism or multilingualism needs to prevail ‘ecological’ multilingualism. Based on the nature of the second languages spoken by our sample, and on their estimated economic return, this study validates fully the free-market theory. However, the returns to literacy skills in the official national language (assumed to be equal to the working language) were expected to be higher. The re-computation of literacy skills as an average of prose, document and quantitative literacy might alter this result in favor of the free-market theory, i.e. in favor of high skills in the language of the market. Finally, a suggestion for further research would be to add a control for the type of professional occupation in order to explain better the differences in returns to language skills by countries, genders, native vs. non-natives, and even individuals of a same group.
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<table><caption>Table I Official language policy of the sample countries</caption><thead><tr><td>Country</td><td>Language Policy</td></tr></thead><tbody><tr><td>Chile</td><td>Mixed policy: Valorization of official language <u>and</u> Differentiate status (minority languages). Non-intervention.</td></tr><tr><td>Czech Republic</td><td>Mixed policy: Non-intervention (official language) <u>and</u> Sector policy (minority languages).</td></tr><tr><td>Denmark</td><td>Sector policy.</td></tr><tr><td>Finland</td><td>Bilingualism based on territorial personal rights.</td></tr><tr><td>Hungary</td><td>Non-intervention and sector policy (minority languages).</td></tr><tr><td>Italy</td><td>Unilingualism (valorization of the <u>national</u> official language).</td></tr><tr><td>Norway (Bokmål)</td><td>Bilingualism based on personal rights.</td></tr><tr><td>Switzerland (Italian-speaking)</td><td>Unilinguism (territorial borders between official languages).</td></tr></tbody></table>

Source: Based on Leclerc (2001).
<table><caption>Table II Literacy Levels and Score Ranges</caption><thead><tr><td>Level</td><td>Score Range</td><td>Definition</td></tr></thead><tbody><tr><td></td><td></td><td><strong>Prose Literacy</strong></td></tr><tr><td>1</td><td>0 to 225</td><td>Most of the tasks at this level require the reader to locate one piece of information in the text that is identical to or synonymous with the information given in the directive. If a plausible incorrect answer is present in the text, it tends not to be near the correct information.</td></tr><tr><td>2</td><td>226 to 275</td><td>Tasks at this level generally require the reader to locate one or more pieces of information in the text, but several distractors may be present, or low-level inferences may be required. Tasks at this level also begin to ask readers to integrate two or more pieces of information, or to compare and contrast information.</td></tr><tr><td>3</td><td>276 to 325</td><td>Tasks at this level generally direct readers to locate information that requires low-level inferences or that meets specified conditions. Sometimes the reader is required to identify several pieces of information that are located in different sentences or paragraphs rather than in a singular sentence. Readers may also be asked to integrate or to compare and contrast information across paragraphs or sections of text.</td></tr><tr><td>4</td><td>326 to 375</td><td>These tasks require readers to perform multiple-feature matching or to provide several responses where the requested information must be identified through text-based inferences. Tasks at this level may also require the reader to integrate or contrast pieces of information, sometimes presented in relatively lengthy texts. Typically, these texts contain more distracting information, and the information requested is more abstract.</td></tr><tr><td>5</td><td>376 to 500</td><td>Tasks at this level typically require the reader to search for information in dense text that contains a number of plausible distractors. Some require readers to make high-level inferences or to use specialized knowledge.</td></tr><tr><td></td><td></td><td><strong>Document Literacy</strong></td></tr><tr><td>1</td><td>0 to 225</td><td>Most of the tasks at this level require the reader to locate a single piece of information based on a literal match. Distracting information, if present, is typically located away from the current answer. Some tasks may direct the readers to enter personal information onto a form.</td></tr><tr><td>2</td><td>226 to 275</td><td>Document tasks at this level are a bit more varied. While some still require the reader to match a single feature, more distracting information may be present or the match may require a low-level inference. Some tasks at this level may require the reader to enter information onto a form or to cycle through information in a document.</td></tr><tr><td>3</td><td>276 to 325</td><td>Tasks at this level are varied. Some require the reader to make literal or synonymous matches, but usually the reader must take conditional information into account or match on the basis of multiple features of information. Some require the reader to integrate information from one or more displays of information. Others ask the reader to cycle through a document to provide multiple responses.</td></tr><tr><td>4</td><td>326 to 375</td><td>Tasks at this level, like those at the previous levels, ask the reader to match on the basis of multiple features of information, to cycle through documents, and to integrate information; frequently, however, these tasks require the reader to make higher-order inferences to arrive at the correct answer. Sometimes the document contains conditional information that must be taken into account by the reader.</td></tr><tr><td>5</td><td>376 to 500</td><td>Tasks at this level require the reader to search through complex displays of information that contain multiple distractors, to make high-level inferences, process conditional information, or use specialized knowledge.</td></tr></tbody></table>
<table><caption>Table II (Cont'd)</caption><thead><tr><td>Level</td><td>Score Range</td><td>Definition</td></tr></thead><tbody><tr><td></td><td></td><td>Quantitative Literacy</td></tr><tr><td>1</td><td>0 to 225</td><td>Although no quantitative tasks used in the assessment fall below the score value of 225, experience suggests that such tasks would require the reader to perform a single, relatively simple operation (usually addition) for which either the numbers are clearly noted in the given document and the operation is stipulated, or the numbers are provided and the operation does not require the reader to find the numbers.</td></tr><tr><td>2</td><td>226 to 275</td><td>Tasks at this level typically require readers to perform a single arithmetic operation (frequently addition or subtraction), using numbers that are easily located in the text or document. The operation to be performed may be easily inferred from the wording of the question or the format of the material (for example, a bank deposit or order forms).</td></tr><tr><td>3</td><td>276 to 325</td><td>Tasks at this level typically require the reader to perform a single operation. However, the operations become more varied – some multiplication and division tasks are included. Sometimes the reader needs to identify two or more numbers from various places in the document, and the numbers are frequently embedded in complex displays. While semantic relation terms such as “how many” or “calculate the difference” are often used, some of the tasks require the reader to make higher-order inferences to determine the appropriate operation.</td></tr><tr><td>4</td><td>326 to 375</td><td>With one exception, the tasks at this level require the reader to perform a single arithmetic operation where typically either the quantities or the operation are not easily determined. That is, for most of the tasks at this level, the question or directive does not provide a semantic relation term such as “how many” or “calculate the difference” to help the reader.</td></tr><tr><td>5</td><td>376 to 500</td><td>These tasks require readers to perform multiple operations sequentially, and they must locate features of the problem embedded in the material or rely on background knowledge to determine the quantities or operations needed.</td></tr></tbody></table>

