rafmacalaba/datause-encoder-data huggingface dataset:
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Policy Research Working Paper 5138

# **Abstract**

In the aftermath of the Lehman Brothers collapse in September 2008, drop in the supply of trade finance, a critical engine for trade transactions, has become an acute concern for the development community. Banks were increasing pricing on trade finance transactions to cover increased funding costs and higher credit risks, and trade was dropping drastically in most countries, with global trade projected to decline in 2009 for the first time in decades. Yet, little was known about the real impact of the crisis on developing country’s capacity to export. The World Bank has commissioned a firm and bank survey on trade and trade finance developments in developing countries during the first quarter of 2009 to collect field information. In total, 425 firms and 78 banks were surveyed in 14 developing countries across five regions.

This paper summarizes the findings of the survey as well as discusses the type of policies governments and international organizations put in place to mitigate the impact of the crisis. In sum, the survey findings confirmed that the global financial crisis has constrained trade finance for exporters and importers in developing countries. But the impact varied by the firm size, sectoral activity, and countries’ integration into the global economy. In particular, SMEs were particularly affected, and export diversification was made more difficult, especially in low income countries. Nevertheless, drop in demand has emerged as the top concern of firms at the time when the survey was conducted in March–April 2009.

This paper—a product of the International Trade Department, Poverty Reduction and Economic Management Network—is part of a larger effort in the department to better understand the role of trade finance in the current global economic crisis. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mmalouche@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 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
The Labor Market Supplementary Pension Fund (ATP) is by far the largest of the supplementary pension schemes. It was set up in 1964 to ensure all wage earners a savings-based supplement to the social pension. Later its coverage was extended to unemployed workers and other recipients of social transfers. Today most citizens between the age of 16 and 65 contribute to the ATP and are eligible for supplementary benefits. ATP benefits take the form of life annuities unless the saved amount is too small in which case a lump sum withdrawal is permitted. ATP pensions can be paid out from the 65<sup>th</sup> birthday or deferred until the 70<sup>th</sup> birthday.

The Special Pension Savings scheme (SP) is the second largest supplementary scheme. It was introduced in 1998 as a fiscal policy tool meant to slow down consumer spending and increase pension saving. As economic winds changed contributions to the scheme were suspended in 2003, but while it was in effect, it was mandatory for all wage earners to contribute one percent of their gross salary. SP benefits depend on the size of contributions and investment returns. They take the form of phased withdrawals over a ten-year period with payouts starting on retirement. But like ATP, small accumulated amounts are paid out as lump-sums.

The third supplementary pension scheme is the Employees’ Capital Pension Fund (LD). LD was established in 1978. Its objective was more an economic policy measure than a pension savings program. In the late 1970s most wages were adjusted upwards several times per year to account for rising price levels. The Danish government wanted to contain this very inflationary practice and decided in 1976 that only one annual wage adjustment to prices would be allowed and the rest would be frozen and held in LD personal accounts until retirement. From 1977 to 1979 two adjustments – amounting to DKK 7.7 billion - were frozen and later transferred into 2.5 million individual accounts in LD. Payout is in the form of a lump sum at any point after the retiree’s 60<sup>th</sup> birthday.

- _1.2 THE DANISH RETIREE_

The Danish pension system is built around the intention of ensuring an adequate pension for everyone. Established in 1892, the social pension is one of the first European public pension schemes and for many decades it has been the main income source for Danish senior citizens. Only since the 1980s have occupational and personal pension plans come to play a significant role in promoting financial security in retirement. However, this shift is seen as a permanent trend, as the table below illustrates.

_Table 1: Average individual income sources and distribution in retirement, 1997 and 2045_

|_Income sources in 1997 prices (DKK 1,000)_|1997<br>DKK|1997<br>Percent|2045<br>DKK|2045<br>Percent|
|---|---|---|---|---|
|National pension and public benefits|73|45|127|33|
|Labor market pensions and individual savings|36|23|136|36|
|Supplementary pensions|5|3|53|14|
|Interest andDividends onSavings|47|29|63|17|
|Total gross income|161|100|379|100|
|Taxes|40|25|103|27|
|Net income|121|75|276|73|

