rafmacalaba/data-use-mentions-extended huggingface dataset:
regularly to reflect flows, i.e. increases in refugee and asylum-seeker numbers (births, new arrivals) and decreases (deaths, departures, durable solutions). In situations of sudden mass influxes, existing registration capacity may not be adequate and the scope of registration data is then rationalized.<sup>65</sup> Additionally, it may not be possible to capture all demographic changes in the case of highly mobile populations. If registration is linked to the provision of services or other entitlements, there may be strong incentives to register births and new arrivals and weak incentives to deregister, leading to the inflation of the register over time or even instances of fraud and abuse (e.g. multiple registration, “borrowing children” etc.) (UNHCR 2003). Consequently, data from a refugee register may overestimate the number of refugees, requiring periodic corrective action through the verification of records. For example, in 2014 a verification of registration records for Somali refugees in the Dadaab camps in Kenya led to the deactivation of tens of thousands of records for individuals that are believed to have returned spontaneously to Somalia (UNHCR 2015). Additional problems with refugee registers include security concerns or inclement weather preventing refugees from accessing registration sites (UNHCR 2003) and the application of data protection principles.

# **_Registration of IDPs_**

Individual registration is not as common a method of estimating numbers of IDPs as it is for refugees. In principle, individual registration of IDPs is not used to determine the ‘status’ of an IDP, since IDPs have the same rights and entitlements as other citizens and do not need to apply or be granted a special legal status.<sup>66</sup> Rather, registration of IDPs can provide a basis for: (a) establishing the number, location, and key demographic characteristics of displaced populations; (b) providing protection and assistance; (c) keeping track of family relationships; (d) preventing fraudulent access to scarce humanitarian assistance;
varies across firms, which corresponds to the incomplete shift-share case described in Borusyak, Hull, and Jaravel (2022). We therefore adopt their proposed correction, by controlling for the interaction between this sum (the export share of sales of the firm in the initial period) and a period dummy in all our regressions, defined as _XSi_ 0 _∗ ιp_ .

The firm fixed effects account for the role of any time-invariant (observable or unobservable) firm attribute. The industry-period effects absorb common shocks to all firms in an industry in each period, while the region-period effects capture the impacts of common shocks across firms operating in the same region in a given period. We report standard errors clustered by firm. Since the forecast error is defined as the difference between actual and forecasted GDP growth in export destinations, in order to isolate its effect on the dependent variable we need to control for the weighted forecast growth, which would be expected to have a direct impact on firm-level outcomes. Our identification assumption is that after controlling for the weighted forecast growth and these fixed effects, subsequent variations in weighted forecast errors are uncorrelated with firm-specific shocks to firm performance and worker compensation.<sup>11</sup>

# **5 Results**

## **5.1 Summary statistics**

Before turning to the econometric analysis, we report descriptive statistics on several variables underlying our empirical strategy. Our firm-level baseline estimation sample is composed of manufacturing firms that exported in the initial year, and for which it is possible to link information from all the data sets described above (except the IPG survey, which is available only for a subset of firms in 2016). Table 1 reports summary statistics on firms from the estimation sample, both in levels and in changes. These statistics reveal that there exists considerable variation across firms and over time with regard to the weighted average of actual and forecast growth. Table A1 in the Appendix provides summary statistics on firms in the estimation sample for each 3-year period considered in the econometric
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# **5.8 Did Poverty Decline?**

The simulations suggest that poverty declined in Tanzania after 1994. This decline is attributable to the key assumption underlying the simulation approach, that positive per capita GDP growth translates into consumption increases for all population groups (and consequently a reduction in poverty). The appropriateness of this assumption can be scrutinized by investigating the implications of the possibility that poverty did _not_ decline. Specifically, if poverty did not decline between 1995 and 2001, while per capita GDP grew at the levels shown in the national accounts, what would the level of inequality had to have been in 1994? The assumption that poverty did not decline would require that all the growth was concentrated among the non-poor, meaning that inequality increased and was therefore lower in 1994 than in 2001.

We consider this scenario by simulating changes backwards from 2001 to 1994, using the 2000/01 HBS data. In this simulation, we set out to keep poverty constant, while adjusting for changes in mean consumption.<sup>11</sup> This can be done by holding consumption constant for those in poverty in 2001 (so that the fraction of poor remains unchanged) and restricting all growth to the non-poor. We also assume that consumption grew at the same rate among those households that experienced some growth.

