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23.1: The Relationship Between Inflation and Unemployment

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The Phillips Curve

The Phillips curve shows the inverse relationship between inflation and unemployment: as unemployment decreases, inflation increases.

learning objectives

• Review the historical evidence regarding the theory of the Phillips curve

The Phillips curve relates the rate of inflation with the rate of unemployment. The Phillips curve argues that unemployment and inflation are inversely related: as levels of unemployment decrease, inflation increases. The relationship, however, is not linear. Graphically, the short-run Phillips curve traces an L-shape when the unemployment rate is on the x-axis and the inflation rate is on the y-axis.

Theoretical Phillips Curve : The Phillips curve shows the inverse trade-off between inflation and unemployment. As one increases, the other must decrease. In this image, an economy can either experience 3% unemployment at the cost of 6% of inflation, or increase unemployment to 5% to bring down the inflation levels to 2%.

The early idea for the Phillips curve was proposed in 1958 by economist A.W. Phillips. In his original paper, Phillips tracked wage changes and unemployment changes in Great Britain from 1861 to 1957, and found that there was a stable, inverse relationship between wages and unemployment. This correlation between wage changes and unemployment seemed to hold for Great Britain and for other industrial countries. In 1960, economists Paul Samuelson and Robert Solow expanded this work to reflect the relationship between inflation and unemployment. Because wages are the largest components of prices, inflation (rather than wage changes) could be inversely linked to unemployment.

The theory of the Phillips curve seemed stable and predictable. Data from the 1960’s modeled the trade-off between unemployment and inflation fairly well. The Phillips curve offered potential economic policy outcomes: fiscal and monetary policy could be used to achieve full employment at the cost of higher price levels, or to lower inflation at the cost of lowered employment. However, when governments attempted to use the Phillips curve to control unemployment and inflation, the relationship fell apart. Data from the 1970’s and onward did not follow the trend of the classic Phillips curve. For many years, both the rate of inflation and the rate of unemployment were higher than the Phillips curve would have predicted, a phenomenon known as “stagflation. ” Ultimately, the Phillips curve was proved to be unstable, and therefore, not usable for policy purposes.

US Phillips Curve (2000 – 2013) : The data points in this graph span every month from January 2000 until April 2013. They do not form the classic L-shape the short-run Phillips curve would predict. Although it was shown to be stable from the 1860’s until the 1960’s, the Phillips curve relationship became unstable – and unusable for policy-making – in the 1970’s.

The Relationship Between the Phillips Curve and AD-AD

Changes in aggregate demand cause movements along the Phillips curve, all other variables held constant.

• Relate aggregate demand to the Phillips curve

The Phillips Curve Related to Aggregate Demand

The Phillips curve shows the inverse trade-off between rates of inflation and rates of unemployment. If unemployment is high, inflation will be low; if unemployment is low, inflation will be high.

The Phillips curve and aggregate demand share similar components. The Phillips curve is the relationship between inflation, which affects the price level aspect of aggregate demand, and unemployment, which is dependent on the real output portion of aggregate demand. Consequently, it is not far-fetched to say that the Phillips curve and aggregate demand are actually closely related.

To see the connection more clearly, consider the example illustrated by. Let’s assume that aggregate supply, AS, is stationary, and that aggregate demand starts with the curve, AD 1 . There is an initial equilibrium price level and real GDP output at point A. Now, imagine there are increases in aggregate demand, causing the curve to shift right to curves AD 2 through AD4. As aggregate demand increases, unemployment decreases as more workers are hired, real GDP output increases, and the price level increases; this situation describes a demand-pull inflation scenario.

Phillips Curve and Aggregate Demand : As aggregate demand increases from AD1 to AD4, the price level and real GDP increases. This translates to corresponding movements along the Phillips curve as inflation increases and unemployment decreases.

As more workers are hired, unemployment decreases. Moreover, the price level increases, leading to increases in inflation. These two factors are captured as equivalent movements along the Phillips curve from points A to D. At the initial equilibrium point A in the aggregate demand and supply graph, there is a corresponding inflation rate and unemployment rate represented by point A in the Phillips curve graph. For every new equilibrium point (points B, C, and D) in the aggregate graph, there is a corresponding point in the Phillips curve. This illustrates an important point: changes in aggregate demand cause movements along the Phillips curve.

The Long-Run Phillips Curve

The long-run Phillips curve is a vertical line at the natural rate of unemployment, so inflation and unemployment are unrelated in the long run.

• Examine the NAIRU and its relationship to the long term Phillips curve

The Phillips curve shows the trade-off between inflation and unemployment, but how accurate is this relationship in the long run? According to economists, there can be no trade-off between inflation and unemployment in the long run. Decreases in unemployment can lead to increases in inflation, but only in the short run. In the long run, inflation and unemployment are unrelated. Graphically, this means the Phillips curve is vertical at the natural rate of unemployment, or the hypothetical unemployment rate if aggregate production is in the long-run level. Attempts to change unemployment rates only serve to move the economy up and down this vertical line.

Natural Rate Hypothesis

The natural rate of unemployment theory, also known as the non-accelerating inflation rate of unemployment (NAIRU) theory, was developed by economists Milton Friedman and Edmund Phelps. According to NAIRU theory, expansionary economic policies will create only temporary decreases in unemployment as the economy will adjust to the natural rate. Moreover, when unemployment is below the natural rate, inflation will accelerate. When unemployment is above the natural rate, inflation will decelerate. When the unemployment rate is equal to the natural rate, inflation is stable, or non-accelerating.

To get a better sense of the long-run Phillips curve, consider the example shown in. Assume the economy starts at point A and has an initial rate of unemployment and inflation rate. If the government decides to pursue expansionary economic policies, inflation will increase as aggregate demand shifts to the right. This is shown as a movement along the short-run Phillips curve, to point B, which is an unstable equilibrium. As aggregate demand increases, more workers will be hired by firms in order to produce more output to meet rising demand, and unemployment will decrease. However, due to the higher inflation, workers’ expectations of future inflation changes, which shifts the short-run Phillips curve to the right, from unstable equilibrium point B to the stable equilibrium point C. At point C, the rate of unemployment has increased back to its natural rate, but inflation remains higher than its initial level.

NAIRU and Phillips Curve : Although the economy starts with an initially low level of inflation at point A, attempts to decrease the unemployment rate are futile and only increase inflation to point C. The unemployment rate cannot fall below the natural rate of unemployment, or NAIRU, without increasing inflation in the long run.

The reason the short-run Phillips curve shifts is due to the changes in inflation expectations. Workers, who are assumed to be completely rational and informed, will recognize their nominal wages have not kept pace with inflation increases (the movement from A to B), so their real wages have been decreased. As such, in the future, they will renegotiate their nominal wages to reflect the higher expected inflation rate, in order to keep their real wages the same. As nominal wages increase, production costs for the supplier increase, which diminishes profits. As profits decline, suppliers will decrease output and employ fewer workers (the movement from B to C). Consequently, an attempt to decrease unemployment at the cost of higher inflation in the short run led to higher inflation and no change in unemployment in the long run.

The NAIRU theory was used to explain the stagflation phenomenon of the 1970’s, when the classic Phillips curve could not. According to the theory, the simultaneously high rates of unemployment and inflation could be explained because workers changed their inflation expectations, shifting the short-run Phillips curve, and increasing the prevailing rate of inflation in the economy. At the same time, unemployment rates were not affected, leading to high inflation and high unemployment.

The Short-Run Phillips Curve

The short-run Phillips curve depicts the inverse trade-off between inflation and unemployment.

• Interpret the short-run Phillips curve

The Phillips curve depicts the relationship between inflation and unemployment rates. The long-run Phillips curve is a vertical line that illustrates that there is no permanent trade-off between inflation and unemployment in the long run. However, the short-run Phillips curve is roughly L-shaped to reflect the initial inverse relationship between the two variables. As unemployment rates increase, inflation decreases; as unemployment rates decrease, inflation increases.

Short-Run Phillips Curve : The short-run Phillips curve shows that in the short-term there is a tradeoff between inflation and unemployment. Contrast it with the long-run Phillips curve (in red), which shows that over the long term, unemployment rate stays more or less steady regardless of inflation rate.

Consider the example shown in. When the unemployment rate is 2%, the corresponding inflation rate is 10%. As unemployment decreases to 1%, the inflation rate increases to 15%. On the other hand, when unemployment increases to 6%, the inflation rate drops to 2%.

Historical application

During the 1960’s, the Phillips curve rose to prominence because it seemed to accurately depict real-world macroeconomics. However, the stagflation of the 1970’s shattered any illusions that the Phillips curve was a stable and predictable policy tool. Nowadays, modern economists reject the idea of a stable Phillips curve, but they agree that there is a trade-off between inflation and unemployment in the short-run. Given a stationary aggregate supply curve, increases in aggregate demand create increases in real output. As output increases, unemployment decreases. With more people employed in the workforce, spending within the economy increases, and demand-pull inflation occurs, raising price levels.

Therefore, the short-run Phillips curve illustrates a real, inverse correlation between inflation and unemployment, but this relationship can only exist in the short run . The idea of a stable trade-off between inflation and unemployment in the long run has been disproved by economic history.

Relationship Between Expectations and Inflation

There are two theories of expectations (adaptive or rational) that predict how people will react to inflation.

• Distinguish adaptive expectations from rational expectations

The short-run Phillips curve is said to shift because of workers’ future inflation expectations. Yet, how are those expectations formed? There are two theories that explain how individuals predict future events.

Real versus Nominal Quantities

To fully appreciate theories of expectations, it is helpful to review the difference between real and nominal concepts. Anything that is nominal is a stated aspect. In contrast, anything that is real has been adjusted for inflation. To make the distinction clearer, consider this example. Suppose you are opening a savings account at a bank that promises a 5% interest rate. This is the nominal, or stated, interest rate. However, suppose inflation is at 3%. The real interest rate would only be 2% (the nominal 5% minus 3% to adjust for inflation).

The difference between real and nominal extends beyond interest rates. In an earlier atom, the difference between real GDP and nominal GDP was discussed. The distinction also applies to wages, income, and exchange rates, among other values.

Adaptive Expectations

The theory of adaptive expectations states that individuals will form future expectations based on past events. For example, if inflation was lower than expected in the past, individuals will change their expectations and anticipate future inflation to be lower than expected.

To connect this to the Phillips curve, consider. Assume the economy starts at point A at the natural rate of unemployment with an initial inflation rate of 2%, which has been constant for the past few years. Accordingly, because of the adaptive expectations theory, workers will expect the 2% inflation rate to continue, so they will incorporate this expected increase into future labor bargaining agreements. This way, their nominal wages will keep up with inflation, and their real wages will stay the same.

Expectations and the Phillips Curve : According to adaptive expectations theory, policies designed to lower unemployment will move the economy from point A through point B, a transition period when unemployment is temporarily lowered at the cost of higher inflation. However, eventually, the economy will move back to the natural rate of unemployment at point C, which produces a net effect of only increasing the inflation rate.According to rational expectations theory, policies designed to lower unemployment will move the economy directly from point A to point C. The transition at point B does not exist as workers are able to anticipate increased inflation and adjust their wage demands accordingly.

Now assume that the government wants to lower the unemployment rate. To do so, it engages in expansionary economic activities and increases aggregate demand. As aggregate demand increases, inflation increases. Because of the higher inflation, the real wages workers receive have decreased. For example, assume each worker receives $100, plus the 2% inflation adjustment. Each worker will make $102 in nominal wages, but $100 in real wages. Now, if the inflation level has risen to 6%. Workers will make $102 in nominal wages, but this is only $96.23 in real wages.

Although the workers’ real purchasing power declines, employers are now able to hire labor for a cheaper real cost. Consequently, employers hire more workers to produce more output, lowering the unemployment rate and increasing real GDP. On, the economy moves from point A to point B.

However, workers eventually realize that inflation has grown faster than expected, their nominal wages have not kept pace, and their real wages have been diminished. They demand a 4% increase in wages to increase their real purchasing power to previous levels, which raises labor costs for employers. As labor costs increase, profits decrease, and some workers are let go, increasing the unemployment rate. Graphically, the economy moves from point B to point C.

This example highlights how the theory of adaptive expectations predicts that there are no long-run trade-offs between unemployment and inflation. In the short run, it is possible to lower unemployment at the cost of higher inflation, but, eventually, worker expectations will catch up, and the economy will correct itself to the natural rate of unemployment with higher inflation.

Rational Expectations

The theory of rational expectations states that individuals will form future expectations based on all available information, with the result that future predictions will be very close to the market equilibrium. For example, assume that inflation was lower than expected in the past. Individuals will take this past information and current information, such as the current inflation rate and current economic policies, to predict future inflation rates.

As an example of how this applies to the Phillips curve, consider again. Assume the economy starts at point A, with an initial inflation rate of 2% and the natural rate of unemployment. However, under rational expectations theory, workers are intelligent and fully aware of past and present economic variables and change their expectations accordingly. They will be able to anticipate increases in aggregate demand and the accompanying increases in inflation. As such, they will raise their nominal wage demands to match the forecasted inflation, and they will not have an adjustment period when their real wages are lower than their nominal wages. Graphically, they will move seamlessly from point A to point C, without transitioning to point B.

In essence, rational expectations theory predicts that attempts to change the unemployment rate will be automatically undermined by rational workers. They can act rationally to protect their interests, which cancels out the intended economic policy effects. Efforts to lower unemployment only raise inflation.

Shifting the Phillips Curve with a Supply Shock

Aggregate supply shocks, such as increases in the costs of resources, can cause the Phillips curve to shift.

• Give examples of aggregate supply shock that shift the Phillips curve

The Phillips curve shows the relationship between inflation and unemployment. In the short-run, inflation and unemployment are inversely related; as one quantity increases, the other decreases. In the long-run, there is no trade-off. In the 1960’s, economists believed that the short-run Phillips curve was stable. By the 1970’s, economic events dashed the idea of a predictable Phillips curve. What could have happened in the 1970’s to ruin an entire theory? Stagflation caused by a aggregate supply shock.

Stagflation and Aggregate Supply Shocks

Stagflation is a combination of the words “stagnant” and “inflation,” which are the characteristics of an economy experiencing stagflation: stagnating economic growth and high unemployment with simultaneously high inflation. The stagflation of the 1970’s was caused by a series of aggregate supply shocks. In this case, huge increases in oil prices by the Organization of Petroleum Exporting Countries (OPEC) created a severe negative supply shock. The increased oil prices represented greatly increased resource prices for other goods, which decreased aggregate supply and shifted the curve to the left. As aggregate supply decreased, real GDP output decreased, which increased unemployment, and price level increased; in other words, the shift in aggregate supply created cost-push inflation.

Aggregate Supply Shock : In this example of a negative supply shock, aggregate supply decreases and shifts to the left. The resulting decrease in output and increase in inflation can cause the situation known as stagflation.

Shifting the Phillips Curve

The aggregate supply shocks caused by the rising price of oil created simultaneously high unemployment and high inflation. At the time, the dominant school of economic thought believed inflation and unemployment to be mutually exclusive; it was not possible to have high levels of both within an economy. Consequently, the Phillips curve could not model this situation. For high levels of unemployment, there were now corresponding levels of inflation that were higher than the Phillips curve predicted; the Phillips curve had shifted upwards and to the right. Thus, the Phillips curve no longer represented a predictable trade-off between unemployment and inflation.

Disinflation

Disinflation is a decline in the rate of inflation, and can be caused by declines in the money supply or recessions in the business cycle.

• Identify situations with disinflation

Inflation is the persistent rise in the general price level of goods and services. Disinflation is a decline in the rate of inflation; it is a slowdown in the rise in price level. As an example, assume inflation in an economy grows from 2% to 6% in Year 1, for a growth rate of four percentage points. In Year 2, inflation grows from 6% to 8%, which is a growth rate of only two percentage points. The economy is experiencing disinflation because inflation did not increase as quickly in Year 2 as it did in Year 1, but the general price level is still rising. Disinflation is not to be confused with deflation, which is a decrease in the general price level.

Disinflation can be caused by decreases in the supply of money available in an economy. It can also be caused by contractions in the business cycle, otherwise known as recessions. The Phillips curve can illustrate this last point more closely. Consider an economy initially at point A on the long-run Phillips curve in. Suppose that during a recession, the rate that aggregate demand increases relative to increases in aggregate supply declines. This reduces price levels, which diminishes supplier profits. As profits decline, employers lay off employees, and unemployment rises, which moves the economy from point A to point B on the graph. Eventually, though, firms and workers adjust their inflation expectations, and firms experience profits once again. As profits increase, employment also increases, returning the unemployment rate to the natural rate as the economy moves from point B to point C. The expected rate of inflation has also decreased due to different inflation expectations, resulting in a shift of the short-run Phillips curve.

