rafmacalaba/data-use-mentions-extended huggingface dataset:
open economy case, and allows us to consider a variety of cases with respect to taxation and government spending: 1) wasteful government spending with lump sum taxes (in which case distortionary taxes are set to zero in the productivity equation); 2) optimal government spending with lump sum taxes; 3) wasteful government spending with distortionary taxes; 4) optimal government spending with distortionary taxes.

For the cross country welfare comparisons, our empirical calculations are based on the following equation, which generalizes (26): where the productivity of the reference country (US in our benchmark case) is: while the productivity for country i is calculated as: ~~i~~ Shares and tax rates used in the calculation of log PRt<sup>andlog PR</sup> t<sup>us</sup> +s<sup>arenowthoseofthe</sup> reference country (the US in our basic set of results) and shares are de...ned as in the within case.

# 5.1 Data and Measurement

In order to discuss how our index of welfare changes over time within a country and how it compares across countries, we use yearly data on consumption, investment, capital and labor services for the years 1985-2005 and for seven countries: the US, the UK, Japan, Canada, France, Italy and Spain. We are unable to include Germany in the sample, since data for uni...ed Germany are available only since 1995 in EU-KLEMS. We use two di¤erent data sets to compare welfare within a country and across countries.

To analyze welfare changes over time within a country, we combine data coming from the OECD 24
- Special attention must be devoted to identifying innovative methods for reducing well-known issues related to the **use of proxy respondents and recall bias** . The adoption of mixed-mode survey designs leveraging the use of new technologies, such as mobile phones and widely used free messaging apps or social media information (e.g., Facebook data), is a promising avenue to explore in the context of international migration, as demonstrated by recent studies (Ndashimye et al., 2022). Combining remote data collections and big data with face-to-face household surveys can be an effective way to improve the quality of migration information as it allows for direct reporting of the subject of interest (i.e., the migrant), rather than relying on a proxy respondent, and for recording migration-related events at a higher frequency (e.g., in between panel waves or even survey visits) while also minimizing potential recall bias.

> 28 It is important to note that the possibility of clearly defining different categories of migrants matters as well for the ‘rare event’ problem as the rarity of the event also depends upon the definition adopted and the type of migrant being studied given that certain types of migration might not be as infrequent in the context of a household survey (De Brauw and Carletto, 2012).

> 29 For example, Gillespie et al. (2021) discuss the main methodological issues in the measurement of migration motives focusing on the use of open-ended questions and the strengths and weaknesses of this method.

19
stages we exploit longitudinal data for Chile (2001-2006), Mexico (2002-2005), and Peru (2002-2006).

In the case of Chile, we use the panel version of the _Socioeconomic Characterization Survey_ (CASEN Panel) carried out by the Ministry of Planning (MIDEPLAN). The first wave was undertaken in 1996 using a random sub-sample of 5,210 households from the cross-sectional CASEN covering four regions of the country, which represent 60 percent of the national

- 8 -
and neonatal asphyxia). The estimates in the tables are (unweighted) mean outcomes across countries, with the country means calculated using countryspecific sampling weights, with 95% confidence intervals in parenthesis (see text for details). Data are from clinical vignettes, unannounced visits, and visual inspections from Kenya (2018), Madagascar (2016), Mozambique (2014), Nigeria (2013), Niger (2017), Sierra Leone (2018), Tanzania (2016), Togo (2014), and Uganda (2013), with number of facilities reported in column 4. The malaria vignette was not used in the Kenya (2018) survey.

30
distribution, holding μ and σ constant. Hence, the share of the population that falls below the poverty line, _x_ , for each year, t, starting in the default year, d, is Where δd+t is the cumulative change in GDP relative to the year prior to default.

