France: Selected Background Issues

International Monetary Fund

doi:10.5089/9781451813470.002.A002

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France: Selected Background Issues

Author: International Monetary Fund

Publication Date:: 02 Jan 1996

eISBN:: 9781451813470

Language:: English

Keywords:: ISCR; CR; cost; competitiveness; unemployment rate; developments in France; affect wage; producer wage; product market liberalization; wage development; Wages; Labor force participation; Unemployment

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This paper analyzes a broad range of price and nonprice indicators to assess developments in the international competitiveness of the French economy during the 1980s and early 1990s. The paper provides a brief review of conceptual issues concerning the competitiveness indicators used in this study. Developments in conventional price- and cost-based indicators, both at the aggregate and bilateral levels, are reported. The paper discusses additional price- and quantity-based measures of competitiveness, and also examines the labor market dynamics and economic policy of France.

Abstract

II. Labor Market Dynamics and Economic Policy ^1/

1. Introduction

As in many other OECD countries, the unemployment rate in France has risen during each cyclical downturn, but has not returned to its pre-recession levels during the subsequent recoveries. As a result, unemployment has gradually increased over the last thirty years, from about 2 percent of the labor force in the 1960s to around 12 percent in 1993 and 1994 (Table 1 and Chart 1). ^2/^3/

Table 1.

Unemployment Rates in Industrial Countries

(In percent of labor force)

Source: OECD, Analytical Database; and IMF, World Economic Outlook.

Table 1.

Unemployment Rates in Industrial Countries

(In percent of labor force)

	Averages
	1960s	1970s	1980s	1993	1994
France	1.7	3.8	9.0	11.6	12.4
United States	4.8	6.2	7.3	6.8	6.1
Japan	1.3	1.7	2.5	2.5	2.9
Germany	0.8	2.4	6.8	8.8	9.6
Italy	5.2	6.4	10.3	10.3	11.3
United Kingdom	1.8	3.5	9.0	10.3	9.3
Canada	5.0	6.7	9.3	11.2	10.4
G-7 average	3.0	4.4	6.9	7.3	7.2

Source: OECD, Analytical Database; and IMF, World Economic Outlook.

Table 1.

Unemployment Rates in Industrial Countries

(In percent of labor force)

	Averages
	1960s	1970s	1980s	1993	1994
France	1.7	3.8	9.0	11.6	12.4
United States	4.8	6.2	7.3	6.8	6.1
Japan	1.3	1.7	2.5	2.5	2.9
Germany	0.8	2.4	6.8	8.8	9.6
Italy	5.2	6.4	10.3	10.3	11.3
United Kingdom	1.8	3.5	9.0	10.3	9.3
Canada	5.0	6.7	9.3	11.2	10.4
G-7 average	3.0	4.4	6.9	7.3	7.2

Source: OECD, Analytical Database; and IMF, World Economic Outlook.

The costs of high unemployment have been substantial. First, output has been lost, with real GDP several percent lower than its full-employment level. Second, the income support provided to more than two million persons (out of about three million unemployed) has placed additional strains on the public finances. ^1/ Third, the human and social costs of rising dependency on public assistance cannot be dismissed; these include the depreciation of human capital, loss of social standing, and the degradation of family structures.

The purpose of this paper is establish a relationship between economic policies and unemployment in France by constructing a macroeconomic model of the labor market. The analysis of the aggregate data suggests that the rise of unemployment has been related mainly to the increasing size of the social security system, which is financed by heavy taxes on labor. More specifically, high levels of social contributions and benefits appear to have severely dampened the growth of employment. ^2/ A second and equally important result is that the minimum wage has a strong influence on aggregate wages, employment, labor force participation, and unemployment.

2. Historical overview of labor market developments and economic policies

A salient feature of the French labor market in the last two decades has been the slow growth of employment relative to population. As shown in Table 2, the working-age population of France increased by about 20 percent since 1970, while employment--especially in the private sector--grew very little by comparison. Put differently, labor force participation fell while unemployment and non-employment increased sharply. ^3/ These developments took place against a backdrop of substantial economic growth. With employment essentially stagnant, the increase in real GDP can be accounted for almost entirely by higher labor productivity, which in turn reflects some combination of capital deepening and technological change (Chart 2).

Table 2.

Demographic and Labor Market Flows Since 1970

(In millions of persons, unless otherwise noted)

^{^1/}Individuals aged 16 to 64.

^{^2/}Unemployed persons in percent of labor force.

^{^3/}Persons of working age not employed, in percent of working-age population.

^{^4/}Labor force in percent of working-age population.

Table 2.

Demographic and Labor Market Flows Since 1970

(In millions of persons, unless otherwise noted)

		1970	1990	Change
Population		50.8	56.7	5.9
Working-age population ^1/		31.6	37.4	5.8
Labor force		21.4	24.8	3.4
Employment		20.9	22.6	1.7
	Public	3.7	5.1	1.5
	Private	17.2	17.5	0.3
Unemployment		0.5	2.2	1.7
Memorandum items:
	Unemployment rate ^2/	2.5	8.9	6.4
	Non-employment rate ^3/	33.9	39.6	5.7
	Labor force participation rate ^4/	67.8	66.3	-1.5

^{^1/}Individuals aged 16 to 64.

^{^2/}Unemployed persons in percent of labor force.

^{^3/}Persons of working age not employed, in percent of working-age population.

^{^4/}Labor force in percent of working-age population.

Table 2.

Demographic and Labor Market Flows Since 1970

(In millions of persons, unless otherwise noted)

		1970	1990	Change
Population		50.8	56.7	5.9
Working-age population ^1/		31.6	37.4	5.8
Labor force		21.4	24.8	3.4
Employment		20.9	22.6	1.7
	Public	3.7	5.1	1.5
	Private	17.2	17.5	0.3
Unemployment		0.5	2.2	1.7
Memorandum items:
	Unemployment rate ^2/	2.5	8.9	6.4
	Non-employment rate ^3/	33.9	39.6	5.7
	Labor force participation rate ^4/	67.8	66.3	-1.5

^{^1/}Individuals aged 16 to 64.

^{^2/}Unemployed persons in percent of labor force.

^{^3/}Persons of working age not employed, in percent of working-age population.

^{^4/}Labor force in percent of working-age population.