Disinflation : Disinflation can be illustrated as movements along the short-run and long-run Phillips curves.

Inflation vs. Deflation vs. Disinflation

To illustrate the differences between inflation, deflation, and disinflation, consider the following example. Assume the following annual price levels as compared to the prices in year 1:

• Year 1: 100% of Year 1 prices
• Year 2: 104% of Year 1 prices
• Year 3: 106% of Year 1 prices
• Year 4: 107% of Year 1 prices
• Year 5: 105% of Year 1 prices

As the economy moves through Year 1 to Year 4, there is a continued growth in the price level. This is an example of inflation; the price level is continually rising. However, between Year 2 and Year 4, the rise in price levels slows down. Between Year 2 and Year 3, the price level only increases by two percentage points, which is lower than the four percentage point increase between Years 1 and 2. The trend continues between Years 3 and 4, where there is only a one percentage point increase. This is an example of disinflation; the overall price level is rising, but it is doing so at a slower rate.

Between Years 4 and 5, the price level does not increase, but decreases by two percentage points. This is an example of deflation; the price rise of previous years has reversed itself.

• The relationship between inflation rates and unemployment rates is inverse. Graphically, this means the short-run Phillips curve is L-shaped.
• A.W. Phillips published his observations about the inverse correlation between wage changes and unemployment in Great Britain in 1958. This relationship was found to hold true for other industrial countries, as well.
• From 1861 until the late 1960’s, the Phillips curve predicted rates of inflation and rates of unemployment. However, from the 1970’s and 1980’s onward, rates of inflation and unemployment differed from the Phillips curve’s prediction. The relationship between the two variables became unstable.
• Aggregate demand and the Phillips curve share similar components. The rate of unemployment and rate of inflation found in the Phillips curve correspond to the real GDP and price level of aggregate demand.
• Changes in aggregate demand translate as movements along the Phillips curve.
• If there is an increase in aggregate demand, such as what is experienced during demand-pull inflation, there will be an upward movement along the Phillips curve. As aggregate demand increases, real GDP and price level increase, which lowers the unemployment rate and increases inflation.
• The natural rate of unemployment is the hypothetical level of unemployment the economy would experience if aggregate production were in the long-run state.
• The natural rate hypothesis, or the non-accelerating inflation rate of unemployment (NAIRU) theory, predicts that inflation is stable only when unemployment is equal to the natural rate of unemployment. If unemployment is below (above) its natural rate, inflation will accelerate (decelerate).
• Expansionary efforts to decrease unemployment below the natural rate of unemployment will result in inflation. This changes the inflation expectations of workers, who will adjust their nominal wages to meet these expectations in the future. This leads to shifts in the short-run Phillips curve.
• The natural rate hypothesis was used to give reasons for stagflation, a phenomenon that the classic Phillips curve could not explain.
• The long-run Phillips curve is a vertical line at the natural rate of unemployment, but the short-run Phillips curve is roughly L-shaped.
• The inverse relationship shown by the short-run Phillips curve only exists in the short-run; there is no trade-off between inflation and unemployment in the long run.
• Economic events of the 1970’s disproved the idea of a permanently stable trade-off between unemployment and inflation.
• Nominal quantities are simply stated values. Real quantities are nominal ones that have been adjusted for inflation.
• Adaptive expectations theory says that people use past information as the best predictor of future events. If inflation was higher than normal in the past, people will expect it to be higher than anticipated in the future.
• Rational expectations theory says that people use all available information, past and current, to predict future events. If inflation was higher than normal in the past, people will take that into consideration, along with current economic indicators, to anticipate its future performance.
• According to adaptive expectations, attempts to reduce unemployment will result in temporary adjustments along the short-run Phillips curve, but will revert to the natural rate of unemployment. According to rational expectations, attempts to reduce unemployment will only result in higher inflation.
• In the 1970’s soaring oil prices increased resource costs for suppliers, which decreased aggregate supply. The resulting cost-push inflation situation led to high unemployment and high inflation ( stagflation ), which shifted the Phillips curve upwards and to the right.
• Stagflation is a situation where economic growth is slow (reducing employment levels) but inflation is high.
• The Phillips curve was thought to represent a fixed and stable trade-off between unemployment and inflation, but the supply shocks of the 1970’s caused the Phillips curve to shift. This ruined its reputation as a predictable relationship.
• Disinflation is not the same as deflation, when inflation drops below zero.
• During periods of disinflation, the general price level is still increasing, but it is occurring slower than before.
• The short-run and long-run Phillips curve may be used to illustrate disinflation.
• Phillips curve : A graph that shows the inverse relationship between the rate of unemployment and the rate of inflation in an economy.
• stagflation : Inflation accompanied by stagnant growth, unemployment, or recession.
• aggregate demand : The the total demand for final goods and services in the economy at a given time and price level.
• Natural Rate of Unemployment : The hypothetical unemployment rate consistent with aggregate production being at the long-run level.
• non-accelerating inflation rate of unemployment : (NAIRU); theory that describes how the short-run Phillips curve shifts in the long run as expectations change.
• adaptive expectations theory : A hypothesized process by which people form their expectations about what will happen in the future based on what has happened in the past.
• rational expectations theory : A hypothesized process by which people form their expectations about what will happen in the future based on all relevant information.
• supply shock : An event that suddenly changes the price of a commodity or service. It may be caused by a sudden increase or decrease in the supply of a particular good.
• disinflation : A decrease in the inflation rate.
• inflation : An increase in the general level of prices or in the cost of living.
• deflation : A decrease in the general price level, that is, in the nominal cost of goods and services.

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The [] luxury unemployment'hypothesis: A review of recent evidence

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• Published: 12 July 2019

The social stigma of unemployment: consequences of stigma consciousness on job search attitudes, behaviour and success

• Gerhard Krug   ORCID: orcid.org/0000-0002-6952-0579 1 , 2 ,
• Katrin Drasch 2 &
• Monika Jungbauer-Gans 3  

Journal for Labour Market Research volume  53 , Article number:  11 ( 2019 ) Cite this article

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Studies show that the unemployed face serious disadvantages in the labour market and that the social stigma of unemployment is one explanation. In this paper, we focus on the unemployed’s expectations of being stigmatized (stigma consciousness) and the consequences of such negative expectations on job search attitudes and behaviour. Using data from the panel study “Labour Market and Social Security” (PASS), we find that the unemployed with high stigma consciousness suffer from reduced well-being and health. Regarding job search, the stigmatized unemployed are more likely to expect that their chances of re-employment are low, but in contrast, they are more likely to place a high value on becoming re-employed. Instead of becoming discouraged and passive, we find that stigmatized unemployed individuals increase their job search effort compared to other unemployed individuals. However, despite their higher job search effort, the stigma-conscious unemployed do not have better re-employment chances.

1 Introduction

Unemployment is associated with adverse consequences. Empirical evidence has been presented for social exclusion (Hirseland and Ramos Lobato 2014 ), network withdrawal (Jones 1988 ), marital dissolution (Hansen 2005 ), financial shame (Rantakeisu et al. 1999 ), ill health (Krug and Eberl 2018 ), as well as reduced wage levels (Gangl 2004 ), reduced well-being (Mousteri et al. 2018 ), even after re-employment. For many of these consequences, social stigma is considered one of the central mechanisms (for an overview, see Brand 2015 ). The social stigma literature, in contrast, rarely addresses the stigma of unemployment, instead focussing on the stigma of mental or physical illness (Baumann 2007 ; Scambler 2009 ), race (Mosley and Rosenberg 2007 ; Pinel et al. 2005 ; Sigelman and Tuch 1997 ), ethnicity (Binggeli et al. 2014 ), sexual orientation (Herek 2010 ; Mattocks et al. 2015 ), etc. If at all, unemployment is only addressed as a potential consequence of other social stigmas such as mental illness or history of incarceration (cf., Link and Phelan 2001 ; LeBel 2008 ; Karren and Sherman 2012 ).

However, there is no doubt that in modern welfare states, there is a number of stereotypical beliefs regarding the attitudes of the unemployed to work and other personal shortcomings that are seen as the main reason for why individuals are getting and remain unemployed (Oschmiansky et al. 2003 ; McFadyen 1998 ). One strand of literature in labour market research explicitly addresses unemployment as a social stigma and shows that it might be these stereotypical beliefs that can hinder the unemployed from getting a job. This literature focusses on the discrimination of the unemployed, especially by firms during the hiring process. This research consistently shows that even if they had the same qualifications and competences as employed applicants, the unemployed and especially the long-term unemployed have significantly lower chances of getting hired. In a recent survey using German data, Rebien and Rothe ( 2018 ) showed that discrimination against the unemployed is very common. The authors found that only 14% of German firms would fill current vacancies with unemployed applicants irrespective of their unemployment duration. Thirty-four percent of these firms would accept such applications only if the applicants were unemployed for less than 1 year. The unemployment discrimination literature mostly focusses on firms’ behaviour towards the unemployed. As a result, we have ample empirical evidence regarding the demand side of the matching process but not so much on the supply side. This empirical one-sidedness can leave the impression that the targets of unemployment stigma are only passive victims of potential employers’ discriminatory hiring behaviour.

In this paper, we aim to contribute to the existing literature by illuminating the role of individuals’ experience with social unemployment stigma in shaping their behavioural responses towards being stigmatized. Specifically, we ask whether this experience helps to co-create the adverse re-employment chances by influencing the job search behaviour and job search success, as is suggested by several authors. To do this, we apply the concept of stigma consciousness to the context of unemployment. This concept explicitly focusses on whether stigmatized individuals internalize the expectation of being stereotyped in social interactions. Stigma consciousness is defined as the extent to which individual targets of specific stereotypes “focus on their stereotyped status and believe it pervades their life experiences” (Pinel et al. 2005 : 482). In other contexts of social stigma (e.g., gender, sexual orientation, or disabilities), it has been shown that the degree to which individuals perceive themselves to be subjected to stigmatization significantly influences their behaviour. For our analysis in the context of unemployment, we use data from the panel study “Labour Market and Social Security” (PASS) (Trappmann et al. 2013 ). A new scale was developed and implemented in 2013 to measure stigma consciousness among the unemployed (Gurr and Jungbauer-Gans 2013 ). We first corroborate that a higher stigma consciousness is associated with lower subjective well-being and lower health satisfaction. Based on expectancy-value theory, we find that those who are more stigma conscious have lower expectations of finding a job but highly value obtaining a job. However, our main result is that instead of leading to a reduced job search effort, those unemployed with a higher stigma consciousness are more likely to engage in an active job search, use more job search methods, spend more time searching for jobs, etc. Despite these positive associations with job search effort, we find that high stigma consciousness is not correlated with re-employment chances.

The remainder of this paper proceeds as follows. Section  2 discusses our definition of social stigma and stigma consciousness. Section  3 presents our theoretical considerations and how we derived our hypotheses. Section  4 presents a literature review regarding the role of unemployment stigma in labour market outcomes. Section  5 outlines our data, operationalization and analytical strategy. Section  6 presents the results of our study and discusses some limitations. Section  7 concludes with a summary and discussion of our results.

2 Definitions of stigma and stigma consciousness

The concept of stigma has received considerable interest in social science research, but as Link and Phelan ( 2001 ) remark, there is great variability in the definitions applied by different researchers. According to the seminal treatment of the topic by sociologist Erving Goffman ( 1963 : 3), stigma is “an attribute that is deeply discrediting” and leads to negative and often hostile behaviour towards the stigmatized. Goffman distinguishes three types of stigmatizing conditions: tribal identities (e.g., ethnicity, religion, nationality or gender), abominations of the body (e.g., physical disabilities or deformities) and blemishes of character (e.g., mental illness, addiction, or previous incarceration). Unemployment can be regarded as an example of stigma of character, where the stigmatized are considered individuals with a “weak will, domineering or unnatural passions, treacherous and rigid beliefs, and dishonesty” (Goffman 1963 : 4).

Several researchers have expanded upon Goffman’s definition of stigma. For example, Link and Phelan ( 2001 ) propose conceptualizing stigma as the interrelation of four processes. First, the differences among members of society are distinguished and labelled. Second, these differences are associated with negative attributes. Third, the labels attached to differences imply a separation of “them” from “us”. Fourth, the labelled person experiences a loss of status and discrimination. “Thus, we apply the term stigma when elements of labelling, stereotyping, separation, status loss and discrimination co-occur in a power situation that allows them to unfold” (Link and Phelan 2001 : 367). This definition has been criticized by Deacon ( 2006 ) because it sees discrimination as an integral part of the stigma concept. In contrast, she defines stigma independent of discrimination. If we apply the definition to the context of unemployment, stigma can be defined as the following social process: Unemployment is construed as preventable and controllable; “immoral” behaviours causing unemployment are identified; these behaviours are associated with carriers of the characteristic in other groups, drawing on existing social constructs of the “other”; the unemployed are thus blamed for their situation; status loss is projected onto the “other”, which may (or may not) result in disadvantage to them (adapted from Deacon 2006 : 421). In contrast to Link and Phelan ( 2001 , see also Besley and Coate 1992 ) Deacon ( 2006 : 421) points out that being stigmatized is not automatically associated with disadvantages caused by discrimination. Furthermore, stigmatization can lead to disadvantages even in the absence of discrimination because it can have negative consequences for the self-concept and actions of the stigmatized individuals.

The reason for disadvantages that are independent of discrimination is that the stigma is internalized by the stigmatized individuals and manifests itself in self-stigma Footnote 1 (Bos et al. 2013 ; Pryor and Reeder 2011 ). According to Stuber and Schlesinger ( 2006 ), self-stigma combines two aspects, i.e., identity and treatment stigma. Identity stigma refers to the internalization of negative labels and stereotypes by the stigmatized individual, resulting in negative self-characterizations. Treatment stigma, in turn, refers to expectations about negative treatment by others. Both identity and treatment stigma are part of the self-stigma and therefore to be distinguished from actual discrimination because both are based on the perceptions of the stigmatized themselves. “For example, administrative practices that are not inherently discriminatory (such as questions about personal finances or living arrangements) may be interpreted by potential recipients as such” (Stuber and Schlesinger 2006 : 935).

In the empirical analysis below, we apply the concept of stigma consciousness to the context of unemployment. Several authors (e.g., Taylor et al. 1994 , cited after LeBel 2008 ) have shown that there is a discrepancy between group discrimination and the extent to which individuals personally experience discrimination. While some stigmatized individuals do, others do not attribute negative outcomes to stereotypes and discriminations. To cover these differences, Pinel ( 1999 ) developed and validated a 10-item ‘Stigma Consciousness Questionnaire’ (SCQ) for several stereotyped groups (e.g., women, lesbians, gay men, African Americans, and Latinos/Latinas). Stigma consciousness reflects the extent to which individual targets of specific stereotypes “focus on their stereotyped status and believe it pervades their life experiences” (Pinel et al. 2005 : 482). According to Pinel ( 1999 ), consciousness of the stigma is a key determinant of the stigmatized individual’s behavioural reactions. Stigma consciousness is argued to increase the perception of being discriminated against and to heighten the belief that group membership influences social interactions and experience (Guyll et al. 2010 ). Negative feedback from others is more often interpreted as discriminatory. Thus, stigma consciousness is viewed as a mechanism mediating the association between group membership and negative outcomes.

3 Consequences of stigma of unemployment: theoretical considerations and hypotheses

Several scholars have examined the consequences of internalizing stigmatizing stereotypes, but the bulk of research focusses on contexts such as ethnicity, gender, medical conditions, sexual orientation or history of incarceration (see e.g., LeBel 2008 ). In this paper, we are interested in the stigma of unemployment and its consequences. Changes in subjective well-being and health are among the most obvious consequences of any type of stigma (Hatzenbuehler et al. 2013 ; Markowitz 1998 ; Rosenfield 1997 ), and the stigma of unemployment should be no exception. However, empirical evidence is scarce, but O’Donnell et al. ( 2015 ) found that anticipated stigma, which is a measure similar to stigma consciousness, has a negative impact on psychological distress and physical health.

We follow this strand of research and argue that stigmatization consciousness is directly connected to subjective well-being and health. Therefore, our first two hypotheses are concerned with the proposition that the stigmatized suffer from their status as unemployed more than the non-stigmatized as follows:

The higher the stigma consciousness among the unemployed, the lower their subjective well-being.

The higher the stigma consciousness among the unemployed, the lower their subjective health.

However, the main concern of the present analysis is the role of stigma consciousness for job search. According to Goffman ( 1963 ), an important dimension of the stigma influencing how it is perceived by the respective targets, is its visibility. As an example of a stigma of character (e.g., Gurr and Jungbauer-Gans 2017 ), unemployment is a stigma that is not highly visible and therefore often concealable. The unemployed are therefore “discreditable” instead of “discredited”. Thus, in social situations, the unemployed can often choose whether they disclose information regarding their status. This is not the case during the job search because to obtain re-employment, per definition, the unemployed have to disclose their status to other individuals.

