rafmacalaba/datause-encoder-data huggingface dataset:
- Keller, Wolfgang (2000), ―Do Trade Patterns and Technology Flows Affect Productivity Growth?‖ _World Bank Economic Review_ , Vol. 14 (1), 17-47.

- Keller, Wolfgang (2002), ―Trade and the Transmission of Technology,‖ _Journal of Economic Growth_ , Vol.7, 5-24.

- Kimura, Fukunari, Mitsuyo Ando and Takamune Fujii (2004a), ―Estimating the Ad Valorem Equivalent of Barriers to Foreign Direct Investment in the Telecommunications Services Sectors in Armenia .‖ Available at http://www.worldbank.org/trade/russia-wto.

- Konan, Denise Eby and Keith E. Maskus (2006), ―Quantifying the impact of services liberalization in a developing country,‖ _Journal of Development Economics_ , Vol. 81, 142-162.

- Levy, Santiago & van Wijnbergen, Sweder (1995), "Transition Problems in Economic Reform: Agriculture in the North American Free Trade Agreement," _American Economic Review_ , Vol. 85(4), September, 738-54.

- Lumenga_Neso, Olivier, Marcelo Olarreaga and Maurice Schiff (2005), On ―Indirect Trade Related Research and Development Spillovers,‖ _European Economic Review_ , Vol. 49 (7), 1785-1798.

- Maliszewska, Maryla, editor (2008), ―Economic Feasibility, General Economic Impact and Implications of a Free Trade Agreement Between the European Union and Armenia,‖  CASE Network Report No. 80. Warsaw: CASE.

- Markusen, James R, Thomas Rutherford and David Tarr (2005), Trade and Direct Investment in Producer Services and the Domestic Market for Expertise,‖ _Canadian Journal of Economics_ , Vol. 38 (3), 758-777.

- Marshall, J.N. (1988), _Services and Uneven Development_ , London: Oxford University Press.

- Martinez de Prera, Josefina (2000), ―Revenue-Neutral Tariff Reform: Welfare Effects of Uniform Tariffs in 13 Developing Countries,‖ Chapter 3 of Ph.D. Dissertation, University of Colorado. Available at http://ussub.colorado.edu/~martindp/

- Modebadze, Grigol and Karine Eroyants (2010), ―Ad Valorem Equivalence to FDI Restrictiveness in Armenia,‖ mimeo. Available at: https://docs.google.com/leaf?id=0B0V-t-Bs4hXYjA3MTQ5MDctNTAxYy00M2I4LWI3NDAtZGRiNDI3NmYwOGU2&hl=en

- Matoo, Aaditya and Carsten Fink (2003), ―Regional agreements and trade services - policy issues, Volume 1,‖ World Bank Policy and Research Working Paper Number 2852.  Available at: http://www-

- wds.worldbank.org/external/default/WDSContentServer/IW3P/IB/2002/07/09/000094946_0206 2104102344/Rendered/PDF/multi0page.pdf.

- Matoo, Aaditya and Pierre Sauve (2008), ―Regionalism in Services Trade,‖ in _A Handbook of International Trade in Services_ , Aaditya Mattoo, Robert M. Stern and Gianni Zanini (eds.), Oxford: Oxford University Press.

36
28 mortality, and improved sanitation particularly slowed in the most crisis-hit countries. A worsening income distribution has accompanied the deterioration in development indicators in several countries in Europe and Central Asia and Sub-Saharan Africa.

