rafmacalaba/datause-encoder-data huggingface dataset:
(for example, Hillberry 2011) and of supply chain trade between countries (for example, Rahman and Zhao 2013). Other studies attempt to estimate the relative contribution of a host of possible drivers to GVC participation. Among the findings is that non‐policy factors (structural characteristics like geography that cannot be changed in the short‐ or medium‐term) matter more than policy factors (Kowalski et al. 2015). However, sector‐level analysis is usually not conducted or conducted at a low level of sectoral disaggregation or while testing only a small range of possible drivers. Firm‐level studies have assessed the determinants of offshoring strategies, identifying the importance of factors like productivity and both skills and capital intensity at the firm level (for example, Corcos et al. 2013; Defever and Toubal 2013; Jabbour 2012). Finally, a separate segment of the literature assesses GVC participation’s impact on economic outcomes such as growth and employment.

So while these studies, along with the policy literature on GVCs, give us a sense of what factors are likely to be important in determining GVC dynamics, the question of which specific drivers matter most and when they matter most for country‐level participation in GVCs remains open. One of the reasons for this is that data that identify clearly what is and is not “GVC trade” are still problematic, despite the significant progress that has been made in recent years. Another reason is that there are significant links across many of these drivers, making endogeneity a problem.

In this context, the purpose of the following empirical exercise is to shed further light for policy makers on where to focus efforts to drive competitiveness for GVC participation. We do this by generating “revealed capability intensity” (RCI) measurements of traded goods, extending the traditional theory of factor‐content of trade to account for the capabilities that would be most relevant in task‐based trade, and utilizing these measurements to illustrate how underlying capabilities shape participation of Southern African Customs Union (SACU) countries<sup>3</sup> in global value chains.

# II. The factor content methodology and its extension

The traditional factor‐content theory of trade is based on relative factor endowments, as expressed in the Heckscher‐Ohlin theory of trade: countries export goods intensive in the factor with which they are well‐ endowed relative to other factors. In this paper, we draw on the methodology employed by Shiritori, Tumurchudur and Cadot (2010), which generated a quantitative measure of “revealed factor intensity” for each traded good from 1971 to 2003 with respect to physical capital per worker, human capital and natural resource endowment. Using this methodology, a product exported predominantly by countries that are richly endowed with physical capital, for example, would be “revealed” to be intensive in that factor.

The _revealed capability intensity_ for each product j for a given capability is summarized by the following equation:

> 3 The SACU region includes Botswana, Lesotho, Namibia, South Africa and Swaziland.

3
responsive to the reform – either because they are more affected by consumers’ response to the incentives, or because the pressure to adopt a POS is higher in these subsectors – we should see a positive treatment effect. Yet, this analysis again confirms our main results of no detectable impact on any tax compliance measure.<sup>24</sup>

# **6 Interpreting the Results**

Overall, we find that the introduction of VAT rebates led to a large increase in the number and volume of card transactions, but had no effect on tax compliance among retail firms. We now discuss the two main factors that explain the lack of a tax compliance response.

First, firms self-select into POS adoption based on a cost-benefit trade-off, and the VAT rebates did not significantly increase POS adoption on the extensive margin. As the analysis in Section 4.2 showed, despite a large increase in consumer demand for electronic transactions, only firms that already accepted card payments prior to the reform increased the number of POS in use. The lack of an extensive-margin POS adoption response is consistent with several additional pieces of evidence. First, we observe a slow and gradual uptake of firm-level POS subsidies, which became available in September 2012 (Figure 4, Panel A). In fact, only 6.5 percent of eligible retail firms had taken up the subsidy within two years of its introduction (2.2 percent of all eligible firms). More importantly, since the subsidy was not restricted to firms that had never used a POS, 87.7 percent of firms that took the subsidy already had used a card machine before, and for 83.9 percent of these, POS adoption preceded subsidy take-up by at least three months. It thus seems that the subsidy program had little impact on the use of the technology. This is consistent with our second piece of additional evidence, suggesting that using a POS is costly for firms that are not yet very tax compliant. Indeed, an event study of firm behavior around the time of POS adoption (Figure 4, Panel B) shows that a firm’s reported output VAT and net liability increases with POS adoption. As a final piece of evidence, we examine firm responses to the January 2012 reduction in tax withholding rates and commissions applied by card companies. Figures A.10-A.12 show no evidence that these changes substantially increased the use of POS on the extensive or intensive margin. These findings suggest that it may be difficult to increase POS take-up among firms via financial incentives. Much larger incentives or a mandate might be needed.

