rafmacalaba/data-use-mentions-extended huggingface dataset:
## **CGE Modeling of Market Access in Services**

Elisabeth Christen<sup>1</sup><sup>_,_2</sup> Joseph Francois<sup>1</sup><sup>_,_3</sup> Bernard Hoekman<sup>3</sup><sup>_,_4</sup>

# **1 Introduction**

Services play an increasingly important role in world trade. According to the WTO, the global value of cross-border services exports in 2007 was $3 trillion, or about 20 percent of world trade in goods and services. Because services are less tradable than goods, this number significantly understates the importance of services in world trade. In practice, much services trade is indirect, in that services are embodied in traded goods, or are sold to foreign consumers through direct investment in affiliates. If account is taken of sales of services by foreign affiliates of multinational firms, then the value of trade in services rises substantially. Data for 15 OECD countries puts the value of such sales at some $1.5 trillion in 2007 (WTO, 2009). If account is taken of the direct and indirect value added content – that is, if trade is measured in terms of the value that is added by processing of imported components into final products for export as opposed to measuring trade flows on the basis of the gross value of goods crossing the border, the share of services in world trade rises to almost 50 percent (Escaith, 2008; Francois, Manchin, and Tomberger 2012).

While the expanding economic importance of services has not gone unnoticed, services have been downplayed in the economic growth and development literature, and have only recently been highlighted in the multi-sector computable general equilibrium (CGE) literature. The trigger for greater attention was the emergence of services on the international policy agenda in the 1980s, in particular the effort that was launched in the Uruguay Round to negotiate multilateral rules on policies affecting trade
10% as a proxy for state control. The use of a comparatively low ownership threshold allows for sensitivity and comparative analyses of state footprint for firms at multiple ownership thresholds levels, including the 25% and 50% commonly used thresholds and as well as cases of minority state control below 25%.

The proposed methodology reconstructs the full ownership structure linking each firm to the state as its ultimate shareholder, including foreign registered firms. For the most part and in contrast to the methodology presented here, existing SOE databases do not capture firms indirectly owned by the state (i.e., subsidiaries of state-owned enterprises), and only a limited number of them capture SOEs at the subnational level. The Global BOS database collects information about firms directly and indirectly linked to the state, mapping out all the subsidiaries of a government-controlled firm operating inside and outside each country with the help of a novel algorithm.

The database adopts a disaggregated sector taxonomy developed by Dall’Olio et. al. (2022) to classify SOEs based on industries’ inherent technological features and market failures. There is a large heterogeneity of sectors in which SOEs are present and efficiency rationale for SOE participation in these sectors varies. The taxonomy provides a tool to assess the economic rationale for SOE presence and also serves to triage SOE interventions.<sup>4</sup> The three categories of classification of sectors are: natural monopolies, partially contestable sectors, and competitive sectors. The methodology to construct the sector taxonomy in Dall’Olio et. al. (2022) is complementary to this paper.

The Global BOS database leverages ORBIS, the proprietary firm-level data from Bureau van Dijk, as a primary source to construct country-level SOE data sets.<sup>5</sup> As pointed out by (Kalemli-Ozcan, Sorensen, Villegas-Sanchez, Volosovych, & Yesiltas, 2015), ORBIS is a good starting point to construct a firm-level data set because it provides administrative data for over 130 million entities worldwide covering all sectors

> 4 For the
pandemic. We focus here on , which captures the difference in achievement change per week of closures. ππ As discussed in the data section, we use four measures of the length of school closures: (1) UNESCO full closures; (2) UNESCO full and partial closures; (3) PISA student reported closures; and (4) PISA principal reported closures. All are expressed in terms of weeks of school closures. We use and to calculate the learning loss for countries with shorter or longer school closures, comparing results at the percentiles of each school closure measure ττ ππ reported.

This model can be further expanded to test for differences in the impact of the pandemic on boys with girls as a baseline, immigrant students with natives as a baseline, or students with different socioeconomic background. To achieve this, we add interaction terms between all variables in the regression model above and individual student characteristics. In this case, the estimated coefficient for the interaction between 2022 and a dummy for boys, for example, shows the differential impact of the pandemic on boys, after controlling for separate time trends DD by gender in each country. Moreover, the interaction term between a dummy for boys and 2022 w shows how boys were differently affected by weeks of school closures. ii DD ∗wwtFinally, we investigate heterogeneity in the impact of school closures by achievement Finally, we investigate heterogeneity in the impact of school closures by achievement level. Some country studies reported more significant losses among low-achieving students, while others showed similar losses across achievement spectrum. To check how globally learning losses vary among students of different proficiency, we re-estimate the main models using quantile regressions, fitting regressions to explain achievement trends for students at different percentiles of PISA scores. To deal with many dummy variables and interactions, we use recent implementations of fast quantile regression algorithms (Chernozhukov et al. 2022).