**Figure 19. Proust index by individual indicators**

<!-- Start of picture text --> “Lost time” in terms of GDP per capita<br>Western Europe<br>Low income<br>South Asia<br>North America Lower middle income<br>Middle East and North Africa<br>Upper middle income<br>Latin America/Caribbean<br>East Asia and Pacific<br>High income: non OECD<br>Central- Eastern Europe<br>Africa High income: OECD<br>number of lost years00 2 4 6 0 1 2 3 4 5 6 7<br><!-- End of picture text --> <!-- Start of picture text --> Western Europe<br>Low income<br>South Asia<br>North America Lower middle income<br>Middle East and North Africa<br>Upper middle income<br>Latin America/Caribbean<br>East Asia and Pacific<br>High income: non OECD<br>Central- Eastern Europe<br>Africa High income: OECD<br>number of lost years00 2 4 6 0 1 2 3 4 5 6 7<br>“Lost time” in terms of household consumption per capita<br>Western Europe<br>Low income<br>South Asia<br>North America Lower middle income<br>Middle East and North Africa<br>Upper middle income<br>Latin America/Caribbean<br>East Asia and Pacific High income: non OECD<br>Central-Eastern Europe<br>Africa High income: OECD<br>number of lost years0 2 4 0 1 2 3 4 5<br>Lost time” in terms of unemployment<br>Western Europe<br>Upper middle income<br>South Asia<br>North America<br>Lower middle income<br>Middle East and North Africa<br>Latin America/Caribbean<br>East Asia and Pacific High income: non OECD<br>Central-Eastern Europe<br>Africa High income: OECD<br>0 4 8 12 16<br>number of lost years 0 5 10 15<br>“Lost time” in terms of asset prices<br>Western Europe<br>Upper middle income<br>South Asia<br>North America Lower middle income<br>Middle East and North Africa<br>Low income<br>Latin America/Caribbean<br>East Asia and Pacific<br>High income: non OECD<br>Central-Eastern Europe<br> Africa High income: OECD<br>number of lost years 0 2 4 6 8 0 2 4 6<br><!-- End of picture text --> _Source_ : Authors’ computations based on data from Bloomberg (equity prices); World Bank WDI for GDP and consumption per capita; and WDI and OECD for the unemployment rate.
investigate if the regulatory environment may be a factor in driving the decision-making process.

Focusing on enterprises with short-term loans for working capital, evidence from approximately 3,500 enterprises in 29 countries suggests that the regulatory environment is a factor in enterprises’ preference for informal creditors. Enterprises facing higher and more frequent demand for bribes, and that deal with onerous regulatory obstacles, are more likely to prefer informal finance.

In predatory regulatory environments, enterprises will forgo opportunities in order to avoid the burden of regulation. Enterprises may forgo opportunities to engage in borrowing from banks because they do not want to make their operations more transparent, want to hide assets in order to minimize tax payments, and do not want to make themselves vulnerable to rent-seeking officials.

14
**Table 12: Number of Accounts in Superannuation Funds**

|End June<br>|1995<br>|2000<br>million<br>|2007<br>|
|---|---|---|---|
|Corporate|1.4|1.2|0.7|
|Industry|4.9|7.0|10.7|
|Public sector|3.1|2.4|2.9|
|Retail|6.0|11.1|15.4|
|Small Funds|0.1||0.7|
|Total|15.5|21.5|30.4|

**Source: APRA**

# **6.2 Life Insurance Companies**

The Australian life insurance industry represents a significant though declining part of the financial services sector. As at end June 2007 total industry assets were $257 billion. Superannuation business represents around 90 percent of the total life insurance office assets. The life insurance industry accounts for 20 percent of superannuation assets, down from 41 percent a decade ago.

There are currently 35 life insurance companies operating in Australia down from 50 ten years ago. The major industry participants include the large wealth management groups including independent, bank-owned and foreign institutions. Major banks have acquired a strategic stake in the industry: they now account for over 50 percent of industry assets. The wealth management companies typically have a range of legal entities to provide for the management of wealth. Despite the large number of players the industry is quite concentrated. The top three life insurance groups accounted for 63 percent of total industry assets and 75 percent of new business premiums in 2006. The top ten life insurance groups represented 93 percent of total assets.

The major products offered by the life insurance industry are pure risk, annuities, investment account and investment-linked (Table 13). The superannuation premiums mainly flow into investment-linked products. Of the two broad types of life insurance products, regular (or annual) premium and single premium, single premium business now accounts for 80 percent of life insurance premiums, of which, 97 percent relates to superannuation business. The vast majority of premiums are directed to investment accounts, of which investment-linked predominate.

**Table 13:  Life Insurance Premiums ($ billion; year to 30 June)**

||2002|2003|2004|2005|2006|2007|
|---|---|---|---|---|---|---|
||||percent o<br>|f total<br>|||
|Investment-linked|61.3|62.0|60.6|65.3|61.7|69.2|
|Investment Account|10.1|8.7|6.3|5.9|5.3|5.1|
|Annuities|16.6|15.9|19.1|12.6|16.8|13.3|
|Risk|10.6|12.5|12.6|15.0|15.2|11.6|
|Conventional|1.4|1.6|1.1|1.2|1.1|1.0|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|
|Total (AUD billion)|36.7|32.1|35.0|34.0|37.6|41.5|
|Total(% of GDP)|4.83%|3.97%|4.17%|3.79%|3.89%|4.01%|

**Source:  APRA Life Office Market Reports**

39
resulting from the fact that the most innovative firms are more likely to answer to the survey.