Note that growth in consumption going backward in time corresponds to reducing consumption. Now consider a household whose consumption in 2001 is just above the poverty line. If we were to reduce its consumption as we move backwards in time, the household would fall below the poverty line and poverty in 1994 would not be identical to poverty in 2001. Through a series of simulation exercises, we find that to keep the level of poverty constant between 2001 and 1994 while adjusting for changes in mean consumption, we must restrict the growth adjustment to those in the top 53% of the consumption distribution in 2001.

With the scenario so defined, we can consider what inequality would have been in 1994 if poverty did
of schools to remain open, it is clear that access to schooling likely declined since the start of the conflict across the entire country (e.g., WFP 2019b). However, there has not been a thorough analysis investigating the relative importance of each of these possible factors that limited access to schooling, which makes it difficult to prioritize factors and regions in an intervention. As discussed above, the relationship between the location of violence and poor education outcomes has been well-documented in other settings. But given the lack of data collection in the midst of such fragile and volatile circumstances, the differential effects of living in close proximity to violence versus conflict shocks that affect regions beyond those experiencing violence are unknown.

# **Section 2a. Shocks during the Survey Period**

Between March 2019 and February 2020, the 12 months during which the WFP survey included questions on school attendance prior to the closure of schools due to COVID-19 in March 2020, there were a number of unexpected and plausibly exogenous conflict-related shocks that could have significantly affected access to schooling. The timeline of these shocks is illustrated in figure 1. First, in August 2019, southern secessionists captured Aden, the capital of the IRG, and the capitals of two additional governorates. The stated aims of the group, which were supporters of the Southern Transition Council, were to overthrow the IRG and claim South Yemen as a separate country from the north as it was prior to unification in 1990 (e.g., OCHA 2020). The capture was arguably a surprise,8 and figure 2 demonstrates that there was a large increase in both violent incidents and fatalities in the districts that were captured.

The capture of the territory began in August 2019, which is prior to the date that schools opened on September 15, 2019. However, the occupation continued through the start of the school year until there was a negotiated transition of power back to the IRG on October 15, 2019 (e.g., Al Jazeera 2019; Peoples’
availability, and safety); (b) we provide evidence on the MNA region which has been understudied so far.<sup>10</sup> The rest of the paper is structured as followed. Section 2 presents the data. Section 3 presents the methodology, including both the construction of measures of accessibility, availability, and safety, as well as the empirical model. Section 4 presents and discusses the results. Section 5 concludes.

# **2. Data**

This section presents the type of data that have been collected to study the effect of the public transportation network in Amman, Beirut, and Cairo on the labor market outcomes of the population, and in particular women. Three types of data have been collected: the transit network of each city, the built environment audits to assess the safety of public transportation stops, and household surveys. For all three types of data, we restrict the analysis to the metropolitan areas (therefore do not include potential commuters living outside of these borders).<sup>11</sup>

## **_2.1. Transit network data and built environment audits_**

Transit network mapping and built environment audits were conducted between May and November 2022 for Beirut and Amman. The transport network map for Cairo was collected as part of the Cairo Mobility Assessment and Public Transport Improvement study which was completed in May 2022. The build environment audits for Cairo were collected in November 2022.

In each city, all fixed route-based public transport modes were surveyed for the transit network mapping.<sup>12</sup> The mapping of the public transit system, started with the collection of official routes (and related) information available from government entities. Known gaps within these official records were filled through desk research. Finally, a team of enumerators were deployed to map the actual routes (and capture other attributes like frequency, fares, etc.) using a mobile based application. The building blocks of the field work were public transit
# 6.2.1. Discontinuity of the Running Variable

In this test, we examine whether the number of observations just above the cutoff is roughly similar to the number of observations below the cutoff. The idea underlying this test is that if individuals do not have precise control over the value of the income score they receive, they should be as likely to receive a score marginally above the cutoff as they are to receive a score marginally below the cutoff. Thus, lumping in the density graph of the running variable, especially near the threshold, may be indicative of manipulation by households. In particular, if we observe lumping just _below_ the cutoff, which would yield beneficiary-status, we could suspect some manipulation by households to get selected into the cash transfer program.

At the same time, there is a growing body of work, which argues that continuity of the running variable’s density function is neither necessary nor sufficient for regression discontinuity identification (McCrary 2008, Sekhon and Titiunik 2016, Dong 2018, and Frölich and Sperlich 2019, Choi and Lee 2020). “The score density continuity should not give a false sense of security when not rejected, and lack of it should not be taken as a reason to give upon the RD study hastily” (p.1. Choi and Lee 2020). In this light, we attempt to check the identifying assumptions of our empirical strategy through multiple tests.