Wages also increased substantially over this period, although their growth rate at times diverged markedly from that of labor productivity (Chart 3). For example, from 1970 to 1983, the ratio of real producer wages to labor productivity (which is equivalent to the share of labor income in GDP) rose markedly. By contrast, during the second half of the 1980s, wages increased much more slowly than productivity. It is not a trivial task to explain these changes in the share of labor. However, they appear to have been strongly correlated with the real price of oil--perhaps because higher energy prices sharply depressed the rate of return on the existing capital stock--perhaps because workers succeeded, in the wage-bargaining process, in being compensated for the increase in real fuel prices.

The period since 1970 has also been characterized by substantial changes in economic policy, many of which fit into a larger pattern of expanding government social programs (Chart 4). This has been reflected in a remarkable rise in the ratio of general government current expenditure and revenue in relation to GDP. Most of this increase is accounted for by higher social spending--on health care, pensions for regular and early retirement, unemployment compensation, welfare--and has been financed largely by continuing increases in social security taxes levied on labor income. It is of course legitimate to ask whether higher social spending and higher social security taxes were principally a cause or an effect of higher unemployment--a question which is further discussed in Section 3 below.

Another way of looking at the expansion of social expenditure is to calculate the ratio of social benefits per capita to the average wage net of social security contributions (Chart 4). ^1/ It appears that, on average, a French resident (of any age) in 1993 received social support payments equivalent to almost 30 percent of the average net wage, and to almost 90 percent of the minimum wage. This represents a massive increase compared with 1970, when these ratios were about half as high as they are now. A possible implication could be that unemployment or non-work has become materially more tolerable than it was a generation ago, and may even be an attractive alternative to certain low-paying jobs. It will be seen that the data support this interpretation.

An important example of the trend towards more remunerative government social programs, and one which is thought by many observers to directly affect the labor market, is the average replacement rate of the unemployment insurance system. This rate became substantially more generous from 1970 until about 1982 (Chart 5). ^2/ A sharp turnaround took place under the government of socialist Prime Minister Laurent Fabius in 1983. However, even under the conservative governments that held office from 1986 to 1988 and since 1993, benefits were never scaled back to the levels that prevailed until the early 1970s.

The legal minimum wage has also risen substantially in real terms. ^3/ However, it has remained in a narrow band relative to average gross producer wages, ranging from a high of 45 percent in 1970 to a low of 40 1/2 percent in 1980 (Chart 5).

3. Single equation models of unemployment and non-employment ^1/

These single-equation models are based on an ad-hoc specification search. ^2/ The statistical determinants of the unemployment rate and of the non-employment rate are analyzed using a general autoregressive distributed lag (ADL) model which includes a large number of explanatory variables (real GDP, real interest rates, the employment rate, the real price of oil, social security benefits, the replacement ratio of the unemployment insurance system, minimum wages, among others). ^3/

The principal result is that for both the unemployment rate and the non-employment rate, the only policy variable that matters is the ratio of social security expenditure to gross income from dependent employment (the “social benefit ratio”), and that this variable has an extremely strong and robust influence. ^4/ Granger-causality tests suggest that the direction of causation runs both from social security expenditure to the dependent variable and the other way around. It is worth noting that an equally strong result is also obtained if the social expenditure ratio is adjusted for the effects of the business cycle.

4. A structural model of the labor market

The single-equation models of unemployment (and non-employment) presented in the previous section offer only an incomplete account of labor market behavior. In particular, the model of the unemployment rate (relative to the labor force) contained the employment rate (relative to working-age population) as an explanatory variable, while the model of the non-employment rate included wages. Both of these variables should be viewed as endogenous to the labor market.

This underscores the need for a structural, multi-equation model of the labor market which simultaneously explains labor force participation (labor supply), employment (labor demand), and the wage bargaining process. Such a model will also provide a more plausible basis for an examination of the dynamic interplay among the main labor market variables.

One question that needs to be addressed at the outset is the rationale for including a separate equation for wages. After all, why would wages not be determined by the intersection of the labor demand and labor supply curves? The most basic answer is that if labor demand and labor supply were equal in equilibrium, there would be no unemployment. Including a wage equation implies that unemployment is being modeled as a “disequilibrium” phenomenon in the sense that the labor market does not “clear.”

Two possible explanations for the disparity--at any point in time-between labor force participation and employment are the following. First, the labor market is generally believed to be imperfectly competitive, with employers’ associations, labor unions, and the public sector exercising substantial power over employment and wage decisions.

Second, the exercise of market power, the presence of adjustment costs, informational lags and similar phenomena will typically imply that wages enter the labor demand and labor supply equations with different lags. Conversely, indicators of labor market conditions, such as the unemployment rate, may influence the wage-setting process with a lag. Thus, unemployment will result from the dynamic interplay of these factors, and may often adjust only slowly in response to changes in policy and other shocks.

The system of equations takes the following form

A(L) y_t = B(L) z_t + v_t

where y_t is a vector of the three endogenous variables, namely employment LE, the labor force participation rate LFR, and real producer wages RWAG_R. A(L) and B(L) are matrix lag polynomials, and z_t is a vector of exogenous variables. ^1/ ^2/ Provided that the structural equations are all identified, the reduced form of this model is simply a vector-autoregression (VAR) which also includes contemporaneous and lagged exogenous variables.

a. The labor force participation rate

It is reasonable to hypothesize that the labor force participation rate depends on the probability of obtaining employment and the prevailing net wage rate. Policy variables might also affect the decision of individuals to join or leave the labor force. For example, minimum wages might induce some individuals to offer their services who might otherwise not find it worthwhile to seek employment. Similarly, the replacement ratio of the unemployment insurance system might increase participation by offering an incentive to stay on the unemployment rolls instead of dropping out of the labor force.

Systematic testing and reduction yielded the model shown in Appendix I. In it, the labor force participation rate is positively correlated with the employment rate (or negatively correlated with the unemployment or non-employment rate). In addition, higher net wages encourage labor force participation. However, none of the policy variables considered in this study (social security benefits, social security taxes, direct taxes, real interest rates, minimum wages, the replacement ratio of the unemployment insurance system) was found to have a significant effect on the labor force participation rate when they were added to the basic economic variables already considered. Only the replacement ratio of the unemployment insurance system even comes close to being significant (p-0.06), but it entered the equation with a negative sign--a counterintuitive result.

b. The employment equation

As expected, employment is affected negatively by real producer wages and positively by a measure of output. In addition, it was found that an indicator of the fiscal weight of the social security system has a strong negative effect on employment (Appendix II). The signs on all of these variables are the same when the model is expressed in error-correction form.

