Eliane El Badaoui
email: eliane.badaoui@u-paris10.fr.

Eric Strobl
email: eric.strobl@polytechnique.edu.

Frank Walsh
email: frank.walsh@ucd.ie.

The Impact of Internal Migration on Local Labour Markets in Thailand

Keywords: JEL Classification: O15, J10 Internal migration, Labour markets, Thailand

We estimate the impact of internal migration on local labour markets in Thailand.

Using an instrumental variable approach based on weather and distance we estimate an exogenous measure of the net migration inflow into each region. Our results show that instrumenting for the possible endogeneity of net inward migration is crucial to the analysis. The results suggest substantial adjustments in hours worked and weekly wages in response to short term changes in labour supply for low skilled males. We find no effect on high skilled workers. A theoretical section shows that a reduction in hours per worker in response to an increase in inward migration is consistent with the predictions of a standard search model.

Introduction

The 2009 Human development Report [ UN (2009)] estimates that at least 740 million people worldwide are internal migrants, i.e., almost four times the number who have moved internationally. Despite this, perhaps because movements within borders often go undocumented, the literature on internal migration and its consequences on local labour markets is relatively small and not very well understood. 1 In this regard, a key difficulty in measuring the impact of internal migration is the endogeneity of migration. More precisely, net migration is expected to be correlated with economic conditions in each region making it difficult to identify the impact of migration on variables such as the wage and employment level, which also depend on these factors. In this study we use exogenous climatic shocks to identify the impact of internal migration on labour market outcomes in Thailand.

The literature on the impact of international migration on labour markets can serve as a first indication of what effects one might expect from internal migration. For example, well known studies such as Card (1991) looked at the impact of exogenous regional migration shocks such as the 1980 Mariel boatlift and found that migration had little impact on native wages. Critics argued that a possible cause for the absence of any observed effect of migration on natives is that natives might move to other local labour markets in response to an influx of migrants, thus masking the impact of migration on wages and employment.

While some studies such as [START_REF] Aydemir | Cross-Country Variation in the Impact of International Migration: Canada, Mexico, and the United States[END_REF] use national data to overcome this problem and find a negative effect of migration on wages, [START_REF] Aydemir | Attenuation Bias in Measuring the Wage Impact of Immigration[END_REF] note that ". . . the national labour market approach may find itself with as many different types of results as the spatial correlation approach that it conceptually and empirically attempted to replace 2 ." An alternative explanation for the absence of important effects on wages and employment prospects for natives from an increase in migration is that native and migrant workers may be imperfect substitutes [START_REF] Manacorda | The impact of immigration on the structure of male wages: theory and evidence from Britain[END_REF], [START_REF] Ottaviano | Rethinking the Effects of Immigration on Wages[END_REF], [START_REF] Peri | Rethinking the area approach: Immigrants and the labor market in California[END_REF]]. In particular [START_REF] Manacorda | The impact of immigration on the structure of male wages: theory and evidence from Britain[END_REF] suggest using U.K. data that, while migrants and natives are imperfect substitutes, migrants are close substitutes for other migrants so that an increase in the stock of migrants lowers the wages of existing migrants but has little impact on natives. Arguably, however, internal migrants will be closer substitutes for native workers than international migrants 1 Also see [START_REF] Lucas | Internal Migration in developing Countries[END_REF] or [START_REF] Mendola | Rural Out-Migration and Economic development at Origin: A Review of the Evidence[END_REF] for reviews of the literature on internal migration in developing countries.

2 Some examples of studies that have examined this question with mixed results are [START_REF] Bonin | Wage and employment effects of immigration to Germany: evidence from a skill group approach[END_REF] who reports a very weak impact of supply shifts on wages in Germany. [START_REF] Bohn | Refining the Estimation of Immigration's Labor Market Effects[END_REF] find a weak wage effect on the Canadian labor market. [START_REF] Aydemir | Cross-Country Variation in the Impact of International Migration: Canada, Mexico, and the United States[END_REF] use data from Canada and Mexico and find a strong negative relationship between wages and supply shifts induced by immigration while [START_REF] Mishra | Emigration and wages in source countries: Evidence from Mexico[END_REF] studies the Mexican labor market and finds a significant positive effect of emigration and wages in Mexico.

so that these effects are less likely to be as important for interprovincial migration within Thailand. [START_REF] Card | Immigration and Inequality[END_REF] concludes that for high school dropouts natives and migrants are perfect substitutes but natives and migrants are imperfect substitutes within higher skilled groups. This conclusion is consistent with the results we present below where we find labour market effects for low skilled workers only.

The little empirical literature that exists on the effect internal migration on local labour markets also presents mixed results. For instance, [START_REF] Beals | Rationality and Migration in Ghana[END_REF] study the migration phenomenon in Ghana and show that income differentials drive migration and that regions of large population are relatively more attractive. [START_REF] Sahota | An Economic Analysis of Internal Migration in Brazil[END_REF] finds that internal migration in Brazil is highly responsive to earning differentials and inversely related to distance. More generally, economic costs and returns dominate the behaviour of migrants. [START_REF] Phan | Inter-provincial migration and inequality during Vietnam's transition[END_REF] analyse inter-provincial migration and inequality during Vietnam's transition. Their analysis suggests that the impact of migration on inequality can be either negative or positive. [START_REF] Boustan | The effect of internal migration on local labor markets: American cities during the great depression[END_REF] study the effect of internal migration in the 1930s on the economic welfare of residents in US destination cities and find a small and not significant impact of in-migration on hourly and weekly earnings while Ham et al.

(2011) use a distance based measure of migration to measure the effect of internal U.S. migration on wage growth and find that migration significantly increases wage growth for college graduates. [START_REF] Kennan | The Effect of Expected Income on Individual Migration[END_REF] show that expected income is an important determinant of interstate migration in the U.S.

In this paper we analyse the effects of inter-provincial migration on wages and employment in Thailand. Using the methodology developed by [START_REF] Boustan | The effect of internal migration on local labor markets: American cities during the great depression[END_REF] we estimate the net inflow rate of migrants into each province thus controlling for the labour supply responses of natives to changes in migration. We take into consideration the geographic distance between sending and receiving provinces. In fact, the distance constitutes an important determinant of the location choice of one migrant and several studies consider that distance has a strong negative effect on migration.3 Furthermore, we consider the fact that, in developing economies, weather conditions might induce a spatial reallocation of the relatively mobile input, labour.4 Thus, local weather conditions can constitute a convenient and readily available instrument for migration rates [START_REF] Boustan | The effect of internal migration on local labor markets: American cities during the great depression[END_REF]. 5We use our framework and data from the Thai Labor Force Survey to identify the impact of internal migration on the weekly wage, hourly wage and on weekly hours worked for males at the destination markets in Thailand. 6 Arguably, Thailand constitutes an ideal case study for the task at hand. Standards of living, economic and cultural structures, and growth rates differ widely among provinces. As a consequence, levels and patterns of internal migration vary within the country. Also, Thailand is one of the earliest Southeast Asian economies to implement an export-led growth strategy, the consequence of which is an increase in rural-urban migration, especially to the service sector in Bangkok [START_REF] Guest | Bridging the Gap: Internal Migration in Asia[END_REF]. Despite the 1997 crisis that altered migration patterns for seasonal and short-term workers, this long-term growth in labour migration has continued. 7 The internal migration of workers is induced by the wage differentials observed among provinces and modifies wages in receiving provinces. Moreover, migrants benefit from a net income increase that they share with household members in the form of money and goods they remit home. In this regard, [START_REF] Yang | Unequal provinces but equal families? An analysis of inequality and migration in Thailand[END_REF] analyses the link between migration and cross-province inequality in Thailand and finds a significant effect of migration on income inequality. More particularly, she reports that a 1 percent increase in the mean fraction of out-migrants to Bangkok entails a 0.058 reduction in the average ratio of Bangkok's income to all other provinces.8

The empirical results we present below males indicate that inward migration has a substantial negative impact on the weekly wage of low skill natives, but this results from a reduction in weekly hours worked rather than in the hourly wage. In this regard, one might have expected an increase in migration to lead to higher hours worked and lower wages in a frictionless labour market. However, in our theoretical section we use the Mortensen- Pissarides (1994) search model modified to include hours worked by [START_REF] Pissarides | Equilibrium Unemployment Theory[END_REF] to show that an increase in migration lowers the search costs associated with finding workers in equilibrium and leads to substitution from hours into workers in line with the empirical results.

The paper is organized as follows. In the next section we outline the theoretical model, section three outlines our data set. The fourth section presents the empirical specification and econometric results. The final section concludes.

2 Theory [START_REF] Kinoshita | Working Hours and Hedonic Wages in the Market Equilibrium[END_REF] derives the equilibrium properties in the standard competitive model where firms combine hours per worker with the number of workers in the production function. 9 We begin by sketching how an increase in migration might affect equilibrium in this type of model and continue by outlining how the results might differ when there are search frictions in the model.

