rafmacalaba/data-use-mentions-extended huggingface dataset:
, while empirical results can be mixed for indirect taxes, none are typically considered to achieve a significant degree of progressivity.<sup>29</sup> Property taxes, while included in the numerator for the first indicator, are excluded from the second and third for several reasons. First, because there remains significant debate in the literature regarding the extent to which recurrent property taxes exhibit features of a benefits tax (and hence are closer in nature to a payment for services thereby having no distributional impact) as opposed to a tax. Second, it avoids the inclusion of highly inefficient (but potentially progressive) property transaction taxes from having a

> 26 The UNU-WIDER dataset draws on data from several sources, primarily the IMF Government Finance Statistics and OECD Global Revenue Statistics databases. See: https://www.wider.unu.edu/about-grd.

> 27 The PIT and CIT categories include “taxes on income, profits and capital gains” of individuals and corporate entities, respectively.

> 28 As noted earlier, empirical evidence does suggest that at least some of the CIT may be borne by workers (in the form of lower wages), which would reduce the progressivity of the tax.

29 While the VAT was historically considered a regressive tax, more recent literature finds the VAT to be roughly proportional on a lifetime basis, and in some cases even sightly progressive (but with minimal redistributive impact) due to the application of reduced rates on consumption items that make up a greater proportion of the expenditure of poorer households such as basic food products (see, e.g., IFS 2011, Thomas, 2022), or due to informality (Bachas et al., 2020). Excise taxes are often found to be regressive, although this is not always the case (e.g., Flues and Thomas, 2015, find transport fuels in some OECD countries to have some progressive impact). Furthermore, beyond the pure fiscal impact, health and environmentally related excise taxes though eliciting
**|0.035***|0.022***|
||(0.005)|(0.005)|(0.012)|(0.005)|(0.004)|(0.004)|(0.012)|(0.004)|
|Logged services exports|0.064***|0.064***|0.067***|0.064***|0.068***|0.068***|0.069***|0.068***|
||(0.007)|(0.007)|(0.011)|(0.007)|(0.007)|(0.007)|(0.011)|(0.007)|
|Logged imports|0.021***|0.021***|0.042**|0.020***|0.028***|0.028***|0.047**|0.028***|
||(0.005)|(0.005)|(0.019)|(0.005)|(0.005)|(0.005)|(0.019)|(0.005)|
|Exposure to imports|0.208***|0.197***|-0.016|0.215***|0.160***|0.151***|0.210|0.177***|
||(0.040)|(0.040)|(0.178)|(0.044)|(0.038)|(0.039)|(0.172
of households)|**Main assumption:**Expenditures on education larger than<br>the mean log-expenditure plus three times the standard<br>deviation (considering only positive values of expenditure)<br>are considered unreliable and are excluded from the<br>analysis.|

_Source: Authors_

# **4.3. Allocation of Healthcare Benefits**

LCMS VI collects health information for 2010, the year the survey was collected (see Appendix 3). However, 2008 is the most recent year for available official information on the number of public healthcare beneficiaries (patients/visits) and for government expenditures on: (i) Health Posts and Clinics; (ii) Provincial Hospitals; and (iii) National and Specialized Hospitals. Ideally, the BIA would obtain from the survey data each member‘s number of visits to public health facilities during the year and then combine that information with official expenditure reports. However, in practice this is not possible. Table 9 describes the assumptions that were necessary in order to resolve a number of inconsistencies in matching the official administrative data with the household-level information recorded in the LCMS.

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performs as well as the traditional diary operation, and comes with less than US$30 additional cost per household compared to R2.P36F<sup>38</sup> On the whole, given the contribution of cassava farming to food security and agricultural commercialization, the evidence underscores the need for adopting improved survey methods to collect better data on cassava production and productivity, facilitating a renewed look at the role of cassava farming in production and welfare outcomes.

