rafmacalaba/datause-encoder-data huggingface dataset:
individual-level wage data is not available. Instead, among all other job-related variables, we choose the duration of the job as the second-best indicator for quality.<sup>27</sup> 111. In order to obtain information about match duration after re-employment and compare the experiences of workers with and without unemployment insurance, we use data from the Unemployment Registry. In this dataset information is collected on the last job held by the individual prior to registration, so we select those individuals who have been registered twice or more times, and view any job in-between unemployment spells as both the last job prior to the current spell or the job after the last spell. We then measure the duration of each job and construct two different tenure distributions, for recipients and nonrecipients.

112. On average, jobs found by workers with unemployment insurance are 10.71 months long, compared to 8.78 months for those with no benefits, and the difference between both numbers is significant at the 1 percent level. In addition, Graph IV presents both distributions. Not only they are visually different, with the one corresponding to workers with no benefits containing a much larger number of very short spells, but the equality of both distributions can be rejected using a Kolmogorov-Smimov test.

**Graph IV**

<!-- Start of picture text --> UIl No Ul<br>213871<br>0- - _____________________<br>- 1 ~ - i n ' 118 -.5 121<br>Source: Unemployment Registry<br><!-- End of picture text --> 113. In sum, workers who receive unemployment insurance search actively for jobs more frequently than those who do not, and appear to be slightly pickier regarding the kind of employment offers they are willing to accept, to the extent that they exhibit a stronger preference for salaried work and less flexibility regarding their work schedule. They also seem to obtain better matches, or at least matches that last longer. Whereas this is really the product of their more intense search, or whether the same would be true were unemployment insurance payments reduced or eliminated is something that, unfortunately, we cannot determine.

> 27 We have also looked at industry and occupation of re-employment, as well as reason for job termination, and found no significant differences across groups. Unfortunately we have no data on contract type (i.e. temporary, open-ended), or work schedule (full/part-time).

32
|**Appendix Table 1. S**|**ummary of Pro**<br>Experienced|**mpted Questio**|**ns**|Neophyte||
|---|---|---|---|---|---|
|Peru|Mexico|Ghana|Peru|Mexico|Ghana|
|**_Panel A: Savings Audits_**||||||
|**_Costs_**||||||
|Interest rate<br>✓|✓|✓|✓|✓|✓|
|APY Explanation<br>✓|✓|||||
|Other costs and penalties<br>✓|✓|✓||||
|**_Other items_**||||||
|Name of staff<br>✓|✓|✓|✓|✓|✓|
|Commercial product name<br>✓|✓|✓|✓|✓|✓|
|Minimum account balance<br>✓|✓|✓|✓|✓|✓|
|Locations to withdraw cash<br>✓|✓|✓|✓|||
|Debit card|✓|✓||||
|Contract<br>✓|✓|✓||||
|**_Panel B: Credit Audits_**||||||
|**_Costs_**||||||
|Payment amount<br>✓|✓|✓|✓|✓|✓|
|Interest rate<br>✓|✓|✓|✓||✓|
|APR explanation<br>✓|✓|✓||||
|Other costs and penalties<br>✓|✓|✓||||
|**_Other items_**||||||
|Name of staff<br>✓|✓|✓|✓|✓|✓|
|Commercial product name<br>✓|✓|✓|✓|✓|✓|
|Available terms<br>✓|✓|✓|✓|✓|✓|
|Available payment frequencies<br>✓|✓|✓|✓|✓|✓|
|Loan amount<br>✓|✓|✓|✓|✓|✓|
|Amount to be received<br>✓|✓|✓||✓|✓|
|Summary sheet / payment plan<br>✓|✓|✓|✓|✓|✓|
|Contract<br>✓<br>This table provides a summary of questions that neophytes <br>information voluntarily. In Peru, auditors specifically asked f<br>account inactivity and falling below the minimum allowed ba<br>asked.|✓<br> and experience<br>or opening, ma<br>lance. In other|✓<br>d auditors had <br>nagement and c<br>countries a gen|to asked if t<br>onsultation fe<br>eral question|he staff did n<br>es, and for p<br>about fees and|ot provide the<br>enalties due to<br> penalties was|
**Table 2: Performance Indicators in the Surve Year**

Sources: GDP **GrwhCPI** nftonGP1989 base: DO. Reform inde and unemployment rate: DeMelo, Denizer and Gelb (1996).Employment share, real wages, real interest rates: EBRD. Aggregate savings rate: Denizer and Wolf (1998).

