Welfare effects of unemployment benefits when informality is high

Liepmann, Hannah; Pignatti, Clemente Working Paper Welfare effects of unemployment benefits when informality is high ILO Working Paper, No. 39 Provided in Cooperation with: International Labour Organization (ILO), Geneva Suggested Citation: Liepmann, Hannah; Pignatti, Clemente (2021) : Welfare effects of unemployment benefits when informality is high, ILO Working Paper, No. 39, ISBN 978-92-2-035184-0, International Labour Organization (ILO), Geneva This Version is available at: https://hdl.handle.net/10419/263105 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/3.0/igo/ XWelfare Effects of Unemployment Benefits when Informality is High Authors / Hannah Liepmann, Clemente Pignatti August / 2021 ILO Working Paper 39 Copyright © International Labour Organization 2021 This is an open access work distributed under the Creative Commons Attribution 3.0 IGO License (http:// creativecommons.org/licenses/by/3.0/igo). Users can reuse, share, adapt and build upon the original work, even for commercial purposes, as detailed in the License. The ILO must be clearly credited as the owner of the original work. The use of the emblem of the ILO is not permitted in connection with users’ work. 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ISBN: 9789220351833 (print) ISBN: 9789220351840 (web-pdf) ISBN: 9789220351857 (epub) ISBN: 9789220351864 (mobi) ISSN: 2708-3446 The designations employed in ILO publications, which are in conformity with United Nations practice, and the presentation of material therein do not imply the expression of any opinion whatsoever on the part of the International Labour Office concerning the legal status of any country, area or territory or of its authorities, or concerning the delimitation of its frontiers. The responsibility for opinions expressed in signed articles, studies and other contributions rests solely with their authors, and publication does not constitute an endorsement by the International Labour Office of the opinions expressed in them. Reference to names of firms and commercial products and processes does not imply their endorsement by the International Labour Office, and any failure to mention a particular firm, commercial product or process is not a sign of disapproval. ILO Working Papers summarize the results of ILO research in progress, and seek to stimulate discussion of a range of issues related to the world of work. Comments on this ILO Working Paper are welcome and can be sent to [email protected], [email protected]. Authorization for publication: Richard Samans, Director of RESEARCH ILO Working Papers can be found at: www.ilo.org/global/publications/working-papers Suggested citation: Liepmann, H., Pignatti, C. 2021. Welfare Effects of Unemployment Benefits when Informality is High, ILO Working Paper 39 (Geneva, ILO). 01 ILO Working Paper 39 Abstract We analyze for the first time the welfare effects of unemployment benefits (UBs) in a context of high informality, exploiting matched administrative and survey data with individual-level information on UB receipt, formal and informal employment, wages and consumption. Using a difference-in-differences approach, we find that dismissal from a formal job causes a large drop in consumption, which is between three to six times larger than estimates for developed economies. This is generated by a permanent shift of UB recipients towards informal employment, where they earn substantially lower wages. We then exploit a kink in benefits and show that more generous UBs delay program exit through a substitution of formal with informal employment. However, the disincentive effects are small and short-lived. Because of the high insurance value and the low efficiency costs, welfare effects from increasing UBs are positive for a range of values of the coefficient of relative risk aversion. About the authors Hannah Liepmann is an Economist in the Research Department of the International Labour Organization. Her fields of interest are in labour economics and applied microeconomics, with a focus on studying how labour market policies and other phenomena affect the integration of marginalized groups into quality employment. Hannah obtained her PhD in Economics from Humboldt-University Berlin. She has been a visiting researcher at the Institute for Research on Labor and Employment at UC Berkeley and the Institute for Employment Research (IAB) in Nuremberg. She is an IZA Research Affiliate. Clemente Pignatti is an Economist in the Research Department of the International Labour Organization. He obtained an MSc in Economics from the London School of Economics and a PhD in Economics from the University of Geneva. His research interests lie at the intersection between labour and development economics, studying how individuals in emerging and developing countries respond to changes in incentives from participation in social protection schemes and labour market policies. 02 ILO Working Paper 39 Abstract 01 About the authors 01 Introduction 07 X1 Theoretical Framework 11 Insurance value and efficiency costs of UBs 11 The role of informal jobs 12 X2 The institutional context of the Mauritian UB scheme 14 X3 Data and descriptive evidence 16 Data 16 Evaluation of the matching procedure and descriptive evidence 17 X4 The insurance value of UBs and underlying labor market mechanisms 22 Empirical approach 22 Main results 23 A. Consumption expenditures 23 B. Labor market status, wages and transfers 25 Robustness tests 28 X5 The efficiency costs of UBs and the substitution between formal and informal employment 30 Empirical approach 30 A. Sample selection and empirical specification 30 B. Imputation of informal employment 32 Assessment of the identifying assumptions 33 Main results 36 A. Benefit duration and time before formal re-employment 36 B. Formal and informal employment 39 Robustness tests 40 Table of contents 03 ILO Working Paper 39 X6 Welfare analysis 42 Conclusion 44 Appendix A: The insurance value of UBs and underlying labor market mechanisms 45 Appendix B: The efficiency costs of UBs and the substitution between formal and informal employment 55 References 70 Acknowledgements 75 04 ILO Working Paper 39 List of Figures Figure 1. Formal employment shares for matched and unmatched observations, social security database 19 Figure 2. Formal and informal employment shares and share of participants receiving UBs, matched sample 20 Figure 3. DiD results on household expenditures 24 Figure 4. DiD results on labor market status 27 Figure 5. DiD results on job characteristics 28 Figure 6. Unemployment benefit entitlements in Mauritian Rupees as a function of the monthly wage at job loss and month of unemployment duration 30 Figure 7. Share informally employed in a given month around job loss, actual versus imputed 33 Figure 8. Probability density function of the running variable around the upper bound 34 Figure 9. Evolution of covariates around the upper bound 35 Figure 10. Scatter bin plots for the duration of UB receipt (in months) at the upper bound 37 Figure 11. Hazard rate of formal employment and share of UB participants finding a formal job in each month after job loss 38 Figure 12. RK-results for the probabilities of being formally and informally employed in a given month around job loss, upper bound 40 Figure A1. DID results on total transfers and consumption expenditures by re-employment status 45 Figure A2. DID results on household transfers 46 Figure A3. Results using an event study approach 47 Figure A4. DID results without propensity score weights 48 Figure A5. DID results on formal employment from panel and cross-sectional regressions 49 Figure A6. DID results on the sample of UB recipients dismissed for economic reasons 50 Figure A7. DID results on PES registration and work absenteeism 51 Figure A8. DID results on wages and total expenditures, by date of formal re-employment 51 Figure A9. DID results on formal and informal wages, different specifications 52 Figure A10. DID results on the sample of UB recipients around the upper bound 53 Figure B1. Probability density function of the running variable around the lower bound 55 Figure B2. Evolution of covariates around the lower bound 56 Figure B3. Scatter bin plot for the duration of UB receipt (in months) at the lower bound 57 Figure B4. Scatter bin plots for formal and informal employment in selected months around job loss (upper bound) 58 Figure B5. Scatter bin plot for the formal and informal employment in selected months around job loss (lower bound) 59 Figure B6. RK-results for the probabilities of being formally and informally employed in a given month around job loss, lower bound 60 Figure B7. RK-results for the probabilities of being formally and informally employed in selected months after job loss, upper bound, by bandwidth 61 05 ILO Working Paper 39 Figure B8. RK results for the duration of receiving UBs (months), upper bound, by bandwidth 62 Figure B9. Detection of actual kink 62 06 ILO Working Paper 39 List of Tables Table 1. Summary statistics for UB recipients, overall sample and matched versus unmatched sub-samples 18 Table 2. Smoothness of covariates at the upper bound 36 Table 3. RK-results for the duration of receiving UBs (months), upper bound 37 Table 4. Welfare effects from increasing UB levels 43 Table A1. Summary statistics for the DID analysis: treated and control groups 54 Table B1. Distribution of participants with UB entitlements at the lower and upper bounds, by year of participation and month of unemployment 63 Table B2. Summary statistics for the RK analysis: overall sample and individuals in the proximity of the upper and lower bounds, respectively 64 Table B3. Imputation regression for the likelihood of being informally employed in a given month around job loss 65 Table B4. Smoothness of covariates at the lower bound 66 Table B5. RK-results for the duration of receiving UBs (months), lower bound 66 Table B6. Sensitivity of results to choice of the polynomial order, focusing on the probability of being formally employed in months 0, 4, 8, and 12 after job loss (upper bound) 67 Table B7. Sensitivity of results to choice of the polynomial order, focusing on the imputed probability of being informally employed in months 0, 4, 8, and 12 after job loss (upper bound) 68 Table B8. Sensitivity of results to choice of the polynomial order, using the duration of receiving UBs (months) as the outcome variable (upper bound) 68 Table B9. RK-results for length of UB receipt using placebo running variable (upper bound) 69 Table B10. Double difference RK-estimates (upper bound) 69 13 ILO Working Paper 39 This means that a priori welfare effects of UBs can be either higher or lower in contexts of high informality. Additionally, a given level of the insurance value (efficiency costs) can be compatible with opposite views on the role of informal employment. In our analysis, we therefore begin by estimating the relevant terms that allow us to characterize Equation 1. Subsequently, we exploit our rich individual-level data to assess the role of informal jobs in determining welfare in line with the two main views presented above. 