rafmacalaba/data-use-mentions-extended huggingface dataset:
Second, we employ the Kubinec (2019) method of adjusting for missing data. Missing data rates in firm surveys can be substantial, and in this case, we have reason to be concerned about non-ignorable missingness. Some firms may not want to report political activities they undertake, either because these are quasi-legal or illegal in their economy or because they do not trust the survey enumerators to keep their data private. As a result, missingness may be correlated with the latent trait, where either high or low influence firms may under- or over-report political activities and connections.

To adjust for this, we implement a two-stage IRT model to account for selection into item response. The first stage is a separate IRT equation with shared influence parameters Θ _i_ but different item parameters _γj′_<sup>and</sup><sup>_β_</sup> _j′_<sup>.Ifaresponse</sup><sup>_yij_ismissing</sup><sup>_yijm_=1,theposteriordistributionisequaltothefollowing:</sup> And if the survey response is observed _yijm_ =0, we have the following posterior distribution: As such, the missingness mechanism allows for the observed responses to be either inflated or deflated depending on whether higher or lower values of the latent trait are correlated with missingness for a given item _j_ , which is determined by the sign and level of the missingness discrimination parameter _γj′_<sup>.14While</sup> relatively straightforward, this missingness adjustment will permit us to handle the most likely scenario in which highly influential firms are less likely to report political activities (or vice versa). Importantly, the missingness adjustment also means that our scores are defined over the whole sample regardless of how many responses each firm _i_ answered. If missingness is ignorable for a given item _j_ (a case in which _γj′_<sup>=0),themodelwillappropriatelyinflatetheposteriorvarianceofΘ</sup><sup>_i_toaccountfordatathatis</sup> missing completely at random.

On its own, the
2016, Maffioli 2017, Fluckinger _et al._ 2018].

Our monitoring data confirms there was an extensive roll out of the ELA program despite the circumstances: (i) 70% of clubs opened on time (by September 2014); (ii) the majority of clubs provided life skills training; (iii) vocational skills training took off after travel quarantines were lifted in January 2015. Moreover, there is high demand from women for accessing ELA clubs: 71% of survey respondents in treated villages _ever_ participated in an ELA club meeting or activity (versus 4% in control villages). In terms of trainings received: (i) 82% participated in life skills; (ii) 25% received financial literacy training; (iii) 34% participated in vocational skills training; (iv) 13% received a microfinance loan from BRAC. The overall pattern of participation in activities is in line with what was reported in terms of club openings and training offered.

We conduct our analysis separately for young girls (aged 12-17 at baseline), and older girls (aged 18-25 at baseline) because (i) these cohorts differ in the economic activities they were engaged in at baseline: the majority of the younger cohort are engaged full time in schooling pre-crisis, while the majority of the older cohort are engaged in income generation; (ii) the policy responses to 3
One source of perceived unfairness in many developing countries is the fact that individuals, and some (particularly small) businesses, have limited understanding of the taxes that they are required to pay. In the most recent round of the Afrobarometer survey, over 55 percent of taxpayers reported that it was “difficult” or “very difficult” to find out what taxes they are supposed to pay to government.<sup>98</sup> This appears particularly true of local-level payments in lower-income countries, which may appear comparatively arbitrary. Recent surveys in Sierra Leone and the DRC both find that taxpayers report surprisingly limited understanding of the taxes that are due.<sup>99</sup> Discussions with small traders in Ghana, and elsewhere, similarly reveal significant difficulty in accessing information about the tax system,<sup>100</sup> while small firms often face substantial complexity and administrative hurdles to enter the formal sector.<sup>101</sup> Reflecting this concern, there has been mounting interest across countries in expanding taxpayer education.<sup>102</sup> However, an important distinction is needed. Tax administrations often conceptualize taxpayer education as an effort to teach taxpayers why paying taxes is important. However, what may be most needed to expand trust is taxpayer education that helps taxpayers to understand the taxes that they must pay, and how to do so.