In general the equilibrium hours per worker hourly wage locus is a set of tangencies where workers who wish to work longer hours match up with like-minded firms. In equilibrium the supply and demand of each worker type are equal and no worker or firm can gain from deviating to another point on the locus. Compensating wage differentials are paid to workers for working a less desirable number of hours.10 Firms are assumed to be able to hire as many workers as they wish at any level of hours (h). If there is only one type of worker and firm, the workers' indifference curve is the equilibrium hours wage locus and the firms' isoprofit curve over hourly wage and hours per worker space.11 Figure 2 illustrates a tangency between the indifference curve of a representative worker and the isoprofit curve of a firm if preferences and the firm's technology are well behaved. We expect that an increase in migration (Labour supply) would shift the equilibrium indifference curve downward and as long as equilibrium is on an upward sloping part of the indifference curve (so that in equilibrium a firm would pay a higher hourly wage if they wished to induce workers to work longer hours) a lower reservation indifference curve will be tangent to the isoprofit curve at higher hours per worker and a lower hourly wage. Even in this simplest version of the competitive model with a representative worker and firm it is theoretically possible that the equilibrium is on a downward sloping part of the indifference curve where workers work short hours and would accept a lower hourly wage to work longer hours and where an increase in labour supply (downward shift in the indifference curve) would lead to lower hours per worker, although arguably most labour economists might expect there to be a positive relationship between hours and the hourly wage for most workers in equilibrium. That is our expectation from the standard model might be that an increase in migration, where migrants are substitutes for natives, would increase labour supply and lead to lower wages and longer working hours.

Another prediction of standard models of the labour market is that an increase in the fixed costs of hiring workers can cause firms to substitute from workers into longer hours per worker [See Hamermesh (1993) Chapter 7 for example]. Most of the modern labour labour literature models labour market flows in a framework where there are search costs associated with firms and workers meeting. In the model presented here we use one of the most common search models: the Mortensen-Pissarides (1994) search model with hours worked included, as outlined in [START_REF] Pissarides | Equilibrium Unemployment Theory[END_REF] and analyse the impact of migration on hours and wages. The intuition is that migration may lower the time and cost of contacting workers and lead to substitution from hours into workers so that hours per worker falls in contrast to the expected prediction from the competitive model outlined above.

Below we present the search model outlined in [START_REF] Pissarides | Equilibrium Unemployment Theory[END_REF] section 7.3. We incorporate a fixed benefit payment to unemployed workers into the model and analyse the impact of an increase in migration (an increase in the labour force) on hours and the number of workers in equilibrium. We show that an increase in migration is associated with a decrease in labour market tightness so that it takes less time for firms to find workers. This increases the employment rate in a stationary equilibrium and reduces the number of hours per worker.

There is a matching function which is homogeneous of degree one so that the number of matches taking place at each unit of time is:

mL = m(uL, vL) (1)
The labour force is L, the unemployment rate is u and the number of vacancies as a fraction of the labour force is v. We define the ratio of unemployment to vacancies (labour market tightness as): θ = v u . The rate at which vacant jobs are filled can be rewritten:

q(θ) = m u v , 1 (2)
The mean duration of a vacancy is: 1 q(θ) and the percent change in q(θ) from a percent change in θ is -1 ≤ η < 0. Similarly the rate at which unemployed workers find jobs is:

θq(θ) = m (uL, vL) uL (3)
The mean duration of unemployment is: 1 θq(θ) and the elasticity is 1 -η. Idiosyncratic shocks destroy jobs at a rate λ. With a fixed labour force the number of workers who enter unemployment during a short interval δt is:

λ(1 -u)Lδt (4)
The mean number of workers who leave unemployment at each point in time is:

mLδt = uθq(θ)Lδt (5)
The unemployment rate in the steady state is:

u = λ λ + θq(θ) (6)
It also follows that if the labour force is L then aggregate employment is:

N = (1 -u)L = θq(θ)L λ + θq(θ) (7)
Equation ( 6) is the Beveridge curve which can be used to graph the relationship between the vacancy rate and unemployment rate in long run equilibrium. This is independent of the size of the labour force. The workers instantaneous utility depends on current income and current hours of work. Instantaneous utility during employment is:

12 w E = whφ(1 -h) φ (.) > 0, φ (.) ≤ 0, 0 ≤ h ≤ 1 (8)
where the hourly wage is w, the length of the day is normalised to unity and the utility function is linear in wage income and non-linear in the disutility of work. We continue by assuming the Cobb-Douglas utility function for hours:

φ(1 -h) = (1 -h) α where 0 < α < 1 (9)
The flow utility from unemployment and employment are:

rU = z + θq(θ)(W -U ) (10)
The model differs from the model in [START_REF] Pissarides | Equilibrium Unemployment Theory[END_REF] in that we include a fixed payment z to unemployed workers. [START_REF] Pissarides | Equilibrium Unemployment Theory[END_REF] discusses the possibility of making this payment proportional to the wage and we might reasonably argue that benefits are a function of earnings in many countries. While this is reasonable in terms of analysing long run equilibrium, we suggest here that if we wish to analyse the short run effects of migration we do not expect benefits to respond instantaneously to any change in wage associated with short run migration flows. This is important for the results in that if benefits are fixed the wage will not be fully proportional to productivity. Because wages are not proportional to productivity there will be a relationship between the equilibrium level of productivity and labour market tightness. This means that if an increase in migration affects employment and the equilibrium level of productivity, this will also lead to an effect on labour market tightness and search costs. We will see that this leads to a change in hours per worker.

rW = whφ(1 -h) + λ(U -W ) (11)
Wages and hours are chosen by Nash bargaining where the flow value of a vacancy is:

rV = -p[N h]c + q(θ)(J -V ) (12)
The cost of maintaining a vacancy per unit of time is pc while there is a probability of that the vacancy will be filled. We assume that productivity is the constant p[Nh] but that the productivity of additional matches is diminishing at the aggregate level, so that the change in productivity from a change in aggregate hours worked NH is negative. The flow value of a filled job is:

rJ = p[N h]h -hw -λJ (13)
Hours and the wage are chosen to maximise:

(W -U ) β (J j -V ) 1-β = whφ(1 -h j -rU (r + λ β [p(N, h) -w]h r + λ -V 1-β (14)
where 0 < β < 1.

We note that when the worker and firm engage in bargaining at the level of the individual worker/job match, the value of the outside option is exogenously given to the worker as is the value of a vacancy to the firm.

Proposition 1 An increase in the labour force (inward migration) leads to a decrease in labour market tightness and lower hours per worker.

Proof. in the Appendix.

Data and Sample Selection

We use data on males from the Thai Labour Force Survey between 1991 and 2000. The survey is conducted several times a year and with increasing frequency in recent years.

We have data for the February and August surveys for each year. The survey is a large cross-section; for example, the February 2000 survey has 164,636 observations. In sum, the complete data is constituted of twenty waves with a total of 2,951,839 observations.

August is the peak of the agricultural season and labour markets are much more buoyant with significant internal migration as workers return to rural areas for harvesting. February clearly represents the off-peak season in Thailand.

The survey contains a wide variety of questions on location, employment status and job characteristics, income as well as demographic characteristics. Data on earnings asks workers if they are paid hourly, daily, weekly, or monthly and what the rate of pay is for the relevant category. Summary statistics for the total sample shows that 91% of workers are paid either daily or monthly, where most waged workers at the low skill end of the labour market are paid daily. There are seventy two provinces in Thailand as shown in Figure 2. In addition to providing the name of the province where they live, individuals answer the following question: "How long have you been living regularly in this village/ municipality?" Respondents can answer from less than a year, one year, two years, up until nine or more than nine years. We exclude people who move into the same province and calculate the number of recent arrivals as those who answer less than or equal to one year.

This number corresponds to 124,185 individuals and represents 52.4% of total movers to new provinces. 13 We use this subsample of movers to compute the inflow and outflow rates. Then we define the province of origin and the destination province of all movers as people are asked "Which is the previous province of your residence before moving here?".

The survey then asks for the reason of migration. Among recently moved people, some 35.71% were looking for a job or occupation. However, 7.62% of respondents migrate for further study, 22.75% follow their family and 28.53% report coming back to their former residence. Only 0.22% of migrants state moving from one province to another in order to be nursed. Concerning the province of destination, Bangkok counts the largest proportion of arrivals with 7.2% of total recent migrants. 14 We construct a sample of non-migrants residing in different areas -municipal, non-municipal, sanitation district -in the 72 Thai provinces. We consider as incumbents all recent migrants who moved within the same province and we reduce our sample to the working age population.

As for the empirical analysis, we reduce the sample to men aged 15-64 who were not attending school at the moment of the survey and who work 95 hours or less a week in the private sector. We drop observations of self-employed, government or public service workers as well as workers who are in unpaid jobs. 4 Econometric Analysis

Construction of Instruments

In order to construct instruments for migration we follow the methodology proposed by [START_REF] Boustan | The effect of internal migration on local labor markets: American cities during the great depression[END_REF], which consists of predicting the total outflow (inflow) from a region induced by weather shocks, and then decomposing this outflow (inflow) into destination regions by estimating the role of geographic distances in determining inter regional flows.

