rafmacalaba/data-use-mentions-extended huggingface dataset:
In section 3 I present my results. Section 4 concludes.

# **2 DATA**

I use two sources of data on arms imports. The first is from the Stockholm International Peace Research Institute (SIPRI). SIPRI is the most trusted source of data on international arms transfers. Since official statistics on arms trade may be patchy, SIPRI has been tracking transfers of major weapons, such as aircraft, drones, rocket launchers, missiles, torpedoes, and reconnaissance satellites, using a variety of sources such as newspapers, annual reports of arms producing companies, blogs, defense white papers, and parliamentary records. It then uses known production costs to estimate the value of the military transfers and construct a trend-indicator value of arms imports. While this indicator does not provide information on the sales prices of arms transfers, it provides a real unit that allows to identify trends over time.

As a robustness check I also use official customs data from the United Nations, i.e. UN COMTRADE. I use reports from both importers and exporters to maximize the data coverage as official trade statistics, especially for arms, may be missing. I include all goods classified as firearms of war and ammunition, that is, section 95 of the SITC Revision 1 classification. This includes artillery weapons and all types of guns and bullets (note that SIPRI does not include small arms). I also include armored fighting vehicles, warships and aircrafts using the 4-digit HS-classification codes 8710, 8802, and 8906, to include major weapons. I convert the raw data, in nominal USD, to constant 2010 USD to focus on real changes in arms transfers.

Figure 2 shows that there is a strong correlation between the two measures of arms imports, both in 1990 and 2010. The two measures seem more in line in 2010 then in 1990. Also, COMTRADE data seem to cover more imports, as per its wider definition. Overall, these two sources provide relevant alternatives to capture countries’ arms imports.

8
Bill in the U.S. and the 1992 Common Agricultural Policy reform in the EU), they typically apply to all commodities of the respective programs (Baffes and De Gorter 2005).

# **7. Supercycles**

The permanent component of most prices follows a U-shaped pattern which captures the end and the beginning of the two post-WWII supercycles (see Figures B1-B27 of Appendix B). The trough, which occurs in the late 1990s and early 2000s, is consistent with the beginning of the last supercycle triggered by a surge in EMDE demand. To examine the contribution of supercycles to permanent shocks, we extended the analysis by using annual data during the 1900-2020 period.<sup>**14**</sup> The decomposition was applied to a subset of commodities (15 instead of 27) by using the same criteria applied to the earlier analysis. Hence, we excluded energy (because for most of the pre-1970 period energy prices, especially oil, were negotiated and changed infrequently), fertilizers (because they were not important during the first half of the sample), and precious metals (because for most of the pre-WWI period, their prices did not vary).<sup>**15**</sup> Summary statistics from the 1900-2020 decomposition analysis are presented in Table 5. The second column reports the share of the permanent component based on annual frequency during 1970-2020 and compares it with the monthly frequency (shown in the first column of Table 5, which is the same as the share reported in Table 3). The comparison confirms that, while the results from the two frequencies are remarkably similar, changing the data frequency from monthly to annual increases marginally the share of the permanent component at the expense of the business cycle and short term fluctuations, as expected. Results based on the longer sample are reported in the last three columns of Table 5: Permanent component (column III), supercycle (column IV), and long term trend (column V)
more efficient than nonexporters. Low and decreasing entry rates can be associated with low productivity at the firm level and/or high costs to export. In any case, this is a point that requires more detailed diagnosis to guide policy actions.

Lack of integration into global value chains can possibly explain the low levels of dynamism in the export sector. The emergence of global production networks has changed the world trade landscape, and this has been a driving force behind emergent trade powerhouses, many of them located in Asia.

Latin America, especially the southern countries, has not been an active participant in this new trade arrangement. The extent to which Brazil is out of the global supply chain can be gauged, at least partially, by looking at trade in parts and components. Looking at trade in parts and components also helps distinguish between assembly activities and participation in value chains. It is likely that a country exporting finished product might have just assembled high-value intermediate inputs made elsewhere. As such, rising imports of parts and components indicate a country’s increased assembly activity, whereas a rise in their exports suggests its growing importance in the global supply chain.

