rafmacalaba/data-use-mentions-extended huggingface dataset:
# **6.5 Processing the Simulated Vectors**

A set of commands that facilitate the processing of the outputs from the simulation are provided. This subroutine is useful for post-estimation calculations based on the output of Mata data created in the simulation stage (6.3). It provides the user the ability to further calculate poverty and inequality indicators. Those indicators may be based on new aggregated levels, new poverty lines, or different weights that were not implemented in the simulation stage. In the case of new weights, it is necessary for the user to have included these new weights in the `addvars` option.

## **6.5.1 Process Indicators**

This sub-routine allows for processing the simulated vectors to obtain poverty and inequality indicators.

The command’s structure is: ``` saeprocindicator,matasource(string)aggids(numlistsort)[INDicators(string)plinevar(string) plines(numlistsort)area(string)weight(string)] ```

- `matasource():` The `matasource` option allows users to specify the source ydump file created by the `sae simulate` routine. Because the size of the file can be quite large, it is advisable to use this with the `numfiles` option.

- `aggids():` The `aggids` option indicates the different aggregation levels for which the indicators are to be obtained, values placed here tell the command how many digits to the left to move to get the indicators at that level. Using the same hierarchical id specified in the `area` option, AAMMEEE, if the user specifies 0, 3, 5, and 7, it would lead to aggregates at each of the levels E, M, A and the national level.

- <u>`indicators():`</u> The `indicators` option is used to request the indicators to be estimated from the simulated vectors of welfare. The list of possible indicators is:
the dependent variable, with the relationship with plot size being again best characterized as L-shaped. Nigeria is an exception in that no statistically significant relationship with plot size is revealed by these two specifications.

These results are in line with the earlier descriptive analysis in that the overall distribution of the bias does not seem to bear much relationship with plot size, as they are largely equally distributed on the positive and negative side. The absolute magnitude of the error does, however, increase somewhat with plot size but less than proportionally. For that reason, in percentage terms the bias actually declines fairly rapidly as plot size increase, stabilizing as plot size reaches the 1-2 acres range.

Of the covariates reflecting physical characteristics that are expected to affect the quality of GPS measures (cloud and canopy cover, plot slope) hardly any are consistently significant across country. What appears to matter most are closing error and the perimeter/area ratio. The former reflects inaccuracy in the CR measure, while the latter is a proxy for the complexity of the plot shape which is likely to affect the accuracy of GPS measures, but can in principle also be capturing noise in the CR measure besides what is captured by the closing error. In Ethiopia and Tanzania (as well as in the fourth specification of the pooled sample regression), there is evidence that heavy canopy cover does increase relative bias. An unsystematic comparison of plot outlines computed from the CR method and collected in the GPS also suggests that it may often be the case that enumerators may tend to simplify the shape of the plot more when collecting CR than GPS data (Figure 6, see Carletto et al. 2016, for additional evidence).

Although the difference between the two objective measurements is relatively small on average, it is worth digging into the problem cases in which the deviation is much larger. Table 9 reports results of a probit model (see equation 2) estimating the probability of a plot being a “problem plot” – defined here as having a relative bias greater than
fact that local governments shelter certain industries in the region from competition) on 1.i.t **i..** Their results show that industries with high past profit margins and high shares of state ownership are less geographically concentrated.' Lu and Tao **(2009)** address that same question using(Iiii. **%..** industry-level data from 1998 to **2005** and show that there is less geographic concentration in industries with higher shares of employment in state-owned enterprises. Fujita and Hu (2001) show an increasing income disparity between China's coastal and interior regions and attribute it to the clustering of manufacturing and the selfselection of FDI into coastal Special Economic Zones. Ge **(2009)** demonstrates that access to foreign trade and FDI is a driving force of unbalanced spatial distribution in China. In particular, industries dependent on foreign trade and FDI are more likely to locate in regions with better access to foreign markets.

