rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

With various degrees of emphasis and depth, most welfare economists of our time have recognized the importance of considering poverty as a state of deprivation which is multidimensional in nature (Sen, 1976, Kolm, 1977, Atkinson and Bourguignon 1982, Maasoumi, 1986, Duclos et al. 2001). Some important critiques to this approach have also emerged suggesting that several indexes of poverty measured on different dimensions may be more effective than a single multidimensional index (Ravallion, 2011). However, the debate around the measurement of multidimensional poverty is not so much about the `if‟ but about the `how‟. The Alkire and Foster (2011) index of multidimensional poverty, for example, has recently generated great attention and controversy witnessing both the importance and the complexity of the topic.

The complexity of measurement of multidimensional poverty arises principally from two main issues. The first issue relates to the direction of aggregation in the construction of the index, whether “horizontally” where dimensions are aggregated across individuals first or “vertically” where individuals are aggregated across dimensions first. These two approaches imply different methodologies and can often lead to different results. The second problem is the weighting of the different dimensions. Some scholars prefer to attribute equal and unitary weights to all dimensions while other scholars use various methods to identify dimension specific weights. The weight choice evidently implies a combination of normative and positive criteria, which makes it difficult to argue that one choice is better than another (partly explaining the controversy around multidimensional indexes).

In this paper we follow the “horizontal” approach by constructing first a composite indicator of welfare and then use this indicator to rank households and measure poverty. In doing so, we follow one of the least beaten tracks by making use of Multiple Correspondence Analysis for the construction of the composite indicator. This technique has been recently rediscovered for the study of multidimensional poverty (Asselin, 2009, Njong, and Dschang, 2008) as it provides a method for assigning weights to different dimensions while it responds to some essential welfare axioms.

Our application is on Morocco using 2001 and 2007 consumption data. During the last decade, Morocco has experienced consistent growth generated by economic reforms and investments in infrastructures. These developments have resulted in improved living conditions as shown by official statistics and reports (Douidich and Ezzrari, 2009). However, it is typically heard from local and foreign commentators that these gains have been unequally shared and/or that living conditions have been improved in certain areas but not in others. Thus, a multidimensional approach to the study of poverty can provide a better sense of whether the gains in Morocco have been robust and comprehensive.

2
# **Estimating Poverty for Refugee Populations: Can Cross-Survey Imputation Methods Substitute for Data Scarcity?**

Hai-Anh H. Dang and Paolo Verme<sup>*</sup>

# **JEL:** C15, I32, O15

**Keywords** : poverty imputation, Syrian refugees, household survey, missing data, Jordan

* Dang (hdang@worldbank.org; corresponding author) is an economist in the Analytics and Tools Unit, Development Data Group, World Bank and is also affiliated with Indiana University, IZA, and Vietnam’s Academy of Social Sciences. Paolo Verme (pverme@worldbank.org) is a lead economist and manager of the Research Program on Forced Displacement at the World Bank. We would like to thank Theresa Beltramo, Jose Cuesta, Talip Kilic, Peter Lanjouw, Christoph Lakner, David Newhouse, Franco Peracchi, Shinya Takamatsu, Matthew Wai-Poi, Tara Wishvanath, and participants at the IARIW-WB conference on poverty measurement and the WB-UNHCR training course on poverty imputation for helpful comments and discussions on earlier versions. This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership''. The program is funded by UK aid from the United Kingdom's Department for International Development (DFID), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. We further thank DFID for additional funding support through its Knowledge for Change (KCP) program. This work does not necessarily reflect the views of DFID, the WBG or UNHCR.
# **Table 9: Meta-Analysis of Results in Table 8**

