rafmacalaba/datause-encoder-data huggingface dataset:
The first vector is obtained by a smoothing process through which each outcome _x ∈ x_<sup>_j_</sup> , is substituted by the arithmetic mean of the outcome in vector _x_<sup>_j_</sup> , denoted by _<u>x</u>_<sup>_j_</sup> = ∑ _NNjjxi_<sup>_j_.The seconddistribution is</sup> obtained by a standardization process which consists of rescaling the individuals’ outcome in each type as follows: _x_ ̃ _i_<sup>_j_=</sup> _<u>xx</u>_<sup>_jx_</sup> _i_<sup>_j_, where</sup> _<u>x</u>_ =<sup><u>∑</u></sup><sup>_<u>N</u>_</sup> _N_<sup>_xi_.Through this process</sup><sup>_XB_eliminates the variation within each type.</sup> Analogously, _XW_ removes the variation between each type, so that we are left only with those inequalities caused by unobservable circumstances.

Now let _I_ : _X →_ R+ be an index of IOp. If _I_ ( _X_ ) is subgroup decomposable (Shorrocks, 1980), then where _I_ ( _XB_ ) accounts for between-type inequality and _I_ ( _XW_ ) accounts for within-type inequality. Provided that the Mean Log Deviation (MLD) is additively subgroup decomposable (Foster and Shneyerov, 2000), I use the MLD (without loss of generality) for my empirical exercises.<sup>1</sup> Note that since in our approach the degree of effort exerted does not play any role in the measurement of IOp, by grouping into types using _C_<sup>_o_</sup> _⊂ C_ , the partition of the population we obtain is coarser than the one we would obtain from using _C_ . This would cause an underestimation of the level of inequality. This is easy to see since if _I_ ( _XW_ ) _>_ 0, then _I_ ( _XB_ ) _< I_ ( _X_ ). Thus, _I_ ( _XB_ ) represents a _lower bound_ estimate of IOp –as it is traditionally defined in the literature.

# **3 An empirical exercise using Demographic Household Surveys**

The data I use come from 27 Demographic Household Surveys (DHS) circa 2008. I focus on kids 0 to 2 years of age because we can confidently assume that children 24 months old or younger have not exerted efforts. Indeed, there is scientific evidence that toddlers start relating their own actions to their surrounding environment after 24 months of age (Rochat, 1998); that is, they become self-aware of their 1 3
years. All measures of training for both genders increased from 1993 to 1999. In terms of the location of firm - North, Center, South, and Mexico City - all firms increased their training between the two years with the exception of southern firms. With respect to the size of the firm, training is positively correlated with firm size in both years. Furthermore, training increased from 1993 to 1999 in all firm sizes except in micro-sized firms.

Table 4 presents the average logarithms of both productivity and wages of the workers. All of the results have been categorized by various types of training - training, no training, in-house training, and external training. By using the average wage of firms in the sample in 1993 and 1999 - 6.89 and 7.81, respectively - we can then compare the effects of training on wages. In all cases, having training, having in-house training, and having external training all appear to be related to higher wages in both 1993 and 1999. Not surprisingly, the reverse was also true in that having no training at all, having no inhouse training, and having no external training, all appear to be related with wages below the average. Concerning productivity, those firms that provided their workers with training were more productive in both 1993 and 1999. Not surprisingly, those firms that did not provide any training were not as productive as the national average.

**Table 4: Mean Value of wages and productivity**

|**Variable**|**Wages**<br>**19931999**<br>**Variable**|**Produ**<br>**1993**|**ctivity**<br>1999|
|---|---|---|---|
|Meanlogwage|6.897.81Mean logproductivity|3.52|4.54|
|Mean log wagenotraining|6.697.63Meanlogproductivityno training|3.37|4.22|
|Mean logwagetraining|7.208.05Meanlogproductivitytraining|375|4.95|
|Meanlogwagenoin-house trauiing|6.88 7.80Mean logproductivityno in-housetraining|3.44|4.41|
|Mean log wage in-house training|6.927.85Meanlogproductivity in-housetraining|3.83|4.98|
|Meanlogwagenoexternaltraining|6.757.70Mean logproductivitynoextemaltraining|3.48|4.44|
|Meanlogwageexternaltraiming|7.498.28Meanlogproductivityextemaltraining|3.67|4.92|
|_Source._Authors'calculationsbasedon ENT|RAM-ENESTYC|||