PISA data are collected through a complex stratified survey with schools sampled as primary sampling units and students sampled at the second stage. We use the sets
The following section describes the data set we have created for this task. Section 3 provides some key summary statistics, and section 4 investigates the relationship between poverty and overall economic growth. Section 5 studies the poverty impact of urban-rural composition of growth in mean consumption, while Section 6 turns to the sectoral composition of growth in net domestic product. A key focus in sections 3-6 is on changing patterns across the pre-reform and post-reform periods. Section 7 concludes.

# **2. Data**

For the purpose of this study, we have derived a new and consistent time series of poverty measures for rural and urban India over the period 1951 to 2012. This is based on consumption distributions from 51 household surveys conducted by the National Sample Survey Organization (NSSO); beginning with the 3<sup>rd</sup> round for August-November 1951 up to the 68<sup>th</sup> round for July 2011 to June 2012. We use the full period for descriptive purposes. Some of the earliest surveys had smaller sample sizes and covered shorter periods. The shorter periods also make for a more imprecise mapping between NSS rounds and the annual national accounts data. Hence, in our main analysis we dropped some of the early NSS rounds that had survey periods considerably shorter than a year, and our first observation for the poverty regressions in this paper is for the 13<sup>th</sup> round for 1957-58.<sup>24</sup> For the econometric analysis we restrict ourselves to the period 1957-2012 (from NSS round 11 to round 68), giving 41 observations including 18 for the post-1991 period.<sup>25</sup> This series significantly improves upon the most widely-used time series on poverty measures in India to date.

Following now well-established practice for India and elsewhere, a household's standard of living is measured by real consumption expenditure per person. The underlying NSS data do not include incomes, though it can be argued that current consumption is
MONGOLIA'S INFORMAL SECTOR Size and Trends 14

## **The Current Situation: Aggregate Informal Income in Ulaanbaatar**

34. The SSO household survey data can also be used to make inferences about the contribution of the informal sector to household income in Ulaanbaatar. The average household in Ulaanbaatar receives 22% of their income from informal sources. However, the aggregate impact is larger due to the fact that low income household are weighted the same as middle and higher income households. Many of the poorest households are pensioner households with no informal income. Reweighting to generate appropriate aggregates we find that 32% of household income in Ulaanbaatar is generated by informal activities.<sup>3</sup> "

# Trends and Current Size of the Informal Sector Implied by Monetary Aggregates

35. One family of techniques for estimating the size of the "underground economy" (economic activity hidden from authorities for the purpose of evading taxation or regulation) utilizes monetary aggregates.<sup>3</sup> " Most of the techniques in this family are based on the premise that cash is the preferred medium of exchange for underground activity, and the population's preference for cash relative to bank deposits serves as an indicator of the size of the underground economy. This concept is certainly no stranger to Mongolian policy makers. Citing "widespread violation of clauses" of several laws "including such practices as not registering their cash transactions in financial statements, hiding revenue for tax evasion purposes and illegal lending and usury activities," the Ministry of Finance and Mongolbank issued a joint resolution in 1993 mandating that cash in excess of 100,000 togrogs must be deposited in commercial banks on a daily basis"<sup>9</sup> A brief survey on informal finance undertaken as part of this study (discussed in more detail later in the report) confirms the isolation of the informal economy from the formal banking system. Of 30 informals questioned about their financial activities, not one used a commercial bank in any way.
in earnings explains only 70 percent of the decline in cash incomes, while falling government transfers in real terms and declines in other income are also quite important (Figure 20). For the non‐vulnerable, government transfers and other types of income actually grew during the crisis times. The positive dynamic of other types of income is mainly driven by top income deciles that enjoyed higher dividends during the crisis due to higher interest rates.

_Figure 20: Decomposition of real cash income change during the crisis (2014‐2016) and the recovery (2017)_ <!-- Start of picture text --> 30<br>20<br>10<br>0<br>-10<br>-20 Other income<br>-30<br>Transfers<br>-40<br>Earnings<br>2014-2016 2016-2017<br>Average Vulnerable Non-vulnerable Average Vulnerable Non-vulnerable<br><!-- End of picture text --> Note: Absolute income change is measured in real 2014 roubles per capita. The income change could be explained by both the income dynamics for the vulnerable and non‐vulnerable groups and by the change in the composition of those groups from one period to the next – during the crisis poverty risks and the vulnerable group expanded, and it could be the influencing factor for the change in income structure of the vulnerable population. Source: Authors’ calculations based on quarterly HBS.

