rafmacalaba/datause-encoder-data huggingface dataset:
structured as a rotating panel, tracking each household across four consecutive months and then dropping it from the sample for 8 months, then reintroducing it again for another 4 months. Each month one fourth of the sample is substituted with a new panel. Thus, after 4 months the whole initial sample has been rotated, after 8 months a third different sample is being surveyed, and after 12 months the initial sample is interviewed. Over a period of two years, three different panels of households are surveyed, and the process starts again with three new panels. To minimize problems induced by attrition that increases with the time between interviews, we focus exclusively in the first two observations. Regrettably, the PME was drastically modified in 2002 and it is not possible to reconcile the new and old definitions for unemployment and job sectors.<sup>7</sup> Hence, our analysis begins in 1983 but stops at 2002.

There is broad consensus in the literature on the definition of informality from a labor market perspective both in the mainstream and Brazilian literature. A comprehensive survey of work studying the size and evolution of the Brazilian informal sector in the labor market can be found in Ulyssea (2005) and a summary of stylized facts of the eighties and nineties is detailed in Ramos and Reis (1997), Ramos (2002), Ramos and Brito (2003), Veras (2004), and Ramos and Ferreira (2005a,b). We follow this literature in definition by dividing employed workers into three sectors: formal salaried (F)-public employees and workers whose contract is registered in his/her work-card or _carteira de trabalho_<sup>_8_</sup> that entitle the worker to labor rights and benefits; informal salaried (I), salaried workers in private firms without _carteira;_ and informal self employed (S.E.). Ideally, following the ILO we would distinguish by firm size as well, focusing on establishments of fewer than 5-10 as informal employees, however the PME does not tabulate this information and hence, we rely purely on the basis of lack of signed _carteira_ - as the critical distinguishing characteristic.<sup>9</sup>

> 7 We are grateful to Lauro Ramos for providing the old PME dataset for 2002.

> 8 According to the Brazilian legislation, registered workers are the ones whose labor contract is registered on their work-card. This registration entitles them to several wage and non-wage benefits such as 30 days of paid holiday per year, contribution for social security, right to request unemployment benefit in case of dismissal, monetary compensation if dismissed without a fair cause, maternity and paternity paid leave and so on.

> 9 The ILO defines informality as consisting of all own-account workers (but excluding administrative workers, professionals and technicians), unpaid family workers, and employers and employees working in

7
depreciation, retained corporate earnings or taxes which are not distributed back to households, all of which are only remotely related to household income. Furthermore, there exists “a basic incongruity in assuming that the relative within-country distributions are measured acceptably well by surveys but their means are not” (Anand and Segal, 2008, p. 70). In addition, the replacement of the survey mean by a typically larger GDP per capita implies an equi-proportional adjustment of all incomes. This type of adjustment, which we call “proportional”, is very unlikely to be correct because it implies the same income underestimation, in relative terms, across the entire distribution.

Compared with GDP, final household consumption expenditure from national accounts is closer to household income (or consumption) recorded in surveys (Anand and Segal, 2008).<sup>18</sup> However, it must be noted, that the data and methods used to estimate national accounts consumption are not necessarily more reliable than household surveys (Anand and Segal, 2008, Deaton, 2005).<sup>19</sup> There are also definitional differences between household surveys and national accounts consumption, such as the inclusion of the imputed value of owner-occupied housing (although conceptually it should be included in both but in practice it often is not included in household surveys), imputed financial services, and consumption by non-profit institutions (Deaton 2005).

# **1.4** **<u>Top incomes</u>**

Recent work on top share inequality using tax records argues that top incomes are understated in standard household surveys. This literature studies inequality at the very top of the distribution, typically expressed in top shares, e.g. the share of total income received by the top 1% (Atkinson and Piketty (2007, 2010)). Tax data might provide more accurate information on top income recipients for a number of reasons: First, it might be harder to enter the gated communities of the rich than to conduct surveys in poor areas, so survey non-response would increase with income (Groves and Couper, 1998). Second, the top 1% are rare by definition, so a household survey with a standard sample size of a few thousand would offer top share estimates with a low

> 18 It might be argued that _income_ recorded in household surveys should be approximated with GDP (rather than national accounts consumption). However, Anand and Segal (2008) argue that even in this case, national accounts consumption is to be preferred because of the unrelated components included in GDP mentioned above.