We then use both weather and distance to construct the predicted inflow (outflow). More specifically, for the case of migration inflow this first involves regressing total outflow rates of each region on a set of climate determinants:

O -rate i,t-1→t = α + δ Z i,t-1 + i,t (15)
where O -rate i,t-1→t is the outflow rate from source region i over time period t -1 to t, Z is a vector of climate specific indicators, and is an error term. Using the estimated coefficients from (1) the predicted flow of migrants leaving each region i, Õi,t-1→t is then just equal to the predicted outflow rate, Ō -rate i,t-1→t , times the population at t -1:

Õi,t-1→t = Ō -rate i,t-1→t * P opulation i,t-1 (16)

One then separately for each sending area i regresses the actual set of destination specific outflow rates to each destination region j on their relative distances and it's squared 15 High-skilled persons are limited to those with an educational level beyond the secondary level.

and cubic value 16 :

O -rate ij,t-1→t = α i + θ i Distance ij + θ i Distance 2 ij + θ i Distance 3 ij + µ it (17)
The instrument for in-migration to region j, Ī( j, t -1 → t), is then just the sum of the predicted number of migrants over all areas (i = j), Ō -rate ij,t-1→t expected to settle in region j:

Ī( j, t -1 → t) = i=1,...,n(i =j) Õi,t-1→t * O -rate ij,t-1→t (18)
One can then in a similar manner construct predicted outflow from area j by predicting the in-migration rates to each receiving area i using climatic determinants, using these rates to predict the number of inflowing migrants into i, and then constructed predicted outflow migrants by multiplying this figure by the from distance and its non-linear terms estimated inflowing rates between regions i and j (i = j).

In order to estimate (15), as well as its analogous specifications for the in-migration, we use for the vector Z a number of measures that capture weather conditions in a region.

In order to identify periods of extreme wetness and dryness in regions we first calculated the local standardized precipitation index (SPI), which has been argued to be particularly good at capturing the cumulative effect of high and low patterns of rainfall over time in a chosen locality, from the mean monthly precipitation values within our regions as calculated from the IPCC data set. 17 Following [START_REF] Mckee | The relationship of drought frequency and duration to time scales[END_REF] we then define a monthly extremely dry (wet) event as starting when the SPI reaches an intensity of -2.0

(2.0) or less (more) and as ending once the index become positive (negative) again. For each time period we then calculate the number of months of extreme dryness (wetness).

To capture the effect of temperature, in particular with respect to its importance for agriculture, we construct a measure of reference evapotranspiration (ET) to represent the evaporative demand of the air within a basin. Following [START_REF] Hargreaves | Reference crop evapotranspiration from temperature[END_REF], evapotranspiration is calculated as:

ET = 0.0023(T avg + 17.8)(T max -T min )0.5Ra ( 19
)
where T avg , T max and T min are mean, maximum and minimum temperature, respectively 16 One should note that [START_REF] Boustan | The effect of internal migration on local labor markets: American cities during the great depression[END_REF] regress these rates only on distance and its squared value. For the case of Thailand we found that including its cubic value substantially increased the specifications fit.

17 The calculation of the SPI is based on modeling the probability distribution of precipitation as derived from long term records by fitting these to a gamma distribution via maximum likelihood. An important component in this regard is the chosen time scale. Since we are interested in cropland productivity and soil moisture conditions are known to respond to precipitation anomalies over a relatively short time period, we use a 12 month scale. See http://www.drought.unl.edu/whatis/indices.htm.

and Ra is the extraterrestrial radiation calculated following [START_REF] Allen | Crop evapotranspiration: Guidelines for computing crop requirements[END_REF]. Since the effects of rainfall shortages and abundance on local agricultural are likely to some extent to depend on the local evaporative demand, we also allow for interactions between ET and W ET and DRY . To construct all these climatic factors at the regional level we resort to information from the Inter-Governmental Panel on Climate Change (IPCC) climatic data set, which provides monthly precipitation and temperature measures across the globe at the 0.5 degree level over the entire 20 th century. This is used to calculate out regional averages of the inputs.

The results of estimating ( 15) for the annual regional out-and in-migration rates, controlling for regional specific fixed effects and region common time specific factors are given in Table 1a. Moreover, we calculate Driscoll and Kray (1998) standard errors corrected for spatial correlation throughout. As can be seen, for both of inflow and outflow rates, the set of climatic variables are almost all significant, producing highly significant F-tests of joint significance. Examining the individual factors, one finds that for the precipitation related factors the signs meet a priori expectations. More specifically, one finds that extremely dry as well as extremely wet weather, indicative of drought and flood like conditions, respectively, act to increase overall outflow from regions. Moreover, the negative impact of rainfall shortage is further exacerbated by a high evapotranspirative demand of the air. Somewhat surprisingly, the direct effect of evapotranspiration is to reduce outflow from a region, although in absolute terms this impact is small. For the inflow rate, one finds that extremely wet periods tend to reduce the inflow rate, while droughts have no significant effect. Furthermore, a high evapotranspirative demand of the air tends to reduce the effect of the latter. Somewhat peculiarly one finds that this demand on its own acts to increase person flowing to the region, although again not substantially so. To construct the predicted inward and outward migration rates by sub-group we proceeded in similar manner as for the overall sample, except restricting construction via (15) through ( 18) to the sub-sample in question. We report the estimation for (15) for the outflow and inflow rates in Tables 1b and1c, respectively. As can be seen, for the outflow rate all climatic variables are significant, where the signs are in congruence with the overall sample. Unsurprisingly the joint F-tests attests to their power as predictive factors. For the inflow rates, the majority of coefficients are significant and similar to those from the overall sample. Similarly, the F-test statistics provides evidence of their predictive power.

In terms of estimating (17), since this involves estimating different specifications for each region, we only provide a brief outline of the results. One may want to first note that since our distance measures do not vary over time, our estimated specification in (17) does not control for region specific effects, but does include a set of time dummies to control for common region time specific factors determining the migration flows. We used Driscoll and Kray (1998) standard errors corrected for spatial correlation as we did for (15). For each region specific regression, we, after estimating the parameters on distance conducted an F-test of the null hypothesis that these were jointly zero. In the case of out-migration rates for only 4 regions, while in the case of in-migration rates for only 6 could the null hypothesis not be rejected. As with the overall sample the F-test of the distance variables suggested strong predictive power in almost all cases for the estimation of (17) for subgroups.

In Table 2 we depict the results from the first stage regression where we use predicted migration rates constructed as outlined above to predict actual net migration rates. Table 2 shows the results for men by skill. As can be seen, and is indicated by the F-Test on the instruments, the predicted inflow rate variables significantly predict an increase in actual net migration, whereas predicted out-migration rate acts to decrease net migration.

Using bootstrapped standard errors and the corresponding Wald tests show similar results although standard errors are somewhat larger.

The Effect of Net Migration on the Local Labour Market

We next examine the impact of net inward migration on the weekly wage, weekly hours and hourly wage for males of working age (15-64 controlling for individual characteristics).

In particular when we look at the impact of migration on the wage we control for age and age squared, marital status (four dummies indicating single, married, widowed or divorced status), a set of twelve educational indicator dummies, ten occupation dummies and ten industry dummies, seven firm size dummies, dummies indicating whether the worker is paid hourly, daily, weekly or monthly and a dummy indicating whether the worker lives in a municipal area. We also include average age and the fraction of workers with no education at the province level as well as province and time specific effects. The results on the estimated coefficient on the net inward migration rate for weekly wage and weekly hours and the hourly wage are reported in Table 3. So for example the coefficient of -.088 on weekly wages in the OLS results in Table 3 indicates that an increase in the net migration rate of 10% (which means the working age population increases by 10% due to net migration) is associated with a decrease in male wages by 8.8%. A clear worry here is that, as noted earlier, we might expect migration inflows to depend on local economic conditions which also affect the wage level or level of hours worked. The results from the instrumental variables regression confirm that this worry is legitimate, showing a weekly wage decrease of 31.5% for males when migration increases population by 10%. We also note that while OLS results indicate a small insignificant coefficient on hours worked the instrumental variables results indicate that a 10% increase in population by migrants lowers weekly hours by 11.753, i.e., a decrease of about 22% based on the summary statistics.

Table 3 provides results by high and low skill group. This makes a substantial difference to the OLS results. Skill specific migration is negatively associated with weekly wages for high skilled workers with a 8.6% fall in wages for a 10% increase in population. Low skill weekly wages fall by 5.3% for a similar increase in migration. 18 Coefficients on weekly hours are small and statistically insignificant for both skill groups. Once again instrumenting makes a substantial difference to the results. For the instrumental variables analysis weekly wages of skilled workers are unaffected by migration while unskilled weekly wages fall by 53.4% in response to a 10% increase in the population. The coefficient on hourly wage is statistically insignificant while hours worked fall by 18.839 if population increases by 10%.

This represents a decrease of 36% in the average hours of low skilled incumbents.

Conclusion

In this paper we have examined the impact of net inward migration on local labour markets in Thailand. To this end we have constructed a data set of regional migration flows and individual labour market outcomes for the period 1991 to 2000 using the Thai Labour Force Survey. Our results show that instrumenting for the possible endogeneity of net inward migration is crucial to the analysis. The results suggest that wages of low skill male workers are highly flexible with substantial adjustments in hours worked and weekly wages in response to short term changes in labour supply. We find no effect on high skilled workers. It may be that wages are slower to adjust for skilled workers due to implicit contracts, firm specific capital or other institutional features that limit firms' ability or willingness to adjust wages in response to possibly temporary shock. 19 Another possibility is that if there is a degree of imperfect substitutability between natives and migrants that this is only the case amongst higher skill groups as [START_REF] Card | Immigration and Inequality[END_REF] suggests.