8
the SHGs; women members simply kept quiet. But in subsequent meetings, the women sang the invocation song and gave an opening speech. They also put questions to KSIRD representatives about loan schemes and other facilities and benefits available to them from the government. They also questioned KSIRD representatives about what they would gain by forming the Stree-Shakti Sanghs (women’s empowerment societies) and the Federation. Furthermore, they have begun to go alone to the Panchayat office and pay their property taxes: a radical change from 2007 where they did not even leave their homes. We should note that NGOs engaged in similar efforts to form self-help groups were present in control villages as well, but those efforts were not effective as those in Jhakuri and Utsavahalli. They tended to focus more on form (creating SHGs) rather than on function (ensuring that those SHGs were effective).

What this shows is that, at its best, the KSIRD intervention was able to tailor the work of the RPs to reflect the needs and social conditions prevalent in the assigned village. Over time, effective RPs were able to channel the civic capacity of poorer citizens in the village towards more effective ends and thus make the accountability process less noisy and “rude.” They were also able to build social institutions that had the potential to carry on citizen initiatives after the RPs had left. Also, as we showed earlier, RPs raised expectations about participatory processes in the community that led to frustration with subsequent visits from NGOs who were less effective trainers and counterparts than KSIRD. Thus, the impact of the intervention sometimes did not manifest itself till after the RPs had ended their association with the GP.

# <u>Conclusion and Summary</u>

This paper shows that even a “failed” intervention can be revealing if enough effort is put into understanding the mechanisms underlying the failure. This requires a combination of quantitative work that is able to rigorously test whether the intervention had an impact, and in-depth qualitative work that carefully tracks the processes of change instituted by the intervention over time. The qualitative analysis is able to unpack mechanisms of change and identify institutional shifts that are hard to capture in quantitative data. Surveys have the advantage of being able to measure predictable outcomes that have impacts that are large enough to be captured by the available units of observation, but major events sometimes occur that are best investigated on the ground, during the moment they happen, by participant observers. Important shifts occur during points of conflict, or during periods of mobilization and coordination. These are not quotidian events and are very hard to predictably measure. Moreover, qualitative investigators are able to build strong relationships within communities that allow them to “see” differently and thus capture insights that a survey interviewer is unable to do. Qualitative and quantitative 24
Policy Research Working Paper 6749

# **Abstract**

This paper investigates whether the Ethiopian economy is undergoing a virtuous process of structural change. In particular, it assesses the relative contributions of withinsector and between-sector productivity to output per capita growth. Based on data disaggregated into eight sectors for the period 1996–2011, the analysis suggests that the structure of output has changed considerably— predominantly from agriculture to services—but changes in the composition of employment have lagged behind. Labor productivity growth has been strong across most sectors, albeit mainly driven by within-sector productivity improvements. Nonetheless, the pace of structural change is accelerating and its relative contribution to output growth is increasing.

This paper is a product of the Poverty Reduction and Economic Management Department, Africa Region. 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://econ.worldbank.org. The author may be contacted at p.martins@odi.org.uk.

_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
last dissolution ended. If we assume that all women with more than one rupture and whose last breakup occurred because of a divorce had previously been widowed, this would give us an upper bound of 21.5% of ever-married women who have experienced widowhood at least once. Conversely, if we assume that the previous dissolution of those identified as having been widowed was a divorce, the estimated upper bound to the proportion of women having suffered a divorce would be 17.3%.