> 5 See Cebeci and others (2012) for a full and detailed description of the database. 6 Cebeci and others (2012) argued that Brazil presents the lowest entry rate among 44 countries in the above-mentioned database.

> 7 _Estado de São Paulo_ , August 3, 2012 (editorial).

13
|_Dependent variable_|Sequence<br>vs. Control<br>(1)|Coaches vs.<br>Control<br>(2)|Wald Test<br>(3)|
|---|---|---|---|
||_Panel A_<br>|||
|Science score|0.550***|0.647***|0.45|
||(0.125)|(0.137)|[0.502]|
|Observations|1,105|1,110||
||_Panel B_<br>|||
|Science score (Basic skills)|0.276***|0.282***|0.01|
||(0.081)|(0.081)|[0.951]|
|Observations|1,105|1,110||
|Science score (Medium skills)|0.571***|0.534***|0.07|
||(0.130)|(0.130)|[0.791]|
|Observations|1,105|1,110||
|Science score (Higher-order skills)|0.416***|0.638***|2.39|
||(0.124)|(0.139)|[0.122]|
|Observations|1,105|1,110||
||_Panel C_|||
|Percent of correct answers|0.103***|0.110***|0.06|
||(0.023)|(0.026)|[0.800]|
|Observations|1,105|1,110||
|Percent of incorrect answers|-0.057***|-
Tax legislation and comprehensive administrative data on direct and indirect taxes, tax rates, tax collection, and executed government expenditure data from MoF and World Bank teams.

- Social protection data from the main social programs on budget, beneficiaries, and allocation rules from the MoSD and the World Bank Social Protection team.

- Data to calculate the subsidy value for domestic electricity distribution and fuel from the Ministry of Energy and the World Bank Energy team.

- Education expenditures and student enrollment from the Ministry of Education and World Bank Education team.

- Health expenditure and health visits from the Ministry of Health and the World Bank Health team

- Supply and use tables from 2017 for the West Bank and Gaza from the World Bank Macroeconomics, Trade, and Investment team.

- The 2017 Labor Force Survey (LFS) on labor informality and public sector employees, also implemented by the PCBS.

## **3.2 Commitment to Equity (CEQ) framework**

### **This study uses the Commitment to Equity (CEQ) methodology to implement a fiscal incidence analysis**

**of the main taxes and public social transfers of the West Bank and Gaza in 2017** . A CEQ Assessment is a rigorous and standardized fiscal incidence methodology that permits systematic analysis of how different tax and public expenditure options affect the welfare distribution. CEQ uses a common framework

21
SYS-REVIEW/HI-LMIC In this review, we found that many studies used data collected for purposes other than the evaluation of insurance schemes. Thus, important questions may be missing from general living standard surveys to allow the assessment of the welfare implications of insurance, such as detailed questions on illnesses. Longitudinal surveys would be more robust in capturing selection effects and the extent to which health insurance schemes provide risk protection against health shocks.

Both the development of rigorous impact evaluation methodology for social programs (Imbens and Wooldridge 2009) and the introduction of health insurance schemes for the poor in lowand middle-income countries are new phenomena. Our report of 19 studies may be encouraging. However, for impact studies to be useful for future considerations of health insurance schemes, greater attention must be given to the rigor and uniformity of welfare measurements, especially in terms of risk protection and evaluation methodologies. In the future, examining a larger number of studies would allow for meta-analyses (regressions), which would facilitate more conclusive remarks regarding program features and outcomes. Further, given that health insurance schemes can differ and exist in different contexts, studies should emphasize pathways through which programs affect outcomes. Although we did not identify such studies, both qualitative and quantitative methods can be used to trace these pathways. Policy makers would benefit from a greater number of rigorous studies that examine the pathways through which programs are likely to affect welfare.

