rafmacalaba/datause-encoder-data huggingface dataset:
household enterprises is dubious. Our calculations showed that in each year, the majority of household enterprises reported negative net income, calculated as the difference between revenues and cost (each was recorded in detail in the survey). Apparently, this was due to both over-estimation of expenditures of the enterprise and under-reporting of revenues. Respondents also had difficulties in separating transactions made for the enterprise from those made bor the household. This problem had been noted already in the first year of the survey and had led the survey managers to add, as of the second year, two direct but aggregate questions about net revenues from enterprises. It turned out that this line of questioning (which most survey practitioners would view with great suspicion) provided an apparently better estimate of household enterprise income - which of course does not mean that it was accurate. Experimental survey work is needed to improve data collection on the costs and revenues of household enterprises. A special study of informal sector enterprises in Nairobi and Dakar is currently underway to understand better how the budget of a household enterprise relates to the budget of the household who owns it. In Madagascar, household enterprises are being asked to keep a very simple type of daily account. These efforts will hopefully lead to better ways to collect data on the enterprise budget.

Second, the survey results show that on average, household savings are negative. (This result was obtained after replacing the negative incomes of household enterprises with the results from the direct questions). This is a customary finding in surveys which collect both income and expenditure data and it is generally believed that this is due to under-reporting of income, especially by high-income earners. This is confirmed in the case of the CILSS since tne negative savings rate increases with the level of income. While this result is therefore not surprising, it is distinctly disappointing that even with an instrument of the detail and complexity of the CILSS, it has not proved possible to improve on the experience of other surveys.

While the under-reporting of household income in the CILSS is clearly bad news, it is by no means disastrous for the analysis of household welfare or poverty. Indeed, most such analyses undertaken with CILSS data have used expenditure data, including the work in this research project. Household expenditure is a conceptually preferred 6
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_CHAPTER 4. AREA LEVEL MODELS_ 23 employment rates for cities with population over 100,000 inhabitants in Canada. Ghosh, Nangia and Kim (1996) proposed a different time series cross-sectional model and used it to estimate the median income of four-person families for US states and the District of Columbia. Datta et al. (1999), You (1999) and Datta, Lahiri and Maiti (2002) replaced the AR(1) time effects in Rao-Yu model by a random walk, and Datta et al. (1999) applied the model, including seasonal variation, to estimate monthly unemployment rates for 49 US states and the District of Columbia. Pfeffermann and Burck (1990) proposed a more complex model with area-by-time random effects and regression coefficients varying by area and time. Esteban et al. (2010; 2012) applied the Rao-Yu model to estimate small area poverty indicators in Spanish provinces by gender.

Various extensions of the FH model have been propose to “borrow strength” from space. A model with area effects following a Conditionally Autoregressive (CAR) process (Besag, 1974) was proposed by Cressie (1991) to estimate the census undercount in US states. Petrucci and Salvati (2006) proposed a model with area effects following a Simultaneously Autoregressive (SAR) process to estimate the amount of erosion delivered to streams in the Rathbun Lake Watershed in Iowa by _D_ = 61 sub-watersheds. Pratesi and Salvati (2008) used the same model to estimate mean PCI in _D_ = 43 sub-regions of Tuscany. A similar spatial FH model was used by Giusti, Masserini and Pratesi (2017) to estimate mean income and poverty rates for the 57 Labor Local Systems of the Tuscany region in Italy for the year 2011. Chandra, Salvati and Chambers (2017) proposed an SAE model for non-stationary spatial data. Marhuenda, Molina and Morales (2013) considered a spatio-temporal model, including simultaneously area effects following a SAR process and time effects following an AR(1) process nested within the area effects. They applied this model to the estimation of poverty indicators for Spanish provinces in 2008, using survey data from 2004-2008.
# _Buoyancy and Income Elasticity of Taxes_

Domestic revenues have been quite buoyant since 2000, rising by 4.9% of GDP in six years (table 2). This buoyancy is mainly attributable to VAT on imports, which adding 4.5% of GDP to tax collections, as result of a boom in imports (fuelled by remittances) and changes in the methodology for calculating the VAT from the origin to the destination principle for trade with CIS countries in the early 2000s (IMF, 2006: 10); i.e. the VAT on goods which are traded between Tajikistan and other CIS countries is levied by the importer (destination) instead of the exporter (origin). As Tajikistan has a large trade deficit with other CIS countries, the change in methodology for applying the VAT served to greatly expand the VAT base.<sup>8</sup> The continued buoyancy of the tax system mainly depends on the extent to which VAT on imports continues to grow as a share of GDP. The only other tax handles to have displayed any buoyancy are the social taxes but these are earmarked for pensions. The natural income elasticity of individual income taxes has been dampened by their very low marginal rates. But it seems unlikely that VAT on imports will continue to be buoyant. Imports are already high as a share of GDP, especially for a landlocked country, and so will probably not rise further. The main factor that led to the buoyancy displayed by VAT on imports during the 2000s, the change in the methodology for computing the VAT, is a one off effect which will not be repeated.