By comparison, the 2010 DHS for Senegal identifies 9.2% of all women aged 15 and older as widows (9.0% in urban and 9.3% in rural), and 1.1% for those in the 15-49 age group. However, because of Senegal’s high remarriage rates, these DHS statistics vastly underestimate the incidence of widowhood within a typical woman’s lifespan. Looking instead at the 2005 DHS which collected more detailed information on marital history (albeit only for the 15-49 age group), 3.6% and 4.6% of women are ever-widowed in urban and rural areas respectively, reflecting rates of 2.1% and 3.8% remarried widows among women aged 15-49. There are far more ever-divorced women. The 2005 DHS identifies 13.1% of all women aged 15-49 as married but previously divorced, while it finds that 5.4% are currently divorced.<sup>12</sup> These numbers are in line with the PSF1 estimates, although PSF1 counts relatively more widows (6.7% of widows or remarried widows in the 15-49 age group) and fewer divorcees whether remarried or not (14.1% in total in that age group).

A notable fact is that most women who divorce eventually remarry, although fewer widows do so. Indeed, according to PSF1, 59% of divorcees and 26% of widowed women remarry. A majority remarry polygamous husbands (47% and 72%, respectively, compared to 36% of first married women). Half of the remarried widows are remarried within a leviratic union, among them 83% in a polygamous union.

Using the latest DHSs for a number of countries for which details on marital histories were collected, Senegal’s characteristically high remarriage rate following divorce appears to be shared with other West African countries (Table 2). In countries in the southern and eastern parts of the subcontinent, divorce rates are higher, but a much larger share of divorcees remain unmarried. Finally, countries in Central Africa have by far the highest divorce rates, with up to one-third of women having been divorced at some point in their lives in Gabon, and about half of them remarried. Table 2 shows that patterns of widowhood rates and remarriage are more equal across the continent; indeed, about 6% of all women aged 15-49 have been widowed and in most countries, nearly half are remarried. There are a few outliers, in particular those most

> 12 Note that separated women are included in the same category as divorced.

9
percent by 2030, even if all other regions including SSA record a zero percent poverty rate, the global poverty rate would be higher than 3 percent. Similarly, if SSA cannot reduce the regional poverty rate to below 17.65 percent by 2030, even if all other regions record a zero percent poverty rate, the global poverty rate will be higher than 3 percent. To achieve the maximum poverty rates allowed, SAR needs to grow at least 1.5 percent annually. This is unlikely to be a huge challenge for SAR countries because all countries in SAR exhibit higher than a 1.5 percent average annual growth rate between 2002 and 2010.

However, the situation is different for the SSA region. To achieve the maximum poverty rate allowed, SSA needs to grow at 4.06 percent annually. If all SSA countries fail to achieve this level of growth on average for the next 20 years, even if all other regions record a zero percent poverty rate, the global poverty rate in 2030 would be higher than 3 percent.

**Table 6: SSA Region Achieves at minimum 4.06 percent Growth Annually while All Other Countries Maintain Growth in the Benchmark** **<u>scenario between 2010 and 2030</u>**

|**Region**|**Headcount rate (%)**|
|---|---|
|East Asia & Pacific|0.78|
|Europe & Central Asia|0.35|
|Latin America & Caribbean|2.69|
|Middle East & North Africa|1.73|
|South Asia|8.92|
|Sub-Saharan Africa|17.06|
|Developing World|6.41|
|World|5.58|

Source: Authors’ projections using PovcalNet (October, 2013) Between 2002 and 2010, only 8 out of 47 countries in SSA have achieved higher than a 4.06 percent annual growth rate after adjusting inequality, but the remainder, which is the majority of SSA, failed to achieve the growth rate. In this sense, maintaining the minimum requirement to achieve the global target is a significant challenge for many countries in SSA.

If all countries with growth rates lower than 4.06 percent between 2002 and 2010 raised their growth rates to 4.06 percent, and all countries with growth rates larger than 4.06 percent continued the pace of growth, the poverty rate for SSA would decrease to 17.06 percent by 2030, but the global poverty rate would still be as high as 5.58 percent unless other regions do not improve their performance. This means that efforts from SSA alone might not be enough.