14 ILO Working Paper 39 X2 The institutional context of the Mauritian UB scheme Mauritius is a country in the Indian Ocean with a population of 1.3 million and a median age of 36.2 years (Statistics Mauritius, 2017). The country has experienced sustained economic growth over the last decades: GDP per capita has more than doubled since the 1990s and is currently comparable to levels in Latin American countries such as Argentina, Chile and Uruguay. The Mauritian service sector accounts for the majority of employment (67.4 per cent), followed by industry and manufacturing (25 per cent) and agriculture (7.6 per cent). Despite rapid economic growth, evidence from the household survey shows that informal employment is still prevalent. An estimated 43.8 per cent of the employed population was formally employed in 2018 (i.e. employed in a job for which compulsory social security contributions to the National Pension Fund are made, as required by the relevant legislation) and this value had barely changed compared to previous years (i.e. it was equal to 44.3 in 2012, when our survey data starts).11 The current system of UBs is in place since 2009. Unemployed individuals are eligible to participate if they have been employed in a full-time job for at least six months without interruption. All reasons for job loss apply (including the expiration of a fixed-term contract), except for voluntary resignations. Eligibility is verified at the time of registration at the local Labor Office, when the dismissed worker needs to present a letter of termination of employment. Additionally, the previous employer is called upon to confirm details of the employment relation. Conditional on meeting these criteria, laid-off individuals coming from both formal and informal jobs can enter the program. When informal workers apply, the government tries to recover social security contributions from the previous employer but fully finances the participation of the individual in the intervention if this proves impossible. In practice, however, very few informal workers apply to UBs upon layoff (i.e. they represent 20 per cent of total participants, despite accounting for roughly 70 per cent of the unemployed in the country). This is because they are both less likely to meet the program eligibility criteria and to apply conditional on being eligible, since program registration requirements (e.g. the need to present a letter of termination of employment and the requirement for the employer to confirm the dismissal) discourage their participation (Liepmann and Pignatti, 2019). The UB received is a function of monthly gross wages earned at the time of job loss as verified from the letter of termination of employment and the unemployment duration. The amount replaced never falls below a lower bound of 3,000 Rupees (USD 184 in PPP) and never exceeds the upper bound in place in a given year t ( UpperBound t). The latter is updated annually to reflect inflation and was equal to 15,000 Rupees in 2017 (USD 920 in PPP). More specifically, UB entitlements are determined as follows: 11 Throughout the analysis, we follow the ILO definition and define formal employment for employees based on the presence of work-related social security contributions to the National Pension Fund (i.e. by both the employer and the worker). This is the relevant definition measuring the fiscal implications of labor supply responses to UB generosity (e.g. foregone tax revenues) and it is also the definition adopted in previous studies. We consistently observe formal employment based on this definition in the household survey (where information on these contributions is elicited) and the administrative data (which records these contributions). Later in the analysis, we will compare the estimates from the two sources. 15 ILO Working Paper 39 U B if rw rw if rw UpperBound UpperBound if rw UpperBound = 3,000 ≤3,000 3,000 << ≥ itm mi mi mi t tmi t        (2) where w i is the monthly wage individual i earned prior to job loss and r mdenotes the replacement ratio in month m of unemployment. In the first three months of unemployment ( m =1,2 ,3 ), the UB replaces 90 per cent of wi ( rm = 0.9). This replacement ratio is reduced to 60 per cent during months 4 to 6 of unemployment ( m =4,5,6 and rm = 0.6). Finally, during months 7 to 12 of unemployment, the replacement ratio further drops to 30 per cent of the initial wage ( m =7−12and rm = 0.3). Upon entry into the program, participants choose among three available active labor market policies: job placement, training and reskilling, or start-up support. The vast majority opts for the job-placement option (85 per cent), but the type of support provided by the job placement services is limited. The maximum duration of UB receipt is 12 months since the time of job loss. Program eligibility ends earlier if a worker finds a new job, in theory independently from its nature. However, the two processes differ significantly between formal and informal re-employment. If the participant finds a formal job, the Ministry of Social Security detects from its records that contributions are being made for a new job and automatically de-registers the UB recipient. If the new job found is instead informal, the participant should report this information to the Labor Office that would proceed with the de-registration. However, this is difficult to enforce and there are no sanctions for failing to report a new job. 16 ILO Working Paper 39 X3 Data and descriptive evidence Data For the purpose of the analysis, we have obtained access to rich administrative records that we have matched with the country’s household survey. The resulting final database represents a unique source of information, allowing us to observe detailed individual and household characteristics of UB recipients independently from their re-employment status. We have combined all data sources using individual’s unique national ID numbers. The starting point is the administrative data collected by the Ministry of Labour on the universe of UB recipients. These records are elicited at the time of registration with the program and include some basic personal characteristics (i.e. age, gender and district of residence) as well as detailed information on the elapsed job-spell (i.e. wage at job loss, tenure, reason for dismissal). This information is taken from the letter of termination of employment and the previous employer needs to confirm its accuracy. When the individual leaves the program, the date of exit is also reported. For the vast majority of UB recipients who opted for the job-placement option, we have additional information from employment centers on individual and household characteristics (e.g. educational levels, marital status, previous occupation). Second, we rely on the social security records from the Ministry of Social Security. These records contain monthly information on individuals’ formal employment biographies. We have these full records (i.e. from the first month of contributions of the individual until December 2018) for two different populations of interest. The first is the universe of UB recipients, for which we can reconstruct all formal employment episodes before and after the unemployment spell. The second is a representative sample of the Mauritian formal labor force, obtained by appending the full employment records of those who were formally employed in a given month in all the years between 2011 and 2018.12 This second sample includes around 90 per cent of those formally employed in Mauritius during the period of analysis.13 In the rest of the analysis, we will jointly refer to these two data sources as the administrative database (or administrative records). For the sample of UB recipients, this will correspond to the panel of social security records from the Ministry of Social Security matched with information collected by the Ministry of Labour at the time of registration into the program. For this sample, we do not consider individuals who lose their job before 2011 (i.e. data from the Ministry of Labor for 2009 and 2010 presents inconsistencies, as program records were initially kept manually) and also restrict the analysis to those who enter the program by the end of 2017 (i.e. to observe them at least for one year in the social security records). We also focus on first-time program participants throughout the analysis. For the sample of the Mauritian formal labor force, the administrative database contains instead only the panel of social security records. For this sample, we restrict the time frame to between 2012 and 2018 to have a comparable period of analysis. 12 We obtained this sample by first extracting the full employment histories of those who were formally employed in July 2018 (i.e. from the date of their first contribution until December 2018). We then moved to July 2017 and added the full employment histories of those who were employed in that month and did not already appear in our database. The same process was reiterated for all the months of July until 2011. July was chosen to maximize sample coverage, as it is considered the peak of labor market participation in the country. 