Job search activities such as visiting the unemployment agency, asking friends for job leads, and attending job interviews make it difficult to conceal one’s status as unemployed and are experienced as humiliating and potentially lead to rejection (Letkemann 2002 ). Therefore, reducing job search effort could be a viable strategy to avoid being stigmatized. The literature on welfare stigma (cf., Andrade 2002 ) even assumes that some of the unemployed will forego welfare benefits they are entitled to in order to avoid their stigma being made visible (Yaniv 1997 ; Moffitt 1983 ; Loewenberg 1981 ). According to Sherman ( 2013 ), for those who do not have other financial options or find themselves unable to get a job despite their best efforts, the eventual acceptance of welfare benefits often leads to self-hatred, shame, and depression. Kerbo ( 1976 ) observes that welfare benefit claimants exhibit lower job search activity than non-claimants and attribute this behaviour to discouragement. He argues that those who felt highly stigmatized because they received welfare were also most likely to be the most passive. Heslin et al. ( 2012 ) develop a theoretical model that relates the labour market experience of members of ethnic minorities to the becoming discouraged workers, i.e., wanting to work but not looking for employment due to negative experiences. They argue that due to the stigma attached to the minority status (lazy, untrustworthy, etc.), they fare worse in the recruitment and selection process of employers. This experience will make them prone to become discouraged workers because among others, it leads to learned helplessness (Seligman 1975 ), that is, they become passive and no longer try to improve the negative situation that they perceive as uncontrollable. According to Abramson et al. ( 1978 ), individuals are more disposed to react with learned helplessness if they see the reason for their negative experience in themselves.

According to Pinel ( 1999 ), an important predictor of whether individuals avoid situations in which stigma is salient is stigma consciousness. She found that those with higher stigma consciousness are more likely to avoid situations where they expect to be stereotyped. For example, compared to those with low stigma consciousness, female workers with high stigma consciousness were more likely to intend to and actually leave their job (Pinel and Paulin 2005 ). Stigma consciousness can cause greater experience of stereotype threat, raise the level of perceived prejudices and feelings of rejection, and reduce the person’s sense of control and self-esteem (Wang et al. 2012 ). Wang and her colleagues show that a person with high stigma consciousness more often views subtle bias as discrimination and becomes angrier.

Thus, there seems to be a consensus in the literature that stigmatization and specifically stigma consciousness should result in reduced job search efforts. However, there are also some indications in the literature that this view might be too one-sided. Based on stress theory, Miller and Kaiser ( 2001 ) note that individuals tend to have two options that the authors call engagement and disengagement behaviour. While the above discussion highlights the potential of social stigma of unemployment to result in disengagement or avoidance behaviour, in other contexts of stigmatization (e.g., mental health), it has been shown that some stigmatized individuals choose engagement behaviour, e.g., raising awareness of social stigma or in the form of problem solving. For example, the above cited Wang et al. ( 2012 ) also find that the high stigma conscious are more often willing to engage in collective action. By directly referencing to the stigma of unemployment, Bretschneider ( 2014 ) draws a similar conclusion using group identity theory (Tajfel and Turner 1979 ). She argues that to the degree that group boundaries are perceived as permeable and social mobility between groups is possible, individuals are more likely to attempt to obtain a more positive sense of self by changing groups. An important way for the unemployed to achieve this goal is by proactively engaging in job search.

To develop testable predictions regarding the potentially ambiguous effect of stigma consciousness on the job search effort, we can draw upon the expectancy-value theory (Vroom 1964 ). This theory was first applied to the job search process by Feather ( 1982 ), and here we extend this theory to incorporate stigma consciousness among the unemployed. Expectancy-value theory assumes that the level of job search effort is determined by two factors, i.e., expectations and value. The first determinant of job search effort expectation refers to the expectations that specific behaviour, such as the job search in our case, will result in the desired outcome, such as obtaining gainful employment. Expectancy-value theory predicts that individuals with high expectation that their job search will be successful will exert more effort in the job search. Regarding the social stigma of unemployment, we hypothesize that their expectations of meeting negative stereotypes during the job search lead the unemployed with higher ratings on the stigma consciousness scale to have lower expectations of succeeding in the job search.

The higher the stigma consciousness among the unemployed, the lower their expectations of their successful re-employment chances.

Value is the second determinant of effort and refers to the degree to which the desired outcome of the job search, i.e., becoming re-employed, is valued by the unemployed. The central behavioural assumption is that the higher the subjective value of the desired outcome, the higher the effort exerted to obtain this outcome. We assume that unemployed individuals with high levels of stigma consciousness are more likely to place a high value on re-employment possibly because the stigmatized are more likely to suffer from adverse consequences due to their status as unemployed compared to other unemployed persons. Our next hypothesis is as follows:

The higher the stigma consciousness among the unemployed, the more value they place on employment.

Given that expectancy-value theory assumes that both expectations and value determine the job search effort, we consider predictions regarding the effect of stigma consciousness on job search effort ambiguous. Regarding expectations, high stigma consciousness should result in lower job search effort; however, regarding the value of re-employment, a higher job search effort is expected. Thus, the overall effect depends on the relative importance of either of the two factors, resulting in two competing hypotheses.

The higher the stigma consciousness among the unemployed, the lower the job search effort.

The higher the stigma consciousness among the unemployed, the higher the job search effort.

Several scholars assume that the actual re-employment chances are crucially determined by the intensity of the job search effort as follows: the higher the job search effort, the higher the probability of obtaining adequate and acceptable job offers (see e.g., Mortensen 1986 ). Therefore, for job search success, we also posit two opposing hypotheses. We expect that stigma consciousness has negative effects on job search success in the case of reduced effort, but in contrast, we expect positive effects in the case of increased job search effort.

The higher the stigma consciousness among the unemployed, the lower is the job search success.

The higher the stigma consciousness among the unemployed, the higher is the job search success.

4 Literature review

Empirical evidence regarding the effect of unemployment stigma on job search behaviour is scarce. In contrast, there is ample and convincing evidence regarding discrimination of the unemployed in employers’ hiring behaviour and so-called true unemployment state dependence. In the following, we review the part of the literature that explicitly connects their results to unemployment stigma as the key explanatory mechanism.

Several studies find that firms are reluctant to fill vacancies with an unemployed job seeker and explain this finding by the prevalence of unemployment stigma. In a correspondence experiment, Oberholzer-Gee ( 2008 ) observes that the callback rates for short-term unemployed individuals are even higher than those for employed job seekers, but if applicants are long-term unemployed, the callback rates decline. He finds that even after controlling for further characteristics of the supply side of a job offer, the duration of unemployment has a crucial negative effect on the likelihood of being invited to a job interview. Several studies have replicated and extended his results. Nüß ( 2017 ) finds that callbacks decline after 10 months of unemployment. Eriksson and Rooth ( 2014 ) do not find that past unemployment spells will lead to differential treatment regarding callbacks, nor will current short-term unemployment for up to 9 months. However, after this point, stigmatization effects arise, and callback rates decline. The authors also observe stronger stigma effects for men than for women. Ghayad ( 2014 ) reports that after more than 6 months of unemployment, work experience will no longer matter. The generally positive effect of an unemployed applicant’s industry-specific human capital disappears, and callback rates are similar to those for unemployed persons without industry-specific human capital. Kroft et al. ( 2013 ) find that discrimination against the unemployed is common if labour markets are tight, and callback rates already start to decline after 6 months of unemployment. However, some studies do not observe any stigma effects. Nunley et al. ( 2017 ) find no effects of unemployment on callback, irrespective of labour market tightness. Similarly, Farber et al. ( 2015 ) do not find that unemployment reduces callback, but they do find reduced callback rates for applicants over the age of 50. In a recent experiment, van Belle et al. ( 2017 ) also conclude that unemployment duration serves as a sorting criterion because employers view it as a signal of low motivation.

In a related stream of research, stigma is considered the reason for the so-called true state dependence in the duration of unemployment, i.e., the effect of past unemployment on one’s current labour market status (e.g., Arulampalam 2001 , 2002 ; Arulampalam et al. 2001 ; Heckman and Borjas 1980 ). Spurious state dependence means that unobserved differences between the unemployed create the impression that re-employment chances diminish with longer unemployment durations. By contrast, true state dependence means that the longer an individual is unemployed, the lower the chances of finding re-employment (e.g., van den Berg and van Ours 1996 ). One explanation provided by the literature for how true state dependence arises is the stigmatization of the (long-term) unemployed by employers because they consider unemployment as a signal of low motivation or productivity (Vishwanath 1989 ).

For example, Biewen and Steffes ( 2010 ) find significant effects of past unemployment on the present unemployment risk; these effects decrease when unemployment rates are high. The authors consider this to be evidence of stigma effects because individual unemployment is less likely to be interpreted as a negative signal if unemployment is high and vice versa (see also Omori 1997 ). Ayllón ( 2013 ) reports similar results but also finds that if unemployment rates are high, discouragement effects counterbalance the lower stigma effect to some extent.

A third strand of literature is addressing the unemployed social experience as stigmatized but is more strongly focussed on psychological coping mechanisms or communication strategies of the unemployed as a reaction to their status as stigmatized. Knabe et al. ( 2018 ) analyse whether social networks can be a substitute for stigmatized unemployed to feel respected and appreciated. Gurr and Jungbauer-Gans ( 2017 ) focus on whether or not the unemployed have internalized society’s view that the unemployed themselves are to be blamed for their situation. Similar, but with a stronger focus on job search requirements, Hirseland and Ramos Lobato ( 2014 ) found that the unemployed react to stigmatizing media discourse (“lazy unemployed”) by either taking over the public opinion, by seeing themselves as an exception to the rule or by complying with the public demands for intensified job search effort. Research using the same data as that used in the following analysis yielded the following results: Lang and Gross ( 2017 ) find that unemployment stigma consciousness is determined by the strength of deviation, the scope of the norm’s application and the intensity of formal social control; Gurr et al. ( 2018 ) find no effects of unemployment benefit sanctions on stigma consciousness; and Linden et al. ( 2018 ) find that being exempted from job search requirements due to ill health does not reduce stigma consciousness among the unemployed.

Overall, empirical evidence regarding how the social stigma of unemployment is related to job search attitudes, behaviour and re-employment success is scarce. To the best of our knowledge, the only studies intersecting with ours are a qualitative data analysis performed by Hirseland and Ramos Lobato ( 2014 ) and a quantitative paper published by Kerbo ( 1976 ); however, in both studies, no formal tests of these relationships were conducted.

5 Data and method

5.1 data and operationalizations.

In the following analysis, we use the German household panel study PASS (Trappmann et al. 2013 ), which began in 2007, and at the time of the writing of this manuscript, ten waves were available. PASS consists of two almost equally large subsamples, a probability sample drawn from all long-term unemployed persons registered with the German federal employment services and a random population sample. We use both samples for our analysis, but because our focus is on the unemployed, the registered unemployed sample dominates our analytical sample. Both subsamples of the PASS were refreshed several times and survey-provided weights are used in the analysis below to account for panel attrition. PASS collected data regarding (un-)employment histories retrospectively, and for each wave, detailed information about the current employment or unemployment situation is available.

Our operationalization of unemployment stigma relies on a scale that measures stigma consciousness among the unemployed who were part of wave 7 of PASS (Gurr and Jungbauer-Gans 2013 ). Footnote 2 This scale builds upon Goffman’s stigma concept and adapts a rather general psychological concept of gender stigmatization for the case of the unemployed (Pinel 1999 ). However, it also uses insights from other concepts and definitions described in the theoretical section of this article (e.g., Link and Phelan 2001 ). We exclude the item “I am trying to find a job as quickly as possible” because this item measures a concept similar to one of our dependent variables job search effort. We construct an index by aggregating all but one of the above mentioned stigma item. We normalize the values of the scale to range from 0 (no stigmatization) to 10 (maximum stigmatization). Table  3 in the appendix provides an overview of the items used in the scale. With a value of Cronbach’s alpha of 0.73, this scale exhibits acceptable reliability. The average value of the scale amounted to 5.09 with a standard deviation of 1.80, indicating on average medium stigmatization and considerable variation between individuals.

To test Hypotheses 1 and 2, we use the PASS questions regarding life and health satisfaction, both of which are measured on an 11-point scale (“In general, how satisfied are you currently with your life overall?”; “How satisfied are you today with your health?”). To test Hypothesis 3, we use a self-assessment of the unemployed’s employment chances (“What do you think are your chances to find a new job in the next 6 months? good/quite good/quite bad/bad”), which was presented to all unemployed individuals regardless of whether they searched for a job. Footnote 3 To test Hypothesis 4, we use a factor score obtained from a set of four items measured on a four-point scale concerning non-monetary and monetary motivations to work as the dependent variable. We maintained the three items Footnote 4 reflecting non-monetary motivation because these items loaded on a common factor, whereas the fourth item constituted an independent factor.

In Hypothesis 5a/b, job search effort is the dependent variable. We use several different measures available in PASS to cover a wide range of potential indicators of higher or lower job search effort (see Table  4 in Appendix for a detailed overview). Our first and most basic indicator uses binary information regarding whether the respondent actively searched for a job within the prior 4 weeks. A second indicator is the sum of all job search methods actively used in the last 4 weeks. The third indicator uses additional information on the intensity with which a specific job search method was used. We use a sum score of all job search intensities from all job search methods used by the respondent. If a method is not used, intensity is coded as 0. Indicator number four is the number of hours spent searching for a job. Indicator five measures the number of times during the last 4 weeks a respondent used one of the following ways to apply for a job: replied to job advertisements, placed an “employment wanted” advertisement with the newspaper, asked for a job at the company itself or submitted an application even though no job opening had been advertised. We set all indicators of the job search effort to zero for those respondents who left the labour force (e.g., “homemaker”) because per definition, these individuals are not searching for a job, and not including them would systematically excludes the most discouraged unemployed.

In Hypotheses 6a/b, we are interested in the job search success. This is measured first as the number of job interviews a respondent had during the last 4 weeks. Second, we construct a dummy variable that assumes the value of one for those who hold a job and zero for those who are unemployed, have withdrawn from the labour force or are in any other state (e.g., retirement).

5.2 Sample selection and analytical design

Because stigma consciousness is measured only in one single wave, our analytical sample is in principle a cross-section. However, the stigma consciousness scale is embedded in an ongoing panel study. Therefore, while stigma consciousness is measured in wave 7, the outcome variables are obtained from wave 8 of the PASS study. We include several important covariates (often time-constant or measured at wave 7) as control variables to account for socio-demographic information, including age, age squared, gender, marital status, migration background, educational attainment, place of residence (East/West Germany), household size, and household income, and a dummy variable representing the general population and the welfare benefit sample. The descriptive statistics (mean, standard deviation, minimum and maximum, and number of cases) of the dependent and independent variables are shown in Table  5 in Appendix .

In addition, we include information regarding previous employment status (unemployed, employed, or out of the labour force), which was measured in wave 6. Furthermore, we included unemployment duration and the number of previous unemployment spells, which were measured at wave 7, as controls because these variables might influence the level of stigma consciousness. However, because we have no information regarding the level of stigma consciousness at the beginning of the unemployment episode, the current unemployment duration can also be an outcome of stigma consciousness, especially if reduced job search effort is the dominant reaction of the stigmatized. Alternatively, we can also consider unemployment duration and previous unemployment as alternative measures of unemployment stigma, albeit potentially confounded by the depreciation of human capital. Because of these alternative views, we also test our hypotheses without including the unemployment duration variables in the model. We find that the choice of including unemployment duration did not substantially influence our results (see Tables  6 and 7 in Appendix ). Footnote 5

In summary, our analytical sample is selected as follows. From the 14,449 respondents in wave 7, we restrict ourselves to 2448 respondents who were eligible to answer the stigma consciousness scale items. The main criterion for eligibility is registered unemployment during wave 7. We exclude individuals with missing stigma scale values, reducing the sample to 2286 individuals. Finally, we exclude those who dropped out in wave 8 (we used the PASS provided weights to account for this panel attrition), resulting in 1779 cases remaining. For Hypothesis 1, we arrive at our analytical sample of 1278, which included individuals who were still unemployed or moved to the silent reserve (“homemaker”) at the time of wave 8. For Hypothesis 2, the same sample is used if the number of job interviews is the dependent variable. If job-finding is the dependent variable, the sample size increases to 1573 because those who found employment between waves 7 and 8 will be included also. To avoid any further loss of cases and, thus, precision in our estimate, we multiply impute (Rubin 1987 ) the missing data in any of two cases. First, we impute the data if the data were missing due to item non-response. Second, we impute the data if the data were missing for respondents who entered the panel survey only during wave 7 because in this case, no information on several variables from wave 6 was available.