A second source of unfairness is arbitrary or abusive enforcement – and extraction – by tax collectors. Surveys indicate that tax agencies are frequently among the least trusted institutions in many developing countries, and/or those most likely to be involved in corruption.<sup>103</sup> Particularly where face-to-face interactions between taxpayers and tax officials are common there is extensive evidence of tax collectors seeking to extract informal payments from taxpayers. There is some evidence that this extraction sometimes targets more vulnerable or less educated groups,<sup>104</sup> or areas in which tax collectors have greater leverage – owing,
Duncan, and Schmidt-Hebbel (2010). Ilzetzki (2011) provides a novel political economy explanation based on successive governments disagreeing on the desired distribution of public spending and examines different theories of procyclicality by running numerical simulations in calibrated models.

> 12 In line with Lane (2003), and to check the robustness of our results, GDP per capita, size of the public sector relative to GDP, and openness (the sum of exports and imports as a share of GDP) were added as controls, one at a time. While GDP per capita was significant at the 5 percent level, the two other control variables were not. In all three cases, the F-test for the joint significance of the three relevant explanatory variables was significantly different from zero at least at the 10 percent level.

> 13 Although fiscal rules may reduce procyclicality, the existence of fiscal procyclicality may prompt policy makers to adopt fiscal rules. To account for this potential endogeneity, instrumental variable estimation (two-stage least squares) is used with bureaucracy quality (from the PRS database) as an instrument.

8
advantage from the opportunities by the political system to make ones voice heard in a constructive way may be involved.

# **4.2 Political participation**

While there are many avenues for political participation, relevant legislation designed village assemblies ( _gram sabhas_ ) as key mechanism to ensure democratic deliberation and accountability at local level. The extent to which previously disadvantaged individuals -including women- participated in such meetings thus allows us a more direct test of the impact of reservation on voice and the ability to effectively articulate issues. Moreover, as individuals will continue to attend meetings only if they find that doing so is worth their while, attendance can also be interpreted as an indicator of the relevance ascribed to these meetings. Using member-level data on meeting attendance in the last four meetings in each village and, for those attending, the nature of participation points, suggests that reservation-induced effects vary markedly by respondents‟ gender (see table 8).

16
level analysis yields new and interesting policy patterns about the dynamics of welfare over time.

Incidentally, we present further validation of the synthetic panel method, which is predicated on certain underlying assumptions. While we are unable to test these assumptions directly with the NSS data, we probe them by validating our methodology with the IHDS data—the one nationally representative panel data set that has been collected in India in recent years. Seeing that the method works well in replicating true panel estimates from the IHDS further adds to the existing validation studies that analyze data from other countries.<sup>3</sup> These offer us scope to comment on patterns of per-capita consumption mobility based on the synthetic panels constructed from the NSS data.

To preview our findings, our analysis indicates that as economic growth in India accelerated in the late 1990s and 2000s, consumption mobility also increased: a larger share of the poor in the later survey years was able to transition out of poverty than in the first half of the study period. Chronic poverty declined. Less encouraging, however, is that relatively few of the poor were able to escape poverty to the extent that they were able to join the secure— i.e. the group that is neither poor nor vulnerable to falling back into poverty. Even in the second half of the study period, transitioning out of poverty and directly into the ‘middle class’ was a very rare occurrence. While fewer people dropped back into poverty over time than managed to escape poverty, it remains important to note that downward mobility was not entirely absent. Indeed, focusing on the characteristics of the population that moved up or down in relative terms reveals that forward castes (neither Schedule Castes and Schedule Tribes, nor Other Backward Castes) exhibited a particularly high propensity to move up in relative rankings. In contrast, Scheduled Castes and Scheduled Tribes were much more likely to see their relative

> 3  See Dang, Jolliffe, and Carletto (2019) for a recent review of studies that validate and employ
Table 1 confirm that using the FO definition, it is still the case that the typical level of education of workers in occupations at risk of automation is secondary education.

