rafmacalaba/data-use-mentions-extended huggingface dataset:
the values recorded in this module design are significantly lower, with the sole exception of Cereals, for which the value reported is not significantly different than the benchmark. It appears that the particular cognitive challenges of the usual month approach, at least in a highly seasonal setting such as rural Tanzania where consumption patterns can vary widely throughout the year, consistently result in overestimation of consumption incidence on most foods and in an underestimation of value.

The two household diaries exhibit a distinct pattern of value reporting error. Because the expected main driver of reporting error between the household diary and the personal diary would be the inability to capture personal consumption outside the home fully, the values reported should diverge most for the relevant food goods. This is indeed what we find. The household diaries record 36 percent – 39 percent lower value in Fruit consumption, 37 percent–42 percent lower value in Beverages, and 35 percent – 36 percent lower value in Meals Outside the Home. In contrast, Cereals, Tubers, and other basic foodstuffs are closer in value to the benchmark, although significantly lower at times, for the frequently supervised household diary. The higher consumption values for the infrequently supervised diary relative to the frequently supervised diary may suggest the partial presence of telescoping or rule of thumb errors (similar to what we observe with the 7-day recall) if, indeed, the infrequently supervised diary defaults to a shortperiod recall survey as a result of less frequent updating of the diary.

Table 6 summarizes how consumption value reporting error covaries with particular household characteristics by listing the precisely estimated interaction terms as given by equation (3).<sup>15</sup> The characteristics of households do mediate the degree of error in the value reported, although the patterns are less clear than in the case of consumption incidence. One fairly clear result concerns the number of adults

> 15 Appendix table 3 relays the magnitude of all estimated interaction terms.

12
7|23.1|22.5|18.2|
|35-49|24.0|33.3|16.8|14.3|11.6|
|50-65|46.1|27.2|9.9|9.0|7.8|
|Total|18.5|27.7|24.1|18.9|10.7|
|_1996 _||||||
|16-25|5.9|23.8|38.6|24.9|6.8|
|26-34|6.9|19.6|27.0|26.1|20.4|
|35-49|15.7|27.4|18.2|18.6|20.1|
|50-65|36.1|29.2|12.2|11.5|11.0|
|Total|12.9|24.4|25.7|21.6|15.4|
|_2002_||||||
|16-25|4.8|20.5|42.8|24.5|7.5|
|26-34|5.6|17.4|32.0|25.6|19.4|
|35-49|10.8|23.9|23.7|20.7|20.9|
|50-65|26.3|29.0|15.5|12.9|16.3|
|Total|10.0|21.9|29.5|21.9|16.7|

_Source:_ Calculations based on the ENEU (third quarter).

Also concerning the interaction between age and education, one can argue that developments in the educational system have more impact on the new generations than on the elderly. To investigate this, it is necessary to contrast the behavior
Dolls, Fuest, Peichl (2012) and Dolls, Fuest, Peichl, Wittneben (2018) analyze the capacity of European tax benefit systems to act as an automatic stabilizer in times of income changes and how this capacity has been affected by policy changes.

# 2. **Assessing vertical redistribution**

## **2.1 Measuring redistribution**

Redistribution is typically measured using the Reynolds-Smolensky index of redistribution as the difference between the Gini coefficients of market income and disposable income: RS��Gini<sup>������</sup> �Gini<sup>����������</sup> � Figure 1, which is based on the OECD Income Distribution and Poverty Database<sup>3</sup> , shows the Reynolds-Smolensky index of redistribution for a selection of countries. The European Union’s average level of redistribution equals 21 Gini points. This is almost twice as large as the redistribution occurring in the United States, amounting to 11 Gini points.

