Balázs Égert

Jaroslava Botev

David Turner

The contribution of human capital and its policies to per capita income in Europe and the OECD

Keywords: E24, I20, I25, I26, I28 human capital, economic growth, per capita income, education policies, OECD

This paper studies empirically the effect of education policies on human capital and per capita income. The results suggest for European and OECD countries that higher attendance at pre-primary education, greater autonomy of schools and universities, a lower student-to-teacher ratio, higher age of first tracking in secondary education and lower barriers to funding to students in tertiary education all tend to boost human capital through amplifying the positive effects of greater public spending on education. Benefits from pre-primary education are particularly high for countries with an above-average share of disadvantaged students. School autonomy yields high benefits especially in countries where schools are subject to external accountability. From a policy perspective, improving the quality of the labour force and value-for-money of education policies are of utmost importance in the future, especially in European countries facing population ageing and ever increasing fiscal constraints. Prompt policy action is needed given the very long delay with which the full effect of reforms in education policy materialises on human capital and per capita income.

Introduction

Much research has been devoted to the impact of educational policies on student and school performance, including equity outcomes. Yet there is much less evidence regarding the macroeconomic impact of such policies. This paper attempts to address this shortcoming by identifying educational policies with a large impact on productivity which act through the channel of human capital.

A severe constraint on the empirical analysis is the poor time series coverage of educational policy measures, which prevents the use of traditional cross-country time-series analysis. In the context of considering the drivers of better health outcomes, [START_REF] Lorenzoni | Which policies increase value for money in health care?[END_REF] recently proposed a novel methodology that could be used to overcome the limited time series availability of the policy measures of interest by assuming they are relatively time invariant and interacting them with time-varying core drivers of the variable of ultimate interest, where the core drivers include public spending. The current paper applies this approach to explain human capital. In such a framework, policy effects can be assessed only indirectly as they will amplify or attenuate the impact of public spending on education on human capital.

The remainder of this document is structured as follows. Section 2 presents the conceptual framework in which the effect of policies on human capital will be analysed. Section 3 discusses the choice of the policy drivers of human capital used in the empirical analysis. Sections 4 and 5 report and scrutinises the estimation results. Section 6 quantifies the effect of educational policies on per capita income levels. Section 7 finally provides concluding remarks.

Educational policies and human capital: the conceptual framework

Recent OECD work by [START_REF] Lorenzoni | Which policies increase value for money in health care?[END_REF] developed a new framework for analysing the policy drivers of health outcomes and the determinants of health spending. This approach overcomes the limited time series availability of health policies and institutions by assuming that they are relatively time invariant and by interacting them with time-varying core drivers of health outcomes and spending on health. In such a framework, health policies and institutions amplify or attenuate the impact of health spending on health outcomes, measured by life expectancy. This framework is well-suited for investigating the policy drivers of human capital, mainly because educational policies vary little over time and because time series availability is very limited. To our knowledge, this is the first attempt to apply this methodology to human capital and education policies. The framework builds on two equations: the first one models the determinants of human capital (output); the second models the drivers of public spending on education (input).

Human capital and spending on education can be modelled by so-called core determinants. Both the dependent variables and the core drivers vary over time. Time-invariant educational policies can have an effect by leveraging the core drivers. For human capital, spending on education is considered as the main core driver. Two other core drivers are life expectancy at birth and, the rate of urbanisation, both of which increase returns to education and hence raise investment in human capital. 2 These choices are inspired by [START_REF] Lorenzoni | Which policies increase value for money in health care?[END_REF] and are also restricted by data availability in terms of country and time series coverage. Equation (1) shows that in this framework, policies will not have a direct impact on 2 Other variables were considered as core drivers but were found to be either highly correlated with the three selected variables giving rise to multi-collinearity or had wrongly signed and/or imprecisely estimated coefficient estimates. These variables included per capita income as well as public and total spending on healthcare per capita (all in PPP terms). human capital. Instead, they will amplify or attenuate the impact of the core drivers on human capital through interactions with the core drivers. (1) where h is human capital per capita, S is public spending on education per capita for country i and time t, respectively. CoreDH is the set of k core drivers of human capital discussed above. Policy stands for a set of educational policies. In the interaction term, both education spending and the policy variables are de-meaned. This ensures that the interaction terms capture marginal effects (! " ) in addition to the base effects of the core variables ( ).

GDP per capita and the share of the young in total population are the core drivers of spending on education. The spending equation including the core drivers and the policy interactions are shown in equation ( 2 where Capita and CoreDS denote GDP per capita and the core driver of spending on education (share of young people in total population) for country i and time t, respectively. In the empirical estimations, both equation (1) and equation ( 2) are estimated using country and time fixed effects.

The dual role that educational policies play in equations ( 1) and ( 2) allows them to be assessed both in terms of their effectiveness in raising human capital as well as how they alleviate or contribute to public spending pressures. In accordance with Egert and Gal (2017), educational policies can be used to evaluate their impact on per capita income through their influence on public spending and spending effectiveness and hence on human capital and multi-factor productivity (Figure 1).

Educational policies and human capital

There is potentially a huge range of educational policies which could influence human capital. Many of them are available only for a subset of OECD countries or are highly correlated with other policies. Therefore, the choice of policies for the current analysis has been guided by selecting one representative policy driver from each of the six broad policy areas in the taxonomy identified by [START_REF] Braga | Educational policies in a long-run perspective[END_REF] as explaining educational attainment in primary, secondary and tertiary education in a set of European countries. The six broad policy areas and the specific variables considered in the empirical analysis are described below. Overall, every country has its weaknesses and strengths, with no particular country being a top performer in all policy areas. 3

Pre-primary education attendance

Pre-primary education has been shown to be very important to the productivity of education in primary education and beyond [START_REF] Braga | Educational policies in a long-run perspective[END_REF]. The recent literature suggests that for disadvantaged children, intervention at a very early stage offers the highest returns [START_REF] Attanasio | The determinants of human capital formation during the early years of life: Theory, measurement and policies[END_REF] by increasing cognitive skills. Early investment pays off only if it is followed by investment later on [START_REF] Heckman | Schools, skills and Synapses[END_REF]. Preschool and formal education systems increase ability rather than cognitive skills, which are also essential to success in life. A permanent boost to cognitive and non-cognitive skills of disadvantaged children enhances the chances of staying in the education system for longer and of becoming a more able member 3 Data sources and definition and descriptive statistics of the variables used in the empirical analysis are provided in the working paper version of this paper (Égert et al., 2019)

Spending on education

Multi-factor productivity

Per capita income

Capital deepening

Employment rate

Human capital

Other core drivers Spending effectiveness Educational policies │ 5 of society. Hence, an increased prevalence of pre-primary education would likely increase both the quantity and quality of educational outcomes.

To represent these effects, a number of alternative variables are available covering most OECD countries, although they tend to be highly correlated with one another.

The variable chosen for the empirical analysis measures the share of PISA students having attended preprimary education for more than one year. This variable is an intermediate policy outcome, which combines into a single measure the impact of the many policies targeted at pre-primary education. In the average OECD country, 74% of children have spent more than one year in pre-primary education, although there is considerable cross-country dispersion: the proportion is only 9% in Turkey; whereas almost all children were enrolled in pre-primary education in Iceland, the Netherlands, Hungary and Japan (Figure 2, Panel A).

Age of first tracking in the school system

"Tracking", namely the practice of dividing students by ability or achievement and by putting them into different types of schools, appears to have an adverse effect on educational attainment and skills. Underachieving pupils with poorer family background tend to be channelled into specific vocational tracks at an early stage. In the shorter run, vocational education can be beneficial because it provides specific skills needed in the labour market. Therefore, graduating from vocational schools can secure a job early on. Nevertheless, in the longer run, early tracking can exacerbate initial socio-economic disadvantages, provides technical skills rather than general ones including non-cognitive skills, necessary to change career in one's lifetime and limit students from pursuing further learning [START_REF] Liu | Education systems, education reforms, and adult skills in the Survey of Adult Skills (PIAAC)[END_REF]. Vocational education can also generate weaker and less diverse lifetime networks compared to later tracking. Indeed, the initial advantage of vocational training compared to general education fades away at the labour market over time [START_REF] Hanushek | General education vocational education and labour market outcomes over life cycle[END_REF][START_REF] Brunello | The effects of vocational education on adult skills and wages[END_REF], Bol and van der Werhorst, 2016). Evidence suggests that postponing tracking to a later age helps mitigate the adverse effect of family background on earnings, educational attainment and skills. [START_REF] Brunello | Does school tracking affect Equality of Opportunity? New International Evidence[END_REF][START_REF] Pekkarinen | School tracking and development of cognitive skills[END_REF][START_REF] Schütz | Education policy and equality of opportunity[END_REF].

The baseline variable considered for the empirical analysis is the age of first tracking. Alternative measures of the tracking system are also considered as robustness checks, including a composite tracking indicator, which combines the age of first tracking, the share of total compulsory curriculum which is tracked (extent of tracking), and the number of tracks available at the age of 15 (see Table A1 for more details).

OECD countries are very different in terms of the age of first tracking and the extent of tracking. In a considerable number of countries including Australia, Canada, Denmark, Spain, Finland, the United Kingdom, Italy, Iceland, Latvia, Norway, New Zealand, Sweden and the United States, tracking occurs at the age of 16. On the other hand, tracking starts at the age of 10 in Austria and Germany, and at the age 11 in the Czech Republic, Hungary Slovakia and Turkey (Figure 2, Panel D).

