rafmacalaba/datause-encoder-data huggingface dataset:
Figure 13 shows that the geographic pattern of the MOLISA poverty rate and our income poverty estimates are rather similar, which indicates that the difference in rankings is not very large.

# _VI.2 Expenditure and income based poverty_

Figure 14 shows that the two different poverty indicators yield similar poverty estimates and a similar ranking at the province level. Differences can be observed for areas with higher levels of poverty, in which case expenditure poverty is found to be higher.

**Figure 14 The expenditure poverty incidence and the income poverty incidence of provinces**

|0<br>20<br>40<br>60<br>80<br>100<br>The expenditure poverty incidence<br>0<br>20<br>40<br>60<br>80<br>100<br>The expenditure poverty incidence||
|---|---|
|0<br>20<br>40<br>60<br>80<br>100<br>The income poverty incidence|0<br>20<br>40<br>60<br>80<br>100<br>The income poverty incidence|
|_Source: Authors’ estimation from VHLS_<br>Table 3 reports the correlation coefficients<br>indicators. It shows that they are strongly correlated.<br>poverty rates are rather high. Interestingly, expen<br>exhibit a stronger relation with the MOLISA pov<br>estimates.<br>**Table 3 Correlation between p**|_S 2006 and ARFC 2006_<br>between the two different poverty<br>Also correlations with the MOLISA<br>diture poverty estimates appear to<br>erty rates than our income poverty<br>**overty estimates**|
|Correlation between the provincial<br>poverty|Correlation between the district poverty<br>(Districts with the percentage of rural<br>population higherthan95%)|
|Expenditure<br>Income<br>MOLISA|Expenditure<br>Income<br>MOLISA|
|povertyrate<br>povertyrate<br>povertyrate|povertyrate<br>povertyrate<br>povertyrate|
|Expenditure<br>poverty rate<br>1|1|
|Income poverty<br>rate<br>0.9575<br>1|0.9615<br>1|
|MOLISA poverty<br>rate<br>0.8693<br>0.8046<br>1|0.8503<br>0.831<br>1|

_Source: Authors’ estimation from VHLSS 2006 and ARFC 2006_ 22
China’s gross exports as commonly perceived. In addition to country size, the distance to consumer markets is another determinant of the participation to GVCs. Thus, New Zealand has one the lowest indexes (below 40%).

**Figure 6. GVC participation index for OECD and selected non-OECD countries, 2008**

<!-- Start of picture text --> 90.0%<br>80.0%<br>70.0%<br>60.0%<br>50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>OECD non OECD<br>Source: OECD, 2012b<br>LuxembourgKoreaSlovak RepublicNorwayHungaryBelgiumCzech RepublicEstoniaSwedenFinlandNetherlandsIrelandAustriaSloveniaPortugalDenmarkPolandSwitzerlandIsraelGermanyAustraliaChileFranceGreeceSpainJapanUnited KingdomItalyTurkeyUnited StatesMexicoCanadaNew ZealandChinese TaipeiSingaporeMalaysiaCambodiaPhilippinesLithuaniaBulgariaMaltaThailandRussiaViet NamLatviaBruneiRomaniaSaudi ArabiaSouth AfricaIndonesiaIndiaChinaBrazilArgentina<br><!-- End of picture text -->

# **(2)** **<u>The length of GVCs</u>**

The participation index does not provide information about the length of the value chain, i.e., the number of stages of production involved. Therefore, additional indicators were developed (Fally, 2011) to measure the actual fragmentation of the production process and identify the domestic and international parts of the GVCs. This measure helps understanding the dynamics of GVCs (e.g., the reduction of the lengths of GVCs after 2008 corresponded to the consolidation of GVCs in times of crisis), as well as sector specificities. The five industries with the highest level of fragmentation are: television and communication equipment, motor vehicles, basic metals, electrical machinery and textiles, leather and footwear; services have on average shorter value chains, with notable exceptions like transport and storage (see Figure 7). They may also give an indication of the scope for countries upgrading within GVCs, assuming that one can argue that longer (more fragmented) value chains provide more opportunities.