The nature of pre-transition savings has been the subject of a lively literature. During the 1980s, a number of transition economies experienced nominal wage growth in excess of the output growth of consumption goods. In the presence of fixed prices, this latent demand could not be converted into goods, and thus resulted into the accumulation of excess savings, mostly held in liquid form. Whether or not his "monetary overhang" indeed existed at the beginning of the transition depends on whether the quantity constraints on goods were in fact binding, or could be circumvented in a black market with scarcity determined prices.<sup>5</sup> Any such pre-existing dis-equilibrium is however unlikely to have persisted until the time the surveys were taken as the price liberalization undertaken in the early 1990s eliminated the excess through a burst in inflation [Dombusch and Wolf (1999)] and, to a more limited extent in the short term, a supply response.

While financial sector liberalization took place before the sample year in all three countries (1989 in Poland, 1991-92 for households in Hungary, 1991 in Bulgaria), both consumer credit and insurance products remained largely unavailable. Insurance markets likewise remained rudimentary. Pension systems were comprehensive but perceived to be fragile, with dependency ratios of 36% for Hungary, 49% for Poland and 87% for Bulgaria [EBRD (1996)], compared to a typical range between ten and twenty percent in market economnies of comparable development. Most households covered by the survey are thus likely to have faced binding borrowing constraints; to have relied on personal savings rather than insurance to cover income and other risks, and to have faced significant uncertainty about expected real pension benefits.

> 5 For alternative views on this issue, see Acharya and Spagat (1993), Alexeev (1988) and Ellis and Naughton (1990)

> 6 Insurance premia as a fraction of GDP in the sample years ranged between 0.5% and 1.8%, compared with 4.2% in the OECD.
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stronger effects for banks with higher exposure to local public debt, consistent with lowering crowding out. More concretely, a one-standard-deviation increase in a bank’s pre-Law exposure to local public debt is associated with a 28 percent reduction in the total volume lent to local governments and a 31 percent increase in the total lending volume to firms. Along with the reduction in loan volume, there is a relative increase in interest rates of loans to highly indebted local governments.

We next study the crowding-out mechanism in more detail using loan-level data. This data allows us to study changes in the loan terms of the average firm and identify adjustments in the supply of credit, by saturating our specifications with _firm*month_ fixed effects. We find that in a state with average ex ante public indebtedness, a one-standard-deviation increase in the ex-ante exposure of a bank to local government debt leads to an 8.8 percent increase in the loan volume extended to firms. This effect is substantially larger in more indebted states.

We then evaluate whether the type of ex ante spending of a local government has any impact on the state’s subsequent economic activity. We find stronger positive effects on economic activity and bank lending to the private sector in states with higher ex ante public spending on social services over infrastructure. For a state with average ex ante public indebtedness, a onestandard-deviation increase in the ex-ante share of spending in non-infrastructure projects is associated with a 0.15 p.p. increase in quarterly growth rate of GDP and a 0.1 p.p. increase in the employment ratio after the passage of the FD Law.<sup>8</sup> Credit effects vary depending on how financially constrained firms are (proxied by shorter credit histories). For firms with short credit histories, the relation between state ex ante indebtedness and firm credit access after the FD Law is significantly stronger—especially for firms headquartered in states with ex ante higher share of fiscal spending on non-infrastructure. Overall, we interpret this finding as evidence that private firms operating in states with lower public infrastructure spending benefit more from the unwinding of the crowding out. In other words, our results suggest that the marginal return of the reallocation of capital from public towards private firms is higher in states that were channeling more funding to non-infrastructure spending (i.e., more public spending on social services).