While there is a large literature estimating the impact of migration on wages, typically such studies do not consider variations in hours per period. The empirical results presented here suggest that this may be an important omission. For example if we restricted our analysis to hourly wages the empirical results would suggest that migration does not affect wages while in fact there are substantial changes in weekly earnings associated with hours. δp(E) δE δE δθ + α(1-h) α-1 X(θ)-X (θ)[1-h(θ)] α

[1-h(θ)] 2α X(θ) 2

(1 -β)z (A.18)

That is we note from (A.18) that labour market tightness is in fact a function of the size of the labour force. We note that δp(E) δE < 0 by assumption. Since δh δL = 0 from equation (A.10) we can say that δE δL = δN δL h , while from (7) δN δL = θq(θ) λ+θq(θ) > 0. It follows that the numerator of equation (A.18) is negative. The first term in the denominator of (A.18):

δp(E) δE δE δθ is positive since -δp (E) δE > 0 by assumption while δE δθ > 0 from (7). Since the workers time endowment is normalised to unity α(1-h) α-1 > 0 while from (A.16) positive productivity implies X(θ) > 0.

Differentiating X(θ) from (A.16) we get:

δX(θ) δθ = -1 + q (θ) α(1 + β)(r + λ)c q(θ) 2 < 0 (A.19)
It follows that the term -X (θ)[1 -h(θ)] α in the denominator of (A.17) is also positive.

Finally since [1 -h(θ)] 2α X(θ) 2 and (1 -β)z > 0 we have shown that the numerator of (A.17) is negative while all the various terms in the denominator are positive so that:

dθ dL < 0 (A.20)
The derivative of hours per worker with respect to a change in migration is:

dh dL = dθ dL δh δθ (A.21)
Since δh δθ > 0 from (A.11) and given inequality (A.20), equation (A.21) implies that an increase in migration will lower labour market tightness and cause firms to reduce hours per workers.

Figure

  Figure 1: Thai Provinces

  After controlling for missing values, the sample used in all the wage and hours regressions below is constituted of 216,282 observations where incumbents account for 194,410 observations and recent migrants represent more than 10% of the sample. According to their skill group, we observe that high-skilled workers account for 64,361 observations of the subsample of incumbents and for 7,869 observations of the subsample of recent migrants.15

	Table A1 in the appendix provides summary statistics separately for the subsamples of
	incumbents and recent migrants for whom all the variables are non-missing. The statistics
	show that 99% of the salaries are paid on a daily or monthly basis. The weekly wage
	is about 1021.2 bahts for recent migrants and 1151.7 bahts on average for incumbents.
	Moreover, recent migrants are younger on average, work more hours per week than incum-
	bents and are more likely to be single. The firm size statistics show that recent migrants
	are more likely to be employed in larger firms. While 25% of incumbents work in firms
	with 50 employees and more, 35% of recent migrants are observed in large firms. More
	distinctions by occupation and industry are observed.

Table 1a :

 1a The Effect of Weather on Migrant Flows

		Outrate	Inrate
	Dry	0.00266*	-2.45e-05
		(0.00130)	(0.000964)
	Wet	0.00352*	-0.00358**
		(0.00135)	(0.000701)
	EVAPO	-0.000222*	0.000710**
		(8.90e-05)	(0.000192)
	EVAPO*Wet	3.04e-05	-5.95e-05**
		(1.55e-05)	(1.16e-05)
	EVAPO*Dry	3.36e-05* -1.34e-05
		(1.56e-05)	(6.74e-06)
	Observations	1,440	1,440
	Number of groups	72	72
	F-Test	4.963	8.616
	Notes: (i) Driscoll and Kray (1998) standard errors corrected
	for spatial and autocorrelation in parentheses; (ii) * * and * are
	1 and 5 per cent significance levels; (iii) Standard errors are in
	parentheses; (iv) Year and binannual dummies included but not
	reported; (v) F-test is test of joint significance of the climatic
	variables.

Table 1b :

 1b The Effect of Weather on Outflow Rates

		(1)	(2)	(3)
	Dry	0.00142*	0.00166*	0.00120
		(0.000625)	(0.000648)	(0.000618)
	Wet	0.00235**	0.00231**	0.00238**
		(0.000709)	(0.000692)	(0.000736)
	EVAPO	-0.000227** -0.000262**	-0.000197**
		(7.61e-05)	(8.85e-05)	(6.81e-05)
	EVAPO*Wet	2.75e-05**	2.54e-05**	2.95e-05**
		(7.25e-06)	(7.64e-06)	(7.35e-06)
	EVAPO*Dry	1.88e-05*	2.14e-05*	1.65e-05*
		(7.68e-06)	(9.34e-06)	(6.40e-06)
	Sample	Men	Low-skilled Men High-skilled Men
	Observations	1,440	1,440	1,440
	Number of groups	72	72	72
	F-Test	9.028	8.172	9.926

Notes: (i)

Driscoll and Kray (1998)

standard errors corrected for spatial and autocorrelation in parentheses; (ii) * * and * are 1 and 5 per cent significance levels; (iii) Standard errors are in parentheses; (iv) Year and binannual dummies included but not reported; (v) F-test is test of joint significance of the climatic variables.

Table 2 :

 2 Relationship between Predicted and Actual Migration for Men Dummies for salaries period of payment (hourly, daily, weekly and monthly) are introduced. (iv) For wage regressions, we introduce the number of weekly working hours and its squared term; (v) We include both the high-skilled and low-skilled net instrumented migration rate for each of the six subsamples shown above; (vi) ***, ** and * denote significance at the level of 1%, 5% and 10% significance levels, respectively.

		Total	Low-skilled High-skilled
		Sample Subsample	Subsample
	WLS Regression
	Predicted in-migration rate	5.332	3.007	3.262
		(2.592)	(1.300)	(1.387)
	Predicted out-migration rate	-9.537	-4.101	-5.539
		(3.302)	(1.128)	(1.219)
	F-Statistic	15.1	16.1	12.4
	Bootstrapped Procedure
	Predicted in-migration rate	5.576	3.103	3.491
		(3.088)	(1.424)	(1.549)
	Predicted out-migration rate	-9.591	-4.168	-5.490
		(2.756)	(1.243)	(1.398)
	Wald's Statistic	2057	1704	3535

Notes: (i) Regressions are estimated using individual data from the Thai LFS from 1991 to 2000; (ii) Standard errors are clustered by provinces and waves;

(iii)

Table 3 :

 3 Effect of Net Migration on Work Time, Weekly and Hourly Wages -Sample of Incumbent Working Men in the Private Sector -Regressions are estimated using individual data from the Thai LFS from 1991 to 2000; (ii) Standard errors are clustered by provinces and waves; (iii) Dummies for salaries period of payment (hourly, daily, weekly and monthly) are introduced. (iv)For weekly wage regressions, we introduce the number of weekly working hours and its squared term; (v) With the instrumental variable technique, we include the net instrumented migration rate specific for each subsample; (vi) ***, ** and * denote significance at 1%, 5% and 10% significance levels, respectively.

		Total	Low-skilled High-skilled
		Sample	Subsample	Subsample
	Ordinary Least Squares
	ln(weekly wage)	-0.088***	-0.053*	-0.086**
		(0.030)	(0.031)	(0.034)
	ln(hourly wage)	-0.105**	-0.073*	-0.124**
		(0.042)	(0.042)	(0.057)
	hours worked	0.610	0.697	1.445
		(2.086)	(2.447)	(2.260)
	Instrumental Variable
	ln(weekly wage)	-0.315***	-0.534**	-0.168
		(0.118)	(0.222)	(0.123)
	ln(hourly wage)	-0.171	-0.334	-0.153
		(0.129)	(0.210)	(0.144)
	hours worked	-11.753*** -18.839**	-0.817
		(5.449)	(8.962)	(4.057)
	Observations	194,410	130,049	64,361
	(i)

Table 4 :

 4 Mean and Standard Deviation migration rates-Subsamples of High-skilled/Low-skilled Men -

		Standard
	Mean	Deviation

See, for instance, Sjaastad (1962),[START_REF] Sahota | An Economic Analysis of Internal Migration in Brazil[END_REF] and[START_REF] Schwartz | Interpreting the effect of distance on migration[END_REF].

In this study, we consider voluntary climate-induced migration and not climate-forced migration.

For instance,[START_REF] Yang | Are Remittances Insurance? Evidence from Rainfall Shocks in the Philippines[END_REF] examine how remittances sent by migrants respond to income shocks experienced by Philippine households. The authors use rainfall shocks as instrumental variables for income changes and show that, in households with migrant members, exogenous income declines are partially covered by foreign remittances. More particularly, households with migrant members enjoy a flat consumption path compared to households without migrants for whom consumption responds strongly to income shocks.

We focus on males because employment rates are much higher than for females and because for the latter estimation is further complicated by needing to model the labour supply decision.

More particularly, the seasonal migration from the northeast of Thailand, facilitated by wide networks of friends and relatives, has continued on a large scale(IOM, 2008). This form of migration represents the main source of remittances for out-migration region.

[START_REF] Vanwey | Ownership as a Determinant of Temporary Migration in Nang Rong, Thailand[END_REF] analyses the role of land ownership in rural temporary migration in Thailand.

The following passage draws heavily on the introduction to[START_REF] Strobl | The ambiguous effect of minimum wages on hours[END_REF].

The models ofLewis (1969) andRosen (1986) are the precursors to this model.

