rafmacalaba/data-use-mentions-extended huggingface dataset:
lower-middle income countries integrate more and more into GVCs, we expect that such domestic policies will play an increasing role.

# **5. Conclusion**

GVCs changed the way we think about trade and its role for development. Initial work on the topic outlined several ways how GVCs can raise growth and value added through increased specialization, knowledge spillovers, or terms of trade effects. However, initial comprehensive empirical tests of the relationship have shown that gains from GVC participation are not self-evident, instead, the effects seem to be dependent on a set of country- and industry-specific characteristics.

Our econometric results confirm that GVC integration—both as a buyer and seller—increases domestic value added, using a large database covering up to 61 countries, 34 industries, and the time period from 1995 to 2011. Interestingly, the results indicate that being a seller (forward links) in GVCs contributes more strongly to economic upgrading than being a buyer (backward links) only.

In a second step, we specifically examined the role of national policies, institutions, and standards for the GVC integration-economic upgrading nexus. We find that the domestic policy environment is indeed a major catalyst for the effects of GVC participation on domestic value added. It can act as either an obstacle or a facilitator for economic upgrading in GVCs and should be a key element of a country’s GVC strategy.

A more detailed analysis shows that several policy areas mediate the gains from GVCs more through integration as a seller, which holds in particular for connectivity, education, and skills, as well as for standards. We consider this as a sensible result since the mechanisms behind the positive effects of GVC integration as a buyer and seller are likely to be different. For instance, backward links might generate gains through increasing the quality of inputs, while forward links have a direct-demand increasing effect for domestic value added. Naturally, domestic policies can influence the demand for domestic goods more than the quality of foreign inputs—our results are in line with such an explanation
.

- Woerner, Wolfgang, Bacy Fleitlich-Bilyk, Rhonda Martinussen, Janet Fletcher, Giulietta Cucchiaro, Paulo Dalgalarrondo, Mariko Lui, and Rosemary Tannock. 2004. The Strengths and Difficulties Questionnaire overseas: evaluations and applications of the SDQ beyond Europe. _European Child and Adolescent Psychiatry_ 13 (2): ii47-ii54.

- Zomba City Assembly. 2009. Zomba district socio economic profile 2009-2012.

41
*AFR**|Ghana*|Nigeria|
|**ow an**<br>**Lower**<br> <br>**EAP**||Indonesia*|
|**Middle**<br>**Income**<br>**ECA**|Armenia*, Moldova*|Azerbaijan|
|**MNA**|Morocco*, Jordan*|Egypt*, Iran, Yemen*|
|**SAR**|India*, Pakistan*||
||**Group B**|**Group D**|
|**Upper-**<br>**Middle**<br>**EAP**||Malaysia*|
|**and High**<br> <br>**ECA**|Turkey*||
|**Income**<br>**LAC**|Chile, Dominican Rep., Peru|Argentina*, Mexico*|

_Note:_ Selected sample based on income, region, energy net import-export

* indicates if the country is characterized by macro unbalances

(either budget deficit higher than respectively 4% of GDP or public debt higher than 40% of GDP).

The case studies were supported by data collection related to direct budgetary subsidies, fuel and electricity tariffs, and household survey data, from official documents, complemented by 2
35 Deming, David J., “The Growing Importance of Decision-Making on the Job,” NBER Working Papers 28733, National Bureau of Economic Research, Inc April 2021.

Flabbi, Luca and Roberta V. Gatti, “A primer on human capital,” Policy Research Working Paper Series 8309, The World Bank January 2018.

Gindling, T. H., Nadwa Mossaad, and David Newhouse, “How Large are Earnings Penalties for Self-Employed and Informal Wage Workers?,” _IZA Journal of Labor & Development_ , Dec 2016, _5_ (1), 20.

Gindling, T.H. and David Newhouse, “Self-Employment in the Developing World,” _World Development_ , 2014, _56_ , 313 – 331.