Source: Statistics Canada (2002).
<table><caption>Table III-A Sample means for wage equations, by gender and place of birth: countries applying a bilingual policy</caption><thead><tr><th rowspan="3">Variables</th><th colspan="4">Born in country of survey</th><th colspan="4">Not born in country of survey</th></tr><tr><th colspan="2">Males</th><th colspan="2">Females</th><th colspan="2">Males</th><th colspan="2">Females</th></tr><tr><th>Mean</th><th>S.D.</th><th>Mean</th><th>S.D.</th><th>Mean</th><th>S.D.</th><th>Mean</th><th>S.D.</th></tr></thead><tbody><tr><td colspan="9"><strong>Finland</strong></td></tr><tr><td>wage</td><td>982256</td><td>2785377</td><td>1038858</td><td>2926830</td><td>1495678</td><td>3536005</td><td>1314993</td><td>3390997</td></tr><tr><td>a7</td><td>12.59</td><td>3.545</td><td>13.11</td><td>3.451</td><td>13.00</td><td>2.867</td><td>14.44</td><td>2.898</td></tr><tr><td>exper</td><td>19.29</td><td>12.879</td><td>18.69</td><td>12.904</td><td>11.68</td><td>10.111</td><td>9.69</td><td>9.075</td></tr><tr><td>prose</td><td>291.72</td><td>41.78</td><td>303.56</td><td>39.17</td><td>282.70</td><td>69.39</td><td>311.01</td><td>59.69</td></tr><tr><td>doc</td><td>298.77</td><td>47.01</td><td>301.23</td><td>44.48</td><td>290.66</td><td>58.67</td><td>300.56</td><td>59.58</td></tr><tr><td>quant</td><td>298.43</td><td>41.41</td><td>291.68</td><td>38.96</td><td>285.37</td><td>55.81</td><td>289.30</td><td>57.57</td></tr><tr><td>lang2</td><td>0.51</td><td>0.500</td><td>0.58</td><td>0.591</td><td>0.79</td><td>0.418</td><td>0.88</td><td>0.342</td></tr><tr><td>Number of observations</td><td>1104</td><td></td><td>1049</td><td></td><td>28</td><td></td><td>16</td><td></td></tr><tr><td colspan="9"><strong>Norway</strong></td></tr><tr><td>wage</td><td>1107591</td><td>2781348</td><td>1546490</td><td>3427086</td><td>1936607</td><td>3765607</td><td>2203896</td><td>4019355</td></tr><tr><td>a7</td><td>12.53</td><td>4.621</td><td>12.42</td><td>3.705</td><td>15.92</td><td>8.942</td><td>14.71</td><td>3.769</td></tr><tr><td>exper</td><td>20.93</td><td>13.726</td><td>20.36</td><td>13.265</td><td>16.01</td><td>13.548</td><td>16.55</td><td>10.754</td></tr><tr><td>prose</td><td>291.68</td><td>37.35</td><td>302.31</td><td>36.03</td><td>266.56</td><td>65.42</td><td>286.05</td><td>62.37</td></tr><tr><td>doc</td><td>306.91</td><td>44.39</td><td>305.11</td><td>43.41</td><td>278.25</td><td>77.33</td><td>289.97</td><td>68.34</td></tr><tr><td>quant</td><td>308.22</td><td>43.65</td><td>303.03</td><td>40.58</td><td>287.56</td><td>65.28</td><td>290.04</td><td>58.38</td></tr><tr><td>lang2</td><td>0.8215</td><td>0,38308</td><td>0.8324</td><td>0.37367</td><td>0.9813</td><td>0.13607</td><td>0.9740</td><td>0.16010</td></tr><tr><td>Number of observations</td><td>1227</td><td></td><td>1241</td><td></td><td>107</td><td></td><td>77</td><td></td></tr></tbody></table>
<table><caption>Table III-B Sample means for wage equations, by gender and place of birth: countries applying a non-bilingual policy</caption><thead><tr><th rowspan="3">Variables</th><th colspan="4">Born in country of survey</th><th colspan="4">Not born in country of survey</th></tr><tr><th colspan="2">Males</th><th colspan="2">Females</th><th colspan="2">Males</th><th colspan="2">Females</th></tr><tr><th>Mean</th><th>S.D.</th><th>Mean</th><th>S.D.</th><th>Mean</th><th>S.D.</th><th>Mean</th><th>S.D.</th></tr></thead><tbody><tr><td><strong>Chile</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>wage</td><td>12960155</td><td>31289941</td><td>17550442</td><td>36682533</td><td>39025000</td><td>50570700</td><td>-</td><td>-</td></tr><tr><td>a7</td><td>9.04</td><td>4.385</td><td>10.47</td><td>4.031</td><td>13.63</td><td>6.093</td><td>-</td><td>-</td></tr><tr><td>exper</td><td>22.54</td><td>14.317</td><td>19.05</td><td>13.376</td><td>14.13</td><td>12.357</td><td>-</td><td>-</td></tr><tr><td>prose</td><td>208.57</td><td>56.116</td><td>228.09</td><td>50.262</td><td>267.99</td><td>52.952</td><td>-</td><td>-</td></tr><tr><td>doc</td><td>212.88</td><td>55.844</td><td>224.11</td><td>48.452</td><td>276.48</td><td>69.506</td><td>-</td><td>-</td></tr><tr><td>quant</td><td>203.40</td><td>71.569</td><td>211.26</td><td>64.172</td><td>281.06</td><td>74.126</td><td>-</td><td>-</td></tr><tr><td>lang2</td><td>0.10</td><td>0.296</td><td>0.08</td><td>0.272</td><td>0.50</td><td>0.535</td><td>-</td><td>-</td></tr><tr><td>Number of observations</td><td>1361</td><td></td><td>873</td><td></td><td>8</td><td></td><td>1</td><td></td></tr><tr><td><strong>Czech Republic</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>wage</td><td>2880509</td><td>4422884.2</td><td>2963418</td><td>4498970</td><td>2079200</td><td>4174691.3</td><td>2107485</td><td>4085639.4</td></tr><tr><td>a7</td><td>13.57</td><td>4.754</td><td>13.02</td><td>4.331</td><td>10.60</td><td>1.430</td><td>13.07</td><td>3.369</td></tr><tr><td>exper</td><td>21.75</td><td>12.451</td><td>22.23</td><td>11.864</td><td>32.10</td><td>10.027</td><td>27.53</td><td>11.281</td></tr><tr><td>prose</td><td>274.32</td><td>39.326</td><td>275.41</td><td>37.546</td><td>250.11</td><td>33.613</td><td>258.82</td><td>47.891</td></tr><tr><td>doc</td><td>292.71</td><td>49.066</td><td>286.00</td><td>47.430</td><td>270.69</td><td>37.604</td><td>272.48</td><td>68.465</td></tr><tr><td>quant</td><td>310.20</td><td>50.701</td><td>301.32</td><td>47.657</td><td>277.93</td><td>45.189</td><td>267.78</td><td>64.347</td></tr><tr><td>lang2</td><td>0.55</td><td>0.498</td><td>0.58</td><td>0.494</td><td>1.00</td><td>0.000</td><td>1.00</td><td>0.000</td></tr><tr><td>Number of observations</td><td>1061</td><td></td><td>1245</td><td></td><td>10</td><td></td><td>10</td><td></td></tr><tr><td><strong>Denmark</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>wage</td><td>4806046</td><td>19880645</td><td>9896876</td><td>28032362</td><td>7974555</td><td>25532464</td><td>19440242</td><td>37425373</td></tr><tr><td>a7</td><td>13.14</td><td>4.051</td><td>13.03</td><td>3.893</td><td>14.04</td><td>3.457</td><td>11.83</td><td>3.099</td></tr><tr><td>exper</td><td>19.13</td><td>13.299</td><td>18.96</td><td>12.944</td><td>22.30</td><td>12.879</td><td>23.09</td><td>12.979</td></tr><tr><td>prose</td><td>278.22</td><td>32.743</td><td>283.46</td><td>32.198</td><td>259.33</td><td>46.675</td><td>266.18</td><td>39.142</td></tr><tr><td>doc</td><td>304.47</td><td>42.036</td><td>297.26</td><td>40.577</td><td>283.51</td><td>59.923</td><td>271.71</td><td>52.740</td></tr><tr><td>quant</td><td>310.80</td><td>40.365</td><td>297.90</td><td>39.552</td><td>292.40</td><td>61.702</td><td>281.01</td><td>49.656</td></tr><tr><td>lang2</td><td>0.86</td><td>0.349</td><td>0.89</td><td>0.309</td><td>0.96</td><td>0.209</td><td>0.96</td><td>0.209</td></tr><tr><td>Number of observations</td><td>1318</td><td></td><td>1197</td><td></td><td>23</td><td></td><td>23</td><td></td></tr><tr><td><strong>Italy</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>wage</td><td>273315</td><td>414803.94</td><td>312356</td><td>445827.69</td><td>274356</td><td>426839.80</td><td>410619</td><td>491194.95</td></tr><tr><td>a7</td><td>12.17</td><td>4.077</td><td>12.26</td><td>3.889</td><td>13.50</td><td>4.194</td><td>12.12</td><td>4.729</td></tr><tr><td>exper</td><td>22.18</td><td>11.701</td><td>19.29</td><td>11.306</td><td>17.43</td><td>8.664</td><td>16.20</td><td>10.275</td></tr><tr><td>prose</td><td>258.56</td><td>53.078</td><td>268.94</td><td>51.041</td><td>262.46</td><td>41.934</td><td>250.92</td><td>55.253</td></tr><tr><td>doc</td><td>258.28</td><td>53.293</td><td>254.60</td><td>49.885</td><td>265.01</td><td>44.743</td><td>241.71</td><td>61.151</td></tr><tr><td>quant</td><td>271.12</td><td>55.567</td><td>263.08</td><td>51.892</td><td>279.52</td><td>42.332</td><td>249.17</td><td>58.619</td></tr><tr><td>lang2</td><td>0.32</td><td>0.467</td><td>0.31</td><td>0.464</td><td>0.54</td><td>0.508</td><td>0.76</td><td>0.436</td></tr><tr><td>Number of observations</td><td>971</td><td></td><td>802</td><td></td><td>28</td><td></td><td>25</td><td></td></tr><tr><td><strong>Slovenia</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>wage</td><td>643475</td><td>761179.81</td><td>542160</td><td>643940.61</td><td>603367</td><td>636767.92</td><td>637158</td><td>745881.44</td></tr><tr><td>a7</td><td>11.47</td><td>2.844</td><td>11.77</td><td>2.874</td><td>10.84</td><td>3.190</td><td>10.54</td><td>3.242</td></tr><tr><td>exper</td><td>19.19</td><td>11.814</td><td>18.20</td><td>11.689</td><td>22.09</td><td>10.599</td><td>22.07</td><td>9.665</td></tr><tr><td>prose</td><td>230.46</td><td>52.332</td><td>249.55</td><td>49.802</td><td>208.65</td><td>60.946</td><td>218.84</td><td>60.914</td></tr><tr><td>doc</td><td>240.64</td><td>59.646</td><td>249.63</td><td>56.567</td><td>212.98</td><td>66.019</td><td>212.94</td><td>67.370</td></tr><tr><td>quant</td><td>252.04</td><td>64.021</td><td>257.46</td><td>58.394</td><td>225.01</td><td>69.251</td><td>222.69</td><td>64.889</td></tr><tr><td>lang2</td><td>0.84</td><td>0.365</td><td>0.73</td><td>0.444</td><td>0.97</td><td>0.172</td><td>0.95</td><td>0.210</td></tr><tr><td>Number of observations</td><td>947</td><td></td><td>904</td><td></td><td>99</td><td></td><td>109</td><td></td></tr></tbody></table>
<table><caption>Table III-B (cont'd)</caption><thead><tr><th rowspan="3">Variables</th><th colspan="4">Born in country of survey</th><th colspan="4">Not born in country of survey</th></tr><tr><th colspan="2">Males</th><th colspan="2">Females</th><th colspan="2">Males</th><th colspan="2">Females</th></tr><tr><th>Mean</th><th>S.D.</th><th>Mean</th><th>S.D.</th><th>Mean</th><th>S.D.</th><th>Mean</th><th>S.D.</th></tr></thead><tbody><tr><td><strong>Switzerland</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>wage</td><td>1567338</td><td>3565222.8</td><td>1866260</td><td>3863863.8</td><td>1601025</td><td>3615315.4</td><td>1645139</td><td>3689831.4</td></tr><tr><td>a7</td><td>13.86</td><td>2.860</td><td>13.20</td><td>5.352</td><td>12.12</td><td>4.652</td><td>11.10</td><td>3.785</td></tr><tr><td>exper</td><td>18.79</td><td>12.820</td><td>19.65</td><td>13.801</td><td>26.45</td><td>13.440</td><td>24.76</td><td>12.360</td></tr><tr><td>prose</td><td>285.16</td><td>38.957</td><td>282.58</td><td>38.746</td><td>245.54</td><td>53.354</td><td>246.63</td><td>58.898</td></tr><tr><td>doc</td><td>292.36</td><td>39.293</td><td>283.11</td><td>39.722</td><td>257.36</td><td>54.155</td><td>246.00</td><td>58.454</td></tr><tr><td>quant</td><td>303.67</td><td>43.996</td><td>288.88</td><td>41.973</td><td>262.19</td><td>59.373</td><td>249.15</td><td>60.239</td></tr><tr><td>lang2</td><td>0.9846</td><td>0.12345</td><td>0.9883</td><td>0.10752</td><td>0.9052</td><td>0.29425</td><td>0.9333</td><td>0.25064</td></tr><tr><td>Number of observations</td><td>324</td><td></td><td>343</td><td></td><td>116</td><td></td><td>105</td><td></td></tr><tr><td><strong>Hungary</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>wage</td><td>20601578</td><td>37899973</td><td>19202275</td><td>36963758</td><td>27951944</td><td>43043159</td><td>13948922</td><td>32496006</td></tr><tr><td>a7</td><td>12.80</td><td>7.698</td><td>12.93</td><td>6.949</td><td>13.67</td><td>3.559</td><td>15.25</td><td>3.980</td></tr><tr><td>exper</td><td>19.64</td><td>13.481</td><td>19.56</td><td>12.728</td><td>24.00</td><td>15.887</td><td>21.92</td><td>12.471</td></tr><tr><td>prose</td><td>241.89</td><td>38.27</td><td>253.61</td><td>37.78</td><td>236.11</td><td>48.34</td><td>282.48</td><td>36.63</td></tr><tr><td>doc</td><td>255.45</td><td>48.97</td><td>256.61</td><td>48.62</td><td>250.07</td><td>50.21</td><td>277.43</td><td>44.14</td></tr><tr><td>quant</td><td>278.04</td><td>48.27</td><td>278.95</td><td>48.57</td><td>265.91</td><td>53.32</td><td>267.40</td><td>89.33</td></tr><tr><td>lang2</td><td>0.19</td><td>0.391</td><td>0.26</td><td>0.438</td><td>0.50</td><td>0.548</td><td>1.00</td><td></td></tr><tr><td>Number of observations</td><td>752</td><td></td><td>763</td><td></td><td>6</td><td></td><td>12</td><td></td></tr></tbody></table>
Table IV-A Estimated coefficients, human capital wage equations: countries applying a bilingual policy

