rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10641

# **Abstract**

This paper assesses the medium-run effects of government support to firms during the COVID-19 crisis and whether the effectiveness of this support varied with its timing. Using data from three rounds of the World Bank’s Enterprise Surveys COVID-19 Follow-up Surveys carried out between May 2020 and April 2022, it relates government support in Round 1 (received in the first half of 2020) and Round 2 (received during the second half of 2020 or early 2021) with firm performance in Round 3 (generally mid-2021). Controlling for a host of background characteristics, firms that received support in Round 1 performed better in terms of Round 3 sales, but only if they did not have continued support. Firms that also received support in Round 2 had similar Round 3 sales as those that received no support and were more likely to decrease employment. Firms that received government support only in Round 2 experienced no boost in Round 3 performance. The findings suggest that government support should be provided promptly, but it should also be phased out quickly.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dsinger@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility. worldbank.org, click **here** for direct access.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
and many of them were able to accessed basic education. The share of college graduates increased by 17 points among most advantaged, while there was no appreciable increase among the least advantaged.

# _Drop-out rate_

A similar picture emerges when looking at the drop-out rate among those who participated in the 2007 TIMSS survey for 8<sup>th</sup> graders. Consider 15-19 year-olds in 2006, most out-of-school children actually never enrolled (about 13 percent of 15-19 year-olds in 2006) or dropped out after obtaining some vocational secondary education (15 percent). Drop-outs after primary (7 percent) or preparatory (4 percent) are less frequent. **Table 4** reports results from a partial correlations analysis of drop-out, i.e. probit estimates of the marginal effects of a set of circumstance variables for the same samples of 15-19 year-olds in 1988 and 2006. Girls have more often dropped out than boys, but the gap decreased from 23 to 8 percentage points between 1988 and 2006. While large geographic differentials persist in drop-out rate, there have been continuous improvements over the last two decades. Children born in rural Upper Egypt continue more likely to drop out than those born in any other region but the gap decreased from 21 to 9 percentage points, compared to Greater Cairo. However some gaps associated with family background have only slightly diminished, e.g. children with uneducated parents are still much more likely to have dropped out in 2006 (about 30 percentage points more than children of college graduates).

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|Title|Author|Date|Contact|
|---|---|---|---|
|WPS13 Objectives and Methods of a World||||
|Health Survey|Trudy Harpham<br>Ian Timaeus|June 1988|A. Menciano<br>33612|
|WPS14 The Optimal Currency Composition<br>of<br>External Debt|Stijn Claessens|June 1988|S. Bertelsmel<br>33768|
|WPS15 Stimulating<br>Agricultural<br>Growth and<br>Rural Development in Sub-Saharan<br>Africa|Vijay S. Vyas<br>Dennis Casley|June 1988|H. Vallanasco<br>37591|
|WPS16 Antidumping<br>Laws and Developing<br>Countries|Patrick Messerlin|June 1988|S. Torrijos<br>33709|
|WPS17 Economic Development and the Debt<br>Crisis|Stanley Fischer|June 1988|C. Papik<br>33396|
|WPS18 China's Vocational<br>and Technical<br>Training|Harold Noah<br>John Middleton|June 1988|W. Ketema<br>33651|
|WPS19 Cote d'lvoire's<br>Vocational and<br>Technical Education|Christiaan<br>Grootaert|<br>June 1988|R. Vartanian<br>34678|
|WPS20 Imports and Growth in Africa|Ramon Lopez<br>Vinod Thomas|June 1988|61679|
|WPS21 Effects of European VERs on Japanese<br>Autos|Jaime de Melo<br>Patrick Messerlin|June 1988|S. Fallon<br>61680|
|WPS22 Methodological<br>Problems in Cross-<br>Country Analyses of Economic Growth|Jean-Paul Azam<br>Sylviane Guillaumont|June 1988<br>|E. Zamora<br>33706|
|WPS23 Cost-Effective Integration<br>of<br>Immunization<br>and Basic Health Services<br>in Developing Countries: The Problem<br>of Joint Costs|A. Mead Over, Jr.|||
|WPS24 Review of World Bank Investments<br>in<br>Vocational<br>Education and Training<br>for Industry|John Middleton<br>Terri Demsky|||
4 The indicators estimated in this paper are defined by two descriptors: the instrument trade reduction index (ITRI) and the instrument welfare reduction index (IWRI). The ITRI (or IWRI) is the ad valorem trade tax rate for a particular policy instrument which, if applied uniformly across all tradable agricultural commodities in a country, would generate the same reduction in trade volume (or same economic welfare loss) as the actual cross-product structure of NRAs and CTEs for that instrument in that country. Because the NRAs and CTEs capture the presence of domestic measures that can distort just farmer or consumer incentives (in addition to trade measures that distort both equally), the ITRI and IWRI are computed from subindexes that herein are called the instrument producer distortion index (IPDI) and the instrument consumer distortion index (ICDI).