Source: The Danish Government, 2000 5
**Table 7: Relative Changes in the Growth of Urban Peripheries across periods**

||**2000-2002**|**2002-2004**|**2004-2006**|**2008-2009**|**2010-2011**|**2013-2017**|**2017-2019**|
|---|---|---|---|---|---|---|---|
|VARIABLES|Recession|Rebound|Expansion|Recession|Rebound|Expansion|Recession|
|||Agglomeratio<br>|ns in the Least<br>|Developed Pr<br>Effects<br>|ovinces with P<br>|rovince Fixed<br>||
|Periphery|-4.61***|8.00***|4.60***|-0.44|4.79*|2.50|5.24**|
||[-3.00]|[6.68]|[3.56]|[-0.23]|[1.85]|[0.94]|[2.18]|
|Constant|-17.57***|16.15***|-3.63***|-18.11***|21.33***|37.23***|-7.91***|
||[-11.68]|[13.77]|[-2.86]|[-9.72]|[8.38]|[14.67]|[-3.44]|
|Observations|22,380|23,015|24,513|24,513|24,513|11,900|11,900|
|R-squared|0.20|0.11|0.12|0.07|0.09|0.07|0.14|
|||Agglomeration|s in the Transi|tion Provinces|with Provinc|e Fixed Effects||
|Periphery|-0.98|3.67***|6.21**|-3.03*|6.34***|9.59***|2.38**|
||[-1.38]|[3.86]|[2.41]|[-1.81]|[3.94]|[3.91]|[2.56]|
|Constant|-0.52|-2.79***|-3.35|10.93***|-6.11***|24.92***|11.02***|
||[-0.75]|[-3.02]|[-1.34]|[6.71]|[-3.91]|[10.86]|[12.68]|
|Observations|36,950|37,531|39,106|39,106|39,106|20,765|20,765|
|R-squared|009|011|011|006|014|006|005|
||.|.<br>Agglomeration|.<br>s in the Develo|.<br> ped Province|.<br>s with Provinc|.<br>e Fixed Effects|.|
|Periphery|-3.75*|7.23***|6.38|-0.41|7.64***|11.28***|2.38*|
||[-1.90]|[7.44]|[1.26]|[-0.24]|[6.40]|[12.48]|[1.91]|
|Constant|-8.11***|5.11***|-3.32***|-5.86***|11.05***|18.95***|11.73***|
||[-1.80e+12]|[4.11e+11]|[-3.66e+11]|[-1.0e+12]|[2.71e+12]|[1.49e+13]|[1.05e+13]|
|Observations|26,493|26,845|27,423|27,423|27,423|16,880|16,880|
|R-squared|0.05|0.06|0.13|0.03|0.09|0.03|0.03|
|||All A|gglomeration|Areas with Pro|vince Fixed E|ffects||
|Periphery|-3.11***|6.44***|5.95**|-1.24|7.03***|9.72***|2.83***|
||[-2.97]|[7.46]|[2.14]|[-1.18]|[8.49]|[8.15]|[3.49]|
|Constant|-8.11***|5.11***|-3.32***|-5.86***|11.05***|18.95***|11.73***|
||[-6.40e+11]|[5.90e+11]|[-1.41e+11]|[-3.30e+11]|[4.83e+11]|[1.47e+12]|[3.15e+12]|
|Observations|85,823|87,391|91,042|91,042|91,042|49,545|49,545|
|R-squared|0.11|0.12|0.11|0.06|0.14|0.09|0.08|
|NTL Data|DMSP|DMSP|DMSP|DMSP|DMSP|VIIRS|VIIRS|

Notes: The average values are based on average DMSP/ VIIRS values emitted by the core and periphery grid cells defined by the methodology described in section 3.2. See Figure 6 for a map showing the coreperiphery delineations across the country. The growth rates are computed by comparing the value in the final year of a period with the value in the year before the period began. For example, the growth during the 2010-2011 rebound is computed as the difference (with both variables in natural logs) of NTL in 2011 and NTL in 2009.