The Gini difference is a measure of the redistributive effectiveness of taxes and transfers at a given point in time: the larger the difference, the larger the impact of taxes and transfers on reducing inequality in market incomes. Market incomes include gross labor incomes and earnings from employment and self-employment (both permanent and temporary jobs), capital income, incomes from property as well as private pensions. Disposable incomes equal market income minus direct taxes and social insurance contributions (for both employees and self-employed), plus any additional transfers (such as pensions, means tested benefits and non-means tested benefits). Direct taxes include for example personal income taxes, taxes on capital dividends and interests, as well as property taxes.<sup>4</sup> Incomes are observed at the household level and weighted with the modified OECD equivalence scale. The Gini coefficients for market income, market income plus pensions, and disposable income for the EU-28 countries have been calculated using the microsimulation model
# **Introduction**

The recently published Europe and Central Asia (ECA) Regional Report on Gender (World Bank 2011) provides summary data on unconditional gender wage gaps across countries, with values ranging from a low 10 percent (Slovenia) to 50 percent (Georgia). Azerbaijan’s gender wage gap (36 percent) is at the higher end of this distribution.

It is not clear whether there are large disparities between the wages of Azerbaijan’s men and women throughout their careers. One possibility, which the empirical literature has started exploring systematically only in the last few years using data for a handful of industrial countries, is that gender wage gaps _evolve_ over time and that the first few years in the labor market, as young people transition from school to work, are an important determinant of the emergence of overall gender wage gaps (Manning and Swaffield, 2008; Bertrand, Goldin and Katz, 2010; Del Bono and Vuri, 2011). One possibility is that young men and women earn equal wages at the beginning of their career, but the childbearing years—in the absence of suitable leave policies, child care, and other supporting mechanisms—gradually place women at a disadvantage. Because this is a fledgling literature (see, for instance, Pastore, 2010; and Pastore, Sattar and Tiongson, 2013), little is known about this possibility, particularly in developing countries and economies in transition such as Azerbaijan.

This paper aims to contribute to the literature on gender wage gaps in early career by studying wage differences among young people in Azerbaijan.

The analysis is based on an _ad hoc_ nationally representative School-to-Work Transition survey carried out in the country in August 2005 on a large sample of young people aged 15 to 29. The age of individuals in the sample allows us to observe the entire process of transition from school to work and to adulthood, including the phase when the young person is going to establish a family and have children. According to official statistics, the average age of
Africa, towards the bottom of the development ladder. Chiquiar and Tobal (2019) similarly show that Mexico reallocated production away from skilled production and towards unskilled production as China’s comparative advantage in skilled production emerged post 2001. Third, countries’ own per-capita GDP and population growth affect the relative size of the market. This can be seen clearly in Asia, where the relative market size in terms of GDP per capita declines over the 2000s, as China gets richer. It is also possible to see how the relative population of Africa’s international market declines in the 2000s, as Africa’s population grows faster than the rest of the world’s.

**Variable profit** We use existing data sets to measure the components of variable profit. To calculate export growth, the first determinant of variable profit, we use the Penn World Tables 9.1 export series (Feenstra, Inklaar, and Timmer, 2015). For total factor productivity growth in agriculture, the second determinant of variable profit, we use the international agricultural productivity series of the United States Department of Agriculture’s Economic Research Service (Fuglie, Wang, Ball, et al., 2012).

17
a companion formal sector census of

## **5.1 Demand Side**

Consumers derive utility from baskets of formal and informal goods, which they combine through a Constant Elasticity of Substitution demand system. Formally: where _Q_ is the final good and _Q_ 1 and _Q_ 2 are formal and informal good baskets that go into the production of the final good.<sup>16</sup> In our calibration, parameters will be set so that the equilibrium expenditure and employment share in the informal sector are low, targeting values of the informal sector share in the U.S. economy. When distortions are identified and fed into the model, the elasticity of substitution _λ_ will be a key determinant of the strength of the response of the informal sector to the distortions.

Formal and informal good baskets, in turn, are produced according to a Constant Elasticity of

> 16 One could have directly defined _Q_ to be aggregate consumption, where the aggregation is done at the level of the household. This is isomorphic to our specification of _Q_ as a good produced by a final good aggregator.