(Note: only cases presenting positive adjusted R² are presented in these tables. For details about cases not presented here, please contact the author)

Finland

<table><thead><tr><th rowspan="2">Gender</th><th rowspan="2">Independent variable</th><th colspan="9">Dependent variable: Natural logarithm of annual wage rate (quintile)</th></tr><tr><th>(1)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th><th>(2)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th><th>(3)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th></tr></thead><tbody><tr><td colspan="11" style="text-align: left; font-weight: bold;">Born in country of survey</td></tr><tr><td rowspan="8">Men</td><td>Intercept</td><td>9.446</td><td>39.746</td><td>0.000</td><td>10.486</td><td>24.663</td><td>0.000</td><td>10.366</td><td>23.999</td><td>0.000</td></tr><tr><td>a7</td><td>0.106</td><td>7.103</td><td>0.000</td><td>0.124</td><td>7.542</td><td>0.000</td><td>0.131</td><td>7.680</td><td>0.000</td></tr><tr><td>exper</td><td>0.051</td><td>12.279</td><td>0.000</td><td>0.046</td><td>10.134</td><td>0.000</td><td>0.045</td><td>9.785</td><td>0.000</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>-0.013</td><td>-4.391</td><td>0.000</td><td>-0.013</td><td>-4.170</td><td>0.000</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>0.007</td><td>2.143</td><td>0.032</td><td>0.008</td><td>2.209</td><td>0.027</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.002</td><td>0.495</td><td>0.621</td><td>0.001</td><td>0.393</td><td>0.694</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.179</td><td>-1.540</td><td>0.124</td></tr><tr><td>Adjusted R²</td><td>0.122</td><td></td><td></td><td>0.136</td><td></td><td></td><td>0.137</td><td></td><td></td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Dependent variable mean: 11.76, Number of observations: 1104.</td></tr><tr><td rowspan="8">Women</td><td>Intercept</td><td>9.644</td><td>35.732</td><td>0.000</td><td>11.253</td><td>20.695</td><td>0.000</td><td>11.289</td><td>20.663</td><td>0.000</td></tr><tr><td>a7</td><td>0.059</td><td>3.522</td><td>0.000</td><td>0.071</td><td>4.044</td><td>0.000</td><td>0.069</td><td>3.831</td><td>0.000</td></tr><tr><td>exper</td><td>0.055</td><td>12.269</td><td>0.000</td><td>0.047</td><td>9.001</td><td>0.000</td><td>0.047</td><td>8.991</td><td>0.000</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>-0.013</td><td>-3.787</td><td>0.000</td><td>-0.013</td><td>-3.821</td><td>0.000</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>0.003</td><td>0.872</td><td>0.383</td><td>0.003</td><td>0.833</td><td>0.405</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.004</td><td>1.119</td><td>0.263</td><td>0.004</td><td>1.147</td><td>0.252</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.070</td><td>0.693</td><td>0.488</td></tr><tr><td>Adjusted R²</td><td>0.125</td><td></td><td></td><td>0.138</td><td></td><td></td><td>0.137</td><td></td><td></td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Dependent variable mean: 11.45, Number of observations: 1049.</td></tr></tbody></table>
Table IV-A (cont'd)

Norway

<table><thead><tr><th rowspan="2">Gender</th><th rowspan="2">Independent variable</th><th colspan="9">Dependent variable: Natural logarithm of annual wage rate (quintile)</th></tr><tr><th>(1)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th><th>(2)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th><th>(3)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th></tr></thead><tbody><tr><td colspan="11" style="text-align: left; font-weight: bold;">Born in country of survey</td></tr><tr><td rowspan="7">Men</td><td>Intercept</td><td>10.816</td><td>66.675</td><td>0.000</td><td>10.963</td><td>29.220</td><td>0.000</td><td>10.976</td><td>29.217</td><td>0.000</td></tr><tr><td>a7</td><td>0.062</td><td>6.446</td><td>0.000</td><td>0.051</td><td>5.007</td><td>0.000</td><td>0.051</td><td>4.989</td><td>0.000</td></tr><tr><td>exper</td><td>0.040</td><td>12.292</td><td>0.000</td><td>0.034</td><td>9.465</td><td>0.000</td><td>0.035</td><td>9.444</td><td>0.000</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>-0.011</td><td>-3.595</td><td>0.000</td><td>-0.011</td><td>-3.633</td><td>0.000</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>0.000</td><td>-0.135</td><td>0.892</td><td>0.000</td><td>-0.151</td><td>0.880</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.011</td><td>3.859</td><td>0.000</td><td>0.011</td><td>3.812</td><td>0.000</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.088</td><td>0.725</td><td>0.469</td></tr><tr><td></td><td>Adjusted R²</td><td>0.112</td><td></td><td></td><td>0.127</td><td></td><td></td><td>0.127</td><td></td><td></td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Dependent variable mean: 12.42, Number of observations: 1227.</td></tr><tr><td rowspan="7">Women</td><td>Intercept</td><td>11.419</td><td>51.626</td><td>0.000</td><td>12.153</td><td>23.448</td><td>0.000</td><td>12.173</td><td>23.474</td><td>0.000</td></tr><tr><td>a7</td><td>0.036</td><td>2.517</td><td>0.012</td><td>0.040</td><td>2.673</td><td>0.008</td><td>0.039</td><td>2.559</td><td>0.011</td></tr><tr><td>exper</td><td>0.022</td><td>5.603</td><td>0.000</td><td>0.017</td><td>3.793</td><td>0.000</td><td>0.018</td><td>3.924</td><td>0.000</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>-0.001</td><td>-0.366</td><td>0.714</td><td>-0.002</td><td>-0.436</td><td>0.663</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>-0.006</td><td>-1.516</td><td>0.130</td><td>-0.006</td><td>-1.590</td><td>0.112</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.005</td><td>1.324</td><td>0.186</td><td>0.005</td><td>1.334</td><td>0.183</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.164</td><td>1.073</td><td>0.284</td></tr><tr><td></td><td>Adjusted R²</td><td>0.024</td><td></td><td></td><td>0.025</td><td></td><td></td><td>0.025</td><td></td><td></td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Dependent variable mean: 12.31, Number of observations: 1241.</td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Not born in country of survey</td></tr><tr><td rowspan="7">Men</td><td>Intercept</td><td>11.516</td><td>15.840</td><td>0.000</td><td>10.790</td><td>9.823</td><td>0.000</td><td>12.217</td><td>6.988</td><td>0.000</td></tr><tr><td>a7</td><td>0.037</td><td>1.298</td><td>0.197</td><td>0.024</td><td>0.770</td><td>0.443</td><td>0.029</td><td>0.928</td><td>0.356</td></tr><tr><td>exper</td><td>0.028</td><td>1.467</td><td>0.146</td><td>0.024</td><td>1.233</td><td>0.220</td><td>0.026</td><td>1.346</td><td>0.181</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>0.001</td><td>0.110</td><td>0.913</td><td>0.001</td><td>0.122</td><td>0.903</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>-0.015</td><td>-1.456</td><td>0.148</td><td>-0.015</td><td>-1.435</td><td>0.154</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.017</td><td>1.647</td><td>0.103</td><td>0.016</td><td>1.586</td><td>0.116</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-1.497</td><td>-1.049</td><td>0.297</td></tr><tr><td></td><td>Adjusted R²</td><td>0.003</td><td></td><td></td><td>0.004</td><td></td><td></td><td>0.005</td><td></td><td></td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Dependent variable mean: 12.56, Number of observations: 107.</td></tr></tbody></table>
Table IV-B Estimated coefficients, human capital wage equations: countries applying a non-bilingual policy

(Note: Only cases presenting positive adjusted R² are presented in these tables. For details about cases not presented here, please contact the author)