11
through wage and rule setting, affects them directly. Furthermore, as labor regulation becomes more modern and supple, it would be important to build the capacity of unions in the private sector to develop and sustain collective bargaining – whereby unions becomes more capable at negotiating with employers at the sector level, according to the sector’s specificities and needs.

# **REFERENCES**

Agénor, P. and K. El Aynaoui, (2003). _Labor Market Policies and Unemployment in Morocco: A Quantitative Analysis_ . World Bank. Washington, DC

- Albrecht, J., L. Navarro, and S. Vroman. (2009). The Effect of Labour Market Policies in an Economy with an Informal Sector," _Economic Journal_ , Vol. 119 (539): 1105:1129

- Angel-Urdinola, Diego F., C. Chartouni, S. Devarajan, and A. Kuddo (2015). _Enhancing Flexibility of the Labor Market for Job Creation in the Arab Region_ . Mimeo. World Bank. Washington DC.

- Angel-Urdinola, Diego F. and Kuddo, Arvo, 2010. "Key characteristics of employment regulation in the Middle East and North Africa," _Social Protection Discussion Papers_ No 55674, The World Bank. Washington, DC

- Benhayoun, G., S. Bazen, Y. Lazzeri, E. Moustier, and P. Guillaumet. (2001). “ _Le salaire minimum au Maroc_ ,” Ministère de l’Emploi, de la Formation Professionnelle, du développement Social et de la Solidarité. Rabat, Maroc.

- Betcherman, G. (2012). _Labor Market Institutions: A Review of the Literature_ . Background Paper for the World Development Report 2013. World Bank. Washington, DC

- Gatti, R., M. Morgandi, R. Grun, S. Brodmann, D.F. Angel-Urdinola; J.M. Moreno; D. Marotta, M. Schiffbauer, E. Mata Lorenzo. (2013). _Jobs for Shared Prosperity: Time for Action in the Middle East and North Africa_ . Editors: Report No. 13284. World Bank. Washington, DC

Margolis, D., L. Navarro, and D. Robalino. (2012). _Unemployment Insurance, Job Search and Informal Employment_ . IZA Discussion Paper Series No. 6660. IZA, Germany.

- Maloney, W. and J. Mendez. (2003). _Measuring the Impact of Minimum Wages. Evidence from Latin America_ , Published in Law and Employment: Lessons from Latin America and the Caribbean, Heckman and Pagés (editors). 2004

- Marouani. M. and D. Robalino. (2008). _Assessing Interactions among Education, Social Insurance, and Labor Market Policies in a General Equilibrium Framework: An Application to Morocco._ Policy Research Working Paper No. 4681. World Bank. Washington D.C.

16
number of imported products (in logarithms), or the number of imported varieties (in logarithms). It is important to note that in this case, the regressions will only account for differences across firms in the extent to which their imports of intermediates are subject to NTMs. In that sense, only firms that import intermediates subject to NTMs are included in the sample whereas firms whose imports of intermediates are not subject to any NTM are excluded. Appendix Table 5 shows averages of NTMs for all sectors and for agribusiness and apparel separately.

The results shown in Table 7 indicate a clear negative correlation between the presence of NTMs on firms’ imported intermediates and total imports as well as numbers of imported products and varieties in all sectors, though the differences in the extent to which imported intermediates are subject to NTMs are less pronounced for agribusiness, hence, the correlations are less significant. The estimates also show that the larger is the actual share of imported intermediates subject to NTMs, the lower are total imports, and numbers of imported products and varieties.