Hall, Robert E. and Charles I. Jones, “Why do Some Countries Produce So Much More Output Per Worker than Others?,” _The Quarterly Journal of Economics_ , 1999, _114_ (1), 83–116.

Hanushek, Eric A. and Dennis D. Kimko, “Schooling, Labor-Force Quality, and the Growth of Nations,” _American Economic Review_ , December 2000, _90_ (5), 1184–1208.

, Jens Ruhose, and Ludger Woessmann, “Knowledge Capital and Aggregate Income Differences: Development Accounting for US States,” _American Economic Journal: Macroeconomics_ , October 2017, _9_ (4), 184–224.

Hanushek, Eric and Ludger Woessmann, “Do better schools lead to more growth? Cognitive skills, economic outcomes, and causation,” _Journal of Economic Growth_ , December 2012, _17_ (4), 267–321.

Heckman, James, _The Heckman Equation Brochure_ , Heckman: The Economics of Human Potential, 2019.

and Richard Robb, “Using Longitudinal Data to Estimate Age, Period and Cohort Effects in Earnings Equations,” in W.M. Mason and S.E. Fienberg S.E., eds., _Cohort Analysis in Social Research_ , Springer, New York, NY, 1985, pp.
districts, and less adherence to lockdown orders specifically amongst those who also exhibit stronger support for the Republican Party between 2000 and 2016 (Bazzi et al., 2021). As a basic test of this “collective inaction” thesis, we regressed the percentage change in NO2 from 2019 Q2 to 2020 Q2 on the margin, of Democratic victory in the 2016 Presidential election at the county level. If the thesis is correct, we should obtain a negative sign on the effect, since this would mean more Democratic-leaning counties were associated with stronger lockdown measures. We obtain the following estimates (robust standard errors in parentheses),

> 1Availability of the stringency index: `https://ourworldindata.org/grapher/covid-stringency-index`

S3
|EPZ dummy||484.7||-0.532||509.1|
|||(292.3)||(0.696)||(332.2)|
|Observations|293|290|248|245|239|236|
|R-squared|0.041|0.259|0.036|0.120|0.044|0.255|

Notes: robust standard errors in parentheses clustered by industry-location. ***, **, and * indicate significance levels of 1%, 5%, and 10%, respectively 15
loans. Currently, there are three stated objectives of KUR: (i) increasing and expanding access to finance for productive businesses; (ii) increasing the capacity and competitiveness of MSMEs; and (iii) encouraging economic growth and development (Regulation No. 1 2022, Section 2).

In its current form, KUR provides public financing to subsidize interest rates on loans for MSMEs and fund a partial credit guarantee for these loans. The government provides interest subsidies directly to participating banks, allowing them to lend to MSMEs at capped interest rates.<sup>6</sup> The interest rate subsidy also covers a guarantee fee which banks pay to selected Credit Guarantee Companies (CGCs), but the loan capital comes from the banks themselves. Each year, the banks and financial institutions which participate in KUR (‘distributors’) submit an estimate of the amount of KUR loans they expect to be able to disburse in the coming year. Based on estimates and past performance, the government formulates annual lending quotas and performance targets for each bank, as well as an aggregate annual target for the KUR program. All participating banks must offer loans according to conditions outlined in the KUR Regulations in order to receive the subsidy.

All banks participating in KUR report into a central Credit Program Information System (SIKP), which registers and tracks each KUR borrower. To become a KUR distributor, financial institutions or cooperatives must meet certain criteria: (i) being healthy and having good performance; (ii) cooperating with a KUR guarantor company in distributing KUR, and (iii) having access to the online KUR data system (SIKP). Financial institutions that meet these criteria can apply to become a distributor by submitting documentation and entering into a financing cooperation agreement (Permenko No 8 of 2019, Article 6). In 2022, there were 44 KUR distributors. However, the majority of KUR loans are disbursed by one distributor, Bank Rakyat Indonesia (BRI), which distributed approximately 70 percent of KUR loans in
**Table 3: Impacts of mental health on refugee partners’ and children’s outcomes**