Teacher qualifications

Good teachers can convey information better to students and they can motivate pupils better by being role models. Good pedagogical sense can make learning and studying a pleasure and not a pain for children. Policies aimed at improving teacher quality should consider offering appropriate wages to teachers and a recognised status in schools and society, although the first option may be costly and the second difficult to achieve in practice. If successfully implemented, such policies are likely to secure a larger pool of potential teachers and increase overall quality. Better teachers will hence raise educational attainment as pupils will be happy to continue to learn and study and will also help students to acquire better skills [START_REF] Braga | Educational policies in a long-run perspective[END_REF]. School-level studies also suggest that teaching quality is one of the most important driver of students learning (OECD, 2005).

It is not only difficult to measure the quality of teaching, but available measures for teacher quality including age, gender and qualification tend to work poorly in the aggregate regression analysis.

For the purpose of the empirical analysis, teacher quality is measured as the average of the students to teaching staff ratios in primary and secondary schools. Ideally, such a ratio should be adjusted for hours worked but no such cross-country data is available. This variable ranges from less than ten students per teacher in Luxembourg and Greece to about 20 in Turkey, France, the United Kingdom and Chile. The number of students per teaching staff in Mexico is almost double the OECD median of 15 (Figure 2, Panel B). An additional variable is also used to check for the robustness of the results: teachers' qualification measured as the share of full-time teachers fully certified.

School autonomy and accountability

If primary and secondary schools are given more autonomy, they will be able to make better decisions in terms of hiring, budgeting, curriculum and other aspects of school life and education for meeting local needs and improving educational outcomes, given existing resources. School autonomy thus enhances the cost-effectiveness of spending on education. Nevertheless, school autonomy alone is not sufficient, it needs to be accompanied by greater accountability to produce superior educational outcomes. Making primary and secondary schools accountable for student achievements provides strong incentives for better performance and for enhancing teaching quality. Empirical research confirms that accountability is associated with better student test scores, in particular when combined with accountability. Relying on a system of central examination provides a powerful instrument to increase school accountability. Such a system makes cross-school comparisons easier and fosters competition between schools for students [START_REF] Braga | Educational policies in a long-run perspective[END_REF]. The OECD's PISA dataset contains numerous measures of school autonomy, which tend to be highly correlated with each other. The indicator measuring a wide range of aspects of autonomy of primary schools is selected for the empirical analysis. The PISA index of school autonomy suggests a divide across OECD countries. In some countries such as Turkey, Greece and Italy, the education system is very centralised and schools have little decision power (Figure 2, Panel C). By contrast, schools enjoy a large amount of freedom in taking decisions with regard to resources and teaching content in the United Kingdom, the Netherlands and the Czech Republic. In order to test whether autonomy influences human capital differently depending on the level of accountability, dummy variables showing the existence of central exams in primary and secondary education are employed.4

University autonomy and selectivity

Universities with more autonomy to manage their financial resources, staff policies and also the selection of students are found to achieve better educational outcomes [START_REF] Oliveira Martins | The policy determinants of investment in tertiary education[END_REF]. Universities with more autonomy and with better funding can impose stricter admission conditions to prospective students, and attract better researchers, professors and teaching staff. This signals higher returns as graduates may expect higher earnings and wages. Signals encourage the best students to go on to university. At the same time, they could deter students with less favourable socio-economic backgrounds.

A variable measuring autonomy of the tertiary education system with regard to inputs compiled by Oliveira [START_REF] Oliveira Martins | The policy determinants of investment in tertiary education[END_REF] is selected for the regression analysis. Countries such as Greece, France, Turkey, Belgium and Germany, in which primary schools have little autonomy, are also those where tertiary education is centralised to a greater extent. In most English-speaking countries (Australia, Canada, United Kingdom, United States and New Zealand) as well as in Japan, Mexico, Slovakia and Sweden, universities enjoy a high degree of autonomy (Figure 2, Panel E).

Barriers to funding to students in tertiary education

Tertiary education can be costly and the returns to it are uncertain, so potential students from less welloff families or who are more risk averse may decide against going to university because of the high costs of tertiary education and because of the difficulties in obtaining the necessary funding to cover tuition fees and living costs. Reducing financial constraints, the costs of tertiary education and the risk relating to expenses (investment) in tertiary education would increase access to tertiary education for individuals with disadvantaged socio-economic background. Public policies eager to increase tertiary graduation rates may want to subsidize tertiary education by waving tuition fees and by providing grants. Yet full subsidisation is regressive, as individuals who would be able to finance their studies will also benefit from it. On the other hand, charging the full costs, even if coupled with student loans may discourage students with a disadvantaged family background. One solution would be student loans that are contingent on income. However, in many countries, students prefer to work part-time or enter the labour force (Braga et al. 2003). Overall, financial support by the government reduce the costs of tertiary education and the related risk , and is likely to raise attendance and graduation rates.

A measure of financial constraints facing students in tertiary education is also taken from Oliveira [START_REF] Oliveira Martins | The policy determinants of investment in tertiary education[END_REF]. It is calculated as the ratio of average annual expenses during study for a tertiary degree to the sum of available financial resources and support [START_REF] Oliveira Martins | The policy determinants of investment in tertiary education[END_REF]. Total resources include loans, grants, part-time work earnings and family financing. The variable indicates that in 2006 it was very costly for students to attend university in Turkey, Korea and Mexico and to a lesser extent in Japan. In those countries, the ratio of total expenses to total resources available to students is almost 100%. This stands in contrast with an observed 20% in the Nordic countries (Sweden, Iceland, Norway, Denmark and Finland), the Netherlands, the Czech Republic, Germany and Switzerland (Figure 2, Panel F).5

Measuring human capital

Mean years of schooling is used in many macroeconomic panel regressions as a proxy measure of human capital [START_REF] Barro | Economic Growth in a Cross Section of Countries[END_REF]. Empirical studies from the late 1990s typically use measures that assumed decreasing marginal returns to education, so that primary education had the biggest marginal returns, followed by secondary education, with tertiary education having the lowest returns. A first wave of studies relied on piece-wise linearity assuming returns of 13.4%, 10.1% and 6.8% for primary, secondary and tertiary education, respectively [START_REF] Hall | Why Do Some Countries Produce So Much More Per Worker Than Others?[END_REF][START_REF] Caselli | Accounting for Cross-Country Income Differences[END_REF]and Feenstra et al. 2013). A second wave relied on a polynominal specification, advocated by [START_REF] Morrisson | The Kuznets Curve of Human Capital Inequality: 1870-2010[END_REF], which smoothed out the step decreases in the piece-wise linear form of decreasing returns. Yet such parameters appear to be inappropriate for a sample of only OECD countries. Average returns vary through time, across countries and levels of education [START_REF] Psacharopoulos | Returns to Investment in Education: A Further Update[END_REF][START_REF] Montenegro | Comparable Estimates of Returns to Schooling Around the World[END_REF].

Against this background, a measure of human capital is calculated and used in this paper, which is calculated using mean years of schooling and realistic rates of return to education based on data from [START_REF] Psacharopoulos | Returns to Investment in Education: A Further Update[END_REF] and [START_REF] Montenegro | Comparable Estimates of Returns to Schooling Around the World[END_REF]. Rates of return are allowed to vary over time and across countries. Cross-country variation is obtained from averaging returns for four country groups, which generates sufficient heterogeneity without producing too much noise. 6 The new indicator suggests that in 2015 among OECD countries, Germany, Japan, Iceland and Australia were the countries with the highest ranking of human capital, whereas Portugal, Spain, Greece and Turkey were the lowest.

Determinants of human capital: estimation results

Let us first look at the core drivers of human capital. An increase in two core drivers, spending on education and life expectancy are estimated to translate into greater human capital. The rate of urbanisation is less robust across different specifications. 8 These positive results imply that policy effects can be transmitted to human capital and spending on education via the core drivers.

Looking at the policy drivers, variables affecting pre-primary, primary and secondary education outcomes have strong robust effects on human capital with statistically significant and stable coefficients across a range of specifications (see equations ( 1) to (2) in Table 1 and equations ( 1) to ( 8) in Table 2). Greater enrolment in pre-primary education has a positive influence on human capital. The literature suggests that children with disadvantaged socio-economic background have the most to gain from attending pre-primary education. This hypothesis is tested by adding a variable which is the interaction of pre-primary education with socio-economic background.9 In the estimations, the effect of pre-primary education has a stronger positive effect on human capital for countries with an above-average share of disadvantaged children (equations ( 4) and ( 8) in Table 2). The effect is particularly large in Chile and Turkey, the two countries with low pre-primary enrolment and a high share of disadvantaged students.

The younger the age of first tracking in secondary education, the greater the negative impact on human capital is (equations ( 1) and ( 2) in Table 1). An index combining several aspects of tracking is also found to have a negative effect on human capital (equations ( 2) and ( 6) in Table 2).

These and the other policy effects in the human capital equations can be interpreted in terms of cost effectiveness as they are obtained as interactions with public spending on education: any given increase in public spending will have a greater effect on human capital if for instance more children attend preprimary education and if tracking in secondary education starts at an older age (Table1).

The measure of school autonomy has estimated coefficients that are statistically significant and stable across all specifications (Tables 1 and2). External accountability is generally thought to boost the positive effects of school autonomy. Estimation results suggest that the positive effect is greater in countries with external central exams as a proxy for external accountability. In primary and lower secondary education, the existence of central exams boosts the positive effect of school autonomy (equation ( 4) and (8) in Table 2). In upper secondary education, greater school autonomy raises human capital only in combination with greater school accountability (not reported here).