As a second reason for the lack of a tax compliance impact, we highlight that firms that

> 24We also considered a cross-regional comparison, but this is less relevant as the share of retail firms with a POS varies little across the 19 regions, while it varies substantially across four-digit subsectors within retail (Figure C.3).

19
ownership on bank values, concentration may also influence the impact of legal protection on bank valuations (LLSV, 2002; Shleifer and Wolfenzon, 2002). These models suggest that a marginal improvement in legal protection may have less of an impact on bank valuations when there is a controlling shareholder. Or, put differently, these models predict that with effective legal protection of minority shareholders, having a controlling shareholder is less important for stemming the expropriation of the minority shareholders’ wealth.

One contribution of this paper is to assemble and analyze detailed data on the ownership of banks around the world. Are banks widely held, or do they tend to have controlling owners? If they have a controlling owner, who tends to control banks? LaPorta, Lopez-de-Silanes, and Shleifer (1999, henceforth LLS) show that the widely held corporation is the exception rather than the norm internationally. Rather, they show that families or the State typically control firms. While there are financial institutions in LLS’s (1999) sample, they do not focus on detailing the ownership structure of banks in each country and their coverage of commercial banks is limited.<sup>4</sup> In this paper, we construct a new database covering 244 banks across 44 countries and trace the ownership of banks to identify the ultimate owners of bank capital and the degree of voting rights and cash-flow rights concentration. As defined in greater detail below, an owner’s voting rights will exceed the owner’s cash-flow rights when the owner controls votes through various affiliated parties without having the rights to all cash flows received by those affiliated parties.

Thus, we provide information on three questions concerning ownership. First, we assess whether banks are widely held or whether they have a controlling owner with significant control and cash-flow rights. We find that banks are generally not widely held. In our average country, only about 25 percent of the banks are widely held, i.e., they do not have a shareholder that owns at least 10

> 4 Similarly, while LLS (2002) and Barth et al. (2001, 2003) provide statistics on the degree of State ownership of banks, these papers do not provide detailed information on the ownership structure of banks.

4
# **Table 3: Accounting decomposition of net interest income to total assets, 2005-14, continued**

