rafmacalaba/data-use-mentions-extended huggingface dataset:
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data which of the factors affect underreporting most. Firm size and location are correlated and investment climate variables are also correlated with firm characteristics. Therefore we also did a descriptive regression analysis with underreporting to the tax office as the dependent variable and firm characteristics as well as the IC constraint variables as independent variables, allowing for heteroskedasticity.<sup>35</sup> The resulting regression shows that the size effect of underreporting disappears with the inclusion of other variables. Firms with more capital underreport significantly less to the tax office while corruption and credit constraints increase underreporting to the tax office. Also firms outside Ulaanbaatar tend to underreport more but less if they are active in the construction sector.

Finally we calculated the percent of _aggregate_ sales underreported to the tax office and in the survey. Aggregate underreporting is 37.5% to the tax office and 22.8% in the survey. These figures are respectively lower and higher than the mean firm-level underreporting reported in Table 4, because underreporting decreases in firms size for sales reported to the tax office but increases for sales reported in the survey.

# **5 Discussion**

We have used matched firm-level survey and official tax data to estimate the true sales and the extent of underreporting in sales by formal firms in Mongolia. Three different approaches have been explored, namely a direct approach, an indirect approach, and a 35 U (robust standard errors in the brackets).

32
additional flood in urban areas is associated with a 0.004 SDs lower RWI. The reverse is the case in rural areas: in rural areas, places with lower wealth are _less_ exposed to flooding.

In low- and middle-income countries, a large share of poor households rely on small, informal firms for work. Hence, to further understand exposure to climate shocks among the poor, we study whether exposure to extreme heat and flooding varies by firm size. This analysis uses spatially granular data on firm size from the Economic Census of India.

Among firms in India, smaller non-agricultural firms are more exposed to floods and heat than larger firms. On average, places with an average temperature of 33 degrees Celsius have 0.25 fewer employees, about 12.5 percent smaller, than a place with an average temperature of 31 degrees 3
10 points higher than the survey-only estimate. Of course, by the same token, we may also be under-estimating inequality. Working with values of _θ_ toward the lower end of our estimated range yields estimates of inequality that are noticeably higher than the Gini coefficient of 47 _._ 0. Using our estimates for _θ_ (and _λ_ ) in a back-of-the-envelope calculation, we find that there are approximately 170 households in Cairo whose household expenditure exceeds 1 million USD per year. Although no other information on the number of millionaires in Egypt is currently available, this estimate seems rather conservative.

# **5 Concluding remarks**

A growing literature has shown that household surveys provide only limited information about top incomes and therefore underestimate income inequality. This paper presents a method that corrects for this underestimation. We use the household survey for the bottom part of the distribution and combine it with another data source that provides a better coverage of the top tail. The existing literature has restricted itself to the use of tax record data to capture the top tail. Unfortunately income tax records are unavailable in many countries, including most of the developing world. Our method permits a much larger set of data for the top tail; the only requirements are that the data (i) contain a good predictor of household income, and (ii) provide a good coverage of the top tail.

We apply this method to Egypt, where estimates of inequality based on household surveys alone are low by international standards. Using publicly available data from real estate listings to estimate the top tail of the income distribution, we find strong evidence that inequality in Egypt is being underestimated. The Gini index for urban Egypt is found to increase from 36 to 47 after correcting for the missing top tail. A natural next step would be to use data on house prices to estimate the top tail of the wealth distribution, and extend the analysis to other countries.

# **References**

- Aguiar, M. and Bils, M. (2015).
||
|Number of observations|2,203|
|p-value for equation|0.00|

_Source:_ Village Fund Survey 2010 and Thailand SES 2009. 2,203 observations. Robust standard errors in parentheses. * p<0.05, ** p<0.01, *** p<0.001. Intercept not shown. Dependent variable is 1 if VF borrows to on-lend, 0 otherwise.

23
different household groups in each survey year, differentiating between consumption quintiles, households below the national poverty line, ethnic minorities and female‐headed households (Figures 2 and A.2 for the standardized values and Table A.2 for the absolute values). Households are then divided into consumption percentiles based on the expenditure data from the VHLSS and split into a rural and urban subsamples for each

> 7 Furthermore, as communes are about a fifth of the size of districts, there is likely to be considerably less variation in environmental risk within communes than within districts.

> 8 For the weather and temperature variability, due to the coarse resolution of the data, the value that the centroid of the commune is taken to represent these risks (rather than the area‐weighted average).

9
who choose to pursue more education are, by revealed preference, forward looking and thus more concerned with the future consequences of climate change. It might not be education that is causing pro-climate beliefs and actions, but rather time preferences. Reverse causality is another challenge: individuals who believe in climate change might choose to pursue more education to better adapt to a changing world.

