rafmacalaba/datause-encoder-data huggingface dataset:
# 5.6. Financial Assets

_Minimal questions._ Many financial assets are held in the name of one individual, clearly identifying them as the owner. Thus, collecting individual level data on some financial assets is relatively simple. The LSMS surveys typically collect data on savings (both formal and informal), and income from retirement pensions and stocks and bonds. It would be useful to have the value of the financial asset, which, in the case of savings, may simply be the balance in the account.

In the Guatemala questionnaire, the respondent is asked whether any household member has deposited money or saved in any type of account during the past 12 months. If so, the ID of the person is listed. Although the survey does not ask about the total amount of money in the account, it does ask whether the current balance is higher, lower or equal to the average balance for the past three months. Without information about the total value of savings in the account, it is not possible to calculate net worth. We recommend, at a minimum, adding a question about the current value of the account.

The Vietnam questionnaire asks whether in the past 12 months anyone in the household earned interest on savings, stocks, or loans, and whether they received income in the form of dowry or bride price and inheritance. In addition, it asks whether anyone received income from the lease or sale of assets or a withdrawal from a savings account or the sale of stocks. These questions provide an indication of whether these items are owned, but only provide information on the flow of income from the asset, not on the value of the asset itself. The questionnaire also whether any member of the household has used any type of savings or liquid assets (savings books, government bonds, US dollars, gold, gemstones, etc.) and if so, the total balance. We recommend adding one question regarding the name of the account holder or owner of each asset.

A brief section is included in the Ghana questionnaire on savings. It asks the key questions needed for individual level analysis: the name of the account holder and the current value of the savings. We would recommend also asking whether there are any other financial assets besides savings.

_Beyond the minimal questions_ . The financial asset that is the most challenging to identify is retirement pensions. These come in myriad forms, both as promised streams of income or as savings accounts that can be drawn down after a specific age. (Some countries call all transfer payments from the government “pensions” but our focus here is on accumulated retirement benefits.) The LSMS surveys typically ask about pension income and this income is usually identified with the particular individual within the household who receives it. Yet, from an asset perspective – and to understand intrahousehold bargaining and also vulnerability at the household and individual levels – it is important to know whether an individual is expecting to receive a pension upon retirement. While it is difficult to capture the value of the expected pension, identifying whether or not an individual expects to receive one may shed light on some of their decisions.

38
|**Tabl**|**e 3.4: Piecem**<br>Para|**eal Sensitivity **<br>meter value<br>Inter-|**Analys**|**is–Welfare **<br>Hicksian eq<br>with corres|**effects**<br>uivalent var<br>ponding par<br>Inter-|iation<sup>b</sup><br>ameter|
|---|---|---|---|---|---|---|
|Parameter<sup>a</sup>|Lower|mediate|Upper|Lower|mediate|Upper|
|Esubs|0.5|1.25|2.0|5.6|7.2|9.7|
|Esub|2.0|3.0|4.0|7.3|7.2|6.8|
|Sigmadm|2.0|3.0|4.0|7.1|7.2|7.3|
|Esubprimary|0.70|1.00|1.30|7.1|7.2|7.2|
|Esubintermed|0.0|0.0|0.25|7.2|7.2|7.4|
|Esubconsumer|0.5|1.0|1.5|6.8|7.2|7.5|
|Etadx|3.0|5.0|7.0|7.1|7.2|7.2|
|Etad|5.0|7.5|10.0|6.9|7.2|7.4|
|Etaf|10.0|15.0|20.0|5.1|7.2|8.7|
|theta_m(i)|s|ee table below||7.1|7.2|7.2|
|theta_fdi(i)|s|ee table below||5.2|7.2|8.4|

- a   The piecemeal sensitivity analysis employs central values for all parameters (see below) other than the tested parameter and lump sum tax replacement.

- b  Hicksian equivalent variation as a percent of the value of consumption in the benchmark equilibrium.