Fewer students per teacher is found to boost the effect of educational spending on human capital. The baseline regressions use the average of the student-to-teacher ratio in primary and secondary education (Table 1). Other, more explicit measures of teaching quality, including the share of full-time teachers who are fully certified, have a positive influence on human capital when using the Dynamic OLS estimator but not with the non-linear least squares estimator.

The two policy variables potentially influencing outcomes in tertiary education work well in regressions estimated using non-linear least squares. Accordingly, if universities enjoy greater autonomy, they can use their resources more efficiently to produce human capital. Also, lowering the barriers to funding to students to pursue tertiary education will raise human capital. The effect of these variables is, however, much less robust because they have very large standard errors in the regressions using the Dynamic OLS estimator (equation (1) in Table 1 and equations ( 1) to (4) in Table 2). 10

The empirical findings for the six educational policies are broadly in line with those reported in [START_REF] Braga | Educational policies in a long-run perspective[END_REF]. They are also consistent with the literature finding that pre-primary education mostly help children from poorer households and that greater school autonomy, especially when coupled with appropriate incentives (accountability) leads to higher educational performance. However, research aimed at explaining international student test scores struggles to pin down any direct impact of the student-to-teacher ratio on educational achievement. Earlier empirical evidence on the negative effect of the extent of tracking is stronger, although not entirely robust. The current results cannot, however, be directly compared with earlier results, particularly because the dependent variable in the current regressions considers quality differently (over the lifetime as opposed to test scores at an early age) and in addition, the quantity of educational outcomes is also taken into account.

The question to be raised is whether these results hold homogenously or whether there are some country differences. Against this background, regression results in

Determinants of education spending: estimation results

Looking at the education spending equations, GDP per capita is found to be a major core driver: it bears a strong positive relationship to spending on education. In other words, a rise in per capita income is accompanied by an increase in spending in education (in PPP terms). However, no robust relationship could be established between the share of young people in the total population, another potential core driver, and education spending.

Policy variables can reinforce, mitigate or remain neutral to the effect of an increase in per capita income on education spending. In this sense, policy effects in the spending equations can be interpreted in terms of dampening or leveraging the effect of per capita income on spending. Any given increase in per capita income will have a different effect on education spending depending on whether policies attenuate or amplify this effect. (Tables 3 and4).

Improvement in some policies such as greater enrolment in pre-primary education, enhanced autonomy in tertiary education and lower barriers to funding to students are associated with a reduction in spending on education (Tables 3 and4). Increased attendance in pre-primary education boosts cognitive and non- cognitive skills, which in turn improves learning skills and outcomes and a reduced need for costly special-care education programmes and institutions. This effect holds true for various country groupings. Greater university autonomy can provide strong incentives to expand the quantity and quality of education including by mobilising private funding and hence easing the strain on public finances. Lower barriers to funding to students may increase the number of students enrolled at university, although easier access to financing would seem more likely to reduce public spending pressure if, for example, it improved access to student loans or part-time jobs rather than relying on increased grants and subsidies.

Other policy improvements go in tandem with a rise in spending on education. Lowering the student to teacher ratio, which boosts human capital, comes at a cost of higher spending, as hiring more teaching staff has direct consequences for the wage bill in the education sector. This effect is particularly pronounced in Anglo-Saxon and less developed OECD countries. Raising the age of first tracking is linked to higher spending, most probably because it enhances the chances of continuing in tertiary education, which is more costly than leaving the education system after secondary school. The estimation results for the composite tracking indicator confirm this finding: a greater extent of tracking is linked to lower spending, and conversely, reducing tracking would result in greater spending.

Finally, improving policies such as school autonomy or teacher qualification does not appear to be related to any additional increase or reduction in spending even though they imply better human capital outcomes. These policies can be viewed as spending-neutral policies (Table 3 and4). It should be noted that school autonomy might reduce spending when coupled with external accountability, but this result is not robust to the estimator used (regressions ( 4) and ( 8) in Table 4). 11

Quantifying the effect of educational policies on human capital

Which policies provide good value for money?

With long-run education expenditure increasing and education services becoming relatively more expensive than other goods, many OECD countries now focus on efficient allocation of school spending (OECD, 2017). A policy can be judged as providing good value for money (VFM) if it improves human capital via the spending channel and at the same time it reduces spending pressures, following the taxonomy of [START_REF] Lorenzoni | Which policies increase value for money in health care?[END_REF]. To illustrate the influence of policies, a series of simulations is conducted in which the preferred set of responses for human capital is taken to be equation ( 2) from Table 1 for pre-primary, primary, secondary and tertiary education policies; equation ( 6) from Table 2 for the alternative measure of tracking (composite indicator of tracking). For the public spending regression, equation ( 2) from Table 3 is the source of coefficient estimates for all policies, but one. For the composite indicator of tracking, equation ( 6) from Table 4 is considered.

To demonstrate these properties, a baseline simulation is first generated by increasing the core drivers of human capital, including public spending on education, by one standard deviation to generate an increase in human capital and public spending. A series of variant simulations are then generated for human capital and public spending by additionally changing each of the educational policy variables by one standard deviation in turn, with the incremental difference that this makes to both human capital and public spending being summarised for each educational policy in Figure 3.

Specification (1) (2) (3) (4) (5) (6) (7) (8)

Policy interactions with the core variables

Pre-primary education (+ greater attendance) -0.

│ 15

On this basis, and in accordance with Section 6, three groups of policies emerge as regards value for money:

• Good value for money policies raise human capital while reducing spending pressure. These include increasing enrolment in pre-primary education, greater university autonomy and lower barriers to funding to students in tertiary education.

• Spending neutral policies include increasing school autonomy in order to enhance educational outcomes.

• Higher-cost policies include reducing the student-to-teacher ratio, increasing the age of first tracking and reducing the extent of tracking.

Simulations to illustrate the importance of educational policies

The system of equations for education spending and human capital, in combination with the wider production function framework for evaluating structural reforms, can be used to evaluate alternative reform scenarios. A first scenario illustrates how strong and weak education policies amplify or attenuate the positive spending effect on productivity and per capita income levels. A second scenario considers the effect of moving national policies to best practice.

Spending moderation Spending pressure Human capital decrease

Human capital increase

Value for money increase

Value for money decrease

Contrasting the effect of weak and strong educational policies

A one standard deviation increase in public spending on education boosts per capita income by about 1% in the long run for a country with average educational policies. To represent weak and strong education policies, values at the 10th and the 90th percentiles are used in the simulation analysis.

Weak and strong values of three policies --namely pre-primary education, the student-to-teacher ratio and school autonomy --attenuate and amplify the spending effect by between 0.5 and one percentage point. As a result, the total long-term impact on per capita income ranges from about 0% to 1.5%. Nevertheless, the effects double in size for pre-primary education and triple for school autonomy once they are made conditional on family background and accountability, respectively. 12 Large effects can be observed for barriers to funding to students in tertiary education. Low barriers, typically observed in Scandinavian countries add an extra 1 percentage point. At the same time, high barriers will worsen per capita income outcomes in the long run (Figure 4).

The long-term effects materialise very slowly, over a horizon of about 30 years. At the two-, five-and ten-year horizons, the policy impacts are considerably smaller. The slow convergence to the total longterm effect is a result of the slow adjustment in the estimated error correction model for productivity (Figure 5), but can be rationalised by the long lags taken before reforms have an impact on the stock of, rather than inflow to, human capital.

Closing the gap to the top performers

Scenarios which close the gap with the top performers are an alternative way to demonstrate policy impacts on economic outcomes. For each country, the size of the reform corresponds to the policy gap to the average of the top three performing OECD countries, so indicating which countries would benefit most from aligning policies with best practices. 13 The total long-term impact on per capita income of closing the gap differs substantially across OECD countries (Figure 6).

-Moving to best practice in pre-primary attendance (as in Japan, Hungary and the Netherlands) would boost per capita income by more than 3% for Chile and Ireland and by nearly double that for Turkey. Once the effect of family background is accounted for, the size of the effect increases by about a third for countries, in which the share of disadvantaged children is above the OECD average, while the effect becomes nil for countries below the OECD average.

-Reducing the student-teacher ratio to the lowest levels of Luxembourg, Greece and Switzerland increase per capita income by more than 1% for Chile, the United Kingdom, France, Turkey, Korea and by more than double that for Mexico. The magnitude of the effects is lower for teachers qualification (share of full-time teachers fully certified) and they might differ considerably for individual counties. For instance, the positive long-term effects of teachers' qualification decrease considerably for the United Kingdom whereas they are substantial for Norway.

-Increasing the age of first tracking in secondary education would increase per capita income by more than 1% for Czech Republic, Hungary, Slovakia and Turkey and by almost 1.5% in Austria and Germany. Reducing the extent of tracking, measured by the composite tracking 12 More uncertainty could arise from estimation uncertainty including alternative estimators and from data limitations. 13 The scope for reform in each country is illustrated in Figure A4 in the Appendix of the working paper version of this paper (Égert et al. 2019).

│ 17

indicator would result in substantial gains Austria and Germany, but also for the Czech Republic, Hungary and Slovakia.

-Increasing school autonomy to best practice (as in the United Kingdom, Czech Republic and Netherlands) would raise per capita income by more than 2% for Turkey, Greece, Italy, Germany, Austria, France and Portugal. Yet again, the impact more than triples for countries in which schools are subject to external accountability while the effect is considerably lower in the absence of accountability.

-Policy effects linked to tertiary education are larger but less certain due to less robust underlying coefficient estimates. Taken at face value, the results suggest that there could be major gains to per capita income from giving greater autonomy to universities, particularly in Greece, France, Turkey and Belgium. There could also be large gains from reducing constraints the funding of tertiary education for students, although these results also need to be qualified as the underlying indicator may be out-of-date for some countries.