**<u>Table 7. Non-Tariff Measures and Imported Intermediates</u>**

||**Log (Import**<br>**Value)**|**Log (Nb.**<br>**Imported**<br>**Products)**|**Log (Nb.**<br>**Imported**<br>**Varieties)**|**Log (Import**<br>**Value)**|**Log (Nb.**<br>**Imported**<br>**Products)**|**Log (Nb.**<br>**Imported**<br>**Varieties)**|
|---|---|---|---|---|---|---|
|||||**All**|||
|**Dummy for Share of Imports**<br>**under NTMs >40%**|‐1.091***<br>(0.157)|‐0.983***<br>(0.080)|‐1.033***<br>(0.092)||||
|**Share of Imports under NTMs**||||‐1.966***<br>(0.224)|‐1.822***<br>(0.110)|‐1.878***<br>(0.128)|
|**Main HS 2‐Digit Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|1,799|1,799|1,799|1,799|1,799|1,799|
|**R‐squared**|0.184|0.220|0.206|0.201|0.273|0.248|
||||**Agrib**|**usiness**|||
|**Dummy for Share of Imports**|||||||
|**under NTMs >40%**|‐0.277|‐0.159|‐0.169||||
||(0.555)|(0.286)|(0.312)||||
|**Share of Imports under NTMs**||||‐1.401*|‐0.863*|‐0.891*|
|||||(0.824)|(0.445)|(0.480)|
|**Main HS 2‐Digit Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|95|95|95|95|95|95|
|**R‐squared**|0.365|0.309|0.303|0.387|0.333|0.325|
||||**App**|**arel**|||
|**Dummy for Share of Imports**<br>**under NTMs >40%**|‐2.364***|‐1.758***|‐1.935***||||
||(0.736)|(0.315)|(0.354)||||
|**Share of Imports under NTMs**||||‐3.389***<br>(0.925)|‐2.822***<br>(0.400)|‐3.099***<br>(0.445)|
|**Main HS 2‐Digit Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|131|131|131|131|131|131|
|**R‐squared**|0.457|0.344|0.343|0.476|0.435|0.433|

Notes: Robust standard errors in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. Only exporter-importers are included in all the regressions.

20
datasets available to measure CBI. For robustness, we also use the data collected by Garriga (2016), which yields similar results.

To capture inequality dynamics, we rely on data from the World Bank’s PovcalNet database. Besides providing an extensive coverage of countries over a comparably long timespan, a key advantage of this dataset is that includes information on income dynamics at the decile level, which allows us to study income dynamics in a more fine-grained manner. Our headline results rely on the share of total income earned by each decile, ordered from the lowest- (bottom 10%) to the highest-earners (top 10%). Our conjecture is that the latter should benefit from CBI, whereas the former should be hurt. Together, this would indicate an increase in inequality.

In addition, we test the validity of the channels through which CBI operates. We discuss these variables in Section 5. Suffice here to say that we examine a range of variables that capture financial, labor market, and social policy deregulation (outputs). In addition, we also investigate the e↵ect of CBI on a range of financial and other economic indicators (outcomes) that should be a↵ected if our argument is correct.

Our baseline models are parsimonious by design. We are wary of including post-treatment control variables that would bias our results (Angrist and Pischke, 2008; Montgomery, Nyhan, and Torres, 2016). Likewise, we are concerned by estimates that depend on complicated modeling strategy (Leamer, 1983). Therefore, we prefer a simple model that uses within-country variation and eliminates time shocks. In the models below, we also control for GDP and population (both logged) to account for di↵erences in baseline economic wealth and size. In the appendix, we include several models with a fuller set of adjustments for potential confounders (see Section 4.2 for a summary). This allows us to rule out competing interpretations of our findings, such as the role played by government ideology.<sup>9</sup> Our main model takes the following form: where countries are indexed by _i_ , years by _t_ ( _↵_ and _⌧_ representing country and year fixed e↵ects,

> 9Adjusting for the ideology of the government does not a↵ect the estimates in meaningful ways (Table A12).