> 36 In assessing the CVIP costs by treatment arm, Table 9 breaks differentiates between two broad categories: (1) Fixed costs applied to all treatment arms i.e. training, listing, purchasing of core survey equipment; and (2) Variable costs across the four treatment arms including main fieldwork costs i.e. staff time and fuel; incentives provided to the households; call center set-up and maintenance costs. Furthermore, although CVIP was implemented using the World Bank Survey Solutions CAPI software, the costs of the Android tablets were not considered in Table 9, since these tablets had been readily available at the NSO. Likewise, only maintenance costs were considered regarding the bicycles used by the enumerators and the motorcycles used by the supervisors, as these items too had been available at the NSO.

> 37 The D2 costs were exacerbated, on one hand, by the relatively low number of households requiring semi-weekly calls; the purchase of mobile phones and solar chargers (due to poor electricity access) for all D2 respondents, and were alleviated, on the other hand, given the small number of call center operators and the NSO experience with the Listening to Malawi (L2M) mobile phone survey. In preparation for the CVIP fieldwork, the NSO management worked closely with the two main carriers in Malawi to determine the most cost-effective airtime bundle. An additional cost saving mechanism would have been to provide mobile phones to only those households not currently owning a working device, which may be much more feasible in other settings for low- and middle-income countries.

> 38
The student language test, which evaluated ability in English (Kenya, Nigeria, Tanzania, and Uganda), French (Senegal and Togo), or Portuguese (Mozambique), ranges from simple tasks that tested letter and word recognition to a more challenging reading comprehension test.<sup>9</sup> The mathematics test ranges in difficulty from recognizing and ordering numbers, to the addition of one- to three-digit numbers, to the subtraction of one- and twodigit numbers, and to the multiplication and division of single-digit numbers. In both language and mathematics, the tests span items from the first four years of the curriculum.<sup>10</sup> In contrast to other approaches to assess teachers’ knowledge, where teachers take exams, teachers were asked to mark (or “grade”) mock student tests in language and in mathematics. This method of assessment has two potential advantages. First, it aims to assess teachers in a way that is consistent with their regular teaching activities—namely, marking student work. Second, by using a different mode of assessment for teachers compared to students, it recognizes teachers as professionals. In the analysis, we use data on language knowledge of those teachers who teach language, and data on mathematics knowledge of those teachers who teach mathematics.

Both the language and mathematics tests for teachers covered items starting at Grade 1 level (simple spelling or grammar exercises, addition and subtraction) and included items up to the upper primary level (Cloze passages to assess vocabulary and reading comprehension, interpretation of information in a diagram and/or a graph and more advanced math story problem).<sup>11</sup> Both the student and the teacher tests have good reliability, with a reliability ratio (estimated by Cronbach’s alpha) above 0.8 in both subjects on the student test and above 0.85 in both subjects on the teacher test.<sup>12</sup>

> 9 For consistency with the other countries
This paper consists of six sections. We describe the data in the next section and the country background and COVID-19 situation in Section 3. We discuss the analytical framework in Section 4, the estimation results in Section 5, and conclude in Section 6.

# **2. Data**

Our main source of data for this study is the Islamic Republic of Iran’s Labor Force Surveys (LFS), which are available in unit records since 2005. Each survey round contains information on about 120,000 households and 700,000 individuals. We analyze eight rounds of the repeated crosssections covering 2015-2022. The large sample sizes of the LFSs allow us to control for province and year-quarter fixed effects in the econometric analysis. To avoid issues related to adult decisions regarding going to school and retirement, we focus on prime age workers (ages 25-54). The summary statistics for this age group are presented in Table 1.