Martincus and Sanguinetti **(2009)** estimate the impact of the interaction between tariffs and distance to the capital city on the regional employment shares of manufacturing industries in Argentina (for years 1974, **1985,** and 1994). They find that lower tariffs were associated with the de-concentration of manufacturing activities away from Buenos Aires and its surrounding region, which they interpret as indicative that with the opening to trade, demand and cost linkages weakened and **1.in-.tii** diseconomies such as high commuting costs or high land rents prevailed.<sup>9</sup> Using measures of openness based on trade volumes rather than trade policies, Martincus (2010) estimates a similar econometric specification to explain the distribution of Brazilian industries across states and shows that more open **industries** tended to locate in states near the country's largest trading partner Argentina, and this tendency increased with the trade liberalization of the 1990s. These studies demonstrate that industrial policies
observed correlations among expert assessments: if these are substantially higher, this would suggest that correlated perceptions errors may be playing a role.<sup>18</sup> If in addition the source of this correlation among commercial business information providers is that they adjust their estimates from year to year to smooth out previous discrepancies from other commercial providers, this would imply that the correlations among commercial business information providers will increase over time as these discrepancies are reduced (see Kaufmann, Kraay and Mastruzzi (2006) for further details).

To assess whether this holds true in the data, Figure 3 reports summary statistics on two sets of correlations over time. We focus on expert assessments from the four main commercial business information providers in our database: Economist Intelligence Unit, Crisis24, Political Risk Services, and S&P Global, and compare them with two firm surveys (World Economic Forum and World Competitiveness Report Executive Opinion Surveys). We focus on these data sources because they are arguably the most homogenous in the sense that they all provide perception of the quality of governance primarily from a business-oriented viewpoint. For each of the six aggregate WGI measures and for every year since 2005, we compute (a) the pairwise correlation between each of the commercial providers and each of the surveys, and (b) the pairwise correlation between each of the commercial providers.<sup>19</sup> We then average all the correlations within (a) and (b) for each indicator and year and plot them over time. The six panels of Figure 3 report these correlations. The first striking feature of this graph is that for five of the six aggregate WGI measures, the expert assessments from commercial providers are no more correlated among themselves than they are with firm surveys, i.e., the solid line (capturing expert-expert correlations) is very close to the dashed line (capturing expert-survey correlations). The second striking feature of the graph is that there is little evidence that the expertexpert correlations are increasing over
<!-- Start of picture text --> 30.5<br>2.4 30 15.00<br>30.0<br>2.2 14.75 35<br>3 14.50 29.5<br>2.0 20 14.25 29.0<br>30<br>2 10 25<br>20<br>2000 2005 2010 2015 2000 2005 2010 2015 2000 2005 2010 2015<br>Mean Max<br>Uncertainty<br><!-- End of picture text --> Figure 2: Time series of temperature variables. Each plot illustrates the annual trajectory of temperature variables across all grids, with separate plots for the temperature uncertainty (left) , mean (middle), and max (right). The black line in each plot denotes the annual mean value of the respective temperature variable, accompanied by a grey shaded area that represents the 95% interval. Additionally, an inset plot highlights the mean values without the 95% interval, emphasizing the overall trend in these mean values over the years.

It is worthwhile to note that the trends and patterns identified in Figure 2 do not necessarily represent regional or global averages of the weather variables. The analysis is specifically based on the grids where firms from the Orbis dataset are located. Therefore, the findings are reflective of the weather variable characteristics in these particular economic areas, rather than providing a broader, generalized view of regional or global weather trends.

Scatterplots of temperature uncertainty and asset growth. To demonstrate the overall relationship between temperature uncertainty and fixed asset growth, we present a scatterplot of these variables in Figure 3. The plot includes all country-grid-year observations; each dot represents the mean value of fixed asset growth and the temperature uncertainty for a specific country, grid, and year. The result shows that there is a strong negative relationship with a slope of -4.77, as shown by the grey linear line.