**Figure 6: Trends in Projections in South Asia Region**

<!-- Start of picture text --> (i) Poverty trends (ii) Poverty headcount rate (iii) Inequality-adjusted growth<br>(%) (%) in 2030 rates (2002-2010)<br>35 20 10.0<br>31.0 17.5<br>8.3<br>30 7.6<br>8.0<br>15<br>25<br>6.0 5.6<br>20 9.6<br>10<br>3.7 3.6<br>15 4.0<br>10 8.9 5 1.8 1.9<br>2.0<br>5 3.0 0.0 0.0 0.1 0.1 0.1<br>0 0.0<br>0<br>2010 2030<br>SAR Target<br><!-- End of picture text --> Source: Authors’ estimations using PovcalNet data (October 1, 2013 version).

18
**Table 8: Savings and Borrowing -- Extensive Margin**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Have a Bank<br>Account|Have a SACCO<br>Account|Belong to a VSLA|Borrowed Money<br>in Past 3 Months|Loan From a<br>Regulated<br>Financial<br>Institution|Loan From an<br>Unregulated<br>Financial<br>Institution|
|Autonomous Trainer Selection (T1)|-0.034|-0.001|0.010|0.003<br>|0.006|-0.004|
||(0.024)|(0.004)|(0.022)|(0.020)<br>|(0.012)|(0.021)|
|Fixed Trainer Selection (T2)|-0.041|-0.003|0.013|-0.008<br>|-0.005|-0.007|
||(0.026)|(0.004)|(0.023)|(0.018)<br>|(0.012)|(0.019)|
|R-squared|0.020|0.004|0.018|0.046<br>|0.003|0.055|
|N|7885|7885|7885|7885<br>|7885|7885|
|Dependent Variable Mean in Control Group|0.286|0.993|0.731|0.555<br>|0.086|0.499|
|F-test(p-value): T1 = T2|0.777|0.732|0.908|0.616<br>|0.394|0.897|

This table presents analysis for savings and borrowing outcomes using household survey data across two endline rounds. All regressions control for survey round dummies as well as stratification controls. Standard errors, clustered at the SACCO level, are reported in parentheses. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
Policy ReseaRch WoRking PaPeR 4899

# **Abstract**

Despite the purported surge in internal migration following the 2004 enlargement of the European Union, data from the 2006 European Union Survey of Income and Living Conditions show that internal migrants are a relatively small share of the European Union’s population. Depending on the exact definition used, only about 1 to 2 percent of the population of European Union-13 countries (members prior to the 2004 enlargement, not including Germany and Luxembourg) were born in other European Union countries, while the corresponding share for European Union-4 countries (Poland, Hungary, Czech Republic, and Slovakia) is even lower. By contrast, about 6 percent of the population of European Union13 countries was born outside the European Union. On examining the demographic and socio-economic background of the migrant population (both from within as well as outside the European Union), this paper finds that migrants tend to include a concentration of both low as well as highly educated workers. Both sets of migrants uniformly contribute to raising the working-age population of receiving countries. Using data on average incomes and taxes paid and benefits received by migrant and non-migrant households, the authors find no evidence to support the contention that migrant workers contribute much less in taxes than the native-born population, or consume significantly higher benefits. On the contrary, our calculations suggest that migrant workers make a net contribution of approximately 42 billion euros to the national tax and benefit systems of European Union-13 countries.

This paper is a product of the Poverty Reduction and Economic Management Unit, Europe and Central Asia Region. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at szaidi5@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
lead to equivalent targets for all schools if the coefficients in the quantile regressions are constant over all quantiles. Annex 1 plots the coefficients for 99 different quantile regression estimations and the plots reveal that the coefficients are not always constant. Of course, each case is different. But in general, one would not expect the coefficients in quantile regressions to always be constant – so there will be differences between the approach followed by JTPA (which implies a constant adjustment to each percentile) and the approach based on quantile regressions. Because it is just as easy to do the quantile regressions in STATA as the OLS adjustments and because the quantile regressions are more flexible, the quantile regression approach would appear to dominate the OLS JTPA-type adjustment.