As a first step, abiding by the conventional wisdom, we check the density of the running variable, i.e., the PMT score (see Figure 4). We find higher density just above the cutoff, i.e., lumping among ineligible households. This is intriguing and unlikely to be explained by intentional manipulation on behalf of 4P households - lest we would have observed lumping just below the cutoff which would have yielded beneficiary status. Still, this may indicate manipulation on behalf of program implementers. However, the income data used to determine eligibility of households was collected – as
The second type of sensitivity analysis compares estimation of govermnent size and growth regressions using Summers-Heston data with that using World Bank data. _Table I_ indicated the difference in the government consumption size measure that arose when using World Bank data in 1987 constant US dollar prices rather than international **prices.** In particular, government size in the low income countries of South Asia and Sub-Saharan Africa would be significantly reduced wvhen using 1987 constant dollar prices, while govenmment size in the high income countries would be increased relative to purchasing power parity values . To test the sensitivity of our results to the deflators, we now re-estimate the growth and size regressions but this time use the World Bank data in constant dollars. _Tables 11_ reports results of the size regression from the OLS and IV estimations for both constant dollar and international prices for the same set of countries.

While the explanatory power of the regression is very much lower using constant dollar values in the size regression (0.3675 versus 0.5791), the signs on the explanatory variables are generally the same. The main difference when using World Bank data is that we now find a significantly positive association bet. --en the income level and government size; an apparent confirmation of Wagner's LW<sup>4 6</sup> . One interpretation of growth - one or one and a half standard deviations greater than the sample mean of the per capita GDP growth rate -were removed. Fourth, observations with low or negative per capita GDP growth - one or one and a half standard deviations less than the sample mean of the per capita GDP growth rate - were removed.

- The greatest sensitivity -- as can be seen from Table **I** -- is in South Asia. To check the robustness of the estimates, we ran the same regressions without South Asia. The sign and size of the coefficients was not substantially affected.

- **46** A simple correlation of government size and per capita income is also
between FAO, ILO and World Bank, is also conducting methodological work in countries to operationalize the 19<sup>th</sup> ICLS definitions in employment surveys.

Rural women are also a crucial demographic as countries look to develop strategies for targeting and collecting data on informal employment, as part of the ILO’s Recommendation No. 204 on transitioning from the informal to formal economy. The recently-revised <u>International Classification of Status in Employment</u> (ICSE-18) during the 20<sup>th</sup> ICLS also includes, relevant for poorer and rural women, the creation of a new employment status category on dependent contractors, including seasonal/casual and short-term workers, as well as recommending a survey question on place of work that will also improve data on types of employment that are typical for women such as home-based work and street vending.

# What additional data could inform rural program design?

Many countries have recently introduced employment-related initiatives where rural women are beneficiaries (Box 1). Many of these programs have faced difficulties in targeting women, however, owing to lack of information about who is employed or available/wanting to work, as well as constraints women face in participating.

This includes the need for regular data collection on earnings and hours worked among men and women, as well as on constraints to participation — as in the case of India’s National Rural Employment Guarantee Scheme, which has had success in keeping poor rural women in the labor force (Sarkar et al, 2018), but suffers from lack of available jobs in some areas, lower female labor force participation (LFP) in poorer states, and women working longer hours to receive the same daily wage rate as men (Dutta et al, 2014; de la O Campos, 2015). Rural skills/technology initiatives could benefit from similar data. Ethiopia’s Rural Capacity Building Project (RCBP), for example, has faced difficulties in targeting agricultural extension services to women farmers, due
are owned by national or central governments and others by subnational, municipal, or local governments. As such, SOEs under different ownership and control levels may face different governance structures, entrusted with distinct objectives, and may be differentially targeted for government support in ways that could generate differences in their measured performances relative to POEs. Jurzyk and Ruane (2021) assessed whether the productivity differential between listed SOEs and POEs in China varied by SOEs owned by the central government and those owned by local governments. Their results suggest that, despite differences in governance and incentives, central and local SOEs both exhibit a similar productivity gap of about 30 percent relative to POEs.

**Other studies have assessed whether SOEs get preferential treatment by receiving more state support through subsidies than POEs** . SOEs can benefit disproportionately from government subsidies because they need to compensate for their higher costs of meeting policy mandates. In China, a study by Harrison et al. (2019), using 1998-2013 data on medium and large firms, found that SOEs were 3 times more likely to receive government subsidies and other support, such as low interest rates on loans, than POEs.They also tended to receive larger values of subsidies compared to POEs. Even when SOEs are privatized, they tend to be favored more by government subsidies and support compared to POEs that were never state-owned. Former SOEs were twice more likely to receive subsidies and in larger values, than POEs that were never state-owned.