28
24 crisis in Russia on nutritional status. This could be understood if Russian households were able to fully smooth food consumption during this period of dramatic expenditure shortfalls. However, it would also be consistent with an overstatement of the increase in poverty in 1998. This latter observation could be obtained if inflation between 1994 and 1998 had somehow been overstated. Indeed, Gibson, Stillman and Trinh (2008) find evidence of a substantial overstatement in the CPI for urban Russia. To the extent that this latter finding holds for Russia more generally, and that the price deflators that accompany the RLMS data track the Russian CPI, it is possible that poverty in 1998, estimated from RLMS data, is also overstated. We apply our poverty prediction method to the RLMS data to probe these alternative narratives.

In our second examination of poverty trends in the face of uncertain pricedeflators we consider the case of Kenya. Two recent household expenditure surveys in Kenya are the 1997 Welfare Monitoring Survey (WMS) and the 2005-6 Kenya Integrated Household Budget Survey (KIHBS). These surveys were implemented during different periods of the year and more detailed consumption data was collected during the KIHBS – raising some questions regarding the comparability of the data.<sup>18</sup> However, it is the choice of the appropriate deflator that was generally considered to pose the greatest challenge to tracking the evolution of poverty during this period in Kenya. The official CPI almost doubled between 1997 and 2005-6, while the deflator based on recalculations of the rural and urban poverty lines suggested a much lower price increase (6 percent in

> 18 The 1997 WMS survey was carried out during 3 months (February -May 1997), while the data collection for the 2005 KIHBS spanned May 2005 till May 2006 during which field work was organized in 17 three week cycles with all 69 districts covered in each cycle.  The consumption data collection during the KIHBS was also more detailed.  During the WMS consumption data was collected for broad (aggregated) categories:  79 food (7 day
Recruitment of schools, ; agreements with principals, Focus groups with IRB submission Enrollment of students Program implementation teachers and parents Jan. 2019 Early Mar. 2019 Apr. — Oct. 2019 Feb. - Mar. 2021 ; eee ow Recruitment of students Baseline data collection Endline data collection Mid-Jan. — Feb. 2019 Mar. 2019 Survey: Oct. - Nov. 2019 Administrative data: February 2020
***|**-2.14*****|**6.32*****|**4.16*****|**-2.16*****|
||(0.044)|(0.043)|(0.062)|(0.048)|(0.046)|(0.066)|
|No. Observations|16957|17022|33979|16957|17022|33979|
|||**Estimate**|**s of Mobility**|**fromQuadra**|**tic CEF**||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|6.32|4.16||**_IGME0 _**|0.52|0.64|
|**_ES5 _**|8.75|7.09||**_IGME5 _**|0.454|0.533|
|**_ES10_**|10.82|9.46||**_IGME10_**|0.384|0.426|
|**_ES16_**|12.85|11.58||**_IGME16_**|0.300|0.298|

Notes: (1) The data used are IHDS 2012 with children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,5,10,16. (4) Robust standard errors in parentheses; **
226,000. These regions are remote, largely rural, and are at a driving distance of 300 km or more from the capital city, and 450 km or more from Dakar, Senegal. These regions were chosen due to their high propensities of irregular migration and poor access to conventional sources of information about migration. According to estimates from the 2018 Gambia Labor Force Survey, URR has the highest share of irregular migrants to working population of all regions (more than 5%), while in CRR about 3% of the population are irregular migrants. Most people work in agriculture, with limited alternative opportunities, making migration to Europe appear particularly attractive. Using estimated population sizes projected from the 2013 Gambian census, we identified settlements that were predicted to have at least 35 males aged between 18 and 30, and that had total population sizes 11
# **1. Introduction**

The estimation of poverty in any given country relies on household surveys that contain information on income, consumption or expenditure (Household Expenditure Surveys, or HESs for short). This information is complex to collect and requires elaborated and time consuming questionnaires that result in costly surveys. For this reason, statistical agencies worldwide have taken to the practice of administering relatively small surveys (usually in between 5,000 and 10,000 households) at intervals of several years (usually every 4-5 years).

This practice is sensible from a logistics- and cost perspective but has two main drawbacks for the measurement of poverty. The first is that small surveys can provide statistically reliable statistics only for highly aggregated areas such as rural and urban areas or large sub-national regions. And the second is that poverty statistics can only be produced in conjunction with the HES surveys every several years, leaving researchers with no information on poverty for the periods between any two surveys or beyond the most recent survey.