As with the other models presented in this paper, this specification was obtained by reducing a general ADL model. A full set of policy variables was included, notably the real interest rate, oil prices, a measure of international competitiveness, the replacement ratio of the unemployment insurance system, and the minimum wage. None of these--with the exception of the social security burden--were found to have a significant influence. It was also not possible to find a significant effect for any of these variables when they were added one by one to the basic specification presented in Appendix II.

In particular, higher real interest rates appear to have no measurable adverse effect on employment. This runs counter to the hypothesis that an anti-inflationary monetary policy is associated with higher unemployment. Possibly, real interest rates affect investment and output, which are taken as exogenous in the present model of employment. However, even this position is not strongly supported by the data. ^1/

From a policy standpoint, the key conclusion of the analysis presented above would appear to be that labor demand is sharply dampened by an increase in the overall burden imposed by the social security system. This burden is measured by social security expenditure, which by definition is the sum of social security taxes and the financial balance of the social security system. This result raises several difficult questions. The first two are statistical in nature, while the third concerns the economic interpretation of the result.

First, it is somewhat surprising that the effect of the social security system on employment is not fully captured by producer wages, which already include social security taxes. To investigate this point further, wages net of social security taxes were substituted for gross wages in the employment equation: the result was that the adverse effect of the social security variable on employment became even larger, while the size of the coefficient on wages dropped sharply. The overall size of the effect of the social security system on employment was unchanged. This was confirmed by a formal parameter restriction test.

Second, it might be hypothesized that causality runs in the opposite direction, from employment to social security spending. Indeed, when employment declines, as in a cyclical downturn, social security spending tends to increase. However, much of the effect of the cycle on employment is already accounted for by the inclusion of GDP in the equation. Furthermore, the adverse and separate effect of social security spending on employment is still found if the social expenditure variable is adjusted for cyclical deviations of actual GDP from potential GDP.

If it is granted that the effect of social expenditure on employment is statistically robust, what is the channel by which it exercises its influence? In particular, economic theory provides little support for the notion that social security benefits would have a direct effect on the hiring decisions of employers. The best explanation is that the employment equation is not a labor demand equation. Indeed, it is not possible, even in principle, to identify a true labor demand equation. The reason is simply that by definition, any equation for employment is also an equation for non-employment. ^1/ It will be recalled that the non-employment rate was shown to depend strongly and positively on social expenditure. The economic rationale for this effect was straightforward: higher benefits encourage workers to remain non-employed. This effect, which rests on a plausible behavioral theory, will therefore also be reflected in the equation for employment. ^2/

c. The wage equation

In the long run, wage developments in France appear to be accounted for mainly by increases in labor productivity. However, as was illustrated in Chart 3, there have been substantial deviations of wage growth from productivity growth. Energy prices have already been identified as one contributing factor to these deviations, but there may be others. A systematic search, once again starting from a general ADL model and reducing it gradually, led to the specification shown in Appendix III. In the long run, the unemployment rate and the minimum wage both have an important effect on the level of real wages. ^1/ The unemployment rate is an indicator of outsider pressure in wage negotiations; higher unemployment tends to act as a constraint on the wage demands of insiders. ^2/

d. Simultaneous structure of the system

The results may be represented most concisely by the static long-run solution to the three-equation model, written in the slightly stylized form

LFR = a₀ + a₁ LERPW + a₂ W_net
E = b₀ + b₁ W_gr + b₂ Y + b₃G
W_gr - LPR = c₀ + c₁ LUR + c₂ SMICR + c₃ POILR

where LFR is the labor force participation rate, LERPW is the ratio of employment to working-age population, W_net are wages net of social security contributions, W_gr are gross wages, Y is real GDP, G is the ratio of social security expenditure to gross labor income, LPR is labor productivity, LUR is the unemployment rate, POILR is the real oil price, and SMICR is the real minimum wage. As all levels are in natural logarithms, LFR, LERPW, LUR, and LPR can be written as differences.

Thus, labor force participation depends on the prospects of obtaining employment and on net wages. Employment depends on gross real producer wages, on real GDP, and on the social benefit ratio. Real producer wages depend on labor productivity, the unemployment rate, the minimum wage, and the price of oil.

On the further simplifying assumption that social security taxes are equal to social security benefits (and expressing the ratio appropriately), one obtains the following identities:

LFR = LF - LPW
LERPW = E - LPW
LUR = LF - E
W_net = W_grf(G)
LPR = Y - E

Substituting these identities in the system of equations set out above, one obtains a system with a rich structure of simultaneous relationships among the endogenous variables. Thus, wages and employment affect the labor force in the first equation; wages affect employment in the second equation; and employment and the labor force affect wages in the third equation. Solving this system in terms of the exogenous variables also yields an expression for the unemployment rate.

5. Dynamic analysis

This section presents an analysis of the aggregate dynamics of the labor market. A first step is to re-estimate the labor force participation, employment, and wage formation equations reported in Section 4 using two-stage least squares in order to correct for the endogeneity of some of the right-hand side variables. ^1/

A system consisting of the re-estimated behavioral equations and the identities described in Section 4 was used to conduct a variety of simulations. A first set of simulations examined the effect on unemployment of both a permanent and a temporary shock (increase in) to labor demand. These shocks took the form of a change in the constant term of the employment equation equal in size to 1 percent of the equilibrium level of employment. As may be seen in Chart 6, a permanent shock of this type permanently lowers the unemployment rate by about 1 3/4 percentage points.

The adjustment is spread over time--close to two years are needed before the transition to the new equilibrium is 90 percent complete (the increments on the horizontal axis represent quarters). The response of wages is the slowest among the three endogenous variables: around four years are needed to accomplish 90 percent of the adjustment. The reaction of the system to a temporary shock is somewhat different: the labor force initially responds more strongly than does employment, reflecting the strong effect of higher employment on wages. Thus, the unemployment rate rises before settling back to its equilibrium value (Chart 7).

The dynamic system was also used to simulate the effect on unemployment of a permanent change in the level of social protection. Chart 8 shows the impact on the unemployment rate of a 1 percentage point reduction in the social security tax and benefit ratios (these ratios are computed relative to gross income from dependent employment). There is some overshooting of the unemployment rate early on, reflecting a stronger short-term effect on labor force participation. In the longer run, the effect on employment predominates, and the unemployment rate declines by about 0.2 percentage points. Note that the incidence of the reduction in social security taxes is mainly on workers: producer wages increase by only 1/2 percent in the long run, largely reflecting the lower equilibrium unemployment rate, while net consumption wages rise by more than 1 1/2 percent.