Table 1: Share of employed workers in occupation at high risk of automation, by educational attainment.

||(1)|(2)|
|---|---|---|
|**Educational attainments**|**GM**|**FO**|
|Primary|.39|.42|
|Secondary|.59|.56|
|Tertiary|.02|.02|

The table reports the share of employment in production occupations at high risk of automation, by the educational attainments of Indonesian workers in 2007. Primary education includes up to completed primary school. Secondary education includes junior and senior high-school. Tertiary education includes education levels from diplomas to PhD. FO indicates that the list of occupations at risk of automation is based on the methodology in Frey and Osborne (2017). GM indicates that the list of occupations at risk of automation is based on the methodology in Graetz and Michaels (2018). Sources: Sakernas (LFS); Frey and Osborne (2017); Graetz and Michaels (2018).

The finding that occupations at risk of automation are dominated by secondary educated workers is consistent with two pieces of evidence. First, the literature on employment polarization suggests that automation technologies tend to replace occupations with an intermediate level of skills.<sup>6</sup> While we do not observe the skill level of plants’ workforce directly, secondary education is both the median and the mean level of education of workers in manufacturing plants in 2006.

Second, an analysis of the characteristics of production occupations compiled by the World Bank for Indonesia, Thailand and Malaysia, suggests that secondary education is the typical educational attainment for all of the occupations at greater risk of automation across countries with similar economic characteristics.<sup>7</sup
wheat and Tk.15 per working day, provided a minimum amount of work is done. Training in awareness-raising and IGAs is conducted from June to November. A participant’s monthly entitlements for the training period are 20 kg of wheat or rice and Tk. 100. Similar to the participant requirements of the IGVGD and FSVGD, saving is compulsory. FFA participants are required to save Tk. 25 per month. FFA follows a 12–24 month project cycle. Although systematic program evaluations are seldom done, available research suggests that cash-transfer programs have helped to alleviate food and health insecurity, and in some cases have enabled beneficiaries to invest in smallscale IGAs.

# **{TABLE 3 HERE}**

A study of the International Food Policy Research Institute (IFPRI) demonstrates that, out of four programs (IGVGD, FSVGD, FFA, and RMP), transfer as a percentage of household expenditure is highest for FFA, at 38.2 percent, followed by RMP (30.2 percent), IGVGD (15.5 percent), and FSVGD (15.0) (Ahmed et al. 2009). The same study finds that, among the four programs, FFA is the best targeted, helping 72 percent of its beneficiaries lift themselves above the poorest 10 percent of the population. Using data from the 2000 and 2005 Household Income and Expenditure Surveys (HIESs), Khandker (2011) finds that VGF lowers poverty among its beneficiaries, and that such programs as FFW lower both seasonal and chronic poverty (Table 3).

# **2.2** **<u>Meeting the Safety-net Need</u>**

Given Bangladesh’s wide range of safety-net programs, are there enough to meet its needs? Driven by rapid urbanization and the breakdown of the extended family system, the country now faces new social-protection challenges, even as it struggles to tackle ongoing ones. Most existing safety-net programs target the rural poor; thus, there is an urgent need to
How about the use of market (rather than PPP) exchange rates in global inequality calculations? This is a useful complement because it gives us a different insight into inequalities. If one is interested in global purchasing power or ability to affect the world economy, then conversion of local incomes into actual US dollars makes sense. But there are relatively few instances where we are interested in this, and most of our interest in global inequality is really based on the desire to compare living standards of different people. For that purpose PPP exchange rates are, of course, better.

# 3. How big is global inequality?

There is a general agreement about the size of global inequality, and there is a general disagreement about the recent direction of change of global inequality. Table 1 shows the results for global inequality obtained by a number of authors using quite different techniques: most of them mix national accounts information (using GDI per capita as mean income) and household survey information, and only a few use household surveys directly. In all the studies however, the recipients are individuals (inequality is expressed on a per capita basis), and national incomes are converted in international (PPP) dollars although the PPP exchange rates may be drawn form different sources. All Gini values for the 1990s, with the exception of the two extremes (61 and 71) lie within a relatively narrow range between 63 and 68. The similarity in the results is even more remarkable when one realizes that the standard errors of these estimates are between 2 and 3 Gini points,<sup>25</sup> and that consequently most of the estimates are within one standard error of each other.