> 8 These states have not only higher share of spending in non-infrastructure, but also over social spending.

5
How can we then address the limitations of SAKERNAS? One approach commonly adopted by previous studies is to supplement SAKERNAS with the IFLS (see e.g., World Bank, 2010). However, this approach is no longer feasible as the IFLS has not been conducted since 2014, and the questionnaire contains outdated measures of informality status (see earlier discussion). A more promising approach would be to design a new survey that will allow more targeted questions to be asked of workers.

In principle, the new survey should include a more extensive set of job characteristics than SAKERNAS as well as measures of worker satisfaction. Well-established surveys such as Australia’s Household, Income, and Labour Dynamics Survey, the British Household Panel Survey, and the German Socioeconomic Panel Survey provide a useful starting point for this exercise. The HILDA Survey, for example, asks respondents to rate their overall job satisfaction as well as their satisfaction with specific dimensions such as job security, work hours, and flexibility. In terms of employment benefits, it is worth asking respondents whether they voluntarily participate in social protection schemes and the reasons for not doing so. Finally, the new survey could also include retrospective questions capturing respondents’ labor market histories, which could provide a more dynamic perspective on informality. For example, this can be used to understand whether informality is an absorbing state or a temporary arrangement until workers find a formal job (Jayachandran, 2021).

# **Analytic gaps**

Shortcomings in the existing data inadvertently create gaps in our understanding of informality in Indonesia. For instance, the lack of a dedicated survey makes it difficult to approximate the size and composition of Indonesia’s informal sector. Without this basic building block, estimating the size of different segments that may co-exist within the broader sector becomes more difficult. This latter issue also applies to informal employment, albeit to a lesser degree. Although overall measures of informal employment have improved substantially since 2016, approximating the relative share of workers for whom the forces of exit and exclusion apply 23
Policy Research Working Paper 10445

# **Abstract**

This paper uses satellite readings of nitrogen (NO2) air pollution, a byproduct of combustion, to improve the measurement of global economic activity. The proposed approach improves upon night light measures for countries where data manipulation, conflict, or other factors have led to poor national accounts. The paper also shows that existing country rankings of gross domestic product accuracy over the past 15 years are unreliable, even among advanced economies. For example, the paper shows that during COVID, in France, the UK and Spain gross domestic product in 2020 was underreported by 76, 181, and 205 basis points respectively. The methodological contribution extends previous Error-Measurement frameworks which, suffer from error-in-variables biases, with an objective, data-driven identification strategy exploiting the plausibly orthogonal measurement errors between nitrogen dioxide and night lights, which are measured at different times.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at drieracrichton@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Table 5: First Stage Regression Results**

Dependent variable: Ln (Freight Rate)

||Control 1|Control 2|Control 3|
|---|---|---|---|
||(1)|(2)|(3)|
|Av. VAT rate at next probable destination|5.557***|5.191***|5.567***|
||(1.405)|(1.410)|(1.401)|
|Ln (Av. VAT rate in own route*Crude price)|0.109***|0.101***|0.115***|
||(0.025)|(0.026)|(0.026)|
|Intercept|-1.468|-3.491**|-1.802|
||(1.664)|(1.756)|(1.653)|
|No. OD pairs|23288|13678|23288|
|No. Absorbed FEs|1080|1003|1080|
|R-squared|0.514|0.514|0.514|

Note: Each column represents a coefficient estimate from separate regressions. The column heading corresponds to the set of controls for regressions results reported in that column. Control 1: ln (distance between origin and destination (OD)) and ln (average VAT in potential next destinations*crude price). Control 2: Control 1 plus ln (diesel price/litre along OD route). Control 3: Control 1 plus ln (average nightlight luminosity along OD route). The sample for column 2 is smaller because diesel price data is available only for 2019. Standard errors are clustered at the destination level. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