Conducted annually by the Statistical Center of Iran since 2005, the Islamic Republic of Iran’s LFS is a nationally representative survey that provides annual and quarterly assessments of the country’s labor markets. The survey follows a two-stage stratification with cluster sampling design, with stratification by rural and urban residence and at the province level and clusters and blocks chosen to yield a random nationally representative sample. The new survey replaced the old Employment and Unemployment Survey and conforms more closely to the International Labour Organization (ILO) guidelines for labor force surveys. The survey covers the country’s non-nomadic households (about 98% of the total population). Each household is on a quarterly rotation in which each household is interviewed for two consecutive quarters in one year (i.e., a household leaves the survey for two quarters and is interviewed again in the same two quarters a year later). By allowing for quarterly rotation, the survey provide estimates of the level as well as 6
sup>|1.4661<sup>∗∗∗</sup>|0.0015|0.9788<sup>∗∗∗</sup>|1.4629<sup>∗∗∗</sup>|0.0021|
|Observations|222,856<br>|230,095<br>|204,868|222,856<br>|230,095|204,868|
|Exporter-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Importer-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-pair FE|Yes|Yes|Yes|Yes|Yes|Yes|

_Source_ : Authors’ estimations using WB-DTA 2.0 and ITPD-E data. Dependent variable: bilateral services trade _Xijt_ . PPML estimation with three-way clustered standard errors. Other policy patterns controlled for but coefficients not reported.

36
.5904||0.5902|0.5890|<br>0.5869||0.5904|
|F test<br>Ptob > F|||||F(14,391<br>0.8|7) = 0.65<br>3|||||

_Note:_ The t-statistics allow for design effects due to the stratification and clustering of the APIS sample. The sample has 168 strata and 3378 primary sampling units. Each of the regressions also allow for strata fixed effects and 32 provincial dummy variables to control for missing _barangay_ data. The estimated income models exclude 125 observations with negative reported incomes.

_Source:_ Calculated from 1998 APIS data.

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For the purposes of obtaining a representative survey of Rohingya displaced it was important to determine the true prevalence of displaced Rohingya outside camps.

Due to the absence of a comprehensive sampling frame for Rohingya outside camps, IPA Bangladesh had conducted a quick population count exercise which provided an opportunity to assess the likely prevalence

> 9 Based on satellite imagery, anecdotal evidence from multiple local actors, and field observations.

> 10 The December 2017 Rohingya Emergency Vulnerability Assessment (REVA) and June 2018 Community-Based Monitoring System (CBMS) surveys, administered by the World Food Programme (WFP), found that over half of Rohingya displaced reached by telephone surveys reported working, despite a formal ban on work for Rohingya displaced (World Bank 2019).

> 11 It was evident from field observations and reports that camp boundaries were largely porous. In addition, other survey data collected in the year after the crisis suggested that Rohingya displaced were not fully confined to camps. This was further corroborated by data collected by the World Food Programme as part of the Rohingya Emergency Vulnerability Assessment (REVA) in December 2017 and the <mark>Community-Based Monitoring System (C</mark> BMS) surveys in June 2018. Over half of Rohingya displaced reached by phone surveys for these assessments reported working, despite a formal ban on work for Rohingya displaced (World Bank 2019). These data suggested that some Rohingya displaced were either leaving and returning to camps every day for work, or else relocating entirely outside camps to pursue work opportunities.

4
Hindu or Muslim, a full set of birth order indicators interacted with the relationship of the child’s mother to the head of the household, twinship indicators, and month-of-birth indicators.<sup>15</sup> Results are presented with and without controls and fixed effects to verify robustness. The mean PSU studied here contains 10 children under 3 used in these regressions.

# **3.2 Regression results**

Table 6 presents results from estimations of equation 2, with simple OLS in Panel A and district fixed effects in Panel B. Districts which saw greater differences in sanitation also present greater differences in child height.