Conclusion

This paper investigated the educational policy drivers of human capital. To deal with the poor time series coverage of comparable data on relevant educational policies, a novel methodology is utilised by interacting educational policies, which are assumed to vary little over time, with time-varying core drivers of human capital such as public spending on education. In such a framework, policy effects can only be assessed indirectly as they amplify or attenuate the effect of education spending on human capital.

Higher attendance at pre-primary education, greater autonomy of schools and universities, a lower student-to-teacher ratio, higher age of first tracking in secondary education and lower barriers to funding to students in tertiary education all tend to boost human capital through amplifying the positive effects of greater public spending on education. Benefits from pre-primary education are particularly high for countries with an above-average share of disadvantaged students. School autonomy yields high benefits especially in countries where schools are subject to external accountability. For a number of OECD countries, aligning any of these educational policies to best practice would generate an increase of more than 1% in per capita GDP, on top of the positive effects of an increase of a one standard deviation in spending on education:

First, moving to best practice in pre-primary attendance (as in Japan, Hungary and the Netherlands) would boost per capita income by more than 3% for Chile and Ireland and by nearly double that for Turkey. The effect is particularly large in countries with a high share of disadvantaged students, coupled with low pre-primary enrolment rates such as in Chile and Turkey;

Second, reducing the student-teacher ratio to the lowest levels of Luxembourg, Greece and Switzerland would increase per capita income by almost 1.5% for the United Kingdom, France, Turkey, Korea and the Netherlands and by more than double that for Chile and Mexico. Better teacher qualifications are also associated with better human capital outcomes.

Third. increasing the age of first tracking would increase per capita income by more than 1% for Czech Republic, Hungary, Slovakia and Turkey and by almost 1.5% in Austria and Germany;

Fourth, increasing school autonomy to best practice (as in the United Kingdom, Czech Republic and Netherlands), if coupled with external accountability, would raise per capita income by more than 2% for Turkey, Greece, Italy, Germany, Austria, France and Portugal;

Finally, results are less robust for tertiary education policies, but suggest that there could be major gains to per capita income from giving greater autonomy to universities, particularly in Greece, France, Turkey and Belgium, as well as gains from reducing constraints on funding of tertiary education for students.

Certain educational policies are identified as good value for money policies because they have a double dividend of boosting human capital as well as reducing spending pressures, namely: more pre-primary education, greater university autonomy and lower barriers to funding to students in tertiary education.

Increasing school autonomy enhances educational outcomes, but does not reduce spending pressures. A lower student-to-teacher ratio, a higher age of first tracking and a reduction in the extent of tracking boost human capital, but at a higher cost.

Taking up the policy challenge cannot wait for decades are needed for the full long-term impact to materialise on productivity, partly because reforms which target a particular cohort will inevitably take a long time before they are fully reflected in the stock of human capital.

These results are informative for policymakers in particular in European OECD countries, which tremendous pressures in the not-so-distant future in terms of fiscal sustainability and population ageing. First, having a sense of what policies provide the most cost-efficient way in increasing the quality of │ 21

educational outcomes and human capital may help prioritise policy measures when facing fiscal constraint and public debt sustainability. Second, population ageing implies a shrinking labour force. The decline in the quantity of labour could be offset by increasing the quality of education and the labour force. This is also important given the fast pace of digitalisation and automation, the emergence of Artificial Intelligence and the challenges posed by climate change, which threaten low-and mid-skill jobs, depreciate any existing skills and create demand for very new skillsets. Some of the policies identified (e.g. pre-primary education and later tracking) improving cognitive skills and the ability to learn after leaving the education system. Nevertheless, identifying new skills and potentially redesigning complete curricula is beyond the scope of this paper and is left to future research.

Figure 1 .

 1 Figure 1. Policy impact on human capital and economic outcomes

Figure 2 .

 2 Figure 2. Policy drivers of human capitalPanel: A. Students with more than one year of pre-primary education, 2012, per cent

  Panel F: Barriers to funding to students in tertiary education, 2006 Total student costs over total student resources, per cent; a higher number implies higher barriers Source: PISA (2012) for pre-primary education and school autonomy; OECD Education at a Glance for the student-to-teacher ratio, Bol and Van de Werfhost (2013) for the age of first tracking and Oliveira[START_REF] Oliveira Martins | The policy determinants of investment in tertiary education[END_REF] for university autonomy and barriers to funding students in tertiary education.

Figure 3 .

 3 Figure 3. Value for money of educational policies Changes in human capital and public spending on education following a positive change in policy, per cent

Figure 4 .

 4 Figure 4. The effect of educational policies through the spending channelPer cent change, total long-run effect on per capita income

Figure 5 .

 5 Figure 5. The effect of educational policies at different time horizonsPer cent change, policy effects on per capita income when policy is at the 90 th percentile of the OECD sample

Figure 6 .

 6 Figure 6. Moving educational policies to the three best-performing countries

Table 2 . Human capital equations -more granular policy effects through interactions

 2 Table 1 are complemented by looking at whether non-European OECD countries, Anglo-Saxon countries and less developed OECD countries are different from the rest of the sample. It turns out that greater school autonomy and postponing the first age of tracking have a bigger effect in non-European countries than in Europe. Pre-primary education is less important in Anglo-Saxon countries, whereas it matters more in less developed OECD countries (TableA1in the Annex). In addition to indirect effects, direct policy effects can be estimated by replacing country fixed effects with the time-invariant policies. The two variables with significant effects on human capital are school and university autonomy. The other variables do not seem to have direct effects. These results are confirmed when human capital is regressed on the time-varying policy dataset used in[START_REF] Braga | Educational policies in a long-run perspective[END_REF]. The vector of policies is interacted with the vector of core drivers in the regressions based on non-linear least squares. When the Dynamic OLS estimator is used, policies are interacted one by one with spending on education (human capital equation). The sample covers OECD countries from 1970 to 2014. The panel is unbalanced. * and ** denote statistical significance at the 10% and 5% levels, respectively, based on robust standard errors. A + or -sign following the variable names indicates the meaning of an increase or decrease in the variable. All regressions include country and time fixed effects.

	Specification	Human capital = f(core,core*policies)
		(1)	(2)
	Estimation method	Dynamic OLS	Non-linear least squares
	Core variables
	Constant	-3.346**	-6.906**
	Log spending on education	0.052**	0.040**
	Log life expectancy	0.871**	1.704**
	Log share of urban population	0.009	-0.005
	Policy interactions with the core variables
	Pre-primary education (+ greater attendance)	0.001**	0.003**
	Student-teacher ratio (+ higher ratio)	-0.008**	-0.008**
	School autonomy (+ greater autonomy)	0.041**	0.061**
	Age of first tracking (+ higher age of tracking)	0.011**	0.011**
	Barriers to funding to students in tertiary education (+ greater barriers)	0.0003	-0.003**
	University autonomy (+ greater autonomy)	-0.001	0.021**
	Adjusted R-squared	0.973	0.972
	No. of observations	956	1044
	No. of countries	28	28
	Country and time fixed effects	YES	YES

Table 1. Human capital equations -policy effects through interactions OECD countries, 1970-2014 10 Note: Note: see Table 1: Source: Authors' calculations.

Table 3 . Spending equations -policy effects through interactions

 3 Two sets of robustness checks are carried out. First, equation (1) in Table1and Table3are re-estimated using 2 Stage Least Squares (2SLS) to control for the potential of endogeneity in the regressions. Second, the dependent variables are used with lags of 3 years. The results are fairly robust to these robustness checks. Results are not reported here but can be obtained upon request from the authors.

	OECD countries, 1970-2014
	Specification	Education spending = f(core,core*policies)
		(1)	(2)
	Estimation method	Dynamic OLS	Non-linear least squares
	Core variables
	Constant	-2.988**	-1.806
	Log GDP per capita	1.009**	0.686**
	Log share of young people in total population	-0.042	0.092
	Policy interactions with the core variables
	Pre-primary education (+ greater attendance)	-0.004**	-0.005**
	Student-teacher ratio (+ higher ratio)	-0.118**	-0.070**
	School autonomy (+ greater autonomy)	0.218**	0.058
	Age of first tracking (+ higher age of tracking)	0.132**	0.079**
	Barriers to funding to students in tertiary education (+ greater barriers)	0.009**	0.009**
	University autonomy (+ greater autonomy)	-0.240**	-0.126**
	Adjusted R-squared	0.956	0.948
	No. of observations	813	905
	No. of countries	28	28
	Country and time fixed effects	YES	YES

11

Table 4 . Spending equations -more granular policy effects through interactions

 4

	OECD countries, 1970-2014

Note: see Table

1

: Source: Authors' calculations.

In primary education, central exams exist in Belgium, Portugal and the USA. In lower secondary education, Belgium, Germany, Denmark, Estonia, France, Ireland, Italy, Latvia, the Netherlands, Norway, Poland, Portugal, Turkey and the USA have such tests. In upper secondary education, a handful of countries including Canada, Switzerland, Iceland, Japan, Mexico and Sweden does not have central exams.

The measure was constructed in 2007, reflecting the situation in 2006, and has not been up-dated since then. In contrast with university autonomy (also measured in 2006), the financing of tertiary education is an area where major changes have taken place in a number of countries over the past decade. The indicator should therefore not be taken as a reflection of the current situation.

Other variables were considered as core drivers but were found to be either highly correlated with the three selected variables giving rise to multi-collinearity or had wrongly-signed and/or imprecisely estimated coefficient estimates. These variables included per capita income as well as public and total spending on healthcare per capita (all in PPP terms).