The last simulation examines the effect of a permanent 10 percent reduction in the real minimum wage (Chart 9). Again, there is some overshooting of the unemployment rate. In the long run, however, employment rises substantially and the unemployment rate drops about 1/2 percentage point. As would be predicted by theory, a reduction in the minimum wage leads to a somewhat smaller decline in average wages (the elasticity appears to be in the neighborhood of 1/2).

6. Concluding remarks

The analysis of the aggregate data suggests that the growing burden of the social security system and the high minimum wage have importantly contributed to France’s unemployment problem. Consequently, the share of the public sector in the economy, which has grown rapidly in the last quarter-century and is now one of the highest among the industrialized countries, may need to be reduced. Cutting the tax and social security burden should stimulate labor demand. Lower taxes, along with less generous benefits, can also be expected to give those who are not employed a greater incentive to seek work. The results presented in this study also provide support for a substantial reduction in the real minimum wage. Over time, it would be possible to lower the real minimum wage simply by avoiding any further increase in nominal terms.

Of course, policy recommendations based on macroeconomic results must be supplemented by a more disaggregated, micro-economic analysis. For example, because unemployment in France is concentrated among low-skilled individuals, measures targeted at this group could be relatively more effective. Thus, an across-the-board reduction in the level of social protection may be neither necessary nor sufficient to reduce unemployment. ^1/ What presumably matters more is the--high--minimum level of social support, which is one of the principal determinants of the reservation wage. The level and comprehensiveness of welfare benefits are a key social choice, entailing a trade-off between the generosity of benefits, on the one hand, and employment objectives on the other. The evidence produced in this paper suggests that, if unemployment is to be substantially reduced, the generous system of social protection would have to be trimmed. To be sure, more effective education and training and labor and product market liberalization are also called for as part of a comprehensive attack on the unemployment problem.

APPENDIX I: Model of Labor Force Participation Rate

Source: Staff calculations.Sample period 1970Q1-1993Q4. Variable definitions are LFR: labor force participation rate, LERPW: employment rate, RCWN_R: real net consumption wage. The operator D denotes first differences; ECM01 is the error-correction term.

APPENDIX II: Model of Employment

Source: Staff calculations.Sample period 1970Q1-1993Q4. Variable definitions are LE: employment, RWAG_R: real producer wages, NGDP_R: real GDP, RNYSS: social benefit ratio. The operator D denotes first differences; ECM03 is the error-correction term.

APPENDIX III: Model of Wage Formation

Source: Staff calculations.Sample period 1970Q1-1993Q4. Variable definitions are: SLAB: real producer wages less labor productivity, LUR: unemployment rate, SMIC_R: real minimum wage, POILR: real oil price. The operator D denotes first differences; ECM05 is the error-correction term.

APPENDIX IV: Single-Equation Models of the Unemployment and Non-employment Rates

1. Unemployment rate

Formally, the model of the unemployment rate may be written as

{L U R}_{t} = a_{0} + a (L) {L U R}_{t - 1} + Σ_{j = 1}^{N} b_{j} (L) z_{j t} + v_{t}

where LUR is the unemployment rate, the Z_j are N explanatory variables, and a(L) and b_j (L) are lag polynomials. The static long-run solution of the model is simply

L U R = {[1 - a (1)]}^{- 1} [a_{0} + Σ_{j = 1}^{N} b_{j} (1) z_{j}]

The model may then be reformulated in the equivalent error-correction form

Δ {L U R}_{t} = \tilde{a} (L) Δ {L U R}_{t - 1} + Σ_{j = 1}^{N} {\tilde{b}}_{j} (L) Δ z_{t} + c {E C M}_{t - 1} + v_{t}

As the focus of this study on the dynamics of the I(1) variables, these error correction forms will not be reported or discussed. ^1/

This general model was reduced by eliminating variables for which the parameter estimates showed the wrong sign or were statistically insignificant, or both. At each stage, the model reduction was tested using F-tests based on the Schwarz criterion. While the minimum wage (relative to the average wage) was found to raise unemployment, the effect was small and statistically insignificant (p=0.98) and this variable was rejected in the first stage of model reduction. The real price of oil had a negative effect whenever it was included. However, it was also insignificant (p=0.40) and was rejected in the second stage of model reduction. Other variables that were excluded in subsequent stages generally showed the correct sign but were insignificant statistically and made only small contributions to the overall fit of the equation. Notably, higher real producer wages were associated with a higher unemployment rate, while higher real GDP was associated with a lower unemployment rate. ^2/

In the final analysis, only two variables were retained in addition to lagged values of the unemployment rate: the rate of employment in the working age population (LERPW), which measures the general prospects of obtaining work, and the social benefit ratio (RNYSS), which is a measure of the overall level of social support payments provided by the public sector. This model reduction was strongly accepted relative to the most general model (p=0.53). It was also accepted relative to all rival models examined in the course of the reduction (p=0.13 for the nearest rival). In addition to real wages, the nearest rival model also included real interest rates, which however again showed the wrong sign.

The parameter estimates for the final model are shown in Table 3. In the long run, a one percentage point increase in the employment rate (relative to working age population) results in a 0.16 percentage point drop in the unemployment rate (the labor force is about 2/3 of working age population), while a 1 percentage point increase in the social benefit ratio (relative to wage income) leads to an increase in the unemployment rate of 0.27 percentage points. ^1/

Table 3.

Model of Unemployment Rate

Source: Staff calculations.Sample period 1970Q1-1993Q4. Variable definitions are LUR: unemployment rate, LERPW: employment ratio, RNYSS: social benefit ratio.

The basic model was further tested by adding other variables one by one. The results are presented in Table 4. None of the variables are significant, even at the 10 percent level. Most show the correct sign, with the exception of real GDP, the real interest rate, and the replacement ratio. Furthermore, both explanatory variables included in the previous model remain significant in all but one case. In summary, the basic model appears to be highly robust.

Table 4.

Effect of Adding Variables to Basic Model of Unemployment Rate

Source: Staff calculations.

^{^1/}Change in unemployment rate (in percentage points) in response to 1 percent (percentage point) change in dependent variable.

^{^2/}F-test for joint significance of current value and two lags.

Table 4.