Government transfers did not help vulnerable households to smooth declining incomes during the crisis; moreover, the decline in transfers exacerbated the decline in earnings and other types of income. During the recovery, transfers also did not play a positive role for the vulnerable – the restoration of incomes was predominantly led by earnings and, to a much smaller extent, by other types of income. For the non‐ vulnerable, on the contrary, other income sources were declining in 2017, reflecting lower interest rates in the aftermath of the crisis.

The reason why government transfers were not
people is about 2.7 billion over this period; this trajectory implies no progress in reducing the number of poor over the coming decade; the rate of progress in reducing the poverty rate is only sufficient to compensate for population growth. The corresponding projections for the PG index are 14% in 2017 and 13% in 2022. (Again, these incorporate the nonlinearity in how the incomegap ratio evolves over time.) Note that the slower rate of progress against poverty by this new measure reflects in part the success against absolute poverty. Economic growth has generally come with a lower absolute poverty rate but it has also meant that many developing countries have moved into the region in which relative considerations become more important. And, as already noted, the new poverty index is naturally less responsive to economic growth, and puts a higher relative weight on inequality.

In setting more ambitious targets for social inclusion, we have to realize that success against extreme absolute poverty will undoubtedly swell the ranks of the relatively poor. Except in the poorest countries, it can be safely assumed that those who will escape absolute poverty under this trajectory will still be relatively poor by the above definition. And the inclusion line will rise with the overall growth. So success against absolute poverty will add to the challenge of fighting relative poverty.

Consider again the World Bank’s current growth projections at country level, and assume again that the mean consumption of all households grows in line with the overall growth rates at country level and there was no increase in inequality. That scenario would get the relative poverty rate down to 41% in 2017.

What poverty target for 2022 for this new measure is consistent with the absolute targets above? One way to address this question is to use the poverty measures across regions and over time in Chen and Ravallion (2012) to estimate the mean relative poverty index when the absolute measure reaches its target holding inequality constant. This gave a mean relative poverty rate of 33% when the absolute rate is 3%, and
’s (2003) study of the poverty implications of China’s WTO accession offers a good example of this approach. In this work, the authors combine disaggregated household survey data with trade 9 A few studies in this general category are: Robilliard, Bourguignon, and Robinson (2003); Ianchovichina, Nicita, and Soloaga (2001); Hertel, Ivanic, Preckel, and Cranfield (2004); and Friedman (2001). Summaries are provided in the tables of Reimer (2002).

10 Informal discussions with authors working in this area confirm this fact. Both Thomas Rutherford and Maros Ivanic report that equilibrium results obtained with a single aggregate household differ little from those obtained with CGE models which disaggregate a large number of households.

17
programs obliges rental companies and exchange programs to keep a smaller variety of pallets for which there is a relatively sizeable market. This lack of access to different pallet sizes limits the destination markets where products do not need to be depalletized and repalletized.

Three options are available to LDCs to comply with pallets sizes requirements:

- buy locally recycled imported pallets from developed countries,

- import pallets from either neighboring countries or developed countries,

- export without pallets.

Locally recycled pallets are the cheapest option for an exporter. In several West-African countries, exporters can select this alternative. However, there are shortcomings to this strategy due to the usual poor quality of the pallet. The risk of the loss of goods is much higher. In addition, the pallet may break and consequently goods can be damaged in the container and pallets can be contaminated. For fruit exports, a whole container may be rejected if pallets are contaminated. In addition, the exporter is constrained by the variety of pallets that are brought in with imported products.

Pallet imports from neighboring countries may be a preferable option to mitigate the risks of losses and contamination. There are however two problems associated with this strategy: transport facilitation and tariff duties. In East Africa, it can take three days to import goods from Nairobi to Kampala. Moreover, the COMESA preferential rate for pallets import is 5% but the non-preferential rate amounts to 15% (25% for imports to Rwanda)<sup>13</sup> .

The last solution is to export without pallets. When labor costs are low and handling equipment inexistent or defective, exporters may choose to export without pallets. In this case, cartons are filled manually in the container. The exported volume is also potentially higher. However, this option is not the panacea. Indeed, risk of product loss is higher. At

> 13 Figures for 2003 from UNCTAD TRAINS database provided by Takako Ikezuki.

13
in 2018). The pace of public debt increase was slower for resource poor countries (18 percentage points of GDP) and minerals and metals abundant countries (15 percentage points).