In this paper, we overcome causal inference challenges by assembling a new database on compulsory schooling laws (CSLs) to estimate the causal effect of human capital accumulation on a series of climate outcomes in Europe. The use of CSLs as a plausibly exogenous shift in educational attainment has a rich tradition in labor and health economics (Angrist and Krueger, 1991; Black, Devereux and Salvanes, 2008; Brunello, Fort and Weber, 2009; Gathmann, J ̈urges and Reinhold, 2015; Goldin and Katz, 1997; Lleras-Muney, 2005; Oreopoulos, 2006), but is much more limited on climate.<sup>2</sup> Moreover, due to data limitations, studies have been largely limited to single countries. We build on this nascent climate literature leveraging 39 CSL reforms in 16 countries, identified via a new reforms database and data-driven definition of CSLs that lead to meaningful educational improvements. In addition, studies to date analyze limited outcomes. We study new climate outcomes which extend well beyond standard measures of beliefs and behaviors, also examining the highly consequential domains of policy preferences and voting.

> 1Human capital captures an individual’s knowledge and skills (Becker, 1962) and is typically measured by education metrics including years of schooling (Barro, 2001) and learning (Angrist et al., 2021).

> 2A small set of studies explore environmental outcomes (Meyer, 2015; Powdthavee, 2021).

2
# **1 Introduction**

The Syrian civil war, raging for almost a decade, has led to some of the largest population displacement of our times. Host nations, even those that had followed an open-border policy (such as Turkey, Jordan and Lebanon), have seen their citizens become wary if not outright hostile toward refugees (Kirişci (2014), Nielsen (2016), Aktas et al. (2018), Getmansky et al. (2018), Yahya et al. (2018)). The European Union is militantly protecting its borders against new migrant flows and Turkey has announced the projected return of over one million Syrian refugees in a 20-mile-deep safe zone in northern Syria that includes the areas of Idlib, Tel Abyad, and Azaz (Hoffman and Makovsky (2021), Gall (2019)). The COVID-19 pandemic-induced border closures and the spread of the disease in crowded refugee camps have further increased calls for refugee return. Despite these hopes, in 2020 only 467,000 Syrian refugees returned home, while 1.8 million were newly displaced (NRC (2021)). Though UNHCR intention surveys indicate that most refugees would like to return at some point, many are unwilling to do so under the current conditions, which are further impacted by the large number of IDPs, and report that they have given up hope of being able to return in the next 5 to 10 years (NRC (2021)). Furthermore, many young and educated Syrians in the capital city of Damascus indicate that they would choose to emigrate if given the opportunity (Jalabi (2021)).

This paper analyzes refugee return and internal displacement in the internet era with a focus on understanding how the widespread use of social media captures considerations on return for Syrian refugees. In doing so, it also considers displacement in Syria as a lens through which to view the difficulties returning refugees may face. Though many have analyzed attitudes around return with surveys (Ghosn et al. (2021), Alrababa’
|2014|Boyd<br>and<br>Curtis|United States|2006 to 2007|energy<br>expenditure<br>over gross output in<br>percentage|merging survey of frm<br>management<br>practices<br>with US Census data<br>on manufacturing|Better managed frms pro-<br>duce more output and are<br>larger.<br>At best, fndings<br>show a modest relation-<br>ship between management<br>and energy efciency, but<br>standard errors are large.|
|---|---|---|---|---|---|---|
|2019|Grover,<br>Ia-<br>covone,<br>and<br>Chakraborty|Croatia|2017|sales<br>per<br>employee,<br>proft|survey on frm capabil-<br>ities in Croatia, includ-<br>ing a module for cap-<br>turing management ca-<br>pabilities|Manufacturing frms are<br>better managed than ser-<br>vice frms. Better manage-<br>ment is consistently and<br>strongly associated with<br>better performance.|
|2019|Bloom, Bryn-<br>jolfsson, Fos-<br>ter,<br>Jarmin,<br>Patnaik,<br>Saporta-<br>Eksten,<br>Van<br>Reenen|United States|2010 to 2015|total factor productiv-<br>ity|survey<br>of<br>structured<br>management<br>practices<br>in 35,000 manufactur-<br>ing plants|The study fnds large dif-<br>ferences<br>in<br>productivity<br>associated with
# **1. Introduction**

Within the next four years, the output capacity of the Panama Canal will roughly double as a new set of locks is installed, enabling ships larger than the current Panamax standard to transit the Canal. Several studies (ACP, 2006a; IMF, 2007) anticipate significant employment creation and growth effects of the Canal expansion through increased domestic resource utilization and large multiplier effects. This view, however, is not consistent with the long-standing characterization of Panama as a dual economy, where a dynamic services exports sector has few linkages with the rest of the economy. More importantly, the methodology of these studies cannot provide insights into the potential distributional consequences of the Canal expansion—an aspect of crucial importance in Panama where inequality is a serious concern.