# **Parameter** **<u>Central</u> Definitions** **<u>of the parameter</u>**

# **Value**

Esubs 1.25 Elasticity of substitution between value-added and business services Esub 3.0 Elasticity of substitution between firm varieties in imperfectly competitive sectors Sigmadm 3.0 "Armington" elasticity of substitution between imports and domestic goods in CRTS sectors Esubprimary 0.0 Elasticity of substitution between primary factors of production in value added Esubintermed 0.0 Elasticity of substitution in intermediate production between composite Armington aggregate goods Esubconsumer 1.0 Elasticity of substitution in consumer demand Etadx 5.0 Elasticity of transformation (domestic output versus exports) Etad 7.5 Elasticity of Russian service firm supply with respect to price of output Etaf 15.0 Elasticity of multinational service firm supply with respect to price of output share of specialized imports V as a share of value added in multinational firms in sector I in the theta_m(i) Varies benchmark equilibrium theta_fdi(i) Varies share of output of service sector I captured by multinationals firms in the benchmark equilibrium

|Parameter values for:||theta_fdi(i)|||theta_m(i)||
|---|---|---|---|---|---|---|
||low|central|high|low|central|high|
|railway transportation|0.01|0.03|0.05|0.02|0.04|0.06|
|truck transportation|0.03|0.05|0.07|0.01|0.03|0.05|
|pipelines transportation|0.01|0.03|0.05|0.05|0.1|0.15|
|maritime transportation|0.25|0.35|0.4|0.01|0.03|0.05|
|air transportation|0.15|0.25|0.35|0.1|0.125|0.15|
|other transportation|0.02|0.04|0.06|0.03|0.05|0.07|
|Telecommunications|0.05|0.15|0.25|0.08|0.1|0.12|
|science and science servicing (market)|0.05|0.1|0.15|0.1|0.15|0.2|
|financial services|0.05|0.1|0.15|0.01|0.03|0.05|

98
it is given by An example of this may be the case in which the seller is sympathetic to the buyer’s objectives such as conservation objectives that a NGO such as Conservation International may have. In this case, the self-interested buyer solves the following maximization program: Because a profit-maximizing buyer only offers a payment that ensures the acceptance of the seller, the seller’s _IRCs_<sup>_a_canberearrangedas</sup> and expression _DICCs_<sup>_a_canberestatedas,</sup> which gives the lower bound on the fixed payment, _p_ , for inducing long-term seller cooperation. By substituting (27) in (28), the optimal distribution of the total compensation among the fixed payment and the performance bonus is established. The optimal stationary REDD contract in this case is defined in Proposition 6.

**Proposition 6.** _If contract enforcement is imperfect and the seller is altruistic, and assuming δ high enough, a self-interested principal offers an optimal stationary REDD contract ⟨p_<sup>_∗_</sup> _, b_<sup>_∗_</sup> ( _q_<sup>_∗_</sup> ) _⟩ that implements forest conservation,_ _<u>q,</u> by satisfying IRSs_<sup>_a,IRS_</sup> _b_<sup>_s,DICC_</sup> _s_<sup>_a,and_</sup>

20
**Table A2: Summary of Output (pre and post sample selection)**

|**COUNTRY AND COMPARATOR GROUPS**|<br>**NUMBER OF**|**MEAN**|**NUMBER OF**|**MEAN**|
|---|---|---|---|---|
||**OBSERVATIONS**|**OUTPUT**|**OBSERVATIONS**|**OUTPUT**|
||**PRE-SAM**|**(IN MILLIONSUS**<br>**PLE SELECTION**|**$)**<br>**POST-SAM**|**(IN MILLIONSUS$)**<br>**PLE SELECTION**|
|**Russia**|833|3,660|738|3,970|
|**OECD**|41,560|6,690|24,661|8,100|
|**Resource rich countries**|4,683|2,770|2,232|3,780|
|**China**|476|60,700|408|66,800|
|**India**|2,096|2,850|1,072|4,290|
|**Korea**|2,092|5,330|1,041|8,600|

Source: Author ’ s calculation from UNIDO 2011 Industrial Output Data (4-digit NACE)

**Table A3: Number of observations removed from UNIDO dataset**

|**E**|**NO. OF**|**NO. OF**<br>**O **|**P**|
|---|---|---|---|
|**CONOMIES**|**OBS. **|**BS. IN**<br>**SAMPLE**|**ERCENT**|
|**Russia**|833|738|88.6%|
|**OECD**|41,560|25,157|60.5%|
|**Resource rich countries**|4,683|2,349|50.2%|
|**China**|476|408|85.7%|
|**India**|2,096|1,137|54.2%|
|**Korea**|2,092|1,097|52.4%|

Source: Author ’ s calculation from UNIDO 2011 Industrial Output Data (4-digit NACE) For the sector analysis, a shortened panel for the period between 1993 and 2009 is used. Since the UNIDO data for Russia start in 1993, this was the earliest period that the pane could begin.