11
<!-- Start of picture text --> 15<br>£ 10<br>x re<br>o@£ |S Bo teahs ° 8 g g<br>= Re Ot<br>a Np Oe<br>© Bp ss 2 SE '<br>55 5 ON GeR eR:ifee &keAapail aeee eeeeeeTh aeBR haSE© ogop<br>E -10 age Rae Sg<br><<br>-15<br>20 30 40 50 60 70<br>Initial Gini index<br>Historical observations — Fitted line<br><!-- End of picture text -->

<!-- Start of picture text --> 10<br>oO<br>& eee teeter eeee<br>x peoeeeeeeerae ee SD<br>3 Da----7 7<br>£4 Te<br>£ ls TT<br>32G -5 see, =hawewee--~a teen<br>“ose<br>-10<br>0 2 4 6 8 10<br>Years<br>— Median —- 25th and 75th percentile<br>--— 10th and 90th percentile---- Sth and 95th percentile<br><!-- End of picture text -->
# 3. Anatomy of the Drop

## **3.1 Age Group and Female Labor Force Participation (FLFP)**

Looking at the drop in FLFP across age groups during the period 2004-05 to 2011-12, we find that approximately 53 percent of the 19.16 million drop had occurred solely within the younger cohort, among the age group of 15 to 24 years old (Table 6). Among the rest of the population, 32 percent of this drop occurred within the age group of 25-34 years, and about 15.6 percent in the age group of 35 years and above.

**Table 6: Distribution of Drop in FLFP Across Age Group During 2004-05 to 2011-12**

|Age group|Rural|Urban|Total|
|---|---|---|---|
|Between 15 and 24 years|(9,364,994)|(790,695)|(10,155,689)|
|Between 25 and 34 years|(7,362,792)|1,226,678|(6,136,114)|
|Between 35 and 44 years|(2,790,808)|525,240|(2,265,568)|
|Between 45 and 65 years|(1,189,800)|855,174|(334,627)|
|Above 65 years|(220,181)|(47,982)|(268,162)|
|Total|(20,928,575)|1,768,415|(19,160,160)|

Source: Authors’ own estimate based on NSSO and census data.

The FLFP rate across different age groups displays an inverted U shape for all years, as expected, in both rural and urban areas (Figure 1). The FLFP rate peaked in the age group of 35 to 44 years. In terms of change, between 1993-94 and 2004-05, there were only marginal variations in agespecific FLFP rates for both rural and urban areas. However, between 2004-05 to 2011-12, the FLFP rate had declined across all age groups, in both rural and urban areas. This decline was most pronounced in rural areas, and held more or less similar for the age groups of 15 to 24, 25 to 34, and 35 to 44 years of age. Beyond these age groups, the FLFP rate converged with that of 200405. In urban areas too, the trends are similar, though the decline in the FLFP rate between 200405 and 2011-12 across age groups had been far more narrow than in rural areas.

11
**Table 4: Decomposition of Math Scores over Time**

|**(asperce**|**ntage of total test score differential)**<br> <br>|
|---|---|
||Endowments<br>Unexplained<br>(Characteristics)<br>(Returns)|
|Constant|0.0<br>-99.4|
|Institutions|0.2<br>19.2|
|Schools|10.9<br>6.6|
|Family|-8.8<br>37.1|
|Student|5.5<br>128.6|
|Total|7.8<br>92.2|
|Overall|100.0|

The bulk of the overall difference resulting from changes in the returns to characteristics was due to student characteristics. That is, for a given set of student characteristics, Indonesian schools were more able to convert those factors into higher levels of learning in 2006 than in 2003. This is a significant finding since more and more children enter the lower secondary school system with every passing year. As we have shown above, most of the new entrants come from poorer backgrounds and from homes with parents who have received less schooling. According to the 2006 math PISA scores, Indonesia was better able to educate students regardless of their age. Math scores for 2003 and 2006 by mean age are presented in Table 5. In 2003 and 2006, the average-aged student achieved the mean score for the country overall. In 2003, there was not much variation in the ages of students who were one standard deviation above the mean or of those who were one standard deviation below the mean age. With a mean age of 15.71 years and a standard deviation of 0.27 years in 2003, students were between 151⁄2 and almost 16 years of age. In 2006, the mean age was 15.78 years and the standard deviation was 0.29 years, thus the range was 151⁄2 to just over 16 years of age. Scores were higher for all age groups in 2006 but also varied more than in 2003, with 16 year olds averaging 400 points.