# **Table 6: Estimation of trade gravity regression: IV Results**

IV: VAT rate in the next potential destination

||(1)<br>Control 1|(2)<br>Control 2|(3)<br>Control 3|
|---|---|---|---|
|**Ln (value of trade)**||||
|Ln (Freight rate)|-3.904***|-3.677***|-3.936***|
||(1.160)|(0.888)|(1.169)|
|**Ln (Volume of trade)**||||
|Ln (Freight rate)|-4.445***|-4.295***|-4.479***|
||(1.142)|(0.884)|(1.152)|
|Kleibergen-Paap F statistic|15.65|28.81|15.79|
|Origin Fixed Effects|Yes|Yes|Yes|
|Destination Fixed Effects|Yes|Yes|Yes|
|No. Absorbed FEs|1080|1003|1080|
|No. OD pairs|23288|13678|23288|

Note: Dependent variables are in bold. Each cell represents a coefficient estimate from separate regressions. The column heading corresponds to the set of controls for regressions results reported in that column. Control 1: ln (distance between origin and destination (OD)) and ln (average VAT in potential next destinations*crude price). Control 2: Control 1 plus ln (diesel price/litre along OD route). Control 3: Control 1 plus ln (average nightlight luminosity along OD route). The sample for column 2 is smaller because diesel price data is available only for 2019. Standard errors are clustered at the destination level. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

28
Table 5. Effects from counterfactual experiments

||Coun<br>|terfactual experi<br>|ments<br>|
|---|---|---|---|
|Variables|Experiment 1|Experiment 2|Experiment 3|
|Tax evasion|-26.72|-18.34|-40.16|
|Output per worker|1.06|6.33|6.26|
|Aggregate TFP|0.16|208.07|184.86|
|Aggregate capital|22.92|173.24|200.22|
|Mass of entry|22.92|173.24|200.22|
|Share of small frms|-0.98|-0.98|-0.98|
|Share of medium frms|8.23|8.71|8.23|
|Share of large frms|2.12|0|2.12|
|Employment share - Small|-16.75|- 0.09|-16.43|
|Employment share - Medium|-6.94|8.86|-6.53|
|Employment share - Large|3.04|-1.04|2.95|
|Average size of frms|21.52|-4.69|20.99|

Notes. Except for the change in output per worker that is reported in fold, all effects are reported in percentage change. Here small, medium and large firms refer to firms having between 1-19, 20-99, and 100 and more employees.

In summary, the counterfactual experiments show that a drop in losses stemming from the business environment along with an improvement in detection probability in Guinea could decrease sales not reported for tax purposes by between 17.78 and 32.86 percent. The drop in tax evasion combined with the improvement in institutional quality generate between a 1.06and 6.33-fold increase output per worker in Guinea through their effects on the aggregate TFP, the mass entering on the market, and the reallocation of firms and employment.

29
The Delhi Metro (DM) is an electric-based mass rapid rail transit system mainly serving the Indian National Capital Territory (NCT) of Delhi. The NCT of Delhi covers an area of 1,483 square kilometers and has a population of 16.8 million people according to the Indian Census of 2011, making it one of the world’s most densely populated cities.<sup>1</sup> The DM was introduced in 2002 and since then it is being continually extended within the NCT and adjoining areas. As of 2012, its total route length was 190 kilometers, and annual ridership was 0.7 billion (DMRC 2012).<sup>2</sup> In this paper we examine whether this important mode of public transportation has had any impact on air pollution in Delhi. We identify the immediate localized effect of extending the DM rail network on air pollution measured at two different locations within the city: _ITO_ , a major traffic intersection in central Delhi, and _Siri Fort,_ a mainly residential neighborhood in south Delhi. Air pollution is measured in terms of three criteria pollutants, namely, nitrogen dioxide **(** _NO_ 2 ), carbon monoxide ( _CO_ ), and fine particulate matter **(** _PM_ 2.5 ).