13 We can check the representativeness of these records by looking at how many UB recipients (for whom we have the full sample and who should all appear in the social security records, at least during the months of UB receipt) are in this sample (which was obtained independently from UB participation). We find that 92.4 per cent of those who are in the UB database appear also in the sample of the Mauritian formal labor force. Moreover, on average there are around 250,000 individuals per month in this sample, which is in line with almost full coverage of the formal work force given that the employed population comprises around 550,000 individuals and formality rates are estimated slightly below 50 per cent. 17 ILO Working Paper 39 We have been able to match our administrative data with the Mauritian household survey administered by the National Institute of Statistics (the so-called Continuous Multi-Purpose Household Survey, or CMPHS). The merge was done at the individual and monthly level. The CMPHS is nationally representative and interviews every year around 30,000 individuals. It has a rotating panel structure, whereby a household can be interviewed four times 15 months apart (i.e. 2-2-2 rotating panel at the quarterly level). The survey has a standard content and reports information on a number of individual and household characteristics, including detailed labor market status (i.e. also covering informal employment). We have data on the CMPHS between 2012, when the survey questionnaire started asking individual ID numbers, which we use to conduct the merging, and 2018. While giving the ID number is not compulsory, the vast majority of survey respondents provide this information (i.e. 85.1 per cent of the sample). Evaluation of the matching procedure and descriptive evidence We now discuss the quality of the matching procedure between the administrative and survey data and present some descriptive evidence. For ease of exposition, in this part of the analysis we focus on the universe of UB recipients only.14 For this sample, we restricted the length of the administrative records to three years before and after job loss. This means that for almost all UB recipients, we have a panel of six years of social security records as well as cross-sectional information reported at the time of registration. We match this database with the household survey and find that 6.66 per cent of UB recipients are interviewed at least once in the CMPHS between 2012 and 2018. These individuals have not necessarily been observed in the CMPHS while receiving UBs, but rather in the three years before or after job loss. A first possible concern is the representativeness of the matched sample with respect to the overall population of UB recipients. Random sampling of the household survey should in theory guarantee this. To verify if this is the case, we compare observable characteristics between matched and unmatched individuals. We take these variables from the administrative records at job loss, so that we have information for everybody at the same point in time. Table 1 shows that there are no statistically significant differences with respect to variables related to program participation (e.g. length of receipt of UB) and characteristics of the previous job (e.g. tenure, wages). The share of individuals formally employed in a given month, as elicited from social security records, is also very similar in the two samples both before and after job loss (Figure 1). A few differences emerge with respect to individuallevel characteristics (i.e. most notably, age and gender), but these are small in magnitude. Thus, the matched sample compares well overall to the universe of UB recipients. 14 For the UB sample we have richer individual and household information from program registration, which we can use to assess the quality of the matching. However, also the administrative sample of the Mauritian formal labor force compares well with the formally employed in the country: the share of men was equal to 58.1 per cent in 2018 in our administrative sample of the Mauritian labor force and to 58.9 among the formally employed interviewed in the CMPHS in that same year. The corresponding average age was 38.9 and 39.1, respectively. 18 ILO Working Paper 39 XTable 1. Summary statistics for UB recipients, overall sample and matched versus unmatched sub-samples Notes: Variables means are shown for the entire sample (column (1)), the sample that we did not match with the household survey (column (2)) and the sample that we matched with the household survey (column (3)). Column (4) displays the difference between columns (2) and (3) and the results from a two-sided t-test, where *, **, and *** denote significance at the 10, 5, and 1 percent levels, respectively. Except for the number of months of benefit receipt and the share exhausting UBs, all variables refer to the point in time of program entry and are taken from the administrative data of the Ministry of Labour. With regard to education, the residual category also contains persons not reporting their educational level, such that the actual share of persons with at least an upper secondary education is higher. We observe that UB recipients (i.e. matched and unmatched observations) are disproportionately men (i.e. almost 60 per cent of participants). This is in line with the prevalence of male workers in the formal labor market in the country. The average age in the sample is almost 36 years and around half of the participants is married and has dependents. Individuals are generally low educated, with less than 20 per cent of UB recipients having attained at least upper secondary education. The monthly wage at job loss is on average slightly below 12,000 Mauritian Rupees, which corresponds to 736 USD in PPP and is marginally below the median wage in the country. Average tenure in the previous job is just above three years and the relative majority of participants (i.e. more than one third) was previously employed in elementary occupations. Finally, the average length of UB receipt is equal to 10.3 months and around 70 per cent of participants stay in the program for its entire duration. 19 ILO Working Paper 39 XFigure 1. Formal employment shares for matched and unmatched observations, social security database Notes: The figure reports the share of formally employed individuals (i.e. relative to the population) in the three years before and after job loss for the sample of UB recipients who has been matched between the administrative and survey data (dashed green line) and the sample of UB recipients who has not been matched (dashed blue line). Formality is measured from the social security records for both samples. A second possible concern relates to the comparability of the information provided between the two data sources. In the context of the present analysis, this is relevant particularly for variables capturing formal and informal employment. The definition of formality that we construct from the household survey is the same as the one that we observe in the administrative database. As mentioned above, this refers to the presence of job-related social security contributions to the National Pension Fund that are compulsory for the legislation. Measurement might nevertheless differ between the two sources, if survey respondents ignore their formality status or decide to misreport it (e.g. for fear that survey responses are used for auditing). To address this concern and provide evidence on the quality of the matching, we exploit the fact that for the matched sample we have information on formal employment from both the administrative records and the household survey. Figure 2 plots the shares of individuals in our UB sample with a formal job before and after the time of job loss. The dotted green line corresponds to the dotted green line in Figure 1. It measures formality from the social security records for the matched sample and is therefore precisely observed at each point in time (i.e. independently from when the individual was interviewed in the household survey, we take the entire series of social security records). The continuous green line also reports the share of individuals who are formally employed for the matched sample, but this time measured from the household survey and using observations as repeated cross-sections (i.e. for each individual, we have a maximum of four observations over time such that different individuals are used to estimate formality rates across months). Sample size is small at the monthly level in the household survey and this leads to some noise in the series, but the figure shows that formality shares are extremely comparable between the two sources. 20 ILO Working Paper 39 XFigure 2. Formal and informal employment shares and share of participants receiving UBs, matched sample Notes: The figure reports the share of formally and informally employed individuals (relative to the population) in the three years before and after job loss as well as the share of participants receiving UBs for the sample of UB recipients who has been matched between the administrative and survey data (i.e. 6.66 per cent of total UB recipients). The dashed green line is obtained from the social security records and corresponds to population means, as it is computed based on the full panel of observations for the universe of the matched individuals (i.e. independently from when the individual was interviewed, we use the entire history of social security records). The same applies to the dashed red line reporting the share of participants receiving UBs. The continuous green and blue lines represent instead population estimates obtained by appending different cross-sections of observations depending on the time at which the individual was interviewed in the CMPHS. This is reassuring, because it suggests that we can credibly rely on information that is only available in the household survey (including on informal employment) to enrich the analysis.15 In Figure 2, we then plot also the informality rates for our matched sample of UB recipients (continuous blue line). A number of interesting findings emerge. First, informality is low in the period before job loss, with around 20 per cent of UB recipients holding an informal job in the year before entering the program. However, the share of individuals holding an informal job rapidly increases and settles between 40 and 50 per cent of the sample starting from six months after job loss. Around 70 per cent of UB recipients is therefore re-employed 12 months after job loss, 40 per cent informally and 30 per cent formally. Informality rates remain high even at the end of program exhaustion, when the incentives not to work formally end: three years after job loss, 40 per cent of UB recipients are still informally employed. 