# _Changes in Tax Policy_

What scope is there for raising more tax revenue through tax policy changes without creating distortions to resource allocation which would impede growth, which requires that tax rates should be moderate and not out of line with international levels? Corporate profits are currently taxed at 25%, a rate which is in line with the unweighted average corporate profit tax for CIS countries and the transition countries of Eastern Europe (Lorrie, 2003: 38). Hence this rate should not be raised if Tajikistan wants to create a regionally competitive tax environment for business.

Personal income tax (PIT) rates are low. The current top marginal rate of 13% is much lower than the equivalent in other CIS countries, which ranges from 20% to

> 8 Levying VAT on the basis of the origin of imports was common to all CIS countries for their intraCIS trade, but most have now switched to the destination basis.

17
unique global dataset provides comprehensive coverage for more than 50 market indicators grouped into five main thematic areas - government and corporate bonds, equity, institutional investors, and sustainability over the period 2015-2020. The construction methodology of the IFC capital markets database includes data collection from both primary and secondary sources. Primary data was collected through desk research using reports of local stock exchanges, stock market regulators, central banks, and other reports on country specific stock markets published by independent bodies. Most of the secondary data collection came from a commercial data provider – Refinitiv – as well as from other prominent sources such as the World Federation of Exchanges (WFE), Organization for Economic Cooperation and Development (OECD), African Development Bank (AfDB), the World Bank Group (WBG), and Asian Development Bank (ADB).

Our sample covers over 150 countries across the globe in the period 2015-2020. In addition to the IFC’s capital markets database, the paper leverages the World Bank’s World Development Indicators (WDI), the World Governance Indicators (WGI) and the International Country Risk Guide (ICRG) databases.

# **_Capturing issuer composition_**

The study relies on the following variables from the IFC capital market database that capture changes in issuer composition:

- _Total number of listed firms_ – measured by the total number of companies listed on a stock market. This captures the market depth, indicative of the barriers to entry for public stock markets.

- _Share of listed domestic firms_ – measured by the share of domestic companies listed on a local stock market as a portion of the total firms listed. A higher share of listed domestic companies suggests that markets are more accessible for local firms.

- _Sectors_ – data are also classified across seven sectors: financials, agriculture, extractives, manufacturing, construction, utilities, others.

- _Share of market capitalization of the top 10 largest domestic companies_ – captures the level of market concentration. A higher value indicates that the stock market is highly concentrated with a few large firms accounting for a higher share of the total value of market capitalization.

6
**Table A8:** Percentiles of Gains and Losses

||(1)<br>Satisfe<br>houseq|(2)<br>d with overall<br>uality (1=Yes)|(3)<br>Satisfed<br>of famil|(4)<br>with quality<br>ylife(1=Yes)|
|---|---|---|---|---|
||Transitory<br>Gains<br>(TG)|Disappointment<br>Losses<br>(DL)|Transitory<br>Gains<br>(TG)|Disappointment<br>Losses<br>(DL)|
|No Delay|6.282|0|2.587|0|
||(12.822)|NA|(2.255)|NA|
|10th pct of∆_T_|6.439|0.046|2.724|0.014|
||(12.801)|(0.03)|(2.24)|(0.011)|
|25th pct of∆_T_|6.726|0.110*|2.975|0.039|
||(12.763)|(0.061)|(2.216)|(0.028)|
|50th pct of∆_T_|6.98|0.149**|3.196|0.059|
||(12.729)|(0.07)|(2.197)|(0.039)|
|75th pct of∆_T_|7.307|0.180**|3.483|0.084*|
||(12.687)|(0.0707)|(2.178)|(0.047)|
|90th pct of∆_T_|7.998|0.206***|4.086*|0.129***|
||(12.599)|(0.061)|(2.155)|(0.046)|

Notes: Gains and losses are computed using the NLS estimated coefficients in Table 2 and are evaluated at different percentiles of ∆ _T_ (time elapsed from announcement until intervention, in months). Standard errors in parentheses are computed using the Delta Method. Following Galiani et al. (2018) the gross discount factor _R_ is the sum of the average mortality rate (by age and gender) in 2018 in the Dominican Republic reported by World Health Organization plus an interest rate of 5%. Coefficients with *, ** and *** indicate that they are statistically significant at the 10, 5 and 1%, respectively.