The use of ITRI (or IWRI) for computing the relative contribution of different policy instruments has the advantage of providing a single theoretically sound partial equilibrium indicator of the trade (or welfare) effects of different policy measures that is comparable across time and countries. Because the Anderson and Valenzuela (2008) dataset covers 5 decades (1955 to 2007), the data can indicate trends over time, which a comparative static CGE model can only do if it is calibrated to a series of past years rather than to just one or a small number of particular years.

The paper is structured as follows: The next section provides the theory for deriving the ITRI and IWRI. The theory is first presented for the import-competing sector of a country and subsequently extended to the exportable sector. This is followed by a description of the World Bank’s Distortions to Agricultural Incentives dataset and its breakdown of the NRA and CTE estimates by instrument. The following section presents and discusses the estimates of the two indexes by policy instrument. Caveats and sensitivity analysis follows, and the final section concludes.

# **Trade and welfare reduction indexes at the policy instrument level**

There is a growing literature that identifies ways to measure the welfare- and tradereducing effects of international trade policy in scalar index numbers. This literature is traditionally used to overcome aggregation problems across products for a country
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# A. BACKGROUND

On November 29, 1996 the Bolivian Government adopted a pension reform law (Law 1732) that modified fundamentally the country's pension system. The Bolivian reform changed the defined-benefit publicly-managed pension system into a defined2 contribution privately-managed system with individual capitalization accounts. The new system, currently under implementation, has many common features with the reforms undertaken in other Latin American countries but has some important differences, particularly during the transition phase from one system to another.

This paper describes the old and the new pension systems, summarizes the particular features of the new system that differentiate it from other reforms, and presents some of the outstanding challenges to successfully complete the reform.<sup>3</sup> Macroeconomic and financial sector effects of the reform are two important issues for further analysis that are not covered in this paper.

# B. THE OLD PENSION SYSTEM

**Institutional framework.**<sup>**4**</sup> The old Bolivian pension system was a defined-benefit system created in 1997 and aiming originally at a replacement rate of about 30% of the last wage. To increase the replacement rate unions and professional groups obtained in 1970 changes in the regulations that allowed for complementary pension funds. In 1996, the system consisted of a basic pension system, administered by a public agency

- 2 Under the old system, retirement benefits were one component of the social security system, which provided also survivorship and disability pensions, and medical insurance. The new system includes also survivorship and disability pensions, but leaves medical benefits untouched.

- 3 The paper is part of a series of papers by the Financial Sector Development Department of the World Bank on private pension funds in Latin American pension reforms. See Queisser (1997), Vittas (1997a, 1997b, 1997c), and Vittas and Iglesias (1992).

- 4 This segment draws on World Bank (1995) and Ballivian (1997).