Outlier observations – identified as growth greater than 3 standard deviations above or below the mean – for each sector in each country were removed. This results in dropping about 45 percent of the observations in the dataset. Table A2, above, provides details on the breakdown of the dataset preand post-sample selection.

20
# **1. Introduction**

Kazakhstan has achieved rapid poverty reduction since independence. Based on the international poverty line of $1.90 a day at 2011 international prices, headcount poverty fell from 10.5 percent in 2001 to 0.04 percent in 2013.<sup>3</sup> However, the rapidity of the change has generated questions about what “poverty” and “middle class” mean.

An emerging middle class is critical because of its potential as an engine of growth (Easterly 2001). Historically, those in the ‘middle’ have vigorously accumulated both physical and human capital (OECD 2011a, 2011b). By driving consumption and domestic demand, consolidating Kazakhstan’s incipient middleincome group into a stable middle class would contribute to a solid foundation for economic progress. The expansion of the middle classes has also contributed to democratic movements and progressive but moderate political reforms, especially those that promote inclusive growth.

While most economists agree that middle class status is characterized by a relatively high income, there is no consensus on where to draw the line, since living above the poverty line does not necessarily ensure middle-class status.

The very concept of middle class has been extensively debated in the economic and sociological literature (for a review, see Atkinson and Brandolini 2013). Sociologists tend to identify the middle class in terms of its functional position in the society. Economists, however, tend to identify it in terms of thresholds in the income or consumption distribution. These thresholds can be either relative (e.g., percentiles or percentage of median income) or absolute (a certain amount of income per day or month).

In advanced economies, where the middle class usually comprises households in the middle of the income distribution, economic thresholds are identified using percentiles or values around the median income, such as 0.75 and 1.25 times the median (Pew Research Center 2016). In developing economies, scholars opt for absolute thresholds because median incomes do not necessarily identify middle class

> 3 World Development Indicators, World Bank (https://datacatalog.worldbank.org/dataset/worlddevelopment-indicators).

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**Table A20: First stage: Log Refugees at 100km instrumented by distance-based IV with decay function represented (i.e. IV generated using 1)mean border distance,** **<u>2)square of mean border distance and 3) square root of mean border distance)</u>**

||Log|of refugees (10|0km) (with dist|ance)|Log o|f refugees (100|km) (with sq dis|tance)|Log of|refugees (100|km) (with sqrt di|stance)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Log of Distance IV (mean border distance)|5.45956***|5.46255***|5.44191***|5.41772***|3.76700***|3.76859***|3.82615***|3.80079***|6.26401***|6.26762***|6.23711***|6.22039***|
||(1.30920)|(1.30982)|(1.47648)|(1.52001)|(0.61038)|(0.61057)|(0.68696)|(0.70140)|(1.76259)|(1.76349)|(1.99892)|(2.05657)|
|HH head age||0.00151|0.00110|0.01251||-0.00043|-0.00062|-0.00039||0.00251|0.00213|0.02061|
|||(0.00185)|(0.00196)|(0.01344)||(0.00129)|(0.00098)|(0.00613)||(0.00241)|(0.00260)|(0.01778)|
|Age squared||-0.00001|-0.00001|-0.00008||0.00001|0.00001|0.00002||-0.00001|-0.00001|-0.00014|
|||(0.00002)|(0.00002)|(0.00012)||(0.00001)|(0.00001)|(0.00006)||(0.00002)|(0.00002)|(0.00016)|
|HH head sex (male)||0.00875|0.01494|0.05091||0.00128|0.00093|-0.02027||0.01192|0.02207|0.08736|
|||(0.00653)|(0.01204)|(0.06553)||(0.00425)|(0.00740)|(0.04185)||(0.00798)|(0.01510)|(0.08069)|
|Household size|||0.00037||||0.00094||||0.00014||
||||(0.00101)||||(0.00071)||||(0.00124)||
|Married monogamously|||-0.03797*||||-0.03172**||||-0.04241*||
||||(0.01928)||||(0.01367)||||(0.02389)||
|Married polygamously|||-0.03500*||||-0.03264**||||-0.03714||
||||(0.02029)||||(0.01334)||||(0.02551)||
|Separated (divorced/widowed)|||-0.01379||||-0.02259||||-0.01116||
||||(0.01832)||||(0.01436)||||(0.02149)||
|Completed primary sch|||-0.00020||||-0.00210||||0.00181||
||||(0.01021)||||(0.00491)||||(0.01352)||
|Commercial farming|||0.01342||||0.00236||||0.01427||
||||(0.01537)||||(0.00798)||||(0.01699)||
|Wage employment|||0.00335||||0.00003||||0.00360||
||||(0.00954)||||(0.00546)||||(0.01208)||
|Non-agricultural self-employment|||-0.01334**||||-0.00900*||||-0.01470*||
||||(0.00646)||||(0.00491)||||(0.00759)||
|Remittances & Others|||0.00383||||-0.00006||||0.00604||
||||(0.01204)||||(0.00729)||||(0.01464)||
|Property income and investments|||-0.01115||||-0.00958||||-0.01172||