With our estimation strategy we follow standard procedures using liner regression, except for that we also use linear regression analyses for binary and ordinal outcome variables because the estimation of marginal effects after multiple imputations is cumbersome (see STATA multiple imputation reference manual release 13: 77). Thus, linear regressions are conducted to analyse life and health satisfaction (ordinal), re-employment expectation (ordinal), non-monetary employment motivation (continuous), active job search (binary), re-employment (binary) and job search intensity (continuous). All other outcomes are count data variables, i.e., hours spent searching, the number of job search methods, the number of applications and the number of job interviews, and we use negative binomial count data regressions, a method similar to Poisson regression, but more generally applicable (Cameron and Trivedi 2010 ).

6.1 Hypotheses tests

This section presents the results of our empirical analysis. Please note that in the following, the terms “effect” and “coefficient” are used interchangeably and without intending to imply causality. See Sect. 7 for a discussion regarding what speaks for or against a causal interpretation of our regression results. In Table  1 , Models 1–4 present the results of Hypotheses 1–4. Hypotheses 1 and 2 posit that people suffer from unemployment stigma, and therefore, higher stigma consciousness is associated with lower subjective well-being and lower health. As shown by Model 1 in Table  1 , the coefficient of stigma consciousness is negative and statistically significant. The coefficient is  −0.319, indicating that a one point (or equivalently a 10%) increase in stigma consciousness is associated with ca. 0.32-point reduction on the 11-point life-satisfaction scale. This reduction is only a slight one, because it accounts for a small part of the standard deviation of the dependent variable (see Table  5 in Appendix ). The results of health satisfaction (Model 2 in Table  1 ) are similar, although the coefficient (0.196) is smaller. As shown, the respective coefficient is also statistically significant and negative, indicating that high stigma consciousness is associated with lower health satisfaction. Therefore, the data support hypotheses 1 and 2.

Hypothesis 3 focusses on the unemployed’s job expectations and posits that a negative association exists between stigma consciousness and self-perceived re-employment chances. Based on Model 3 in Table  1 , the coefficient of the stigma consciousness scale on the 4-point scale of self-assessed chances of re-employment is − 0.047, which as predicted, is negative and statistically significant. The higher the unemployed’s stigma consciousness, the lower their expectations of transitioning from unemployment to employment. Hypothesis 4 concerns the positive relationship between unemployment stigma and the value placed on re-employment. The respective coefficient (Model 4 in Table  1 ) is 0.125, which is statistically significant. Thus, Hypotheses 3 and 4 are also supported by our data.

In Table  2 , the results of Hypotheses 5a/b and 6a/b are presented. In both hypotheses, the theoretical prediction is ambiguous, allowing for both positive and negative associations between unemployment stigma and job search effort and job search success.

In Model 1 in Table  2 , the dependent variable is active job search. Instead of the negative regression coefficient expected from Hypothesis 3a, Model 1 reports that stigma consciousness has a positive effect on whether unemployed individuals actively search for a job, and this effect is significant at the 0.1% level. For every additional point on the ten-point stigma consciousness scale, the probability of actively searching for a job increases by 2.6% points. Thus, two hypothetical individuals who are located at the opposite ends of the stigma consciousness scale could differ by 26% points in their probability of engaging in active job search, whereas the average probability of an active job search is approximately 52% in our analytical sample (see Table  5 in Appendix ).

Model 2 in Table  2 extends the binary outcome variable by not only looking at active search but at the number of job search methods used during job search. With each additional point on the stigma consciousness scale, the number of methods significantly increases on average by 0.067, which is also only a slight increase. Model 3 then takes into account that job search intensity can vary within each method used for job search and uses the sum score of all the respondents’ values for each method used as the dependent variable. Again, we observe a significantly positive but rather small effect. Our next indicator of job search effort is the number of hours spent searching for a job, where the coefficient of stigma consciousness is significant only at the 5% level. On average, an additional point on the stigma scale results in approximately 4.6 min per week (60 min * 0.077) more time spent searching for a job. For Model 5, the number of times the unemployed applied for a job is the dependent variable. We observe a positive and statistically significant coefficient of 0.069, indicating that a one-point increase in the stigma variable is associated with ca. 0.07 more applications.

Our empirical analysis based on several different indicators of job search effort finds empirical support for Hypothesis 5b, i.e., stigma-conscious unemployed individuals increase their job search effort.

Turning to Hypothesis 6, we have two different indicators of an unemployed individual’s job search success. The first indicator focusses on the number of job interviews during the last 4 weeks of those still unemployed (Model 6). Here, the association with stigma consciousness is positive but the coefficient is small and statistically insignificant. Considering Model 7 in which the dependent variable is actual re-employment, we find that stigma consciousness is slightly negatively related to re-employment probability, but again, this effect is not statistically significant.

6.2 Robustness checks

Here, we report some robustness checks and tests of potential alternative explanations for our empirical results.

A first alternative explanation concerns the relationship between stigma consciousness and job-search effort. We cannot exclude the possibility that the level of job search effort could lead to high stigma consciousness and not the other way around. For example, we might assume that those who are starting their job search with above average job search effort are more prone to interpret their experience of continued unemployment as the result of stigmatization. In both cases, a positive regression coefficient of job search effort on stigma consciousness will arise in cross-sectional data. However, if the regression coefficients reflect such differences in job search effort at the beginning of the unemployment spell, the positive relationship should disappear once these initial differences are accounted for. Thus, as a robustness check, we measure the job search effort at the start of the unemployment episode. Specifically, we use the job search effort from the start of the unemployment spell or if observations remain censored because the respondents entered the panel survey during unemployment, job search effort from the first observed wave. Footnote 6 The underlying idea here is that job search effort during the early stage of the unemployment spell is not influenced by stigma consciousness because it is defined as the expectation of being subjected to negative stereotypes during the job search. To the degree that these expectations are based on actual experiences, measuring job search behaviour at the beginning of the job search suggests that only minimal experiences have been gathered and that the level of job search effort should still be relatively independent of such negative experiences.

Unfortunately, information regarding the initial job search effort is only available for active job search, number of job search methods and number of job interviews. However, at least for these outcomes, we can perform a robustness check by including the respective initial values as additional control variables. We extend this procedure to the analysis of life and health satisfaction and we control for the initial value at the first observed wave in unemployment, too. As shown in Appendix , Table  8 , including the initial levels of the dependent variables reduces the size of the coefficients of stigma consciousness in all cases, but the basic conclusions remain unchanged. The coefficients tend to be smaller but are still positive and statistically significant. However, using past values of the dependent variable (so-called lags) has been criticized not only in the context of panel data (Nickell 1981 ) but also, more recently, in pooled cross-sectional data analysis by Vaisey and Miles ( 2017 ). Thus, this strategy might not be able to remove the bias entirely. Footnote 7

A second alternative explanation is concerned with whether increasing the job search effort is really based on the autonomous decision of the unemployed as suggested by our theoretical framework. In contrast, as posited by self-determination theory (Ryan and Deci 2000 ), increased effort can also be the result of externally controlled behaviour. Following Hirseland and Ramos Lobato ( 2014 ), we might assume that the increased job search effort of those unemployed experiencing high stigma consciousness could be the result of their attempts to comply with the demands placed upon them by case workers at local unemployment offices. To “activate” the unemployed, case workers often monitor their job search effort and sanction those who do not comply with what in Germany is called “Mitwirkungspflicht” (duty to cooperate). If this monitoring is successful such that it leads to increased job search efforts but simultaneously makes the unemployed feel depreciated and stereotyped as the “lazy unemployed”, high stigma consciousness could arise as a by-product of such monitoring practices. Consequently, the positive effect of stigma consciousness could be spurious and solely based on the level of monitoring through unemployment offices and their case workers as a common cause. To test this alternative explanation, we included information on whether an integration agreement was signed between the unemployed and the case worker as an additional covariate. Furthermore, we included a factor score measuring the self-perceived quality of the unemployeds’ experiences with the job center and their staff members. The factor sore was derived from a factor analyses on items of a respective items set. Footnote 8 We found that the inclusion of these variables did not substantially change the results (see Tables  9 and 10 in Appendix ).

Third, a further alternative explanation, especially for the results concerning job search effort, is social desirability bias. Social desirability bias refers to survey respondents’ tendency to adapt their answers towards what they perceive to be the social norm. To explain the positive association between stigma consciousness and effort, social desirability bias must upward bias the reporting of both job search effort and stigma consciousness, net of all covariates, such as age, gender and education. Clearly, there is a danger that individuals tend to overstate their job search effort in an interview situation because given the public debate regarding the lazy unemployed, high effort to end unemployment is normatively more acceptable than low effort. However, regarding stigma consciousness, the nature of social stigma is that it is rather hidden in social interactions than overemphasized. Therefore, social desirability bias is more likely to lead to a downward bias in reporting the level of stigma experienced during unemployment. Footnote 9 Overall, this logic argues against social desirability bias as an alternative explanation.

Fourth, in accordance with our theoretical framework, many of our dependent variables are based on self-assessment and measure respondent’s perceptions, e.g., of being subjected to stereotypes or of their labour market chances. Therefore, personality traits such as self-efficacy or the “Big 5” personality traits might be a common cause for the negative relationship of stigma consciousness and subjective well-being and/or with the positive association with job search effort. Therefore, in a further robustness check, we conducted our analysis controlling for these traits. Self-efficacy is defined as generalized self-efficacy and was measured as sum score obtained from five items Footnote 10 measured in wave 7. These items focus on the personal assessment of one’s own competences to deal with difficulties and barriers in everyday life (Schwarzer and Jerusalem 1995 , 1999 ) and were used as sum indices. Further personality traits were measured using the 21-item version of the Big Five Inventory (BFI-K) which covers rather broad personality dimensions extraversion, agreeableness, conscientiousness, neuroticism and openness to experience (Rammstedt and John 2005 ). Those traits are only available in wave 5 and for those respondents in our sample who did not already participate in that wave, values had to be multiply imputed. We found that even if some of the personality traits are significantly correlated with some outcomes, including self-efficacy or the “Big 5” personality traits does not substantially change the results. Whereas the coefficients of stigma consciousness for well-being and health become smaller, those for job-search effort even slightly increase (see Tables  11 and 12 in Appendix ).

Finally, the Appendix also documents that the results without multiple imputation are, except for higher standard errors, similar to those after multiple imputation (see Tables  13 and 14 in Appendix ).

6.3 Limitations

An important caveat of our analysis is that our results might only apply to the German context and need not necessarily extend to countries with different systems of social security or welfare traditions. In general, Germany is assumed to be characterized by a social security system that has a strong focus on status maintenance. For example, Paugam and Russell ( 2000 ) argue that due to the high importance of employment for social status in Germany, unemployment is likely to lead to social stigma. Within the German context, our analytical sample is characterized by a high share of long-term unemployed, mostly recipients of welfare benefits. In Germany, unemployment insurance benefit receipt is limited to 12 months for the general population and 24 month for workers 55 years or older. After the insurance benefits expire, the unemployed can receive means-tested basic income support. Basic income support consists of a flat rate, and the unemployed are only eligible for support if their household income is below a certain threshold. However, our indicator of stigma consciousness does not focus on the stigma of welfare receipt but on the general stigma of unemployment. Therefore, we cannot answer the question regarding whether a measure of stigma consciousness focusing more on welfare state dependency could lead to different results. In addition, due to our analytical sample restrictions, we focus on unsuccessful job searches, increasing the tendency to over represent the long-term unemployed. However, the long-term unemployed should be subject to stronger stigma than the short-term unemployed, and unsuccessful job searches should be more likely to lead to passivity. Therefore, notably, even in this analytical sample (average unemployment duration in wave 6 is slightly over 5 years), the association with job search effort is positive.

A second limitation concerns the results on actual re-employment. The results for job search effort and the number of job interviews on the one hand and re-employment chances on the other hand are observed on systematically divergent populations (the former is only observed among those who did not find re-employment), the interpretation that increased job search effort does not increase re-employment chances can thus be challenged. In addition for re-employment, a duration analysis might have been the more informative and appropriate method. However, even if our focal independent variable stigma consciousness is in principle time-varying, it was measured in PASS at only one point in time. This point in time is different for all respondents with respect to their previous unemployment duration. Given that unemployment duration influences stigma consciousness, it is crucial for our analysis to control for this variable. In a duration analysis, unemployment duration would already be the dependent variable, therefore controlling for elapsed unemployment duration until the stigma consciousness was measured would induce endogeneity. Therefore, we rely on the simpler logistic regression model, where we can control for unemployment duration. We acknowledge, however, that this discards a lot of information and is only a workaround.

7 Discussion and conclusion

An important strand of literature in labour market research is concerned with the effect of unemployment stigma on re-employment chances. This literature shows that the unemployed are stigmatized in the sense that they face serious disadvantages on the labour market, irrespective of their actual motivation, skills and behaviour and that unemployment can create a vicious cycle where unemployment begets further unemployment. In contrast, literature on the behaviour of the unemployed themselves is scarce and prone to assume that the typical reaction of the unemployed to being stigmatized is passivity and withdrawal behaviour.

Our paper is the first to present an empirical test of how stigma consciousness relates to job search attitudes and behaviour. We tested several hypotheses and interpreted the empirical evidence as follows. First, we corroborated the results of other studies showing that being stigmatized has negative consequences on individual well-being and health. Those who rated higher on the stigma consciousness scale also showed significantly lower life and health satisfaction. Based on expectancy-value theory, we found that being subjected to the social stigma of unemployment leads the unemployed to have lower expectations of successfully leaving unemployment. In contrast, the value of employment increases most likely because re-employment is an effective way to free the unemployed from unemployment stigma.

While the literature on unemployment suggests discouragement or withdrawal among the unemployed, we posit two possible reactions of the unemployed towards perceiving themselves stigmatized with respect to job search behaviour. If the low expected employment chances dominate the stigmatized’s behaviour, these individuals should decrease their job search effort. In contrast, if a higher value of employment dominates, the stigmatized should actually increase their job search effort. By presenting empirical evidence from several different indicators of job search effort, we found that high values of stigma consciousness were associated with more rather than less effort, e.g., in terms of engaging in an active job search, the number of hours spent searching or the number of job applications. We interpret this as evidence that the stigmatized unemployed are not characterized by passiveness or learned helplessness as the literature sometimes suggests. In contrast, we interpret this finding as evidence that the stigmatized suffer from their experience of joblessness even more than the average unemployed and aim to leave unemployment to change their social status and eliminate the social stigma. However, for individuals subjected to unemployment stigma, such increased effort does not lead to better actual re-employment chances. Despite its positive association with job search effort, we found no statistically significant association between stigma consciousness and the number of job-interviews or even re-employment probability.

Overall, we interpret our results as evidence that those who experience unemployment stigma during their job search suffer more from their experience of joblessness but do not tend to react with withdrawal and passivity. These individuals do not quit the job search and instead increase their effort by utilizing more methods to search for a job, spend more hours searching and send more job applications to potential employers. However, no empirical evidence supports that this increased effort helps the unemployed improve their situation by leaving unemployment. This result is in line with Gielen and van Ours ( 2014 ), who find that even if the unhappy unemployed search more actively for a job, it does not impact their unemployment duration. The results are also in line with Hohmeyer and Wolff ( 2018 ) who find that One-Euro-Job announcements increase job search effort but does not lead to higher employment probability.

Because we mainly rely on cross-sectional data, alternative explanations of the observed pattern of associations are possible. For example, high stigma consciousness could instead be a reaction of high-effort job seekers who become frustrated by the absence of re-employment success. Furthermore, pressure from employment offices might be a common cause of both high stigma consciousness and high job search effort. We attempted to test these alternative explanations as much as possible and found no evidence supporting these explanations over our own. However, we must acknowledge that these alternative explanations cannot be entirely dismissed given that there seems to be no bulletproof solution, especially regarding reverse causality, which is particularly true for cross-sectional data but in many regards also extends to longitudinal data (see e.g., Vaisey and Miles 2017 ). With longitudinal data, more sophisticated methods are available (cf., Leszczensky and Wolbring 2018 ), but these methods are also not without their own problems. Therefore, further research is needed to corroborate our results. Such research should preferably be based on longitudinal data that includes the measurement of unemployment stigma at several points in time, including the beginning of unemployment. To gain such data, refining the existing scale could be worth the effort to obtain a shorter scale that is more easily incorporated into panel surveys to measure unemployment stigma.

Availability of data and materials

The datasets analysed in the current study are available in the Forschungsdatenzentrum der Bundesagentur für Arbeit (BA) im Institut für Arbeitsmarkt- und Berufsforschung (IAB), https://fdz.iab.de/de/FDZ_Individual_Data/PASS.aspx .