Having found the expected poverty headcount if growth impacted all individuals across the income distribution equally, we compare this figure with observed poverty headcounts sourced directly from surveys: Excess poverty. After filtering PIP data for observations coming from compatible surveys (i.e. same type of coverage: rural, national, urban) and observations only up to 6 years apart, we obtain 649 observations for change in _excess poverty_ .<sup>30</sup> Of these, only 30 observations occur in the t to t+3 period of a default spell, we use these data for our first descriptive exercise. When looking at the effects of growth in average per capita income on growth, Kraay (2006) finds that much of the variation in poverty can be attributed to “growth in average incomes,” while little is attributable to a high sensitivity of poverty to the same. Dollar and Kraay (2002) find that mean income for the bottom quintile of the distribution grows equiproportionally to overall per capita income.

Yet, the cases we can explore below suggest that, at least on occasion, poverty increases beyond what would be expected for a given GDP decline. Figure 7 below suggests that poverty headcounts are somewhat elastic to income during the first few years of a default. The slight downward slope of the observations within the default window implies that in these cases growth translated to larger than expected changes in excess poverty between surveys. This elasticity is estimated at -0.03% per every 1% change in mean income and it is statistically significant at the

> 30 <mark>Note that the observations for change in excess poverty can thus be taken from occurrences up to 6 years apart, but the window is
make at least initial inferences on whether the consumption or the production channel is the most relevant channel for project impacts to materialize, we complement household-level with administrative data on the total number of days of employment provided under MNREGS, in total and for irrigation-related activities that could be expected to have an immediate productivity-enhancing impact. Summary statistics for these data, which unfortunately were available only from Andhra Pradesh, Bihar, Chhattisgarh, Haryana, Madhya Pradesh, Maharashtra, Rajasthan, and Tamil Nadu, in panel C. With 38.8 days of employment generated per household in treated villages (vs. 6.5 in control villages, pointing to some contamination), the program’s size is not inconsequential. Also, with 17.1, almost half this employment was in irrigation-related activities which, if effectively implemented, should have an almost immediate impact on agricultural production.

Information on agricultural production outcomes in table 3 points towards modest increases in value of output (from Rs. 4,129 to Rs. 4,746/ ac.) and inputs (from Rs. 1,072 to 1,339/ac.) as well as profits from agricultural cultivation (Rs. 3,057 to 3,407/ac.). The incidence of irrigation increased markedly, from 38% to 56%, possibly reflecting MNREGS-related investment. While total number of hired labor used increased from 30 to 42 days, family labor use decreased from 91 to 79 days. Intensity of hired labor was much higher in treated areas than in controls while more family labor was used in control compared to treatment areas.

> 15 Functioning of rural land sales markets is affected by land ceilings imposed to facilitate land reforms. Rental markets are outlawed in many states, leading to static efficiency losses (Deininger _et al._ 2008) and depressing investment (Deininger _et al._ 2013b). Ill-functioning land markets have also been linked to significant inefficiencies in the industrial context (Duranton _et al._ 2014).

10
**Figure 2:  Growth Incidence Curves, 2005-2008, for Egypt, Urban and Rural Areas**

_Note: grey areas show95 percent confidence intervals of the estimated growth rate. Source: own estimates based on panel HIECPS survey 2005-2008._ This assessment should be now put to test. As stated above, there are limitations of growth incidence curves that the panel data can help to overcome.

# **V. Was growth in Egypt biased against the poor?  Assessment based on panel data**

Longitudinal changes capturing actual movement of households suggest a very different pattern of mobility compared to growth incidence curves. Growth incidence curves constructed on panel data allow us to see how each person’s welfare evolved over time. Panel based GICs are no anonymous, and they show the growth rates depending on where in a distribution a given person or household started. Therefore, it is not surprising to find that moving from a simple comparison across periods (Figure 2) to a longitudinal perspective produces a dramatically different picture **_._** Figure 3 plots actual household growth rates over 2005-2008, ranking all households by their position in the 2005 distribution.