15 In theory, one might still suspect that information on informal employment is misreported in the CMPHS even if information on formal employment is adequately provided. While we cannot totally rule out this hypothesis, we note that in the six months before UB registration, when everybody should have been in a job in order to be eligible to later receive UBs, all individuals report being in a job in the CMPHS (i.e. summing the green and blue continuous line adds almost perfectly to one in all the months). Additionally, the structure of the CMPHS makes it unlikely that an informal employee strategically misreports her labor market status as questions on social security contributions are only asked when respondents are already responding to the employment module. 21 ILO Working Paper 39 The evidence from the survey data shows that 12 months after job loss, only 30 per cent of program participants is without a job and is therefore still entitled to the benefit. However, we also see from the social security records that 70 per cent of UB recipients in the matched sample is still receiving UBs at that point in time (dashed red line in Figure 2). This means that 40 per cent of UB recipients are working while also receiving the benefit at the time of UB exhaustion. Therefore, the vast majority of those who find a new informal job do not report it to the Labor Office. Among the individuals who in our survey data appear to be informally re-employed in the first year after job loss, 92.5 per cent are still registered as receiving UBs in the administrative records in the same month of the survey interview. This is an important finding, as we provide the first estimates of the share of individuals working informally while receiving UBs. It implies that the level of non-compliance with program regulations is relatively high. De-registration from the program is instead automatic for individuals who find a formal job and the excessive UBs eventually transferred (e.g. due to administrative delays) are recovered. 22 ILO Working Paper 39 X4 The insurance value of UBs and underlying labor market mechanisms We conduct a difference-in-differences (DiD) analysis centered 36 months around job loss to estimate the effects for UB recipients of losing a formal job on main outcomes of interest. The first set of outcome variables captures consumption expenditures, as this directly determines the insurance value of UBs. We then focus on a series of labor market outcomes to understand the mechanisms through which the consumption effect materializes and how the availability of informal jobs affects the insurance value of UBs. Empirical approach Starting from the universe of UB recipients, we restrict the sample to those who enter unemployment from a formal job, which has lasted at least 36 months before layoff as measured from the social security records.16 As common in the literature, we define a control group of individuals who never experience job loss (Gerard and Naritomi, 2021; Kolsrud et al., 2020; Landais and Spinnewijn, 2020). This group is taken from the sample of the Mauritian formal labor force for which we have full social security records, restricting the analysis to those who are reported in the same formal job for 72 consecutive months. We conduct the analysis only on observations in the treatment and controls groups that are matched in the household survey. Imposing these restrictions, we end up with a final sample of 14,535 individuals (1,041 treated and 13,494 controls) who have been interviewed in the CMPHS in the three years around the (placebo) job loss. Table A1 in the Appendix shows selected descriptive statistics for this sample, as measured in the CMPHS. As expected, the treatment and control groups differ on a number of dimensions. In particular, UB recipients are more likely to be men and have lower educational attainments. The average age is instead similar between control and treated observations.The baseline equation takes this form: ∑∑ Y αUBQ βQ UB Year MonthDistric tε =+ ++++ + icst i t t t tic cs icst =−12 11 =−12 11 (3) where UBi is a dummy variable taking the value of one if the individual belongs to the treatment group of UB recipients; Q tare a set of event time dummies for each quarter before and after the (placebo) job loss at t =0; Y earcand Mo nth c are calendar year and month dummies for the time in which the individual is observed in the CMPHS; and District sis a vector of dummies for the district of residence. We group observations at the quarterly level in order to have adequate sample size in each event time. Given differences in observable characteristics between the treated and control groups, we re-weight observations to balance the first two moments of the covariate distributions (see Gerard and Naritomi, 2019; Landais and Spinnewijn, 2020). The re-weighting is based on the variables of sex, age, age squared as well as full sets of dummies for marital status, kinship relation and educational attainments. We apply the same re-weighting to all DiD results unless stated otherwise. Robustness tests will show that results are 16 The restriction on formality is imposed to identify a comparable control group of individuals who never lose their jobs for the entire time period (i.e. we do not observe informal workers consecutively for 72 months from the CMPHS, as the maximum length of the panel is 15 months and there might be employment gaps between interviews). The tenure requirement raises the comparability between treatment and control groups and follows previous studies that have similarly focused on high-tenure workers (Jacobsen et al., 1993). While the 36 month cut-off is arbitrary, modifying it does not substantially change the results. 29 ILO Working Paper 39 A second set of tests verifies if UB recipients anticipate job loss, which would represent a threat to identification. First, we restrict the treatment group to individuals dismissed for economic cause.24 This is done to look at more exogenous forms of job separation, in line with studies that have focused on firm closures. Results are very similar to those obtained for the overall sample (Figure A6). We then look at the probability of being registered at the public employment services (PES). The PES provide job-search support also to employed people willing to change jobs, so we should see an increase in registrations before job loss if individuals were anticipating dismissal. Instead, PES registrations go up exactly at the time of job loss (Figure A7, panel A). Similarly, we look at patterns of work absenteeism before job loss. Individuals could anticipate or even trigger the layoff by missing days of work. However, we do not find support for this hypothesis (Figure A7, panel B). A final set of tests checks if the results by re-employment status are driven by composition effects, given that neither the timing of re-employment nor the type of job found can be taken as exogenous. First, we exploit the fact that we know the exact date of formal employment from social security records and conduct the analysis separately by groups of workers according to the month of formal re-employment. Results in Figure A8 show that all groups experience a long-term drop in wages (panel A) and expenditures (panel B), even though those who find a new job earlier do relatively better in the short-run. Second, we investigate if differences in wages between formal and informal jobs are driven by selection bias. This is done by comparing specifications which add different sets of controls. In particular, we present results, (i) with no controls (i.e. no weights), (ii) with weights obtained as in the baseline model (i.e. baseline model), and (iii) with weights obtained adding also dummies for industry (i.e. at the one digit level), enterprise type (i.e. public, private or other types) and establishment size (i.e. less than five workers, between five and nine, ten and above) (i.e. augmented specification). If selection bias was driving differences in wages, adding controls should reduce the estimated wage gap between formal and informal jobs. However, this does not appear to be the case (Figure A9). 24 This has been classified as dismissals for: firm's closure, economic dismissal, financial difficulties, in receivership, redundancy, structural dismissal and technological reasons. 