A first indication that pre-primary education interacts with social background comes from the inclusion of the variable on family background as a separate variable. It has a strong negative link to human capital. This result is not reported here.

Annex -Further regression results

Table A1. Estimation results for different country groups, Dynamic OLS estimates. 1970-2014 Note: Regressions (1) and (5) replicate equations (1) in Table 1 and Table 3 respectively. Effects for "differences for country groups" are obtained by including the six policy times core variable interactions multiplied by a dummy, which takes the value of 1 for non-European / Anglo-Saxon / less developed OECD countries, and is zero otherwise. The coefficient estimates on these variables tell the difference of these countries compared to the remaining countries. Non-European countries include Australia, Canada, Chile, Israel, Japan, Korea, Mexico, New Zealand, USA; Anglo-Saxon countries include Australia, Canada, Ireland, New Zealand, the UK and the USA; less developed OECD countries are the Eastern European OECD countries, Chile, Mexico, Turkey, Greece, Israel, Portugal and Spain. See also note to Table 1. Source: Authors' calculations


Michael Phomsoupha

G Laffaye

Keywords: Badminton, Blessure, Traumatisme, Épidémiologie Badminton, Injury, Trauma, Epidemiology 50

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Badminton is originated in China and created in England and is one of the most popular sports in the world with 200 million adherents [START_REF] Phomsoupha | The science of badminton: game characteristics, anthropometry, physiology, visual fitness and biomechanics[END_REF]. It is considered as the fastest racket game [START_REF] Phomsoupha | How to use the elasticity of a badminton racket to increase its speed by 80%?[END_REF]. This sport has five events: men's and women's singles and doubles and mixed doubles. The decision to include badminton in the 1992 Olympics Game increased participation in the game. This sport can be practiced by anyone, regardless of age or experience [START_REF] Shariff | Musculoskeletal injuries among Malaysian badminton players[END_REF]. During a tournament, badminton match are generally played with one to three matches over the course of 4 or 5 days.

Badminton is an individual non-contact sport that is considered very safe because it involves no physical impact with other players [START_REF] Backx | Sports injuries epidemiologic study[END_REF]. Quick changes of direction, jumps, lunges at the net and rapid arm movements in order to strike the shuttlecock from a variety of postural positions increase the risk of injury [START_REF] Hoy | Badminton injuries--a prospective epidemiological and socioeconomic study[END_REF]. Existing studies have demonstrated the risk of injury in badminton to be 0.85 injuries per year [START_REF] Boesen | Evidence of accumulated stress in Achilles and anterior knee tendons in elite badminton players[END_REF], 1.6 to 2.9 injuries per 1000 hours of play [START_REF] Shariff | Musculoskeletal injuries among Malaysian badminton players[END_REF], 2 to 5 injuries per 1000 players [START_REF] Fahlström | Acute badminton injuries[END_REF] and 1% to 5% of all sports injuries [START_REF] Kroner | Badminton injuries[END_REF]. The way those injuries occur will be discussed in this review.

Participation in badminton training and tournaments was found to increase the risk of injury by 15 to 39% [START_REF] Engebretsen | Sports injuries and illnesses during the London summer Olympic Games 2012[END_REF]. The injury rate was higher in training than in competition [START_REF] Jørgensen | Injuries in badminton[END_REF]. Non-contact trauma occurs frequently in badminton playing (35%) and a higher injury rate was found among athletes competing in badminton tournaments at the London Olympic Games in 2012 (11%) [START_REF] Engebretsen | Sports injuries and illnesses during the London summer Olympic Games 2012[END_REF]. Study has reported a higher injury risk among recreational rather than elite players [START_REF] Yung | Epidemiology of injuries in Hong Kong elite badminton athletes[END_REF].

However, other studies have reported similar risks among these players [START_REF] Jørgensen | Epidemiology of badminton injuries[END_REF].

Male badminton players were found to be more frequently injured than females [START_REF] Jørgensen | Injuries in badminton[END_REF], [START_REF] Jørgensen | Epidemiology of badminton injuries[END_REF], [START_REF] Jones | One year of severe eye injuries in sport[END_REF]. However, when the higher participant rate of male players was considered, the incidence rate did not differ much between the genders [START_REF] Weir | A twelve month study of sports injuries in one Irish school[END_REF]; 0.09 for males and 0.14 for females per person and per year [START_REF] Hensley | A survey of badminton injuries[END_REF]. Most badminton injuries can be classified as chronic overuse injuries (~74%) [START_REF] Fahlström | Decreased shoulder function and pain common in recreational badminton players[END_REF]. The lower limbs account for 58 to 76% of all badminton injuries, the upper limbs for 19 to 32%, and 11 to 16% of back injuries [START_REF] Fahlström | Decreased shoulder function and pain common in recreational badminton players[END_REF]. Eye injuries in sports have been linked to racket sports (42%) [START_REF] Macewen | Sport associated eye injury: a casualty department survey[END_REF], [START_REF] Jones | Eye injuries in sport: an increasing problem[END_REF]. Based on this finding, the objective of this review is to summarize the existing literature of badminton injuries, their location, nature and causes. In addition, this explain how the injury occurred and as well as medical and training recommendations for athletic population.

Methods

We conducted a literature search for English language and non-English language papers on the following databases: PubMed, EBSCOhost, PsycINFO, ScienceDirect, Cairn and Web of Science. An additional search was performed on the Internet using Google Scholar and ResearchGate. Keywords and combinations of these words were used to carry out a comprehensive search of the databases: badminton injury, badminton knee, badminton eye, badminton legs, badminton trunk, racket sports injury, performance injury badminton, shoulder badminton, elbow badminton, muscle badminton and arm badminton. Database searches covered the period from 1974 to 2015.

Frequency, epidemiology and traumatology of injuries

Although it is widely accepted that warming up is vital for the attainment of optimum and safe performance [START_REF] Mcgowan | Warm-up strategies for sport and exercise: mechanisms and applications[END_REF], most players still only spend less than 15 minutes on warm-up activities.

Only 20.0% of players spend 30 minutes or more before playing and players older than 30 years old spend less time warming up than younger players [START_REF] Mcgowan | Warm-up strategies for sport and exercise: mechanisms and applications[END_REF]. Studies have shown that badminton injuries constitute 4.1% of the total number of registered injuries in the literature of sport injury [START_REF] Kroner | Badminton injuries[END_REF], [START_REF] Laoruengthana | The epidemiology of sports injury during the 37th Thailand National Games 2008 in Phitsanulok[END_REF].

Temporal distribution of badminton injuries

Study was found that most injuries occur at the beginning of the season, in September, and in January, after the Christmas holidays [START_REF] Kroner | Badminton injuries[END_REF] (Figure 1). Most of July is spent to rest and holidays for the athletes. In addition, many tournaments begin in September in different countries, which may explain the increased risk of injury in this month. In the study of [START_REF] Kroner | Badminton injuries[END_REF], around 75% of the badminton players included were recreational players [START_REF] Kroner | Badminton injuries[END_REF]. To prepare the beginning of the season, greater care should be taken at these times. In addition, Achilles tendon rupture occurred during the winter months (October to March) for recreational players [START_REF] Fahlström | Acute Achilles tendon rupture in badminton 28 players[END_REF].

Injured players' characteristics

Age has been seen as a risk factor for sport injuries [START_REF] Reeves | A retrospective review from 2006 to 2011 of lower extremity injuries in badminton in New Zealand[END_REF]. The mean age of players reported is 29.5 ± 8.9 years, for males 28.9 ± 8.5 (Table 1) and for females 30.5 ± 8.4 years (Table 2). The age bands most frequently injured are 20 to 29 years (52%), 30 to 39 years (13%), and 40 to 49 years (35%); specifically for males 20 to 29 years (52%), 30 to 39 years (20%), and 40 to 49 years (28%) and for females 20 to 29 years (52%), and 40 to 49 years (48%), according to the entire studies in this literature.

*** Please insert

Distribution of badminton injuries

Even if training sessions are highly necessary to avoid further injuries, existing literature has shown that injuries could occur during the training, with a high frequency of occurrences at the end of training (48%), followed by the middle of training (41%) and the beginning of training (11%). This can be explained by a decline in force output and proprioception brought about by muscle fatigue at the end of training [START_REF] Wassinger | The influence of upper body fatigue on dynamic standing balance[END_REF]. Furthermore, rapid changes in direction, jumps, lunges and the high repetition of 'stop-and-go' maneuvers [START_REF] Phomsoupha | The science of badminton: game characteristics, anthropometry, physiology, visual fitness and biomechanics[END_REF] are required in badminton and these constitute a major source of muscle fatigue. Thus, according to the literature, the lower body extremities are involved in 49.7% of injuries, whereas the upper body extremities are involved in 42.3% of injuries (Figure 2). The head and eyes are injured in 2.0% of cases in different studies [START_REF] Kroner | Badminton injuries[END_REF], [START_REF] Hensley | A survey of badminton injuries[END_REF]. Most injuries have been shown to occur to the joints and ligaments, such as sprains (35.8%) and strains (35.2%), followed by overuse injuries (i.e., tendons, tissue) (22.4%) and bone fractures (3.8%) (Figure 2).

Identification of injury type

A sprain can be defined as a minor ligamentous injury that requires no surgery and causes no major time off from training or competition [START_REF] Yung | Epidemiology of injuries in Hong Kong elite badminton athletes[END_REF]. Ligamentous injury can be defined as major ligamentous damage that requires surgery and leads to major time off (more than one month) from training or competition [START_REF] Yung | Epidemiology of injuries in Hong Kong elite badminton athletes[END_REF]. A strain can be defined as muscular fiber, or tendon tears, including acute or chronic tendinopathy [START_REF] Yung | Epidemiology of injuries in Hong Kong elite badminton athletes[END_REF]. A fracture can be defined as bony damage that results in discontinuity of trabeculae, with radiographic confirmation [START_REF] Yung | Epidemiology of injuries in Hong Kong elite badminton athletes[END_REF].

