rafmacalaba/datause-encoder-data huggingface dataset:
27 These results should be taken with caution, though. As inflows into public employment and wage subsidy schemes are large relative to hirings<sup>26</sup> and as the long-term unemployed are specifically targeted for these measures the overrepresentation of the long-term unemployed among participants can lead to a strong upward bias of the job accession rates for this latter group.

The estimated transition probabilities would imply little or no state dependence for the pool of the unemployed. Below we will return to this issue when an augmented matching function is estimated for Poland. Nevertheless, whether there is state dependence or not, targeting the long-term unemployed with ALMPs in Poland seems rather an equity than an efficiency issue. With an overall demand for labour which is very weak the long-term unemployed should be targeted because some of the scarce resource employment should be redistributed to those who have suffered from a long spell of relatively low income..

The last disaggregation of the LFS flow data that will be discussed concerns education. Several points can be made from the inspection of tables 8 and 9 which give labour market transition probabilities for men and women separately and of table 10 which presents participation rates, unemployment rates and LTU shares, again for men and women separately.

First, for men, if one ignores the elementary and incomplete schooling categories which have far lower participation rates than the other categories, education and the level of unemployment are inversely related. For women this correlation is also present but less strong.

Secondly, secondary general, but above all elementary and incomplete schooling means a far lower attachment to the labour market than the other educational categories. In fact, the only two educational categories with decreasing participation rates for men and women are elementary and incomplete schooling. These two categories have by far the largest labour force exit rates from the state of employment (the probability PEN), for both men and women while their inflow rates into unemployment (the sum of PEU and PNU) are compared to most other categories low. The already weak labour market attachment of the least qualified groups seems to become even more marginal as transition occurs. In the Polish case, targeting ALMPs at these two groups in order to strengthen their labour market attachment seems under the conditions of the transition, i.e. weak labour demand

> **26Between May 1992** and **May** 1993 the ratio of public employment and **wage** subsidy participants to hirings **was 0.31.**
Policy Research Working Paper 5883

# **Abstract**

Despite their explicit focus on reaching the poor, many community driven development (CDD) projects have been found to be only mildly pro-poor in their funding allocations. This paper presents evidence of an explanation that has been overlooked in the CDD literature to date: the requirement that beneficiaries must apply for projects in order to receive support. The authors first examine data on the universe of project applications and funding under Tanzania’s flagship CDD program, Tanzania’s Social Action Fund, and then use a census of 100 program villages to examine the determinants of both program awareness and program participation at the household level. The data paint a consistent picture at both levels: wealth, access to information, and political capital are important correlates of the ability to navigate the application process successfully. The centrally dictated features of this decentralized program appear to be the most effective mechanisms in directing funds to the poor. The results suggest that unless demand-driven projects can develop ways of soliciting engagement from a broader cross-section of the population, they are unlikely to achieve truly progressive targeting.

This paper is a product of the Poverty and Inequality Team, Development Research Group. 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 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
academic year 2022-2023, 0.2% of students enrolled in the Italian school system were Ukrainian refugees (this share slightly decreases from grade 10 and is negligible in grade 13).

_Table 1- Group of students by grade during the academic year 2022-2023 (Source: MoE)_

||**Grade6**|**Grade7**|**Grade8**|**Grade9**|**Grade10**|**Grade11**|**Grade12**|**Grade13**|**Total**|
|---|---|---|---|---|---|---|---|---|---|
|**Ukr post Feb**||||||||||
|**2022**|1,444|1,245|1,263|1,591|896|821|477|275|8,012|
|**Ukr pre Feb**<br>**2022**|1,429|1,670|1,757|1,372|1,273|1,160|995|900|10,556|
|**Migrant post**<br>**Feb 2022**|10,725|8,207|6,871|16,660|9,891|8,859|6,365|4,900|72,478|
|**Migrant pre**<br>**Feb 2022**|53,886|43,939|44,317|43,002|31,905|30,901|27,702|25,195|300,847|
|**Italians**|470,319|495,155|501,083|508,873|484,926|475,036|457,203|486,018|3,878,613|
|**Total**|537,803|550,216|555,291|571,498|528,891|516,777|492,742|517,288|4,270,506|
|_Share of Ukr_<br>_post Feb 2022_<br>_over total_<br>_students_|_0.27%_|_0.23%_|_0.23%_|_0.28%_|_0.17%_|_0.16%_|_0.10%_|_0.06%_|_0.19%_|

Administrative data on enrollment for the academic years 2021-2022 and 2023-2024 are also used to examine the evolution of Ukrainian refugees’ enrollment in Italian schools since February 2022.<sup>10</sup> Figure 1 shows that in 2021-2022, the number of Ukrainian refugees was very low in all grades, with a total of 3,320 Ukrainian refugees that arrived directly after the start of Russia’s invasion of their country. In 2023-2024, the number of Ukrainian refugees enrolled increases in all grades with the exception of Grade 8.<sup>11</sup>

> 10 The distributions of students by grade for academic years 2021-22 and 2023-2024 are shown in Tables B1 and B2 in Appendix B.