Effect of Adding Variables to Basic Model of Unemployment Rate

Variable	Long-run elasticity ^1/	Significance level ^2/
Real interest rate	-0.21	0.19
Real consumption wages	4.52	0.23
Real producer wages	3.44	0.37
Real oil price	0.41	0.71
Replacement ratio	-0.07	0.75
Relative minimum wage	0.20	0.87
Real GDP	0.50	0.98

Source: Staff calculations.

^{^1/}Change in unemployment rate (in percentage points) in response to 1 percent (percentage point) change in dependent variable.

^{^2/}F-test for joint significance of current value and two lags.

Table 4.

Effect of Adding Variables to Basic Model of Unemployment Rate

Variable	Long-run elasticity ^1/	Significance level ^2/
Real interest rate	-0.21	0.19
Real consumption wages	4.52	0.23
Real producer wages	3.44	0.37
Real oil price	0.41	0.71
Replacement ratio	-0.07	0.75
Relative minimum wage	0.20	0.87
Real GDP	0.50	0.98

Source: Staff calculations.

^{^1/}Change in unemployment rate (in percentage points) in response to 1 percent (percentage point) change in dependent variable.

^{^2/}F-test for joint significance of current value and two lags.

Very similar results were obtained if the overall social support ratio or the ratio of general government current expenditure to GDP (both shown in Chart 4) were substituted for the social benefit ratio in the basic model. The ratio of social security taxes to gross income from dependent employment also had a strong positive effect on the unemployment rate. This is not surprising, as all of the substitute variables are highly collinear.

2. The non-employment rate

If it were true that more generous social expenditure has added to unemployment by making it more tolerable or indeed attractive in some cases, the rise of unemployment would in part reflect an increase in voluntary unemployment. ^1/ In addition, one should be able to observe a decline in labor force participation. Thus, the effect of more generous social expenditure on the labor market might be better captured by the non-employment rate, which is defined as the percentage of working-age individuals who are not employed.

A single-equation model of the non-employment rate in presented in Table 5. ^1/ As with the unemployment rate, there is a strong positive effect of social benefits, both in the long run (the model in levels) and in the short run (the same model in first differences). Real consumption wages have a negative effect, both in the short run and in the long run, which means that people are drawn into the labor market by the prospect of higher net wages. Although stronger economic growth helps to reduce non-employment in the short run, in the longer term this demand effect appears to be outweighed by the tendency of non-employment to rise in tandem with higher levels of real income. Again, the positive association, both in the short term and in the long run, between social benefits (however measured) and the non-employment rate is highly robust to changes in the detailed specification of the model.

Table 5.

Model of Non-Employment Rate

Source: Staff calculations.Variable definitions are LNER: non-employment rate (in percent of working-age population, RCWN_R: real net consumption wage, RNYSS: social benefit ratio, NGDP_R: real GDP, POILR: real price of petroleum.

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Prepared by Karl F. Habermeier.

These issues are discussed in Bean (1994), who provides a useful survey of labor market developments in Europe.

The staff estimate that the portion of the unemployment rate not attributable to the cycle amounts to 9 to 10 percent of the labor force. In the copious literature on labor market issues, this non-cyclical portion of the unemployment rate is generally attributed to structural factors which reduce the incentives of workers to accept employment and of employers to create jobs. These factors are thought to include labor market mismatch, regulations governing hiring or dismissal, minimum wage laws, social benefits (especially unemployment benefits), and the tax system.

The expenditure of the unemployment insurance funds alone came to more than 1 1/2 percent of GDP in 1994. This does not include the amounts allocated for early retirement and social welfare payments.

Schmitt and Wadsworth (1993) conclude that job search is not much affected by the level of unemployment benefits. However, their investigation was limited to the United Kingdom and did not take into consideration the broader range of benefits available to non-employed individuals. Other studies, e.g., Layard and Nickell (1991) show that the duration of benefits, rather than their level, is what most affects the labor market. Many social benefits in France are independent of efforts to participate in the labor market and of essentially unlimited duration.

The overall decline in the labor force participation rate masks a strong increase in the rate for females and an even sharper decline in the rate for males.

Ideally, one would want to calculate the benefits obtained by persons of working age who are not employed, and set them in relation to the net wages (including benefits) received by those persons who are employed. These data are not readily available.

The rate here is calculated by taking the ratio of unemployment benefits per unemployed person to gross wages per employed person. Of course, the replacement ratio is substantially higher for a newly employed person. Under the new system of “allocation unique degressive” adopted in 1993, the maximal gross replacement ratio is equal to 75 percent (for an individual previously earning the legal minimum wage). See Moghadam (1994) for further details.

The minimum wage rate may be increased for three reasons: (i) automatic increases whenever the CPI has increased by more than 2 percent; (ii) automatic increases equal to one-half the rate of increase in gross real wages; and (iii) increases at the discretion of the government.

The non-employment rate is defined (in levels) as the share of the working age population that is not employed.

Details are found in Appendix IV.

All variables were tested for order of integration. With only one exception, a unit root cannot be rejected in levels but is strongly rejected in first differences.

Suitable proxies of this variable, such as the ratio of general government expenditure to GDP, perform similarly well.

Exogenous here means non-modeled but not necessarily exogenous in a statistical sense.

All the variables in levels are measured in natural logarithms.

By contrast, Bierens and Broersma (1993), using an ARMAX model and monthly data, find a positive effect of interest rates on unemployment.

The sum of employment and non-employment is the working-age population, which is an exogenous variable over the time horizons studied here.

This argument can also be cast in a somewhat different light. Higher social benefits reduce what might be called “effective labor force participation”, which is the sum of measured labor force participation less those unemployed persons who are voluntarily unemployed (and therefore not really seeking work). The smaller the effective labor force, the lower is employment in a search and matching framework, since the lower will be the probability that any given employer will find a new recruit (at any given wage offer).

The effect of the minimum wage found in this equation, and the implied relationship between the unemployment rate and the minimum wage, stand in contrast to a number of other studies. Elmeskov (1993), in a review of explanations for unemployment in the OECD countries, concludes that “most empirical evidence points to rather modest effects [of the minimum wage] on total equilibrium unemployment.” See also Brown (1988), who states “The effects of the minimum wage on employment are smaller than I would have supposed.” The evidence in favor of an effect on wages is stronger. For example, Bazen and Martin (1993) show that a higher minimum wage raises the cost of youth labor. Still, the effect on employment appears to be rather weak.

A systematic treatment of the insider-outsider theory of employment and unemployment may be found in Lindbeck and Snower (1988).