Eye injury

It has been shown that the extent of eye injuries depends on the type of sports played in a particular country or area, with racket sports appearing to generate the most eye injuries in Europe and Asia [START_REF] Jones | One year of severe eye injuries in sport[END_REF], [START_REF] Zamora | Multicenter survey of badminton-related eye injuries[END_REF]. All injuries were found to be unilateral [START_REF] Zamora | Multicenter survey of badminton-related eye injuries[END_REF]. In the United Kingdom and Australia, in particular, studies have shown that squash and badminton account for half of all sports-related eye injuries [START_REF] Barr | Ocular sports injuries: the current picture[END_REF]. Whilst in Malaysia, badminton generates two-thirds of all ocular sports injuries [START_REF] Chandran | Ocular hazards of playing badminton[END_REF]. The evolution of badminton playing with an increasing shuttlecock velocity during the last 30 years [START_REF] Laffaye | Changes in the game characteristics of a badminton match: a longitudinal study through the Olympic Game finals analysis in men's singles[END_REF] can be associated with physical and ocular injuries [START_REF] Zamora | Multicenter survey of badminton-related eye injuries[END_REF].

Several research studies have examined the causes and types of eye injuries (Table 3). 3 here *** Table 3: Different eye injuries on badminton. NS: no specify.

*** Please insert Table

4.1

Cause of eye injuries [START_REF] Hensley | A survey of badminton injuries[END_REF] have shown that when eye injuries occur by an opponent (65%), by a partner (26%), and self-inflicted (9%) [START_REF] Kelly | Serious eye injury in badminton players[END_REF]. Generally, 6% are related by shuttlecock, whilst 7% related by racket [START_REF] Hensley | A survey of badminton injuries[END_REF]. Patient records have indicated that eye injuries are generated during doubles matches (78%) [START_REF] Zamora | Multicenter survey of badminton-related eye injuries[END_REF] (Table 3). Indeed, existing studies have reported that doubles matches are associated with more injuries than singles matches [START_REF] Jones | Eye injury in sport[END_REF]. Injuries in these matches occur when a player turns round to look at his or her partner [START_REF] Barr | Ocular sports injuries: the current picture[END_REF] or when the shaft of the racket head becomes separated from the handle of one of the players [START_REF] Pappano | Penetrating missile-type head injury from a defective badminton racquet[END_REF]. In singles matches, injuries can be seen to occur when a player is close to the net and hit by a shuttlecock from the opponent's smash [START_REF] Kelly | Serious eye injury in badminton players[END_REF] (Table 3). In these cases, the high speeds involved are enough to cause injury when the shuttlecock hits the eye [START_REF] Zamora | Multicenter survey of badminton-related eye injuries[END_REF]. The shuttlecock head has a small diameter that fits the human orbits [START_REF] Zamora | Multicenter survey of badminton-related eye injuries[END_REF].

Types of related eye injuries

Modifications of racket properties and technology innovation have a great influence on shuttlecock velocity [START_REF] Phomsoupha | How to use the elasticity of a badminton racket to increase its speed by 80%?[END_REF]. The speed at which shuttlecocks travel increases with the players' level of skill [START_REF] Phomsoupha | Shuttlecock velocity during a smash stroke in badminton evolves linearly with skill level[END_REF]. Studies have shown that when eye injuries occur, the right eye is involved in 52% of cases, the left in 48% of cases (Table 3). No instances have been recorded in which both eyes are involved [START_REF] Jones | One year of severe eye injuries in sport[END_REF]. Generally, the injuries are all unilateral with none of the players wearing eye protection [START_REF] Kelly | Serious eye injury in badminton players[END_REF]. Hyphemia is seen to be the most common eye injury [START_REF] Zamora | Multicenter survey of badminton-related eye injuries[END_REF], [START_REF] Barr | Ocular sports injuries: the current picture[END_REF], [START_REF] Kelly | Serious eye injury in badminton players[END_REF], with macular changes, traumatic cataracts, and glaucoma being the main causes of visual impairment in badminton injuries [START_REF] Chandran | Ocular hazards of playing badminton[END_REF] (Table 3).

Thus, badminton players and coaches need to be aware of the risk associated with delivering powerful strokes in doubles matches by the opponent or partner. To prevent the occurrence of eye injuries (such as hyphemia), athletes should hold their racket to protect their head or use protective eyewear. The forward players should hold the racket in the front of the face while awaiting the smash [START_REF] Kelly | Serious eye injury in badminton players[END_REF]. The illusion that contact lenses [START_REF] Jones | One year of severe eye injuries in sport[END_REF] or glass lenses [START_REF] Kelly | Serious eye injury in badminton players[END_REF] offer partial protection should be dispelled, they merely complicate an injury. In some sports, there is already an awareness of the risk of eye injuries [START_REF] Jones | One year of severe eye injuries in sport[END_REF]. However, the lack of knowledge is responsible for athletes failing to use eye protection [START_REF] Jones | One year of severe eye injuries in sport[END_REF]. The value of ocular protection in sport has already been demonstrated [START_REF] Pashby | Eye injuries in canadian amateur hockey[END_REF]. Eye injuries in sports can easily be prevented with the proper use of protective eyewear [START_REF] Filipe | Modern sports eye injuries[END_REF]. Protective polycarbonate spectacles are available for wearing on the court; these are designed to deflect blows [START_REF] Jones | One year of severe eye injuries in sport[END_REF].They merely complicate an injury [START_REF] Jones | One year of severe eye injuries in sport[END_REF]. While vision loss is uncommon, these injuries can lead to permanently diminished visual function.

Upper limb injury

Badminton is a sport that requires a large number of overhead strokes (30%) with clear drop shots and smash shots [START_REF] Laffaye | Changes in the game characteristics of a badminton match: a longitudinal study through the Olympic Game finals analysis in men's singles[END_REF]. Studies have shown that female players play a higher percentage of shots from the back of the court [START_REF] Abian-Vicen | Temporal and notational comparison of badminton matches between men's singles and women's singles[END_REF] and that the upper extremity injuries that occur are unique to racket sports [START_REF] Lees | Science and the major racket sports: a review[END_REF]. A retrospective study found that the incidence rate of injuries is 5.04 per 1000 hours of play, and that the shoulder [START_REF] Yung | Epidemiology of injuries in Hong Kong elite badminton athletes[END_REF] is the most frequently affected location, followed by the hand (Table 4).

*** Please insert Table 4 here *** Table 4: Different upper limb injury on badminton. NS: No specify.

Shoulder pain

Badminton requires a considerable amount of over-shoulder motion, with the shoulder in abduction or external rotation [START_REF] Fahlström | Decreased shoulder function and pain common in recreational badminton players[END_REF]. It has been reported that shoulder pain occurs frequently, as does impingement of the rotator cuff caused by anterior instability of the shoulder joint [START_REF] Ticker | Instability and impingement in the athlete's shoulder[END_REF]. A large proportion of badminton players continue to play with an ongoing injury to the shoulder (17-28%) [START_REF] Arora | Over half of badminton players suffer from shoulder pain: Is impingement to blame?[END_REF]. Studies have observed no differences between male and female players [START_REF] Fahlström | Shoulder pain -a common problem in world-class badminton players[END_REF].

During the World Mixed Team Championship in 2003, shoulder pain on the dominant side was reported by 52% of players, with 37% of players reporting previous shoulder pain and 20%

ongoing pain [START_REF] Fahlström | Shoulder pain -a common problem in world-class badminton players[END_REF]. With regard to badminton players in Hong Kong, shoulder injuries accounted for 19% of all injuries [START_REF] Yung | Epidemiology of injuries in Hong Kong elite badminton athletes[END_REF]. Similarly, a Swedish study found that 52% of players had existing or present pain in the dominant shoulder, with 16% of them experiencing ongoing pain [START_REF] Fahlström | Decreased shoulder function and pain common in recreational badminton players[END_REF]. Injury does not prevent a player from playing; however, it does affect the quality of his or her performance [START_REF] Arora | Over half of badminton players suffer from shoulder pain: Is impingement to blame?[END_REF].

Stroke repetition that is suggestive of an overuse epiphyseal injury to the shoulder may occur as the result of a repetitive explosive action [START_REF] Boyd | Stress fracture of the proximal humeral epiphysis in an elite junior badminton player[END_REF]. It has been noted that the repetitive movement from the cocking to the follow-through phases generates a stress to the epiphyseal plate. In fact, a study has shown that the rotator cuff in badminton players on the dominant arm is stronger than that of the non-dominant arm [START_REF] Arora | Over half of badminton players suffer from shoulder pain: Is impingement to blame?[END_REF]. The ratio between eccentric antagonist and concentric agonist strength is important when analyzing shoulder symptoms experienced by badminton players [START_REF] Ng | A study of antagonist/agonist isokinetic work ratios of shoulder rotators in men who play badminton[END_REF]. During the cocking phase, the ratios of strength were found to be 2.15:1 for the dominant side and 1.71:1 for the non-dominant side; whereas the ratios were 0.97:1 for the dominant side and 1.08:1 for the non-dominant side during the deceleration phase [START_REF] Ng | A study of antagonist/agonist isokinetic work ratios of shoulder rotators in men who play badminton[END_REF].