29
billion disclosed to the authorities in foreign stocks, equivalent to 9.9% of the 2016 GDP.<sup>6</sup> In a similar vein, nearly a quarter of the overall change in reported assets from 2015 to 2016 stemmed from foreign bank accounts, amounting to over Arg$360 billion or around US$25 billion. Another quarter of the total change in reported assets is attributed to real estate, evenly split between foreign and domestic properties. To our knowledge, no other tax amnesty, whether attempted by any previous Argentine administration or in another country, has yielded such a substantial amount of disclosed assets.

A substantial portion of these assets were concealed in countries traditionally regarded as tax havens or those with low tax rates, such as Switzerland, the British Virgin Islands, and Uruguay. Perhaps surprisingly, a significant portion—30% of foreign stocks, 45% of foreign bank accounts, and 37% of foreign real estate—was actually situated in the US (Figure A.11).

# **3.2 Disclosures by top wealth groups**

Wealth disclosures can have distributional if tax evasion is con- significant implications centrated among the wealthiest individuals. To explore this possibility, we analyze the wealth distribution and compare the average assets owned by the wealthiest Argentines over time. More specifically, we rank individuals based on the assets they report each year (meaning the groups consist of different people each year) and compare the average reported assets for each group before and after 2016. To examine the extreme upper end of the distribution, we break down the top 2% of tax units, which includes individuals aged 20 and above, into bins of increasing assets all the way up to the top 0.01%: P98 to P99, P99 to P99.5, P99.5 to P99.9, P99.9 to P99.95, P99.95 to P99.99, and P99.99.

To assess which groups experienced the most significant increases in reported assets, we begin by comparing the assets declared annually by different percentile groups before and after 2016. Figure 7 shows that individuals below the top 1% displayed a moderate 13% increase in their average declared assets in 2016 compared to 2015. However, the wealthiest 0.5% of taxpayers reported a considerably higher level of capital after 2016, with a particularly noteworthy increase among the top 0.1%, where their assets more than doubled. Indeed, the increase in reported assets is highly concentrated at the top, with the wealthiest 0.1% accounting for almost two-thirds of the overall increase between 2015 and

> 6 These values, measured in constant 2015 pesos, are roughly equivalent to Arg$755 billion or US$50 billion in 2016 pesos, which aligns more closely with the official figures reported by AFIP (Table A.2). It is important to note that our data is based on individuals who filed the wealth tax returns. but individuals below the wealth tax filing threshold can also voluntarily disclose their assets. Furthermore, the figures indicate that the majority of disclosures occurred within the context of the tax amnesty program, which is in contrast to the ’quiet disclosures’ that happened in the US, as reported by Johannesen et al. (2020).

10
## 4. Analysis of the role of remittances in ex-post responses and ex-ante preparedness using household surveys

Remittances may have a positive impact on consumption, housing and human capital accumulation in remittance-receiving households when compared to households that do not receive remittances. We also analyze whether receiving remittances enable households to be better prepared for unforeseen shocks. We test the following hypotheses using household survey data: (1) remittances are positively associated with absolute levels of household per capita consumption; and (2) remittance-receiving households have concrete houses and better access to communication that can reduce vulnerability to natural disasters such as earthquakes and floods.

# **_4.1 Data and methodology_**

We use household survey data for Burkina Faso (2003), Ghana (2005) and Bangladesh (1998-99), and Ethiopia (2004). In particular for Bangladesh, we have three rounds of data collected on households after the devastating flood of July-September 1998. We use the nationally-representative Ghana Living Standards Survey (GLSS V) conducted in 2005, the Burkina Faso Core Welfare Indicators Questionnaire Survey conducted in 2003, and the Ethiopia Welfare Monitoring Survey in 2004.

To assess the long-term effects of remittances on current consumption, we first have to deal with the issue of self-selection: many of the factors that determine remittance-recipient status could determine the level of per capita household consumption. We use propensity-score matching techniques to construct a counter-factual group of households that don’t remittances, but are otherwise similar in observable characteristics to that of the remittance-receiving households for Bangladesh, Ghana and Burkina Faso (Heckman, Ichimura, and Todd, 1997, 1998). This procedure helps us to control for the endogeneity of remittance-receiving status to a large extent on the basis of observable characteristics of the households. The findings for Ethiopia on the differences in coping strategies for households that receive international remittances and other households are suggestive and do not attempt to control for endogeneity.