Chile

<table><thead><tr><th rowspan="2">Gender</th><th rowspan="2">Independent variable</th><th colspan="9">Dependent variable: Natural logarithm of annual wage rate (quintile)</th></tr><tr><th>(1)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th><th>(2)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th><th>(3)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th></tr></thead><tbody><tr><td colspan="11" style="text-align: left; font-weight: bold;">Born in country of survey</td></tr><tr><td rowspan="7">Men</td><td>Intercept</td><td>12.835</td><td>69.509</td><td>0.000</td><td>12.492</td><td>41.889</td><td>0.000</td><td>12.485</td><td>41.780</td><td>0.000</td></tr><tr><td>a7</td><td>0.138</td><td>10.915</td><td>0.000</td><td>0.107</td><td>6.032</td><td>0.000</td><td>0.108</td><td>6.035</td><td>0.000</td></tr><tr><td>exper</td><td>0.015</td><td>3.781</td><td>0.000</td><td>0.015</td><td>3.726</td><td>0.000</td><td>0.015</td><td>3.746</td><td>0.000</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>0.001</td><td>0.243</td><td>0.808</td><td>0.001</td><td>0.280</td><td>0.779</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>0.001</td><td>0.403</td><td>0.687</td><td>0.001</td><td>0.373</td><td>0.710</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.001</td><td>0.384</td><td>0.701</td><td>0.001</td><td>0.384</td><td>0.701</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.063</td><td>-0.402</td><td>0.688</td></tr><tr><td colspan="2">Adjusted R²</td><td>0.085</td><td></td><td></td><td>0.087</td><td></td><td></td><td>0.087</td><td></td><td></td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Dependent variable mean: 14.41, Number of observations: 1361.</td></tr><tr><td rowspan="7">Women</td><td>Intercept</td><td>12.073</td><td>41.692</td><td>0.000</td><td>11.454</td><td>22.730</td><td>0.000</td><td>11.490</td><td>22.776</td><td>0.000</td></tr><tr><td>a7</td><td>0.154</td><td>7.821</td><td>0.000</td><td>0.094</td><td>3.570</td><td>0.000</td><td>0.088</td><td>3.313</td><td>0.000</td></tr><tr><td>exper</td><td>0.036</td><td>6.065</td><td>0.000</td><td>0.036</td><td>6.141</td><td>0.000</td><td>0.037</td><td>6.150</td><td>0.000</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>0.004</td><td>0.997</td><td>0.319</td><td>0.005</td><td>1.061</td><td>0.289</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>-0.002</td><td>-0.456</td><td>0.648</td><td>-0.003</td><td>-0.497</td><td>0.619</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.004</td><td>0.941</td><td>0.347</td><td>0.003</td><td>0.914</td><td>0.361</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.336</td><td>1.300</td><td>0.194</td></tr><tr><td colspan="2">Adjusted R²</td><td>0.069</td><td></td><td></td><td>0.078</td><td></td><td></td><td>0.079</td><td></td><td></td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Dependent variable mean: 14.37, Number of observations: 873.</td></tr></tbody></table>

Czech Republic

<table><thead><tr><th rowspan="2">Gender</th><th rowspan="2">Independent variable</th><th colspan="9">Dependent variable: Natural logarithm of annual wage rate (quintile)</th></tr><tr><th>(1)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th><th>(2)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th><th>(3)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th></tr></thead><tbody><tr><td colspan="11" style="text-align: left; font-weight: bold;">Born in country of survey</td></tr><tr><td rowspan="7">Men</td><td>Intercept</td><td>12.192</td><td>45.991</td><td>0.000</td><td>12.685</td><td>23.782</td><td>0.000</td><td>12.732</td><td>23.715</td><td>0.000</td></tr><tr><td>a7</td><td>0.026</td><td>1.824</td><td>0.069</td><td>0.025</td><td>1.729</td><td>0.084</td><td>0.023</td><td>1.586</td><td>0.113</td></tr><tr><td>exper</td><td>0.014</td><td>2.474</td><td>0.014</td><td>0.008</td><td>1.356</td><td>0.175</td><td>0.008</td><td>1.404</td><td>0.160</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>-0.009</td><td>-2.392</td><td>0.017</td><td>-0.009</td><td>-2.469</td><td>0.014</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>-0.012</td><td>-3.134</td><td>0.002</td><td>-0.012</td><td>-3.167</td><td>0.002</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.018</td><td>5.036</td><td>0.000</td><td>0.018</td><td>5.073</td><td>0.000</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.108</td><td>0.780</td><td>0.436</td></tr><tr><td colspan="2">Adjusted R²</td><td>0.005</td><td></td><td></td><td>0.027</td><td></td><td></td><td>0.026</td><td></td><td></td></tr><tr><td rowspan="7">Women</td><td>Intercept</td><td>12.275</td><td>40.892</td><td>0.000</td><td>13.538</td><td>22.796</td><td>0.000</td><td>13.541</td><td>22.802</td><td>0.000</td></tr><tr><td>a7</td><td>0.032</td><td>1.967</td><td>0.049</td><td>0.031</td><td>1.869</td><td>0.062</td><td>0.029</td><td>1.760</td><td>0.079</td></tr><tr><td>exper</td><td>-0.004</td><td>-0.731</td><td>0.465</td><td>-0.012</td><td>-1.986</td><td>0.047</td><td>-0.012</td><td>-1.845</td><td>0.065</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>-0.010</td><td>-2.575</td><td>0.010</td><td>-0.010</td><td>-2.634</td><td>0.009</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>-0.014</td><td>-3.338</td><td>0.001</td><td>-0.014</td><td>-3.360</td><td>0.001</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.019</td><td>4.746</td><td>0.000</td><td>0.019</td><td>4.755</td><td>0.000</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.149</td><td>1.078</td><td>0.281</td></tr><tr><td colspan="2">Adjusted R²</td><td>0.004</td><td></td><td></td><td>0.024</td><td></td><td></td><td>0.024</td><td></td><td></td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Dependent variable mean: 12.60, Number of observations: 1245.</td></tr></tbody></table>
<caption>Table IV-B (cont'd)</caption>

Denmark

<table><thead><tr><th rowspan="2">Gender</th><th rowspan="2">Independent variable</th><th colspan="9">Dependent variable: Natural logarithm of annual wage rate (quintile)</th></tr><tr><th>(1)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th><th>(2)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th><th>(3)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th></tr></thead><tbody><tr><td colspan="11" style="text-align: left; font-weight: bold;">Born in country of survey</td></tr><tr><td rowspan="7">Men</td><td>Intercept</td><td>10.529</td><td>58.758</td><td>0.000</td><td>11.271</td><td>27.542</td><td>0.000</td><td>11.268</td><td>27.513</td><td>0.000</td></tr><tr><td>a7</td><td>0.080</td><td>7.184</td><td>0.000</td><td>0.084</td><td>7.117</td><td>0.000</td><td>0.083</td><td>7.052</td><td>0.000</td></tr><tr><td>exper</td><td>0.045</td><td>13.215</td><td>0.000</td><td>0.045</td><td>12.075</td><td>0.000</td><td>0.045</td><td>11.751</td><td>0.000</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>-0.015</td><td>-3.986</td><td>0.000</td><td>-0.015</td><td>-3.985</td><td>0.000</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>0.009</td><td>2.335</td><td>0.020</td><td>0.009</td><td>2.312</td><td>0.021</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.002</td><td>0.736</td><td>0.462</td><td>0.002</td><td>0.752</td><td>0.452</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.032</td><td>0.223</td><td>0.824</td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Dependent variable mean: 12.43, Number of observations: 1318.</td></tr><tr><td rowspan="7">Women</td><td>Intercept</td><td>11.318</td><td>39.978</td><td>0.000</td><td>12.773</td><td>19.817</td><td>0.000</td><td>12.698</td><td>19.717</td><td>0.000</td></tr><tr><td>a7</td><td>0.048</td><td>2.739</td><td>0.006</td><td>0.064</td><td>3.459</td><td>0.001</td><td>0.063</td><td>3.452</td><td>0.001</td></tr><tr><td>exper</td><td>0.027</td><td>5.139</td><td>0.000</td><td>0.027</td><td>4.677</td><td>0.000</td><td>0.030</td><td>5.060</td><td>0.000</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>-0.011</td><td>-2.042</td><td>0.041</td><td>-0.013</td><td>-2.404</td><td>0.016</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>0.008</td><td>1.527</td><td>0.127</td><td>0.008</td><td>1.504</td><td>0.133</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>-0.003</td><td>-0.729</td><td>0.466</td><td>-0.003</td><td>-0.612</td><td>0.540</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.555</td><td>2.423</td><td>0.016</td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Dependent variable mean: 12.45, Number of observations: 1197.</td></tr></tbody></table>

Hungary

<table><thead><tr><th rowspan="2">Gender</th><th rowspan="2">Independent variable</th><th colspan="9">Dependent variable: Natural logarithm of annual wage rate (quintile)</th></tr><tr><th>(1)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th><th>(2)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th><th>(3)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th></tr></thead><tbody><tr><td colspan="11" style="text-align: left; font-weight: bold;">Born in country of survey</td></tr><tr><td rowspan="7">Men</td><td>Intercept</td><td>10.984</td><td>27.442</td><td>0.000</td><td>10.299</td><td>11.522</td><td>0.000</td><td>10.476</td><td>11.726</td><td>0.000</td></tr><tr><td>a7</td><td>0.057</td><td>3.129</td><td>0.002</td><td>0.046</td><td>2.521</td><td>0.012</td><td>0.043</td><td>2.325</td><td>0.020</td></tr><tr><td>exper</td><td>0.034</td><td>3.292</td><td>0.001</td><td>0.028</td><td>2.539</td><td>0.011</td><td>0.030</td><td>2.682</td><td>0.007</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>-0.021</td><td>-3.183</td><td>0.002</td><td>-0.021</td><td>-3.237</td><td>0.001</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>0.002</td><td>0.413</td><td>0.680</td><td>0.002</td><td>0.305</td><td>0.761</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.019</td><td>3.458</td><td>0.001</td><td>0.019</td><td>3.417</td><td>0.001</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.788</td><td>2.529</td><td>0.012</td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Dependent variable mean: 12.39, Number of observations: 752.</td></tr><tr><td rowspan="7">Women</td><td>Intercept</td><td>11.517</td><td>27.629</td><td>0.000</td><td>10.129</td><td>10.147</td><td>0.000</td><td>10.199</td><td>10.221</td><td>0.000</td></tr><tr><td>a7</td><td>0.024</td><td>1.203</td><td>0.229</td><td>0.011</td><td>0.534</td><td>0.593</td><td>0.012</td><td>0.606</td><td>0.545</td></tr><tr><td>exper</td><td>0.014</td><td>1.347</td><td>0.178</td><td>0.015</td><td>1.323</td><td>0.186</td><td>0.018</td><td>1.591</td><td>0.112</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>-0.005</td><td>-0.758</td><td>0.448</td><td>-0.006</td><td>-0.857</td><td>0.391</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>-0.005</td><td>-0.948</td><td>0.343</td><td>-0.005</td><td>-0.961</td><td>0.337</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.015</td><td>2.810</td><td>0.005</td><td>0.015</td><td>2.757</td><td>0.006</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.481</td><td>1.689</td><td>0.092</td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Dependent variable mean: 12.10, Number of observations: 763.</td></tr></tbody></table>
Table IV-B (cont'd)