25
, Elementary Workers (14 percent), and Craft and Related Trades Workers (12 percent). There are some groups with a share between 6 and 9 percent (Technicians and Associate Professionals; Clerical Support Workers; Skilled Agricultural, Forestry and Fishery Workers; and Plant and Machine Operators and Assemblers). Finally, the number of observations is small for Managers (3 percent) and negligible for Armed Forces Occupations (0.27 percent).

# **3.3 Comparison with National Household Surveys**

In this section we compare the structure of occupations in the HFPS before the pandemic with the one constructed with microdata drawn from national household surveys (NHS). To that aim we take advantage of the Socioeconomic Database for Latin America and the Caribbean (SEDLAC), a large project of harmonization of national household surveys that has been carried out by CEDLAS-UNLP and The World Bank since 2002. In particular, we use the NHS of the 13 Latin American countries included in the HFPS project for circa 2019.

The occupation variables are already codified in the household surveys by the national statistical offices although, unfortunately, the codification is not uniform across countries. Latin American countries use different systems of occupation codes: they use different

> 6The codes that carry out the matching are available upon request.

7
13 administrations not too different from those of the EU countries. In theory, one could assume that all these countries had to do was to make copies of the tax laws of some EU country and make them their own. This, however, would be missing completely what tax reform is and how tax systems must be nested in the economy of a country. They must reflect the structural characteristics of a country‟s economy if they are to be successful. (...) Some of these changes are still taking place. Thus, in some ways, their tax systems have been adapting themselves to moving targets. It is to be expected that this process will continue for a few more years until the economies of these countries become fully market oriented, with characteristics, structures, and institutions similar to those of the other European countries” (p. xiv). This analysis complements the previous studies that have explored tax morale in the 1990s (Torgler 2003, 2007b, Frey & Torgler 2007).

# **III. EMPIRICAL APPROACH**

We explore the newest European Values Study (EVS) 2008 in conjunction with the data from 1999/2000. The EVS is a European-wide investigation of socio-cultural and political change. The survey collects data on the basic values and beliefs of people throughout Europe and is seen as the most comprehensive research project on human values in Europe. The EVS was first carried out from 1981 to 1983. The methodological approach is explained in detail in the EVS (1999) source book, which provides information on response rates, the stages of sampling procedures, the translation of the questionnaire, and field work, along with measures of coding consistency, reliability of data, and data checks. Country surveys are conducted by experienced professional survey organizations. Interviews were face-to-face and those interviewed were adult citizens aged 18 years and older. Clear guidelines are provided to guarantee the use of standardized information in the surveys and the national representativeness of the data. To avoid framing biases, the questions were
9 due to the reduction in the productive capital stock. Gross revenue net of - - adjustment costs is p0 (t)[F(.) G(,)] wagebill.

An alternative or an additional cost that **may** be included in the revenue function is the convex cost of installation associated with new planting, denoted by C(I(t),K(t,v)). The total cost of new investment is the **sum** of the direct cost, denoted by q(t)I(t), and the installation cost q(t)C(.). Therefore, at time t and for vintage v, the net after-tax revenue is equal to the revenue from the output net of "lost output", minus the wage-bill, and minus the direct cost of installation of the investment goods. The decision problem is to choose the time paths of new planting and replanting and labor input to maximize the present value of net after-tax receipts, given the time-paths of prices of all inputs and output, the technology and the capital accumulation equation.

Since the general solution to this decision problem is complex, or even intractable, important simplifying assumptions are made. It will assumed that a **firm** is either replanting (uprooting and then replacing) or undertaking new planting, but does not simultaneously undertake both activities. This allows us to ignore the complication arising from possible interdependence in the cost of adjustment associated with the two types of activities. 2 It also enables us to develop the view-point that the essential difference between new planting and replanting as ways of expanding productive capacity lies in the different costs of adjustment associated with each. Specifically, it is suggested that replanting is different from new planting because it involves higher output losses in 2 While it is difficult to judge the realism of this assumption for the Bahia cocoa data, it will be a reasonable assumption if new planting and replanting is undertaken by distinctly different groups of farmers.
# TABLE OF CONTENTS