|||As a p<br>|ercentage of n<br>|et interest inco<br>|me<br>||As a per<br>|centage of tota<br>|l assets<br>||
|---|---|---|---|---|---|---|---|---|---|---|
|[1] Economy|[2]<br>Net interest<br>income to<br>total assets|[3]<br>Overhead -<br>noninterest<br>income|[4]<br>Tax<br>expenditure|[5]<br>Loan loss<br>provisions|[6]<br>After-tax<br>profit|[7]<br>Noninterest<br>income|[8]<br>Overhead|[9]<br>Tax<br>expenditure|[10]<br>Loan loss<br>provisions|[11]<br>After-tax<br>profit|
|Mexico|5.748|34.5|9.6|27.2|28.7|2.341|4.327|0.554|1.562|1.647|
|Moldova|3.878|16.0|6.4|23.7|53.9|3.609|4.229|0.247|0.919|2.092|
|Mongolia|2.879|33.8|10.8|20.3|35.1|1.178|2.150|0.312|0.585|1.010|
|Montenegro|3.150|55.8|0.7|50.1|-6.6|1.936|3.693|0.023|1.579|-0.209|
|Morocco|2.881|30.5|19.2|14.4|35.9|1.257|2.137|0.553|0.414|1.035|
|Mozambique|5.926|32.8|10.1|11.4|45.7|3.900|5.846|0.600|0.674|2.706|
|Myanmar|-0.689|143.0|-33.0|0.0|-10.1|1.828|0.843|0.227|0.000|0.069|
|Namibia|4.387|30.3|19.8|6.0|43.8|2.940|4.271|0.871|0.263|1.922|
|Nepal|3.836|28.5|17.8|8.1|45.6|1.079|2.172|0.685|0.311|1.748|
|Netherlands|1.157|47.4|6.1|22.8|23.8|0.337|0.886|0.070|0.264|0.275|
|New Zealand|1.854|28.0|17.7|10.2|44.2|0.854|1.373|0.328|0.189|0.819|
|Nicaragua|6.323|36.0|14.0|18.5|31.4|2.601|4.879|0.888|1.169|1.988|
|Niger|4.362|25.0|16.4|24.6|34.0|3.714|4.804|0.717|1.074|1.482|
|Nigeria|5.621|48.1|7.0|18.1|26.9|3.069|5.771|0.391|1.015|1.513|
|Norway|1.526|25.8|17.0|7.7|49.5|0.734|1.128|0.259|0.118|0.756|
|Oman|3.006|21.0|8.9|9.5|60.6|1.208|1.838|0.268|0.286|1.822|
|Pakistan|4.036|32.1|17.5|17.9|32.5|1.218|2.515|0.705|0.723|1.312|
|Panama|3.500|33.6|9.7|16.3|40.3|1.596|2.772|0.341|0.572|1.412|
|Papua New Guinea|4.454|-19.9|32.5|5.6|81.8|4.690|3.803|1.446|0.250|3.645|
|Paraguay|5.758|38.3|5.2|12.1|44.5|4.684|6.889|0.298|0.694|2.560|
|Peru|5.208|27.3|14.9|16.8|40.9|2.387|3.811|0.776|0.876|2.132|
|Philippines|3.127|41.7|7.6|13.2|37.5|1.575|2.879|0.237|0.414|1.172|
|Poland|3.074|24.8|10.7|18.6|45.9|1.979|2.741|0.328|0.572|1.412|
|Portugal|1.595|56.4|3.6|39.7|0.3|0.817|1.717|0.057|0.633|0.004|
|Qatar|2.463|-8.1|1.2|8.0|99.0|1.159|0.959|0.029|0.196|2.437|
|Romania|3.765|31.0|5.3|41.8|21.9|2.444|3.611|0.199|1.576|0.823|
|Russian Federation|4.144|29.4|13.7|22.9|34.0|15.224|16.443|0.568|0.949|1.409|
|Rwanda|7.299|49.3|12.0|14.4|24.4|4.216|7.811|0.876|1.048|1.779|
|Saudi Arabia|2.629|-0.4|0.0|13.2|87.1|1.353|1.343|0.000|0.348|2.291|
|Senegal|4.549|41.4|9.0|18.9|30.6|2.907|4.792|0.408|0.861|1.393|
|Serbia|4.405|40.8|2.4|35.6|21.2|7.253|9.049|0.104|1.570|0.936|
|Seychelles|2.946|-13.1|32.9|7.1|73.2|2.438|2.050|0.969|0.209|2.155|

28
- **Security and fiscal issues are closely interlinked (see Section 3.1).** For example, low domestic revenue is both a consequence and a cause of insecurity.  Costs of reconstruction tend to be higher due to insecurity, while the security sector is a source of considerable fiscal pressure.  Pay and grading decisions with respect to the security services, made outside national budget processes, have important direct fiscal implications and secondary consequences through pressures on civilian pay.

Trends in security sector expenditures, including both Core Budget (spending included in budget documents and going through Treasury channels) and External Budget (spending directly executed by donors and their contractors), illustrate some of these points (Table 4).

**Table 4: Security Sector Expenditures by Program (US $ million)**

||**2003/04**<br>**Estimate**|**2004/05**<br>**Estimate**|**2005/06**<br>**Budget**|
|---|---|---|---|
|**Total**|**1,105**|**1,328**|**1,615**|
|Afghan National Army|797|788|830|
|National Police and Law Enforcement|209|340|597|
|Justice|16|45|63|
|Mine Action|63|96|77|
|DDR|21|59|48|

_Source: Afghanistan Ministry of Finance budget documents and fiscal reports; authors’ estimates._ From the perspective of this paper, key challenges facing Afghanistan’s security sector include the following:

- **The role of policy-based budgeting** (see Section 5.1; these points are reviewed in more detail in Section 6.3). First, an integrated security sector strategy and policy framework is needed; otherwise sound programmatic—e.g. on force sizes, equipment—and public expenditure decisions cannot be made.  So far the National Security Strategy has not yet been finalized, although reportedly its preparation is at an advanced stage.  Second, very high donor-executed spending through the external budget renders achieving coherence of spending across and within subsectors much more difficult, and also raises questions about the fiscal sustainability of the security sector in the medium term.  Third, there are many difficulties in coordinating and prioritizing security sector expenditures and actions, reflecting the above problems and fragmentation of decision-making across donors.  These factors make it more difficult for the National Security Council (NSC) to fully carry out its mandated strategic, leadership, and coordination functions, although the situation in this regard improved in 2004 and 2005.