The firm chooses the number of workers optimally and we substitute the first order condition on the number of workers into the profit function to trace out the isoprofit curve.

We note that φ (.) is the marginal utility of leisure which is positive. In the analysis we will differentiate with respect to work hours where δφ(1-h) δ(h) = -φ (1 -h).

Note that the category of movers within provinces represents 29% of all movers and that 49.4% of the sample of movers from this category moved one year ago at most.

The second best destination province is Udon Thani with 4.26% of total recent migrants.

We explored the possibility that low/high skill worker's wages and hours might be affected by the migration rate of the other group: that is that to explore whether these groups were complements/substitutes for one another. Unfortunately migration rates for are highly correlated across groups (even when instrumented) making it difficult to identify a separate

effect.19 See[START_REF] Beaudry | The Effect of Implicit Contracts on the Movement of Wages Over the Business Cycle: Evidence From Micro Data[END_REF] for an example and some evidence for an implicit contracts model while[START_REF] Hall | Employment Fluctuations with Equilibrium Wage Stickiness[END_REF] shows that the local monopoly rents in a search matching model mean that wages can be sticky without violating rationality.

APPENDIX: Proof of Proposition One

The first order conditions for the wage and hours of work satisfy:

and

Firms continue to create vacancies until the marginal product of an additional vacancy is zero so that:

From ( 9) and ( 10) we calculate:

Hence by using V=0, the value of J from A(3), the value of W -U from (A.4) and using

[p(N h)-w]hq(θ) pc

= r + λ from (A.5) (18), we get the wage equation from the first order condition on the wage [equation (A.1)] as:

Using the value for β(J j -V )φ(1 -h) from (A.1) in (A.2) we see that h is chosen such that:

The job creation equation is (A.5) and can be rewritten in terms of the hourly wage as:

Equation (A.7) can be rewritten for the Cobb-Douglas utility function [equation ( 9)] as:

Equating equations (A.7) and (A.9) we can solve for hours as:

We note that since q (θ) < 0:

Hours per worker increases with labour market tightness. The implication of this is that if an increase in migration leads to a fall in labour market tightness, hours per worker will fall. We also note that:

Using equation ( 7) for the aggregate number of workers and equation (A.10) for hours per worker we get:

We note from equation ( 7) that:

q(θ)

[λ + θq(θ)] 2 > 0 (A.14)

Since: d(N h) dθ = δN δθ h + δh δθ N it follows from (A.11) and (A.14) that:

Equating (A.8) and (A.6) yields the following solution for productivity per worker:

To get the relationship between migration and labour market tightness we totally differentiate (A.16) over the labour force and labour market tightness:

Rearranging equation (A.17) we can write the derivative of labour market tightness from


Ali Abcha
email: ali.abcha@u-paris10.f

Imperfect competition, government spending and estimated markup

Keywords: JEL Classification: E3, E62 Mark-up, Imperfect competition, Fiscal Policy

). It also provides an interpretation of the estimated markups that depend on the type of market structure. An application of a VAR model is used to examine the relationship between imperfect competition and the effects fiscal policy on output and markup, based on the method of Rotemberg and Woodford (1999).

Introduction

In recent decades, contemporary economies are often characterized by a lower degree of competition between firms. This competition is assessed by the degree of market power of each firm. Indeed, this power is determined by the ability of the price differential compared to its marginal cost. In perfect competition, firms must choose the amount of production factors that equalize the prices with the marginal cost. Under imperfect competition, such an equalization is invalid. The firms have an incentive to increase their profit by setting a price higher than their marginal cost. Therefore, imperfect competition can lead to adverse effects on growth. However, these effects can be reduced by public intervention through taxation and public spending (Barro (2000) and Tabellini (2005))2 .

In the respective literature, the mark-up plays an important role in models of public policy.

According to [START_REF] Rotemberg | Mark-ups and the Business Cycle[END_REF], an increase in government spending leads to an increase in real wages due to imperfect competition generated by a counter-cyclical mark-up.

While [START_REF] Goodfriend | The New Neo-Classical Synthesis and the Role of Monetary Policy[END_REF] showed that price rigidity combined with procyclical marginal cost leads-to a decrease in the average mark-up, other studies e.g., [START_REF] Bils | The Cyclical Behaviour of Marginal Cost and Price[END_REF] and Galí et al. (2007) estimated marginal cost under the assumption of overtime, showing that mark-ups are cyclical. However, using the relationship between price and average cost, Domowitz et al. (1986), concluded that there is a positive correlation between mark-ups and demand, with a procyclical rate of mark-up. More recently, Nekarda and Ramey (2010) presented an empirical study for the OECD countries; whose markups are shown to be mostly procyclical.

Due to the difficulty of finding an accurate accounting rule to measure this cost, some economists have estimated the marginal cost by econometric methods (Bresnahan (1989)). The first work on the estimation of the mark-up is realized by [START_REF] Hall | Market Structure and Macroeconomic Fluctuations[END_REF][START_REF] Hall | The Relation between Price and Marginal Cost in U.S. Industry[END_REF]. Then, [START_REF] Roeger | Can lmperfect Competition explain the Difference between Primal and Dual Productivity Measures? Estimates for US manufacturing[END_REF] improved the original model of Hall, solving the problem of endogeneity. Indeed, the methodologies of Hall and Roeger are most commonly used to estimate the mark-up behavior.

Other studies have tried to estimate marginal costs. Morrison (1990) uses gross data to estimate the cost function. He shows that mark-up varies between 1.2 and 1.4 for 16 manufacturing industries. [START_REF] Hall | The Relation between Price and Marginal Cost in U.S. Industry[END_REF] presents an alternative approach using a method of instrumental variables. He assumes that these variables are orthogonal to exogenous technical progress, and he relaxes the assumption of endogeneity. Some authors like Gali (1996), Rotemberg and Woodford (1996) introduce imperfect competition in markets, to study its influence on the emergence of economic fluctuations.

Imperfect competition is compatible with increasing returns to scale and allows for the existence of a positive rate of mark-up. They stresse that the variability mark-up and the level of increasing returns can also be a source of local indeterminacy of the steady state by fostering the emergence of endogenous fluctuations (Porter (1995[START_REF] Seegmuller | Concurrence Imparfaite, Variabilité du Taux de Marge et Fluctuations Endogènes[END_REF]).

In its first section the paper presents the empirical study that seeks to explain economic fluctuations and behavior mark-up. Inspired by the method of [START_REF] Roeger | Can lmperfect Competition explain the Difference between Primal and Dual Productivity Measures? Estimates for US manufacturing[END_REF], an estimate of the mark-up is performed on four OECD countries (Denmark, Finland, Italy and the United States) for 17 manufacturing industries during the period 1986-2008. This study provides a comparison between our estimates of mark-up and other (Roger (1995), Oliveira, Scarpetta and[START_REF] Martins | Mark-up Ratios in Manufacturing Industries : Estimates for 14 OECD Countries[END_REF] and [START_REF] Rotemberg | Mark-ups and the Business Cycle[END_REF]). It also provides an interpretation of the estimated markup depending on the type of market structure. The manufacturing industries are classified according to the degree of competition and the markup cyclicality. In the second section, we examine the relationship between imperfect competition and fiscal policy, based on Rotemberg and Woodford (1999)'s method. Therefore, an empirical study is made in the context of a VAR model with annual data for the same set of countries, but over a wider period .

Estimating mark-ups

Methodology

The Lerner index (B) is an indicator that refers to the gap between prices (P) and marginal costs (MC). It takes values between 0 and 1. Under perfect competition, this index is equal to zero because the firm has no market power. When pries exceed marginal cost, the B index becomes positive ( ), it claims a certain degree of competition in price. Therefore, it is necessary to explore the relationship between the Lerner index and the mark-up ratio ( = P/MC) as follows:

The Solow residual (SR) indicates the difference between the growth rate of output and a weighted average of the growth rate of factor inputs. This method is defined in the [START_REF] Hall | Market Structure and Macroeconomic Fluctuations[END_REF] approach:3

Where , and represent respectively, the growth rate of real value added, labor and capital, is the share of labor in total value added and is the rate of technical progress.

Under perfect competition, the SR correspond to the productivity term (B = 0). A necessary condition is that the SR should not be correlated with the growth rate of the output / capital ratio.

However, [START_REF] Hall | Market Structure and Macroeconomic Fluctuations[END_REF] shows that the assumption of perfect competition is rejected because the hypothesis of no correlation is not always true.

Assuming that the rate of technological progress is a random spread and mark-up rate is constant over time, so we can estimate the mark-up ratio ( ) from the following equation:

Under imperfect competition, [START_REF] Hall | Market Structure and Macroeconomic Fluctuations[END_REF] shows that the labor / capital ratio is correlated with productivity term. Consequently, the estimates OLS (ordinary least square) of equation ( 3) will be biased. Frequently, this bias is corrected by using instrumental variables to replace the labor / capital ratio. But, this method has been criticized by [START_REF] Roeger | Can lmperfect Competition explain the Difference between Primal and Dual Productivity Measures? Estimates for US manufacturing[END_REF], who proposes an alternative method to estimate the mark-up. He calculates the difference between the traditional Solow residual (SR) and the dual Solow residual based on the price (SRP).

Where , and represent respectively, the growth rate of output price, wages and the rental price of capital.