30 ILO Working Paper 39 X5 The efficiency costs of UBs and the substitution between formal and informal employment We now turn to analyzing the efficiency costs of UBs, which are determined by the effect of UB generosity on benefit duration and time until formal re-employment (recall Equation 1 in Section 2 above). To investigate the role of informal jobs in determining the efficiency costs, we subsequently assess whether UB generosity leads to a substitution from formal to informal employment. For effect identification we employ regression kink (RK) analysis, following Card et al. (2015, 2017) and Landais (2015), among others. Empirical approach A. Sample selection and empirical specification XFigure 6. Unemployment benefit entitlements in Mauritian Rupees as a function of the monthly wage at job loss and month of unemployment duration Notes: Graphical illustration of UB entitlements (see Equation 2) during months 1-3 (blue), months 4-6 (orange), and months 7-12 (gray) of unemployment duration. UB entitlements were inferred using information on monthly wages at the time of job loss for UB recipients entering the program between January 2011 and April 2018. For ease of exposition, in this figure we do not adjust for inflation. As explained in Section 3, the Mauritian UB schedule exhibits kinks at the upper and lower end, which we illustrate in Figure 6. Monthly UB entitlements decline rather strongly with time, from 90 percent of the 31 ILO Working Paper 39 monthly wage at job loss (during months 1-3 of unemployment), to 60 percent (months 4-6) and finally to 30 percent (months 7-12). The share of individuals for which the upper bound is binding is highest during the first three months of unemployment (equal to 13 percent, see Appendix Table B1). In contrast, the share of participants entitled only to the lower bound of the UBs peaks during the last six months of program participation (51.7 percent, Appendix Table B1). Given this distribution of participants and its implications for sample sizes, we focus on the kink at the upper bound of UB entitlements during the first three months of unemployment and the kink at the lower bound during the last six months. We additionally focus on benefit entitlements rather than benefits actually received. Entitlements are exogenous to participants’ behaviour, whereas benefits received are determined by when individuals leave the program upon re-employment (see Section 3). Regarding the selection of our sample, we drop a small fraction of UB participants for whom the wage at job loss is implausibly small (below 1,500 Rupees) or high (top 1 percent of the real wage distribution). We also focus on individuals who worked in a formal job in the month prior to program participation. 25 After imposing these restrictions, we obtain a sample of 17,791 individuals. Summary statistics are shown in Appendix Table B2 for the overall sample and for individuals in the proximity of the upper and lower bounds, respectively. We employ a sharp RK design and estimate the following model for the upper bound:  () EY ββww δw www[w]= +(−)+−[≥ ] kk kk 01 (4) and for the lower bound:  () EY ββww δw www[w]= +(−)+−[≤ ] kk kk 01 (5) [] is an indicator function equal to 1 whenever an individual receives UBs corresponding to the respective bound. w is a worker’s monthly wage at job loss and w kdenotes the wage at the respective kink points. We express both variables in 2017 Mauritius Rupees and divide them by 1,000. The models are estimated for ww−≤h k, where h is the bandwidth size. In our main specifications, we use the mean squared error (MSE) optimal bandwidth of h =0. 248 around the normalized kink points (see Calonico et al., 2017). We vary the bandwidth in robustness tests.26 The average treatment effect is given by αδ τ =/ kk k , where τ k is the change in slope in the relationship between the UB level and w . αk identifies the average impact of an additional 1,000 Mauritius Rupees of UBs. For the upper bound, τ=−0.9 k. Relative to initial wages, workers above this kink receive lower benefits than workers below the kink. In our analysis of the lower bound, τ=0.3 k.27 While α kis estimated locally, the investigation of two different kinks provides a more comprehensive understanding for different wage levels. As explained below, we show results for the upper bound in the main text and merely reference those for the lower bound, placing the corresponding results in the Appendix. In our preferred specification, we include year and district fixed effects to account for unobservable general time trends and time-invariant regional characteristics. This is important since the upper bound changes 25 Policy take-up is extremely low among informal workers who lose their job (while being almost complete among previously formal workers) and those who participate are not representative of informal workers in Mauritius overall (Liepmann and Pignatti, 2019). This might lead to unobserved heterogeneity, where it is not clear how this affects individuals around the kink (see Landais, 2015). Additionally and differently from formal workers entering the UB scheme, the wage information for informal workers cannot be verified by the caseworker at the time of registration, increasing the risk of measurement error of the running variable (see Section 3). 26 We identify this MSE-optimal bandwidth for the upper bound and based on the outcome of UB duration, as this outcome is central for our subsequent welfare analysis. We additionally consider the coverage error rate (CER) optimal bandwidth of h =0. 142 and a range of values chosen without theoretical foundation. 27 For workers below the kink, the unemployment benefit schedule is flat at the lower bound, whereas workers above the kink receive 30 percent of their initial wages. Therefore, workers below the kink receive higher unemployment benefits – relative to their initial wages – than workers above the kink. 32 ILO Working Paper 39 across years and registration to the program occurs at the district level. We also investigate how the inclusion of additional covariates affects our results. These include age and its square, marital status (captured by 4 categories), the number of dependents (3 categories) and educational attainment (3 categories). These covariates should not substantially affect our estimates provided that the identifying assumptions of the RK analysis are satisfied. B. Imputation of informal employment In contrast to all other outcome variables analyzed in this section, we observe informal employment only for the sub-sample of program participants we could match with the survey data, which means that sample sizes in the proximity of the kinks are very small. Based on the matched sub-sample, we thus revert to imputing the probability for individuals in the unmatched sub-sample to be informally employed in a given month. We build on the literature that started with Blundell et al. (2008) and predicts non-durable consumption based on food expenditure (see also Browning et al., 2003; Blundell et al., 2004; Attanasio et al., 2015; Kaplan et al., 2020). This methodology distinguishes between time-varying proxy variables (i.e., the main explanatory variables in the imputation regression) and variables that serve as controls. Both types of variables need to be available for the matched and unmatched sub-samples. Our imputed outcome variable, infemplim , is defined as the probability for an individual i to be informally employed in month m={−24,…,23} around job loss. We focus on this two-year window to show pre-treatment trends and dynamics over time. Two empirical observations guide our choice of proxy variables. First, the likelihood of being informally employed changes around job loss. It is low in the months before job loss, but then significantly increases with time spent in the program (recall the discussion of Figure 2 above). Second, the likelihood of being informally employed in a given month does not increase for those who hold a formal job, an information that we observe for our entire sample (where the residual category is non-employment). Therefore, we include as proxy variables a dummy variable formempl im, capturing whether an individual worked formally in a given month according to social security records, month since job loss fixed effects γm , and full interactions between these two sets of variables.28 We additionally include demographic control variables, thereby accounting for any observed differences between matched and unmatched sub-samples in terms of age, gender, marital status, number of dependents or education (see Table 1 above).29 The imputation regression is then estimated for individuals we could match with the household survey and takes the following form: infemplγformempl γγformempl γγXε=++++ im im mimm iim 12 ′ 3 ′ (6) The results for this regression are shown in Appendix Table B3. Its R-squared is 0.26 and the partial R-squared pertaining to the proxy variables is 0.16, which is reasonably high given the relatively sparse set of variables. We next predict the likelihood of being informally employed in a given month for the unmatched observations. Figure 7 shows that we are able to replicate well the pattern of informal employment around job loss. However, since this prediction is naturally associated with imprecision in the imputed outcome variable, we need to account for the fact that the usual standard errors of the coefficients relating UBs to informal employment (i.e., of δk in equations 4 and 5) are too small. Moreover, due to non-classical, or Berkson type, measurement error in the dependent variable, these coefficients are downward biased. We correct for both phenomena relying on the procedure and Stata routine of Crossley et al. (2020) for our RK results on informal employment. 28 Because control variables need to be identical in the imputation regression and the RK-regression of interest (Crossley et al., 2020), we include year and district fixed effects and the first term of the RK-specification (i.e., ( w w−) ik ) in the imputation regression. 29 These variables are time-constant and are measured at the time of job loss. We deliberately include them as controls, not proxies. Indeed, these controls should not yield differential predictions around the kink points of the UB schedule, given that the RK design hinges on the assumption of smooth covariate evolution. 