Other manifestations are public stigma (shared attitudes and behaviour of a society towards the stigmatized), structural stigma (legitimization of a social stigma by being embedded in a society’s institutions) and stigma by association (people’s reaction of being associated with a stigmatized person) (Pryor and Reeder 2011 ).

The scale was originally designed to derive different distinct factors of stigmatization and distinguish between factors pertaining expectations with respect to other unemployed persons (in-group) and the general population (out-group) and strategies of action (Gurr and Jungbauer-Gans 2013 ). However, neither the pretest of the study nor the main study of PASS wave 7 confirms these theoretical expectations, though other non-congruent factors (social relations, avoidance of situations, pressure to act, awareness of prejudices) develop. In the PASS dataset factor analyses only confirms these factors to some extent. This could either be due to the low number of cases in the pretest (N = 104) or the fact that distinct factors are difficult to measure within the stigmatization framework. Therefore, and in line with Gurr et al. ( 2018 ) and Linden et al. ( 2018 ) we use a sum score for our analyses.

For several other items regarding job search expectations and attitudes, this was not the case.

Let us now deal with the topic of work and gainful employment. Regardless of whether you currently work or not: To what extent do you agree to the following opinions on work? Please think very generally about working in a job. Please tell me whether you “strongly agree”, “somewhat agree”, “somewhat disagree” or “strongly disagree” with these opinions. “Having work is the most important thing in life.”; “Work is important because it gives you the feeling of being a part of something/belonging.”; “I would also like to work if I didn’t need the money”. (Official translation provided by PASS).

Because unemployment duration is often seen to be negatively related to job-search effort, this stability might seem surprising. However, Schels and Bethmann ( 2018 ) found that for most unemployed, job search effort remains stable over time.

For example, if an individual entered unemployment during wave 4 and is still observed as unemployed during wave 7 (our main sample selection criterion), the indicator measures job search effort during wave 4. If the individual entered the panel survey during wave 5 as unemployed and was unemployed at least until wave 7, the job search effort is measured at wave 5. If the individual was employed before wave 7, the job search effort on the job is measured at wave 6.

In our application, lagging the dependent variable could pose a problem if in technical terms, large reverse causality bias and large bias due to unobserved confounders coincide. According to results from a Monte Carlo simulation by Vaisey and Miles ( 2017 ), if bias due to unobserved confounders is low to medium, the results of a regression with and without lagged dependent variables can be considered the lower and upper bounds, e.g., for the dependent variable “active job search (yes)”, the upper bound is 0.026 (Table  2 , Model 1) and the lower bound is 0.014 (see Table  8 , Model 3). However, we emphasize that there is an important difference between our strategy and the strategy criticized by Vaisey and Miles ( 2017 ). We do not simply lag the dependent variable for one period, which was the approach used by Vaisey and Mills ( 2017 ). In that case, we would still use a value of the dependent variable (job search effort) that has potentially been influenced by prior values of the focal independent variable (stigma consciousness). Instead, we aim to control for the initial (or at least the earliest observed) value of job search effort. Thus, we aim to measure the dependent variable at a point in its history when it is still “uncontaminated” by the focal independent variable. In the simulated data reported by Vaisey and Miles ( 2017 ), there is never such a point in the common history of the dependent and independent variables, which may explain why lagging does not work as intended. To clarify this distinction, we refer to “initial values” instead of “lags” of the dependent variables (see Table  8 in Appendix ).

How far do the following statements apply to your personal experience with the Job centre and their staff members? Please tell me whether these statements “Apply completely”, “Tend to apply”, “Tend not to apply” or “Do not apply at all”. (A) The staff dictate too much what I am to do; (B) They really want to help me there; (C) I expect that my situation will improve through the counselling; (D) They support me in finding a job again; (E) Only demands are put forward by them, but I don’t get any support; (F) I trust the staff; (G) My ideas are taken into consideration in counselling; (v) The staff members are friendly and helpful to me; One item (C) had to be excluded from the scale because of positive and negative correlations with the other items, two items (A and E) were reversed so that higher values indicate a more positive experience. The scale proved to be one-dimensional and is sufficiently reliable (Cronbach’s alpha 0.85). The scale was only presented to those unemployed that were actually registered with the job center, i.e., recipients of welfare benefits, thus the number of observations are slightly lower compared to the main analysis.

Since the PASS is a dual mode survey, one way to test for social desirability bias is to determine whether both job-search effort and stigma consciousness are higher among those engaged in face-to-face interviews compared to those engaged in telephone interviews (under the assumption that social desirable answers are more common face-to-face). However, we observe lower instead of higher job-search effort in face-to-face interviews, which is not consistent with social desirability bias.

Whenever unexpected difficulties or problems show up, there are different ways of reacting to that. We grouped some opinions about that topic here. Please tell me, whether to you those opinions “Apply completely”, “Tend to apply”, “Tend not to apply” or “Do not apply at all”. (A) I have a solution for every problem. (B) Even when things happen surprisingly, I believe that I can cope with them. (C) I have no difficulties in achieving my aims. (D) I always know how to act in unforeseeable situations. (E) I can always solve difficult problems if I try to.

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Acknowledgements

We thank the guest editor Katrin Auspurg, two anonymous reviewers, Katrin Hohmeyer and Jens Stegmaier for the helpful and constructive comments. Previous versions of the manuscript have also profited from comments by the participants of the Second PASS user conference in Nürnberg and the participants of the Session of the Sektion ‘Sozialpolitik’ at the 2018 DGS Kongress in Göttingen, especially Sigrid Betzelt and Carolin Freier. We also thank Huyen Nguyen Ngoc and Luca Reinold for help with preparing the manuscript.

Monika Jungbauer-Gans received funding for this article from the Deutsche Forschungsgemeinschaft under grant DFG JU 414/15-1.

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Krug, G., Drasch, K. & Jungbauer-Gans, M. The social stigma of unemployment: consequences of stigma consciousness on job search attitudes, behaviour and success. J Labour Market Res 53 , 11 (2019). https://doi.org/10.1186/s12651-019-0261-4

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The Fair Wage-Effort Hypothesis and Unemployment

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George A. Akerlof, Janet L. Yellen, The Fair Wage-Effort Hypothesis and Unemployment, The Quarterly Journal of Economics , Volume 105, Issue 2, May 1990, Pages 255–283, https://doi.org/10.2307/2937787

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This paper introduces the fair wage-effort hypothesis and explores its implications. This hypothesis is motivated by equity theory in social psychology and social exchange theory in sociology. According to the fair wage-effort hypothesis, workers proportionately withdraw effort as their actual wage falls short of their fair wage. Such behavior causes unemployment and is also consistent with observed cross-section wage differentials and unemployment patterns.

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The Natural Rate of Unemployment

• Definition: The natural rate of unemployment is the rate of unemployment when the labour market is in equilibrium. It is unemployment caused by structural (supply-side) factors. (e.g. mismatched skills)

Diagram showing the natural rate of unemployment

• The natural rate of unemployment is the difference between those who would like a job at the current wage rate – and those who are willing and able to take a job. In the above diagram, it is the level (Q2-Q1)
• Frictional unemployment
• Structural unemployment . For example, a worker who is not able to get a job because he doesn’t have the right skills
• The natural rate of unemployment is unemployment caused by supply-side factors rather than demand side factors

What Determines the Natural Rate of Unemployment?

Milton Freidman argued the natural rate of unemployment would be determined by institutional factors such as.

• Availability of job information . A factor in determining frictional unemployment and how quickly the unemployed find a job.
• The level of benefits . Generous benefits may discourage workers from taking jobs at the existing wage rate.
• Skills and education. The quality of education and retraining schemes will influence the level of occupational mobilities.
• The degree of labour mobility. See: labour mobility
• Flexibility of the labour market E.g. powerful trades unions may be able to restrict the supply of labour to certain labour markets
• Hysteresis . A rise in unemployment caused by a recession may cause the natural rate of unemployment to increase. This is because when workers are unemployed for a time period they become deskilled and demotivated and are less able to get new jobs.

Explaining Changing Natural Rates of Unemployment

In the post-war period, structural unemployment was very low. During the 1980s, the natural rate of unemployment rose, due to rapid deindustrialisation and a rise in geographical and structural unemployment.

Since 2005, the natural rate of unemployment has fallen.

• Increased labour market flexibility, e.g. trade unions less powerful.
• Privatisation has helped increased competitiveness of industry, leading to more flexible labour markets.
• Rise in self-employment and gig economy, have created new types of jobs.
• Increased monopsony power of employers, who have kept wage growth low, enabling firms to employ more workers.
• Harder to claim unemployment benefits.

Natural Rate of Unemployment in EU

Even during the period of economic growth 2000-2007, unemployment in Eurozone is higher than US and UK. This suggests the Eurozone has a higher natural rate of unemployment.

• Rigidity in EU labour markets e.g. minimum wages and the maximum working week
• Restrictions on closing factories and mandatory severance pay for workers made unemployed, and this makes firms more reluctant to set up in these countries.
• Higher degrees of unionisation resulting in wage rigidity.
• Generous benefits which lessen the pain of unemployment.
• Hysteresis effects . The cyclical recessions of the 1970s and 1980s had long-lasting effects resulting in more unemployment. However, this does not appear to have affected the UK
• Growing competition from Asian countries, lead to structural unemployment from increased job competition.

During 2012-14, the higher unemployment was partly due to lower rates of economic growth – caused by austerity, and deflationary pressures of the Eurozone single currency.

Reducing the natural rate of unemployment

To reduce the natural rate of unemployment, we need to implement supply-side policies, such as:

• Better education and training to reduce occupational immobilities.
• Making it easier for workers and firms to relocated, e.g. more flexible housing market and greater supply in areas of high job demand.
• Making labour markets more flexible, e.g. reducing minimum wages and trade unions.
• Easier to hire and fire workers.

NAIRU and Non-Accelerating Rate of Unemployment

• A very similar concept to the natural rate of unemployment is the NAIRU – the non-accelerating rate of unemployment.
• This is the rate of unemployment consistent with a stable rate of inflation. If you try to reduce unemployment by increasing aggregate demand, then you will get a higher rate of inflation, and the fall in unemployment will prove temporary.

NAIRU explained

• If there is an increase in AD, firms pay higher wages to workers in order to increase in output, this increase in nominal wages encourage workers to supply more labour and therefore unemployment falls.
• However, the increase in AD also causes inflation to increase and therefore real wages do not actually increased but remain the same. Later workers realise that the increase in wages was only nominal and not a real increase.
• Therefore they no longer work overtime. Therefore the supply of labour falls, and unemployment returns to its original or Natural rate of unemployment. It is only possible to reduce unemployment by causing an increase in the rate of inflation. Therefore the natural rate is also known as the NAIRU (non accelerating rate of unemployment.
• This model assumes workers do not correctly predict the rate of inflation but have adaptive expectations .
• Some economists argue workers will correctly predict higher AD causes higher inflation and therefore there will not be even a short term fall in unemployment; this is known as rational expectations .

Example of NAIRU

• In the above example, the natural rate of unemployment is 6%. If you try to reduce unemployment through increased demand, we get a temporary fall in unemployment, but higher inflation. (point A)
• However, this fall in unemployment is unsustainable and the short-run Phillips Curve shifts to SRPC2, and we move to (point C) and unemployment of 6%.
• Causes of Unemployment
• Voluntary unemployment
• Essay on: Natural Rate of Unemployment

Types and Theories of Unemployment

Unemployment is a situation in which an individual who is actively looking for employment is unable to secure one. The unemployment rate represents the number of these unemployed individuals in the workforce. Nonetheless, the prevalence of joblessness is one of the indicators of economic performance.

But what causes unemployment? What are the factors leading to high unemployment rates? Note that there are several categories or types of unemployment. Also, there are also different theories explaining the factors of or reasons behind the prevalence of unemployment within a given economy. Understanding these types and theories provide several explanations of the causes of unemployment.

Causes Based on the Categories and Types of Unemployment

The two broad categories of unemployment are voluntary unemployment and involuntary unemployment. Unemployment is voluntary when an individual willingly left his or her job in search of a new one. There are specific and more personal causes of voluntary unemployment including a desire to look for a higher salary, professional development, migration or relocation, and conflicts with the employer or colleagues, among others.

On the other hand, unemployment is involuntary when an individual has been laid off and has no other choice but to look for another job or when he or she is willing to work at the prevailing wage but remains jobless. A high rate of involuntary unemployment within an economy indicates a surplus of labor.

There are more specific types of unemployment apart from the aforementioned categories. These are structural unemployment, frictional unemployment, and cyclical unemployment. They provide a partial explanation of the causes of unemployment. Take note of the following:

• Structural Unemployment: Structural unemployment arises from the inability of the labor market to provide jobs for every member of the workforce because of a mismatch between the skills of the unemployed individuals and the skills requirements of specific jobs, or because of technological advances in which people are replaced by machines or their skills become outdated because of inability to keep up with latest trends.

• Frictional Unemployment: Frictional unemployment occurs when an individual is in between jobs. It corresponds to the time when this individual has to find another job after leaving his or her employer or when he or she is transitioning from one job to another. Note that this type of unemployment has some overlaps with structural unemployment. However, its defining characteristic is that it is usually short-lived.

• Cyclical Unemployment: Cyclical unemployment represents the offshoots of the business cycle and the boom-and-bust cycle of the economy. Keynesian economics explains that the frequent shifts in the business cycle and severe economic downturns, such as in the case of the Great Depression , lead to a shortage in aggregate demand that is not enough to provide employment for everyone who wants to work.

Major Theories Explaining the Causes of Unemployment

There are several theories of unemployment. Each theory provides an explanation of the factors and causes of unemployment.

1. Classical Unemployment Theory

Several schools of thought in economics such as classical economics and the Austrian School of economics argue that unemployment increases with government regulation or intervention. Their arguments collectively form the classical unemployment theory.

There are different ways regulations and interventions contribute to unemployment. For example, raising the minimum wage increases the labor costs more than the economic value of the actual labor, especially the value of jobs that merely require low competencies. Businesses respond to these minimum wage laws by refusing to hire more laborers to reduce their costs and optimize their operations.

Labor laws that restrict layoffs or downsizing, promote the security of tenure, and mandate the provision of benefits beyond wages are another example. Some businesses are less likely to hire or expand their workforce because of the legal and financial risks stemming from stringent labor laws.

Note that there are other theories related to the classical unemployment theory. These are implicit contract theory and efficiency wage theory.

2. Implicit Contract Theory

Greek macroeconomist Costas Azariadis and American economist Joseph Stiglitz introduced the implicit contract theory of unemployment in 1983. They developed this theory to explain why there are quantity adjustments or layoffs instead of price adjustments or wage adjustments in the labor market, especially during economic downturns. In other words, this theory tries to explain the primary cause of unemployment during a recession.

The implicit contract theory specifically claims that labor contracts and labor laws make it difficult for employers to cut the wage of their existing laborers. Hence, during a recession in which businesses need to save costs and optimize their operations, they usually choose to layoff their laborers or downsize their workforce instead of implementing wage reductions.

3. Efficiency Wage Theory

Renowned economist Alfred Marshall introduced the term “efficiency-wages” in his 1890 book “Principles of Economics” to indicate the equivalent wage per efficiency unity of labor. Proponents of this preliminary concept argued that employers should pay their workers differently based on their efficiency. In other words, a more efficient worker should have a higher wage than a less efficient worker.

The Marshallian concept evolved until it became the efficiency wage theory. It argues that businesses can operate more efficiently and become more productive if they provide wages above the equilibrium level. To be specific, increasing wages beyond the current labor benchmark could lead to better efforts from the employees, decrease employee turnover, attract highly competent employees, and promote the wellbeing of employees.

However, there is a downside to paying high wages beyond the equilibrium level. A high-paying employer will naturally attract more employees. Other employers might also offer higher payouts to keep up with the competition in the labor market. Unemployment might transpire if this practice becomes widespread because it not only makes labor costlier, thus compelling employers not to expand their workforce, but also creates unrealistic expectations in the labor market in which employees would not dare offer to work for lower wage and employers would rather stay away from hiring individuals offering work for a lesser payout because such might be an indicator of incompetence.

4. Keynesian Theory of Unemployment

Keynesian economics provides an alternative theory of unemployment. John Maynard Keynes and adherents of the Keynesian school of thought have explained that unemployment occurs when there is not enough aggregate demand in the economy. After all, if demands for goods and services decrease, then there is a lesser need for production and consequently, lesser needs for workers.

Take note that Keynesian economics also argues that market economies or capitalist economic systems naturally undergo a boom-and-bust cycle. Low aggregate demand and unemployment characterize the bust phase of the economy. Employment rate will normalize if the economy manages to reenter the boom phase. Hence, the Keynesian theory of unemployment serves as the basis for explaining cyclical unemployment because it describes the effects of frequent shifts in business and economic cycle on the labor market.