31
Page 12 either suppliers or banks against<sup>non-delivery and export contract frustration.</sup> Some insurers provide coverage<sup>directly to banks, while in the case of others</sup> who generally do not (e.g., Lloyds<sup>of London), suppliers may assign their rights</sup> under these policies to their lending<sup>banks. On the other hand, a significant</sup> volume of African exports is financed<sup>directly by suppliers under insurance</sup> cover, without the intervention of<sup>banks. It isalso</sup> true that banks do not necessarily seek insurance coverage<sup>for large pre-export finance facilities,</sup> given the high cost of such insurance<sup>and the anticipated delays in making</sup> recoveries should problems arise.

# 3. Asset-Based Finance

Banks have demonstrated a willingness to finance high-value,<sup>moveable assets such</sup> as aircraft, vessels and mobile equipment,<sup>based on a lien on the asset typically</sup> for a reasonable percentage (50 to<sup>70 percent) of the asset's market value. The</sup> private insurance industry generally<sup>supports such transactions</sup> by providing loss and/or repossession coverage.

Except in the case of Liberia's vessel<sup>registry, asset-based loans tend to be</sup> recorded in the World Bank statistics<sup>as cross-border</sup> credits, unless structured as leasing arrangements, which often<sup>applies in the aviation sector.</sup>

# 4. Secured Lending

Secured lending encompasses loans that<sup>are collateralized by an offshore cash</sup> deposit or protected by an identified<sup>external irrevocable source of repayment.</sup> Unless guaranteed by an official agency<sup>(such as an export credit agency), these</sup> loans tend to appear in the World Bank<sup>statistics, but typically are not included</sup> in the BIS reports.

Typical security includes cash deposits,<sup>cross guarantees among suppliers, buyers</sup> and direct investors, commitments<sup>or undertakings from official agencies, most</sup> times conditional on borrower performance, and private political risk insurance.

Increasingly, commercial banks are involved in the<sup>intermediation</sup> of donor funds, which involves the provision of normal<sup>correspondent banking services against</sup> cash deposits or reimbursement guarantees<sup>from donors. To some extent, these</sup> facilities, which also can enable<sup>banks to take some form of "clean" risk with</sup> funded credit, are replacing the unsecured<sup>short-term credit lines that the banks</sup> have traditionally provided to their<sup>subsidiaries and to correspondents in a</sup> number of countries.

Another form of secured lending involves<sup>the provision of guarantees to the banks</sup> by offshore private entities, such as<sup>oil exploration and production companies.</sup> These guarantees can vary from "comfort<sup>letters" to legally-binding full or</sup> partial guarantees, depending on<sup>the nature of the relationship between the</sup> corporation and its bank, and the<sup>degree of risk associated with a given</sup> transaction.
# Literature

Alstadsaeter, A., Johannensen N. and Zuman, G. (2017), Who Owns the Wealth in Tax Havens? Macro Evidence and Implications for Global Inequality, Journal of Public Economics, 162, 89-100.

Auerbach, A. (2017), Demystifying the Destination Based Cash-Flow Tax. NBER Working Paper Series, 23881.

Beer, S, De Mooij R, and Liu, L (2018), International corporate tax avoidance: a review of the channels, effect size, and blind spots. IMF Working Paper No 18/168.

Beer, S. and J. Loeprick (2015). Profit-shifting: drivers of transfer (mis)pricing and the potential of countermeasures. International Tax and Public Finance, 22 (3), 426-451.

Beer, S. and Loeprick J. (2018) The Cost and Benefits of Tax Treaties with Investment Hubs: Findings from Sub-Saharan Africa. World Bank Policy Research Paper (forthcoming).

Buettner. T., Overesch, M. and Wamser G. (2018), Anti profit-shifting rules and foreign direct investment. International Tax and Public Finance, 25, 553-580.

Cooper, J., Fox, R., Loeprick, J., and Mohindra. K. (2016), Transfer Pricing and Developing Economies, World Bank, Washington DC.