> 11 The available data do not allow to estimate the number of refugees enrolled in Grade 6 during the academic year 2023-2024.

7
The literature offers many definitions of noncognitive skills. In general, however, the term pertains to the competencies, behaviors, attitudes, and personal qualities that enable people to navigate their environment, work well with others, perform well in different settings, and achieve their goals; they are different from academic or technical/vocational skills. Although termed noncognitive skills, these skills require cognition, as psychologists have emphasized (Borghans, et al., 2008).<sup>12</sup> Both cognitive and noncognitive skills have roots in early childhood but are also learned and shaped throughout a person’s life experiences and in various situations—at home, in the playground, in schools, at work and in the streets.

We therefore expand the basic log-earnings model given by [1] as follows, where human capital is measured by years of schooling ( _S_ )<sup>13</sup> , years of work experience (E) proxied by age, a set of measured cognitive and noncognitive skills ( _C_ and _NC,_ respectively), and _R_ is metropolitan area of residence.<sup>14,15</sup>

> 12 The terms used for this cluster of skills have varied by discipline. Psychologists distinguish between traits and skills, where traits underlie and influence multiple behaviors and attitudes, and are considered relatively stable, though not immutable. These have been consistently found, especially conscientiousness, to relate to workforce outcomes (Lippman, et al., 2015). On the other hand, skills are specific, teachable and malleable. The psychology literature on intelligence points to different types of intelligences—that it is about how well a person deals with environmental changes throughout life, processes that involve knowledge acquisition, problem-solving, decision-making and creation—and that there is more than one ability associated with intelligence. For example, Dweck’s “growth mindset” and Duckworth’s “grit” scales have been used to predict educational achievement and attainment as well as workforce outcomes (Duckworth et al., 2007; Dweck et al., 2011).

> 13 We specify years of schooling as a spline variable with knots at 9 and 13 years of education, although we also estimated our model using total years of schooling.

> 14 The survey data for the countries included in our study are restricted to urban households. We construct a variable to distinguish locations for each country that we considered to be a metropolis, or significantly different in terms of population density and economic activity.

> 15 Throughout the analysis we refer to _Υ_ as “returns” to skills inasmuch as the _β1, β2_ and _β3_ coefficients have been typically referred to as estimates of returns to schooling and experience. However, we acknowledge that the term “returns” is used in a loose sense because the costs of producing schooling and skills are ignored, as is a potentially causal relationship between skills and schooling.

7
first shipment, i.e. the one of cotton, and as double-counting in the second one, when cotton is embedded in the shipment of apparel. Summing the value-added terms in the two export flows, we end up with consistent aggregate figures at the country level, i.e. cotton classified once as value-added and once as double counting.

Consider now a different goal, i.e. assessing the value-added exposed to a specific trade barrier imposed by a partner. To this end, suppose that a tariff impairs the exports of apparel. Now, when considering the value-added embedded in the second flow, the one exposed to the tariff, also that one of cotton needs to be taken into account. In this way, we are able to correctly assess the value-added that could be impaired by the tariff.

More in general, depending on the type of trade flow and the objective of the analysis, it is necessary to define the “perimeter” according to which something is classified as “value-added” or “double counted”, i.e. a specific accounting “perspective” has to be chosen.

Each perspective is better suited to address specific empirical issues. Whenever the empirical application requires to retrieve the entire value-added of a country/sector of origin that is embedded in a given trade flow, as in the second example above, the accounting perspective to be chosen should match the level of disaggregation of the trade flow considered, as reported in the first column of Table 1 (e.g., “exporting country” perspective for aggregate exports, “bilateral” perspective for an aggregate bilateral trade flow, “sectoral-bilateral” perspective for a trade flow between two countries in a given sector, etc.).<sup>8</sup> For instance, suppose a tariff is imposed on the imports of a given sector from a certain partner, and we are interested in evaluating what part of the exporting country GDP is exposed to the tariff. In this case we want to consider as “value-added” the entire GDP that is involved in this sectoral-bilateral relationship, even if part of that was previously exported to other countries/sectors (i.e. double counted in an “exporting country” perspective). The specific sectoral-bilateral relationship becomes the new relevant perimeter, and only the items that enter multiple times in this trade flow are considered as “double counted”. Indeed, this is what is called a “sectoral-bilateral perspective”, the one used in the second example on cotton.

However, these measures cannot be summed up to get a precise assessment of value-added contents in more aggregate trade flows, e.g. value added in the total exports of a country. In other words, they are non-additive.<sup>9</sup> Thus, if we

> 8See Section E for a complete overview of the perspectives available in `icio` ).