|I.|INTRODUCTION .........................................<br>1|
|---|---|
|II.|MEASURING WELFARE EFFECTS .........................................<br>3|
|m.|RESULTS .............<br>6|
||A.<br>PROGRESS IN AGRICULTURAL<br>TRADELIBERALIZATION<br>.6|
||B.<br>ESTIMATES OFPRE- AND POST-URAIMPORT RESTRICTIONS<br>.7|
||C.<br>EST[MATES<br>OF AGRICULTURAL<br>PRICE CHANGES<br>.8|
||D.<br>TRADE DATA.12|
||E.<br>TRADE AND DOMESTIC DISTORTIONS<br>IN LEAST-DEVELOPED<br>COUNTRiES.<br>13|
||F.<br>ESTIMATES OF WELFARE IMPACTS .14|
|IV.|CONCLUSIONS AND POLICY IMPLICATIONS ..........<br>...............................<br>19|
|RE|FERENCES ........................................<br>28|
Fink et al. (2002), they regressed (log) transport price _p_ on the (log) marginal transport cost _mc_ and (log) markup _μ_ with _mc_ being a function of the average country-specific port efficiency and _μ_ being determined by the product fixed effects and liner connectivity index:<sup>38</sup> where subscripts _j_ , _k_ , and _t_ index country-importer, HS2 product, and year, respectively.

Their estimation results indicate that a 0.1 increase in port efficiency reduces the (unit) maritime transport cost of exporting to the US by 2.3 percent. Put differently, moving from the 25th to the 75th percentile reduces transport costs by 3.5%. With similar data sources and empirical strategies, Herrera Dappe et al. (2017) extended this analysis by focusing on the impacts of port efficiency on bilateral trade flows with the US: The same 25th to the 75th percentile efficiency increases the value of exports to the US by roughly 0.56 percent.

Furthermore, Herrera Dappe et al. (2021) find that increasing port efficiency (based on Stochastic Frontier Analysis) from the 25th-75th percentile lowers transport costs by approximately 4 percent. Their point estimate of the unit transportation cost elasticity with respect to efficiency, namely _−_ 0.23, is consistent with the Data Envelopment Analysis efficiency measures of Indian/Western Pa- _− −_ cific Ocean ports which are between 0.22 and 0.24 (Herrera Dappe and Suárez-Alemán, 2016; Herrera Dappe et al., 2017).

# **4.4.2 Surveys of Port Efficiencies and Linking Them to Transportation Costs**

Clark et al. (2004) used the US Import Waterborne Databank alongside the World Economic Forum’s 1999 _Global Competitiveness Report_ (WEF GCR hereafter) to show that moving from the 25th to the 75th percentile of port efficiency reduces transport costs by approximately 12 percent. Their main variables were: (i) liner transportation charges per weight at the
paper complements this work by focusing on effective labor vs. capital tax rates in addition to total tax revenues, and by implementing several identification strategies to capture the causal effect of trade.5

# **3**

This section describes the construction of our database of effective tax rates on labor and capital. The data covers the 150 most populous countries from 1965 to 2018, with exceptions only for pre-independence, civil war, and command economy eras. The database is available online at http://globaltaxation.world along with country-specific visualizations and notes. Here we focus on the general methodological principles.

## **3.1 Conceptual framework and methodology**

### **3.1.1 Factor shares**

We begin by decomposing each country’s output into a labor and capital component. Following standard national accounts definitions, net domestic output _Y_ at factor prices (i.e., before indirect taxes) can be expressed as: where _CE_ is compensation of employees (wages, salaries, plus supplements to wages and salaries such as contributions to pensions); _OSCORP_ is the operating surplus of corporations (profits, net of depreciation); _OSHH_ is the operating surplus of households (actual and imputed rental income); and _OSPUE_ is the operating surplus of private unincorporated enterprises, or mixed income.

> 5Cage and Gadenne (2018) find that trade liberalization led to a decrease in overall tax revenue pre-1995. Our paper highlights positive revenue effects of trade liberalization but looking at different outcomes (effective capital and labor tax rates, as opposed to indirect tax revenues) and sample periods (the positive effects we obtain are concentrated after 1995). We also complement Buettner and Madzharova (2018) who show that lost tariff revenue from WTO accession events in the 1990s were fully compensated.