|Dependent variable:|Partner<br>employment<br>status|Partner life<br>satisfaction|Child mental<br>health scores|Child school<br>performance<br>below average|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Standardized mental health<br>scores|-0.004|-1.018*|3.170*|0.016***|
||(0.005)|(0.570)|(1.756)|(0.005)|
|_First stage of 2SLS (depende_|_nt variable is stand_|_ardized mental heal_|_th scores)_||
|Trauma experience*Year<br>after arrival|-0.023***<br>(0.006)|-0.228*<br>(0.130)|||
|Trauma experience|||0.537***<br>(0.183)|0.415***<br>(0.133)|
|Kleibergen-Paap F stat.|35.644|28.205|8.766|9.710|
|AR 95-CIs|[-0.014, 0.004]|[-2.406, -0.465]|[-0.582, 8.685]|[0.001, 0.476]|
|Dep. Mean|0.200|7.608|8.954|0.061|
|Other controls|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes
reforms is ensuring that the labor market opportunities generated under the new economic model are widely shared among the population. This paper describes key bottlenecks to these goals. Without action to address the forces slowing domestic migration, theory and international experience suggest that output and productivity growth will be muted, while incomes and employment will remain below potential.

The government has set about rolling back some of the impediments to urban growth. A presidential decree in January 2019 laid out a high-level vision for urban development over the coming years, including:

- Creating conditions for free movement of people from rural to urban areas

- Providing special measures to support investment in residential construction

> 4 Soviet Demographic Yearbook 1991. 5 No national population census has been conducted in Uzbekistan since independence from the Soviet Union in 1991.

6
Policy Research Working Paper 10522

# **Abstract**

This paper provides a framework to study how different allocation systems of public procurement contracts affect firm dynamics and long-run macroeconomic outcomes. It builds a novel panel dataset for Spain that merges public procurement data, credit register loan data, and quasi-census firm-level data. The paper provides evidence consistent with the hypothesis that procurement contracts act as collateral for firms and help them grow out of their financial constraints. The paper then builds a model of firm dynamics with asset- and earnings-based borrowing constraints and a government that buys goods and services from private sector firms, and uses it to quantify the long-run macroeconomic consequences of alternative procurement allocation systems. The findings show that policies which promote the participation of small firms have sizeable macroeconomic effects, but the net impact on aggregate output is ambiguous. While these policies help small firms grow and overcome financial constraints, which increases output in the long run, these policies also increase the cost of government purchases and reduce saving incentives for large firms, decreasing the effective provision of public goods and output in the private sector, respectively. The relative importance of these forces depends on how the policy is implemented and the type and strength of financial frictions.

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 mgarciasantana@worldbank.org.

_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
Policy Research Working Paper 5436

# **Abstract**

This paper examines the evolving, cross-country use of antidumping, safeguard, and countervailing duty policies—temporary trade barriers (TTBs)—over the period 1990-2009. The author constructs two new measures of imported products subject to the combined use of these TTBs before applying these measures to new data drawn from the World Bank’s Temporary Trade Barriers Database. The research establishes a number of facts regarding trends in historical use to benchmark against policy activity during the global economic crisis of 2008–2009. The 2008–2009 economic shock mostly accentuates patterns and trends already visible in the pre-crisis data: e.g., while the major users of such policies overall combined to increase the product lines subject to TTBs by 25 percent during the crisis, this was driven almost entirely by developing economies which increased their product coverage by 40 percent. On the export side, a previously unidentified feature of the data is that a much larger share of China’s exports to other developing economies is subject to foreign-imposed antidumping than its exports to developed economies. The evidence confirms this feature is shared by a number of other major developing economy exporters, deepening concern that these discriminatory trade barriers are increasingly a “South-South” phenomenon.