26
||Theoretical<br>energy demand|Disposable household<br>income before energy<br>expenditure for adequate<br>space heating|BSO|Requires<br>standard on<br>adequate<br>space heating|
|---|---|---|---|---|
||Type of dwelling|Type of dwelling|EU-SILC|Yes|
||Children exposed<br>to leakages.|Share of children (aged 0<br>to 17) living in a dwelling<br>with a leaking roof, damp<br>walls,<br>floors,<br>or<br>foundation, or rot in<br>window frames or floor|EU-SILC|Yes|
||Ability to afford<br>to keep home<br>adequately warm|Share of the population<br>able to afford to keep<br>home adequately warm|Eurobarometer|Yes|
|Consensual|Risk of falling<br>behind with<br>paying utility bills<br>on time|Share of the population at<br>risk of falling behind with<br>paying utility bills on time|Eurobarometer|Yes|
||Dwelling is<br>leaking, damp, or<br>rotting.|Share of the population<br>who<br>report<br>leaking,<br>damp,rotting|Eurobarometer<br>|Yes|
||Energy usage and<br>efficiency of<br>housing and<br>households|Energy-related data for<br>housing units occupied as<br>primary residences and<br>their households|Residential<br>Energy<br>Consumption<br>Survey (United<br>States)|No|
||Energy efficiency<br>of housing|Collects<br>information<br>about people's housing<br>circumstances and the<br>energy<br>efficiency<br>of<br>housing|English<br>Housing<br>Survey<br>(England)|No|
||Several<br>indicators of<br>energy efficiency<br>and affordability|Several|European<br>Quality of Life<br>Survey|Yes|
|Several|Several<br>indicators of<br>energy efficiency<br>and affordability|Several|Generation<br>and Gender<br>Programme<br>Survey|Yes|
||Multidimensional<br>energy poverty<br>indicators|Combination of several<br>indicators|Several (see<br>main text)|Yes|
|Direct|Energy<br>consumption|Approximation of energy<br>consumption, compared<br>to a norm|Direct meters,<br>Satellite data,<br>and other big<br>data (see main<br>text)|Yes|

_Source: Compilation of findings by Gouveia et al. (2022) and additional literature (as shown in this section)._ 10
Table A10: Medium-term impacts of _Mais Educação_ on educational outcomes for schools that chose environmental education field

|Dependent variable|Dropout rates|Test score<br>mathematics|Test score<br>Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
|Panel A: Imp|acts on fifth-grade va|riables||
|Dummy_Mais Educação_* year 2011|2.346|–5.090|–1.626|
||(2.009)|(3.967)|(3.373)|
|_R_ <sup>2</sup>|0.755|0.811|0.823|
|Observations|296|296|296|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
|Panel B: Imp|acts on ninth-grade v|ariables||
|Dummy_Mais Educação_* year 2011|3.569|–5.655|–6.054|
||(2.650)|(4.563)|(4.450)|
|_R_ <sup>2</sup>|0.801|0.793|0.764|
|Observations|236|236|236|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined environmental education field in 2008 and remained in the activity until 2011. This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper, where the dependent variable is in column (1) is Portuguese grades in _Prova Brasil_ , in column (2) mathematics grades in _Prova Brasil_ , and in column (3) dropout rates. All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for schools from the fifth-grade sample. Panel B reports these estimates for schools from the ninth-grade sample.
# 1. Introduction

All members of the World Trade Organization (WTO) signed at least one Preferential Trade Agreement (PTA).<sup>2</sup> The content of these agreements changed over time as they now encompass a number of disciplines that go beyond traditional trade policy (Hofmann, Osnago and Ruta, 2017). Through PTAs, member countries commit to cut their tariffs and undertake additional obligations in policy areas covered by the WTO such as customs administration or contingent protection. But they more and more break new grounds in policy domains that are not regulated by the WTO, such as investment and competition policy. This new generation of “deep” trade agreements is at the core of a number of policy and research debates, as economists try to assess their economic effects and provide guidance on how to efficiently design and implement them.

This paper contributes to this broader debate on trade agreements by empirically investigating the relationship between deep trade agreements and Global Value Chains (GVCs). Using a new data set on the content of PTAs developed by the World Bank, our analysis allows us to i) quantify the impact of deep trade agreements on GVC integration among member countries, ii) disentangle the importance of specific sets of provisions in PTAs, and iii) shed light on the role of deep trade agreements in shaping the pattern of integration across countries with different levels of development. Our key finding is that the depth of trade agreements contributes to increase GVC trade among parties. This impact is higher for industries with higher share of value added in total production, suggesting that deeper trade arrangements help countries to integrate in industries with higher levels of value added. We also find that for trade agreements between developed and developing countries, this effect is mostly driven by the presence of provisions that are currently outside the domain of the WTO and that deal with behind the border policies, such as investment and competition policy. For trade agreements between developing countries, the impact of trade agreements on GVC trade is mostly driven by the reduction of traditional trade barriers such as tariffs and other border measures.