> 19 The measurement issues in national accounts include the measurement of illegal transactions or the measurement of intermediate inputs. Furthermore, because consumption is computed as a residual, measurement errors are compounded (Anand and Segal, 2008).

12
Table 4: The 2012 reform effect on employment rates - Triple difference estimation with additional interaction terms

||Logit|Logit|Logit|Logit|Probit|OLS|
|---|---|---|---|---|---|---|
|DiDiD efect|-0.0394|-0.00163|-0.00110|-0.000124|-0.00474|-0.0907|
||(0.0413)|(0.0161)|(0.0166)|(0.0157)|(0.0182)|(0.0451)|
|Mean (Dep. Var)|.5262|.5262|.5262|.5262|.5262|.5262|
|St. Dv. (Dep. Var.)|.4994|.4994|.4994|.4994|.4994|.4994|
|Demographic controls|No|Yes|Yes|Yes|Yes|Yes|
|Year fxed efects|No|No|Yes|Yes|Yes|Yes|
|State fxed efects|No|No|Yes|Yes|Yes|Yes|
|Region by year unemployment rate|No|No|No|Yes|Yes|Yes|
|N|2509|2502|2502|2502|2502|2509|

Standard errors in parentheses Source: EU-SILC (2010-2020). Standard errors are clustered. The sample consists of mothers of children between 3 and 4 or 6 years old.

# **6 Hours Worked, Part-time Employment, and Earnings**

One particularity of the 2012 reform is that compulsory, preparatory classes only took place during morning classes. Consequently, the childcare subsidy by the public sector only took place during half of the day. It is therefore not surprising that we find an increase in the probability of being employed part-time as a consequence of the reform (Table 5). Nevertheless, the reform resulted in a slight increase in the average number of usual hours worked per week (Table 6).

Table 7 demonstrates a significant increase in gross monthly employee cash or near cash income for affected women. The coefficient in Column 4, the baseline specification, is equivalent to a 1.7 percent increase. The provision of public childcare could therefore also contribute to closing the gender pay gap. Nevertheless, the coefficients presented are insignificant at the common significance levels. Future research should investigate if these results are entirely driven by women working more, or because some of them also face earning increases.

19
Figure B3: Changes in APA and Household Survey indices (2016-2018) <!-- Start of picture text --> Urban planning Fiduciary responsibility<br>0 1 2 -2 0 2<br>Changes in DLI Changes in DLI<br>(a) Urban planning (b) Fiduciary responsibility<br>Infrastructure Accountability<br>-4 -2 0 2 -2 0 2 4<br>Changes in DLI Changes in DLI<br>(c) Infrastructure (d) Accountability<br>1<br>.6<br>.8<br>.4<br>.6 .2<br>.4 0<br>Changes in Survey Index Changes in Survey Index<br>.2 -.2<br>1 1.5<br>1<br>.5<br>.5<br>0<br>0<br>Changes in Survey Index Changes in Survey Index<br>-.5<br>-.5 -1<br><!-- End of picture text --> _Note:_ This figure presents a scatter of changes in DLI subcomponent scores (x-axis) and survey indices (x-axis) by LGA. DLI scores are normalized so that changes can be interpreted as standard deviations in the distribution of the 4th APA scores.

Figure B4: Correlation between APA and household survey measures <!-- Start of picture text --> Planning Baseline<br>Fiduciary Baseline<br>Infrastructure Baseline<br>Accountability Baseline<br>Taxation Baseline<br>Planning Endline<br>Fiduciary Endline<br>Infrastructure Endline<br>Accountability Endline<br>Planning Change<br>Fiduciary Change<br>Infrastructure Change<br>Accountability Change<br>-.5 0 .5<br>Correlation<br><!-- End of picture text --> _Note:_ This figure presents correlations across the 18 ULGSP LGAs for baseline, endline and changes in outcomes. Endline and changes are not reported for the taxation subcomponent since it was not collected in the 6th APA.