# _Comparison of Empirical and CAP Distributions_

The discussion in the paper has focused on the setting of targets and, for that, one need only choose the percentile that is considered most relevant and then estimate a single quantile regression at that percentile. In addition to target setting, a policy maker might be interested in ascertaining whether an observed change in an indicator reflects performance that is good, bad or indifferent. For that it is useful to have some context against which to compare the outcome. One could compare the observed change with the empirical distribution of all past changes. However, the same considerations come into play as with target setting. The empirical distribution does not take into account the particular circumstances of the agent and to properly judge whether the performance has been good, bad or indifferent some consideration of the particular circumstances of the agent needs to be taken into account. Just as one estimated a target based on the quantile regression of the 67<sup>th</sup> and 25<sup>th</sup> percentiles, one can estimate 99 different quantile regressions and use the estimated coefficients to generate an estimated value if performance was equivalent to that of the 10<sup>th</sup> , 20<sup>th</sup> or nth percentile. We call the distribution of these estimated percentiles, the **CAP** or **C** haracteristic **A** djusted **P** erformance distribution Figure 6 plots the empirical distribution for the change in Portuguese proficiency between 2007 and 2006, together with the **CAP** distributions for the change in Portuguese proficiency for the same period for two selected schools in Minas Gerais.<sup>11</sup> School A was randomly selected from the set of schools that were in the top 25 percent of all schools in terms of the number of students in 2007 and in terms of the level of Portuguese proficiency in the 5<sup>th</sup> grade. School B was randomly selected from the set of schools that were in the bottom 25 percent of all schools in terms of the number of students in 2007 and in the level of Portuguese proficiency in the 5<sup>th</sup> grade. The red vertical lines indicate the values at the 25<sup>th</sup> and 67<sup>th</sup> percentiles.

> 11 Annex 2 provides information on a STATA program that has been developed to automate the process of generating the CAP distribution.

13
<!-- Start of picture text --> 0.30 0.025<br>Kakwani Coefficient Marginal Contribution<br>0.20<br>0.015<br>0.10<br>0.005<br>0.00<br>-0.005<br>-0.10<br>-0.015<br>-0.20<br>-0.30 -0.025<br>b. Indirect taxes in selected countries<br>0.05 Kakwani Index Marginal Contribution 0.015<br>0.00<br>0.005<br>-0.05<br>-0.005<br>-0.10<br>-0.015<br>-0.15<br>-0.025<br>-0.20<br>-0.25 -0.035<br>KAKWANI INDEX<br>(CHANGE IN GINI POINTS)<br>REGRESSIVE <-------------> PROGRESSIVE MARGINAL CONTRIBUTION TO EQUITY<br>All indirect taxes VAT on durables VAT on clothing VAT on individual... Petroluem excise on... SCT on durables VAT on education<br>SCT on alcohol and tobacco VAT on energy products VAT on motor vehicles VAT on alcohol and tobacco SCT on energy products SCT on motor vehicles Other Indirect Taxes VAT on food and beverages VAT on communication... SCT on communication... SCT on sports and hobbies VAT on medical products VAT on sports and hobbies VAT on entertainment and...<br>KAKWANI INDEX<br>TO EQUITY<br>(CHANGE IN GINI POINTS)<br>REGRESSIVE <-----> PROGRESSIVE MARGINAL CONTRIBUTION<br>Croatia (2014) Poland (2014) USA (2016) Russia (2014) Brazil (2009) Chile (2013) Mexico (2010) Argentina (2012) Turkey (2016)<br><!-- End of picture text --> Source: Argentina (Rossignolo 2017); Brazil (Higgins and Pereira 2017); Chile (Martinez-Aguilar et al. 2016); Croatia (Inchauste and Rubil 2015); Mexico (Scott 2013); Poland (Goraus and Inchauste 2016); Russia (Popova et al. 2018); US (Higgins et al. 2018). Turkey: Own estimates based on Turkey 2016 HBS. Note: Marginal contribution to equality is the difference between the Gini coefficient without the particular fiscal intervention and the Gini coefficient of all income components together. There is no marginal contribution calculated for Argentina. USA results are preliminary.

# **_6.3 Taxes and Transfers and Their Effects on Poverty_**

Panels (a) and (b) of Figure 12 show the increase in poverty due to each direct and indirect tax, respectively. These results vary widely depending on the taxes considered. On the direct tax side, the payroll income tax has the highest poverty-increasing effect, raising the share of poor by about 30