Italy

<table><thead><tr><th rowspan="2">Gender</th><th rowspan="2">Independent variable</th><th colspan="9">Dependent variable: Natural logarithm of annual wage rate (quintile)</th></tr><tr><th>(1)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th><th>(2)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th><th>(3)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th></tr></thead><tbody><tr><td colspan="11">Born in country of survey</td></tr><tr><td rowspan="7">Men</td><td>Intercept</td><td>10.257</td><td>42.300</td><td>0.000</td><td>10.090</td><td>29.367</td><td>0.000</td><td>10.086</td><td>29.303</td><td>0.000</td></tr><tr><td>a7</td><td>0.044</td><td>3.123</td><td>0.002</td><td>0.036</td><td>2.226</td><td>0.026</td><td>0.037</td><td>2.217</td><td>0.027</td></tr><tr><td>exper</td><td>0.018</td><td>3.586</td><td>0.000</td><td>0.015</td><td>3.010</td><td>0.003</td><td>0.015</td><td>3.007</td><td>0.003</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>-0.007</td><td>-2.682</td><td>0.007</td><td>-0.007</td><td>-2.658</td><td>0.008</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>-0.002</td><td>-0.642</td><td>0.521</td><td>-0.002</td><td>-0.655</td><td>0.513</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.010</td><td>2.895</td><td>0.004</td><td>0.010</td><td>2.898</td><td>0.004</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.027</td><td>-0.226</td><td>0.821</td></tr><tr><td colspan="2">Adjusted R<sup>2</sup></td><td>0.014</td><td></td><td></td><td>0.026</td><td></td><td></td><td>0.025</td><td></td><td></td></tr><tr><td colspan="11">Dependent variable mean: 11.18, Number of observations: 971.</td></tr><tr><td rowspan="7">Women</td><td>Intercept</td><td>10.563</td><td>30.121</td><td>0.000</td><td>10.850</td><td>21.998</td><td>0.000</td><td>10.863</td><td>21.923</td><td>0.000</td></tr><tr><td>a7</td><td>0.015</td><td>0.733</td><td>0.464</td><td>0.014</td><td>0.591</td><td>0.555</td><td>0.012</td><td>0.509</td><td>0.611</td></tr><tr><td>exper</td><td>0.013</td><td>1.795</td><td>0.073</td><td>0.008</td><td>1.107</td><td>0.269</td><td>0.008</td><td>1.116</td><td>0.265</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>-0.009</td><td>-2.364</td><td>0.018</td><td>-0.009</td><td>-2.373</td><td>0.018</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>-0.008</td><td>-1.599</td><td>0.110</td><td>-0.008</td><td>-1.603</td><td>0.109</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.016</td><td>3.655</td><td>0.000</td><td>0.016</td><td>3.660</td><td>0.000</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.046</td><td>0.289</td><td>0.773</td></tr><tr><td colspan="2">Adjusted R<sup>2</sup></td><td>0.002</td><td></td><td></td><td>0.017</td><td></td><td></td><td>0.016</td><td></td><td></td></tr><tr><td colspan="11">Dependent variable mean: 11.00, Number of observations: 802.</td></tr></tbody></table>
Table IV-B (cont'd)

Switzerland (Italian-speaking)

<table><thead><tr><th rowspan="2">Gender</th><th rowspan="2">Independent variable</th><th colspan="9">Dependent variable: Natural logarithm of annual wage rate (quintile)</th></tr><tr><th>(1)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th><th>(2)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th><th>(3)<br>Parameter<br>estimate</th><th>t-statistic<br>H₀: β=0</th><th>Prob&gt;<br>|t|</th></tr></thead><tbody><tr><td colspan="11" style="text-align: left; font-weight: bold;">Born in country of survey</td></tr><tr><td rowspan="7">Men</td><td>Intercept</td><td>9.754</td><td>14.325</td><td>0.000</td><td>10.387</td><td>9.896</td><td>0.000</td><td>9.207</td><td>7.090</td><td>0.000</td></tr><tr><td>a7</td><td>0.079</td><td>1.860</td><td>0.064</td><td>0.093</td><td>2.022</td><td>0.044</td><td>0.083</td><td>1.791</td><td>0.074</td></tr><tr><td>exper</td><td>0.041</td><td>4.312</td><td>0.000</td><td>0.038</td><td>3.794</td><td>0.000</td><td>0.036</td><td>3.549</td><td>0.000</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>-0.005</td><td>-0.873</td><td>0.383</td><td>-0.006</td><td>-0.907</td><td>0.365</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>-0.001</td><td>-0.204</td><td>0.838</td><td>-0.001</td><td>-0.223</td><td>0.824</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.004</td><td>0.676</td><td>0.500</td><td>0.004</td><td>0.696</td><td>0.487</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.442</td><td>1.538</td><td>0.125</td></tr><tr><td colspan="2">Adjusted R²</td><td>0.049</td><td></td><td></td><td>0.044</td><td></td><td></td><td>0.048</td><td></td><td></td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Dependent variable mean: 11.61, Number of observations: 324.</td></tr><tr><td rowspan="7">Women</td><td>Intercept</td><td>10.543</td><td>19.201</td><td>0.000</td><td>13.125</td><td>10.633</td><td>0.000</td><td>13.142</td><td>7.844</td><td>0.000</td></tr><tr><td>a7</td><td>0.017</td><td>0.573</td><td>0.567</td><td>0.027</td><td>0.906</td><td>0.366</td><td>0.027</td><td>0.905</td><td>0.366</td></tr><tr><td>exper</td><td>0.025</td><td>2.204</td><td>2.204</td><td>0.019</td><td>1.535</td><td>0.126</td><td>0.019</td><td>1.530</td><td>0.127</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>-0.015</td><td>-2.079</td><td>0.038</td><td>-0.015</td><td>-2.060</td><td>0.040</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>0.004</td><td>0.438</td><td>0.662</td><td>0.004</td><td>0.434</td><td>0.664</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.002</td><td>0.303</td><td>0.762</td><td>0.002</td><td>0.303</td><td>0.762</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.018</td><td>-0.014</td><td>0.988</td></tr><tr><td colspan="2">Adjusted R²</td><td>0.009</td><td></td><td></td><td>0.024</td><td></td><td></td><td>0.021</td><td></td><td></td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Dependent variable mean: 11.25, Number of observations: 343.</td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Not born in country of survey</td></tr><tr><td rowspan="7">Men</td><td>Intercept</td><td>11.578</td><td>13.367</td><td>0.000</td><td>12.154</td><td>10.358</td><td>0.000</td><td>11.829</td><td>9.680</td><td>0.000</td></tr><tr><td>a7</td><td>-0.016</td><td>-0.355</td><td>0.723</td><td>0.033</td><td>0.593</td><td>0.554</td><td>0.028</td><td>0.507</td><td>0.614</td></tr><tr><td>exper</td><td>0.009</td><td>0.546</td><td>0.586</td><td>0.010</td><td>0.642</td><td>0.522</td><td>0.008</td><td>0.490</td><td>0.625</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>-0.027</td><td>-2.717</td><td>0.008</td><td>-0.027</td><td>-2.699</td><td>0.008</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>0.019</td><td>1.901</td><td>0.060</td><td>0.018</td><td>1.816</td><td>0.072</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>0.002</td><td>0.261</td><td>0.795</td><td>0.002</td><td>0.279</td><td>0.781</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.624</td><td>0.958</td><td>0.340</td></tr><tr><td colspan="2">Adjusted R²</td><td>-0.011</td><td></td><td></td><td>0.032</td><td></td><td></td><td>0.032</td><td></td><td></td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Dependent variable mean: 11.61, Number of observations: 116.</td></tr><tr><td rowspan="7">Women</td><td>Intercept</td><td>8.643</td><td>7.921</td><td>0.000</td><td>8.586</td><td>6.162</td><td>0.000</td><td>8.381</td><td>5.152</td><td>0.000</td></tr><tr><td>a7</td><td>0.052</td><td>0.782</td><td>0.436</td><td>0.043</td><td>0.543</td><td>0.588</td><td>0.040</td><td>0.500</td><td>0.618</td></tr><tr><td>exper</td><td>0.072</td><td>3.527</td><td>0.001</td><td>0.074</td><td>3.517</td><td>0.001</td><td>0.074</td><td>3.485</td><td>0.001</td></tr><tr><td>prose</td><td></td><td></td><td></td><td>0.009</td><td>0.800</td><td>0.426</td><td>0.009</td><td>0.807</td><td>0.422</td></tr><tr><td>doc</td><td></td><td></td><td></td><td>0.001</td><td>0.118</td><td>0.907</td><td>0.002</td><td>0.127</td><td>0.899</td></tr><tr><td>quant</td><td></td><td></td><td></td><td>-0.010</td><td>-0.756</td><td>0.451</td><td>-0.010</td><td>-0.762</td><td>0.448</td></tr><tr><td>lang2</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.228</td><td>0.247</td><td>0.806</td></tr><tr><td colspan="2">Adjusted R²</td><td>0.098</td><td></td><td></td><td>0.078</td><td></td><td></td><td>0.069</td><td></td><td></td></tr><tr><td colspan="11" style="text-align: left; font-weight: bold;">Dependent variable mean: 11.01, Number of observations: 105.</td></tr></tbody></table>
<figure><img src="image_19.png" /></figure>
# ANNEXES
<figure><img src="image_20.png" /></figure>
<table><caption>Table A Current language settings of countries in Study I ('U'=Unilingual; 'B'=Bilingual; and 'T'=Trilingual) (1/7)</caption><thead><tr><th></th><th>Colonial Language(s)</th><th>Official Language(s)</th><th>National Language(s)</th><th>Total Number of Language(s)</th><th>Language(s) of Instruction</th><th>LiE System</th><th>Language(s) of the Labor Market</th><th>Linguistic Group (U, B, T)</th></tr></thead><tbody><tr><td>Angola</td><td>Portuguese</td><td>Portuguese (de facto)</td><td>Umbundu, Kimbundu, Kikongo, Quioco and Ganguela.</td><td>41</td><td>Portuguese/Angolan Portuguese</td><td>Portuguese unilingual system</td><td>Angolan Portuguese</td><td>Lusophone (U)</td></tr><tr><td>Benin</td><td>French</td><td>French</td><td>&gt; 50, among which Fon, Yorouba, Bariba, Adja, Goun and Ayizo are the most spoken.</td><td>51</td><td>French (the national languages are only used in non-formal education for adult literacy).</td><td>French unilingual system</td><td>French</td><td>Francophone (U)</td></tr><tr><td>Botswana</td><td>English</td><td>English</td><td>Tswana</td><td>30</td><td>Primary: Tswana; Secondary and upward: English</td><td>Bilingual system</td><td>English and Tswana</td><td>English-speaking (U)</td></tr><tr><td>Burkina Faso</td><td>French</td><td>French</td><td>Mooré (or Mossi), Dioula and Foulfoudé (also called Fulbé, Peular or Peul)</td><td>71</td><td>French and National Languages<sup>1</sup>.</td><td>French unilingual system</td><td>French</td><td>Francophone (U)</td></tr><tr><td>Burundi</td><td>French</td><td>Kirundi (de jure) and French (de facto)</td><td>N/A</td><td>3</td><td>Grades 1-3: Kirundi; Grades 4-6: Inclusion of French; Secondary upward: French only</td><td>Bilingual system</td><td>Kirundi, French (formal and official settings), Swahili (mainly in SME) and English (business and trade)</td><td>Francophone (B)</td></tr></tbody></table>