As is usual in classical two-stage least squares, the fitted values of the reduced form model, as well as all of the exogenous variables, were used as instruments. The long-run parameters had the same sign as those obtained by ordinary least squares, although they were not always the same in magnitude. In particular, the effect of real wages on employment was noticeably smaller using two-stage estimation.

Reducing social contributions and benefits for the top twenty percent of wage earners, or even the top sixty or eighty percent, is likely to have relatively little effect on either their employment or their unemployment.

However, all of the relationships in this paper were also estimated in error-correction form, and tests of cointegration were conducted, with uniformly plausible and statistically coherent results.

However, real interest rates at times showed an incorrect sign.

The model was also expressed in error-correction form in order to confirm the long-run results.

Distinguishing between voluntary and involuntary unemployment is of course not possible on the basis of existing statistics.

Again, the model was obtained by reducing a general autoregressive distributed lag to a form in which the retained variables were significant (or close to significant in the case of real GDP) and the model reduction could not be rejected at any stage by the Schwarz criterion.

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France: Selected Background Issues

Author: International Monetary Fund

Volume/Issue:: Volume 1995: Issue 141

Publisher:: International Monetary Fund

ISBN:: 9781451813470

ISSN:: 1934-7685

Pages:: 95

DOI:: https://doi.org/10.5089/9781451813470.002

Keywords
II. Labor Market Dynamics and Economic Policy 1/
References

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FRANCE: Unemployment Rate

(In Percent)

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FRANCE: Labor Market Indicators and Economic Performance

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FRANCE: Wage Developments

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FRANCE: Indicators of Public Sector Activity

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FRANCE: Indicators of Labor Market Policy

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FRANCE: Response of Labor Market to Permanent Labor Demand Shock ^1/

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FRANCE: Response of Labor Market to Temporary Labor Demand Shock ^1/

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FRANCE: Response of Labor Market to Permanent Alleviation of Social Security Taxes and Expenditure ^1/

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FRANCE: Response of Labor Market to Permanent Reduction of the Minimum Wage ^1/

Model in levels
	Variable		Coefficient	Std.Error	t-value	t-prob	PartR²
	Constant		-0.034427	0.012732	-2.704	0.0083	0.0828
	LFR_1		1.4427	0.090960	15.861	0.0000	0.7564
	LFR_2		-0.53175	0.090488	-5.876	0.0000	0.2989
	LERPW		0.56407	0.066284	8.510	0.0000	0.4720
	LERPW_1		-0.91223	0.12828	-7.111	0.0000	0.3843
	LERPW_2		0.38509	0.077088	4.996	0.0000	0.2355
	RCWN_R		-0.017519	0.019342	-0.906	0.3678	0.0100
	RCWN_R_1		0.024407	0.019466	1.254	0.2135	0.0190
	R² = 0.99334		DW = 2.02
	Solved Static Long Run equation
	$\begin{matrix} \begin{matrix} \begin{matrix} (S E) & L F R = - \underset{(0.04966)}{0.3867} \end{matrix} & + \end{matrix} \underset{(0.05224)}{0.4148} L E R P W & + \underset{(0.0267)}{0.07737} R C W N̲ R \end{matrix}$
	Tests on the significance of each variable
	variable		F(num, denom)	Value	Probability		Unit Root t-test
		LFR	F( 2, 81) =	503.89	[0.0000]	**	-2.9254
		Constant	F( 1, 81) =	7.3113	[0.0083]	**	-2.7039
		LERPW	F( 3, 81) =	25.788	[0.0000]	**	2.9721
		RCWN_R	F( 2, 81) =	2.7753	[0.0683]		2.1317
Error-correction form
	Variable		Coefficient	Std.Error	t-value	t-prob	PartR²
	DLFR_1		0.53175	0.087296	6.091	0.0000	0.3064
	DLERPW		0.56407	0.061526	9.168	0.0000	0.5002
	DLERPW 1		-0.38509	0.071480	-5.387	0.0000	0.2568
	DRCWN_R		-0.017519	0.015683	-1.117	0.2671	0.0146
	ECM01_1		-0.089027	0.027358	-3.254	0.0016	0.1120
	R² = 0.611546		DW = 2.02

Model in levels
	Variable		Coefficient	Std.Error	t-value	t-prob	PartR²
	Constant		-0.034427	0.012732	-2.704	0.0083	0.0828
	LFR_1		1.4427	0.090960	15.861	0.0000	0.7564
	LFR_2		-0.53175	0.090488	-5.876	0.0000	0.2989
	LERPW		0.56407	0.066284	8.510	0.0000	0.4720
	LERPW_1		-0.91223	0.12828	-7.111	0.0000	0.3843
	LERPW_2		0.38509	0.077088	4.996	0.0000	0.2355
	RCWN_R		-0.017519	0.019342	-0.906	0.3678	0.0100
	RCWN_R_1		0.024407	0.019466	1.254	0.2135	0.0190
	R² = 0.99334		DW = 2.02
	Solved Static Long Run equation
	$\begin{matrix} \begin{matrix} \begin{matrix} (S E) & L F R = - \underset{(0.04966)}{0.3867} \end{matrix} & + \end{matrix} \underset{(0.05224)}{0.4148} L E R P W & + \underset{(0.0267)}{0.07737} R C W N̲ R \end{matrix}$
	Tests on the significance of each variable
	variable		F(num, denom)	Value	Probability		Unit Root t-test
		LFR	F( 2, 81) =	503.89	[0.0000]	**	-2.9254
		Constant	F( 1, 81) =	7.3113	[0.0083]	**	-2.7039
		LERPW	F( 3, 81) =	25.788	[0.0000]	**	2.9721
		RCWN_R	F( 2, 81) =	2.7753	[0.0683]		2.1317
Error-correction form
	Variable		Coefficient	Std.Error	t-value	t-prob	PartR²
	DLFR_1		0.53175	0.087296	6.091	0.0000	0.3064
	DLERPW		0.56407	0.061526	9.168	0.0000	0.5002
	DLERPW 1		-0.38509	0.071480	-5.387	0.0000	0.2568
	DRCWN_R		-0.017519	0.015683	-1.117	0.2671	0.0146
	ECM01_1		-0.089027	0.027358	-3.254	0.0016	0.1120
	R² = 0.611546		DW = 2.02