13
0.160)|(0.257)|(0.088)|(0.156)|
|_NO_MINWAGE_: the country<br>|1.092|1.130|1.394|0.640|1.705|2.235|
|does not have a minimum wage|(1.562)|(1.642)|(1.598)|(1.302)|(1.681)|(1.514)|
|log GDP per capita, PPP USD|3.633***|3.221**|1.828|0.544|-1.519|-1.791|
|(annual average, lagged 1 year)|(1.265)|(1.303)|(1.786)|(2.282)|(0.984)|(1.173)|
|Real GDP growth, y-o-y,|0.114***|0.117*|0.067|0.119**|-0.145<sup>(</sup>*<sup>)</sup>|-0.226***|
|quarterly data lagged 1 quarter|(0.050)|(0.060)|(0.076)|(0.064)|(0.089)|(0.060)|
|<br>_LMP_ACTIVE_: Active labor||||<br>-33.216***||<br>-41.139***|
|market measures spending, %||||(12.587)||(8.886)|
|<br>GDP per percentage point of<br>unemployment (lagged 1 yr)|||||||
|_LMP_PASSIVE_: Defined in the||6.522|
# **_Estimating investment flows_**

# _Estimates for the period 2003 to the present_

For the period after 2002, the steps taken to generate sectoral series for gross fixed capital formation are as follows:

- For the industrial sector, use the industrial enterprise data to measure gross investment in fixed assets measured as the difference between the original value of fixed assets in adjacent years.

- For the service sectors in urban areas, GFCF has been estimated using the fixed asset investment data for outlays on structures and equipment.  Outlays on the asset class “other” have been excluded as they are largely outside the scope of the SNA definition of gross fixed capital formation. No data for the asset type “other” could be found for 2003 and so it was assumed that the share of “other” investment in total fixed asset investment in urban areas was the same in 2003 as in 2004 for all sectors.

- For agriculture, the national data for FAI have been used.

- For the industrial and service sectors in rural areas, the difference between the national and urban FAI data has been added to the urban series as defined above. Since 2011, the extent of this additional investment has fallen to a very low level following a change in definitions used by the NBS. No explanation is available for this change but it may be that the NBS has adopted a functional definition of urban and rural areas (as in the population data) rather than a definition based on the administrative status of the local area. Thus, most industrial investment in rural areas may have been reclassified as being in urban areas, reflecting the population density in the area where the enterprise is located.

- The business sector is estimated as the sum of the four estimates above.

- For the government and infrastructure sectors, the FAI data for urban areas have been used excluding the category “other” investment.

_Estimates for the period 1981 to 2002_ For the period prior to 2003, two different methodologies have been adopted. Between 1981 and 2002
) or their use as fodder for livestock or provider of organic fertilizer. None of these indirect aspects is typically addressed in the LSMS-ISA surveys (or household surveys more generally).

Information on the harvested amounts of the tree products and their different uses (sale, autoconsumption, or other uses<sup>3</sup> ) was directly obtained in all surveys, except in Tanzania. Second, to assess their contribution to household income, we estimated the share of gross income derived from trees on farms as part of gross agricultural and gross overall income.<sup>4</sup> Gross household income was calculated

> 3 This concerns their use as inputs into another production process (e.g. fodder for livestock, fruits for jam, timber for own house construction or fencing).

> 4 A more refined measure would be to calculate the share of net income from trees on farms over the total net income per household. However, complete expense data were not collected in all study countries and when collected, they were not

5
Studies also show the important contribution of social networks on the desire and decision to migrate (Boyd, 1989). Krissman (2005) calls for a larger conception of what constitute the migrant’s network, by including potential employers in the destination countries and labor smugglers, in order to understand international migration. Access to information about living abroad through siblings and friends increases intention to migrate (Cairns & Smyth, 2011) but social network from the home country decreases it, namely when there are strong links with family and local community. For instance, using data from Rwanda, Blumenstock et al. (2019) find that the probability of leaving home decreases proportional to the size of the home network. Bryan et al. (2014) point out that migration is a risky investment. Inasmuch as social networks could reduce the noise and perception of risks, their model suggests that firsthand, personal experience of migration is needed to overcome the information friction. They argue that this explains why there are fewer migrants than expected despite huge potential returns from migration.