|(1)|(2)|(3)|
|---|---|---|
|No. ofobservations<br>0.000|-0.000|-0.000|
|(0.006)|(0.000)*|(0.001)|
|DummyAustria<br>-7.165|||
|(3.105)**<br> <br>|||
|DummyBelgium<br>7.470|||
|(0.595)***|||
|Dummy Chile<br>-5.549|||
|(7.598)|||
|Dummy China<br>-19.002|||
|(152.837)|||
|Dummy Colombia<br>-9.104|||
|(13.141)|||
|DummyDenmark|||
|DummyEast Germany<br>-8.512|||
|(11.361)|||
|DummyFrance<br>-8.070|||
|(8.639)|||
|DummyIreland<br>-8.182|||
|(1.008)***|||
|DummyItaly<br>-2.515|||
|(6.470)|||
|Dummy Spain<br>-3.356|||
|(0.609)***|||
|Dummy Sweden<br>-5.924|||
|(20.730)|||
|Dummy UK<br>-2.818|||
|(2.260)|||
|Dummy West Germany<br>-8.819|||
|(39.565)|||
|Share export starters|0.322|0.361|
||(0.384)|(0.763)|
|Dummy establishmentleveldata|1.217|0.927|
||(2.720)|(2.863)|
|Average year|0.351|0.612|
||(0.213)|(0.665)|
|GDP||-0.000|
|||(0.000)|
|GDPpercapita||-0.000|
|||(0.000)|
|Openness||0.015|
|||(0.078)|
|Ease ofdoing business||-0.761|
|||(21.434)|
|Government effectiveness||1.504|
|||(10.343)|
|Regulatory quality||-3.493|
|||(7.312)|
|Observations<br>57|57|57|
|R-squared<br>0.24|0.07|0.10|
|Robust standard errors in parentheses<br>* significant at 10%; ** significant at 5%; *** signific<br>Regression includes constant term|ant at 1%||

53
# **1 Introduction**

Most governments imposed measures restricting the mobility of people and the operation of certain sectors, referred to as lockdowns, to avert the spread of Coronavirus (COVID-19) and mitigate its impact from early 2020. These measures have upended economic order worldwide: the global economy shrank by 3.2% in 2020 while the gross domestic product (GDP) of advanced economies contracted by 4.6% in 2020 (IMF, 2021). International trade was also affected. The volume and value of merchandise trade in the world declined by 5.3% and 7.6%, respectively, in 2020 (WTO, 2021). Empirical evidence on the effect of the pandemic on international trade is still nascent, mainly covering developed economies and primarily descriptive.

This study builds on this debate by investigating the impact of lockdowns on the international trade of the Philippines. We conduct an event-study analysis on monthly 10-digit Harmonized System (HS) level bilateral import and export trade ranging from January 2019 to December 2020. The lockdown indicator is a dummy variable signaling the month when the Philippines and its trading partners imposed one of these policies: workplace closure, closure of public transport, stay at home requirements, restrictions on internal movement, and international travel controls.

The Philippines makes for an important case in this regard. The country is among the top traders in East Asia (Thorbecke and Pai, 2015) and it was one of the most affected nations by the virus in the region. It accounted for 31% of total East Asian COVID-19 cases by the end of December 2020, making it the second most infected country in the region after Indonesia. The first case of COVID-19 was discovered on January 30, 2020.<sup>1</sup> The number of infected people was 3 by the end of February 2020 but it spiraled from mid-March where the cumulative number of infected people rose from 111 on March 14, 2020, to 2,084 by March 31, 2020. The Philippines had about 8,500 COVID-19 cases by the end of April and more than 37,500 infections by the end of June 2020. The number of infections was

> 1See Figure A1.

1
-20-

Table 2--Nutritional status by age group (Na1,078)

|Age Group|Percent<br>below<br>80% weight-<br>for-height<br>-|Percent<br>below<br>90% height-<br> <br>-for-age|
|---|---|---|
|Below 6 months|4.8|39.2|
|7-11 months|7.0|43.0|
|12-24months|8.8|53.6|
|25-36 months|6.7|55.2|
|37-48 months|2.3|56.9|
|49-60 months|3.2|49.7|

Source: IFPR! Rural Survey of Pakistan (Round 3), 1986/87.
Policy Research Working Paper 10134

# **Abstract**

Measuring job quality across countries has been challenging and has relied typically on a single indicator, such as formality or wages. To contribute to this critical policy issue, this paper presents a first global estimate of job quality departing from microdata. It assembles a harmonized global data set of labor force and household surveys to produce a measure of job quality across four dimensions: sufficient income, access to employment benefits, job stability, and adequate working conditions. The results for 40 developing countries show significant variation in job quality across countries, economic sectors, and sociodemographic characteristics, including age, location, and educational attainment. Countries in the Latin America and the Caribbean region have relatively higher levels of job quality, while countries in Sub-Saharan Africa display the lowest levels of job quality. Most workers in the sectors of finance and business services, public administration, and utilities have, on average, better jobs. Higher education matters in securing greater job quality, while the average job quality of wage employment is relatively similar between men and women but with some variation in income and working conditions.