- **Concerns about the growing security sector wage bill and pressures for fiscally unaffordable salary increases, as well as the fiscal sustainability of security sector staffing levels.** The ANA salary structure, determined without reference to fiscal constraints or pay in the civil service, set a fiscally costly precedent which also resulted in a large pay increase for the police, and which other sectors aspired to (this point is discussed in more detail in the next section).  Subsequent further increases in ANA salaries have exacerbated such pressures and tendencies.

- **The role of good governance** (discussed in Section 5.2).  Public administration reforms and capacity building in security sector management and oversight

28
||COM2XrL||
|---|---|---|
|||Page|
|I.|INTRODUCTION|1|
|II.|PLANNING AND BUDGESING PROCESSES|2|
||Planning and Budgeting-<br>Ideology and Stereotypes<br>Ministry of Finance vs Ministry of Planning<br>Dilapidation of Planning/Budgeting Systems<br>The Importance of Budget Process and Presentation<br>Budget Methodology- the Importance of Sequence<br>Economic Analysis of Projects and the Preparation of PIPs<br>Monitoring and Managing Plans and Investment Programmes|2<br>3<br>5<br>12<br>14<br> <br>16<br>20|
|:-v.|RECURRENT BUDGET ISSUES|23|
||Budget Classification<br>"Recurrent costs arising"<br>Recurrent Expenditure "norms" and an approach to the<br>restructuring of recurrent budgets.|23<br>24<br>25|
|IV.|AID AGENCIES, AID COORDINATION AND TECHNICAL ASSISTANCE|31|
||Effects of the Increasing Relative Importance of Aid<br>Aid Management and Aid Coordination<br>Technical Assistance|31<br>32<br>35|
|Annex|A: Main Sources||
|Boxes|||
|1.|Deterioration of Planning and Budgeting Systems<br>- A Typical Scenario|7|
|2.|Institutional Contrasts between Countries|8|
|3.|Devaluation of the Budget Process- an Example|10|
|4.|Annual Plans|1)|
|5.|"Objective" Ranking Criteria for Project Selection|17|
|6.|Project Profiles|19|
- For every pair of factors

- only a few variables should have near-zero loadings on both.

- some variables should load heavily on one and not at all on the other.

- several variables should have near-zero loadings on both factors.

Two classes of methods have evolved that produce simple structures. The first class of – – methods orthogonal rotation maintains the uncorrelatedness of the factors, while the second class of methods – oblique rotations - seeks to find simple structures with correlated factors. Since the latter relax the constraint on orthogonality of factors, they are capable of producing even simpler structures than orthogonal rotations.

Technically, rotations work as follows. In the Stata code, a _k × k_ rotation matrix **T** rotates the factor loadings in Step 4 (see Section 2.1) so that the rotated factor loadings matrix, ˆ denoted **AR** , is given by If **T** is an orthogonal transformation matrix, the rotation preserves the orthogonality of the factor score matrix **F** . Otherwise, the rotation is oblique, that is, the factors are correlated. Table 4.3 displays the oblique rotation matrix **T** that produces the simple structure that we use to proceed with our analysis and computations. In PC analysis, even after rotation the total variance explained by the factors is still the same (95.30%), but the portion accounted by each factor is now different. As Table 4.4 shows, rotation distributes the portion of the variance explained more evenly than the unrotated factors. This is the point of the simple structure: to identify a factor associated with only few variables.

A graphical analysis makes the connection between rotation and simple structure clear. Figure A1 (see appendix) plots the unrotated factor loadings for _k_ = 6 factors. A row of 20
decreases the chance of working in the public sector.<sup>15</sup> Living in a large city (such as Lima or Arequipa) increases further the chance to work in the formal private sector.

Based on the results of these estimations, the inverse Mills ratios were constructed and added as an additional explanatory variable (the “selection term” ) to the wage equations (2) and (3). This approach (Heckman, 1979) allows us to estimate the wage equations consistently using OLS (see Section 3 – Equations 8 and 9).

Estimations of wage equations (8) and (9) show that returns to education are significant, both in the public and the formal private sector (see Table 4). The comparison of estimation results shows that returns to education are generally higher in the public sector. However, returns to postgraduate education are significantly higher in the formal private sector. In addition, note how returns to non-university (technical) post-secondary education are not significant in the formal private sector. This contrasts with the positive impact that completed non-university post-secondary education has on wages within the public sector, as shown in the fourth graph of Figure 3. As discussed above, this may be related to the importance of formal qualifications to determine compensation within the public sector.