Because of the cyclical nature of unemployment and based on one of the primary tenets of Keynesian economics about the importance of government interventions, the Keynesian theory of unemployment recommends government-driven aggregate demand to reduce unemployment, promote consumer confidence, and revitalize production during economic recessions. Government intervention was demonstrated during the Great Depression and the 2008 Financial Crisis.

5. Marxian Theory of Unemployment

Somehow similar to the Keynesian theory, the Marxian theory of unemployment also believes that there is a relationship between economic demand and employment rate. In his manuscript “Theories of Surplus Value,” German philosopher and economist Karl Marx argued that unemployment is not only inherent in a capitalist system but also necessary.

Marx specifically argued that the purpose of the proletariat or the class of wage earners in a capitalist system is to provide a “reserve army of labor” necessary to create downward pressure on wages. He divided further this class into two subgroups: the surplus labor or the employed individuals and the under-employment or the unemployed individuals.

Nevertheless, members of this reserve army of labor compete for scarce jobs while driving wages lower and lower. The capitalist system allows capitalists or the owners of the means of production to manipulate the labor market by perpetuating unemployment and thus, limit the capacity of laborers to demand higher and fairer wages. The situation also demonstrates the theory of alienation of Marx in which workers are alienated from other workers, as well as from their species-essence.

FURTHER READINGS AND REFERENCES

• Azariadis, C. and Stiglitz, J. 1983. “Implicit Contracts and Fixed Price Equilibria.” Quarterly Journal of Economics . 98. DOI: 10.7916/D83R13GF
• Keynes, J. M. 1936. The General Theory of Employment, Interest, and Money . Britain: Palgrave MacMillan. ISBN: 978-0-230-00476-4
• Marshall, A. 1890. Principles of Economics . London: MacMillan
• Marx, K. 1859. “Theories of Surplus Value.” A Contribution to the Critique of Political Economy . Germany

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• Natural Unemployment Rate

Understanding Natural Unemployment

• Inflation and Unemployment

The Bottom Line

What is the natural unemployment rate.

Julia Kagan is a financial/consumer journalist and former senior editor, personal finance, of Investopedia.

The natural unemployment rate is the minimum unemployment rate resulting from real or voluntary economic forces. Natural unemployment reflects workers moving from job to job, the number of unemployed replaced by technology, or those lacking the skills to gain employment.

Key Takeaways

• The natural unemployment rate is the minimum unemployment rate resulting from real or voluntary economic forces.
• It represents the number of people unemployed due to the structure of the labor force, such as those replaced by technology or those who lack the skills to get hired.
• Natural unemployment is commonplace in the labor market as workers flow to and from jobs or companies.
• Unemployment is not considered natural if it is cyclical, institutional, or policy-based unemployment.
• Because of natural unemployment, 100% full employment is unattainable in an economy.

Investopedia / Theresa Chiechi

The term “ full employment ” is often a target to achieve when the U.S. economy is performing well. The term is a misnomer because there are always workers looking for employment, including new college graduates or those displaced by technological advances. There is always movement of labor throughout the economy that represents natural unemployment.

Unemployment is not considered natural if it is cyclical, institutional, or policy-based unemployment. An economic crash or steep recession might increase the natural unemployment rate if workers lose the skills necessary to find full-time work or if certain businesses close and are unable to reopen due to excessive loss of revenue. Economists call this effect “ hysteresis .”

Important contributors to the theory of natural unemployment include Milton Friedman , Edmund Phelps , and Friedrich Hayek , all Nobel prize recipients. The works of Friedman and Phelps were instrumental in developing the non-accelerating inflation rate of unemployment (NAIRU).

Natural unemployment can occur from both voluntary and involuntary factors. Hysteresis often occurs following extreme or prolonged economic events such as a recession, where the unemployment rate may continue to increase despite economic growth.

Causes of Natural Unemployment

Economists commonly held that if unemployment existed, it was due to a lack of demand for labor or workers and the economy would need to be stimulated through fiscal or monetary measures. However, history reveals the natural flow of workers to and from companies even during robust economic periods.

Full Employment

Full employment means 100% of the workforce is employed. History shows that this is unattainable as workers move from job to job. A zero unemployment rate is also undesired as it requires an inflexible labor market, where workers cannot quit their current job or leave to find a better one.

According to the general equilibrium model of economics, natural unemployment is equal to the level of unemployment in a labor market at perfect equilibrium. This is the difference between workers who want a job at the current wage rate and those willing and able to perform such work. Under this definition of natural unemployment, it is possible for institutional factors, such as the minimum wage or high degrees of unionization, to increase the natural rate over the long run.

Effects of Inflation on Unemployment

John Maynard Keynes wrote The General Theory of Employment, Interest and Money in 1936, leading many economists to believe there is a direct relationship between the level of unemployment in an economy and the level of inflation.

This direct relationship was formally codified in the Phillips curve , which showed that unemployment moved in the opposite direction of inflation . If the economy was to be fully employed, there must be inflation, and conversely, with periods of low inflation, unemployment must increase or persist.

The Phillips curve fell out of favor after the great stagflation of the 1970s. During stagflation, unemployment and inflation both rise , questioning the implied correlation between strong economic activity and inflation, or between deflation and unemployment.

What Is Natural vs. Cyclical Unemployment?

The cyclical unemployment rate is the difference between the natural unemployment rate and the current rate of unemployment as defined by the U.S. Bureau of Labor Statistics.

Why Is the Natural Unemployment Rate Significant?

The natural rate of unemployment is considered the lowest acceptable level that a healthy economy can sustain without creating inflation.

How Does a Recovering Economy Impact the Natural Unemployment Rate?

The natural rate of unemployment typically rises after a downturn in the economy or a recession as workers become more confident that they can move from job to job.

The natural unemployment rate is the minimum unemployment rate stemming from real or voluntary economic forces. It is common in the labor market as workers flow to and from jobs or companies, and because of natural unemployment, full employment is unattainable in an economy. Unemployment is not considered natural if it is cyclical, institutional, or policy-based unemployment.

• What Is Unemployment? Causes, Types, and Measurement 1 of 43
• What Does Termination of Employment Mean? 2 of 43
• What Is an Unemployment Claim? 3 of 43
• Unemployment Compensation: Definition, Requirements, and Example 4 of 43
• What Is Severance Pay? Definition and Why It's Offered 5 of 43
• The Layoff Payoff: A Severance Package 6 of 43
• 7 Considerations When You Negotiate Severance 7 of 43
• 7 Effective Ways to Prepare for a Layoff 8 of 43
• Unemployment Insurance (UI): How It Works, Requirements, and Funding 9 of 43
• How to Apply for Unemployment Insurance Now 10 of 43
• Who Doesn't Get Unemployment Insurance? 11 of 43
• What Was Private Unemployment Insurance? 12 of 43
• How to Pay Your Bills When You Lose Your Job 13 of 43
• Can I Access Money in My 401(k) If I Am Unemployed? 14 of 43
• All About COBRA Health Insurance 15 of 43
• Medical Debt: What to Do When You Can’t Pay 16 of 43
• Help, My Unemployment Benefits Are Running Out 17 of 43
• What Is the Unemployment Rate? Rates by State 18 of 43
• How Is the U.S. Monthly Unemployment Rate Calculated? 19 of 43
• Unemployment Rates: The Highest and Lowest Worldwide 20 of 43
• What You Need to Know About the Employment Report 21 of 43
• U-3 vs. U-6 Unemployment Rate: What's the Difference? 22 of 43
• Participation Rate vs. Unemployment Rate: What's the Difference? 23 of 43
• What the Unemployment Rate Does Not Tell Us 24 of 43
• How the Unemployment Rate Affects Everybody 25 of 43
• How Inflation and Unemployment Are Related 26 of 43
• How the Minimum Wage Impacts Unemployment 27 of 43
• The Cost of Unemployment to the Economy 28 of 43
• Okun’s Law: Economic Growth and Unemployment 29 of 43
• What Can Policymakers Do To Decrease Cyclical Unemployment? 30 of 43
• What Happens When Inflation and Unemployment Are Positively Correlated? 31 of 43
• The Downside of Low Unemployment 32 of 43
• Frictional vs. Structural Unemployment: What’s the Difference? 33 of 43
• Structural vs. Cyclical Unemployment: What's the Difference? 34 of 43
• Cyclical Unemployment: Definition, Cause, Types, and Example 35 of 43
• Disguised Unemployment: Definition and Different Types 36 of 43
• Employment-to-Population Ratio: Definition and What It Measures 37 of 43
• Frictional Unemployment: Definition, Causes, and Quit Rate Explained 38 of 43
• Full Employment: Definition, Types, and Examples 39 of 43
• Labor Force Participation Rate: Purpose, Formula, and Trends 40 of 43
• Labor Market Explained: Theories and Who Is Included 41 of 43
• What Is the Natural Unemployment Rate? 42 of 43
• Structural Unemployment: Definition, Causes, and Examples 43 of 43

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The Reporter

The Economics of Generative AI

Artificial intelligence (AI) is not a new field. The term was coined in 1956, but the field has only recently begun having significant effects on the economy.

Research in AI went through three eras. Early work focused primarily on symbolic systems with hand-coded rules and instructions. In the 1980s, expert systems, which consisted of hundreds or thousands of “if…then” rules drawn from interviews with human experts, helped diagnose diseases and make loan recommendations, but with limited commercial success.

Later, the focus shifted to machine learning systems, including “supervised learning” systems trained to make predictions based on large datasets of human-labeled examples. As computational power increased, deep learning algorithms became increasingly successful, leading to an explosion of interest in AI in the 2010s.

More recently, even larger models using unsupervised or self-supervised systems have become a major focus of the field. Large-language models (LLMs) — trained on massive amounts of text to simply predict the next word in a sequence — have astounded the public with their ability to produce meaningful and remarkable output. These systems have been found to outperform humans for a growing range of knowledge-intensive tasks: taking the bar exam, for instance. In addition, studies show that access to LLMs and other types of generative AI tools can help human workers improve their own performance.

In the past year, a growing body of work has explored how new AI tools might impact productivity in applications as diverse as coding, writing, and management consulting. 1

In research with Lindsey Raymond, we analyze the effects of generative AI on worker productivity in the context of technical customer support. 2 Our study is based on data from over 5,179 agents, about 1,300 of whom were given access to an LLM-based assistant that provided real-time suggestions for communicating with customers. The system, trained on millions of examples of successful and unsuccessful conversations, provided suggestions that the agents could use, adapt, or reject. The tool was rolled out in phases, creating quasi-experimental evidence on its causal effects.

We found significant improvements in worker productivity as measured by the number of customer issues workers were able to resolve per hour. Within four months, treated agents were outperforming nontreated agents who had been on the job for over twice as long.

On average, worker productivity increased by 14 percent. These gains were concentrated among the lowest quintile of workers, whether measured by experience or prior productivity, where there were productivity gains of up to 35 percent. In contrast, the top quintile saw negligible gains and, in some cases, even small decreases in the quality of conversations, as measured by customer satisfaction. This pattern is reflective of how the system is trained: by observing successful conversations, the system is able to glean the behavior of the most skilled agents and pass on these behaviors as suggestions to novice workers.

Did the system deskill the workforce? Another natural experiment suggests not. As with most large systems, there were occasional outages when the system unexpectedly became unavailable. Workers who had previously been using the system now had to answer questions without access to it, and nonetheless they continued to outperform those who had never used the system. This suggests that the system helped them learn, and retain, answers.

Our results point to the possibility that — in contrast with earlier waves of information technology that largely benefited higher-skill workers — generative AI technologies could particularly benefit workers at the lower or middle levels of the skills distribution. Drawing on these and other results, David Autor sees opportunities for the recent waves of AI to help rebuild the middle class by increasing the value of output from their labor. 3

Advances in AI technologies and algorithmic design can yield improvements beyond direct measures of productivity. For example, we saw evidence in our study that AI assistance improves the experience of work for treated agents, as measured by the processing of conversation transcripts: customers spoke more kindly to agents and were less likely to ask to speak to a supervisor. These effects were likely driven both by agents’ improved social skills and increased access to technical knowledge as a result of chat assistance.

Indeed, there is growing evidence that generative AI tools may outperform humans in an area traditionally considered a source of strength for humans relative to machines: empathy and social skills. One study of doctors’ responses to patient questions found that an LLM-based chatbot provided answers that were judged by expert human evaluators to be more detailed, higher quality, and 10 times more likely to be considered empathetic. 4

Finally, innovations in AI systems may further improve the functioning of current AI tools. For example, Li, Raymond, and Peter Bergman explore how algorithm design can improve the quality of interview decisions in the context of professional services hiring. They find that while traditional supervised learning systems — which look for workers who match historical patterns of success in the firm’s training data — select higher-quality workers relative to human hiring, they are also far less likely to select applicants who are Black or Hispanic. In contrast, reinforcement learning and contextual bandit models — which value learning about workers who have not traditionally been represented in the firm’s training data — are able to deliver similar improvements in worker quality while also distributing job opportunities more broadly.

While the effects of AI on productivity and work practices are now evident not only in a number of laboratory settings but also in business applications, it may take longer for them to show up in aggregate statistics. Brynjolfsson, Daniel Rock, and Chad Syverson discuss a set of reasons why the effects of AI might not quickly change aggregate productivity numbers. 5

For one thing, labor productivity is typically defined as GDP per hour worked. But GDP as it is traditionally measured may miss many of the benefits of an increasingly digital economy that creates free goods and makes them more widely available while also improving the quality, variety, or convenience of existing goods. An alternative metric, GDP-B, seeks to address these challenges by assessing the benefits of goods and services, not the amount spent. 6

Furthermore, general purpose technologies like AI are likely to experience a lag between their initial adoption and observable improvements in productivity. In a second study, Brynjolfsson, Rock, and Syverson model this “Productivity J-Curve.” 7 As with other types of information technology, the initial phase of AI adoption is characterized by time-consuming complementary investments, including the realignment of business processes, the integration of new technologies into existing workflows, and the upskilling of the workforce. As noted by Brynjolfsson and Lorin Hitt, these adjustments are costly and may create valuable intangible assets, but neither the costs nor the benefits are typically accounted for when measuring a firm’s output. 8 As a result, productivity as it is conventionally measured may initially be seen as stagnating or even falling. However, as these technological and organizational complements are gradually implemented, the productivity benefits of AI begin to materialize, marked by an upward trajectory in the J-curve.

The Productivity J-Curve model implies that productivity metrics fail to capture the full extent of benefits during the initial stages of AI adoption, leading to underestimation of AI’s potential.

The ultimate economic effects of generative AI will depend not only upon how much it boosts productivity and changes work in specific cases, but also on how much of the economy it is likely to affect. As noted by Daron Acemoglu and Autor, occupations can be broken down into specific tasks. 9 Applying this insight, Brynjolfsson, Tom Mitchell, and Rock look at 18,156 tasks in the O-NET taxonomy and find that most occupations include at least some tasks that could be automated or augmented by machine learning, though significant redesign would typically be required to realize the full potential of the technology. 10 Building on this work, Tyna Eloundou, Sam Manning, Pamela Mishkin, and Rock estimate that approximately 80 percent of the US workforce could have at least 10 percent of their work tasks either automated or augmented by the introduction of LLMs, while around 19 percent of workers could see at least half of their tasks affected. 11

Hulten’s theorem states that a first-order approximation of the productivity effects of a technology is the share of the economy affected multiplied by its average productivity impact. There is evidence that both the potential productivity impact and the potential share of the economy affected are significant in the case of generative AI, suggesting that the ultimate effects may be substantial, though, as implied by the Productivity J-Curve, they may take some time to be realized. 12

The field of economics itself is not immune to the effects of generative AI. Students of economics are using the tools to help with their assignments, requiring a rethinking of teaching methods. We and our colleagues are using the tools to help with research and writing; we used LLMs to help with aspects of the preparation of this article. Anton Korinek described six ways that LLMs can assist economists: ideation and feedback, writing, background research, data analysis, coding, and mathematical derivations. 13 Jens Ludwig and Sendhil Mullainathan go further, showing that AI models can be used to make the first stage of the scientific process — hypothesis generation — more systematic. 14

As discussed by Brynjolfsson and Gabriel Unger, important policy choices are emerging regarding AI’s effects on productivity, industrial concentration, and inequality. 15 For instance, on the question of inequality, the distinction between technology used for automation versus augmentation or, more formally, AI that substitutes for rather than complements labor, can have significant effects on the distribution of income and bargaining power. 16 Brynjolfsson has argued that either approach can boost productivity but has noted that a focus on human-like AI can lead to a “Turing Trap” by reducing worker bargaining power. As AI continues to grow in power, so too does the need for economic research to better understand how we can harness its benefits while mitigating its risks.