Rehabilitation for these players should include muscle-strengthening exercises (i.e., shoulder stabilizers and rotator cuff muscles), and exercises to avoid loss of motion [START_REF] Fahlström | Decreased shoulder function and pain common in recreational badminton players[END_REF]. The ratio between eccentric antagonist and concentric agonist strength is important when analyzing shoulder symptoms experienced by badminton players [START_REF] Ng | A study of antagonist/agonist isokinetic work ratios of shoulder rotators in men who play badminton[END_REF].

Elbow injuries

Tennis elbow and golfer's elbow are the most commonly reported diagnoses related to the elbow in badminton players [START_REF] Shariff | Musculoskeletal injuries among Malaysian badminton players[END_REF] (Table 4). Tennis elbow is mainly caused by lateral epicondylitis [START_REF] Chard | Racquet sports--patterns of injury presenting to a sports injury clinic[END_REF] whereas golfer's elbow is caused by medial epicondylitis [START_REF] Karahan | Kienbock's disease that manifested in a badminton player: case report[END_REF]. Training errors are one of most common causes of overuse injuries [START_REF] Karahan | Kienbock's disease that manifested in a badminton player: case report[END_REF]. Players who have hitting technique problems are at an increased risk of injury [START_REF] Mills | Injuries in badminton[END_REF]. Poor technique can often be related to an incorrect grip or the holding of the racket too tightly by the beginner.

Hand injuries

Players were found to rarely use a 'wrist snap' [START_REF] Gowitzke | Myths and realities in badminton and tennis strokes[END_REF]; however, they can use it to increase the power of the smash. The grip, the intensive practice of the badminton game and the repetitive hyperextension movements of the wrist are risk factors for scaphoid fractures [START_REF] Brutus | Could this unusual scaphoid fracture occurring in a badminton player be a stress fracture?[END_REF], even without axial load (Table 4). The scaphoid bone is a connecting rod between the two carpal rows and may be exposed to shearing and torsional forces by excessive and repetitive movement of wrist flexion and extension [START_REF] Inagaki | Stress fracture of the scaphoid combined with the distal radial epiphysiolysis[END_REF].

Trunk function to prevent injury

Studies have shown that badminton players hold a racket in their dominant hand, which limits their arm position and leads to an asymmetric posture through lateral trunk flexion, especially during overhead strokes [START_REF] Kimura | Mechanisms for anterior cruciate ligament injuries in badminton[END_REF]. In general, core stability is essential to the control of movements of the trunk and distal segments [START_REF] Huang | How does knee pain affect trunk and knee motion during badminton forehand lunges?[END_REF].

Trunk sway greatly contributes to a change of direction in the knee adduction movement.

The badminton-specific movement in the upper extremity and trunk motion during an overhead stroke may affect the hip and knee joint kinematics and kinetics [START_REF] Kimura | Increased knee valgus alignment and moment during single-leg landing after overhead stroke as a potential risk factor of anterior cruciate ligament injury in badminton[END_REF]. During an overhead stroke in the left rear court, players have to laterally bend their trunk to their left side as their arm comes through [START_REF] Kimura | Trunk motion and muscular strength affect knee valgus moment during single-leg landing after overhead stroke in badminton[END_REF]. Deficits in neuromuscular control of the trunk may contribute to lower extremity joint instability and injury [START_REF] Kimura | Trunk motion and muscular strength affect knee valgus moment during single-leg landing after overhead stroke in badminton[END_REF]. Neuromuscular training should be carried out to increase trunk and hip control, thus preventing injury to the lower limb [START_REF] Myer | The effects of plyometric versus dynamic stabilization and balance training on lower extremity biomechanics[END_REF].

Stroke repetition and hitting technique problems can increase the risk of arm injuries. Most players continue to play through the pain, but the discomfort affects their quality of play. The badminton-specific movement engages the trunk, which makes a specific contribution during a stroke, forming a link between lower limb movement and upper limb stroke.

Lower limb injury

Injuries are common in badminton, and overuse injuries are frequent, particularly those localized to the lower extremities [START_REF] Fahlström | Acute badminton injuries[END_REF]; in studies, these accounted for 58% of the injuries [START_REF] Boesen | Evidence of accumulated stress in Achilles and anterior knee tendons in elite badminton players[END_REF] (Table 5).

*** Please insert Table 5 here ***

Table 5: Different lower limb injury on badminton. NS: No specify.

Achilles tendon rupture

The most severe overuse injuries were found to be tendon related, including Achilles tendinopathy [START_REF] Boesen | Evidence of accumulated stress in Achilles and anterior knee tendons in elite badminton players[END_REF], and patella tendinopathies [START_REF] Jørgensen | Epidemiology of badminton injuries[END_REF]. Indeed, Hess [START_REF] Hess | Achilles tendon rupture: a review of etiology, population, anatomy, risk factors, and injury prevention[END_REF] has shown a relatively high incidence of acute Achilles tendon ruptures. Such injuries are a major problem among athletes [START_REF] Fahlström | Painful conditions in the Achilles tendon region in elite badminton players[END_REF]. Badminton is a sport where tendons are placed under a heavy strain [START_REF] Boesen | Evidence of accumulated stress in Achilles and anterior knee tendons in elite badminton players[END_REF]. Existing studies of badminton players and fencers have used magnetic resonance imaging to show that the cross sectional area of the patella tendon on the dominant leg is up to 28% greater than that of the non-dominant patella tendon [START_REF] Couppé | Differences in tendon properties in elite badminton players with or without patellar tendinopathy[END_REF]. Chronic Achilles tendon pain associated with structural tendon changes is often located in the middle portion of the tendon [START_REF] Alfredson | Chronic Achilles tendinosis: recommendations for treatment and prevention[END_REF].

Badminton development has increased the number of hours per person and per day [START_REF] Boesen | Ultrasonographic investigation of the Achilles tendon in elite badminton players using color Doppler[END_REF],

increasing the risk of injuries as well. The cause of Achilles tendon ruptures is based on mechanical and degenerative factors [START_REF] Khan | Time to abandon the "tendinitis" myth[END_REF] and the weight of the player [START_REF] Kaalund | Achilles tendon rupture in badminton[END_REF]. The Achilles tendon is most loaded during badminton [START_REF] Boesen | Evidence of accumulated stress in Achilles and anterior knee tendons in elite badminton players[END_REF], because of the abrupt repetitive jumping and sprinting movements involved [START_REF] Houshian | The epidemiology of achilles tendon rupture in a Danish county[END_REF]. Studies have shown that Achilles tendon rupture occurs in the middle or at the end of a match or training session in 87% of cases [START_REF] Kaalund | Achilles tendon rupture in badminton[END_REF]. One reason for this is that players tend to tire at this stage of play. Tiredness can lead to poor muscle coordination and increased tension in the Achilles tendon with acceleration and deceleration [START_REF] Kaalund | Achilles tendon rupture in badminton[END_REF]. The occurrence of Achilles tendon injuries in athletes has been shown to increase with age [START_REF] Saltzman | Achilles tendon injuries[END_REF]. Athletes with acute Achilles tendon ruptures reported previous Achilles tendon pain in 15 to 21% of cases [START_REF] Fahlström | Acute Achilles tendon rupture in badminton 28 players[END_REF].

Ultrasound is used in rehabilitation and to diagnose Achilles tendon ruptures and other tendon pathology [START_REF] Aström | Imaging in chronic achilles tendinopathy: a comparison of ultrasonography, magnetic resonance imaging and surgical findings in 27 histologically verified cases[END_REF]. This technique has been supplemented by the color Doppler technique for finding and treating hyperemia in tendinopathy in the Achilles tendon [START_REF] Malliaras | Achilles tendon shape and echogenicity on ultrasound among active badminton players[END_REF]. Surgical techniques and rehabilitation programs should lead to a decrease in complications [START_REF] Sjöström | Achilles tendon injury[END_REF], whilst treatment by Kinesio taping is aimed at decreasing tenderness and pain [START_REF] Lee | Treatment of chronic Achilles tendon pain by Kinesio taping in an amateur badminton player[END_REF]. The majority of players play with orthoses (inserts in the shoes) [START_REF] Boesen | Ultrasonographic investigation of the Achilles tendon in elite badminton players using color Doppler[END_REF]. However, it has not yet been established that orthoses prevent Achilles tendinopathy or achillodynia [START_REF] Boesen | Ultrasonographic investigation of the Achilles tendon in elite badminton players using color Doppler[END_REF].

Anterior cruciate ligament injuries and prevention

Most anterior cruciate ligament (ACL) injuries occur during sports activity (Table 5).

According to existing research, up to 70% of all incidents are non-contact injuries [START_REF] Kimura | Increased knee valgus alignment and moment during single-leg landing after overhead stroke as a potential risk factor of anterior cruciate ligament injury in badminton[END_REF], whilst ACL injuries account for 37% of all injuries requiring surgically treatment [START_REF] Uchiyama | Sports injuries in different athletic events, authored by national team physicians[END_REF]. The greater activation of the knee extensor is a predictor of knee injury, because of increased tension on the ACL [START_REF] Kimura | Increased knee valgus alignment and moment during single-leg landing after overhead stroke as a potential risk factor of anterior cruciate ligament injury in badminton[END_REF].