This paper is a product of the Poverty and Equity Global Practice and the International Finance Corporation. 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 shovhannisyan@ifc.org, vmontalvatalledo@worldbank.org, crodriguezc@worldbank.org, and kstamm@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
Policy Research Working Paper 9827

# **Abstract**

This paper conducts a large, narrative literature review of interventions that seek to (1) increase educational attainment, (2) delay childbearing, and/or (3) delay marriage for adolescent girls in developing countries. Using 104 interventions from 70 studies, predominantly in developing countries, the paper summarizes the performance of 16 categories of interventions in improving each of the three outcomes of interest. It then provides high-level policy strategies to improve each outcome, informed by this review. Finally, the paper discusses several promising future research avenues to help close knowledge gaps and, thus, improve policy guidance for enhancing the well-being of adolescent girls in developing settings.

This paper is a product of the Development Research Group, Development Economics and the Health, Nutrition and Population Global Practice. 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 kbergstrom@worldbank.org or bozler@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
of ordinary exports during the sample period is around 0.9, substantially higher than that of processing exports but consistent with similar ...ndings by KWW12. Moreover, the DVAR of ordinary exports has declined slightly between 2000 and 2007, from 0.92 to 0.90. However, given the small decline compared to the much larger increase in the DVAR of processing exports, coupled with the fact that the share of processing exports in China’s total exports has been stabilized at around 55% throughout the sample period, the DVAR of Chinese aggregate exports increased from 0.65 to 0.70 between 2000 and 2007 (see Figure 5, Table 2 and Figure A7 in the appendix). In short, China’s DVAR has increased signi...cantly in recent years, almost entirely driven by the rise in the DVAR in the processing export sector.

# 4 Time-series Trend of Firm DVAR

In this section, we provide reduced-form evidence of the time-series changes in ...rms’DVAR and other related variables. A formal analysis of the determinants of China’s rising DVAR will be presented in the next section. Given the ...nding in the previous section that the entire increase in the DVAR is caused by processing exports instead of ordinary exports, we will focus on providing ...rm-level evidence based on processing exporters only from this section and on. We start o¤ by estimating the following speci...cation at the ...rm level: where i stands for ...rm, t represents year, and �it is the regression residual. The ...rm and year ...xed e¤ects are �i and �t respectively, with the year e¤ect for 2000 dropped to avoid the dummy variable trap. Thus, positive and rising �<sup>0</sup> t<sup>s(i.e.0 < �</sup> t<sup>< �</sup> t+1<sup>;8t > 2000)will</sup> imply a within-...rm increase in the DVAR over time.

Control variables in Xit include a ...rm’s material-to-sales ratio, <u>PPY</u><sup>M</sup> <u>M</u><sup>itslabor</sup> � �it<sup>and</sup> 19
**The global crisis has already produced considerable spillover effects to bank credit conditions in the rest of the world** (Exhibits V.1 and V.2). Emerging markets began to experience significant tightening of bank credit conditions in 2010 reaching a trough in 2011Q4, particularly in emerging Europe. At that time, all emerging markets across the world indicated their credit standards had been severely affected by European financial turmoil (Exhibit V.3). Especially international funding conditions for emerging markets deteriorated excessively (Exhibit V.4). As discussed above, the ECB LTROs provided welcome relief and overall bank credit conditions, credit standards, and local and international funding conditions continued to tighten in 2012, but to a lesser degree. It also appears the ECB statement to “preserve the Euro whatever it takes” and the launch of the OMT program slowed the tightening pace in funding conditions for the first time since 2012Q2 and has recently slightly loosened. On the demand side, conditions have continued to be expansionary, but at an increasingly slower pace since 2010. Demand was stagnant during end-2011 and has been increasing slightly since 2012Q2 (Exhibit V.3).