Then, by subtracting the two equations (4-2) to obtain the following equation:

Where:

The variable represents the Solow residual, and the variable is the growth rate of the nominal output / capital ratio. The advantage of this equation is to estimate the Lerner index with OLS estimation. In addition, to [START_REF] Roeger | Can lmperfect Competition explain the Difference between Primal and Dual Productivity Measures? Estimates for US manufacturing[END_REF] approach we can introduce other intermediate inputs and set the mark-up ratio on gross output (not value added) in order to obtain a clear upward biais in the estimation. Therefore, equation (5) becomes:

Where and denote gross output and price, and m and correspond to intermediate inputs and their prices, and and are the respective shares of labor and intermediate costs of gross output.

Data

The construction of data is described by Martins Oliveira, and Scarpetta Pilat (1996). The The method of calculating the cost of capital is determined in Annex 1. The database does not include price series for the rental price of capital. However, we used the method of Hall and Jorgensen (1967) in order to resolve the problem of lack in data at the sectorial level.

This study is to estimate the mark-up (equation 6) in the 17 manufacturing sectors for four OECD countries (Denmark, Finland, Italy and the United States) .

The behavior mark-up

Our empirical study is based on the work of Oliveira [START_REF] Martins | Mark-up Ratios in Manufacturing Industries : Estimates for 14 OECD Countries[END_REF] applied to annual data for 14 OECD countries during the period 1970-1992. However, our study analyzed the behavior mark-up ratio during a more recent and extended period marked by economic upheaval. We analyze the cyclical behavior of mark-up according to market structure in order to assess the degree of competition over time. It should be noticed that the period under study was characterized by economic tensions in some OECD countries such as the creation of the Economic and Monetary Union (EMU) and structural policies of market liberalization. These factors may adversely affect the market by promoting the appearance of large firms that dominate the market. Consequently, there was an increase in degree monopoly, which results in a higher rate range. In addition, we find some points of difference between our study with other previous works such as Oliveira, Scarpetta and Pilat (1996) for the period 1980-1992. Indeed, our results show that with the exception of Italy the mark-ups are lower in all countries over the period 1980-1992.

At sectorial level, the comparison of our estimates of mark-ups in the U.S. with those of Oliveira, [START_REF] Martins | Mark-up Ratios in Manufacturing Industries : Estimates for 14 OECD Countries[END_REF], show that the higher mark-ups are remarked especially in sectors of pulp, paper, paper products, printing and publishing, chemicals and chemical products, and other non-metallic mineral products. Indeed, our estimates of mark-ups are the majority between 1.14 and 1.20. These values are generally lower than the estimates observed in other studies. The cause of the decline in value of mark-ups is the economic development in some markets area.

Our results differ from those of [START_REF] Roeger | Can lmperfect Competition explain the Difference between Primal and Dual Productivity Measures? Estimates for US manufacturing[END_REF], because of the introduction of intermediate inputs in the estimated equation which produces lower values. Indeed, in some sectors, intermediate goods are an important part in the overall production. Consequently, the estimated mark-ups differ from one model to another (Bresnahan (1989) and Schmalensee ( 1989))

according to the method of measurement used. However, a high rate mark-up ratio cannot be taken as some evidence of the presence of the profits generated by market power. For example, in technologically innovative sectors, temporary monopoly rents for a fixed term may be causing a high rate markup.

The market structure

In monopolistic competition, we determined the degree of market power by two factors: the scale and the product differentiation (Dixit and Stiglitz (1977)). According to Sutton (1995), the market power depends on specific structural variables, such as firm size and R&D intensity and capital. In other words, these variables are used to distinguish between different market structures.

In this study, the 17 sectors are grouped according to the type of market structure (fragmented or segmented). In fragmented sectors, firms are medium in size and their number increases with the size of the market. In segmented sectors, firms are large and the concentration becomes relatively stable when the market size increases. The type of competition in each sector is determined by two variables, the R & D intensity to identify homogeneous or differentiated nature of the industry. Then, the size of each industry (fragmented or segmented) is determined according to the number of employees incurred.

We present in Table (2), the average mark-ups of the four groups of sectors. The degree of competition varies from one country to another. The segmented sectors are marked by higher mark-ups than in the fragmented sectors. This shows the existence of large-size firms in a dominant position on the market; these firms are price makers to the extent that a decline in demand does not affect sale price. However, the distinction between homogeneous and differentiated sectors helps to explain the different variables affecting mark-ups ( In addition, the level of product differentiation is an important element in interpreting the degree of competition. For example, in segmented differentiated sector, the structure of market is characterized by the existence of a monopolistic or oligopolistic competition.

To conclude, the explanation of mark-ups differs from one country to another, in line with such specific measures as the conditions of entry into a country, openness to international trade, the asymmetry information and investment in new technology. These factors consolidated the market power and contributed to the development of mark-ups.

The business cycle

Some econometric studies have that the mark-up ratios are pro or counter cyclical. The cyclical nature of mark-up is determined as follows:

Where denotes the average fixed mark-up ratios over the business cycle, CYCL is a variable for business fluctuation. The coefficient represents the cyclical nature of mark-ups. So with the sign of we determine the cyclicality of mark-ups. The negative (positive) sign of implies a countercyclical (procyclical) markup.

The new expression of equation ( 6) becomes:

The cyclical variable is measured by the ratio between the actual and trend sectorial production. We estimate equation ( 8) by varying the parameter between sectors and countries.

Then we vary depending on the market structure.

Indeed, Table (3) explores the cyclicality of mark-up by sector. Under most sectors, the mark-ups are countercyclical, the coefficient of the cyclical variable is negative in 60% of the cases (Italy, Denmark and the United States). However, in Finland, the estimated coefficient is positive in most sectors, indicating that mark-ups are procyliques. Our results are confirmed by the findings of [START_REF] Bils | The Cyclical Behaviour of Marginal Cost and Price[END_REF], [START_REF] Rotemberg | Mark-ups and the Business Cycle[END_REF], Oliveira (1994) and Oliveira, [START_REF] Martins | Mark-up Ratios in Manufacturing Industries : Estimates for 14 OECD Countries[END_REF]. They showed the existence of countercyclical mark-ups in the U.S.

Then, table (4) has the coefficient estimates based on the structure of market. The market is composed of four sector groups (fragmented; homogeneous or differentiated, segmented; homogeneous or differentiated). Indeed, that the sensitivity of mark-ups cycle differs from one group to another. But always the cyclicality character dominates the various estimates. Therefore, the estimated mark-ups are higher in segmented than in fragmented sectors. The countercyclical nature of mark-ups is a strong competitive pressure when the economy is in a period of expansion. The importance of our estimates is to provide explanations of macroeconomic theory during the study period . Indeed, Oliveira (1994) showed that countercyclical markup presents a means for examining the procyclical real wages and their effects on the economy, when real wages increase with aggregate output and employment during periods of expansion. In particular, this study shows that there are factors other than technology shocks in explaining macroeconomic fluctuations (Rotemberg and Woodford, 1991). The results :

The main results of the estimates of mark-ups ratios in four OECD countries are the following:

-Most of the estimated mark-ups rates are significantly higher than 1. This implies the existence of a market with imperfect competition in all sectors.

-The level of mark-up depends on the market structure. The estimated mark-ups are higher in segmented differentiated sectors than in fragmented homogeneous sectors.

-Some economic policies by some countries as barriers to entry or taxation affect market structure and affect the level of mark-ups.

-The results on the cyclicality of mark-ups show that mark-ups are countercyclical in all countries except for Finland.

-The estimated mark-ups in our study are very close to those found in other studies (see for instance [START_REF] Rotemberg | Mark-ups and the Business Cycle[END_REF] and Oliveira, Scarpetta and Pilat (1996)).

In summary, under imperfect competition, it is important to analyze the components of a mark-up. On the one hand, the estimated mark-up is not a technique surely adapted to determine the degree of competition. It is considered as a simple measure of corporate behavior on prices in a given area. However, it is necessary to take into account other variables to determine the degree of monopoly. On the other hand, empirical data on mark-up in the manufacturing industry may be affected by constraints such as the measurement error due to information asymmetry.

Fiscal policy and estimated mark-up

The interaction between imperfect competition and economic policy has occupied an important place in the theory. Some authors such as Hall (2009) showed that fiscal policy can increase the degree of monopoly in the market. This is characterized by high mark-ups due to the multiplier effect of short-term profits or increasing returns to scale in the long term. Any time, empirical studies examining the nature of the relationship between the degree of competition and the effects of fiscal shocks are limited because of a lack of time series for mark-ups.

Additionally, it is important to mention some paper empirical study. Indeed, [START_REF] Monacelli | Fiscal Policy, Wealth Effects, and Markups[END_REF] used a VAR model with quarterly data to explain the interaction between the markups and government spending in the United States. Their results show a rate of mark-up countercyclical with fiscal shocks. Any time, Afonso and Costa (2010) followed the same approach as [START_REF] Monacelli | Fiscal Policy, Wealth Effects, and Markups[END_REF] but on a wider sample (OECD), showing that the majority of mark-ups are procyclical with productivity shocks and light behavior countercyclical impact of public spending.

This section based on the approach of Rotemberg and Woodford (1999) and, [START_REF] Afonso | Market Power Fiscal Policy in OECD Countries[END_REF]. We produce results with annual data for a group of four OECD countries, over the period 1975-2011: Denmark, Finland, Italy and the United States. Then, we studied the interaction between fiscal policy and different macroeconomic variables using a VAR modeling.