An impact study of the DM on air pollution is important for two reasons. First, there is substantial scientific evidence on the adverse effects of air pollution on human health. Block et al. (2012) provide a review of epidemiological research that shows the link between air pollution and damage to the central nervous system, which may manifest in the form of decreased cognitive function, low test scores in children, and increased risk of autism and of neurodegenerative diseases such as Parkinson’s and Alzheimer’s. They also cite other studies which show that air pollution causes cardiovascular disease (Brook et al. 2010) and worsens asthma

> 1. According to a worldwide ranking of cities by City Mayors Statistics, Delhi ranked thirteenth in terms of population density with 11,050 persons per square kilometer. Mumbai ranked first, and Beijing ranked twelfth, with population densities of 29,650 and 11,500, respectively. The data are compiled from various sources and are the most recent available. Source url: http://www.citymayors.com/statistics/largest-cities-density-125.html. Last accessed on March 31, 2015.

> 2. As of 2014, the Beijing Subway had a route length of 527 kilometers and annual ridership of 3.4 billion. This information is accessed from Wikipedia. Source url: http://en.wikipedia.org/wiki/Beijing_Subway. Last accessed on March 31, 2015.

2
- [22] buehren.n, s.chakravarty, m.goldstein, v.slavchecska and m.sulaiman (2018) Girls’ Empowerment and Conflict: Evidence from South Sudan, mimeo World Bank.

- [23] bullough.v and b.bullough (1987) _Women and Prostitution: A Social History_ , Prometheus Books, Buffalo.

- [24] callen.m, m.isaqzadeh, j.d.long and c.sprenger (2014) “Violence and Risk Preference: Experimental Evidence from Afghanistan,” _American Economic Review_ 104: 123-48.

- [25] cameron.l and m.shah (2015) “Risk-Taking Behavior in the Wake of Natural Disasters,” _Journal of Human Resources_ 50: 484-515.

- [26] casey.k, r.glennerster and e.miguel (2012) “Reshaping Institutions: Evidence on Aid Impacts Using a Pre-Analysis Plan,” _Quarterly Journal of Economics_ 127: 1755-812.

- [27] casey.k, r.glennerster and t.suri (2017) The Economic Impacts of Ebola on Firms in Sierra Leone, IGC Report F-39204-SLE-1.

- [28] cunningham.s and m.shah (2017) “Decriminalizing Indoor Prostitution: Implications for Sexual Violence and Public Health,” _Review of Economic Studies_ 85: 1683-715.

- [29] dhar.d, t.jain and s.jayachandran (2018) Reshaping Adolescents’ Gender Attitudes: Evidence from a School-based Experiment in India, mimeo, Northwestern.

- [30] doepke.m, m.tertilt and a.voena (2012) “The Economics and Politics of Women’s Rights,” _Annual Review of Economics_ 4: 339-72

- [31] duflo.e (2012) “Women Empowerment and Economic Development,” _Journal of Economic Literature_ 50: 1051-79.

- [32] duflo.e, p.dupas and m.kremer (2015) “Education, HIV, and Early Fertility: Experimental Evidence from Kenya,” _American Economic Review_ 105: 2757-97.

- [33] dupas.p (2011) “Do Teenagers Respond to HIV Risk Information? Evidence for a Field Experiment in Kenya,” _American Economic Journal: Applied Economics_ 3: 1-34.

- [34] dupas.p, and j.robinson (2012) “The (hidden) Costs of Political Instability: Evidence from Kenya’s 2007 Election Crisis,” _Journal of Development Economics_ 99: 314-29.

- [35] eckel.c.c, m.el-gamal and r.k.wilson (2009) “Risk Loving after The Storm: A Bayesian-Network Study of Hurricane-Katrina Evacuees,” _Journal of Economic Behavior and Organization_ 69: 110-124.

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