As for the direction of change—comparison between the 1990s and 1980s—there is no unanimity. Sala-i-Martin and Bhalla, using very similar methodologies, argue that global inequality has declined by between 3 and 4 Gini points. Dikhonov and Ward, and Bourguignon and Morrison find an increase of about 1 Gini point. Sutcliffe concludes

> 25 One
internal mobility as an alternative to hiring and firing or obtain special arrangements with the authorities to dilute the associated costs.

- _Innovating firms – those that have upgraded their production process or their products -- are more likely to face severe constraints from strict labor regulations._ This points to the importance of labor regulations for innovation adoption of new technology and potentially long-term growth.

- _Firms react to strict employment regulations by investing more in training and making greater use of temporary employment_ .  Not all firms can use these strategies, though.  Medium and large firms tend to use both training and temporary employment, while small firms tend to rely more on the use of temporary employment when facing strict regulations.  Innovating firms make greater use of training than temporary employment to upgrade skills.

All in all, this paper suggests that labor laws have an impact on firms’ performance. Medium-size firms -- those that often have the greatest potential for expansion and creating more jobs – are the most severely affected as are innovating firms that require adjustment in the workforce to match the requirements of new technologies. Training and temporary employment are used to circumvent the costs associated with strict labor regulations for regular contracts, but again not all firms can take advantage of these strategies.

For the empirical research agenda, the good news is that _de jure_ indicators of the stringency of labor regulations are broadly consistent with employers’ survey data. Hence results from studies relying on either one or the other type of information can be compared and used for informing policy makers. But our results clearly suggest that EPL affects firms differently, depending on their characteristics and performance. And although some of them use training or temporary employment to compensate for the high costs associated with EPL, we do not know how costly these strategies are for firms and their job creation potential. These are issues that deserve a closer look in future empirical work.

25
4 reveals that exposure of a barangay to a typhoon in the 6 months before the month of interview, is associated with a statistically significant decline in PCE by 6.7 percent. This estimate is very comparable to the impact estimates of ββ typhoons on PCE in the literature. For example, for the Philippines, Anttila-Hughes and Hsiang (2013) using provincial panel data, estimate a reduction between 5.9% and 7.1% in consumption, whereas Ishizawa and Miranda (2016), estimate a decline ranging between 2% and 4% for Central American countries. Baez et al. (2015) using panel data also show a fall in the consumption of Guatemalan households by 12.6%.

Along similar lines, PCE on food decline by 5.1% (see col. 2) and PCE on non-food decline by 7.8%. Also, PCE on protein seem to be negatively affected (decline by 10.1 pp) whereas PCE on fruits and vegetables, cereals, education and medical services appear to be unaffected.<sup>21</sup>

> 21 We have also confirmed that changing the reference period of the shock measure based on the binary variable W119_bhy, does not result in any significant lagged effects of typhoons on total household PCE or any of its components (as is the case with the _f_ index used in Table 3).

18
ready aware of PEP’s role in formalization exhibit different responses to the treatment. Tables F.7–F.10 present these results. The findings indicate that prior knowledge of PEP’s impact on formalization does not lead to significantly different responses across key outcome measures, including voting intentions, social capital, attitudes toward Venezuelan migrants, and altruism toward migrants. This suggests that awareness of PEP’s benefits does not meaningfully amplify the treatment effect, reinforcing our argument that concerns about the program’s economic impact on individual welfare are not the primary drivers of attitudes toward Venezuelan migrants. Consistent with this interpretation, evidence from the 2021 Venezuela Migration Project Observatory Perception Survey shows that most Colombians do not oppose migrant integration (Semana 2023). The survey found that 80% of Colombians support granting nationality to children of migrants born in Colombia, while 72% agree that migrants should have access to basic rights such as health and education. Support for granting legal documentation to migrants increased from 59% to 66% within a few months. Additionally, over 70% of respondents favor policies that facilitate migrant integration into the labor market and entrepreneurship. This broader of further with our un- public acceptance migrant integration aligns findings, derscoring that economic concerns are not the primary determinant of attitudes toward Venezuelan migrants.