This paper—a product of the Trade and Integration Team, Development Research Group—is part of a larger effort in the department to understand the changing nature of market access in the world trading system. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cbown@worldbank.org.

_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
38 percent increase in gas price would increase poverty incidence to 24.1 percent. Thus, an additional 0.6 percent of Armenian households would be classified as poor with the April 2010 gas price hike alone.

# **_What is the cost of protecting the poor and/or all affected households?_**

The aggregate cost to the households of the gas price increase is about 0.73 percent of 2009 GDP (or about 22.5 billion drams). Table 8 presents the aggregate increase in expenditures on gas estimated to account for the increase in the gas price. The estimates are the upper bound since our simple model assumes consumption levels are unchanged (no substitution). More importantly, the calculations provide a rough cost of protecting the poor or any share of the affected households to ensure that they are as better off as they were before the gas price increase. For example, protecting the energy consumption of households from the two poorest quintiles requires energy subsidies of about 0.18 percent of GDP (or about 5.8 billion drams). However, these calculations assume perfect targeting by the subsidy program of the intended beneficiaries. In practice, it is impossible to achieve perfect targeting.

The Government of Armenia used the Family Benefit (FB) program targeting scheme to provide compensation against the gas price hike. It introduced a lifeline tariffs for poor gas consumers who are beneficiaries of the FB program.<sup>9</sup> Under this arrangement, poor households pay AMD 100/m3 instead of regular AMD132 on the first 300 m3 of gas consumed during the year. This benefit became effective on April 1, 2011. Based on the FB database, the total cost of the lifeline tariffs for one year was about 1.056 billion drams (about 0.03% of GDP). For comparison, assuming the FB targeting mechanism was used and the poorest two quintiles were targeted, the fiscal cost of full compensation would have been about 0.22 percent of GDP
increased opacity in the debt profiles of low-income countries.<sup>24</sup> This has resulted in greater risks associated with massive hidden debt operations and greater (than expected) interest payments, which leads to heavier government burdens (World Bank 2020b). Empirical evidence shows that countries with greater public debt exposure to private creditors tend to experience a larger decline in economic growth as the coefficients of PPG external debt to private creditors are negative and, in some cases, significant in Table 1 (i.e. -0.019 in [5] of Table1), while the ones of PPG external debt to public creditors are robustly positive and significant in Table 1 (i.e. 0.0935 points in regression [5] of Table 1).

Reducing general government gross debt also helps mitigate macroeconomic vulnerability in the event of economic shocks. For instance, the coefficients of the government primary balance and to the COVID-19 shock, oil exporters excluding Nigeria accumulated some reserves from oil export proceeds, and consequently, the resulting fiscal space from these revenues may have helped finance policy actions and reduce the adverse impact from the pandemic even if their economic structure was not as diversified. Oil abundant countries registered more favorable fiscal and current account balances in 2019, therefore, those countries had some resources in their coffers prior to the COVID-19 crisis. For instance, oil exporting countries in Sub-Saharan Africa recorded a fiscal surplus that exceeded 1 percent of GDP in 2019 and exhibited a current account deficit of around 2 percent—the smallest among commodity exporting groups in the region.

> 24 The recently published debt transparency heatmap by the World Bank shows that although there has been some progress in terms of the coverage of debt instruments reported as well as its publication, there is still a lot of work to do in terms of making available information to the public on annual borrowing plans and contingent liabilities (World Bank 2020b).

14
Language fluency is a strong predictor of the type of jobs in which migrant workers are employed and the associated level of vulnerability that surfaces in the multinomial logit regression (Column 5, tables 1-33-5). Compared to workers for whom the language used in their place of residence is also their mother tongue, those with beginner or intermediate levels of language fluency are about 10 percentage points more likely to be employed in a safe job (Column 3, Table 3). This effect is very large, given that less than half of the employed population has jobs with neither income nor health risks. Moreover, language ability explains a significant gap in the share of “safe jobs” for several migrant groups. For example, the gap between migrants from NMS13 countries and natives’ likelihood of having held a “safe” job during the pandemic falls from 16 percentage points to 9 percentage points when language skills are taken into account; the gap falls from 19 percentage points to 13 percentage points for migrants from other European countries, from 13 percentage points to 7 percentage points for Asians, from 10 percentage points to 6 percentage points for those from Sub-Saharan Africa, and from 9 percentage points to 3 percentage points for those from MENA countries.