This paper adopts a methodological framework that is focused on the likely effects of the Canal expansion on the distribution of income . The findings of the paper are obtained by linking a dynamic computational general equilibrium (CGE) model of Panama with a micro-simulation framework based on a recent Panamanian household survey. The objective of the simulations is to contrast the counterfactual income distribution that would have resulted in the absence of the Canal expansion project with the income distribution resulting from the Canal expansion during both the construction and operation phases. Compared to earlier studies, this framework is much less suited for comprehensive growth analysis, especially in the near to medium term; on the other hand, it has the advantages of explicitly recognizing the inter-sectoral linkages in Panama‘s economy, clearly identifying the income sources of households, and providing a direct mapping of changes in macroeconomic aggregates to household welfare. Thus, it is important to interpret the results of this paper not as forecasts, but as a consistent set of scenarios for the likely poverty and inequality consequences of the Canal expansion.

The paper is organized as follows. The next section discusses the data and presents some background information on Panama and the Canal shock. Section 3 summarizes the model framework, while section 4 discusses the macro
Comparing (2) and (3) provides a clear picture of the simplifying assumptions that are required for the NBR based measure to be robust. First, it assumes that there is no demand response to consumption price changes through substitution eliminating the cross-elasticity term (the second term) in equation (2). Second, the proportional price changes are assumed the same for both consumer prices and producer prices, and therefore the first and the third terms in equation (2) are combined. Third, it is also assumed that producers do not exhibit any supply responses allowing the fourth term to be ignored. Finally, there are no significant changes in input prices including wage rates thereby ignoring the last two terms of the second-order approximation in (2).

In practice, household survey data limitations often constrain the extent to which these assumptions can be relaxed in the welfare analysis. Even though the micro-econometric approach described above provide many insights, the partial equilibrium nature of this analysis fails to account for various macroeconomic factors that could change the welfare incidence of food price spikes. For instance, economy-wide changes in relative prices, factors affecting structure of production and wage rates in the economy can alter households’ real income. Past work has found that while certain sections of the population are affected negatively in the short-run due to rising food prices, the long-run effect could be zero or positive after accounting for changes in wage rates (Rashid, 2002; Ravallion, 2000).

Understanding the macroeconomic processes is particularly relevant to the 2016 Mozambique case given the underlying currency market shocks. Studies employing general equilibrium methods range from simple simulations to full scale CGE models (Wodon & Zaman (2008) provide a literature overview). In particular, micro-simulation approaches, in which survey households are embedded in the CGE model, can provide a more comprehensive picture of the welfare and poverty impacts of food price spikes (De Janvry & Sadoulet, 2008). In this approach, a CGE model is used to estimate changes in the average income for representative household
the growth of private schools has had a positive impact on educational outcomes, both in terms of enrollment and learning outcomes. In a representative sample of households in the country (the Pakistan Integrated Household Survey 1998), overall enrollment is higher for villages with private schools (61 percent versus 46 percent), as is female enrollment (56 percent versus 35 percent). Moreover, Kim et al. (1999) provide strong causal evidence that private schools increase enrollment by showing that a randomly 4
**Figure 5:** Transition matrix for selected countries

<!-- Start of picture text --> fraction by parental attainment fraction by parental attainment<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>1 1<br>.8 .8<br>.6 Less than primary .6 Less than primary<br>Primary completed Primary completed<br>Secondary completed Secondary completed<br>.4 University completed .4 University completed<br>.2 .2<br>0 0<br>(a) Jamaica (b) Guatemala<br>Less than primary Primary completed Secondary completedUniversity completed Less than primary Primary completedSecondary completedUniversity completed<br>likelihood of child attainment likelihood of child attainment<br><!-- End of picture text --> Notes: The sample is constructed with individuals older than 25 that coreside with at least one individual of the generation above. The figures display the transition matrix between the educational attainment of individuals in the sample and their parents. The horizontal axis is divided according to the share of parents with each level of educational attainment. The height of each rectangle within each figure is the likelihood of child educational attainment conditional on the attainment of their parents.

intergenerational mobility that reflects the likelihood that a child completes a strictly higher or lower education level than the members of the immediately previous generation in the household (parents and/or extended family members, such as aunts and uncles).