Model in levels
	Variable		Coefficient	Std.Error	t-value	t-prob	PartR²
	Constant		0.30455	0.084455	3.606	0.0005	0.1413
	LE_1		1.0897	0.11300	9.644	0.0000	0.5407
	LE_2		-0.31369	0.091642	-3.423	0.0010	0.1292
	RWAG_R		-0.10595	0.035977	-2.945	0.0042	0.0989
	RWAG_R_1		0.093294	0.034855	2.677	0.0090	0.0831
	NGDP_R		0.11753	0.030671	3.832	0.0003	0.1567
	NGDP_R_1		-0.0018453	0.043577	-0.042	0.9663	0.0000
	NGDP_R_2		-0.043865	0.030296	-1.448	0.1516	0.0258
	RNYSS		-0.14385	0.045631	-3.153	0.0023	0.1117
	RNYSS_1		0.0073610	0.046926	0.157	0.8758	0.0003
	R2 = 0.993328		DW = 2.04
	Solved Static Long Run equation
$\begin{matrix} (S E) & L E = \underset{(0.1088)}{+ 1.36} & \underset{(0.02111)}{- 0.05653} R W A G̲ R & \underset{(0.02488)}{+ 0.3207} N G D P ̲ R & \underset{(0.06038)}{- 0.6095} R N Y S S \end{matrix}$
	Tests on the significance of each variable
	variable		F(num, denom)	Value	Probability		Unit Root t-test
		LE	F( 2, 79) =	97.309	[0.0000]	**	-3.9924*
		Constant	F( 1, 79) -	13.004	[0.0005]	**	3.6061
		RWAG_R	F( 2, 79) =	6.1713	[0.0032]	**	-2.7154
		NGDP_R	F( 3, 79) =	11.173	[0.0000]	**	4.163
		RNYSS	F( 2, 79) =	9.6489	[0.0002]	**	-3.9502
Error-correction form
	Variable		Coefficient	Std.Error	t-value	t-prob	PartR²
	DLE_1		0.31369	0.084498	3.712	0.0004	0.1424
	DRWAG_R		-0.10595	0.024406	-4.341	0.0000	0.1850
	DNGDP_R		0.11753	0.027730	4.238	0.0001	0.1779
	DNGDP_R_1		0.043865	0.023841	1.840	0.0694	0.0392
	DRNYSS		-0.0014385	0.00038011	-3.784	0.0003	0.1472
	ECM03_1		-0.22394	0.046624	-4.803	0.0000	0.2175
	R2 = 0.669495		DW = 2.04

Model in levels
	Variable		Coefficient	Std.Error	t-value	t-prob	PartR²
	Constant		0.30455	0.084455	3.606	0.0005	0.1413
	LE_1		1.0897	0.11300	9.644	0.0000	0.5407
	LE_2		-0.31369	0.091642	-3.423	0.0010	0.1292
	RWAG_R		-0.10595	0.035977	-2.945	0.0042	0.0989
	RWAG_R_1		0.093294	0.034855	2.677	0.0090	0.0831
	NGDP_R		0.11753	0.030671	3.832	0.0003	0.1567
	NGDP_R_1		-0.0018453	0.043577	-0.042	0.9663	0.0000
	NGDP_R_2		-0.043865	0.030296	-1.448	0.1516	0.0258
	RNYSS		-0.14385	0.045631	-3.153	0.0023	0.1117
	RNYSS_1		0.0073610	0.046926	0.157	0.8758	0.0003
	R2 = 0.993328		DW = 2.04
	Solved Static Long Run equation
$\begin{matrix} (S E) & L E = \underset{(0.1088)}{+ 1.36} & \underset{(0.02111)}{- 0.05653} R W A G̲ R & \underset{(0.02488)}{+ 0.3207} N G D P ̲ R & \underset{(0.06038)}{- 0.6095} R N Y S S \end{matrix}$
	Tests on the significance of each variable
	variable		F(num, denom)	Value	Probability		Unit Root t-test
		LE	F( 2, 79) =	97.309	[0.0000]	**	-3.9924*
		Constant	F( 1, 79) -	13.004	[0.0005]	**	3.6061
		RWAG_R	F( 2, 79) =	6.1713	[0.0032]	**	-2.7154
		NGDP_R	F( 3, 79) =	11.173	[0.0000]	**	4.163
		RNYSS	F( 2, 79) =	9.6489	[0.0002]	**	-3.9502
Error-correction form
	Variable		Coefficient	Std.Error	t-value	t-prob	PartR²
	DLE_1		0.31369	0.084498	3.712	0.0004	0.1424
	DRWAG_R		-0.10595	0.024406	-4.341	0.0000	0.1850
	DNGDP_R		0.11753	0.027730	4.238	0.0001	0.1779
	DNGDP_R_1		0.043865	0.023841	1.840	0.0694	0.0392
	DRNYSS		-0.0014385	0.00038011	-3.784	0.0003	0.1472
	ECM03_1		-0.22394	0.046624	-4.803	0.0000	0.2175
	R2 = 0.669495		DW = 2.04

Model in levels
	Variable		Coefficient	Std.Error	t-value	t-prob	PartR²
	Constant		-0.22770	0.048238	-4.720	0.0000	0.2244
	SLAB_1		0.75614	0.10882	6.949	0.0000	0.3854
	SLAB_2		0.077073	0.098664	0.781	0.4371	0.0079
	LUR		-0.45208	0.48994	-0.923	0.3590	0.0109
	LUR_1		1.6090	0.89595	1.796	0.0764	0.0402
	LUR_2		-1.5095	0.50553	-2.986	0.0038	0.1038
	SMIC_R		-0.049732	0.041953	-1.185	0.2395	0.0179
	SMIC_R_1		0.072992	0.052233	1.397	0.1663	0.0247
	SMIC_R_2		0.021418	0.042160	0.508	0.6129	0.0033
	POILR		0.0088981	0.0033344	2.669	0.0093	0.0847
	POILR_1		0.0012442	0.0044508	0.280	0.7806	0.0010
	POILR_2		-0.0027867	0.0035991	-0.774	0.4411	0.0077
	R2 = 0.979976		DW = 1.90
	Solved Static Long Run equation
	$\begin{matrix} (S E) & S L A B = - \underset{(0.1912)}{1.365} & \underset{(0.6467)}{- 2.114} L U R & + \underset{(0.09891)}{0.2679} S M I C̲ R & \underset{(0.009772)}{+ 0.0441 P O I L R} \end{matrix}$
	Tests on the significance of each variable
	variable		F(num, denom)	Value	Probability		Unit Root t-test
		SLAB	F( 2, 77) =	217.62	[0.0000]	**	-4.1551*
		Constant	F( 1, 77) =	22.282	[0.0000]	**	-4.7204
		LUR	F( 3, 77) =	7.874	[0.0001]	**	-4.105
		SMIC_R	F( 3, 77) =	4.2033	[0.0083]	**	3.1905
		POILR	F( 3, 77) =	4.6933	[0.0046]	**	2.5551
Error-correction form
	Variable		Coefficient	Std.Error	t-value	t-prob	PartR²
	DSLAB_1		-0.077073	0.093034	-0.828	0.4099	0.0084
	DLUR		-0.45208	0.45513	-0.993	0.3235	0.0120
	DLUR_1		1.5095	0.48821	3.092	0.0027	0.1056
	DSMIC_R		-0.049732	0.036570	-1.360	0.1776	0.0223
	DSMIC_R_1		-0.021418	0.037745	-0.567	0.5720	0.0040
	DPOILR		0.0088981	0.0030537	2.914	0.0046	0.0949
	DPOILR_1		0.0027867	0.0033064	0.843	0.4018	0.0087
	ECM05A_1		-0.16678	0.028231	-5.908	0.0000	0.3011
	R2 - 0.439572		σ = 0.00574453	DW = 1.90