Researchers

More from nber.

“ Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence, ” Noy S, Zhang W. Science 381(6654), July 2023, pp. 187–192. “ Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality, ” Dell’Acqua F, McFowland III E, Mollick E, Lifshitz-Assaf H, Kellogg KC, Rajendran S, Krayer L, Candelon F, Lakhani KR. Harvard Business School Working Paper No. 24-013, September 2023.

“ Generative AI at Work, ” Brynjolfsson E, Li D, Raymond LR. NBER Working Paper 31161, November 2023.

“ Applying AI to Rebuild Middle Class Jobs, ” Autor D. NBER Working Paper 32140. February 2024.

“ Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum, ” Ayers JW, Poliak A, Dredze M, Leas EC, Zhu Z, Kelley JB, Faix DJ, Goodman AM, et. al. JAMA Internal Medicine 183(6), April 2023, pp. 589–596.

“ Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics, ” Brynjolfsson E, Rock D, Syverson C. NBER Working Paper 24001, November 2017.

“ GDP-B: Accounting for the Value of New and Free Goods in the Digital Economy, ” Brynjolfsson E, Collis A, Diewert WE, Eggers F, Fox KJ. NBER Working Paper 25695, March 2019.

“ The Productivity J-Curve: How Intangibles Complement General Purpose Technologies, ” Brynjolfsson E, Rock D, Syverson C. NBER Working Paper 25148, January 2020, and American Economic Journal: Macroeconomics 13(1), January 2021, pp. 333–372.

“ Beyond Computation: Information Technology, Organizational Transformation and Business Performance, ” Brynjolfsson E, Hitt LM. Journal of Economic Perspectives , 14(4), Fall 2000, pp. 23–48.

“ Skills, Tasks and Technologies: Implications for Employment and Earnings, ” Acemoglu D, Autor D. NBER Working Paper 16082, June 2010. Published as “Chapter 12 - Skills, Tasks and Technologies: Implications for Employment and Earnings” in Handbook of Labor Economics 4(B), 2011, pp. 1043–1171.

“ What Can Machines Learn, and What Does It Mean for Occupations and the Economy? ” Brynjolfsson E, Mitchell T, Rock D. AEA Papers and Proceedings 108, May 2018, pp. 43–47.

“ GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models, ” Eloundou T, Manning S, Mishkin P, Rock D. arXiv , August 2023.

“ Machines of Mind: The Case for an AI-Powered Productivity Boom, ” Baily MN, Brynjolfsson E, Korinek A. Brookings Institution, May 10, 2023.

“ Generative AI for Economic Research: Use Cases and Implications for Economists, ” Korinek A, Journal of Economic Literature 61(4), December 2023, pp. 1281–1317.

“ Machine Learning as a Tool for Hypothesis Generation, ” Ludwig J, Mullainathan S. NBER Working Paper 31017, March 2023.

“ The Macroeconomics of Artificial Intelligence, ” Brynjolfsson E, Unger G. International Monetary Fund, December 2023.

“ The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence, ” Brynjolfsson E. Daedalus 151(2), Spring 2022, pp. 272–287. An earlier version of this argument was published as Race Against the Machine: How the Digital Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy , Brynjolfsson E, McAfee A. Digital Frontier Press, 2011.

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In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship  — as well as online conference reports , video lectures , and interviews .

© 2023 National Bureau of Economic Research. Periodical content may be reproduced freely with appropriate attribution.

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India’s high-stakes election

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• Hilary McQuilkin
• Meghna Chakrabarti

In 2023, professor Ashutosh Varshney joined us to talk about democracy in India.

"India is ceasing to be a liberal democracy but it is an electoral democracy," Varshney said. "If ... the next election in India is not competitive and opposition party leaders are put in jail, then we are heading towards an electoral autocracy."

Since then, prominent opposition leader Arvind Kejriwal has been arrested. And India's elections are currently underway. What does Varshney think now?

Today, On Point:  a test for democracy in the world's largest democracy.

Ashutosh Varshney, director of the Saxena Center for Contemporary South Asia. Sol Goldman Professor of International Studies and the Social Sciences at Brown University. Author of eight books, including " Battles Half Won: India’s Improbable Democracy ."

Vivan Marwaha, author of " What Millennials Want: Decoding the World's Largest Generation ."

MEGHNA CHAKRABARTI: I’m Meghna Chakrabarti. And this is On Point.

Vikram Chandra is considered one of India’s leading journalists. He’s covered his nation for more than 30 years, including at the pioneering independent news network NDTV, and now at his multilingual news platform Editorji Technologies.

In that time, he’s reported on several wars in Kashmir, interviewed world leaders, and probed India’s domestic politics and foreign affairs. Now, he’s covering one of the biggest events of them all. India’s elections.

VIKRAM CHANDRA: It's one of the greatest spectacles on this planet, probably the greatest show on the earth. The fact that you can actually get 970 million people, that's a billion people who are going to probably, who are able to cast their vote.

CHAKRABARTI: India’s elections unfold over the course of 40 days. They began on April 19. Voting ends on June 1st. Results are tabulated and released on June 4th.

Now, Indian law says voters must have access to polling stations no farther than 1.2 miles from where they live. So that means millions of election workers and a whole lot of electronic voting machines are deployed to make it happen – across rivers, up into mountains, into some of the most remotest places in the country.

CHANDRA: If you think about it, the way it sometimes works in the U.S. is that everyone goes to the polls on the same day and you're sort of done. Here, it's actually the counting is a miracle. All of those boxes are going to be put in and within three hours, India is going to count a billion votes or nearly a billion votes. I mean, okay, not all of them are going to go out and vote, but you get what I mean.

Yes, the voting process is spread out over six weeks. Because the security has to be done and the officials have to move from place to place to conduct the election, to tally all those votes. All of them are then put into these electronic voting machines. And then it's all counted and tallied in like four hours flat on the 4th of June.

CHAKRABARTI: Amazing, when you think about it. At this point, it’s expected that the current ruling party, the BJP, will make a strong showing this election. And likely returning prime minister Narendra Modi to India’s leadership for a third term. Chandra says the BJP’s strength is due to a number of reasons, including a disjointed and rudderless opposition.

CHANDRA: Therefore, what the BJP always tries to do and has been able to do very successfully is say, "Alright, here's Narendra Modi. Who's on the other side?" And then you've got this, invariably put up this picture of 20 squabbling opposition leaders, all sort of fighting with each other, and none of them apparently with the status or the stature or the ability to articulate a vision for the future. And that's why what the BJP wants to do and what the BJP tries to do is will be to make this election into a referendum on Modi. Modi versus who?

CHAKRABARTI: Chandra says that when Narendra Modi is on the ballot, the opposition party has a tough time winning seats. He says they need to make this election about something OTHER than Modi and his cult of personality. Something like the high rate of unemployment in India, which is currently plaguing the economy.

CHANDRA: The opposition is trying to make that instead of saying, Okay, let's not have a referendum on Modi. Let's have a referendum on inflation and on unemployment, because we think there are much better grounds out there. The only issue with that particular card is that what happens if people say yes, we're very worried about unemployment but we think that Modi is the person who's most likely to fix it.

CHAKRABARTI: Earlier this year Prime Minister Modi predicted that his party, the BJP-led alliance and its allies would win 400 out of 543 parliamentary seats available, that would be a huge majority. Of course, we don't yet know what's going to happen because elections are still ongoing. Vikram Chandra says the opposition is warning of the dangers of a win like that.

CHANDRA: The opposition is certainly painting this election in rather apocalyptic terms, they are saying that this is going to be the last election. And if Modi comes back with a thumping majority, in a two thirds majority, then the constitution is going to be amended and you're not going to have elections ever again. And that sort of a thing, that's probably slightly more scary than the reality, but that's the way it's being painted.

And it was seeming till a couple of months ago, that's exactly what's going to happen. You're going to have Modi coming back with a very thumping majority. Now that the election campaign has already started, you never quite know, right? There's a lot of people, there's a billion people, and you never quite know what's going to happen.

CHAKRABARTI: That was veteran TV journalist Vikram Chandra. He’s founder of Editorji, a short video news and information platform based in India.

Now here's the thing. India has always been a massive and dynamic laboratory for the expansive possibilities and limits of modern democratic systems. We are talking about the governance of the most populous, multi-religious, multi-lingual, multi-ethnic country in the world.

And that’s why many look upon the current election with both admiration and trepidation. Democracy advocates say Prime Minister Modi has championed a fundamentally anti-democratic, Hindu-nationalist vision of India. They warn that an overpowering win for the BJP could accelerate India’s transition from messy, but open democracy, to what our guest today once called an electoral autocracy.

And that guest is Ashutosh Varshney. He’s Professor of International Studies and the Social Sciences at Brown University, he's also director of the Center for Contemporary South Asia. I should say, the Saxena Center for Contemporary South Asia. And author of many books, including "Battles Half Won: India’s Improbable Democracy." Ashutosh Varshney, welcome back to On Point.

ASHUTOSH VARSHNEY: Pleasure to join you, Meghna.

CHAKRABARTI: Okay, so you are the man who about a year ago on our show, when we were doing a show about India and populism used this phrase, electoral autocracy.

So I actually want to first go back and listen to what you said a year ago and then ask you where you see India now, but here it is.

VARSHNEY: I have made the claim thus far that India is not a, India is seizing to be a liberal democracy, but it is an electoral democracy. If, for example, the next election in India is not competitive and opposition party leaders are put in jail, then we are heading towards an electoral autocracy.

CHAKRABARTI: So Professor Varshney, that was you one year ago. And one year on, what are your thoughts now?

VARSHNEY: Yeah, the claim about electoral autocracy, one should note what democratic theorists say, is on a scale, zero to one, it's not zero or one, it's not a binary. And India, by arresting the current government, Modi government, by arresting not only Arvind Kejriwal, whom your reporter mentioned, the Delhi chief minister, some weeks ago, but also another chief minister and chief minister in India's head of, elected head of state government, arresting him.

And then also trying to freeze the bank accounts of the leading opposition party has taken several steps down the ladder. It's not yet zero, which would be electoral autocracy, but it has taken several steps down that one to zero ladder.

There is no doubt that Mr. Modi or the Modi government, through its actions, was trying to restrict electoral competition.

CHAKRABARTI: You have no doubt about that? I have no doubt about it, because this is how you injure the opposition parties and politicians. So one of them cannot campaign. He's in jail. Two of them cannot campaign, they're in jail, and the financial health of the leading opposition party, the Congress party, has been severely damaged.

CHAKRABARTI: Now, so about these arrests and the jailing of opposition members, the chief ministers of the states that you had talked about, if I understand correctly, though, they have been arrested under accusations of corruption, correct?

VARSHNEY: That's right.

CHAKRABARTI: The reason why I point that out is because, supporters of Prime Minister Modi would say, corruption is not unfamiliar in India, right?

I was just looking at Transparency International's 2023 Corruption Perception Index, which rates countries zero to 100. The closer you are to zero, the more corrupt a nation is. India scores 39 out of 100. Is there not perhaps a legitimate cause behind the arrest of these opposition leaders?

VARSHNEY: So two answers to that. First, if the Modi regime did mean its anti-corruption campaign seriously, then it would not invite some major politicians in the opposition. Who are accused of corruption and have been charge sheeted. Prima charge sheet in India means a prima facie case, before it goes to the court.

Before the conviction. More than 20 leaders, opposition leaders, have been either forced to join BJP or enticed into joining BJP. And their corruption cases have been, they cannot be dropped legally, but they've been relegated for a much later date and perhaps not invoked at all.

So if you want a serious attack on corruption, you wouldn't let corrupt leaders from the opposition join your party and in such large numbers. So corruption here is a political weapon. Rather than an honest attempt to cleanse India, the corruption campaign.

CHAKRABARTI: Can I just ask you? We have about a minute before our first break, Professor. ... So share a common analysis with you and I and all of our listeners today.

What do you think is at stake if the BJP does win an overwhelming majority of seats in this election?

VARSHNEY: If the BJP wins two thirds of India's parliamentary seats, that's over 365 and he has already claimed that he would like to win 370. For himself, for the party, BJP party and 400 for the alliance, that will give them certainly the power to --

CHAKRABARTI: Change the constitution.

VARSHNEY: The constitutional amendment requires two thirds of parliament and half of state governments. They have half of state governments. And two thirds of parliament, if they win. Then there is a real chance of serious constitutional changes.

CHAKRABARTI: Today we are taking a look at India's ongoing elections, are happening right now in the world's largest democracy. And we're talking about what the outcome of those elections would mean, will mean for India and also the example it sets for democracies around the world, including right here in the United States.

And Professor Ashutosh Varshney joins us today. He's director of Brown University's Saxena Center for Contemporary South Asia, author of a number of books on India, including "Battles Half Won" and "Ethnic Conflict and Civic Life." Professor Varshney, actually, before we go forward about implications of this election, can we just take a step back and talk about Modi himself a little bit more?

And why in your eyes, and in the eyes of other political scientists, he's often put in this group of autocratic leaning leaders of nations, like in the break, we were talking about Turkey, Hungary, et cetera.

VARSHNEY: Prime Minister Modi grew up in a movement and in an organization, 100 years old, that organization, which has always believed in, with some inflections of some marginal changes here and there, but not fundamentally altering its view, fundamental commitment to Hindu nationalism. Which essentially means, if you use in American terms, Hindu supremacy, like white supremacy, right? In politics. And what would that mean? That would certainly mean a marginalization of minorities. India's current constitution is very clear that all religions are equal.

Point number one, and that India's state will be committed to religious neutrality. Point number two, religious neutrality of the state and religious equality of all citizens. The Hindu nationalists are opposed to both, and they have argued for 100 years now that there have been two kinds of invaders in India, the Muslim invaders who came from Middle East and the British invaders who came in, started occupying India, starting with Bengal in 1757 and ruled India for 200 years.

The British are gone. The Brits are gone. But Muslim invaders are not gone. How are they not gone? Because their children are still in India. Their children and grandchildren and progeny are still in India.

CHKRABARTI: and great grandchildren.

VARSHNEY: And great grandchildren. Now the question for democracy theorists is this.

How can the great grandchildren off those who came, plus those who were converted to Islam, who didn't come from anywhere, who were born in India. But in any case, those Muslims born in India. And there's 200 odd million of them. How are they responsible for the invasions of 11th century, 8th century, 16th century, and the Muslim empires of the time?

How can the argument be made that they be punished for what their ancestors did, and not all of them had ancestors in the Middle East, right? Most of them were born in India. So how can they be punished for that? And how can they be made second class citizens for those reasons? And Hindu nationalism believes in Hindu supremacy, Hindu primacy, and at least a second order citizenship or secondary citizenship given to Muslims, if not the expulsion, certainly not the expulsion, as the Jews were expelled from Germany. Certainly not that, but turning them into second class citizens with Hindu primacy ruling.

CHAKRABARTI: Just for a second, I want to go back in time to the creation of the modern Indian constitution, mostly because it happened at a moment.

The context really matters here. It very much matters, right? Because we're talking about a constitution that was, what, created after the British left India and partition took place. And if I remember correctly, and please do point me in the right direction here. The vision of a sort of multi religious India was one championed by Gandhi, right?

But it was contentious even at the time within India. Is that right?

VARSHNEY: It was the contentions came from Hindu traditionalists or Hindu nationalists; the mainstream of Congress party was simply not opposed to it. And the constituent assembly had only one person. Actually, you can say two out of a large number, who were uncomfortable with that.

So the fundamental core, primary commitment of the freedom movement was, regardless, even after 1947 or 1940. 1940, when the Pakistan resolution came on board, even after that, the fundamental commitment of the freedom movement led by the Congress party was that regardless of what happens to British India, whether it's partitioned or not, whether Pakistan is born or not, India will be committed to religious neutrality and religious equality.

But of course, it was a Hindu nationalist who murdered Gandhi. A Hindu nationalist murdered Gandhi a few months, just a few months after India's independence, and before the constitution came into being. So the constitution making remained committed.

An overwhelmingly large number of constituent assembly members remained committed to the idea of a multi religious India, with religious equality of citizens and religious neutrality of the state.

CHAKRABARTI: The reason why I wanted to just point that out is because it helps explain why you said that for a century, even with this federal commitment to religious neutrality, there has been this strong strain of Hindu nationalism that never really went away in India.

And it's now expressing its greatest power through Modi's leadership. But at the same time, he is being elected. The BJP as a party is winning seats in parliament. It's not exclusively because of the Hindu nationalism. There are many Indians who would say he's also delivered, the BJP has also delivered on development promises, for example.

VARSHNEY: There's two more issues that explain Modi's popularity. One is indeed delivery of welfare benefits to vast numbers of people. The deprived sections of society, modern toilets are not needed by the middle class and by the rich, but the poor needed them. Cooking gas was not a problem for the middle class or the rich, but the poor needed cooking gas, cylinders subsidized, so on and so forth.