District-level open defecation rates do not statistically significantly predict child height once village-level open defecation is included, which plausibly suggests that villages are much nearer than districts (which are much larger than villages) to capturing the geographic extent of sanitation externalities. Village-level open defecation predicts child height with or without district fixed effects and with or without individual controls.<sup>16</sup> The coefficient on village open defecation is smallest in absolute value with fixed effects and controls,<sup>17</sup> although it is not statistically significantly different from the other estimates; this could reflect the well-known attenuating bias of fixed effects, if much of the important variation in sanitation that is causing variation in height has been captured by other controls, leaving noise remaining.<sup>18</sup> 15If the survey rounds were conducted in different places in different times of year, different children would be under 36 months old. Month of birth is correlated with early-life human capital inputs ( _cf_ . Doblhammer and Vaupel, 2001, about developed countries).

> 16If, instead of omitting observations with height-for-age _z_ -score beyond _±_ 6, a cut-off of _±_ 10 is used, then results are very similar. For example, the coefficient in column 1 of panel A becomes -0.768 (0.222); the smallest coefficient in absolute value
but are they large enough to matter? The estimated treatment effects translate into increases in height and weight of 2.35 cm and 0.75 kg respectively, equivalent to increases on the counterfactual 1997/98 height and weight respectively of 2% and 4%. Assuming the effects occurred continuously throughout the five years following the 1992/93 survey, these effects are equivalent to an extra 0.47 cm extra height and 0.15 kg extra weight per year. To express the effects another way, the annualized VHI impacts are equivalent to 7% and 9% of counterfactual annual growth of height and weight, while the total cumulative impacts are equivalent to 37% and 46% of annual growth of height and weight .<sup>13</sup> How do these impacts compare to those reported for other programs? Behrman and Hoddinott (2000) estimate that young children within Mexico’s PROGRESA program grew by an additional centimeter in the year following the program’s introduction, equivalent to 17% of the counterfactual mean growth per year, a little over twice that achieved by VHI. Case (2002) estimates that five years after its introduction South Africa’s pension scheme had caused a 3-4 cm increase in height among Colored and Black children aged 0-6. This (cumulative) impact was equivalent to 44% of a year’s growth, a little more than VHI’s cumulative impact after a similar period of 37%. South Africa’s pension is estimated to have increased weight among young children as well, but only among Colored children. The estimated 1 kg cumulative impact for this group is a little larger than the 0.75 kg VHI estimated cumulative impact. Chaudhuri (2003) has estimated that Bangladesh’s maternal and child health program increased heights and weights of children under the age of 14 by 0.07 and 0.06 standard deviations respectively. Mackintosh et al. (2002) estimate that a Save-the Children integrated nutrition project in northern Vietnam increased weight-for-age by
(or may not) induce firms to reorganize production, adding layers and increasing overall productivity. Empirical evidence related to this theory can be found in Tag (2013), Bernini, Guillou and Treibich (2016), Caliendo, Monte and Rossi-Hansberg (2015), Caliendo, Monte and Rossi-Hansberg (2017), Caliendo, Mion, Opromolla and Rossi-Hansberg (2016), Cruz, Bussolo and Iacovone (2016), Friedrich (2016) and Spanos (2016).

Our results for Chile can be rationalized by this theory. The lower layer of production would include the non-specialized blue-collar workers. Engineers supervise blue-collar workers and, plausibly, can work in production as well. Exposed to increases in export opportunities, firms reorganize their structure by expanding the layer of engineers needed to supervise blue-collar workers (and, again, possibly to substitute some of them in production as well). This would explain why exporters demand more skilled tasks and, in turn, why the utilization of engineers increases more than the utilization of directors or administrative skilled workers. These impacts may be particularly large at the extensive margin. In this context, our paper contributes to this literature by providing evidence of these mechanisms using less detailed data for lower developed or advanced middle-income countries rather than the employer-employee datasets used in the studies referenced above. This can shed light on the role of exporting and employment in relatively more advanced developing countries.

# **4 Conclusions**

In this paper, we have explored the link between exports and the demand for skilled tasks using a panel of Chilean firms. We have found that exporting raises the demand for specific sets of skills, favoring technical skills such as engineering skills over other profession skills such as “desk” skills. In addition, engineers seem to replace blue-collar unskilled labor in certain tasks associated with exporting.