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|**Table B.2: Variables, Elasticities And Parameters**<br>**Variables**|**Value**|
|---|---|
|Trade Restrictiveness Index of Albania in fixed line telecom, TRI(Albania)|0.07|
|Trade Restrictiveness Index of Albania in mobile line telecom, TRI(Albania)|0.24|
|Current quantity of fixed line per 100 inhabitants in Albania (<br>_m_<br>_Albania_<br>_QI_<br>)|10.39|
|Current quantity of mobile line per 100 inhabitants in Albania (<br>_f_<br>_Albania_<br>_QI_<br>)|84.02|
|**Elasticities and Parameters**||
|The estimated value of b from Warren for fixed line telecom services (ei)|5.26|
|The estimated value of b from Warren for mobile line telecom services (ei)|3.05|
|Substitution elasticity from Dee and others (b)|-1.2|

# **Calculation of AVE in fixed line telecom services in Albania**

Based on equation 35 (a), the quantity index in fixed line services in Albania ( _QI Albaniaf_ ) is calculated: Based on Equation (36a) and considering the value of _QI Albaniaf_ estimated in (36a), and the obtained elasticity ( e= -1.2) , we have : 72
Figure 6: EAP: Infrastructure, environment, health, and education indicators

# A. Ranking of overall infrastructure

# B. Quality of trade and transport-related infrastructure

<!-- Start of picture text --> Ranking 2019 2011-12 Index 2018 2010 World<br>150 4<br>120<br>3<br>90<br>2<br>60<br>1<br>30<br>0 0<br>C. Share of urban population living in D. Environmental performance<br>slums<br>Percent of urban population Index 2020 2010<br>60 2020 2010 World 60<br>45<br>40<br>30<br>20 15<br>0<br>0<br>-15<br>E. Under-5 mortality rate F. Ranking of capacity to retain or<br>attract talent<br>Number per 1000 live births Ranking 2019-2021 2016-18<br>2020 2010 World 120<br>75<br>90<br>50<br>60<br>25<br>30<br>0 0<br>East Asia Island economies<br>Malaysia China Thailand Indonesia Vietnam Lao PDR Philippines Mongolia Cambodia<br>Myanmar Cambodia Philippines China Mongolia Thailand Vietnam<br>China Malaysia Mongolia Vietnam Indonesia Cambodia Myanmar Lao PDR Tonga Palau Samoa Solomon Is. Tuvalu Vanuatu Fiji Nauru Marshall Is. PNG Kiribati<br>Lao PDR Indonesia<br>Thailand Philippines Micronesia Timor-Leste<br>Malaysia Thailand Philippines Indonesia China Lao, PDR Cambodia Vietnam Mongolia Myanmar<br>China Malaysia Thailand Vietnam Indonesia Philippines Lao PDR Cambodia Mongolia Myanmar<br>Malaysia China Philippines Thailand Indonesia Mongolia Vietnam Lao PDR Cambodia<br><!-- End of picture text --> Sources: Environmental Performance Index; World Economic Forum; World Bank, World Development Indicators database.

A. World Economic Forum ranking of 140 countries according to the quality of their infrastructure. 1= best, 140 = worst. B. Logistic Performance Index Surveys conducted by World Bank and Turku School of Economics. 1 = extremely underdeveloped by international standards; 7 = well developed and efficient by international standards. D. The Environmental Performance Index is constructed through the calculation and aggregation of 20 indicators reflecting national level environmental data, including child mortality, wastewater treatment, access to drinking water, access to sanitation, and air pollution (average exposure to PM2.5). These indicators use a “proximity-to-target” methodology, which assesses how close a particular country is to an identified policy target. Scores are then converted to a scale of 0 to 100, with 0 being the farthest from the target (worst observed value) and 100 being closest to the target (best observed value). E. Probability of dying between birth and exactly five years of age expressed per 1000 live birth. Latest data are for 2020.
# **Estimates of Ad Valorem Equivalents of Barriers Against Foreign Suppliers of Services in Eleven Services Sectors and 103 Countries**

**Yaghoob Jafari and David G. Tarr***

**Keywords** : services barriers; ad valorem equivalents; tariff equivalents; World Bank services database; Services Trade Restrictiveness Index; Australian Productivity Commission.

JEL classification: F23; F13; F16 ; F19.