The second Survey of Development Activities and Technological Innovation for Colombian manufacturing firms was conducted by the _Departamento Administrativo Nacional de Estadística_ (DANE) and contains data for the years 2003 and 2004. The total number of companies in the manufacturing sector in Colombia is estimated to be 6,670; the survey reports data on 6,172 of them.<sup>14</sup> In the next two subsections we report the list of dependent and explanatory variables used in the analysis, organized according to the scheme/structure mentioned above. A more detailed list of the variables is reported in the Annexes, table A.2.1 for Chile and table A.2.2 for Colombia.

# **4.1 DEPENDENT VARIABLES**

Innovation expenditures are divided in the surveys into different areas, such as:

- Expenses for new machinery

- Training in innovation and product testing

- Patents, licenses, introduction and commercialization of new products

- R&D

A firm is regarded as innovative from an input side (and therefore y=1) if the sum of **expenditure on innovation** , in one of the forms reported above, is larger than 0.

From an output perspective, a firm is regarded as innovative if it innovates in products or processes, if it increases the percentage of innovation sales or produces or applies for new patents.

In this work a firm is regarded as innovative in terms of **product** if it has introduced at least one of the following:<sup>15</sup>

- A new technology for production

- A product that is known to the market but new to the firm

- A product that is new both to the market and to the firm

Product innovations are aimed at increasing market share or to open a new market space for the firm. Process innovations, on the other side, are developed to improve the existing value chain of the firm. The improved process should lower the cost of production and therefore make the product more profitable, reducing the break-even point, either by competing on price or by increasing sales. Though not as radical in their effect as product innovation, process innovations also provide a substantial economic benefit to the firm.

A firm is considered here as innovative in terms of **process** if it has introduced at least one of the following:

- A new technology that the firm deems important to improve existing processes.

> 14 There is no use of weights in the survey. 15 In the last 3 years in the case of the Chilean survey.

8
<!-- Start of picture text --> stress 45-degree Line — Market Income seeeee —_ —_ —_<br>-—--— PIT Deductions —— Income Taxes<br>—— Social Security Contributions (Employee) VAT An<br>100 — — Excise Tax - oe on<br>30 wewe oe vt<br>Ue 4 wn<br>80 aa - a c7 o rc<br>70 a _ s -“ - -<4 ” wo ”<br>2 awna 7 “7 2 7" ae“ oo we on<br>- “ oe<br>50 -ww- - -— - - 22of * wo we<br>40 er - = oe<br><0 ae a _c -= ue"ae & a<br>=we -= "7<br>20 we - -- _-<br>a == a a<br>10 =e = = een we<br>wr eer we<br>° foo-7-~<br>1 n 2 31 a s1 6 n si 91 101<br>awe LOIa<br>set a 7<br>oeo a7 7<br>aeo “a oo c 2<br>at “a o<br>7 A* 7<br>oo vd<br>” on - 2<br>oo - 7<br>a se Z 4 - -7<br>Pe hd<br><!-- End of picture text -->
# **_5.1 Local linearity of learning gains_**

In the initial example presented in this paper, _LAYS_ is calculated using scores of 8<sup>th</sup> - graders. When we apply the relative learning in 8<sup>th</sup> grade to different average numbers of years of schooling, ranging from about 6 to 14 years of schooling, we are implicitly assuming that this learning ratio across countries remains constant—that each year of schooling is worth the same amount, in terms of learning, in any given country over that range (even as the learning rate differs across countries). While this assumption will not literally be true, we can test to see whether it makes sense as a rough estimate of cross-country differences. As the following sections will show, the evidence suggests that it does.

# _ASER data from India_

One way to investigate learning trajectories is through an assessment that tests the same content across multiple grades. Tests such as PISA and TIMSS are tailored to each grade and age in which they are administered and are normed at the relevant level. This approach might produce linearity by test construction or scaling, even if the underlying learning trajectory on a constant measure would not be linear. To get around this problem, we analyze learning data from India collected for the Annual Status of Education Report (ASER), for which the NGO Pratham administers the same exact test to students from ages 5 to 16 across Grades 1 to 12 (ASER Centre 2017). The ASER data enable us to assess the rate of learning with a stable, comparable metric across grades and over time. To allow us to map out the specific trajectory for learning in school, we restrict our sample to school-going children.<sup>18</sup> Figure 5.1 shows that students learn to divide along an S-shaped learning trajectory, with a locally linear interval from Grades 6 to 10. Figure 5.2 shows how often this locally linear interval appears at the subnational level, using 2012 ASER data for 31 Indian states. We compare observed learning trajectories to a projected linear trend and find remarkable alignment, indicating local linearity across most states in that year. We see this trend repeated across nearly all states and all years from 2008 to 2012 (see Annex 1, Figure 3 for all results).