Our findings have implications for trade theory and should illustrate the importance of recent assignment models of factors to tasks in international trade. The results have implications for empirical research and policy design as well. The
little discernable rational basis decided to omit a number of survey responses from the publicly released data base. Consider one example. The OCC survey asked people without bank accounts why they did not have an account and read a list of reasons that they could agree with, including such reasons as “don’t have enough money,” “bank fees are too high,” etc. The public data set includes peoples’ responses to this question. The OCC survey followed this question with an identical question with a second set of possible reasons, such as “you need a Social Security number to open an account,” “bank won’t let you open an account,” etc. The public data set does not include peoples’ responses to this question. Despite these weaknesses, the OCC data provides a number of insights into the use of financial services by lower-income urban households.

Table 1 presents an overview of the socioeconomic characteristics of the adults in the survey. As indicated in the table, almost 60 percent of the adults work and almost an equal percentage have household incomes of $30,000 or less. We will refer to these households as lowerincome households.<sup>6</sup> More than half (53%) of the surveyed population identified themselves as Latino and 33 percent as black non-Latinos. Slightly more than one-third (37%) of the adults did not have a deposit account of any type. In contrasting the data between the two cities, the most striking differences is the much greater representation of Latinos (63%) in Los Angeles, and the greater presence of younger individuals and individuals in the lowest income category in this city.

> 5 9.1%

> 6 In 2000, the median household income in New York City was $39,939 and the median household income in Los Angeles was $35,955.

12
that examines the ETR-firm-size relationship (see Jansk ́y (2022) for a survey and Table A.1 for a summary). These papers differ in their methods (data and ETR definition) and results. We make progress by using tax administrative data for many countries and a common ETR definition. Unlike previously used financial accounts data with narrow coverage in low- and middle-income countries (LMICs), our data cover all formal sector firms. We use an ETR definition that can be measured consistently across countries to expose common patterns in the ETR-firm-size relationship. The previous literature—probably due to methodological differences—has not come to a

> 5 Since tax evasion and avoidance have been shown to have real effects, it is likely that tax expenditures have similar real effects. For instance, Egger and Wamser (2015), de Mooij and Liu (2020) and de Mooij and Liu (2021) show that the tightening of anti-avoidance rules reduced MNEs’ investment in affected destination countries. Su ́arez Serrato (2022) and Bilicka (2022) document labor-market consequences of anti-avoidance rules. Alstadsæter et al. (2022) show that firms engaging in profit shifting pay higher wages for managers, a finding echoed in Souillard (2022).

4
in the economy. However, the above mentioned findings show that more openness to trade does not necessarily decrease overall wage inequality, as this group will not loose its privileges due to increased international competition, except for in textiles.

# 7. **Conclusion**

This paper applies individual-level data on employees matched with firm-level data to estimate wage and production functions of employees with different demographic and other characteristics.

Most standard wage regressions do not control for firm characteristics that can affect the wage determination process. The reason is that information on firms and employees are often not available. We control for firm characteristics in this paper. Additionally, we go a step further in that we jointly estimate wage and production functions. This approach allows not only for assessing the marginal impact of demographic and other characteristics on wages but also for comparing the impact of these variables on productivity and wages.

For certain groups of workers, wage differentials match productivity differentials, while for others they do not. First, female employees are generally paid less than male employees and the negative wage premium they receive do not reflect a corresponding negative productivity premium. This may suggest gender discrimination. The wage differential between men and women is estimated at around 37 percent. However, for the case of the textile sector, no discrimination seems to be present.

Second, we find that employees' experience as measured by age is reflected equally in wages and productivity differentials over the life-cycle, both wages and productivity increase, but at a decreasing rate. No clear differences exist in returns to education due to gender. No direct link between firm-specific experience is established. Rather, the link is indirect, as firm-specific experience is implicitly captured by the age-variable.

32