Model in levels of LNER
	Variable			Coefficient	Std.Error	t-value	t-prob	PartR²
	Constant			4.5868	2.2500	2.039	0.0449	0.0512
	LNER_1			1.3174	0.10352	12.727	0.0000	0.6778
	LNER_2			-0.45333	0.094257	-4.810	0.0000	0.2310
	RCWN_R_1			-4.0421	1.5838	-2.552	0.0127	0.0780
	RCWN_R_2			2.9081	1.4651	1.985	0.0507	0.0487
	RNYSS			0.069197	0.029745	2.326	0.0226	0.0657
	RNYSS_1			0.011646	0.032844	0.355	0.7239	0.0016
	NGDP_R			-2.5095	1.9594	-1.281	0.2041	0.0209
	NGDP_R_1			-1.5270	2.7758	-0.550	0.5838	0.0039
	NGDP_R_2			4.1136	1.9089	2.155	0.0343	0.0569
	POILR_1			0.039475	0.059426	0.664	0.5085	0.0057
	POILR_2			0.097166	0.059849	1.624	0.1086	0.0331
	R² = 0.998773			DW = 2.12
Solved Static Long Run equation
$\begin{matrix} L N E R = & \underset{+ 0.5672 N G D P̲ R}{\begin{matrix} + 33.75 \end{matrix}} & \underset{+ 1.006 P O I L R}{- 8.345 R C W N̲ R} & + 0.5949 R N Y S S \end{matrix}$
Tests on the significance of each variable
	variable			F(num, denom)	Value	Probability		Unit Root t-test
		LNER		F( 2, 77) =	261.37	[0.0000]	**	-3.5489
		Constant		F( 1, 77) =	4.1559	[0.0449]	*	2.0386
		RCWN_R		F( 2, 77) =	4.0031	[0.0222]	*	-2.1041
		RNYSS		F( 2, 77) =	5.1853	[0.0077]	**	2.902
		NGDP_R		F( 3, 77) =	2.4446	[0.0703]		0.19427
		POILR		F( 2, 77) =	4.839	[0.0105]	*	3.0721
	Tests on the significance of each lag
		Lag		F(num, denom)	Value	Probability
			1	F( 5, 77) =	35.261	[0.0000]	**
			2	F( 4, 77) =	9.7353	[0.0000]	**

Model in Levels of LUR
Variable	Coefficient	Std.Error	t-value	t-prob	PartR²
Constant	-0.011672	0.0060901	-1.916	0.0588	0.0429
LUR_1	1.3912	0.071313	19.509	0.0000	0.8227
LUR_2	-0.45528	0.070501	-6.458	0.0000	0.3371
LERPW	-0.38551	0.057870	-6.662	0.0000	0.3512
LERPW_1	0.37387	0.057596	6.491	0.0000	0.3394
RNYSS	0.058819	0.027607	2.131	0.0361	0.0525
RNYSS_1	-0.028703	0.027918	-1.028	0.3069	0.0127
Solved Static Long Run equation
$\begin{matrix} (S E) & L U R = \underset{(0.04034)}{- 0.1823} & \underset{(0.235)}{- 0.1818} L E R P W & \underset{(0.2041)}{+ 0.4702} R N Y S S \end{matrix}$
R² = 0.998968
F(6, 82) = 13224 [0.0000]
σ = 0.00103287
DW = 1.85

Model in Levels of LUR
Variable	Coefficient	Std.Error	t-value	t-prob	PartR²
Constant	-0.011672	0.0060901	-1.916	0.0588	0.0429
LUR_1	1.3912	0.071313	19.509	0.0000	0.8227
LUR_2	-0.45528	0.070501	-6.458	0.0000	0.3371
LERPW	-0.38551	0.057870	-6.662	0.0000	0.3512
LERPW_1	0.37387	0.057596	6.491	0.0000	0.3394
RNYSS	0.058819	0.027607	2.131	0.0361	0.0525
RNYSS_1	-0.028703	0.027918	-1.028	0.3069	0.0127
Solved Static Long Run equation
$\begin{matrix} (S E) & L U R = \underset{(0.04034)}{- 0.1823} & \underset{(0.235)}{- 0.1818} L E R P W & \underset{(0.2041)}{+ 0.4702} R N Y S S \end{matrix}$
R² = 0.998968
F(6, 82) = 13224 [0.0000]
σ = 0.00103287
DW = 1.85

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Abstract

II. Labor Market Dynamics and Economic Policy 1/

1. Introduction

2. Historical overview of labor market developments and economic policies

3. Single equation models of unemployment and non-employment 1/

4. A structural model of the labor market

a. The labor force participation rate

b. The employment equation

c. The wage equation

d. Simultaneous structure of the system

5. Dynamic analysis

6. Concluding remarks

APPENDIX I: Model of Labor Force Participation Rate

APPENDIX II: Model of Employment

APPENDIX III: Model of Wage Formation

APPENDIX IV: Single-Equation Models of the Unemployment and Non-employment Rates

1. Unemployment rate

2. The non-employment rate

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II. Labor Market Dynamics and Economic Policy ^1/

3. Single equation models of unemployment and non-employment ^1/