> 2 As is common in the recent trade literature, the term PTAs will be used throughout the paper and is preferred to the term ‘regional trade agreements (RTAs)’ since some of these agreements are not necessarily between countries within the same region or in regional proximity. We will also often refer to PTAs as ‘deep (trade) agreements’, in recognition of the fact that several provisions in PTAs are not preferential in nature (Baldwin and Low, 2007).

2
6 In conclusion, the evidence suggests that women's participation rat. has risen significantly since the 1950s. The rise was due to greater participation rtes of prime-age women. In addiion, the labor force in the countries examined in this paper has become more homogenous with respect to women's participation. This o,servation applies to all three agegroups (young, prime-age and older women). Something that cannot be ruled out whether the increase was the result of market forces or an expanding public sector.

# 3. The Determinants of Female Participation

An obvious precondition fur any policy targeted at female participation is to understand the factors that affect a woman's decision to join or not to join the labor market, especially a comparison of her marginal product at home and the wage labor force. In order to cast more light in this area, we fitted a series of Logit regressions on "a wom in's decision to participate" in the labor force. The dependent variable took the value of zero, if the woman was not working, and unity, if the woman was observed to be in the labor force. The independent variables included a number of what can be labelled as exogenous factors such as age, years of schooling, husband's earnings and other income, location (rural/urban or regional dummies), family size and so on. The exact specification was dictated by the availability of information in the respective country household surveys at our disposition."5 Table 2 summarizes the effects of some key variables on women's decision to participate in the labor market in selected countries.

Education has a significant effect on participation. For exampl-, in Argentina a woman with less than primary education has a ceteris paribus probability **oiL** participation of only 28 percent compared with a probability of 58 percent of a woman who is a university graduate. In Venezuela the probability of participation for the corresponding education groups rises from about 30 percent to more than 85 percent.

Women's family characteristics exercise a strong effect on participation, too. Married women's probability of participating in the labor force is about half the probability for single women. For example, the probability for married women in Chile drops to 14 percent compared to 41 percent for single women. In Costa Rica the corresponding decrease is from 40 percent to 18 perce1it, in Venezuela from 56 percent to 33 percent and in Guatemala from 33 percent to 14 percent.

Not surpisingly, being head of household increases the probability of participation in all countries under consideration. In fact, this demographic aspect seems to be one of the strongest determinants of female labor force participation. For example, in Colombia the probability for a woman who is head of household jumps .o 47 percent (from 21 percent for non-heads of 15 The full results are reported in the country studies contained in Psacharopoulos and Tzannatos (1991).
# **2.3 NTM data**

We use an updated version of a novel comprehensive NTM database for Indonesia<sup>3</sup> built by the World Bank. The data is hand collected through extensive regulatory checks. It has the most disaggregated NTM classification available, i.e., 3-digit MAST level, and the highest frequency available, monthly series. This classification is the most appropriate for policy analysis as it identifies individual measures introduced or modified by each agency. There are over 600 regulations spanning 13 government ministries and agencies. A total of over 60 3-digit NTMs are available in the data set, each with values coded between 0 and 1 to signify when they were in effect on the corresponding products, that is, the data varies at the month-product pair. The starting point of this database is data collection by the Economic Research Institute for ASEAN and East Asia (ERIA) in collaboration with UNCTAD. Then the regulations are traced forward and backward from 2015 and coded accordingly, using various other sources, primarily through government repositories. This data is the most up-to-date data on NTMs and at a high-level of frequency, both of which make the data most relevant for the aim of this paper.

As non-tariff measures usually serve a public policy objective, extensive evaluation needs to be made to identify those that may require reform. We focus on four measures in this analysis that previous research has found to be more restrictive to trade than necessary and requiring elimination or reform (see Cal`ı et al. (2022) and Cal`ı and Montfaucon (2021) based on data up to 2018). These measures are pre-shipment inspections (PSI, MAST code C1), mandatory certification with Indonesian product standards or _Standar Nasional Indonesia_ (SNI, MAST code B7), port of entry restrictions (PoE, MAST code C3), and import approvals (IA, MAST code B14). Additionally, three of these measures have been found to have higher tariff ad valorem equivalents compared to similar measures in other countries in ASEAN (Montfaucon et al., 2022).

> 3Cali et al. (2021), Cal`ı et al. (2022), and Cal`ı and Montfaucon (2021) also use an older version of this data set.

9