**Upward mobility at the country level.** To estimate upward IGM, I estimate the following econometric specification, pooling observations from all the censuses and countries:

where _yicoyt_<sup>_up_is a dummy variable that takes a value equal to one when individual</sup><sup>_i_completes</sup> at least primary education and zero otherwise. The parameters _γo_<sup>_b_,</sup><sup>_γ_<
in the high income category, where 23% of the group has a privately administered system.

# **5. Conclusion**

This comprehensive database provides detailed information on the deposit insurance schemes across the world as of 2003. It improves significantly over the earlier DemirgüçKunt and Sobaci (2001) cross-country database. First, the database includes 14 new countries that have adopted deposit insurance schemes since 1999 and identifies 12 other countries with DISs as of 1999 that were not covered before. Second, the database uses various country sources and surveys of deposit insurance agencies and officials around the world, and hence completes and further details the other collected data. Third, this dataset adds historical time series data, and covers the values of deposit insurance coverage amounts, co-insurance, and coverage ratios since the inception of the first nationwide scheme by the United States in 1934. Fourth, other new variables are incorporated that include the level of co-insurance requirements, percentage of the value of deposits covered, and whether the payments are per depositor or per depositor per account.

The work here is part of a broader research project in understanding and characterizing the design, and implementation of deposit insurance as analyzed in Demirgüç-Kunt, Kane and Laeven (2005) using this data. Moreover, it will help and hopefully stimulate further research on the effect of deposit insurance on financial development, financial stability, fragility and market discipline. We provide the data in an empirically usable format to contribute to this growing literature.

13
Taken collectively, the experiments suggest previous findings demonstrating increased vaccination intentions due to financial incentives may be context specific and may not fully generalize outside of the context of those studies. The risk of a “backfire” effect suggests that it is prudent to systematically pretest incentive programs in novel settings.

# Context

Vaccination campaigns against COVID-19 in Central Asia began from February to April 2021 and were focused first among high-risk groups. By March 2021, widespread vaccination campaigns had begun in all three countries, with Kazakhstan typically leading in terms of the share of the target population vaccinated.

Different vaccines were available in each of the three countries, and among those who planned to be vaccinated, preferences varied substantially. In Kazakhstan, there were three main vaccine options available at the time of the study: QazCovid, produced in Kazakhstan jointly with Chinese manufacturers, Sputnik V, produced in the Russian Federation alongside some local production in Kazakhstan under license, and Sinovac, produced in China. Survey results show that Sputnik V was the most trusted option, particularly when manufactured in the Russian Federation. In Uzbekistan, four different vaccines were available. The first on the market was ZFSW, a locally produced vaccine, followed by AstraZeneca and Sputnik V (imported from producers in Russia). These were later supplemented with the Moderna COVID19 vaccine through the COVAX platform in late July 2021. In Tajikistan, the COVAX platform first supplied the AstraZeneca vaccine, follow by shipments of Sinovac, a small number of Sputnik V doses, and later the Moderna vaccine.

Panel surveys conducted throughout the COVID-19 pandemic by the World Bank in Central Asia (see details in the following section) revealed a varied and complex set of challenges to achieve widespread vaccination. In June 2021, only 40 percent of respondents in Kazakhstan said they were either already or planned to be vaccinated, compared to 43 percent who would “definitely not” be vaccinated (Table 1). In the same month, at 54 percent, the share in Uzbekistan open to vaccination was consistently higher than Kazakhstan,
)|(0.36)|
|Highlands‐drought|0.39<br>|0.37<br>|
||(0.49)|(0.48)|
|Highlandsreliable|0.38|0.34|
||(0.48)|(0.47)|
|Lowlandsreliable|0.03|0.11|
||(0.18)|(0.3)1|
|Lowlandsenset|0.19|0.18|
||(0.40)|(0.39)|
|Goodroof|0.22|0.34|
||(0.42)|(0.47)|
|Electricity|0.00|0.06|
||(0.00)|(0.24)|
|Improved loo|0.21|0.59|
||(0.41)|(0.49)|
|Householdsize|4.91|5.00|
||(2.28)|(2.20<br>|
|Number obs|8431|)<br>8807|

# **b. Regression results**

In this section we discuss the result for the vulnerability relationships tested. We modelled the dependent variable log of consumption per adult equivalent in the household, and included (the same list of ) household and community characteristics in each model. We then estimated a baseline model with simply the LEAP % loss in linear form.

We tested for a non‐linear relationship of LEAP but as results below show, the linear model is the preferred specification. We noted above that in line with the disaster modelling, we explicitly incorporate characteristics of households that may mitigate or exacerbate the impact of the drought shock. The analogy can be drawn with table 2.2 above which shows characteristics of buildings with different wall structures. Table 3.2 below shows the list of