Barriers to recognition of foreign credentials are also significantly correlated with the exposure to the labor-market risks of the pandemic. Migrants who report a lack of recognition of their credentials as a main barrier to integrate in the host-country labor market are 16 percentage points less likely to be employed in “safe jobs” compared to similar migrants who did not face difficulties validating their degrees (Column 6, Table 1). Although skill validation is an important factor, it does not explain a large part of the overall higher vulnerability of migrants in the labor market; as captured in the EU-LFS, it represents a main barrier for only 6 percent of working-age migrants. Moreover, when we compare the coefficients for different world regions (tables 1-3, columns 5- 6), we do not see much of a change
43|0.07||
|WRSI|0.21|0.13|<br>0.12|0.16|0.22|0.15|0.18|0.11|0.15|-0.11|0.01|0.02|
||||**Pastoral**|||||**Ag**|**ropastora**|**l**|||
||Total seas<br>rainfal|onal<br>l|Soil<br>moisture|SPEI|<br>NDVI|To|tal seas<br>rainfall|onal<br> <br>|Soil<br>moisture|SPEI|NDVI||
|Soil moisture|0.46||||||0.32||||||
|SPEI|0.45||0.23||||0.51||0.39||||
|NDVI|0.57||0.54|0.28|||0.33||0.60|0.31|||
|WRSI|0.29||0.09|0.24|<br>0.26||0.26||-0.11|0.17|0.10||

_Source:_ World Bank. _Note:_ NDVI = Normalized Difference Vegetation Index; SPEI = Standardized Precipitation Evapotranspiration Index; WRSI = Water Requirement Satisfaction Index.

# 3.2. Hazard simulations data

For the analysis, we asked Verisk’s AIR Worldwide (AIR) to produce 10,000-year drought-related stochastic catalogs of synthetic simulations for Malawi at a spatial resolution of 0.05° and dekadal temporal resolution. Stochastic catalogs are designed to provide a view of hazard that goes beyond the data available in the historical record. AIR used historical data on rainfall to
**Table 14: Rice Yields with Improve Practice**

|**Ecosystem**|**Traditional Practice**<br>**t/ha**|**Improved Practice**<br>**t/ha**|**% Change**|
|---|---|---|---|
|Uplands|0.8|1.4|75.0|
|IVS|1.7|2.3|35.3|
|Boliland|1.5|2.0|33.3|
|Mangrove Swamp|2.8|4.0|42.9|

Source: Baggie I., Lamin A.S., Lahai M.T. Inland Valley Research and Development, Sierra Leone The model is used to simulate the result of an experiment, which assumes that rice yields across the four ecologies covered in the trials increase to levels indicated for improved practice. Since the trials did not cover the Riverain ecology the yield from this ecosystem was held at the current level (2016 survey baseline) for the simulations. The results of the simulation are shown in Table 15. Producers clearly benefit as domestic production of rice rises by 40 percent from the 2016 survey baseline to 974,833.7 tons. Consumers also benefit as the price of rice would fall from US$768.64/ton to US$594/ton, a reduction of 23 percent. This benefit to both consumers and producers is reflected in the fact that the Consumer/Producer surplus would increase from US$1,328.3 million to US$1,486.0 million.

The simulation also suggests that the increased yields relative to baseline is both land and labor augmenting as the entire Upland and a substantial part of the Boliland ecologies would be unutilized and the total employment in the rice sector would fall from 669,340 in the baseline to 377,600