To address these two shortcomings, we advocate the use of imputation methods to fill these data gaps. Imputation methods have a long history in statistics and economics and have been used to address a variety of missing data problems; see e.g. Rubin (1978 and 1987). While originally conceived to fill data gaps within surveys, these methods have also been extended to cross-survey imputation where one survey is used to fill data gaps of another survey belonging to the same population. A recent review of these methodologies by Ridder and Moffit (2007) shows how widespread these methodologies have become, and how they can be adapted to respond to different types of missing data problems. See also Fujii and van der Weide (2013) and the references therein.

In the context of poverty analyses, imputation methods have found numerous applications to address statistical inference problems across space and time. For example, Elbers et al. (2002, 2003, 2005) combine census and survey data to estimate poverty and inequality for areas
# **1. Introduction**

Globally, about 55 percent of the world’s population resides in urban settlements. It is expected that this proportion will increase significantly by 2030, adding an estimated 1.6 billion people to urban areas. About 90 percent of this increase is projected to take place in Asia and Sub-Saharan Africa, particularly in India, China, and Nigeria (UN 2020). Unprecedented population growth and urbanization put pressure on the housing market as increased demand for housing often outpaces the construction of new homes. The demand for housing, particularly in urban areas, will very likely further rise due to socio-demographic transition processes, changes in intra-household (HH) dynamics, or increased migration flows due to climate change. To understand the forward-looking housing demand that economies will face in the coming decade (the flow deficit), we need a thorough understanding of the inadequacy of existing housing (the stock deficit).

Therefore, in this paper, we introduce a new _Adequate Housing Index (AHI)_ , which is based on a uniform methodology to quantify housing deficits across emerging markets. The _AHI_ draws on countries’ latest HH expenditure and consumption surveys (HCES) to estimate the adequacy of housing conditions at the HH level. The uniform measure of the _AHI_ allows for comparability across countries, provides an assessment of the general state of housing within a given economy, and allows to raise awareness regarding the overall housing situation within a given country. We implement the _AHI_ for 64 emerging economies representing about 88 percent of emerging markets’ population, excluding China. We select these 64 emerging economies based on data availability, income group, population size, and region.

Access to an adequate place to live is fundamental for economic well-being, human dignity, physical and mental health, and overall quality of life. Low-quality housing is often observed in slums or informal settlements, in which most urban poor HHs live and work (Bah et al. 2018). Yet, finding an adequate house is also often a
account. The effect of region on formality also becomes somewhat mixed when additional regressors are added. Firms in the Alexandria and Suez Canal region are more likely to be formal only in 2012 but not in 2018. Thus, region is also not a reliable predictor of formality. Firm age is more likely to be significant in 2012 than 2018 and effects are attenuated as more regressors are added.

As we found with the EcC data, firms with collective ownership are more likely to be formal relative to sole proprietorships across years and across the two models, but the effect is larger in 2018 than in 2012. Having more capital increases the odds of being formal, but the effect appears to kick in only when the capital exceeds EGP 1000. As in the case of labor productivity in Model 2a above, higher log net revenue per worker is associated with a higher odds of being formal.

27
which these effects are expected to operate are often not clearly spelt out, either in analytical work or public debate. The reduced marginal effective rates associated with the 2017 Tax Cuts and Jobs Act in the United States, for example, prompted concerns in Canada with the impact on investment there. But how exactly the marginal effective rate in one country, constructed with a closed economy in mind, should impact investment in another is by no means obvious. Similar doubts apply more generally to the helpfulness of the much-noted cross-country ‘league tables’ of marginal effective tax rates<sup>10</sup> in understanding cross-border investment decisions.