> 18 Note that this is comparing different cohorts of students at different grades, not the same students over time.

26
uncertainty. They address this problem by considering three distinct scenarios, with decreasing depth of the future agreement between the UK and the rest of the EU. The first scenario assumes that the PTA between the UK and the EU will be as deep as the agreement the EU has with Norway (called, “soft” Brexit). In the second scenario, the UK and the EU will sign a PTA as deep as the average PTA the EU currently has with third countries (referred to as “hard” Brexit). Finally, the third scenario (“very hard” Brexit) has no agreement.

This analysis indicates that UK-EU trade declines under all scenarios and that this drop is sharper the lower the depth of the future arrangement relative to the depth of the EU agreement. In terms of value added trade, the decline ranges from 6 percent of the “Norway” scenario to 28 percent of no agreement. In all scenarios, the largest declines are for UK services and GVC trade with the rest of the EU. Summary results are reported in Table 1. These predictions should be seen as average effects. As it takes time for trade flows to respond to changes in trade costs, the expectation is that the impact in the short-run to be smaller than in the longer term. Another caveat is that the analysis assumes that the processes of integration and disintegration are symmetric. In practice, this may not be the case: once GVC linkages are formed, they may be more resilient to changes in trade policy than the analysis predicts.

Table 1: Changes in UK’s bilateral trade with the EU under different scenarios

||"Norway"<br>scenario|"average<br>PTA"<br>scenario|"no-agreement"<br>scenario|
|---|---|---|---|
|Goods|-12%|-38%|-50%|
|Services|-16%|-48%|-62%|
|Domestic<br>Value Added|-6%|-20%|-28%|
|GVC forward<br>linkages|-5%|-18%|-26%|
|GVC||||
|backward|-7%|-25%|-34%|
|linkages||||

_Notes_ : Depth decreases from 44 to 36 in the “Norway” scenario, to 14 in the “average PTA”, and to 0 in the “noagreement” scenario.

Source: Mulabdic et al. (2017) 13
# **Table 6: Probit regressions for estimating employment**

|Dependent variable: Employed =1; not<br>employed=0|**All indi**|**viduals**|**Males**|**Females**|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Primary education|0.0748***|0.249***|0.0551**|0.00602|
||(0.0193)|(0.0162)|(0.0214)|(0.0150)|
|Low secondary education|0.0523**|0.247***|-0.0148|0.0778***|
||(0.0241)|(0.0205)|(0.0268)|(0.0244)|
|Secondary education|0.100***|0.280***|-0.0129|0.160***|
||(0.0275)|(0.0228)|(0.0303)|(0.0329)|
|Tertiary education|0.301***|0.386***|0.0789**|0.330***|
||(0.0341)|(0.0258)|(0.0342)|(0.0489)|
|Married|0.0256|-0.00215|0.308***|-0.113***|
||(0.0177)|(0.0155)|(0.0242)|(0.0130)|
|Age|0.0476***|0.0416***|0.0422***|0.0169***|
||(0.00366)|(0.00326)|(0.00403)|(0.00270)|
|Age squared|-0.000576***|-0.000459***|-0.000599***|-0.000187***|
||(4.55e-05)|(4.06e-05)|(4.93e-05)|(3.40e-05)|
|Urban|-0.0818***|-0.118***|-0.105***|-0.00689|
||(0.0170)|(0.0150)|(0.0187)|(0.0130)|
|Male|0.601***||||
||(0.0106)||||
|Region dummies|Yes|Yes|Yes|Yes|
|Observations|6,451|6,451|3112|3339|
|PseudoR2|0.33|0.07|0.15|0.15|

Source: MHYS 2009-2010. Note: Probit estimations. Marginal effects, evaluated at sample means for continuous variables, are shown. Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1.

Table 7 reports the relative labor deprivation (RLD) values for different sub-populations based on different specifications of the reference group. When all individuals constitute the reference group, we find women to have higher levels of relative deprivation than men. This can be seen by decomposing the societal value by gender, which shows that total female RLD is almost three times as that of males for the society for a whole. This is expected as women in Morocco are also considerably more excluded from the labor market in an objective or absolute sense. If women compare their situation with betteroff men, they would naturally feel more deprived than men. If we focus on youth only, the gender gap is reduced but the ranking is the same with females being more deprived than males.