Crivelli, E., De Mooij, R. and Keen, M (2016), Base Erosion, Profit Shifting and Developing Countries, FinanzArchiv: Public Finance Analysis, 72, issue 3, p. 268-301.

De Simone, L., Lester, R., Markle, K. (2018), Transparency and Tax Evasion: Evidence from the Foreign Account Tax Compliance Act (FATCA). Stanford University Graduate School of Business Research Paper No. 17-62.

De Mooij and Liu (2018), At a Cost: The real effects of transfer pricing regulations. IMF Working Paper No. 18/69.

Devereux, M. (2016), Residual Profit Allocation Proposal, July 14<sup>th</sup> , 2016. Presentation available at: <u>https://www.taxpolicycenter.org/sites/default/files/residual-profit-allocationproposal_1.pdf</u> Dischinger, M. and N. Riedel (2011), Corporate taxes and the location of intangible assets within multinational firms. Journal of Public Economics 95, 691-707.

Durst, Michael C. (2018), “A Corporate Tax Policy Agenda for Lower-Income Countries”, Tax Notes International, July 16, 2018, P. 255.

Howe, B.A. and Khomenko, J. (2017) Australia’s Diverted Profit Tax: Applications and Exclusions, Tax Notes International, April, pp. 349 - 354.

Johannesen, N., Langetieg, P., Reck, D., Risch, M., Slemrod., J. (2018), Taxing Hidden Wealth: The Consequences of U.S. Enforcement Initiatives on Evasive Foreign Accounts Working Paper (National Bureau of Economic Research).

26
<!-- Start of picture text --> Figure 9(c): Philippines: Return to skill by sector over time<br>0.9 (Tertiary and above)<br>0.8<br>0.7<br>0.6<br>0.5<br>Agriculture/Mining<br>0.4<br>Manufacturing<br>0.3<br>Trade/Services<br>0.2<br>0.1<br>0<br>1988 1994 2001 2006<br>Year<br>Return<br><!-- End of picture text -->

**Figure 9(d): Philippines: Proportion of skilled workers by sector over time (Tertiary and above)**

<!-- Start of picture text --> 0.5<br>0.4<br>0.3<br>Agriculture/Minin<br>g<br>0.2<br>Manufacturing<br>0.1<br>Trade/Services<br>0<br>1988 1994 2001 2006<br>Year<br>% skilled<br><!-- End of picture text --> 48
the key variables of interest as shown in the equation below: _Starijt_ = _α_ 0 + _β_ 1 _× Log_ ( _Assets_ ) _it_ + _β_ 2 _× Log_ ( _Age_ ) _it_ + _β_ 3 _× Market Shareit_ + _β_ 4 _× Markupsit_ + _β_ 5 _× HHIjt_ + _β_ 6 _× Log_ ( _Assets_ ) _it × Industry Skillj_ + _β_ 7 _× Market Shareit × IndustrySkillj_ + _β_ 8 _× Markupsit × Industry Skillj_ + _β_ 9 _× HHIjt × Industry Skillj_ In Table 4, we examine the role of industry skill in predicting star status. We have the industry skills data only for manufacturing following Ayyagari and Maksimovic [2017] and hence these regressions are based on just the manufacturing industries. Across all regressions, the main effects on size and markups are positive and significantly associated with predicting superstar status. In columns 1-4 we look at the role of RMAN, in columns 5-8 we look at the role of CPS, and in columns 9-12 we look at the role of NRCOG. The interaction of RMAN with size, markups and HHI are all significant at the 1% level in columns 1-3 suggesting that in industries that rely heavily on routine manual skills (low skilled industries), large firms, firms with high markups and those in concentrated industries are less likely to be in the top 10% of ROIC firms in a year. We find similar results with industries that rely on high complex problem solving skills in columns 5-8 where large firms and firms with high markups in these industries are more likely to star firms. In columns 9-12 we see that in industries that rely on high non-routine cognitive analytical skills, large firms are more likely to be ROIC stars.

In unreported tests, we estimate unconditional quantile effects of the interaction of skill and markups (models 2, 6, and 10 of panel A) and find that the impact of skill and markups is stronger at higher levels of quantiles, especially for large firms.

# **3.3 Future Performance of ROIC Stars**

Thus far, we have defined a firm as a star firm in a given year if its return on invested capital is in the top 10% of firms in that year. It could be the case that there is a lot of churning in this top 10% of firms each year with different sets of firms randomly realizing high returns each year. In the 20