The approach taken here is to ground the empirics in a simple but reasonably general model of the investment decisions of a multinational that potentially operate in several countries. This generates a sufficient statistic which summarizes the impact of the international and domestic tax system on inward investment, encapsulating effects through statutory tax rates, in both the host country and alternative locations abroad, and King-Fullerton-type marginal effective tax rates. In doing so, it highlights one form of spillover that has been largely neglected in the international tax literature: the direct effect that investment in one affiliate within a multinational may have on the profitability of others. It might be, for example, that investing in one, and expanding its sales, reduces the price at which others can sell; or expanding one may require using scarce expertise within the group and so reduce the profitability of others. We refer to this, for want of a better term, as ‘implicit’ profit shifting, and distinguish it from ‘paper’ profit shifting of the kind that, as mentioned above, has become the primary focus of attention. In the empirics, we allow for, and attempt to differentiate between, these two forms of profit shifting and the tax effects that operate through them.

The paper is structured as follows. Section 2 sets out the broad framework that articulates potential channels for tax spillovers on real investment and guides the empirical work. Section
Some benchmarking suggests that this estimation represents a conservative scenario. Tobacco-attributable medical expenses are much lower than the calculation by the UNDP et al. (2018) of GEL 327.3 million in 2016 reviewed above.

Tobacco-related mortality Data on smoking-attributable death events in Georgia are taken from the Global Burden of Disease project (GBD 2017). It is estimated that 6,799 people died in Georgia from smoking-attributable causes in 2017 (Table 5).<sup>14</sup> Ischemic heart disease accounts for over one-third of those deaths. And, consistent with the prevalence rates, 91 percent of smoking-related deaths occur among males.

According to the GBD data, smoking-attributable diseases would be responsible for 13 percent of all premature death in Georgia in 2017.<sup>15</sup> This figure represents a conservative scenario on the health burden of tobacco, relative to previous estimates. The NCDCPH reports that tobacco is the leading cause in cardiovascular disease, cancer and respiratory diseases in Georgia, accounting for up to one-fifth of all premature deaths in the country (NCDCPH et al. 2016).

Years of working life lost (WYLL) The smoking-attributable years of life lost among the working population (YWLL) in Georgia are calculated based on the mortality data from the GBD (2017). For this calculation, the retirement age is assumed at 65 years old for both males and females.<sup>16</sup> The resulting years of productive life lost at the national level totaled 28,794 years in 2017 (Table 6).

> 14 Includes death events from smoking-attributable causes. The GBD reports smoking-attributable death events only for adults over 30 years old. Deaths related to second-hand smoking and chewing of tobacco are not included.

> 15 The overall number of death events reported by the GBD was compared with national records to test consistency. According to Geostat, 50,771 people died in the country in 2016 (Geostat 2016).

> 16 Hence, 2,972 smoking-attributable death events for the
Bank, Washington, DC.</mark>

- <mark>Beyer, R. C., Chhabra, E., Galdo, V., and Rama, M. (2018).</mark> _<mark>M</mark> easuring districts' monthly economic activity from outer spac_ _<mark>e</mark>_ <mark>. The World Bank.</mark>

- <mark>Beyer, R. C., Franco-Bedoya, S., and Galdo, V. (2021). Examining the economic impact of COVID-19 in India through daily electricity consumption and nighttime light intensity.</mark> _World Development_ , _140_ <mark>, 105287.</mark>

- <mark>Bundervoet, T., Davalos, M. E., and Garcia, N. (2021). The short-term impacts of COVID-19 on households in developing countries: An overview based on a harmonized data set of highfrequency surveys. Policy Research Working Paper No. 9582. World Bank, Washington, DC.</mark>

- <mark>Cho, Y., Robalino, D., and Watson, S. (2016). Supporting self-employment and small-scale entrepreneurship: potential programs to improve livelihoods for vulnerable workers.</mark> _<mark>IZA Journal of Labor Policy</mark>_ <mark>,</mark> _<mark>5</mark>_ <mark>(1), 1-26.</mark>

- <mark>Dietrich, S., Giuffrida, V., Martorano, B., & Schmerzeck, G. (2021). COVID‐19 policy responses, mobility, and food prices.</mark> _American Journal of Agricultural Economics_ .

- <mark>Donaldson, D., and Storeygard, A. (2016). The view from above: Applications of satellite data in economics.</mark> _<