<footnote n="1">¹ However, in practice, only French is used as medium of instruction in formal education, and the national languages only for literacy programs.</footnote>
<table><caption>Table A Cont'd (2/7)</caption><thead><tr><th></th><th>Colonial Language(s)</th><th>Official Language(s)</th><th>National Language(s)</th><th>Total Number of Language(s)</th><th>Language(s) of Instruction</th><th>LiE System</th><th>Language(s) of the Labor Market</th><th>Linguistic Group (U, B, T)</th></tr></thead><tbody><tr><td>Cameroon</td><td>German, English and French</td><td>French and English</td><td>280 National languages</td><td>279</td><td>Primary and Secondary: English or French (according to the linguistic geographic area), and teaching of the other language in Grade 6 upward.</td><td>French and British system with national incentive for biculturalism</td><td>French and English</td><td>Francophone + English-speaking (B)</td></tr><tr><td>Central Africa</td><td>French</td><td>Sango and French</td><td>Sango</td><td>68</td><td>French (Sango is only used in non-formal education for adult literacy)</td><td>French unilingual system</td><td>French (written) and Sango (oral)</td><td>Francophone (B)</td></tr><tr><td>Congo-Brazzaville</td><td>French</td><td>French</td><td>Lingala and Munukutuba</td><td>60</td><td>French</td><td>French unilingual system</td><td>French (all sectors)</td><td>Francophone (U)</td></tr><tr><td>Congo-Kinshasa</td><td>French</td><td>French and English</td><td>Kikongo, Lingala, Kiswahili, and Tshiluba.</td><td>221</td><td>French</td><td>French unilingual system</td><td>French, English and vernacular languages</td><td>Francophone (U)<sup>2</sup></td></tr><tr><td>Cote d'Ivoire</td><td>French</td><td>French</td><td>70 National languages</td><td>73</td><td>French (English is introduced as compulsory second language at Secondary level)</td><td>French unilingual system</td><td>French</td><td>Francophone (U)</td></tr><tr><td>Egypt</td><td>French and English</td><td>Arabic (de jure)</td><td>N/A</td><td>11</td><td>Classic Arabic<sup>3</sup></td><td>Arabization policy</td><td>Arabic and English</td><td>Arabic-speaking (U)</td></tr></tbody></table>

<footnote n="2">² Congo-Kinshasa is still unilingual although it pursues bilingualism in French and English. In order to reach that goal, it needs to redefine the LiE policy.</footnote>

<footnote n="3">³ Private schools offer instruction in other languages in addition to Arabic (e.g., French, English). These schools are targeted to the socio-economic elite.</footnote>
<table><caption>Table A Cont'd (3/7)</caption><thead><tr><th></th><th>Colonial Language(s)</th><th>Official Language(s)</th><th>National Language(s)</th><th>Total Number of Language(s)</th><th>Language(s) of Instruction</th><th>LiE System</th><th>Language(s) of the Labor Market</th><th>Linguistic Group (U, B, T)</th></tr></thead><tbody><tr><td>Gabon</td><td>French</td><td>French</td><td>All Gabonese languages</td><td>40</td><td>French</td><td>French unilingual system</td><td>French</td><td>Francophone (U)</td></tr><tr><td>Gambia</td><td>English</td><td>English (de facto)</td><td>N/A</td><td>20</td><td>English</td><td>British unilingual system</td><td>English</td><td>English-speaking (U)</td></tr><tr><td>Ghana</td><td>English</td><td>English (de facto)</td><td>N/A</td><td>72</td><td>English<sup>4</sup></td><td>British unilingual system</td><td>Akan</td><td>English-speaking (U)</td></tr><tr><td>Guinea-Bissau</td><td>Portuguese</td><td>Portuguese (de facto)</td><td>N/A</td><td>23</td><td>Portuguese</td><td>Portuguese unilingual system</td><td>Portuguese</td><td>Lusophone (U)</td></tr><tr><td>Kenya</td><td>English</td><td>English (de facto) and Swahili (de facto)</td><td>N/A</td><td>61</td><td>English (Swahili only taught as second language)</td><td>British unilingual system</td><td>English and Swahili</td><td>English-speaking (B)</td></tr><tr><td>Lesotho</td><td>English</td><td>Sotho and English</td><td>Sotho</td><td>3</td><td>Primary (up to Grade 5): Sotho; Grades 6-7: Progress introduction of English; Secondary: English only (Sotho taught as second language)</td><td>Bilingual system</td><td>English and Sotho</td><td>English-speaking (B)</td></tr><tr><td>Libya</td><td>Italian, French and English</td><td>Arabic</td><td>N/A</td><td>12</td><td>Classic Arabic</td><td>Arabization policy</td><td>Libyan Arabic</td><td>Arabic-speaking (U)</td></tr></tbody></table>

<footnote n="4">⁴ Some vernacular languages, e.g. akan, ćwě, ga, etc., are taught in Primary, but not the local mother-tongues.</footnote>
<table><caption>Table A Cont'd (4/7)</caption><thead><tr><th></th><th>Colonial Language(s)</th><th>Official Language(s)</th><th>National Language(s)</th><th>Total Number of Language(s)</th><th>Language(s) of Instruction</th><th>LiE System</th><th>Language(s) of the Labor Market</th><th>Linguistic Group (U, B, T)</th></tr></thead><tbody><tr><td>Mali</td><td>French</td><td>French</td><td>All Malian languages</td><td>32</td><td>Pre-primary: Mother-tongue; Grades 1-3; Mother-tongue; Grade 4 upward: French<sup>5</sup>.</td><td>Officially: bilingualism; In practice: French unilingual system</td><td>French and Bambara</td><td>Francophone (U)</td></tr><tr><td>Mauritania</td><td>French</td><td>Classic Arabic</td><td>Modern Arabic, Pouler, Soninké and Wolof</td><td>8</td><td>Modern Arabic and French</td><td>Bilingual arabization policy</td><td>Classic Arabic and French</td><td>Francophone + Arab-speaking (B)</td></tr><tr><td>Morocco</td><td>French</td><td>Classic Arabic</td><td>N/A</td><td>11</td><td>Classic Arabic and French (compulsory second language)</td><td>Arabization policy</td><td>Arabic, French and Berbère</td><td>Arabic-speaking (U)</td></tr><tr><td>Mozambique</td><td>Portuguese</td><td>Portuguese (de jure)</td><td>14 National languages</td><td>33</td><td>Portuguese<sup>6</sup></td><td>Portuguese unilingual system</td><td>Portuguese</td><td>Lusophone (U)</td></tr><tr><td>Namibia</td><td>German and English</td><td>English (de facto)</td><td>12 National languages</td><td>28</td><td>Grades 1-3: Mother-tongue</td><td>Multilingual system</td><td>Afrikaans, English and German</td><td>English-speaking (B)</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Grade 4: Introduction of English</td><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Grade 5 upward: English</td><td></td><td></td><td></td></tr></tbody></table>

<footnote n="5">⁵ In practice, this bilingual system is only applied in 300 schools over the country and French remains the sole medium of instruction from Grade 1 upward in most schools.</footnote>

<footnote n="6">⁶ Currently, a new system of education is experimented, including 1 bilingual education program (Portuguese and a Mozambique language); 1 monolingual program where the Mozambique languages are only used as pedagogical support; and 1 unilingual system where Mozambique languages are taught as second languages.</footnote>
<table><caption>Table A Cont'd (5/7)</caption><thead><tr><th></th><th>Colonial Language(s)</th><th>Official Language(s)</th><th>National Language(s)</th><th>Total Number of Language(s)</th><th>Language(s) of Instruction</th><th>LiE System</th><th>Language(s) of the Labor Market</th><th>Linguistic Group (U, B, T)</th></tr></thead><tbody><tr><td>Niger</td><td>French</td><td>French</td><td>20 National languages</td><td>21</td><td>Pre-primary: Mother-tongue<br>Grades 1-3: National languages<br>Grade 4 upward: French</td><td>Bilingual system</td><td>French (written) and National languages (oral)</td><td>Francophone (U)</td></tr><tr><td>Nigeria</td><td>English</td><td>English</td><td>Hausa, Ibo and Yorouba</td><td>470</td><td>Grades 1-3: National Languages (English as second language);<br>Grade 4 upward: English</td><td>Foreign language priority system</td><td>Hausa, Ibo, Yorouba and Pidgin English</td><td>English-speaking (U)</td></tr><tr><td>Rwanda</td><td>French</td><td>Kinyarwanda, French and English</td><td>N/A</td><td>3</td><td>Grades 1-3: Kinyarwanda; Grades 4-6: French; Secondary: Kinyarwanda and French (English as second language)<br>Tertiary: French or English<sup>7</sup></td><td>Multilingual system</td><td>Kinyarwanda, French and English</td><td>Francophone + English-speaking (T)</td></tr><tr><td>Senegal</td><td>French</td><td>French</td><td>Diola, Malinké, Poular, Sérère, Soninké and Wolof</td><td>39</td><td>Pre-primary: Mother-tongue; Grades 1-3: Mother-tongue; Primary and Secondary: Wolof and French</td><td>Multilingual system</td><td>Wolof and French</td><td>Francophone (U)</td></tr><tr><td>Sierra Leona</td><td>English</td><td>English (de facto)</td><td>N/A</td><td>23</td><td>English</td><td>British unilingual system</td><td>English, Kriol, Mendé and Themné</td><td>English-speaking (U)</td></tr></tbody></table>