**Regarding the role of SOEs during economic downturns, recent literature analyzed whether SOEs acted as buffers during economic downturns** . SOEs and state-owned banks or other financial institutions (SOBs or SOFIs) can act as economic stabilizers in  economic crises by attenuating job losses and sustaining workers’ wages in ways that POEs **c** annot do. Even if governments during various crises aspired to stabilize demand, employment, and investment through state support to SOEs, the evidence on the effectiveness of SOEs in fulfilling those objectives is not clear cut (Beuselinck et al
0.067|0.519***|0.299***|0.335***|0.262***|0.166**|0.372***|0.378***|
||(0.048)|(0.056)|(0.057)|(0.054)|(0.041)|(0.139)|(0.049)|(0.078)|(0.046)|(0.064)|(0.049)|(0.080)|
|Spillover students|0.022|0.064|0.034|−0.025|0.024|0.139|0.043|0.061|0.051|0.031|0.015|0.011|
||(0.044)|(0.053)|(0.054)|(0.059)|(0.044)|(0.147)|(0.050)|(0.082)|(0.054)|(0.064)|(0.050)|(0.085)|
|Control for baseline<br>value of dep. var.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|4,373|3,867|4,354|3,859|4,353|3,836|4,388|3,874|4,384|3,867|4,388|3,903|

_Source_ : Authors’ analysis from student surveys.

_Note_ : Dependent variable is an index of related questions. All components of the indices are standardized to mean 0 and standard deviation 1, based on the sample frame at baseline
progressivity, detailing the pros and cons of each approach, and illustrating their use drawing on results from the literature, where possible for IDA countries. Section 6 then considers the use of aggregate revenue data to help assess tax progressivity. Drawing on the preceding sections, Section 7 then proposes a set of indicators to use to assess the progressivity of tax systems in IDA countries.

# 1 Defining and measuring progressivity

The commonly accepted definition of a progressive tax is one where the average tax rate increases as income (or other relevant welfare metric<sup>5</sup> ) increases. Meanwhile, for a regressive tax the average tax rate decreases as income increases, and for a proportional tax the average tax rate is constant as income increases. Progressivity can equivalently be specified as requiring the marginal tax rate to be higher than the average tax rate (see, e.g., Musgrave and Thin, 1948).

This concept of progressivity derives from concepts of vertical equity, in particular the well-known “abilityto-pay” principle, as well as “equal sacrifice” principles. The ability-to-pay principle, which has its origins in Smith’s (1776) _Wealth of Nations_ , states that someone with greater ability to pay tax should pay more tax (although how much more is not articulated).<sup>6</sup> Ability-to-pay is typically measured in relation to income. Equal sacrifice, which originates from Mill (1848), instead is linked more closely with utility. The principles of equal _absolute_ sacrifice and equal _proportional_ sacrifice, respectively, require that taxpayers should face the same absolute and proportional reduction, respectively, in utility from the imposition of a tax. Assuming that the marginal utility of income decreases with income, both concepts imply that richer households should pay more in tax than poorer households, although they do not necessarily imply that they should pay more as a proportion of their income, as this will depend on the underlying utility functions (see, e.g., Young, 1987)
concatenated based on selected variables, and various imputation models are employed, including linear regression, predictive mean matching (PMM), and truncated regression. The best-performing imputation model determined based on alignment with observed distribution, consistency of results, and performance is the predictive mean matching (PMM) technique. Furthermore, a comparison of model specifications reveals that a weighted PMM slightly outperforms the unweighted PMM. By imputing many variables of tax income, we recognize the uncertainty of the matching process. Predictive mean matching usually imputes less implausible values than other imputation techniques. It is also recommended if the distribution of the original variable is skewed, which is true in the case of income data. We imputed ten values of tax income into the EU-SILC data set based on the ten closest neighbors (k=10). The imputation was handled in Stata using the multiple imputation module. EU-SILC includes survey weights, which increase the representativeness of the sample. We use the weights in the estimations reported below.

**We evaluate the performance of the different imputation methods by comparing several moments of** There are **the distribution of the imputed tax income to the ones of the true observable tax income.** several possibilities for evaluating the performance of statistical matching techniques (Leulecscu and Agafitei, 2013). We start by comparing the imputed mean and standard deviation to the observed mean and standard deviation of tax income. We then compare the distribution of population subgroups (by income percentiles), which is crucial because it shows how the method works in the context of distributional analyses. The chosen imputation method should effectively maintain the marginal distributions of the variables before and after imputation. However, results based on matched datasets should be interpreted with caution, as they can be sensitive to the variables included in the imputation, and the specific objectives of the imputation, such as estimating the marginal distribution of taxable income rather than estimating joint distributions.

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