Figure 8
- (iv) Reduce the visa processing time;

- (v) Increase the duration of stay for service suppliers across sectors;

- (vi) Move towards multiple-entry visas for service providers in all sectors;

- (vii) Provide opportunities for e-visas/visas on arrival for service providers in all sectors; (viii) Exempt visas for temporary entry/transit of service providers.

This said, it must be recognized that discussing a TFiS agenda on Mode 4 trade is becoming increasingly challenging given the refugee crisis in the West. While this highlights the need to separate the issue of ‘temporary entry for supply of services’- which is the essence of Mode 4, from the overall migration debate, pragmatic solutions to address sensitive issues, including through specific visas for service suppliers in committed sectors, and implementing the concept of ‘authorized economic operator<sup>7</sup> ’ also need to be discussed. Moreover, innovative ways must also be deployed to address the inherent bias that continues to prevail in any discussion on Mode 4 against unskilled and semi-skilled service providers.

At the same time, providing empirical estimates of the costs imposed by specific regulatory and administrative barriers on Mode 4 trade would also inform the discussion on an MNP TFiS agenda and enable a prioritization of TFiS measures for policy makers. The WTO Secretariat has recently released services trade data by modes of delivery, which could be deployed in regression analysis to estimate the regulatory costs on Mode 4 trade, using the quantitative measures of MNP restrictiveness constructed in this paper, at least for the country sample in the OECD STRI database. This could be an agenda for future research on this subject.

Finally, one additional input into this analysis, which would substantively inform a TFiS agenda on MNP, would be constructive feedback from the actual stakeholders - i.e. sector organizations or representatives and services professionals that cross borders frequently to deliver services – on the actual regulatory, compliance and even investment climate issues that impose unnecessary costs on them. Conducting country-
different combinations of spending/savings and spending allocations: BBR, BBRHR, SSR, and SSR-HR (see Table 1).<sup>3</sup>

**<u>Table 1: Taxonomy of fiscal rules for Government resource revenues</u>**

|||**Spending**<br>|**allocation**<br>|
|---|---|---|---|
|||**Mostly Govt. consumption**<br>(Historical budget shares)|**Public investment**<br>(Hartwick rule)|
|**ng of**<br>**ding**|**Spent on impact**<br>(Balanced budget rule)|BBR|BBR-HR<br>(“HR” in Default submodel)|
|**imi**<br>**pen**|**Save for the future**||SSR-HR|
|**T**<br>**s**|(Structural surplus rule)|SSR|(Not in Default submodel)|

BBR=Balanced Budget Rule; SSR=Structural Surplus Rule; HR=Hartwick Rule The LTGM-NR-External-Balance also analyzes how resource revenues and different fiscal rules interact with international capital flows. This mostly affects the SSR, which often leads to large movements in international borrowing/lending. Most important is the effect on the current account balance and private investment. If the current account and private savings are relatively fixed, the extra public savings through an SSR can crowd in private investment dollar-for-dollar, leading the SSR to have a similar path for investment and growth as a BBR-Hartwick rule. In contrast, without crowding-in of investment (full adjustment of the current account), the SSR in the External-Balance model performs similarly to that of the simple model (which abstracts from international flows). Of course, real-world countries are somewhere in between
by the Penn State Commuting Zones/labor Markets Data Repository.<sup>8</sup> This crosswalk uses the commuting zone methodology devised by the Economist Research Service (ERS) at the US Department of Agriculture, updated to 2010 zones and modified to correct for some discrepancies by Fowler et al. (2016).<sup>9</sup>