In contrast, if the relevant reference populations are along gender lines, the situation is reversed. First, overall societal relative deprivation falls in comparison to the level when the entire population is the reference group. This is due to the fact that the _between_ male-female difference does not play a role when the reference group is gender specific. Moreover, in this new gender specific scenario, the 17
These two different trends since 1999 show that TIETP contributed to the broadening tax base for self‐employed businesses while it alleviated wage and salary earners’ tax burdens. Since the introduction of TIETP in 1999, wage and salary earners have actively used credit cards to get the tax incentives and, as a result, self‐employed business income earners have reported their business income more honestly, mindful that third parties were reporting transactions to the tax authorities. These trends give broad support to the theoretical framework—the TIETP‐induced equilibrium change from “Not use CC—Not report” to “Use CC—Report”—discussed in the previous chapter. It should be noted, however, that the upward trend in the effective PIT rate of business income may also be strengthened by an income tax creeping effect caused by nominal income increases due to economic growth and inflation, fixed income tax brackets, and PTI exemption thresholds, as in the case of the active taxpayer ratio described in the previous section. However, such creeping effects might not have been realized automatically without TIETP, because business income tends to be underreported to offset such effects.

One notable phenomenon in this PIT rate trend is that the effective PIT rate of business income is greater than that of wage and salary income, without an exception, for the whole period considered. This is seemingly against the common understanding that self‐employed workers’ business income tends to be underreported. One possible interpretation is that the Korean PIT system allows more generous income deductions to wage and salary earners, considering that wage and salary earners’ incomes are correctly reported by third parties, while self‐employed workers’ business income is underreported in the absence of third‐party information. Another interpretation is that the average income of the self‐employed is greater than that of employed workers, and as a result the self‐employed are subject to a higher progressive income tax schedule.

# 2. Quantitative Analysis for TIETP

## Methodology and Data

TIETP’s effects were analyzed using micro‐simulation methods through counterfactual analysis adopted by Song and Sung (2012). This study updated the Song and Sung (2012) approach, using 2014 the HIES data set. Three different scenarios were set up to evaluate policy effects by establishing the counterfactual and the observable. When estimating true business income in each scenario, income data obtained from HIES were adjusted under the assumption that they were also underreported, as in tax reporting.<sup>52</sup> Real business incomes were estimated by

> 52 . Hurst, Li, and Pugsley (2014) showed that in U.S. household surveys the self‐employed systematically underreport their income by about 25 percent.

35
# I. Introduction

In many economies, informality predominates: it provides an undeniably large source of employment as well as economic activity. While a formal definition of informality can be hard to come by, it is useful to distinguish between informal employment and those businesses that operate informally. Informal employment can be defined as simply as work that is not covered by labor taxes and public pension contributions. These jobs are almost certainly off-the-books but can exist in either formal or informal firms. Somewhat apart, informal sector businesses operate outside of the official channels by which formalized firms are registered, engage the legal system, pay taxes, and deal with government officials. These businesses largely consist of self-employed operators, who occasionally employ others, who are almost invariably informally employed. On its face, then, operating informally can expose these businesses to frequent (but unstructured) encounters with police and government officials. In some cases, these interactions leave informal businesses exposed to harassment and demands for bribes. Yet while there are substantial literatures on both informality and corruption, there are few attempts to join the two, particularly using micro-level data.<sup>2</sup> One reason for this gap in the literature is a lack of adequate data sources. Those data that are commonly available rely on household-level measurement, and in turn, focus on labor dynamics between formal and informal employment. However, households do not necessarily encounter bribery the same way that businesses do, even if those businesses are operated out of the home. This paper leverages unique, survey data on informal sector businesses in Harare, Zimbabwe, collected in 2017. These data were collected using a geographically based, adaptive sampling approach and can be considered representative. Observations are, by construction, centered on informal businesses, a fact which makes the data well-suited to assess under what conditions those activities are exposed to extractions of bribes and harassment at the hands of public officials. This is likely important when assessing the informal sector’s exposure to corruption: operators of informal businesses may directly face a demand for a bribe so that they can avoid a fine or being shut down. By contrast, the bribe exposure associated with off-the-books labor is, on its face, more likely to be borne by the tax-evading employer rather than the informal employee.

> 2 Lavallée and Roubaud (2018) provide one recent exception. Emerson (2006) models an informal-formal divide but is concerned with bribery at the entry of firms into the formal sector. Dutta _et al_ (2011), similarly, discuss both informal and corruption, but they rely on generalized perceptions of corruption for Indian states.

(2)