All in all, our findings suggest that conditional on having a large informal sector and controlled inflows of migrants, native’s political behaviors can be unaffected by policies that ease the economic integration of migrants. This could be mainly explained by the fact that these programs do not have negative economic effects for natives in the short-run. As discussed earlier other potential reasons explaining voter’s indifference to the program may also be related to the cultural proximity between migrants, the low political and media saliency of the PEP program in Colombia, and the fact that the migration flows into Colombia stabilized after 2018.

41
# **4 Empirical Strategy**

I identify the causal effect of debt relief using a regression discontinuity design based on the program eligibility criteria and data from a household survey of 2,897 debt relief beneficiaries. The identification strategy exploits the fact that, unlike previous debt relief initiatives, eligibility for debt relief under the program depended on the amount of land pledged as collateral at the time a loan was disbursed. This creates a discontinuity in the amount of debt relief around the eligibility threshold of two hectares and induces quasi-random variation in debt relief status: households below the two hectare threshold received 100% unconditional debt relief, while those above the cutoff qualified for 25% of relief conditional on settling the remainder of their outstanding balance.

Presuming that banks followed the rules of the debt relief program faithfully, the causal effect of debt relief can be estimated using a _sharp_ regression discontinuity design (Imbens and Lemieux, 2008a; Hahn, Todd and Van der Klaauw, 2001) that compares households in the immediate vicinity of the program cutoff. Identification using the sharp regression discontinuity design rests on the assumption that treatment status is determined by a cutoff score _<u>x</u>_ along a forcing variable _xi_ and therefore quasirandomly assigned. In the setting studied here, the forcing variable is the amount of land pledged as collateral at the time the loan was disbursed. Without loss of generality, I rescale this variable so that the program eligibility cutoff is centered at zero and use “hectares from cutoff” as the assignment variable throughout the analysis.

Because assignment to treatment ( _Ti_ = 1) or control ( _Ti_ = 0) follows the known rule _Ti_ = **1** _{xi >_ _<u>x</u>_ _~~}~~ ∀ i_ , treatment effect of debt relief, _τRD_ on an outcome _yi_ can be estimated as the difference between the regression functions at the discontinuity _<u>x</u>_ .

Intuitively, if
|**0.676****|
|||**[0.110]**||**[0.322]**|
|Dummy: physician’s availability||-0.030||-0.330|
|||[0.135]||[0.358]|
|Dummy: management service||**0.461****||**-1.223***|
|||**[0.195]**||**[0.670]**|
|Dummy: formal fin inst.||-0.260||**1.189****|
|||[0.168]||**[0.483]**|
|Dummy: theft security||0.193||-0.550|
|||[0.167]||[0.454]|
|Dummy (disaster)||-0.099||**1.053***|
|||[0.213]||**[0.552]**|
|ln (rice price)||0.253||**-6.943*****|
|||[0.485]||**[1.267]**|
|**Dummy**<br>Rural|0.186|**0.406****|-0.182|-0.622|
||[0.136]|**[0.174]**|[0.356]|[0.491]|
|Observations|663|663|221|221|

_Source:_ 2010 Yemen RIC Survey; Notes: Sampling weights are applied; Standard errors are in parentheses
br>`Capital Regulatory Index`|<br>`-0.0463`<br>`(0.0316)`|<br>`0.00379`<br>`(0.00497)`|<br>`0.00184`<br>`(0.00271)`|<br>`-0.0162`<br>`(0.0104)`|
|`Private Monitoring Index`|`0.0204`<br>`(0.0406)`|`-0.00945`<br>`(0.00612)`|`-0.00841**`<br>`(0.00330)`|`-0.0101`<br>`(0.0111)`|
|`Official Supervisory Power Index`|`0.00419`<br>`(0.0191)`|`0.00335`<br>`(0.00333)`|`0.00104`<br>`(0.00166)`|`0.000772`<br>`(0.00532)`|
|`Entry Requirements Index`|`-0.00297`<br>`(0.0225)`|`0.00248`<br>`(0.00281)`|`0.00282`<br>`(0.00199)`|`0.00802`<br>`(0.0112)`|
|`Restrictions on Activities Index`|`-0.0699**`<br>`(0.0313)`|`0.00787**`<br>`(0.00332)`|`0.00226`<br>`(0.00224)`|`0.00262`<br>`(0.00950)`|
|