**<u>Table 5: Average Math Scores by Age</u>**

||Below (mean – 1 sd)|Between(mean - 1 sd)and(mean + 1 sd)|Above(mean + 1 sd)|
|---|---|---|---|
|2003|357|360|363|
|2006|383|390|400|

_Source_ : PISA 2003 and 2006 As Annex Table 2 shows, two other characteristics are important in explaining the differences in test scores between 2003 and 2006. An adequate supply of teachers, both in terms of endowments and coefficients, played a positive effect in increasing test scores between 2003 and 2006. Also, in terms of the unexplained part of the decomposition, the coefficient associated with being a female changed from -7.57 in 2003 to -18.6 in 2006.

14
**LLECE.** The network of national education systems in Latin American and Caribbean countries, known as the Latin American Laboratory for Assessment of the Quality of Education (LLECE), was formed in 1994 and is coordinated by the UNESCO Regional Bureau for Education in Latin America and the Caribbean.

Assessments conducted by the LLECE focus on achievement in reading and mathematics. The first round was conducted in 1998 across grades 3 and 4 in 13 countries (Casassus et al., 1998, 2002). These countries include: Argentina, Bolivia, Brazil, Chile, Columbia, Costa Rica, Cuba, Dominican Republic, Honduras, Mexico, Paraguay, Peru and Venezuela (Casassus et al., 1998). The second round of the LLECE survey was initiated in 2006 in the same countries as LLECE I. In round two, called the Second Regional Comparative and Explanatory Study (SERCE), pupils were tested in grade 3 and grade 6. The Third Regional Comparative and Explanatory Study (TERCE), was done in 2013 across grades 3 and 6 and included 15 Latin American and Caribbean countries. Our analysis will include both SERCE and TERCE results, since these assessments are most similar and cover comparable grades. Table 1 summarizes availability and details of the various international and regional assessments listed above.

We link the above assessments and obtain two datasets<sup>10</sup> . The cross-section dataset provides measures of education quality which are aggregated at the education level. We obtain at least one measure of education quality for approximately 163 countries/areas. This covers 82.5 percent of all countries with a population greater than one million, and 90.9 percent of the global population. Out of the 163 countries/areas included, more than 100 are developing economies. Of these, 131 are unique countries. We also obtain a panel database which provides over time comparable scores for education quality between 1965 and 2015. On average, our dataset includes 3.3 observations per country at the primary level and 4.5 observations at the secondary level. Developed countries are over-represented at the secondary level. However, at the primary level, the two groups have similar coverage. Figures 12.0-12.6 present HLO data availability by 5-year interval and coverage 10
<!-- Start of picture text --> 2.5 USD/PPP 2005<br>100<br>£ c 80 ——<br>= 60<br>a<br>a<br>°<br>=<br>° 40<br>&<br>20<br>0<br>consumption income<br>@ 2003 @ 2007 @ 2009<br><!-- End of picture text --> <!-- Start of picture text --> w 100<br>a B80<br>= =60<br>Zon<br>wig wm510<br>=~ 4<br>¢ Ba<br>7003 7007 7009<br>B primaryand less § basic education<br>lB general secondary § specialized secondary<br>SM tertiary<br><!-- End of picture text --> <!-- Start of picture text --> 2 100<br># 80<br>= =60<br>=n<br>uiB50om<br>=a<br>g 230<br>2005 2007 2009<br>@ primaryand less § basic education<br>@ general secondary B§ specialized secondary<br>M@ tertiary<br><!-- End of picture text -->
in this paper (the bold row of Table 1). This shows that choosing an optimal threshold to combine the two datasets and explicitly considering the underreporting of incomes in the household survey when computing the Gini coefficients for conditional distributions can have dramatic on the measured<sup>11</sup>