<footnote n="7">⁷ After 1996, the large amount of refugees returning from Uganda and Tanzania has raised a new issue: thousands of children could only read or write in English and not at all in French. Therefore, the Rwandan Ministry of Education has created intensive support classes in French and English in the schools where it was judged necessary (especially at the Secondary and Tertiary levels).</footnote>
<table><caption>Table A Cont'd (6/7)</caption><thead><tr><th></th><th>Colonial Language(s)</th><th>Official Language(s)</th><th>National Language(s)</th><th>Total Number of Language(s)</th><th>Language(s) of Instruction</th><th>LiE System</th><th>Language(s) of the Labor Market</th><th>Linguistic Group (U, B, T)</th></tr></thead><tbody><tr><td>South Africa</td><td>English and Afrikaans</td><td>National: English, Afrikaans<br>Provincial: Ndébélé, Sotho, Sesotho, Swazi, Tsonga, Tswana, Venda, Xhosa and Zoulou.</td><td>N/A</td><td>27</td><td>Pre-primary and Primary: mother-tongue instruction (if one of the local official languages) and second language learning (often English or Afrokaans);<br>Secondary upward: Progressive move towards English and/or Afrikaans.</td><td>Multilingual system</td><td>English</td><td>English-speaking (B)</td></tr><tr><td>Swaziland</td><td>Afrikaans and English</td><td>English and Swati (de jure)</td><td>N/A</td><td>3</td><td>Primary: Swati (English as second language);<br>Secondary: English (Swati as second language)</td><td>Mainly British system</td><td>English</td><td>English-speaking (B)</td></tr><tr><td>Tanzania</td><td>English</td><td>English (de facto) and Swahili (de facto)</td><td>N/A</td><td>131</td><td>Primary: Swahili;<br>Secondary: English</td><td>Bilingual system</td><td>Swahili (oral), English (written)</td><td>English-speaking (B)</td></tr><tr><td>Togo</td><td>French</td><td>French</td><td>Ewé and Tem</td><td>43</td><td>Pre-primary: National languages and French;<br>Primary upward: French</td><td>French unilingual system</td><td>French (written) and national languages (oral)</td><td>Francophone (U)</td></tr><tr><td>Tunisia</td><td>French</td><td>Classic Arabic</td><td>N/A</td><td>9</td><td>Pre-primary and Grades 1-3: Arabic;<br>Grade 4 upward: Arabic and French</td><td>Bilingual system</td><td>Tunisian dialectic Arabic and French</td><td>Arabic-speaking (U)</td></tr><tr><td>Zambia</td><td>English</td><td>English</td><td>7 National languages</td><td>39</td><td>Primary: English;<br>Secondary: English and National languages (not applied much in practice).</td><td>Bilingual system</td><td>English</td><td>English-speaking (U)</td></tr></tbody></table>
<table><caption>Table A Cont'd (7/7)</caption><thead><tr><th></th><th>Colonial Language(s)</th><th>Official Language(s)</th><th>National Language(s)</th><th>Total Number of Language(s)</th><th>Language(s) of Instruction</th><th>LiE System</th><th>Language(s) of the Labor Market</th><th>Linguistic Group (U, B, T)</th></tr></thead><tbody><tr><td>Zimbabwe</td><td>English and Afrikaans</td><td>English, Shona, Ndébélé, Venda, Nambya, Shangaan, Kalanga, Southou and Tonga (de jure)</td><td>Shona, Ndébélé, and other Zimbabwean languages</td><td>19</td><td>Pre-primary: National languages (English only in large urban areas);<br>Grades 1-3: Mother-tongue;<br>Grade 4 upward: English.</td><td>Multilingual system</td><td>English, Shona and Ndébélé</td><td>English-speaking (T)</td></tr></tbody></table>

Source: Adapted from Leclerc, J. (2006). *L'aménagement linguistique dans le monde*. Québec: TLFQ, Université Laval. URL: [http://www.tlfq.ulaval.ca/axl.htm] (June 17, 2006).

Note: In all the above countries, wherever applicable, Classic Arabic is the medium of instruction in Qur'anic schools.
<table><caption>Table B Current external settings of countries in Study I
(economic and financial dependency)</caption><thead><tr><th></th><th>Primary Trade Partners (Export-Import)</th><th>Primary Bilateral Donor</th></tr></thead><tbody><tr><td>Angola</td><td>US-South Corea</td><td>Portugal</td></tr><tr><td>Benin</td><td>Nigeria-China</td><td>France</td></tr><tr><td>Botswana</td><td>European Free Trade Association (EFTA)-<br>Southern African Customs Union (SACU)</td><td>U.S.</td></tr><tr><td>Burkina Faso</td><td>Ghana-France</td><td>France</td></tr><tr><td>Burundi</td><td>Switzerland-Kenya</td><td>U.S.</td></tr><tr><td>Cameroon</td><td>Spain-France</td><td>France</td></tr><tr><td>Central Africa</td><td>Belgium-France</td><td>France</td></tr><tr><td>Comoros</td><td>US-France</td><td>France</td></tr><tr><td>Congo-Brazzaville</td><td>China-France</td><td>France</td></tr><tr><td>Congo-Kinshasa</td><td>Belgium-South Africa</td><td>U.S.</td></tr><tr><td>Cote d'Ivoire</td><td>France-France</td><td>France</td></tr><tr><td>Egypt</td><td>Italy-US</td><td>U.S.</td></tr><tr><td>Gabon</td><td>US-France</td><td>France</td></tr><tr><td>Gambia</td><td>India-China</td><td>Japan</td></tr><tr><td>Ghana</td><td>Netherlands-Nigeria</td><td>Japan</td></tr><tr><td>Guinea-Bissau</td><td>India-Senegal</td><td>Italy</td></tr><tr><td>Kenya</td><td>Uganda-UAE</td><td>U.S.</td></tr><tr><td>Lesotho</td><td>US-Hong Kong</td><td>Ireland</td></tr><tr><td>Liberia</td><td>Denmark-South Korea</td><td>U.S.</td></tr><tr><td>Libya</td><td>Italy-Italy</td><td>France</td></tr><tr><td>Mali</td><td>China-France</td><td>France</td></tr><tr><td>Mauritania</td><td>Japan-France</td><td>Japan</td></tr><tr><td>Morocco</td><td>France-France</td><td>France</td></tr><tr><td>Mozambique</td><td>Netherlands-South Africa</td><td>U.S.</td></tr><tr><td>Namibia</td><td>South Africa-South Africa</td><td>Germany</td></tr><tr><td>Niger</td><td>France-France</td><td>France</td></tr><tr><td>Nigeria</td><td>US-China</td><td>U.S.</td></tr><tr><td>Rwanda</td><td>Indonesia-Kenya</td><td>U.S.</td></tr><tr><td>Senegal</td><td>India-France</td><td>France</td></tr><tr><td>Sierra Leone</td><td>Belgium-Germany</td><td>U.K.</td></tr><tr><td>South Africa</td><td>US-Germany</td><td>U.K.</td></tr><tr><td>Swaziland</td><td>South Africa-South Africa</td><td>Netherlands</td></tr><tr><td>Tanzania</td><td>India-South Africa</td><td>U.K.</td></tr><tr><td>Togo</td><td>Burkina Faso-China</td><td>France</td></tr><tr><td>Tunisia</td><td>France-France</td><td>France</td></tr><tr><td>Zambia</td><td>South Africa-South Africa</td><td>U.K.</td></tr><tr><td>Zimbabwe</td><td>South Africa-South Africa</td><td>U.K.</td></tr></tbody></table>

Sources:

CIA. (2006). The World Fact Book 2006. URL: [http://www.cia.gov/cia/publications/factbook/], (June 18, 2006).

OECD. (2006). Aid Statistics, Recipient Aid Charts. URL: [http://www.oecd.org/countrylist/0,2578,en_2649_34447_25602317_1_1_1_1_1,00.html], (June 18, 2006).

WTO. (2006). Statistics Country Profiles March 2006. URL: [http://stat.wto.org/CountryProfiles/], (June 18, 2006).
Publications

Studies in International and Comparative Education

1. **Sixten Marklund** Educational Administration and Educational Development, ISBN: 91-85996-00-2. October 1979

2. **Sixten Marklund** The Democratization of Education in Sweden. A Unesco Case Study, ISBN: 91-85966-01-0. October 1980

3. **Alex Gorham** Education and Social Change in a Pastoral Society: Government Initiatives and Local Responses to Primary School Provision in Kenya Maasailand, ISBN: 91-85966-02-9. December 1981

4. **Ulla Kann** Career Development in a Changing Society: The Case of Botswana, ISBN: 91-85966-03-7. March 1981

5. **Mauno Mbamba** Primary Education for an Independent Namibia: Planning in a Situation of Uncertainty and Instability, ISBN: 91-22-0661-7. March 1982

6. **Roland Duberg** Schooling, Work Experience and Earnings: A Study of Determinants of Earning in a Third World Corporate Setting, ISBN: 91-85966-05-3. June 1982

7. **Ann-Margret Fris** Policies for Minority Education: A Comparative Study of Britain and Sweden, ISBN: 91-85966-06-1. October 1982

8. **Vinayagum Chinapah** Participation and Performance in Primary Schooling: A Study of Equality of Educational Opportunity in Mauritius, ISBN: 91-7146-417-4. September 1983

9. **Kjell Nyström** Schooling and Disparities: A Study of Regional Differences in Sri Lanka, ISBN: 91-7146-453-0. November 1985

10. **Ingemar Gustafsson** Schools and the Transformation of Work: A Comparative Study of Four Productive Work Programmes in Southern Africa,, ISBN: 91-7146-479-4. 1987

11. **Vasilios Makrakis** Computers in School Education: The Cases of Sweden and Greece, ISBN: 91-7146-398-4. March 1988

12. **Agneta Lind** Adult Literacy Lessons and Promises: Mozambican Literacy Campaigns 1978-1982, ISBN: 91-7146-587-1. July 1988

13. **Birgit Negussie** Traditional Wisdom and Modern Development: A Case Study of Traditional Peri-Natal Knowledge Among Elderly Women in Southern Shewa, Ethiopia, ISBN: 91-7146-712-2. December 1988

14. **Anton Johnston** Study, Produce and Combat! Education and the Mozambican State 1962-1984, ISBN: 91-7146-720-3. March 1989

15. **Christine McNab** Language Policy and Language Practice: Implementation Dilemmas in Ethiopian Education, ISBN: 91-7146-732-7. April 1989