33 ILO Working Paper 39 XFigure 7. Share informally employed in a given month around job loss, actual versus imputed Notes: The figure shows the actual share of informally employed individuals in a given month around job loss for the matched sub-sample (dashed green line) and the imputed mean probability of being informally employed for the unmatched sub-sample (solid blue line). The imputation is based on Equation 6 in the main text and results are shown in Appendix Table B3. Assessment of the identifying assumptions The RK methodology hinges on two identifying assumptions. First, the density and the partial derivative of the density of the assignment variable are assumed to evolve smoothly around the kink. Intuitively, this rules out that observed changes in the outcomes of interest are generated by sample selection. Figure 8 displays the number of observations in each bin of the wage distribution at layoff, normalized by the wage at the upper bound for a bin size of 0.0125. Panel A shows the entire distribution of participants, while panel B focuses on observations close to the upper bound according to an MSE-optimal bandwidth. The figure shows no sign of discontinuity around the kink, which is confirmed by the McCrary (2008)-test. We also test for a possible discontinuity of the partial derivative of the density as in Landais (2015) and cannot reject the null hypothesis of no discontinuity (see Figure 8).30 30 We regress the number of observations in each bin on polynomials of the running variable centered at the kink ww(−) kand the interaction term for being above the upper bound ww ww(−)[≥ ] kk . The coefficient of the interaction term for the first-order polynomial is a test for the change in slope of the derivative of the density. 34 ILO Working Paper 39 XFigure 8. Probability density function of the running variable around the upper bound Notes: The figure plots the probability density function of the running variable (i.e. wage at layoff) around the upper bound. Wages are normalized by the wage at the upper bound and the bin size is equal to 0.0125. Panel A presents the entire distribution of participants, while panel B focuses on observations around the upper bound using the MSE-optimal bandwidth size. In panel B, we also report results of two tests of discontinuity. The first one is a classical McCrary test for the continuity of the probability density function at the kink. The second test follows Landais (2015) and aims to check the continuity of the partial derivative of the probability density function at the kink. Lack of sorting at the upper bound is coherent with the institutional setting faced by UB recipients. The upper bound has changed every year during the period of analysis and is computed as 90 per cent of the threshold at which social security contributions are capped. Identifying these values is complex and participants would also need to optimize based on the presumed schedule that will apply when they can expect to be dismissed. In contrast with this understanding, there is evidence of sorting at the lower bound (see Appendix Figure B1). Although we cannot directly test the reasons behind this, the lower bound was binding throughout the study period for individuals whose initial wage was equal to 10,000 Rupees. This is a round amount, where a mass of individuals is naturally concentrated. Coherently, we see similar discontinuities for placebo kinks at pre-layoff wages of 8,000 and 12,000 Rupees. The second assumption needed for RK is that the marginal effect of the assignment variable on the outcomes of interest is smooth at the kink. Figure 9 shows that observable individual-level characteristics measured at the time of job loss (i.e. gender, age, marital status, presence of dependents and educational attainments) evolve smoothly around the kink at the upper bound. We confirm this result by running regressions in the form of Equation 4, but using the covariates as dependent variables (Table 2). Finally, we estimate the main outcome of interest (i.e. length of UB receipt) based on pre-determined covariates (as in Britto, 2016; Ye, 2020).31 Panel E of Figure 9 shows that the predicted length of UB receipt evolves smoothly around the kink. Instead, the presence of bunching at the lower bound implies that some of the covariates do not evolve smoothly around that kink (i.e., gender and age, see Appendix Figure B2 and Table B4). This leads us to focus on the upper bound in the rest of the analysis and present results for the lower bound only as a matter of comparison. 31 In particular, we predict the length of UB receipt based on individual-level characteristics (i.e. gender, age, age square and educational attainments), household characteristics (i.e. dummies for marital status and number of dependents) as well as occupation in the previous job and year and district fixed effects. All of these variables are measured at the time of job loss and are therefore not affected by treatment. 35 ILO Working Paper 39 XFigure 9. Evolution of covariates around the upper bound Notes: The figure plots the evolution of covariates and predicted outcomes around the upper bound of benefit levels, using a bin size of 0.0125 and an MSE-optimal bandwidth of 0.248. All variables are measured at the time of job loss, using administrative data from the Ministry of Labour. The predicted length of UB receipt is obtained with a regression using as explanatory variables: gender, age, age square, marital status, number of dependents, educational attainments, previous occupation and year and district fixed effects. 36 ILO Working Paper 39 XTable 2. Smoothness of covariates at the upper bound Notes: The table shows RK-results for selected individual level characteristics measured at the time of job loss using the administrative data of the Ministry of Labour. Estimates are obtained at the upper bound with an MSE-optimal bandwidth of 0.248 around the normalized kink point. Each column refers to a separate regression, with and without year and region fixed effects. Robust standard errors are given in parentheses, where *** , **, and * denote significance at the 1, 5, and 10 percent level, respectively. Main results A. Benefit duration and time before formal re-employment As a first outcome, we analyze how increased UBs impact the duration of benefit receipt for individuals affected by the upper bound of the benefit schedule. The emerging pattern stands in clear contrast to the smooth evolution of covariates and the predicted outcome presented above. The scatter bin plot for length of UB receipt shows a change in the slope around the kink point (Figure 10). To the left of the kink, where benefits amount to 90 per cent of previous earnings, we observe a positive relationship between underlying wages and UB duration. That relationship becomes flat to the right of the kink, where UBs are capped at the maximum level. This suggests that replacement rates below 90 per cent induce individuals to exit the program faster and to receive UBs for a shorter time. Results are similar without controls (panel A) and with the inclusion of year and district fixed effects (panel B, which plots regression residuals). The RK-results in Table 3 corroborate this finding. Our preferred specification includes only district and year fixed effects and indicates that benefit receipt increases by 0.184 months due to a 1,000 Rupee (USD 61 in PPP) increase in the UB level (column 2). The effect is virtually the same when additional control variables are added (column 3). It is larger in the specification without any control variables (column 1), which demonstrates the importance of accounting for district and year. The elasticity corresponding to our preferred specification equals 0.27 (s.e. 0.11). Although they must be interpreted with some caution, we find similar effects at the lower bound (see Figure B3 and Table B5). 32 In comparison, Schmieder and von Wachter (2016) report a median elasticity of UB receipt to benefit levels of 0.30 for studies in the US and Europe (authors’ calculation based on Table 2 in their review).33 Our estimates are thus below the median of those typically found for high-income economies, despite informality being low in those countries. 32 We again find that higher benefits increase UB duration, with an effect of 0.897 months due to a 1,000 Rupee increase and a resulting elasticity of 0.25 (s.e. 0.05) in our preferred specification. This suggests that the effects hold and are very similar in magnitude at a lower point of the income distribution. 33 The few studies focusing on a middle-income country context (Britto 2021; Gerard and Gonzaga, 2021) analyze benefit duration rather than levels and are thus not directly comparable. 37 ILO Working Paper 39 XFigure 10. Scatter bin plots for the duration of UB receipt (in months) at the upper bound Notes: The figures show mean values and 90 per cent confidence intervals of UB receipt in months per bin of size 0.0125 around the normalized kink point, based on raw data (panel A) and after controlling for region and year fixed effects (panel B). The MSEoptimal bandwidth of 0.248 was used. Linear models were fitted on both sides and independently of the choice of bin size; i.e., all programme participants are weighted equally in the linear fits. The figure might seem to be influenced by the presence of outliers at the two extremes of the bandwidth. In reality, their exclusion would change the slope of the lines on the two sides of the cutoff while still maintaining a change in slope at the cut-off. The robustness tests will check the sensitivity of the results to bandwidth choice. XTable 3. RK-results for the duration of receiving UBs (months), upper bound Notes: The table shows RK-results for the duration of receiving UBs (in months) at the upper bound and an MSE-optimal bandwidth of 0.248 around the normalized kink point. Each column refers to a separate regression. Robust standard errors are given in parentheses, where *** , **, and * denote significance at the 1, 5, and 10 percent level, respectively. Additional controls include age and its square and dummy variables capturing gender, marital status (4 categories), number of dependents (3 categories) and educational attainment (3 categories). α kis the average treatment effect due to a 1,000 Rupee increase in UBs. 38 ILO Working Paper 39 Elasticities are given by α kmultiplied by the maximum benefit b in thousands (averaged across years) over the mean UB duration D at the kink point. XFigure 11. Hazard rate of formal employment and share of UB participants finding a formal job in each month after