*Corresponding author: David G. Tarr. David Tarr is Consultant and former Lead Economist at the World Bank. Yaghoob Jafari is a consultant at the World Bank. The authors wish to thank Ana M. Fernandes (who led the project), Aaditya Mattoo, Ingo Borchert, Batshur Gootiiz, Georgi Idrisov, an anonymous referee of this Working Paper Series and Michelle Chester for their comments and contributions. We thank the Government of the Netherlands for financial support to the Bank-Netherlands Partnership Program under the project TF012466 “Reducing Trade Costs in East Africa: Analytical Development and Capacity Building.”
||EXPORT<br>|-RELATED BARRIERS<br>|Ai t Ril<sup>Ld</sup>|
|---|---|---|---|
|Baniers|Possible Conreive Measures|Progress with Current Measures|Recommended ctons a egona<sup>ev</sup>|
|Lack of<br>International<br>Marketing|- Allocation of foreign exchange to<sup>exporters or</sup><br>export groups to cover costs of establishing<sup>out-</sup><br>of-country logistical and commercial<sup>facilities.</sup><br>- Use of all-media advertising campaigns<sup>to</sup><br>promote marketing at national and<br>interprofessiona**l**evels.<br>- Conversion of FOB selling contracts<sup>to a C&F</sup><br>basis.<br>- Establishment of processing plants,<sup>locally or</sup><br>abroad, to give greater value added to<sup>raw export</sup><br>products (the implication being that all<sup>necessary</sup><br>government facilities would be made available<sup>for</sup><br>the purpose 1/m foreign exchange, credits,<sup>etc.).</sup>|||
|Transit Charges|- Trade missions (making use of the<sup>resources of</sup><br>interprofessional organizations) to<sup>discuss and</sup><br>negotiate transit charges at destination<sup>and to</sup><br>have them treated as transport expenses<br>deductible from the proceeds of export sales.<br>Morooco: Study of the high excess<sup>costs</sup><br>generated for TIR traffic by passage<sup>of the Strait</sup><br>of Gibraltar.|||
|Brokerage<br>Charges|- Development of means of monitoring<br>commission agents at destination through<sup>the</sup><br>professional association active in each<sup>sector.</sup><br>- Availability of government facilities<sup>for each</sup><br>case in point.|||

IM Intra-Maghreb EM = Europe/Maghreb
## 3.1.3 Other Variables

Estimating Eq. (5) also requires the use of plant-level data on sales and materials costs to construct value added, the book value of machinery and equipment to measure the capital stock, industry and region dummy variables. To reduce the in fl uence of extreme values, we eliminate from the estimation the top and bottom 1% of observations for the ratio of capital to value added in each industry.<sup>32</sup> The industry dummy variables are based on the industries shown in Table 2.<sup>33</sup> The region dummy variables represent thirteen states in Brazil and fi ve cities in China. Finally, we rely on information on the plant’s main manager level of education as a proxy for manager quality using three dummy variables: managers with secondary education, managers with college education and managers with graduate education.

# 3.2. Cross-Tabulation Results

The results from cross-tabulations between the wage bill shares of different measures of skilled labor and either measures of absorptive capacity or international economic activities are presented in Table 4 for Brazil (Panel A) and China (Panel B).<sup>34</sup> For each country, we calculate the difference between the average wage bill share of skilled workers for (i) plants with "better" absorptive capacity or (ii) plants "more" engaged in international economic activities and the average wage bill share of skilled workers for other plants and we test whether those differences in averages are statistically signi fi cant.<sup>35</sup> In Brazil, there is a positive and signi fi cant correlation between the wage bill share of skilled workers and both measures of absorptive capacity and international activities, however skills are measured. In China, the measures of absorptive capacity and FDI are also positively and signi fi cantly associated with wage bill

> 32As industries differ signi fi cantly in their capital intensity, the elimination is done for each industry separately.

> 33All the empirical results obtained for Brazil and China described in Section 4 are maintained considering industry dummy variables at a more disaggregated level: 4-digit ISIC revision 3.

> 34For Brazil, Table 4 shows employment shares of workers with college education instead of the corresponding wage bill shares which are unavailable.

> 35For dummy variables, a plant with "better" absorptive capacity or "more" engaged in international economic activities has the corresponding dummy variable equal to one.