So there is delivery of welfare benefits, which is the second basis for his popularity. And the third is his personal incorruptibility, though not the corruptibility of his party, it has become very clear, after an electoral bond scheme, which the Supreme Court turned down, overturned some weeks ago.

It's become very clear that the BJP and India's business have collusive links. Very corrupt links. So it's not the corruption of the party, but he's personally incorruptible.

CHAKRABARTI: And he's the figurehead of the party.

VARSHNEY: And he's the figurehead of the party. So other than Hindu nationalism, you also have welfare benefits and his personal incorruptibility.

CHAKRABARTI: What's fascinating to me is that at least from what we can view externally, in this election, recently Modi has been as vocal, perhaps as ever, about his view of Muslim Indians. And because just recently he gave a speech to a crowd on Sunday in the state of Rajasthan, said some pretty inflammatory things that made their way around the world but here it is.

(MODI TAPE)

CHAKRABARTI: So the Prime Minister there is saying, when they, the Congress Party, were in power, they said Muslims have the first right on India's resources and wealth. This means that they will collect all your wealth and distribute it among those who have more children.

They will distribute it among infiltrators. Should your hard-earned money be given to infiltrators? Are you okay with that? This is what the Congress Manifesto says.

CHAKRABARTI: Is that what the Congress Manifesto says, Professor?

VARSHNEY: I re-read it yesterday. At length, it doesn't say that. First of all, even Prime Minister Manmohan Singh, the head of the pre-BJP, UPA government led by the Congress Party, did not say that the Muslims had the first claims on India's resources. He said all deprived communities, including Dalits, had the first claims on India's resources. Scheduled tribes, some lower castes and minorities had the first claims, not Muslims alone, first of all.

All the deprived sections of India, both Hindu and Muslim. Secondly, the manifesto that I read, nowhere says that even if a socioeconomic census of India is taken, and it's been taken and it was taken in 2011, it can establish, for example, which castes, which groups have, what is the economic status?

What is their status in terms of literacy? What is their status in terms of health? ... Are they women headed households or men headed households? Is there an adult person in the household who is income earning? All of those issues are there in that sense. It is not about your property alone.

And even if that is counted, and certainly in that census, in the manifesto, which is a promise to the electorate, it doesn't say that the property will be transferred to Muslims of India. And the third big point there is calling them infiltrators, which is invaders, and calling them also as a community that keeps on producing more children than the Hindus.

The demographers are very clear about that. You cannot compare Hindu family size with Muslim family size. First of all, Muslim family size is declining and may soon become, reach a level which is, what is called below the replacement, in several states of India, not everywhere. But Hindu, the Muslim family size is slightly bigger by the 2011 census than the Hindu family size.

However, that's not the comparison. Since most Muslims are poor.

CHAKRABARTI: Yeah.

VARSHNEY: And poverty has a lot to do with family size. The right comparison is of Muslim family size with the Dalit family size and the Adivasi family size. These are the two other very large, deprived communities of India. And if you do that, Muslims can't be called producing many more children than others.

CHAKRABARTI: We know that income and also female education, increases in female education rate are the two biggest drivers of reductions in family size.

VARSHNEY: That is correct.

CHAKRABARTI: But this caught a lot of people's attention both inside and outside of India. Because it is using that now familiar demonizing language that we hear in various forms, in other places where democracy is quavering a little, if I can put it that way. I've read that Modi may have resorted to this kind of speech in the past few days, perhaps not because of his fire breathing Hindu nationalism, but something that Vikram Chandra mentioned earlier, that there is a high unemployment rate right now in India.

And people are concerned about that. And that rate is happening under Modi's leadership.

VARSHNEY: Yes.

CHAKRABARTI: So he's trying to distract from that.

VARSHNEY: So that is a very good hypothesis. We need to, it will be hard to prove that conclusively, but that is as plausible a hypothesis as observers and democracy scholars can find. The first round of elections took place on April 19th, and this language emerged after that.

CHAKRABARTI: Oh, okay.

VARSHNEY: This language was last used when Mr. Modi was the chief minister of Gujarat state, when he was heading a state government, not heading India's federal or national government. This language has come back after a very long time.

So the question, that's why this hypothesis, the question is why. So it is possible that his own ground reports, the ground reports coming to the party, right? Put to the party leadership, are saying that things are not going very well, or did not go very well in the first round, especially because the voter turnout has dropped considerably or dropped considerably in the first round.

He relies heavily on Increasing turnouts. And the assumption is the more people vote, the more it will show that I am raising the levels of voter enthusiasm. And that's why they're coming out to vote to elect me to power.

CHAKRABARTI: Fascinating. So a drop in turnout and perhaps internal reports coming from the ground may have generated anxiety about what the election results might be. And is it that unemployment and economic issues of several kinds, is it that's going to undermine his likely election victory?

CHAKRABARTI: I see. Or at least undermine his aim to get two thirds.

VARSHNEY: Undermine his aim to get two thirds, which he's been insisting on that for almost six months now.

CHAKRABARTI: So that gets us back to if he were, if the BJP wins two thirds of the parliamentary seats, that gives the party the power to change the Indian election. Because they also have the requisite number of state leaderships. We have about just a couple of minutes before our next break, Professor, but can you just briefly summarize the kinds of constitutional changes that they would seek to make?

VARSHNEY: This has been an object of much analysis. All speculative at this point, but not necessarily wrong for that reason. How would Hindu supremacy, the idea of Hindu primacy and supremacy be turned into laws? Only one, or maybe you can say two steps have been taken in that direction. They were taken right after the elections in 2019.

One, on which immigrants from neighboring countries can become citizens. That was called Citizenship Amendment Act in December of 2019. It was passed by his control over parliament. Right? Now, that says all communities can come to India from the neighboring states, from the neighboring three Muslim majority states, but Muslims cannot. Only non-Muslim migrants.

Immigrants from those societies can become citizens, not Muslims, because Muslims are not persecuted. Minorities are persecuted, even though many Muslim communities claim that they are persecuted in their own Muslim majority countries. Okay, that's point number one. Point number two, the only Muslim majority state of India lost its status as a state.

That was also done through Parliament. Kashmir lost its status as a state. It's no longer a state of India, and that was done through Parliament. More of that we can discuss later in what other form it might come, but two steps have been taken in that direction. What other steps might be will be a matter of speculation right now, but not wrong for that reason.

CHAKRABARTI: We're talking today about India's elections and Ashutosh Varshney joins us in the studio and now Professor Varshney, I just want to bring in another voice here, Vivan Marwaha joins us. He's author of "What Millennials Want: Decoding the Largest Generation," and he's working on a sequel to his book on that, writing about Gen Z in India. Vivan, welcome to On Point.

VIVAN MARWAHA: Thanks, Meghna. It's great to be here.

CHAKRABARTI: When we talk about India, I just want to continuously remind folks about the incredible diversity of the nation that we're speaking about, but also its particular demographics in terms of age, right?

There's a large cohort of younger Indians. And you've written extensively, about them. Is it possible at all to generalize what such a large number of young people want for their country?

MARWAHA: It's a bit hard because there's this saying we have when it comes to things related to India, which is you can say one thing and it will be very true.

And you can say the exact opposite, and that will also be very true. That being said, we can make a few generalizations based on data. And based on geography, whether we're talking about North India, South India, middle class India versus folks with less income, but it is getting a bit hard to make generalizations given how diverse the country is.

CHAKRABARTI: So let me ask it then in a slightly different way. Because folks of your generation and Gen Zs, I think it's fair to say have grown up in a completely different India than parents of people who say, my parents' generation, right? Every time my parents go back and visit, they say India has become unrecognizable to them, because of the vast amounts of development and the growth of the economy in India.

So that's one thing. It's a different India now. And Vivan, I wonder what you think about this story too. Because, about 20 years ago, when I was there visiting family. And my family hails from both Bengal and Mumbai, so we were in Mumbai, walking down the street one day just to go to the market, and there was this large public rally going on.

And, I didn't realize it at the time, but it was a Hindu nationalist rally. But the thing that gave it away later, as I was thinking about it, was that there was this giant poster, very large poster, depicting the god Krishna. And the one that's famously depicted with the blue skin, and in this image, though, he was very, he looked like a bodybuilder, like bodybuilder Krishna, and he had a quiver, which is typically full of arrows, but this quiver in the image was full of nuclear weapons.

It was absolutely fascinating. And that was 20 years ago, but it was really out in the open. And I wonder if that's also different than what previous generations of Indians had experienced in terms of the vocal openness of Hindu nationalism. So given that, and you can disagree, Vivan, if you want, but given that, is it fair to say that younger Indians have grown up in a nation where they have different expectations due to, let's say, ideological changes and economic changes?

MARWAHA: Absolutely. That's a very fair statement. And on that point, I actually have a book on my desk right now that's called H-pop or Hindutva POP, which is how a lot of Hindutva today is, traditionally it was you went to a temple or you went to a political rally, but today you can be on your phone and listen to pop songs that have some of these Hindutva themes.

Just to step back for a second though, and set the stage, India has a median age of 29.So that means that roughly 700 million people are, you know, 29 years or younger in India today, and just 20 million Indians will be voting for the first time in this election. Now, these Indians are not getting their news from the TV or the radio and newspapers, but from Instagram, YouTube and WhatsApp.

And these are mediums that the BJP has dominated for nearly a decade. And these people are who, they form what I call an emerging India or a new India that have very little loyalty to the India of the past and the politicians of the past. And so going to what you were asking me is that this is what binds a lot of these young Indians together. Is that they are not, they're less tied to the establishment in terms of not just getting their news, but of also forming their opinions and ultimately deciding who they're going to vote for. The number one issue today is definitely unemployment.

And just one in five young Indians today is unemployed. You're seeing stories of young Indians lining up for jobs in Israel. You're seeing stories of young Indian men actually even fighting. It's claimed that they're fighting on behalf of Russia and Ukraine. And some are even illegally crossing the border in Mexico to come to the United States.

And these are very new stories. They didn't take place 20 years ago in rural areas, working age Indians are lining up for manual labor guaranteed by government programs. And so there's a lot of anxiety out there, but like the professor mentioned earlier, the opposition is not viewed as having an answer to these problems.

And where people ultimately get the news and their opinions from, they see Modi as, and the BJP, as people who may not have an answer to these problems, but they're people who are giving them an identity of being young, proud Hindus. And that identity is very powerful that never existed earlier for previous generations.

CHAKRABARTI: That is so interesting. So is another way of what you're saying is that for, I'm just reflecting on my parents generation, right? They are extremely proud of India's, modern India's example of being a democracy, that the state has no specific religious affiliation.

It's a multi-religious nation in a place where religious and ethnic wars have gone back for centuries. It was like proving the impossible, almost. And it was a point of pride. Vivan, are you saying that's not necessarily where young Indians place their political or ideological loyalties anymore?

MARWAHA: Not necessarily. While India has been secular on paper, there have been many incidents and laws that were passed by the Congress where you could actually question some of those secular credentials. And that's also why a lot of Modi's speeches, some of these very communal speeches, in a sense, have a lot of traction amongst young Indians, because they view the Congress and previous governments as almost, there's this term in India called minority appeasement, that they appeased minority communities, particularly the Muslims, by creating favored policies for them.

In the 1980s, the Congress government even subverted a very momentous Supreme Court decision in favor of a small group of Muslim clerics who did not want equal rights for Muslim women who were getting divorced. And the young Indians look at these as examples that India has never been secular, and that the time has come for a government that actually now plays and favors the majority community and Hindus and they don't see anything wrong in that.

CHAKRABARTI: Professor Varshney. I see you wanting to respond. Go ahead.

VARSHNEY: Yes. I think one of the great political victories of Mr. Modi and his organization are, would be precisely this, that a lot of people don't see Hindu primacy, Hindu majoritarianism, Hindu supremacy, as wrong, that all communities should have equal rights, even if Congress did something wrong earlier, let's say.

And this particular instance of 1980s when Congress used its supermajority in Parliament to overturn a Supreme Court judgment about equal rights of men and women in Islam on when it came to divorce. That was a reformist thrust that you could see in the Supreme Court's move. And Congress politically overturned it.

Yes. So Congress made the mistakes. But a very large number of people, it's not, we can give you better numbers after our surveys now and over the next few weeks. But when we asked in 2019, our surveyors asked in 2019, did you vote for BJP or did you vote for Mr. Modi?

CHAKRABARTI: Interesting.

VARSHNEY: The Modi voters, the BJP voters, 25% said they only voted for Modi. They didn't vote for BJP, which means from that you can infer with considerable plausibility that 75% of the 38% who voted for BJP, three fourths of the 38% who voted for BJP have some kind of belief in Hindu supremacy, Hindu primacy, Hindu nationalism.

But 25%, for them, Hindu nationalism was not the issue. They believed in Mr. Modi.

VARSHNEY: And his leadership and his personality and his character and all of those things, right? So yes, the very fact that 75% of 38% can be inferred to have voted on grounds of Hindu nationalism.

It's a very new development.

CHAKRABARTI: Interesting. Vivan, what do you think about that? Because again, to your point a majority of Indians are under the age of 30, right? So this is not just an election for the present of India. It's an election regarding the future. I suppose every one, every election is like that. But this one especially, what is your sense of what the younger Indians actually want for the future of their nation?

MARWAHA: Yeah. In my book which is on millennials. I write something that I believe still holds true today, is that young Indians today want leaders who speak like them, who pray like them, and who eat like them. And so the professor's point regards to the prayer and religion. And I think that's a big part of identity.

But where Modi and today's BJP leaders fall on the other two issues is that they speak and they look like young India. And a lot of these leaders are not traditional elites or diners. They don't come from political dynasties or powerful families and young Indians, particularly in Mr. Modi, they look at the someone who's one of us. He was, as the story goes, he was the son of a tea seller who rose up and became a party worker. And then with the chief executive of the state of Gujarat for many years, and then became prime minister. And when he goes abroad, he speaks in Hindi.

He speaks in Hindi to foreign leaders. He sells out stadiums in New York City and Sydney and London. And when he speaks, people listen. And young Indians see that, and they see if he could do it, then so could I. And so that's a very powerful connection that he's formed with young India today, that I don't see any other leader has such a big grip on the youth of a country, but while not being a young person themselves. And so that, when I go and talk to young people, that sort of sense of connection is something that's very palpable, even more than the religious element, which does exist.

But I see these other sort of new forms of connection with their leader, where they almost call him their guardian. Someone who's looking out for them, which hasn't really existed before.

CHAKRABARTI: Vivan, just quickly, because I think I may have misheard you a little bit earlier when you said, did you say someone who eats like us or speaks like us?

MARWAHA: Speaks and eats like us.

CHAKRABARTI: So the eats part, can you just take a quick second to elaborate on that? Do you mean someone who doesn't practice halal eating? What do you mean by that?

MARWAHA: Yeah. Modi's of course, vegetarian. A lot of Indians, of course, not vegetarian.

CHAKRABARTI: Are not.

MARWAHA: They do eat meat, but one of the big promises of, and initiatives of the BJP has been to clamp down on the sale of beef. Which naturally, most Hindus don't do not eat beef.

And so a lot of BJP states, where they're in the government, they've clamped down on the sale of cow slaughter and on the sale of beef. And this is a very popular move amongst young people who I've met because they see that as something very against their values. And Modi is someone who they believe represents their values.

CHAKRABARTI: Okay. Thank you for clarifying that. That is absolutely fascinating. Now we have about a minute left. Professor Varshney, I'm going to give you the last word. There's so many ways that we could think about what's at stake in India, because there's that tension between a democracy representing the majority of a nation, versus a democracy protecting the rights of a minority.

The two don't always have to be in conflict, but perhaps they are in India. But what would you say for external observers, for people here in the United States, what is, what are the lessons that India right now, in these elections, has to teach other democracies?

VARSHENEY: Some of these issues in a different form will appear here and have already appeared here in the United States.

Is Trump a believer in white supremacy? Is Trump, it's not old style Jim Crow white supremacy, that can't return easily. And I don't think can return. I've been studying that period quite extensively. But if you believe in majoritarianism, either racial majoritarianism or religious majoritarianism, can you really keep and you win elections? Can you really keep societies together? Or would you have virtually interminable conflict? This is something at stake in India's election, and it will be at stake if Trump wins power here.

This program aired on April 23, 2024.

• India kicks off elections lasting 6 weeks. Here's what to know
• India begins voting in elections with a prominent opposition leader in jail
• Why critics are concerned about H-Pop, India's Hindu nationalist genre

Hilary McQuilkin Producer, On Point Hilary McQuilkin is a producer for On Point.

Meghna Chakrabarti Host, On Point Meghna Chakrabarti is the host of On Point.

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