# **4 Conclusion**

This paper proposes a new methodology to optimally measure inequality by combining household survey and tax records data. The motivation stems from the fact that household surveys poorly represent the top of the income distribution, while tax records cover the top but not the bottom of the distribution. The key innovation of the proposed methodology is the choice of an income threshold _b_ used to combine the two data sources; the paper shows that _b_ should not be chosen arbitrarily, but should be chosen optimally to minimize the distortions from household survey underreporting at the top of the income distribution. In particular, we discuss why the optimal income threshold _b_ corresponds to the minimum income level required to file a tax return (i.e., the lowest income captured in the tax records).

After presenting the methodology, we apply it to the case of Colombia. We find that the Gini coefficient for Colombia in 2010 is 0.5978 when computed using an optimal income threshold of 81 million pesos, which is significantly different from the one that would be obtained if using an (arbitrary) threshold corresponding to the top 1 percent of the income distribution (0.5960). The difference of 0.2 points is not trivial, particularly when we consider that over the last 10 years the average annual reduction of the Gini coefficient in Latin America was 0.51 percentage points. The methodology presented in this paper builds on and improves upon the methodologies proposed by Atkinson (2007) and Alvaredo (2011).

> 11When applying the methodology presented in Alvaredo (2011) and Alvaredo and Londoño (2013), instead of estimating the Gini coefficient for the bottom 99 percent of the population using the bottom 99 percent of the household survey’s distribution, an alternative could be to simply use the Gini coefficient of the full distribution of the household survey as an estimator of the Gini coefficient for the bottom 99 percent of the true income distribution. This would be correct under the assumption that the household survey underreports incomes exactly above the 99th percentile. The Gini coefficient computed under this assumption (which generally will not be true) would be 0.6468.

16
<!-- Start of picture text --> (Kireka_Namugongo) ri \e * a<br>° _—page poiont ii ; }I||, Nasa :, Natonko WePF a<br>y Static Location as ans Buwate if at a<br>Fri_Apr_19_2019_all_records ; Kira! s, a © pamoyt<br>|, - Elawate a Jianda ‘. Cave eo<br>ieee {| ts ae e | : ie<br>B Komamboga . | “ i sin pas<br>= 150 -— 250 ‘ 5 * ‘ il : ig! hyirize a Bajo<br>@ >55- 150 "4 Yeze vefre st? iF Kyallsyemtata | I WL vy<br>a > 35-55 \ Kulambire i nee Ne a ss<br>aes ye i iy ~~ oe, ah, ; a _g@iSeeta<br>B o-12 ‘& ne “ , | a yt em a.<br>Bukoto be ‘s kyamecod q - a eo =< mm ( y 4<br>_ gore, Co ae ee fa<br>i Se ae \ op ee ere ag ase<br>Peo Naguru oF i my 5 600) ell.<br>ia. | 5 - Yaa i oh Ee , Ben<br>ee ; ie a Pu te Z cued Eo “S. Namilyang<br>aay © al ae So ar ie ee . |] P\<br>iS, iy, PS a ot<br>2km<br>a<br><!-- End of picture text -->

<!-- Start of picture text --> .<br>: ¢<br>:<br>wu me =<br>s * “ ;<br>Je Vee A<br>: et 5 oe Py<br>halTo. me ao ie<br>| E aS \<br>2"ea<br><!-- End of picture text -->

<!-- Start of picture text --> Kireka Kampala Latest<br>oe<br>Very unhealthy<br>Last updated on 7-Jul-2019 at 12:23 hrs EAT<br>Past 24 hrs Past1 hr Latest<br>| roll ad<br>400<br>= 300<br>a<br>2 200<br>nt"PISOQMMDADLAONG PPAAo bE PHnit a LEib kis OMDN D r<br><!-- End of picture text -->