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sector as in the non-state sector<sup>10</sup> . Both the average capital and labor productivity are much higher in the non-state sector -- in 1997, nearly 100 percent and 60 percent more respectively. Over time, the average productivity of labor stagnates in the public sector, while it rises in the non-state sector (by about 50 percent over the four years), resulting in a greater divergence over time.

Second, the average productivity of capital has declined in both the state and non-state sector from 1994-97, with the fall in the state sector especially large. It is interesting to observe that in spite of (or perhaps because of) the decline in the efficiency of capital, there has been a fast rise in capital intensity in both the state and non-state sectors, which nearly double during the four years." Surprisingly, the capital to labor ratios in the two sectors is about equal.12 1' Since 1996, an increasing number of workers called "Xiagang worker" have been made redundant while remaining on the state firms' wage bill, typically receiving minimum wage and some social services. By the end of 1997, some estimated about 11.5 million (some millions are from industry). However, in China Statistics Yearbook, the Xiagang workers remain included in the formal labor statistics. This introduces some bias in our analysis, but there are no reliable state-level estimates on the actual number of Xiagang workers that we can rely on to remove this bias at this stage.

" Similar findings were obtained by Enos (1984) when he examined five small rural industries: iron and steel, chemical fertilizers, farm machinery, cement, and energy (coal and electricity).

12 This reflects mixes of small and large firms in both the public and private sectors, with the first owning millions of small and medium size firms in addition to a few thousands towering firms, while the latter is comprised of small TVEs and large joint-ventures. It is evident that TVEs has attracted most of rural household saving towards its capital formation, averaged annual rural household saving is about 40 percent.

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liabilities created by PPPs are the noncontractual liabilities that the partnership and incomplete contracts create in various states of the world. For example, even though the government might not contractually promise any guarantees to the private party in the event of a default, considering the fact that the government is the ultimate guarantor of public services in most societies, the government might have to bail out the private party or assume the remaining debt and service obligations of the private party to avoid service disruption. It means that when a PPP contract is agreed upon, the government assumes the ultimate insolvency risk (Irwin 2007). The most — common implicit contingent liabilities of PPPs stem from renegotiation and early termination when the compensation is beyond what is included in the termination clauses.

The current public-sector accounting principles do not provide an adequate framework for valuating and reporting the liabilities created by PPPs, which exacerbates the fiscal risks from PPPs. The cash-based accounting practices, which are still popular in the developing world, do not provide a way to include the liabilities into government finances. Even the financial accounting frameworks recommended by the IMF and the EU limit their inclusion based on assessment of the risks and control borne by the government (Heald and Georgiou 2010; de Vries 2013). Furthermore, governments facing fiscal constraints tend to increase their levels of PPP investments as a percentage of GDP without proper institutional mechanisms to deal with the liabilities they create (Reyes-Tagle and Garbacik 2016).

# **3. PPPs in infrastructure in South Asia**

The use of PPPs in infrastructure in South Asia grew exponentially from the early 1990s to the early 2010s, slowing down after 2012. PPP investments accelerated between 2005 and 2012, increasing the value of the active portfolio by more than five-fold from $45 billion to $267 billion, and from 3.9 to 11.4 percent of the region’s GDP (figure 1). After 2012, investment growth slowed down and fell behind GDP growth, as indicated by the decline in investments as a percentage of GDP (figure 1, panel b). At the end of 2018, cumulative investment in the active portfolio of PPP projects was just over $320 billion,<sup>8</sup> according to the World Bank Private Participation in Infrastructure Project Database (PPI),<sup>9</sup> which corresponds to 8.9 percent of the region’s total GDP.

The number of active PPP projects in infrastructure in South Asia increased exponentially between the early 1990s and the early 2010s but slowed down after 2012. However, the increase in the number of projects is slower between 2005 and 2012 than the increase in investment volumes, which indicates that the countries in the region increased the number of projects as well as their average size (figure 2).

> 8 All dollar figures in the study have been expressed in 2019 US dollars, inflated using the US CPI series in WDI. 9 Version 2019 H1, available at https://ppi.worldbank.org/en/ppidata.

6