**Figure 4. Public Debt in Sub-Saharan Africa, by resource abundance** _(% GDP)_

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Resource Poor Minerals & Metals Oil Rich Oil Rich excl.<br>Rich NGA<br>2007 2013 2018<br><!-- End of picture text --> _Source: The figure reports GDP-weighted averages by region. IMF – World Economic Outlook Database, October 2018_ Fast borrowers had among the highest debt levels in 2018.<sup>5</sup> **Figure 5** and **Figure 6** add more granularity to the broader debt trends over the past five years. The figures show the 75th percentile of countries with the highest debt levels in 2018, as well as the sharpest increases in public debt between 2013 and 2018, respectively. Eight of the twelve fast-borrowing countries over the past five years can be found in the 75<sup>th</sup> percentile of debt-to-GDP in 2018. This implies that these eight countries (Angola, Cabo Verde, Congo Rep., Mauritania, Mozambique, Sierra Leone, Sudan, and Zimbabwe) not only exhibit the largest levels of indebtedness but also their debt levels grew at the fastest pace. Additionally, seven of the 12 fastest-borrowing countries are commodity dependent (of which 4 are oil abundant countries). Yet, 40 percent of the fastest borrowers are not commodity exporters, such as Sudan, Mozambique, and Cabo Verde.

> 5 Fast borrowers are defined as those countries in the upper quartile of the distribution of the change in public debt between 2013 and
.

In examining the Russian Federation’s services trade, this report identifies trends in the development of services both domestically and as exporters, assesses the potential for increasing services exports to neighboring markets, examines linkages between services and other sectors, and analyzes how the main service industries are regulated. Among questions addressed are: (1) Is there untapped potential to develop the services sector in Russia? (2) How do Russia’s services exports perform in comparison to other countries? (3) How close are the linkages of services to other economic activities compared to direct services exports? (4) What is the trade potential for services? (5) What factors lie behind services trade performance? and (6) What is the regional pattern of Russia’s services trade?

The paper uses the analytical model proposed in Sáez et al. (2014), which provides for integrated analysis and diagnostics of the trade competitiveness of the services sector using a wide range of indicators. Cross‐country comparisons are based on World Development Indicators (WDI) and other publicly available World Bank data, and on two new databases: (1) The Export in Value‐Added database measures trade on a value added basis based on national input‐ output tables from the Global Trade Analysis Project (GTAP) project for 1992, 1995, 1997, 2001, 2004, 2007, and 2011. It also measures not only the direct contribution of services to total exports in terms of value added but also their indirect contribution through linkages. In other words, the data set

> 1 Producer services include telecommunications, transport and distribution services, financial intermediation, and business services, among others.

> 2 For the positive link between liberalization of services trade and manufacturing productivity, see Arnold, et al 2011 and Arnold, et al 2010) and Arnold et al (2008).

2
<!-- Start of picture text --> Professional Management PROFESSIONAL MANAGEMENT<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>T1 T2 T3<br>Source: LAC SOEs Governance Database, The World Bank, 2009. THRESHOLD LEVELS<br>VALUE 0-1<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>T1 T2 JAM ECU D R BZL COL ARG URU T T MEX BOL C R PER PAR<br><!-- End of picture text --> The Professional Management Index shows the highest percentage of companies at T3 levels.

# **COMPONENT 4: TRANSPARENCY AND DISCLOSURE**

The transparency index measures the existence of mechanisms that allow transparent disclosure of the company’s financial and non-financial information, the involvement of civil society in decision-making, and the independent auditing of SOEs’ accounts. The three Tier analysis indicates that the majority of SOEs have minimum conditions to achieve the open disclosure of their performance and accounts. In the sample, no SOEs fulfill the desirable criteria.

<!-- Start of picture text --> Transparency and Disclosure TRANSPARENCY AND DISCLOSURE<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>T1 T2 T3<br>Source: LAC SOEs Governance Database, The World Bank, 2009. THRESHOLD LEVELS<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>T1 URU PER BZL BOL C R JAM COL MEX PAR ARG ECU T2 D R T T<br>NUMBER OF COMPANIES<br><!-- End of picture
compared to a standard deviation of 119 days in the DB indicator).

In contrast, there is a set of firms that do find regulations an obstacle, and the extent of delay for those firms does vary widely across countries. The ―disfavored‖ firms—those in the 90<sup>th</sup> percentile—show huge differences across countries, the standard deviation for getting a construction permit of the 90<sup>th</sup> percentile is 106 days. Even though there are large differences, these are not well explained by a simple linear association with DB. Rather, there is a sharply non-linear pattern in which increases in DB days from low levels are associated with a rapid increase in the time reported by ―disfavored firms‖ (90<sup>th</sup> percentile), but for increases above the first tercile of the data (and at least by the median) there is no association between the DB indicator and the ES 90<sup>th</sup> percentile responses.

Fourth, several countries have repeated ES surveys that span a change in the corresponding DB indicator. While these are many substantial _reductions_ in the DB reported time, there were almost no instances in which this was associated with a fall in the ES reported time for compliance. In fact, the change in average responses reported by firms often _rose_ . This 5