59
3.2.1 Merger of the EPA and LRD files.

The calculation of pollution intensity required merging the EPA and LRD data at the facility level. Unfortunately, no common code numbers link the same establishments within the EPA databases or between the EPA and LRD databases. This necessitated a complex matching process which used the facility names, addresses and SIC codes. Of some 20,000 plants reporting TRI information in 1987, about 13,000 were matched to the corresponding LRD data for that year. For medium-specific intensities, data from all 200,000 plants in the LRD, 20,000 plants in the TRI, 20,000 plants in the AIRS database, and 13,000 facilities in the NPDES were combined to the extert possible.

# 3.2.2 The Choice of a Numerator

A number of options existed for the choice of total pollutant risk to be used as the numerator. First, a decision had to be made regarding the choice of disposal medium. As noted above, the TRI data identify a range of releases and transfers, including emissions to air, water, land, underground injection, and off-site disposal in both landfill and public waste-water facilities. Initially pollution across all media was used, aggregating all releases and transfers of a given chemical from each facility.?

4In this regard, it is worth noting that there is little comprehensive analysis of the impact environmental regulation has had on total pollution at the plant level. Both regulation and research have generally focused on particular media, especially stressing releases to air and water. It is therefore unclear how much total “pollutant intensity" has been reduced in the US. Consider, for example, the 13
26 actuarially fair premiums have been paid on the basis of demographic characteristics; and second one must take account of the fact that some groups systematically receive less than their formal characteristics would suggest. Both these adjustments lead to the conclusion that r.,any programs overstate the degree to which they redistribute to the poor.

Life Cycle Income Shifts Closely related, some programs that appear to be redistributive merely shift income from one stage of the life cycle to another. For example, social security is sometimes thought of as a payment to low income retirees but, to the extent it is actuarially fair, merely represents compulsory savings when young to finance consumption when older and not working. Thus, it is less redistributive to the poor from a life cycle than from a static cross-sectional point of view. Indeed, in some countries people with high lifetime incomes (albeit relatively low current incomes) receive a disproportionate share of total benefits so the net redistribution may actually be perverse. In Brazil, for example, the poor are much less likely to benefit from pension benefits since they are less able to demonstrate the minimum necessary number of years of continuous attachment to the labor force. In 1986, 28? of benefits were absorbed by early retirees in Brazil, who represented just 92 of all recipients, and relatively few of these were poor (World Bank, 1988b).

Another example of a life cycle effect (which creates the opposite kind of distortion) concerns the provision of public education. We may think of people as paying for public education throughout their working lives but receiving the benefits at particular points in time, a life cycle shift in real income. Primary school students usually have young parents, whose
A descriptive analysis of the satisfaction with the reform indicates no obvious links between being a winner (loser) of the reform and being satisfied (unsatisfied) with the reform. Through out the surveys a simple t-test fail to reject the null that the satisfaction rates are significantly different across groups (e.g. between losers and winners). Also, the overall lack of popularity of the reform does not appear to differ significantly across a range of characteristics such as income or the source of fuel for cooking. For example, the approval of the reform was highest among the richest respondents to the survey conducted in January 2011 (around 50 percent of those in the top 15 percent of income were satisfied with the reform compared to 30 percent for rest of the sample) but the reform turned out to be more popular among poor people in later surveys (for instance in May 2012). Males appear slightly more favorable than females at any point in time but again, not significantly so. Unsurprisingly, there was a visible difference across education levels in January 2011 (more educated people resulted more favorable to the policy change) but already in May 2012 the difference across levels was not significant.