job loss Notes: The figure shows the share of UB participants finding a formal job in any given month after job loss as well as the hazard rate of formal employment, computed as the ratio between the share formally re-employed in that month and the survival rate out of formal employment in the previous month. These variables are computed on the entire sample of UB participants with data from social security records. The three vertical lines denote when UB replacement rates vary. UB efficiency costs also depend on the total time spent out of employment, independently from the length of benefit receipt. In this regard, we follow Gerard and Gonzaga (2021) who show that, even in a context characterized by high informality, efficiency costs arise only from the effect of UB generosity on delayed formal (but not informal) re-employment. Using our preferred specification at the upper bound, we obtain elasticity estimates of the duration out of a formal job with respect to benefit levels of 0.28 (s.e. 0.14) when we cap formal re-employment at one year after job loss and 0.21 (statistically not-significant) when we cap it at two years. These estimates are substantially lower than those generally found in developed economies (the median estimate is equal to 0.57 in the review by Schmieder and von Wachter (2016)). This can be explained by the fact that hazard rates of formal employment remain low in our sample even after individuals lose benefit eligibility (Figure 11), while they spike right after UB exhaustion in other contexts (Card et al., 2007).34 34 While the evidence for emerging economies is more limited, Gerard and Gonzaga (2021) find that in Brazil hazard rates of formal employment more than double in the month after benefit exhaustion. 45 ILO Working Paper 39 Appendix A: The insurance value of UBs and underlying labor market mechanisms XFigure A1. DID results on total transfers and consumption expenditures by re-employment status Notes: The figure reports estimates and confidence intervals for the coefficient β, as presented in Equation 3. Confidence intervals refer to the 90 per cent level; significance at the 5 and 1 percent levels are illustrated by markers shaped in diamond and triangle form, respectively. Regressions are run in levels, but to facilitate the interpretation results are presented in relative changes by dividing the estimates by the mean in the outcome of interest in the treatment group in the quarter before job loss. Figures showing results for different sub-groups according to their re-employment status include the whole sample in the period before layoff (i.e. independently from their re-employment patterns after job loss), which is why pre-treatment trends coincide and they are denoted by a unique line. All outcomes of interest are taken from the CMPHS. 46 ILO Working Paper 39 XFigure A2. DID results on household transfers Notes: The figure reports estimates and confidence intervals for the coefficient β as presented in equation 3. Confidence intervals refer to the 90 per cent level; significance at the 5 and 1 percent levels are illustrated by markers shaped in diamond and triangle form, respectively. Regressions are run in levels and for this set of outcomes of interest they are not rescaled by dividing estimates by the mean in the treatment group in the quarter before job loss, since this mean would be equal to zero in most of the cases. All outcomes of interest are taken from the CMPHS. 47 ILO Working Paper 39 XFigure A3. Results using an event study approach Notes: The figure reports estimates and confidence intervals for the coefficient β, as presented in equation 3. Confidence intervals refer to the 90 per cent level; significance at the 5 and 1 percent levels are illustrated by markers shaped in diamond and triangle form, respectively. Regressions are run in levels, but to facilitate the interpretation results are presented in relative changes by dividing the estimates by the mean in the outcome of interest in the treatment group in the quarter before job loss. Compared to the baseline specification, these results are obtained with a pure event study approach in which we do not use any control group. All outcomes of interest are taken from the CMPHS. 48 ILO Working Paper 39 XFigure A4. DID results without propensity score weights Notes: The figure reports estimates and confidence intervals for the coefficient β, as presented in Equation 3. Confidence intervals refer to the 90 per cent level; significance at the 5 and 1 percent levels are illustrated by markers shaped in diamond and triangle form, respectively. Regressions are run in levels, but to facilitate the interpretation results are presented in relative changes by dividing the estimates by the mean in the outcome of interest in the treatment group in the quarter before job loss. Compared to the baseline specification, these results are obtained without re-weighting observations using the propensity score. All outcomes of interest are taken from the CMPHS. 49 ILO Working Paper 39 XFigure A5. DID results on formal employment from panel and cross-sectional regressions Notes: The figure reports estimates and confidence intervals for the coefficient β, as presented in Equation 3. Confidence intervals refer to the 90 per cent level; significance at the 5 and 1 percent levels are illustrated by markers shaped in diamond and triangle form, respectively. The figure reports results for formal employment obtained using two different methods and data sources, (i) panel regression using social security records, and (ii) cross-sectional regression using CMPHS data. For comparison purposes, the panel regression is only conducted on individuals that are matched in the CMPHS, but using their full social security records. For both regressions, we conduct an event study approach so the cross-sectional results are the same as those presented in panel A of Figure A3. 50 ILO Working Paper 39 XFigure A6. DID results on the sample of UB recipients dismissed for economic reasons Notes: The figure reports estimates and confidence intervals for the coefficient β, as presented in Equation 3. Confidence intervals refer to the 90 per cent level; significance at the 5 and 1 percent levels are illustrated by markers shaped in diamond and triangle form, respectively. Regressions are run in levels, but to facilitate the interpretation results are presented in relative changes by dividing the estimates by the mean in the outcome of interest in the treatment group in the quarter before job loss. Compared to the baseline specification, these results are obtained by restricting the treatment sample to individuals who have been dismissed for economic reasons as defined in the main text. All outcomes of interest are taken from the CMPHS. 51 ILO Working Paper 39 XFigure A7. DID results on PES registration and work absenteeism Notes: The figure reports estimates and confidence intervals for the coefficient β, as presented in Equation 3. Confidence intervals refer to the 90 per cent level; significance at the 5 and 1 percent levels are illustrated by markers shaped in diamond and triangle form, respectively. This figure plots results on the probability of being registered at the PES (panel A) and the probability of having been absent at work in the reference week (panel B). All outcomes of interest are taken from the CMPHS. XFigure A8. DID results on wages and total expenditures, by date of formal re-employment Notes: The figure reports estimates and confidence intervals for the coefficient β, as presented in Equation 3. Confidence intervals refer to the 90 per cent level; significance at the 5 and 1 percent levels are illustrated by markers shaped in diamond and triangle form, respectively. Regressions are run in levels, but to facilitate the interpretation results are presented in relative changes by dividing the estimates by the mean in the outcome of interest in the treatment group in the quarter before job loss. This figure reports results on overall wages (panel A) and total expenditures (panel B) for three groups: those who found a formal job (i) within six months, (ii) within 12 months, and (iii) beyond 12 months. The entire treatment group is included before job loss, which is why pre-treatment trends coincide and they are denoted by a unique line. The date of formal re-employment is taken from the social security records, while the outcomes of interests are observed in the CMPHS. 52 ILO Working Paper 39 XFigure A9. DID results on formal and informal wages, different specifications Notes: The figure reports estimates and confidence intervals for the coefficient β, as presented in Equation 3. Confidence intervals refer to the 90 per cent level; significance at the 5 and 1 percent levels are illustrated by markers shaped in diamond and triangle form, respectively. Regressions are run in levels, but to facilitate the interpretation results are presented in relative changes by dividing the estimates by the mean in the outcome of interest in the treatment group in the quarter before job loss. This figure plots results on formal wages (panel A) and informal wages (panel B) using three different specifications: (i) with no weights, (ii) with the weights used in the baseline results, and (iii) with weights obtained with an augmented specification (see text for details). The entire treatment group is included before job loss, which is why pre-treatment trends coincide and they are denoted by a unique line. All outcomes of interest are taken from the CMPHS. 53 ILO Working Paper 39 XFigure A10. DID results on the sample of UB recipients around the upper bound Notes: The figure reports estimates and confidence intervals for the coefficient β, as presented in Equation 3. Confidence intervals refer to the 90 per cent level; significance at the 5 and 1 percent levels are illustrated by markers shaped in diamond and triangle form, respectively. Regressions are run in levels, but to facilitate the interpretation results are presented in relative changes by dividing the estimates by the mean in the outcome of interest in the treatment group in the quarter before job loss. Compared to the baseline specification, these results are obtained by restricting the treatment sample to individuals who are around the upper bound based on the MSE-optimal bandwidth used in the RK analysis. All outcomes of interest are taken from the CMPHS. 