Players need to move from back to front and side to side rapidly; they also need to return to the center of the court to prepare for the next shot by performing footwork that is unique to badminton [START_REF] Kimura | Increased knee valgus alignment and moment during single-leg landing after overhead stroke as a potential risk factor of anterior cruciate ligament injury in badminton[END_REF]. The injury patterns recorded appear to be the result of frequently performed movements. In badminton, ACL injuries were found to be the result of two injury mechanisms (Table 5). First, the knee opposite the racket-hand side is at risk of injury during a single-leg landing after an overhead stroke. This usually occurs in the backhand side of the court (48%) [START_REF] Kimura | Mechanisms for anterior cruciate ligament injuries in badminton[END_REF]. The knee of the racket hand side is susceptible to injury because of plant-and-cut movements during side or backward stepping in the forehand side of the court (38%). ACL injury can also be explained by a greater increase in the knee valgus angle and movement during single-leg landing following back-steps to the backhand-side rear than with back-steps to the forehand-side rear [START_REF] Kimura | Increased knee valgus alignment and moment during single-leg landing after overhead stroke as a potential risk factor of anterior cruciate ligament injury in badminton[END_REF]. Landing requires high levels of neuromuscular control to maintain stability and performance [START_REF] Loram | Human balancing of an inverted pendulum: position control by small, ballistic-like, throw and catch movements[END_REF].

In order to prevent ACL injuries, it is important to study the biomechanics of players' movements and to put physiological knowledge into action with respect to knee joints [START_REF] Kimura | Increased knee valgus alignment and moment during single-leg landing after overhead stroke as a potential risk factor of anterior cruciate ligament injury in badminton[END_REF].

Adequate muscular training of the lower extremities is necessary to prevent badminton injuries [START_REF] Fahlström | Acute badminton injuries[END_REF].

Impact of the lunge on the knee

Lunge motions require a high level of knee dynamic stability; thus, they place a high physical demand on the lower limbs [START_REF] Kuntze | Biomechanical analysis of common lunge tasks in badminton[END_REF]. Two types of lunges exist according to the axe of the foot, the standard one and the "break-foot lunge" [START_REF] Phomsoupha | Vertical ground reactions of two types of lunges according to foot axis[END_REF]. The repetition of lunges on the same leg can lead to players adopting an asymmetric posture [START_REF] Kimura | Mechanisms for anterior cruciate ligament injuries in badminton[END_REF]. The intensive braking phase of the dominant leg during the net stroke can increase the stress on the Achilles tendon by 6 to 12 times the player's body weight and by 5 times for the knee patellar tendon [START_REF] Lee | Treatment of chronic Achilles tendon pain by Kinesio taping in an amateur badminton player[END_REF]. Core stability plays a key role in preventing knee joint injury [START_REF] Kimura | Trunk motion and muscular strength affect knee valgus moment during single-leg landing after overhead stroke in badminton[END_REF]. The contraction of the core muscles provides a stable base for the movement of the lower extremities [START_REF] Huang | How does knee pain affect trunk and knee motion during badminton forehand lunges?[END_REF]. With unbalanced core muscle strength, players are at a higher risk of knee injury [START_REF] Devlin | Recurrent posterior thigh symptoms detrimental to performance in rugby union: predisposing factors[END_REF]. Thus, the trunk position affects the recruitment of the knee flexors [START_REF] Farrokhi | Trunk position influences the kinematics, kinetics, and muscle activity of the lead lower extremity during the forward lunge exercise[END_REF]. During the performance of forehand and backhand backward lunges, the trunk movement affects the knee joint kinematics [START_REF] Kimura | Increased knee valgus alignment and moment during single-leg landing after overhead stroke as a potential risk factor of anterior cruciate ligament injury in badminton[END_REF]. The skill level of players affects their lunge performance [START_REF] Kuntze | Biomechanical analysis of common lunge tasks in badminton[END_REF] (Table 5). The combination of greater knee flexion and a smaller root mean square of knee extensor in the injured player was seen to be the compensatory strategy required to complete the task and avoid injury [START_REF] Huang | How does knee pain affect trunk and knee motion during badminton forehand lunges?[END_REF].

Conclusion

This review takes a global approach to badminton-related injuries related to the eyes, and upper and lower limbs. Each of these factors may play an important role in the prevention of injuries to badminton players. Future badminton research is likely to continue to be heavily influenced by the evolution of temporal and notational structure. It is apparent that the movement patterns and movement demands of badminton are related to an increase in injuries and the generation of new injuries. Furthermore, it would be of interest to compare badminton with other racket sports.

Summary

Studies that have reported on eye injuries have determined the nature of these injuries and the way they occur. They have also suggested possible preventive measures. The main reason for eye injury is shuttlecock impact from an opponent's stroke; thus, by wearing glasses, badminton players can considerably reduce the risk of eye injury. Injuries to the arm and shoulder in badminton are largely due to faulty technique, while leg and back injuries are caused mainly by a lack of strength or mobility. The contribution of the trunk to the prevention of lower limb injuries suggests that specific attention should be paid to this area.

In general, the patellar tendon is put under serious stress in badminton [START_REF] Boesen | Evidence of accumulated stress in Achilles and anterior knee tendons in elite badminton players[END_REF]. Studies have shown that a substantial number of elite badminton players have patellar tendon pain and 'jumper's knee' [START_REF] Koenig | Doppler ultrasonography of the anterior knee tendons in elite badminton players: colour fraction before and after match[END_REF]. Male badminton players have a significantly higher incidence of painful patellar tendons [START_REF] Koenig | Doppler ultrasonography of the anterior knee tendons in elite badminton players: colour fraction before and after match[END_REF].

Rapid changes in the direction of movement, jumps and lunges can all result in injuries to the Achilles tendon and anterior cruciate ligament. Tendons are put under high pressure during each movement and, in general, injuries occur at the end of a training session or tournament.

Fatigue influences the way that lunges are performed and the way that the jump is received by making these tendons less powerful and more unstable.

Such knowledge could help coaches and fitness trainers focus on the specific muscular activities required to prevent injuries. For example, they could design a training program that increases body strength to prevent injuries to badminton players. The relationship between coaches and scientists, particularly in terms of biomechanics and physiotherapy, will help improve performance and prevent injury.

Figure 1 :

 1 Figure 1: Percentage in each month according to the total number of injuries during one year

Figure 2 :

 2 Figure 2: Body sites distribution of badminton injuries

Table 1 here ***Table 1 :

 1 Male players characteristics on the different studies. NS: no specify.

*

** Please insert Table 2 here ***Table 2 :

 2 Female players characteristics on the different studies. NS: no specify.

Table 1 :

 1 Male players characteristics on the different studies. NS: no specify. 356

			Weight	Height	BMI	Experience
	Study	Age (years)
			(kg)	(cm)	(kg/m²)	(years)
	Boesen et al. [6]	25.9 ± 3.2	76.4 ± 6.4 183.5 ± 6.6 22.7 ± 1.0	18.8 ± 4.1
	Fahlström et al. [7]	32.9 ± NS	NS	NS	NS	24.5 ± NS
	Fahlström et al. [16]	43.1 ± 7.0	NS	NS	NS	22.1 ± 10.9
	Fahlström et al. [53]	24.4 ± 4.3	76.9 ± 8.8 183.0 ± 6.6 22.9 ± 1.4	15.6 ± 4.4
	Fahlström et al. [72]	44.7 ± 6.1	NS	NS	25.0 ± 3.2	29.8 ± 4.3
	Fahlström et al. [38]	23.5 ± 4.3	NS	NS	NS	14.2 ± 4.9
	Fu [73]	20.1 ± 1.9	71.1 ± 7.6 179.1 ± 3.4	NS	10.7 ± 2.5
	Fu et al [74]	20.1 ± 1.9	71.1 ± 7.6 179.1 ± 3.4 10.7 ± 2.5	NS
	Hensley and Paup
		33 ± 13.1	73.8 ± 9.2 177.8 ± 7.3	NS	13.4 ± 10.1
	[15]
	Huang et al. [48]	24.9 ± 5.2 75.0 ± 11.9 178.0 ± 8.7 13.6 ± 2.6	11.6 ± 5.3
				179.1 ±
	Koenig et al. [71]	25.0 ± 3.5	73.5 ± 9.2		22.5 1.7	17.9 ± 4.3
				14.0

Table 2 :

 2 Female players characteristics on the different studies. NS: no specify. 357

	Study	Players	Main injury (%)

Table 4 :

 4 Different upper limb injury on badminton. NS: No specify. 359

	Study	Players	Main injury (%)	Causes (%)
			Leg
			Sprain (40), fracture (3), contusion (5)
	Goh et al. [76]	34 males and 24 females (range 13-16)	apophysistis (10), bursitis (3), tendinopathy (7), stress fracture (5), patellofemoral joint	Acute and overuse
			syndrome (2)
	Shariff et al. [3]	190 players	Patellar tendinopathy (42.7), muscle strain (11.8) meniscus or ligamentous injuries (10.9)	NS
	Yung et al. [11]	14 males and 16 females elite players (range 13-28)	Strains (64), sprains (14), facet injury (14)	NS
			Knee
	Boesen et al. [78]	96 semi-professional	Dominant tendon pain (23), non-dominant tendon pain (13)	NS
	Chard and Lachmann [41]	74 males and 54 females college (4: 15 and under years, 60: 16-25 years, 64: 26 and over)	Collateral ligament (28), cruciate ligament (12.5), meniscal injury (25)	NS
	Couppé et al. [54]	7 professionals	Tendinopathy	Overuse and repetitive load
	Fahlström et al. [7]	67 males and 11 females (range 11-52)	Knee injuries (16.7)	Overuse
	Huang et al. [48]	17 injured players	Knee pain (100)	NS
	Jafari et al. [75]	23 males and 7 females professional	Muscle tendon (56.2), joint ligaments (28.1), bone (9.4)	NS
	Kimura et al. [47]	6 males and 15 females	Anterior cruciate ligament	Single leg landing (48), plant-and-cut (38)

Table 5 :

 5 Different lower limb injury on badminton. NS: No specify. 360

Disclosure of interest