Age is also an important determinant of migration, with younger people having a stronger incentive to migrate than older individuals (Cairns & Smyth, 2011; Kahanec & Fabo, 2013; Milasi, 2020). One of the reasons why age is such an important driver of migration is related to an individual’s familial/marital status which affects the likelihood of migration, namely, not having partner ties and childbearing (Smith & Floro, 2020; Van Mol, 2016). Milasi (2020) shows that women are less likely than men to desire to migrate, but that the probability to plan to migrate does not significantly depend on gender. He explains this by the fact that the actual planning of migration is guided by traditional drivers (e.g. income and education) that do not necessarily depend on gender whereas the desire to migrate in itself is more influenced by gender-related factors (Milasi, 2020; Ruyssen & Salomone, 2018).

Studies find that
It is clear that the direction of the change in prices of domestic goods and factors cannot be detennined a priori. For example, the above discussion suggested that the excess demand for labor at initial prices would put upward pressure on the wage rate. However, if the price of domestic goods declines, as seems likely if the domestic goods are good substitutes for imports, the demand for labor will be reduced. Thus, wages and goods prices could either rise or fall in the final equilibrium. The discussion of the model does suggest several factors that are likely to affect the magnitude of the price changes. First, markets with greater levels of imports for final consumption and directly competing imported intermediates are likely to experience declines in prices, since there will be greater substitution away from domestic goods. This will also reduce the returns to owners of specific factors in these sectors, because of the decline in output price. Second, prices are also likely to fall in sectors where significant levels of imported inputs are used, because the reduction in production cost will increase supply. In these sectors specific factor owners will increase incomes, though, because the cost of inputs declines relative to the price of output. In this case imports will be a complemen **t** o domestic production rather than a substitute. Third, the impact on domestic prices is likely to be larger the greater is the elasticity of substitution between domestic goods and imported goods. Finally, the impact on prices is likely to be small when there is a significant level of exporting and a high elasticity of substitution between domestic sales and export sales.

We now turn to a parameterization of the model for the case of Chile, to illustrate how a preferential tariff reduction affects domestic prices once equilibrium has been restored in all markets.

# B. Simulation Results for Chile

The domestic production data for Chile contained information on 69 domestic production sectors for Chile. Of these sectors, 41 produce traded goods and 28 sectors produce non-traded goods.<sup>5</
stable political institutions than South Africa. Some studies have shown tendencies exists for ethnic fractionalization to negatively influence economic success and the quality of institutions (see, e.g., Easterly and Levine, 1997; La Porta et al., 1999; Alesina et al., 2003). Easterly and Levine (1997) found that for Africa, greater ethnic diversity goes in line with low schooling, underdeveloped financial systems, distorted foreign exchange markets, and insufficient infrastructure.

In general, these results were confirmed using a survey data set from the Afrobarometer, a relatively new survey measuring the social, political and economic atmosphere in more than ten countries in Africa. Examining these results, we observe that individuals in Botswana are more compliant than those in South Africa. The marginal effects indicate that being a resident of Botswana rather than of South Africa increases the probability of reporting the highest tax honesty by around 6 percentage points. This result is robust across various specifications. _Figure A1_ in the Appendix reports the results of the experiment. It shows a clear difference in behavior between subjects from Botswana and South Africa throughout most of the different experimental treatments. Moreover, the results obtained also indicate that tax culture effect overwhelms the enforcement effort. Thus, increasing audit probability and/or the penalty for South African subjects leads to lower compliance.