> 9See also Johnson (2018) and Los and Timmer (2018) on this point. More specifically, when we use an accounting perspective based on a narrower perimeter to define the double counted items (e.g. the sectoral-bilateral one), by summing these indicators we obtain value-added content

11
Helliwell, Layard & Sachs, 2017; Graham & Nikolova, 2015; Manski, 2004; Oswald & Wu, 2010). We define subjective well-being, also known as happiness or life satisfaction, as the degree to which individuals judge the overall quality of their lives favorably (Veenhoven, 1984). Biologically, high levels of subjective well-being are a signal that people are thriving and indicate the presence of good life chances in society, such as income, education, access to infrastructure, and high-quality institutions. When basic human needs are satisfied and there is a good fit between opportunities in a society and people’s capacities, levels of subjective wellbeing are likely to be high (Veenhoven, 2000). Thus defined, subjective well-being is something on one’s mind that can be measured using surveys.

In this paper, we measure subjective well-being using the life evaluation index developed by the Gallup World Poll, which surveyed a nationally representative sample of individuals in more than 160 countries from 2006 onwards.<sup>5</sup> In most high-income countries, the interviews are conducted over the phone, whereas in low- and middle-income countries the surveys are administered face-to-face. The sample size is typically 1,000 respondents per country-year and 2,000 for larger countries such as China, India and the Russian Federation. In the case of small island states such as Haiti, Trinidad and Tobago, Iceland and Malta, the sample size is between 500 and 1,000. The life evaluation index is based on the Cantril ladder, which measures an individual’s evaluation of their life as a whole, both now and in the future. This indicator contrasts indicators of the pleasantness of people’s emotional lives, which are often measured using _experiential_ questions about smiling a lot or feeling happy (Diener, Kahneman, Arora, Harter & Tov, 2009). The life evaluation index is constructed from the answers to the following two questions:

> 5 Samples are not representative at the national level in cases where the safety of the interviewing staff is threatened or in the case of scarcely populated islands (which are omitted in some countries). For Cuba, Chad, the Central African Republic, the Democratic Republic of Congo, Madagascar and Sudan, the excluded regions account for more than 15% of the population.

11
_Figure 3: Decomposition of the incidence of the oil shock_ <!-- Start of picture text --> ‐4<br>‐5 GIC 2: GIC 1 + Δ in<br>Returns to Skills<br>‐6<br>GIC tot: GIC 1 + GIC 2<br>+ Δ Goods Prices<br>‐7<br>GIC 1: Labor Movement across sectors<br>‐8<br>‐9<br>0 20 40 60 80<br>Population percentiles<br>consumption)<br>Change in welfare (% change in per capita<br><!-- End of picture text --> Source: HBS 2011 and authors calculations using microsimulations The progressive effect of the change in wage premia is _reduced_ by another round of unequal adjustments: the change in relative prices of consumption goods. Not all households consume the same proportions of food items. As shown in Figure 4, the share of total expenditure devoted to food is much higher for poorer households: the average Russian household spends 33 percent of its total expenditures on food, while a household in the bottom 40 percent spends 43 percent, and one in the top 60 only 16 percent. Therefore, richer households benefit disproportionally more from the stronger reduction of prices of non‐food items, including non‐tradables. This is reflected by a counterclockwise tilting of the GIC (from GIC 2 to GIC tot in Figure 3) which makes the overall incidence less progressive.