54 ILO Working Paper 39 XTable A1. Summary statistics for the DID analysis: treated and control groups Notes: Variables means are shown for the treatment group of UB recipients (column (1)), and the control group of individuals who never lose their job according to social security records (column (2)). Column (3) displays the difference between columns (2) and (1) and the results from a two-sided t-test, where *, **, and *** denote significance at the 10, 5, and 1 percent levels, respectively. All variables are measured from the CMPHS and refer to the time of the survey interview, which might take place in the three years around the (placebo) layoff. 61 ILO Working Paper 39 XFigure B7. RK-results for the probabilities of being formally and informally employed in selected months after job loss, upper bound, by bandwidth Notes: The figures show RK-results for the probabilities of being formally and informally employed in selected months after job loss, referring to the upper bound. Specifically, we display α_k, i.e., the average treatment effect due to a 1,000 Rupee increase in UBs. For each bandwidth, a separate regression was run. Confidence intervals refer to the 90 percent level; significance at the 5 and 1 percent levels are illustrated by markers shaped in diamond and triangle form, respectively. The specification follows Figure 12, Panel (a). For informality, the outcomes are imputed probabilities of being informally employed in a given month, with details on the imputation provided in the text; coefficients and standard errors were adjusted using the imputation correction of Crossley et al. (2020). For comparison, we highlight the bandwidths of 0.248 and 0.142 as in the previous figure. 62 ILO Working Paper 39 XFigure B8. RK results for the duration of receiving UBs (months), upper bound, by bandwidth Notes: The figure plots RK-results and 90 per cent confidence intervals for the effect of a 1,000 Rupee increase in UBs on the duration of UB receipt (in months); significance at the 5 percent level is illustrated by markers shaped in diamond form. The specification is the same as in Table 3, column (2). Each estimate stems from a separate regression, obtained by varying the bandwidth size from 0.05 to 0.35 on each side of the cut-off. The long-dashed vertical line corresponds to the MSE-optimal bandwidth (0.248), while the short-dashed vertical line to the CER-optimal bandwidth (0.142). XFigure B9. Detection of actual kink Notes: The figure plots the R-squared of different specifications using length of UB receipt as the outcome variable without controls (panel A) or with year and region fixed effects (panel B). The different R-squared are obtained by varying the location of the kink over the support of the running variable. The continuous vertical line denotes the true location of the kink. 63 ILO Working Paper 39 XTable B1. Distribution of participants with UB entitlements at the lower and upper bounds, by year of participation and month of unemployment Notes: The table reports the number and shares of individuals for which the upper and lower bound is binding at different points in time during the twelve months of the program, for each year separately and in the overall period of analysis. The sample is defined as explained in Section 6.1. 64 ILO Working Paper 39 XTable B2. Summary statistics for the RK analysis: overall sample and individuals in the proximity of the upper and lower bounds, respectively Notes: The table shows summary statistics for the RKD analysis, where the proximity of the two bounds is defined in terms of the MSE-optimal bandwidth underlying our main specification. 65 ILO Working Paper 39 XTable B3. Imputation regression for the likelihood of being informally employed in a given month around job loss Notes: The table shows the results of the imputation regression for informal employment (equation 6), estimated for the matched sub-sample (i.e., individuals are included whenever survey data is available for them in a given month in a two year window around job loss). The dependent variable is a dummy variable capturing whether an individual was informally employed in a given month. All coefficients refer to a single regression. Robust standard errors are shown in parenthesis, where ***, **, and * denote significance at the 1, 5 and 10 percent levels, respectively. Proxy variables for informality include a dummy variable for being formally employed in a given month, fixed effects capturing the months around job loss (the omitted category is the month of job loss itself) and interaction terms between these two sets of variables. The partial R-squared refers to the variation that is attributable to the proxy variables. It was calculated by regressing the dependent variable on the proxy variables, after having partialled out the effect of the control variables in a previous regression. It is an important parameter for the corrections of standard errors and coefficients in the later RKD-regressions (see Crossley et al., 2020). Demographic control variables capture age and its square, gender, marital status, dependents, and education. 66 ILO Working Paper 39 XTable B4. Smoothness of covariates at the lower bound Notes: The table shows RK-results for selected individual level characteristics measured at the time of job loss using the administrative data of the Ministry of Labour. Estimates are obtained at the lower bound with an MSE-optimal bandwidth of 0.248 around the normalized kink point. Each column refers to a separate regression, with and without year and region fixed effects. Robust standard errors are given in parentheses, where *** , **, and * denote significance at the 1, 5, and 10 percent level, respectively. XTable B5. RK-results for the duration of receiving UBs (months), lower bound Notes: The table shows RK-results for the duration of receiving UBs (in months) at the lower bound and a bandwidth of 0.248 around the normalized kink point. Each column refers to a separate regression. Robust standard errors are given in parentheses, where *** , **, and * denote significance at the 1, 5, and 10 percent level, respectively. Additional controls include age and its square and dummy variables capturing gender, marital status (4 categories), number of dependents (3 categories) and educational attainment (3 categories). α_k is the average treatment effect due to a 1,000 Rupee increase in UBs. Elasticities are given by α_k multiplied by the minimum benefit b = 3,000 in thousands over the mean UB duration D at the kink point. 67 ILO Working Paper 39 XTable B6. Sensitivity of results to choice of the polynomial order, focusing on the probability of being formally employed in months 0, 4, 8, and 12 after job loss (upper bound) Notes: The table shows RK-results for the effects of UB generosity on the probability of being formally employed in selected months after job loss. The table reports three types of specifications (i.e. with no controls, with only year and region dummies and with additional individual level controls) and three types of models (i.e. linear, quadratic and cubic) as well as the results of the AIC and BIC tests. Robust standard errors are given in parentheses, where *** , **, and * denote significance at the 1, 5, and 10 percent level, respectively. 68 ILO Working Paper 39 XTable B7. Sensitivity of results to choice of the polynomial order, focusing on the imputed probability of being informally employed in months 0, 4, 8, and 12 after job loss (upper bound) Notes: The table shows RK-results for the effects of UB generosity on the probability of being informally employed in selected months after job loss. Details on the imputation are provided in the text and coefficients and standard errors were adjusted using the imputation correction of Crossley et al. (2020). The table reports three types of specifications (i.e. with no controls, with only year and region dummies and with additional individual level controls) and three types of models (i.e. linear, quadratic and cubic) as well as the results of the AIC and BIC tests. Robust standard errors are given in parentheses, where *** , **, and * denote significance at the 1, 5, and 10 percent level, respectively. XTable B8. Sensitivity of results to choice of the polynomial order, using the duration of receiving UBs (months) as the outcome variable (upper bound) Notes: The table shows RK-results for the effects of UB generosity on length of UB receipt, focusing on the upper bound. The table reports three types of specifications (i.e. with no controls, with only year and region dummies and with additional individual level controls) and three types of models (i.e. linear, quadratic and cubic) as well as the results of the AIC and BIC tests. Robust standard errors are given in parentheses, where *** , **, and * denote significance at the 1, 5, and 10 percent level, respectively. 69 ILO Working Paper 39 XTable B9. RK-results for length of UB receipt using placebo running variable (upper bound) Notes: The table shows RK results for the effects of UB generosity on length of UB receipt using as placebo running variable the wage upon re-employment. These are observed from the social security data. Robust standard errors are given in parentheses, where *** , **, and * denote significance at the 1, 5, and 10 percent level, respectively. XTable B10. Double difference RK-estimates (upper bound) Notes: The table shows RK-results for the effects of UB generosity on length of UB receipt using a double difference RK design. Robust standard errors are given in parentheses, where *** , **, and * denote significance at the 1, 5, and 10 percent level, respectively. 70 ILO Working Paper 39 References Amarante, V., R. Arim, and A. 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