# **3.3 Local labor market exposure**

We proxy for local labor market exposure using the pre-trade war sectoral employment composition of commuting zones. The employment data are originally produced by the US Census Bureau in the form of the County Business Pattern (CBP) database, and consists of employment per county and sector at the North American Industry Classification (NAICS) 6- digit level, representing the most detailed view of the United States’ industrial structure available to the public. The CBP data are extracted from the US Census Bureau’s Business Register, consisting of administrative tax records of all private US non-farm employer establishments, supplemented by additional information from sources such as the Economic Censuses, Annual Surveys, Current Business Surveys, and Company Organization Surveys, as well as the Bureau of Labor Statistics and the Social Security Administration.<sup>10</sup> We then aggregate the county-level variables up to the commuting zone level using the crosswalk discussed in Section 3.2. This leads to 625 commuting zones, with total postings over the period ranging from 98 to 277,0274 per commuting zone, and total employment (excluding farm employment) ranging from 279 to 6,830,871.

In order to analyse the relationship between job postings and tariff exposure at the local labor market level, we weight national sectoral exposure measures using baseline employment shares within a local labor market for each industry. The regional (commuting zone) tariff exposure measure is defined as:

> 8https://sites.psu.edu/psucz/data/

> 9The term ‘labor-shed’ is used in this data repository as analogous to commuting
respondents who have the most to lose from energy subsidy reforms (e.g., those who use subsidized energy sources) are just as likely to increase their support for reforms in response to information about the negative impacts of energy subsidies as those who have little to lose from reforms. Collectively, the findings from this study provide useful guidance for policy makers to consider as they try to steer their countries away from energy subsidies. There appears to be value in broad-based “reform” campaigns that inform the general population about the negative impacts of energy subsidies and that implement credible alternative policies.

This study contributes to the existing knowledge base in at least two ways. First, this is the largest cross-country survey examining attitudes toward energy subsidy reforms, and it provides novel insights into the similarities (and differences) in levels of support for reform, as well as the characteristics and beliefs associated with support across middle-income countries. There is an absence of standardized, cross-country evidence as prior research has typically involved detailed case studies in specific countries (e.g., Inchauste and Victor 2017), and survey work examining public support for reforms has been limited; some examples of surveys include Aklin et al. (2015) for India; Pradiptyo et al. (2016) for Indonesia; and McCulloch et al. (2021) for Nigeria. Two very recent cross-country surveys have begun to fill this knowledge gap. One study across five middle-income countries involving a brief survey showed that levels of public support for energy subsidy removal were similar to levels of support for carbon taxation (Harring et al. 2023). The other study focused on Latin American countries (Vieites et al. 2023) and found a lack of knowledge about subsidies and low levels of support for reform. The current study builds on this prior work by systematically studying support for energy subsidy reform in more detail and across a much larger number and more diverse set of countries, thereby generating novel evidence that is likely to have considerable external validity in other settings and
3|276.0|1,332.9|
|Bricks|5L42.2|<br>69.4|715.7|
|Iron and Steel|2,223.8|1,082.8|15,783.2|
|Radio and TV Receivers|447.3|205.3|1,542.5|
|Motor Vehicles|1,772.6|1,163.3|21,465.9|
||-------------------------------------|-||
|Total in our sample|12,472.8|6,840.9|86,645.4|
|as%of Total Manufacturing|33.04|38.90|19.58|

a) indirect taxes and subsidies are deducted (see table 2.3).

b) includes 571.8 million US$ (excl. indirect taxes and subsidies) for petroleum refining, which are not shown in the census Resumen General but taken from Sistema de Cuentas Nacionales de Mexico.

Notes: Figures are converted at the exchange rate of 8.13 cruzeiros to the US$ and 12.5 pesos to the US$.

Source: Figures for Brazil from Censo Industrial, figures for Mexico from Resumen General (except for figures mentioned under footnotes a) and b)), and figures for USA from the Annual Survey of Manufa^tures 1975-1976.
OECDcountries, thus stimulating capital flows.