In the regression analysis, we include factors that determine remittance-receiving status as follows: (1) age of the household head; (2) educational attainment as shown by the number of household members with primary, secondary and tertiary education; (3) physical capital such as land and other assets, (4) household’s maximum education attainment or the highest number of years of education of any household member, (5) current area of residence (urban or rural), (6) number of children below the age of 5, (7) number of adult male members, and (8) regional dummies. In some specifications, we 10
<mark>of 0.15kg/USD. Similarly, Colombia's cement export emissions are 0.37kg/USD, below the EU's average of 0.5kg/USD.</mark>

**Figure 5 Top 5 and bottom 5 countries in Aggregate Relative CBAM Exposure Index**

<!-- Start of picture text --> 0.10<br>0.08<br>0.06<br>0.04<br>0.02<br>0.00<br>-0.02<br>-0.04<br><!-- End of picture text --> _Source:_ Maliszewska et al. (2023). _Notes:_ Underlying data for relative CBAM exposure index includes carbon emissions intensity data in 2017 and trade data in 2019. The formula of the Aggregate Relative CBAM Exposure index can be found at <u>https://www.worldbank.org/en/topic/trade/brief/technical-note-for-the-cbam-exposure-index.</u>

**Regarding developing countries’ economic exposure, the economies of Mozambique and Ukraine are the most susceptible to CBAM.**<sup>20</sup> In 2022, affected product exports to the EU accounted for 9.3 percent of Mozambique's GDP to the world and 2.8 percent of Ukraine’s.

**The CBAM sets a precedent that is likely to motivate other countries to implement similar schemes, potentially exacerbating effects on developing countries.** Canada, the UK, the US, and Japan are all contemplating the creation of their own carbon border adjustment mechanisms (Law 2023).  Additionally, the range of products and emission scopes included under the EU CBAM is expected to broaden over time.

**The EU CS3D regulation is set to impact GVC metal exports to the EU, both directly and indirectly.** Figure 6 highlights Armenia’s metal GVC sector as the most affected, with over half its domestic metal products output destined directly or indirectly for the EU. Macedonia and Bosnia and Herzegovina follow closely. Other countries may also be exposed indirectly if they supply metal inputs to firms serving the EU market.

> 20 Note that the underlying trade data for the relative CBAM exposure index is from 2019, prior to Russia’s invasion of Ukraine. Using 2022 data, the CBAM exposure index value for Ukraine is likely to be higher due to its heightened export concentration towards the EU.

16
**Table 6: Is more Innovation Associated with Higher Profits?** OLS regression, Dependent Variable Log Profits

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Firm Size Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm Characteristic Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Owner Characteristic Control|s No|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm asset controls|No|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Any Form of Innovation<br><br>|0.156*** <br>(0.0549)|0.150*** <br> (0.0544)|0.140***<br> (0.0534)||||||
|Product Innovation||||0.0973<br>(0.0666)||||0.042<br>(0.075)|
|Process Innovation|||||0.166<br>(0.113)|||0.107<br>(0.131)|
|Marketing Innovation||||||0.0987*<br>(0.057)||0.066<br>(0.061)|
|Organizational Innovation|||||||0.116<br>(0.102)|0.005<br>(0.114)|
|Observations|1459|1459|1459|1459|1459|1459|1459|1459|
|R-squared|0.485|0.544|0.559|0.557|0.558|0.558|0.557|0.558|
|_Notes:_|||||||||

Firm controls are as per Table 4, column 6. Owner characteristic controls as per Table 6, column 9. Firm asset controls are log of firm total assets.

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

- 31 -
main findings of the 2010 survey.

# **2.1 Structure**

High-income economies have a long tradition of export promotion agencies, but 65 percent of current EPAs worldwide were created in the late 1990s, mostly in developing countries.<sup>7</sup> With few exceptions, EPAs are relatively new institutions in the developing world. This fact might explain several recent institutional changes in EPAs worldwide. In 2010, the large majority (60 percent) of agencies across different regions were established as joint public-private entities (table 1). Among the 79 agencies with information available in 2005 and 2010, 23 percent of them have embarked on at least one type of institutional reform (e.g merging or separating the foreign direct investiment - FDI attraction and export promotion activities, changing the legal status from private to joint public-private entity). Most of them (12 out of 18) became joint public-private or private institutions. Among those that changed in this relatively short period, none of them became a public agency.

Table 1: Legal and organizational status of the agency

|Structure|Asia|ECA|LAC|MENA|OECD|SSA|Total|
|---|---|---|---|---|---|---|---|
|Public (Sub-unit of a ministry)|0|0|2|0|1|1|4|
|Public (Autonomous)|0|0|1|0|3|2|6|
|Public-Private|5|10|13|10|9|12|59|
|Private|8|4|5|1|3|2|23|
|Other|0|0|4|1|1|0|6|
|Total|13|14|25|12|17|17|98|

An important feature regarding export promotion agencies refers to the composition of their executive board. Lederman et al. [2010] find that exports increase with a larger share of the EPA executive board seats that are held by the private sector. A median EPA, in our sample, has about nine members with approximately 50 percent of the board composed of representatives from the private sector (table 2). Yet, this feature may differ across regions. In Asia and Eastern Europe (ECA), EPAs tend to be smaller with proportionally fewer members from the private sector.

> 7In our sample, Canada was the first to create an EPA in 1896, followed by Finland (1919) and Switzerland (1927).

4