7
across the world over recent decades. In advanced economies, this share reached its lowest point between 2007 and 2008 (49.9 percent), while, among emerging markets, it reached only 34.7 percent in 2006 (IMF WEO 2017). Data from the U.S. Bureau of Labor Statistics also show a decline in the labor share of income (in the nonfarm business sector), which implies that the share of income going to capital has risen (Furman and Orzag 2015). The empirical evidence suggests that the profit rate (measured as markups) has also risen globally (De Loecker and Eekhout 2017). Such an increase in the profit rate and capital share of incomes has been associated with rising inequality (Crane 2016; Khan and Vaheesan 2017; Stiglitz 2012). These recent trends indicating a global rise in market power and market concentration demand analytical tools that allow to assess in a rapid and cost-efficient manner the distributional effects of changes in market concentration. However, current empirical studies that rigorously evaluate the distributional effects of variations in competition are time consuming, costly, and intensive in data requirements.

To fill this gap, this paper introduces a simple, yet novel microsimulation approach to assess the distributional impact of competition that combines expenditure data from household surveys with parameters on market structure in certain industries. This simulation approach adapts a set of flexible theoretical models (monopoly, oligopoly with a homogeneous good, and partial collusive oligopoly) into a user-friendly interface, WELCOM, which can be used to assess the impact of variations in market concentration on household welfare, poverty, and inequality. WELCOM provides a sound, rapid, and efficient option for predicting ex ante likely distributional impacts of competition in a user23
Second, in Section 3 we propose three hypothetical risk financing strategies and present assumptions about the economic and commercial environment. Finally, in Section 4 we analyze the cost of each risk financing strategy, both on an average basis and for different shock severities. Sensitivity and scenario testing of results is presented to illustrate how the costs might differ under different assumptions, or under different specifications for PSNP scalability. A glossary is included in Annex 1 outlining key terminology used in this paper.

# **Approach and Limitations**

The analysis presented in this paper makes assumptions about disaster risk, economic environment, and risk transfer instruments. The analysis considers hypothetical risk financing strategies for the PSNP costs, assuming perfect knowledge of the planned PSNP expenditures for different drought severities. This analysis is based on historic drought and population information. Information used in the analysis was both quantitative (e.g. additional drought-affected people based on historic rainfall) and qualitative (e.g. description of the PSNP) in nature. The information was of a high quality and broadly sufficient for the intended purpose. Where possible relevant sensitivity analyses were performed on the assumptions made.

For the avoidance of doubt, this paper does not present a World Bank or Government Actuary’s Department view of actual poverty estimates or the actual contingent liability of the PSNP to drought in 2
Overall, both the McCrary and balance tests suggest that it is unlikely that the close election outcomes we analyze were subject to an unobservable influence, and may thus constitute a credible setting to quasi-experimentally evaluate the political determinant of road investment in Mexico.

# **V. Main Results**

## **A. Highway Politics in Mexico**

Table 2: Impact of Ruling Party Status on Federal Highways, RD Estimates

||LLR|with bandw|idth <sup>ˆ</sup>_h_|Polyn|omial of or|der_p_|
|---|---|---|---|---|---|---|
||ˆ_h_1= .3|ˆ_h_2= .2|ˆ_h_3= .1|<br>_p_= 2|<br>_p_= 3|<br>_p_= 4|
||(1)|(2)|(3)|(4)|(5)|(6)|
|A. No covariat|es||||||
|President|1.066**|1.062*|2.436***|1.039**|1.371***|1.396**|
||(0.453)|(0.565)|(0.880)|(0.426)|(0.525)|(0.631)|
|Observations|4,988|3,400|1,752|9,220|9,220|9,220|
|R-squared|0.374|0.359|0.392|0.346|0.346|0.346|
|B. Covariates i|ncluded||||||
|President|1.018**|1.118**|2.303***|0.943*