**Table 4. Selectivity Corrected Compensation Equations Estimation Results**

|**Variables**|**Public Sector**|**Formal Private Sector**|
|---|---|---|
|Secondary Education|0.508***|0.105**|
|Non Completed Post-Secondary|0.210***|0.161***|
|Completed Post-Sec (non University)|0.282***|0.064|
|Completed Post-Sec (University)|0.567***|0.411***|
|Postgraduate|0.295***|0.617***|
|Age|0.063***|0.007|
|Age Squared (/100)|-0.055***|0.011|
|Female|-0.075***|-0.159***|
|Married|0.083***|0.161***|
|Urban|0.041|0.030|
|Selection Term|0.197***|-0.279***|
|Observations|3670|4032|
|Chi-squared|541.0|853.5|
|*** p<0.01, ** p<0.05, * p<0.1|||

Compensation is also significantly affected by experience, gender and family status. However, experience (proxied by age) influences compensation only in the public sector. The impact is positive until workers reach 57 years of age, then its sign switches.

> 15 In a large city, the percentage of opportunities in the public sector is likely to be smaller.

13
is also worth noting that these groups are not exactly the same ones by opportunity (although rural households with less educated heads speaking Guarani only are the least advantaged in both cases), which underscores the potential need for different target groups across equalizing interventions.

**Figure 12:  Share of Public Spending on** **_Secondary_ Education by Circumstance Group (Ages 15– 17), 2009**

<!-- Start of picture text --> Share of public expenditure on secondary education<br>by circumstance groups in 2009<br>19.4<br>17.9<br>19.1<br>12.2<br>13.6<br>11.6<br>8.5 8.9<br>6.6 6.4 7.0<br>7.6<br>4.84.0 2.72.6 4.63.6 4.6 1.50.8 0.80.4 5.4 1.91.6 5.6 4.93.3 1.40.6 6.3<br>g1 g5 g3 g9 g7 g2 g11 g13 g6 g10 g4 g15 g14 g8 g12 g16<br>0-5 6 0-5 7-9 6 0-5 7-9 10+ 6 7-9 0-5 10+ 10+ 6 7-9 10+<br>G G S-M G S-M G S-M G G G S-M S-M G S-M S-M S-M<br>R R R R R U R R U U U R U U U U<br>Population (age 15-17) Public Expenditure<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> _Source_ : Author’s estimates from INEC’s EPH (2009); Ministerio de Educación (2009).

_Notes_ : Groups sorted by average probability of attending public school. Household head’s education (highest level attended): “0–5” less than 6<sup>th</sup> grade; “6” completed primary (2<sup>nd</sup> cycle); “7–9” (attended 3<sup>rd</sup> cycle of elemental); “10+” attended high school or higher. Main language spoken by household head: “G” Guarani; “S-M” Spanish or both. Area of residence: “R” rural; “U” urban.

**Figure 13: Share of Public Expenditure on Hospital Health Care by Circumstance Group**

<!-- Start of picture text --> Share of public expenditure on hospital care<br>by circumstance groups in 2009<br>30.7<br>23.7<br>17.7<br>11.9<br>10.6<br>9.4<br>8.2<br>6.9 7.4<br>5.1 4.9 7.4 3.73.0 3.02.1 3.02.1 2.11.4 5.3 1.00.1 5.23.4 3.7 1.20.6 6.8 1.20.1 7.1<br>g1 g5 g2 g3 g9 g7 g11 g6 g13 g4 g15 g10 g8 g14 g12 g16<br>0-5 6 0-5 0-5 7-9 6 7-9 6 10+ 0-5 10+ 7-9 6 10+ 7-9 10+<br>G G G S-M G S-M S-M G G S-M S-M G S-M G S-M S-M<br>R R U R R R R U R U R U U U U U<br>Population (age 0-17 ill only) Public Expenditure<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br><!-- End of picture text --> _Source_ : Author’s estimates from INEC’s EPH (2009); MSPyBS (2010); IPS (2011). _Notes_ : Groups sorted by average probability of accessing public health services. Household head’s education (highest level attended): “0–5” less than 6<sup>th</sup> grade; “6” completed primary (2<sup>nd</sup> cycle); “7–9” (attended 3<sup>rd</sup> cycle of elemental); “10+” attended high school or higher. Main language spoken by household head: “G” Guarani; “S-M” Spanish or both. Area of residence: “R” rural; “U” urban.

26