The group of pull-factors is split into policy factors, i.e., "reform efforts," initial conditions, and "outcomes." Obviously, it is difficult to quantify the degree of policy reform a country has been undertaken in absolute terms. The very similar starting position of most of the transition economies-controlled prices, little private sector activity, limited institutional development, etc.-makes it somewhat easier to quantify at least the relative degree of policy reform in CEE/FSU. We use the liberalization index from Ele Melo et al, 1997, to rank countries in their relative reform efforts. This index, an indicator between 0 and 1, is available for each country and for each year and aims to measure how far the country has progressed in liberalizing prices, trade and private sector activities, including privatization. The initial conditions and outcomes variables are more difficult to separate, as capital flows are likely to interact with and affect current outcomes, which then become initial conditions for subsequent capital flows. We use the country's GDP-growth rates, inflation, fiscal balance, private savings, and, as a creditworthiness indicator, the change in the country's reserves. We lag the change in the country's reserves and the two savings variables by one period to avoid possible simultaneity (as the sum of private, public and foreign savings adds up to the change in reserves). In addition, we also use a dummy for the ten CEE-countries likely to become EU-member.12

> 12 We use the following ten countries that have been identified by the EU as candidates: Czech Republic, Poland, Hungary, Slovenia, Estonia, Romania, Bulgaria, Slovak Republic, Lithuania and Latvia. The first five have recently started negotiations with the EU; we set the dummy equal to two for these countries. For the other five countries, the dummy is set to one, and zero for all other countries.

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|**$47 053**|
|**South Africa**|$20|$155|**$175**|$364|$2 170|**$2 534**|**$126 798**|
|**Ghana**|$116|$10|**$126**|$152|$155|**$307**|**$14 596**|

aIncludes Carpets, Celebration, Other, Paperware, Wickerware, and Yarn. bIncludes Art Crafts, Audio Visuals, Design, New Media, Performing Arts, Publishing, and Visual Arts. So ‘goods’ here probably implies goods and services given the composition of the items included.<sup>c</sup> BPM6 = Balance of Payments and International Investment Position Manual Sixth Edition.

Source: UNCTADstat Table 2.1 <u>|</u> **Art** **<u>Crafts</u>** <u>, 2011 | Total Trade analysis in millions (USD)</u>

|**Country**|**Egypt, Arab**<br>**Rep.**|**South Africa**|**Ghana**|
|---|---|---|---|
|Art Crafts Trade/Trade in all Creative Goods|759÷1794 =<br>~0.423077|175÷2 534 =<br>~0.069061|126÷307 =<br>~0.4104235|
|Percentage of Art Crafts Trade in Total Trade<br>inCreative Goods|~ 42.3077%|~ 6.9061%|~ 41.04235%|
|Art Crafts Trade/Total Trade in all Goods<br>and Services|759÷47 053 =<br>~0.0161307|175÷
the former, and they are randomly selected on the basis of the rules and the geographical areas covered by the program for the latter.

When indirect subsidies are added to disposable income, and indirect taxes are deducted, the result is **_consumable income, or postfiscal income_** , that is, household income after the various fiscal interventions. Agricultural subsidies are the only type of indirect subsidies considered. Indirect taxes include VAT, import taxes, and other indirect taxes. Given the importance of the informal sector in Niger (informality contributes approximately 70 percent of the GDP according to the National Institute of Statistics), the risk of tax evasion is high. The rates used for the various indirect taxes are therefore effective rates, and they are calculated based on the Niger 2012 Social Accounting Matrix (SAM) rather than the statutory rates. Moreover, in addition to the direct effects of indirect taxes, indirect effects are included using the input-output matrix from the 2012 SAM. The overall effect of indirect taxes on household welfare is therefore equal to the sum of the direct effect and the indirect effect, and this is calculated following the Paasche variation by assuming price-inelastic demand. The **_final income_** is computed by adding the monetized value of public education and health services net of payments made by households to benefit from those services.

Once all the income concepts are defined, the measures defined in Section 3 can easily be applied, considering market income as the prefiscal income and final income as the postfiscal income, to determine the fiscal impoverishment effects.

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