# **IV. HOW STABLE IS TAX MORALE AND HOW IMPORTANT ARE SOCIAL INTERACTIONS?**

We have seen in the previous section that values and preferences are affected by policy or institutional changes. Thus, these results support Bowles‘ (1998) argument that ―we can neither accurately predict nor coherently evaluate the likely consequences of new policies or institutions without taking account of preference endogeneity‖ (p. 75). This can be seen as an indicator that tax morale does not remain stable over time. Changes in the political system have a substantial impact on tax morale. In the fields of behavioral law and economics, the importance of social norms in studying compliance with the law has been
|Sisben eligible|1,363,200|
|---|---|
|Poor|1,059,720|
|Indigent|385,274|

A more detailed look at the data, however, yields a more complicated picture. Table 2 shows the distribution of all age-eligible individuals according to their eligibility by the Sisben criterion and their poverty status. The shares represent weighted averages of each category over all survey years. While table 1 shows that the total number of age-eligible elderly with low Sisben scores is about equal to the number of elderly living in conditions of poverty, table 2 shows that these two groups overlap only partially. Over 35 percent of the poor are not considered eligible according to their Sisben scores. The same is true of almost one-third of the indigent elderly.

**Table 2: Comparison of Sisben Score and Poverty Status,** **<u>percent</u>**

|_Eligibility_|_Nonpoor_|_Poor_|_Nonindigent_|_Indigent_|_Total_|
|---|---|---|---|---|---|
|Noneligible by Sisben score|52.73|10.60|60.22|3.11|63.33|
|<br>Eligible by Sisben score|17.71|18.96|28.62|8.06|36.67|
|<br>Total|70.44|29.56|88.83|11.17||

The most important question is how the eligibility criteria translate into actual receipt of the benefit. Table 3 shows the share of individuals who report living in a household where at least one person receives the benefit. We present weighted averages from the 2013 round of the ENCV. As can be seen, coverage levels are fairly low in each of the target groups. Even among individuals who are older than 65 years of age, only slightly over 50 percent of the
Several of these surveys were harmonized by the Economic Research Forum (Egypt’s 20122013, 2015, 2017-2018 HIECSs; Iraq’s 2007 and 2012 IHSESs; Jordan’s 2010-2011, and 20132014 HIESs; the West Bank and Gaza’s 2009 and 2011 PECSs; and Tunisia’s 2005 and 2010 NSHBCs. The most recent surveys for Egypt (2019-2020), the West Bank and Gaza (2016-2017), Tunisia (2015 and 2021), and the Mauritanian EPCVs were obtained from national statistical agencies CAPMAS, PCBS, INS and ONS, respectively. We implemented careful harmonization of these surveys with the previous survey years and translated the variables from Arabic or French to English.

We present the poverty lines for the six countries in Tables A.2-A.7 (Appendix A), compiling them from official sources and World Bank publications. We used region-specific poverty lines within each country to account for spatial differences in consumption (expenditure) patterns and price levels.<sup>7</sup> Since our focus is on poverty analysis, we used consumption values and national poverty lines in local currency units and in survey-year prices to sidestep conversion issues (e.g., with the PPP or market exchange rates) and adjustment for inflation.

# **3.2. Living-standards indicator**

Expenditure is widely regarded as a better indicator of permanent income when households, particularly in poorer countries, exercise consumption smoothing and use savings to augment unstable incomes due to seasonal or informal employment or unexpected shocks (Deaton, 1997; Deaton and Zaidi, 2002; Mancini and Vecchi, 2022). We use household consumption expenditures

> 7 We were able to do this for all countries in our sample except for Jordan. According to DOS reports, Jordan’s Department of Statistics (DOS) did not publish region-specific poverty lines and used a single poverty line for all of Jordan in 2010 and 2013. Jordan’s DOS does not publish region-specific Consumer Price Indices so we were unable to take spatial price differences into
show that intangible capital includes what might be termed the ‘stock equivalent of TFP’ (total factor productivity).

Table 1 displays one of the key findings from the World Bank wealth accounts. At least in cross section, there is a clear tendency for natural capital to shrink as a share of total wealth as countries become wealthier, while there is a corresponding increase in the share of intangible capital. However, because the World Bank does not have estimates of human capital by country, the result is that intangible capital – the sum of human capital and the stock equivalent of TFP – constitutes 50% to 80% of total wealth. Pulling this intangible capital residual apart, and using the values of human capital in selected countries estimated by the OECD, is one of the primary contributions of this paper.

4