Rather, the level of information about the reform appears to be associated with the views of the respondents. Most of the respondents had limited information and acknowledged it so. In January 2011 only 18 percent of people considered themselves to be well (or very well) informed about the policy change. The lack of information was also reflected in the fact that only 15 percent of the respondents correctly identified the true price of LPG in the absence of a subsidy. Around 25 percent of the population did not know what the price without subsidy would be and a further 22 percent underestimated it by more than five dollars (see figure 3). It is noteworthy that those that considered themselves to be informed had different priors about the consequences of the reform (see table 3).

Among those respondents that were well-informed (top panel of table 3) the satisfaction with the reform was relatively high at 54 percent (compared to 24 percent among the badly informed). Among the wellinformed 46 percent mentioned that the reform would have at least one positive effect, for example improving the lives of the poor. At the same time, those well-informed were also able to come up with negative effects at a similar rate (45 percent mentioned at least one negative effect). In contrast, among those that were badly informed (bottom panel) only 13 percent mentioned any positive effects while 69 percent were able to identify a negative consequence.

The that the uninformed came up more easily with negative than positive consequences of the 15
where _X1_ is the indicator for the period of birth occurrence, _X2_ is the indicator for exposure to the voucher program, and _Xi_ is the vector of the control variables in the model for birth _i_ from village _j_ in parish _k_ . The parameter _α0_ represents the likelihood of the outcome for nonexposed individuals at baseline, _α1_ is the difference in the outcomes for nonexposed individuals over time, _α2_ is the difference in outcomes between exposed and nonexposed individuals at baseline, _α3_ is the difference in the changes in outcomes between exposed and nonexposed individuals over time (i.e., the difference-in-differences estimate); _βi_ is the vector of parameters for the control variables in the model, and  _jk_ are the unobserved characteristics of individuals from the same village and parish that might be correlated with the outcome.

A key assumption of the difference-in-differences estimation is that preexisting trends between intervention and comparison groups are similar. Table 1 presents trends in the distribution of facility-based births occurring two years before the voucher program began among multiparous voucher and nonvoucher respondents who were interviewed in the 2010–11 survey. The results show no significant baseline differences between the two groups of respondents in the proportion of births delivered in public or private facilities in the two years preceding the voucher program launch.

Table 1 Trends in Facility-Based Births among Multiparous Voucher and Nonvoucher Respondents for Births Occurring Two Years before the Voucher Program, 2010–11

||Public|health facility||Privat|e health facility||
|---|---|---|---|---|---|---|
|Year of<br>birth|Voucher<br>respondents|Nonvoucher<br>respondents|_P_<br>value|Voucher<br>respondents|Nonvoucher<br>respondents|_P_<br>value|
|2006|50.0%|51.7%|0.83|<br>20.0%|12.8%|0.20|
||(_N_= 50)|(_N_= 180)||(_N_= 50)|(_N_= 180)||
|2007|43.9%|38.9%|0.46|<br>24.2%|21.1%|0.59|
||(_N_= 66)|(_N_= 247)||(_N_= 66)|(_N_= 247)||

_Source:_ Population Council, Household Survey 2010–11.

_Note: P_ values are from significance tests of proportions for differences between voucher and nonvoucher respondents.

18
|Std. error|(5.8)|(8.7)|(15.8)|
|---|---|---|---|
|Number of observations|935|935|932|
|Siri Fort||||
|Blue Line second extension<br>(Nov 11, 2006)|-14.2|0.3|—|
|Std. error|(10.5)|(6.2)|—|
|Number of observations|934|871|—|

Source: Authors’ analysis based on data described in the text.

Missing observations in each included week do not exceed 20 percent. Each estimate is calculated from a separate regression (equation (1)), where the dependent variable is the natural logarithm of hourly pollutant. Controls are the same as used in table 4. Std. errors are clustered at one week. * indicates significantly different from zero at 10 percent level, ** at 5 percent level and *** at 1 percent level.