Data

Macroeconomic data are obtained from the AMECO database of the European Commission (2012). We examine a set of four OECD countries (Denmark, Finland, Italy, and the United States) during the period 1975-2011. Indeed, the choice of the sample period and the countries is justified by some elements. First, the availability of data does not allow to choose only four OECD countries. Second, the study period has known various economic and political factors that have changed the OECD events.

The data are as follows (annex 2): represents real GDP per capita (between 15 and 64 years), is the real capital stock per capita, is the total hours worked, and represents the adjusted wage share in total income.

is the GDP deflator, it is calculated as the ratio between nominal GDP and real GDP. We divide the price deflator by (1+ ), where means the ratio of indirect taxes less subsidies and nominal GDP. is the ratio of adjusted hourly nominal wage rate obtained by .

The study of the effect of fiscal policy on mark-up requires the addition of certain series: the final consumption expenditure of government, gross fixed capital formation, tax revenues, the amount of taxes direct and indirect, and social security contributions.

Data on average mark-ups are drawn from the results found in the first section. We assume that the mark-ups are constant in long-term (Table 1).

Mark up Italy

Denmark United States Finland 1.17 1.09 1.14 1.13

The (1-) is given by Hoddrick-Prescott (HP) ( =100). The series is obtained by application of the HP filter (the right side of equation (A.3: annex 2)).

We obtain the series of mark-up as follows:

is a measure of cyclical fluctuations in inputs.

VAR modeling

We estimate a VAR model with five variables for the period 1975-2011. The variables in the model are: government spending, G, real output, Y, the real taxes T, the rate of mark-up and the level of productivity, A (PTF). All variables are in logarithms except for the rate of mark-up in level.

An econometric study with time series requires two steps. First, the application of unit root tests to identify stationarity and order of integration of the series. Then we will choose the order of the VAR model to estimate. Second, the estimation of the VAR model and the identification of structural shocks from the impulse response functions.

The VAR (P) can be written as follows:

Where denotes the vector of 5 endogenous variables, is the matrix of autoregressive coefficients, and is the error vector, . The number of lags of the endogenous variables, p, is determined by the information criteria.

The order of variables in the VAR model is from the most exogenous to the least exogenous one. We make TFP in first position because a productivity shock can have an immediate impact on all other variables. Any time, the response of productivity due to other variables, cannot be simultaneous to all structural disturbances. The same reasoning is true of government spending that does not directly react to any disturbances caused by taxation, GDP or mark-up (delay in government decision making). In case of a fiscal shock (taxation is the third variable), the impact is not immediate public spending or technology of production, but the effect on the GDP and mark-up.

The determination of structural shocks 4

The interest results are the impact of fiscal shocks on macroeconomic variables in the model. Figure 2 shows that for Denmark, where a shock of public expenditure, the mark-up is marked by alternating positive and negative effects, and an expansionary early in the second period with a peak followed by a recession (recess in the third period). The maximum mark-up increase was recorded at 0.03%.

Figure 1: The impulse response functions: Italy

In Italy, a positive shock on public expenditure is reflected in the mark-up, by alternating positive and negative effects manifested by increases phases (periods: [1, 2], [3, 4], [5,6]) and lower (periods [2, 3], [4,5]). The impact of fiscal innovation leads to sharp drop in the pace of mark-ups in the short term. Indeed, the impact of the shock on the mark-up also reflects the degree of competition on the market and the number of firms attracted to the sector concerned.

4 See annex 3. But the effect of this shock reflects moderate fluctuations over the medium term and stable over the long term. However, in the United States, there is a slight expansion in the short term [1,3] and stabilized over time. In Finland, the effect of a positive shock to government spending is insignificant.

A positive impact on public expenditure is reflected in the production of a positive mediumterm before stabilizing effect until the end of the period. These effects are similar for the three countries (Denmark, Italy and Finland). With the exception of Finland, we observe alternating negative and positive effects, negative effects during the first two periods, then a positive impact between periods 2 and 5.

The impact of a fiscal shock on the mark-up rate in Denmark is negative and more significant than those public spending with lower that exceeds 0.05 % (second period). This phenomenon was not observed in Italy, where the tax shock is accompanied by a transient decrease in production and mark-ups, then followed by temporary increases. This increase is explained by the multiplier effect profits of short-term or by increasing returns to scale (Hall (2009)). The maximum recessionary impact observed only in Italy (-0.003 %). Indeed, responses mark-ups to tax shock were positive from the third period. Any time, this trend is not confirmed in Finland, since the responses are almost neutral (annex 3). The effects of the shock are very low and often close to zero. In the United States, the fiscal shock resulted in a maximal decrease does not exceed the bar 0.002 %.

The results reported in the graphs above indicate that the response functions of production and mark-ups appear satisfactory and consistent with economic theory, all reactions with the expected pace. We used the techniques of time series analysis. We based on a structural VAR model to analyze the effects of public spending, as well as taxes on the degree of competition and economic growth in four countries studied. We have shown, in a system with five variables, it is possible to study the reactions of the markup and production to the various shocks of government spending; including changes in the conduct of public policy depends on the rate of taxation.

In conclusion, the VAR model is an important tool for determining economic fluctuations.

However, the unavailability of sufficient long time series and temporal precision statistics, as well as the limits of the structural VAR technique, prevent to provide a reliable estimate. The use of panel data could help provide more information. Response of Mark-up to T

Response to Cholesky One S.D. Innovations ± 2 S.E.

The panel VAR model

In this section, we use recent developments in econometrics associated with panel data.

These data comprise repeated formed on a set of individuals (4 countries) at different dates observations. We retain the same temporal dimension studied previously. We add a modeling panel that will analyze the evolution of variables for all countries and not those of each country.

The Regarding the markup, a slight increase is observed caused by a shock on government spending (line 2, column 1) with a peak of 0.03%, then a recession that will persist in the medium term as we studied in previous estimates. In addition, we have noticed a priori positive relationship with taxes. The shock appears in the second period, since the impact on taxes first causes a small decrease in mark-ups before starting up until the fifth period. Several reasons can be put forward to explain this result, as the strong cyclicality of mark-up caused by purely exogenous factors do not allow the tax to have a significant influence.

Finally, we note that the results of a panel VAR model are more robust than a structural VAR. But often the impact of a shock dissipates quickly in time. Like most empirical studies, we have sought to identify the effects of fiscal shocks on mark-up and production. We felt it appropriate to use the VAR model to determine the different channels of transmission more operational for OECD countries. Part of this result is consistent with findings of earlier research.

Conclusion

This paper has focused on the variability of the markup in the presence of market imperfections. This markup is considered as a tool for determining the degree of competition.

Indeed, we have presented empirical methodology discussed by [START_REF] Hall | The Relation between Price and Marginal Cost in U.S. Industry[END_REF] and [START_REF] Roeger | Can lmperfect Competition explain the Difference between Primal and Dual Productivity Measures? Estimates for US manufacturing[END_REF] before estimating empirically the effect of public spending on the markup and the level of competition. This work has made it possible for us to explain economic fluctuations and the behavior markup, based on the method of [START_REF] Roeger | Can lmperfect Competition explain the Difference between Primal and Dual Productivity Measures? Estimates for US manufacturing[END_REF]. Also, we have examined the nature of the relationship between imperfect competition and fiscal policy, based on the method of Rotemberg and Woodford (1999). This study to compare our estimates of the markup with others like Roger (1995), Oliveira, Scarpetta and[START_REF] Martins | Mark-up Ratios in Manufacturing Industries : Estimates for 14 OECD Countries[END_REF] and [START_REF] Rotemberg | Mark-ups and the Business Cycle[END_REF].

The main findings of our two econometric studies are as follows: in sectors results, there is a slight deviation from the overall results. The markup is cyclical and involves strengthening the effectiveness of fiscal policy on output and this is particularly relevant when the fiscal multiplier is positive.

However, this work has some limitations. It does not establish a detailed description of the nature of government intervention in the level of taxation, as is the case with are various property sectors in the economy. It is therefore, necessary to identify policy specified for each public sector. These problems concern directions for future research. Second, in the econometric level, the use of a VAR model to change plans (time series or panel data) could help us to explain well the impact of public spending on the degree of competition. But the unavailability of long series of statistics for the study sample will continue to influence the robustness of the results.

Annex 3. Impulse-response functions

Denmark :

-.015 Response of PTF to T

Response to Cholesky One S.D. Innovations ± 2 S.E.

  database has been developed to construct series for the period 1988-2008: gross output, employment, wages, gross capital stock and intermediate inputs are provided by the STAN database of the OECD (2009).

Figure 2 :

 2 Figure 2: The impulse response functions: Denmark

  study panel data has two dimensions: first one concerns the heterogeneity of the countries between them, indicated by N the number of countries; the second one concerns the change in macroeconomic variables over time, indicated by T the number of observations in each country. Applying a modeling VAR panel exposes several advantages. The use of a large number of observations in this study increases the accuracy of estimates and will yield fluctuations consistent with theory. In addition, the individual dimension increases the number of data, adding information on different countries and thus leads a multi-country analysis. Second, it allows controlling individual heterogeneity of OECD countries, and identifying effects that cannot be detected by a simple series of temporal data. Among the major works on the VAR model with panel data, we cite the work of Gilchrist and Himmelberg (1995) and Love and Zicchino (2002), which analyzed the relationship between investment and capital productivity. In this work, the use of panel data should allow us to solve the problem of heterogeneity of response functions of key variables such as the responses of real output and mark-up rate to a shock in public spending. Consider the following system: , the indices i = 1,...,N et t=1,...,T reflecting individual and time dimension of the sample (N is the total number of individuals of the panel, T the total number of observations).