# _Robustness and validation_

The incidence results discussed up to this point rely on two assumptions: the first is that the shock affects the allocation of labor across sectors but without generating unemployment; the second is that the reduction in oil royalties does not influence the level of government expenditure and its household transfers. The implications of the two assumptions are related, as it can be inferred by looking at the employment share along the welfare distribution depicted in Figure 4. The intensity of the use of labor, the main source of income for most households, is not uniformly distributed. Households at the bottom 40 percent record an average employment rate of 45 percent against an average of 53 percent for 18
|**Table 14: Exploring the Presen**|**ce of Omitted Varia**<br>**Category of**|**ble Bias in Base OL**<br>**AdditionalCovariat**<br>|**S Regression Results Un**<br>**es Integrated to the Base**|**derlying the Mean D**<br>**Regression**<br>|**ecomposition North,**|**Female**<br>|
|---|---|---|---|---|---|---|
|_VARIABLES_|_Base North_|_Manager_<br>_characteristics_|_Plot characteristics_|_Geospatial_<br>_characteristics_|_Crop fixed effects_|_Community_<br>_characteristics_|
|Female manager=1|||||||
|Age|0.0728**|0.0317|0.0778**|0.0822**|0.0727*|0.0613|
||(0.0357)|(0.0396)|(0.0381)|(0.0405)|(0.0410)|(0.0378)|
|Age Squared|-0.000879**|-0.000471|-0.000918**|-0.000920**|-0.000911**|-0.000747*|
||(0.000381)|(0.000416)|(0.000403)|(0.000427)|(0.000412)|(0.000394)|
|Years of schooling|-0.0122|-0.00985|-0.00226|-0.00510|0.00789|-0.00543|
||(0.0291)|(0.0329)|(0.0304)|(0.0312)|(0.0353)|(0.0338)|
|Manager is nonmuslim†|-0.487*|-0.565*|-0.532*|-0.656**|-0.531*|-0.535*|
||(0.278)|(0.314)|(0.279)|(0.309)|(0.292)|(0.283)|
|Manager has nonfarm activity†|0.343|0.243|0.366|-0.00115|0.348|0.315|
||(0.262)|(0.317)|(0.266)|(0.228)|(0.288)|(0.291)|
|Household female adult size (age 12-60)|-0.178|-0.112|-0.169|-0.207*|-0.0879|-0.240*|
||(0.108)|(0.175)|(0.108)|(0.111)|(0.117)|(0.126)|
|Household male adult size (age 12-60)|<br>0.0463|<br>0.186|<br>0.0493|<br>0.0840|<br>0.0206|<br>0.00493|
||(0.0933)|(0.152)|(0.0933)|(0.0937)|(0.101)|(0.135)|
|Child dependency ratio|0.0187|0.209|0.0165|0.0237|0.00515|0.164|
||(0.130)|(0.233)|(0.130)|(0.123)|(0.138)|(0.154)|
|Log [GPS-Land Area (HA)]|-0.723***|-0.687***|-0.716***|-0.625***|-0.735***|-0.637**|
||(0.185)|(0.199)|(0.183)|(0.137)|(0.196)|(0.271)|
|Log [GPS-Land Area (HA) Squared]|-0.0251|-0.0129|-0.0234|0.0315|-0.00796|-0.0442|
||(0.0565)|(0.0582)|(0.0558)|(0.0391)|(0.0571)|(0.0778)|
|Plot distance to household|<br>0.00996|<br>0.00546|<br>0.00872|<br>0.00569|<br>0.0135*|<br>0.0277|
||(0.00862)|(0.01000)|(0.00880)|(0.00805)|(0.00761)|(0.0190)|
|Access to extension†|0.178|0.0658|0.216|0.171|0.291|0.354|
||(0.320)|(0.346)|(0.327)|(0.337)|(0.342)|(0.369)|
|Grows cash crop†|<br>0.574**|<br>0.585*|<br>0.625**|<br>0.577*|<br>1.318**|<br>0.500*|
||(0.280)|(0.325)|(0.289)|(0.300)|(0.584)|(0.264)|
|Only one crop on plot †|<br>-0.161|<br>-0.136|<br>-0.197|<br>-0.0589|<br>0.203|<br>-0.278|
|Robust standard errors in parentheses. *** p<|(0.198)<br>0.01, ** p<0.05, * p<|(0.215)<br>0.1. State fixed effec|(0.205)<br>ts not displayed. † indicate|(0.194)<br>s dummy variable|(0.256)|(0.230)|

48
destinations were computed using GIS software. Data on agro-ecological zones are drawn from the Bangladesh water board database.

Table 1 reports the incidence of poverty and extent of inequality during the decade covered by the two surveys. The incidence of poverty is measured by the head count ratio which was in turn determined from an o¢ cial poverty line decided by the Bangladesh government. The estimated head count ratio shows a signi...cant decline in poverty in Bangladesh from 49 percent in 2000 to 35 percent in 2010. Poverty declined substantially in both urban and rural areas. Poverty incidence is much lower in urban areas compared with rural areas. The inequality in per capita expenditure measured by the Gini coe¢ cient shows that inequality at the national remained nearly unchanged during this decade (Gini=0.31 in both survey years). Inequality in urban areas declined somewhat, from 0.37 to 0.33 between the survey years, but it has started to inch up in rural areas.

The summary statistics from the surveys reported in appendix table A.1 show considerable increase in per capita expenditure between 2000 and 2010. Among the variables representing access to infrastructure, the proportion of households with electricity and phone showed dramatic changes between these two years. The average education level of the workforce has increased though only in rural areas. Employment pattern has become more diverse in rural areas and more specialized in urban areas. For instance, agriculture’s share in rural employment fell from 65 percent in 2000 to 53 percent in 2010. Urban employment has become more concentrated in manufacturing and service activities. The di¤erences between rural and urban areas in most measures are evident for both survey years with rural areas trailing behind urban areas.

# 4. Empirical Results

Our estimation of spatial disparity index is based on equation (4). In all di¤erent speci...cations of equation (4), age dummies for the household head and his/her gender are included as controls.

14