<!-- Start of picture text --> V.1 As the Euro Area crisis deepened, emerging V.2 The intensification of the Euro Crisis in<br>market credit conditions deteriorated significantly. 2011Q4 was strongly felt in all emerging markets<br>Conversely, they improved after ECB intervention across the world<br>Emerging Market Overall Credit Conditions 2011Q4 Impact of Euro Crisis on Credit Standards<br>Diffusion index, below 50 indicates tightening relative to Diffusion index, below 50 indicates tightening relative<br>previous quarter to previous quarter<br>70 60<br>65<br>60 50<br>55<br>40<br>50<br>45<br>30<br>40<br>35 20<br>10<br>GLOBAL AFME LATAM<br>0<br>EUROPE ASIA GLOBAL AFME LATAM EUROPE ASIA<br>Source: IIF Lending Survey 2013Q1 Source: IIF Lending Survey 2011Q4<br><!-- End of picture text --> 21
14 U.S. husbands and wives to housework provided more support for **the** bargaining approach (Carlin,.1988). But data from Thailand strongly confirm its usefulness (Schultz, 1988c).

In the 1981 Socioeconomic Survey of Ttailand, participation by women aged 25 to 54 in the labor force is reduced by three **times as** much for a given flow of nonearned incom (from rentals, interest or dividends) if that nonearned income **is** owned by the woman as compared to the effect of that income if it is owned by her husband. Conversely, men agegd 25 to 54 reduce their participation by three times the amount when **the** family's nonearned income is owned by the man rather than his wife (See Appenlix Tables A-3 **and** A-4). In Thailand women are frequently in the labor force, and while marriage has been nearly universal, they often divorce and remarry. It **might** be more difficult in some other societies to collect meaningful data on the ownership of nonearned income for each individual in a family. For example, in a survey of rural Northeast Brazil, few women report nonearned income, though the proportion increases in urban areas.

At a more anecdotal level, there is evidence that as women's education and marketable skills improve, women are treated better and consume more of their family's resources. But there reomin relatively few studies of household surveys from which to draw such generalizations. The neoclassical family demand model emphasizes that the human capital embodied in women affects their value of time and influences the allocation of time and investments within the family (Mincer. 1963; Becker, 1965). Consequently, evidence that time allocations, consumption, and investment patterns within the family respond to differences in male, female and child wages does not discriminate between the family demnd and bargaining models. But **the** cooperative Nash-
structuralist models of development, rather it assumes that aggregate employment is determined by factors such as labor market norms and regulation that are largely independent of trade policy in the long run. Absent sufficient detail on these employment drivers, we look to wage changes to clear farm and non-farm the labor markets in each country.<sup>5</sup> _Rich Country Farm Household Impacts:_ The potential for adverse impacts on rich country farm household incomes has received far less attention than the distributional impacts in poor countries, yet it represents a key component of the political economy of WTO trade reform. A primary factor in determining the impact of agricultural reforms on farm household welfare in rich countries is the share of their income that currently comes from the farm sector. If farm income is only 10% of total household income, then a 10% drop in farm income translates into just a 1% drop in overall household income (for constant non-farm income). Recent OECD (2003) statistics report the on and off-farm income split for farm households in numerous member countries – see Annex Table A.6.1. Farm income provides only 8% of the total income of US farm households and 10% and 12% in Canada and Japan respectively. In Europe the share is larger, in 60%-70% range.

In the global CGE model, we model a representative farm household for each region and explicitly track the allocation of its labor and capital between the farm and non-farm sectors and the allocation of its land across agricultural uses. As returns in agriculture fall when subsidies are removed, farm households reallocate some farm-

> 5 This market clearing assumption means that our model does not generate the large changes in competitiveness that Polaski finds when real wages become misaligned.