Figure 3 :

 3 Figure 3: The impulse response functions

Table 1

 1

	presents the results of estimates of mark-up ratio for each country during the period
	1988-2008. The highest mark-ups are observed in Italy (1.17), while they are lower for the
	United States (1.14), Finland (1.13) and Denmark (1.09). Therefore, the estimation results imply
	the existence of imperfect competitive markets due to the presence of externalities and increasing
	returns to scale in some sector.
	However, the sectorial results show that the values of the estimated mark-ups are in the range
	[1.06;1.32], 1.06 in Denmark, in the transport equipment industry and, 1.32 in Italy in the
	production of wood, products of wood and cork. Indeed, these results show that some value of
	mark-ups exceeds 1.20, for example in sector of electrical machinery and apparatus, and sector of
	the pulp and paper (United States and Italy). The estimated mark-ups are relatively low in some
	industries such as transport equipment (1.06 in Denmark), or motor vehicles and trailers and
	semi-trailers (1.04 in the U.S.).

Table 1 . Estimated markup ratios for manufacturing industries Period 1988-2008 Sector Italy Denmark United States Finland Food products, beverages and tobacco

 1

	1.19	1.06	1.19	1.10

Machinery and equipment, n.e.c.

Motor vehicles, trailers and semi- trailers

		1.07	1.11	1.04	1.12
	Transport equipment	1.07	1.06	1.08	1.07
	Country	1.17	1.09	1.14	1.13

Average mark-up ratios by the type of market structure (1988-2008)

  Table2). First, the fragmented sectors are homogeneous with a low degree of competition in all countries, i.e., the mark-ups rates are lowest, but Italy is the exception. the fragmented differentiated sectors are characterized by very high markups compared to the homogeneous fragmented sectors, showing the presence of imperfect competition and innovation rents.

	Table2. Country Fragmented industries	Segmented industries	Fragmented homogeneous industries	Fragmented differentiated industries	Segmented homogeneous industries	Segmented differentiated industries
	United States	1.13	1.15	1.12	1.13	1.14	1.16
	Finland	1.14	1.15	1.11	1.13	1.13	1.15
	Italy	1.17	1.19	1.23	1.13	1.20	1.21
	Denmark	1.09	1.10	1.09	1.11	1.09	1.12

Table 3 . The business cycle by sector

 3

	Sector	Italy	Denmark	United States	Finland
	Food products, beverages and tobacco	-0.318 (-2.88)	-0.54 (-3.07)	-0.173 (-2.18)	-0.027 (0.55)
	Wood and products of wood and cork	-0.011 (-0.193)	0.015 (0.99)	-0.045 (-0.14)	0.328 (3.43)
	Pulp, paper and paper products	-0.607 (-5.88)	0.202 (3.12)	1.08 (2.89)	0.24 (1.81)
	Pulp, paper, paper products, printing	0.138	-0.217	-0.02	0.09
	and publishing	(1.49)	(-2.09)	(-0.14)	(0.87)
	Printing and publishing	0.183 (2.92)	-0.191 (-4.22)	0.512 (2.26)	0.046 (1.98)
	Coke, refined petroleum products and	-0.199	0.182	-0.031	-0.105
	nuclear fuel	(-3.02)	(0.99)	(-0.33)	(-1.97)
	Chemical, rubber, plastics and fuel	-0.052	-0.086	-0.098	-0.076
	products	(-1.32)	(-2.62)	(-1.95)	(-2.09)
	Chemicals and chemical products	-0.17 (-2.81)	-0.056 (-1.81)	-0.038 (-0.56)	0.009 (0.19)
	Rubber and plastics products	-0.077 (-2.22)	-0.019 (-0.87)	-0.003 (-0.08)	0.22 (2.54)
	Other non-metallic mineral products	-0.79 (-3.06)	0.085 (1.63)	0.095 (0.84)	0.160 (1.92)

Fabricated metal products, except machinery and equipment

	-0.013	-0.164	-0.033	0.029
	(-0.21)	(-1.63)	(-0.39)	(1.16)

Machinery and equipment, n.e.c.

		-0.043	-0.348	-0.25	0.031
		(-0.60)	(-6.23)	(-3.39)	(0.89)
	Machinery and equipment	-0.102 (-3.11)	-0.192 (-2.74)	0.006 (0.03)	0.083 (1.68)
	Electrical machinery and apparatus,	-0.203	-0.021	-0.167	0.046
	n.e.c.	(-3.23)	(-0.47)	(-1.59)	(2.74)
	Radio, television and communication	-0.041	0.095	-0.68	0.271
	equipment	(-0.52)	(2.09)	(-1.94)	(2.33)
	Motor vehicles, trailers and semi-trailers	0.771 (1.88)	-0.016 (-1.23)	0.091 (0.43)	-0.12 (-1.98)
	Transport equipment	-0.096 (-1.14)	-0.086 (-2.58)	0.06 (0.37)	0.26 (2.36)

*The values in parentheses are t-statistics of the estimated coefficients.

Table 4 . Business cycle according to market structure

 4

	Country	Fragmented industries	Segmented industries	Fragmented homogeneous industries	Fragmented differentiated industries	Segmented homogeneous industries	Segmented differentiated industries
	United States	-0.093	-0.034	-0.023	-0.126	-0.019	-0.052
	Finland	0.053	-0.006	0.305	0.044	0.001	-0.11
	Italy	-0.093	-0.341	-0.198	-0.085	-0.421	-0.01
	Denmark	-0.071	-0.008	0.020	0.085	-0.052	0.001

  Response to Cholesky One S.D. Innovations ± 2 S.E.

				Response of Y to G							Response of Y to T
	.008									.008
	.004									.004
	.000									.000
	-.004									-.004
	-.008									-.008
	1	2	3	4	5	6	7	8	9	10	1	2	3	4	5	6	7	8	9	10
				Response of Mark-up to G							Response of Mark-up to T
	.04									.04
	.03									.03
	.02									.02
	.01									.01
	.00									.00
	-.01									-.01
	-.02									-.02
	-.03									-.03
	1	2	3	4	5	6	7	8	9	10	1	2	3	4	5	6	7	8	9	10

Barro (2000) andTabellini (2005) found empirically that public intervention has a negative effect on growth, but if the government invests in infrastructure, for example, it is likely to support growth.

For further explanation of equation (2) see Annex I in[START_REF] Hall | Market Structure and Macroeconomic Fluctuations[END_REF].

Equation (11) becomes as follows:

The results of unit root tests are presented in the Appendix. All variables in the VAR model are introduced in first differences, except the rate of margin level. Then, we estimate the VAR model in first differences. We apply the test of delays to determine the order of the model.

The determination of structural shocks

We try to push our analysis further by seeking to identify the transmission channels of fiscal policy in the OECD by a panel VAR model.

Figure 3 shows the response functions as well as bands of error of 5 % generated by Monte Carlo simulation. These response functions show the maximum variation with time of each variable following a shock. Thus, this graph shows the impact of the variable in the variable row i column j, with orders for the Cholesky decomposition.

We estimate that the responses of output (Y) and mark-ups face on impact of public spending (G) we remark after is significant. These results are confirmed by Figure 3 (row 1, column 1), the simulation of an impact on public spending would lead to a positive effect on output in the short term [1, 2] followed by a recession during periods [2,4]. In the long term, the effect is neutral. For against, the effect of a fiscal shock (T) is more significant, there is alternating positive and negative effects before dissipating completely (row 1, column 2). Thus, this result shows that the impact of public intervention is always short term.

ANNEX Annex 1. The rental price of capital

The rental price of capital was defined as follows:

Where i is the long-run nominal interest rate and is the expected inflation rate. The denotes the depreciation rate of gross capital stock (5%). is the economy-wide deflator for fixed business investment.

Annex 2. The mark-up ratio

We assume the production function of the following representative firm (Rotemberg and Woodford (1991)): 0 < <1, and > 0

Where is the output, and represent respectively the capital stock and the labour input.

is a (non-observable) measure of TFP. indicates the fixed cost, it implies that the economy is imperfect competition (increasing returns to scale).

The real pure profit function of the representative firm is given by: Where is the total cost of production:

Where is the nominal rental price of capital and is the aggregate price index relevant for producers. Under imperfect competition in product markets, real factor prices are not equal to their marginal products:

Where and represent the marginal productivity of capital and the marginal productivity of labor.

Thus, if we replace (A.1), (A.2) and (A.3) into (A.4) yields equation advantage as follows:

Then we define the average share of labor in total income:

Using the above equations, we obtain the equation of mark-up ratio:

where is a measure of increasing returns given by:

In the long term, , so the equation becomes :

Therefore, using (A.2) and (A.3) we can obtain the share of wages given by: Italy :

-.03 Response of PTF to T

Response to Cholesky One S.D. Innovations ± 2 S.E.