# **Table 6. Effect of Delhi Metro on Air Quality: Longer than Nine-Week**

# **Symmetric Window**

||NO2|CO|
|---|---|---|
||(Percentage ch|ange in level of pollutant)|
|ITO|||
|Yellow Line first extension<br>(Jul 03, 2005)|-5.5|-18.8|
|Std. error|(11.5)|(18.7)|
|No. of obs. (window length in<br>weeks)|3259 (19)|7118 (41)|
|Blue Line introduction (Dec|-3.1|—|

46
# _6.1. Village-level Poverty Targeting Using Detailed Household Surveys_

We begin the analysis of the household-level data by using the subset of our observations among which detailed household surveys were collected. These data allow us to calculate household-level consumption aggregates, and therefore to conduct an analysis of poverty at the local level. We use sampling weights to make this sample representative of the population in the 100 study villages.

Figure 2 shows the CDF of the consumption aggregate by stratum. We can see that the village elite have the lowest likelihood of being poor followed by non-vulnerable individuals and TASAF II VG group leaders. Vulnerable households, exogenously defined using the criteria devised by TASAF II, are poorer than non-vulnerable households and those who ended up in TASAF II VG groups as „rank and file‟ members (i.e. **not** group leaders) form the poorest stratum. Using a lower bound poverty line (of 18,000 Tanzanian Shillings per capita per month), fewer than 20% of village elites are defined as being in poverty, whereas almost 60% of TASAF VG group „rank and file‟ members are poor.

The vulnerability criterion is, in and of itself, a decent targeting criterion: the poverty rate among vulnerable households is 10 percentage points higher than non-vulnerable households (51% vs. 41%). However, inclusion and exclusion errors are many: there are many poor households that are not defined as vulnerable and many vulnerable households who are not poor. The within-village targeting, conditional on the vulnerability criterion, is very different for group leaders (who are substantially richer than the average eligible beneficiary) and group rank and file (who are substantially poorer). Hence, while the villages do a good job of forming TASAF II VG groups that are poorer than the eligible population in general, group leaders (who comprise 20-30% of the membership in these groups) are significantly richer than the average eligible household. Again, the combination of the targeting criteria from the center (vulnerability) and the within-village targeting process produces a targeting performance that is mildly pro-poor at the local level.

To understand this process more clearly, we now proceed to examine group membership under the TASAF II VG component, using a sequence of probit regressions. Table 5 moves through three steps in the selection process, clustering standard errors at the village level. In columns (1) and (2), we compare vulnerable households to the entire population of the 100 villages, thereby assessing the extent to which this centrally defined component of the targeting 16
- Modest definition: Forest and community are registered _or_ proposed CFs (CForPROPOSED)

- Broad definition: Forest and community are NCFs, but village leaders are able to report the year forest user groups were established (CanIDFUGyear)

We also include environmental variables, such as total forest area, altitude, forest type, plot slope, ecosystem type (hill versus _Terai_ ), soil quality and whether plots have evidence of erosion and fire as explanatory variables. All these variables are expected to affect plot biomass and carbon, but are not of primary interest. In addition, we adjust for the baseline vegetation level in forests using the 1990 normalized difference vegetation index (NDVI) for our forests. These forest level vegetation indices are calculated from Landsat data images collected in November/December 1990, which is before any of the group formation years in our dataset and three years before the Forest Act of 1993 that established CFs was passed. These particular months are chosen, because the sky is typically very clear in Nepal and Landsat images are unimpeded by clouds.<sup>10</sup> We view the 1990 NDVI variable as a particularly important independent variable. Forests that, for example, had sparse vegetative cover before groups were formed or CFs established are likely to have less carbon in 2013 than forests that started out well-forested. The 1990 NDVI adjusts for this historical baseline and helps avoid endogeneity bias.

Our last three independent variables are at the community level and capture extraction pressures. The first variable is the total number of households in forest user groups and the second is total forest area in hectares per household. The third variable is the forest user group migration rate, which is defined as the fraction of sample household members in forest user groups that are reported to have migrated. This variable is included, because migration is

> 10 We thank Charles Maxwell of Portland State Universityfor assuring clear satellite imagery and estimating the NDVI.

**17 |** P a g e