16. **Wendy Duncan** Engendering School Learning: Science, Attitudes and Achievement Among Girls and Boys in Botswana, ISBN: 91-7146-75-8. April 1989
17. **Eva Olkiewicz** *Invandrarfamiljer i förändring: En studie över finska och jugoslaviska invandrar-ungdomar och deras föräldrar*, ISBN: 91-7146-836-6. April 1990

18. **Hanne Bang** *Religious Identity over Two Generations: Roman Catholic Immigrant and Convert Families in Sweden*, ISBN: 91-7146-834-X. April 1990

19. **Maria Emília Catela** *Education Reform and Political Transition: A Study of Change in Portuguese Education in the 1970s*, ISBN: 91-7146-842-0. July 1990

20. **Ingemar Fägerlind, Sixten Marklund and Vinayagum Chinapah (eds)** *Torsten Husén. An Educator*, ISBN: 91-630-0377-5. 1991

21. **Ulla Sirén** *Minority Language Transmission in Early Childhood: Parental Intention and Language Use*, ISBN: 91-7146-920-6. May 1991

22. **Alf Carlsson** *Cooperatives and the State: Partners in Development? A Human Resource Perspective*, ISBN: 91-7153-013-4. May 1992

23. **Vinayagum Chinapah (ed.)** *Strategies and Modalities for Educational Financing in Africa*, ISBN: 91-7153-049-5. December 1992

24. **Holger Daun** *Childhood Learning and Adult Life: The Functions of Indigenous, Islamic and Western Education in an African Context*, ISBN: 91-7153-062-2. 1992

25. **Gary Miron** *Choice and the Use of Market Forces in Schooling: Swedish Education Reforms for the 1990s*, ISBN: 91-7153-107-6. 1993

26. **Zhao Shangwu** *Chinese Science Education: A Comparative Study of Achievement in Secondary Schools Related to Student, Home and School Factors*, ISBN: 91-7153-119-X. 1993

27. **Paul Pawar** *Training for the New Competition: Applications of Critical Theory to Industrial Relations Training in Kenya and Nigeria*, ISBN: 91-7153-155-6. 1993

28. **Jan-Åke Engström** *Science Achievement and Student Interest: Determinants of Success in Science among Swedish Compulsory School Students*, ISBN: 91-7153-180-7. 1994

29. **Geoffrey Nambira** *Educating Children with Hearing Impairments in Tanzania: Comparing Regular and Special Schools*, ISBN: 91-7153-239-0. September 1994

30. **Fatima Salah** *Media Selection: Measuring the Effectiveness of a Media Selection Procedure in Moroccan Secondary School Context*, ISBN: 91-7153-240-4. 1994

31. **Riitta Eriksson** *Biculturalism in Upper Secondary Education: The Long Term Effects of Finnish Language Programs on Students' Educational and Occupational Careers*, ISBN: 91-7153-238-2. June 1994

32. **Gary Miron** *Special Needs Education in Nicaragua: A Study of the Prevalence of Children with Disabilities in Primary Schools and the Factors Affecting Their Successful Participation*, ISBN: 91-7153-292-7. 1994

33. **Nelly Kostoulas-Makrakis** *Language Maintenance or Shift? A Study of Greek Background Students in Sweden*, ISBN: 91-7153-328-1. April 1995
34. **Elisei Basil Temu** Successful Schools in Tanzania: A Case Study of Academic and Production Programs in Primary and Secondary Schools, ISBN: 91-7153-382-6. 1995

36. **Torsten Husén** Printed Publications 1981 – 1995 (In Commemoration of his 80th Birthday), ISBN: 91-7153-439-3. 1996

37. **Xiaoda Chen** Quality Schooling with Limited Resources: An International Comparison of Mathematics and Science Education in China, Korea and Hungary, ISBN: 91-7153-465-2. June 1996

38. **Anne Welle-Strand** Policy, Evaluation and Leadership: The Context of Educational Change in Zimbabwe, ISBN: 91-7353-468-7. June 1996

39. **Sixten Marklund** Printed Publications 1956-1996 (In Commemoration of his 75th Birthday), ISBN: 91-7153-507-1. September 1996

39. **(a) Romanus Ejiaga** Higher Education and the Labor Market: A Study of University Access and Graduate Employment Opportunities in Nigeria, ISBN: 91-7153-669-8. November 1997

40. **(a) Paul Vedder and Mina O'Dowd** Social Competence in Swedish Primary School Pupils: The Validity of the RCP-Scale, ISBN: 91-7153-560-8. 1996

40. **Karen Sørensen** Polish Higher Education En Rout to the Market: Institutional Change and Autonomy at Two Economics Academies, ISBN: 91-7153-672-8. December 1997

41. **Luciana Benincasa** A Journey, a Struggle, a Ritual: Higher Education and the Entrance Examinations in a Greek Province Town, ISBN: 91-7153-688-4. 1997

42. **Anne Welle-Strand** Educational Evaluation in Norwegian Development Aid: Does Competence in Education Make a Difference?, ISBN: 91-7153-730-9. 1998

43. **Catherine A. Odora Hoppers** Structural Violences as a Constraint to African Policy Formation in the 1990s: Repositioning Education in International Relations, ISBN: 91-7153-806-2. 1998

44. **Liu Bing** The State, Ethnic Identity and Education: A Study of Primary Schooling for Minorities in Xinjiang and Inner Mongolia in China, ISBN: 91-7153-832-1. November 1998

45. **Teshome Nekatibeb** Media Utilization and School Improvement: A Case Study of Primary Education Radio Support Programs in Ethiopia, ISBN: 91-7153-837-2. November 1998

46. **Kah Slenning** Future School Management. Do Plans and Needs Match? A Comparative Study with Examples from Four OECD Countries, ISBN: 91-7153-914-X. May 1999

47. **Indra Dedze** Reading Ability of Latvian Students. Results from an International Study, ISBN: 91-7153-981-6. October 1999

48. **Ingemar Fägerlind, İçara Holmesland, and Görel Strömqvist (eds)** Higher Education at the Crossroads: Tradition or Transformation?, ISBN: 91-7265-021-4. 1999

49. **Mina O'Dowd** The Changing Nature of Knowledge: Mapping the Discourse of the Malmö Longitudinal Study, 1939-1995, ISBN: 91-7265-088-5. May 2000

50. **Alexander N. Kanaev** Civic Education in Central Asia: Re-conceptualization of Citizenship in Newly Independent States, ISBN: 91-7265-083-4. 2000
51. **Wycliffe H. Odiwour** HIV/Aids and Primary Education in Kenya. Effects and Strategies, ISBN: 91-72-65-089-3. June 2000

52. **Mina O'Dowd and Ingemar Fägerlind**
(eds) Mapping European Comparative Education Research Perspectives. The PRESTIGE TMR Network, ISBN: 91-7265-128-8. 2000

53. **Yu Qiang** Bilingual Education, Cognitive Development and School Achievement: A Study of the Bilingual Programs for Tibetan Children, ISBN: 91-7265-132-6. 2000

54. **Wycliffe Humphrey Odiwuor** The Impact of HIV/Aids on Primary Education. A Case Study of Selected Districts of Kenya, ISBN: 91-7265-140-7. Sep. 2000

55. **Elizabeth F. Heen** The Research University: Quo Vadis? Doctoral Research Training in Economics – Case Studies from France and Norway, ISBN: 91-7265-190-3. December 2000

56. **Marit Storeng** Giving Learners a Chance: Learner-Centredness in the Reform of Namibian Teaching, ISBN: 91-7265-236-5. March 2001

57. **Emmanuel Boudard** Literacy Proficiency, Earnings, and Recurrent Training: A Ten Country Comparative Study, ISBN: 91-7265-356-6. December 2001.

58. **Anne Smehaugen** Inclusion & Exclusion in Culture, Learning and Education: A European Perspective. The Cases of Spain and Norway, ISBN: 91-7265-366-3. November 2001

59. **Dinah Richard Mmbaga** The Inclusive Classroom in Tanzania: Dream or Reality? 2002, ISBN: 91-7265-441-4. May 2002

60. **Ulf Fredriksson** Reading Skills among Students of Immigrant Origin in Stockholm, ISBN: 91-7265-460-0. May 2002

61. **Ann-Kristin Boström** Lifelong Learning, Intergenerational Learning, and Social Capital, ISBN: 91-7265-571-2. February 2003

62. **Katrina Hugosson** Working Life, Sustainable Health and Retirement for Women, ISBN: 91-7265-662-X. May 2003

63. **Michiyo Kiwako Okuma-Nyström** God Turns the Chapter and Everything Changes: Children's Socialization in Two Gambian Villages, ISBN: 91-7265-656-5 ISSN: 0348-095-023. June 2003

64. **Nuzzly Ruiz de Forsberg** School Community Voices: Implementation of the Autonomous School Program in Nicaragua, ISBN: 91-7265-739-1 ISSN: 0348-095-023. October 2003

65. **Richard Desjardins** Learning for Well Being: Studies using the International Adult Literacy Survey, ISBN: 91-7265-792-8. March 2004

66. **Lidija Kolouh-Westin** Learning Democracy Together in School? Student and Teacher Attitudes in Bosnia and Hercegovina, ISBN: 91-7265-828-2. April 2004.

67. **Richard Desjardins, Christelle Garrouste-Norelius, Shawn Mendes.** Benchmarking Education and Training Systems in Europe, ISBN: 91-7265-915-7 June 2004

68. **Lihong Huang** Elitism and Equality in Chinese Higher Education. Studies of Student Socio-economic Background, Investment in Education, and Career Aspirations, ISBN: 91-7155-063-1 June 2005
69. Sheila Brooks Learning Motivation in International Primary Schools: The Voices of Children, ISBN: 91-7155-129-8 October 2005

70. **Ernesto Villalba** The uniqueness of knowledge management in small companies: Knowledge management as a strategy for lifelong learning, ISBN: 91-7155-252-9 June 2006

71. Mikiko Cars Project Evaluation in Development Cooperation: A Meta-Evaluative Case Study in Tanzania; ISBN: 91-7155-319-3. October 2006

72. **Pia Karlsson, Amir Mansory** An Afghan Dilemma: Education, Gender and Globalisation in an Islamic Context; ISBN: November 2007

73. Björn Söderqvist School Leaders' View on Market Forces and Decentralization; ISBN: November 2007

74. **Christelle Garrouste** Determinants and consequences of Language-in-Education Policies: Essays in Economics of Education; ISBN: 978-91-7155-517-5. December 2007