11
Table 4: Decomposition of return from RNFE

||Rural+<br>|Rural <br>|only|
|---|---|---|---|
||Gross<br>Net|Gross|Net|
|Ethiopia||||
|Prediction for Female|6.69<sup>_∗∗∗_</sup><br>6.46<sup>_∗∗∗_</sup><br><br>|6.44<sup>_∗∗∗_</sup><br>|6.26<sup>_∗∗∗_</sup><br>|
||(0.04)<br>(0.04)|(0.05)|(0.05)|
|Prediction for Male|7.04<sup>_∗∗∗_</sup><br>6.92<sup>_∗∗∗_</sup><br><br>|6.78<sup>_∗∗∗_</sup><br>|6.77<sup>_∗∗∗_</sup><br>|
||(0.07)<br>(0.07)|(0.11)|(0.09)|
|Dierence|-0.34<sup>_∗∗∗_</sup><br>-0.46<sup>_∗∗∗_</sup><br><br>|-0.35<sup>_∗∗_</sup><br>|-0.51<sup>_∗∗∗_</sup><br>|
||(0.09)<br>(0.08)|(0.12)|(0.10)|
|Explained|-0.05<br>0.00<br><br>|-0.01<br>|0.03<br>|
||(0.05)<br>(0.04)|(0.05)|(0.05)|
|Unexplained|-0.29<sup>_∗∗∗_</sup><br>-0.46<sup>_∗∗∗_</sup><br><br>|-0.33<sup>_∗∗_</sup><br>|-0.55<sup>_∗∗∗_</sup><br>|
||(0.08)<br>(0.08)|(0.11)|(0.10)|
|_N_|3,296<br>3,277|2,723|2,708|
|Uganda||||
|Prediction for Female|11.92<sup>_∗∗∗_</sup><br>11.79<sup>_∗∗∗_</sup><br><br>|11.64<sup>_∗∗∗_</sup><br>|11.53<sup>_∗∗∗_</sup><br>|
||(0.05)<br>(0.05)|(0.05)|(0.05)|
|Prediction for Male|12.85<sup>_∗∗∗_</sup><br>12.63<sup>_∗∗∗_</sup><br><br>|12.69<sup>_∗∗∗_</sup><br>|12.45<sup>_∗∗∗_</sup><br>|
||(0.11)<br>(0.09)|(0.11)|(0.10)|
|Dierence|-0.93<sup>_∗∗∗_</sup><br>-0.83<sup>_∗∗∗_</sup><br><br>|-1.06<sup>_∗∗∗_</sup><br>|-0.92<sup>_∗∗∗_</sup><br>|
||(0.12)<br>(0.10)|(0.12)|(0.12)|
|Explained|-0.34<sup>_∗∗∗_</sup><br>-0.30<sup>_∗∗∗_</sup>|-0.35<sup>_∗∗∗_</sup>|-0.31<sup>_∗∗∗_</sup>|
||(0.04)<br>(0.04)|(0.04)|(0.04)|
|Unexplained|-0.59<sup>_∗∗∗_</sup><br>-0.53<sup>_∗∗∗_</sup><br><br>|-0.71<sup>_∗∗∗_</sup><br>|-0.61<sup>_∗∗∗_</sup><br>|
||(0.12)<br>(0.10)|(0.12)|(0.11)|
|_N_|3,832<br>3,774|3,086|3,048|

Note: Cluster robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. The standard errors are clustered at zone level in Ethiopia and region-urban/rural level in Uganda. Rural<sup>+</sup> includes small towns. Return is in per capita terms, log(ETB) or log(UShs). To convert the logarithmic dierence ( _β_ ) into percentage dierence, we use ( _e_<sup>_β_</sup> _−_ 1) _∗_ 100 as an approximation. In Ethiopia, the gross return gaps in rural<sup>+</sup> areas and rural areas are 29 and 30 percent, respectively. In Uganda, the corresponding return gaps are 61 and 65 percent. In terms of net-return, the gender gaps in rural<sup>+</sup> areas (& rural areas) in Ethiopia and Uganda are 37 (& 40) and 56 (& 60) percent, respectively.

Heterogeneity by marital status : There are substantial heterogeneities in return gap between female- and male-headed households with dierent marital status, especially in Uganda. Widow household heads in Uganda earn substantially less than widower heads. Similarly, divorced and married female-heads earn less than male-heads with the corresponding marital status. Whereas, households with never married female heads 26