rafmacalaba/datause-encoder-data huggingface dataset:
selected by PMT targeting in the same database as well, and we assess the performance of these CBT and PMT schemes to reach the poorest 20 percent of the population. As mentioned earlier, this approach enables us to make meaningful comparisons, net of threshold effects, between PMT and CBT _within_ databases.

When we rely on targeting error rates to assess targeting performance, PMT systematically selects individuals with the lowest per-capita consumption relative to CBT for all targeting schemes (Table 3). This is consistent with the literature and with the design of PMT formulas, but it is particularly striking across our nine data sets. The median targeting error for CBT is 50 percent, while that of PMT is 39 percent. This means that 50 and 39 percent of households selected by CBT and PMT targeting scheme respectively were incorrectly identified. PMT still delivers a significant improvement over a random allocation: the CGH index is 20 for PMT and 6 for CBT. In other words, the median PMT targeting scheme provides 20 percent more resources to the poor than a random allocation would, versus 6 percent for the median CBT targeting scheme.

While the median difference in the performance between PMT and CBT is large, an important variation exists across databases. This variation depends largely on the selection threshold employed in each database. The difference in the CGH index between PMT and CBT is lowest in Mali (i.e. with a difference of 7 percentage points), where the selection threshold is among the highest (at 66%). On the other hand, the difference in the CGH index between PMT (2.18) and CBT (1.47) is highest in Burkina Faso 2, where the selection threshold is lowest (21%). In sum, when the selection threshold is higher (i.e. more people selected), there is a mechanical trend due to the targeting measures used to see lower targeting errors rates (i.e. more exclusion/inclusion) and lower CGH index (i.e. less budget directed towards the poor).

When relying on distribution-sensitive measures for performance based on poverty, the differences between PMT and CBT are relatively small (Table 4). The CBT simulated poverty-gap reduction of the median targeting scheme is 3.3 versus 3.7 for PMT. The small difference in poverty-gap reductions is partly mechanical, given that an important share of households is well below the poverty line in most databases. Nevertheless, results are similar when measuring the simulated reduction in poverty severity, which values more transfers made to the poorest of the poor. The CBT poverty-severity reduction of the median targeting scheme is 3.3 versus 3.6 for PMT.

Perhaps more striking is how close the FGT poverty-reduction targeting measures are when random or universal selection is compared to CBT and PMT. When looking at the poverty gap, CBT and universal targeting are virtually identical, except in Senegal 2 where CBT is slightly above. But when looking at the poverty severity, universal targeting outperforms CBT in Cameroon and Niger 2 – and is almost identical in other countries except the two data sets with the lowest selection rates (Senegal 1 and Burkina Faso 2).

16
is a vector of control variables including trade regime and firm ownership type variables, measures of product differentiation, etc.;<sup>**γ**</sup> is the coefficient vector for Z; and<sup>e</sup> i<sup>is the</sup> error term.

To test the second hypothesis, we examine how the tax evasion motive varies with product and firm characteristics. Similar to Javorcik and Narciso (2008) and Mishra, Subramanian, and Topalova (2008), we argue that evasion through price underreporting might be easier for differentiated products than homogenous products whose prices are often standard. Rauch (1999) defines homogeneous goods as products whose price is set on organized exchanges, defines reference priced goods as those not traded on organized exchanges but possessing a benchmark price, and defines the goods whose price is not set on organized exchanges and which lack a reference price because of their intrinsic features as differentiated products. We would expect to find stronger support for the evasion hypothesis from differentiated products, while weaker evidence from referenced and especially homogenous products. We will run regressions for different subsamples grouped based on the level of product differentiation or include interaction terms between export tax rate and the trade regime and firm ownership type variables.<sup>25</sup>

# **4.2. Customs trade data**

China’s shipment level trade data are released by the China Customs Office. The data are organized monthly based on the shipment documents that firms submit to the corresponding customs offices. In our paper, we use only the information of destination/source country, HS 8-digit product codes, firm IDs, firm names, trade regimes, ownership types, and the values of the shipments. As a commitment made under the WTO,

> 25 The different findings from homogenous and differentiated products can also help to invalidate alternative interpretations other than tax evasion. For instance, in a standard Melitz-type model, higher export tax rates would reduce profits from direct exporting, making indirect exporting relatively more profitable. In terms of Ahn, Khandelwal, and Wei (2011), this means that the cutoff productivity level for direct exporting increases, and thus indirect export share also increases. Although our empirical findings of a positive correlation between unrebated VAT rates and indirect exports share are also consistent with the productivity sorting mechanism, this mechanism cannot explain the stronger support from differentiated products.

16
Figure 30: Regional Level Water Deprivation a) b) <!-- Start of picture text --> Urban Water Deprivation - by Geopolitical Zone - GHS Rural Water Deprivation - by Geopolitical Zone - GHS<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Wave 1_NC Wave 2_NC Wave 3_NC Wave 4_NC Wave 1_NC Wave 2_NC Wave 3_NC Wave 4_NC<br>Wave 1_NE Wave 2_NE Wave 3_NE Wave 4_NE Wave 1_NE Wave 2_NE Wave 3_NE Wave 4_NE<br>Wave 1_NW Wave 2_NW Wave 3_NW Wave 4_NW Wave 1_NW Wave 2_NW Wave 3_NW Wave 4_NW<br>Wave 1_SE Wave 2_SE Wave 3_SE Wave 4_SE Wave 1_SE Wave 2_SE Wave 3_SE Wave 4_SE<br>Wave 1_SS Wave 2_SS Wave 3_SS Wave 4_SS Wave 1_SS Wave 2_SS Wave 3_SS Wave 4_SS<br>Wave 1_SW Wave 2_SW Wave 3_SW Wave 4_SW Wave 1_SW Wave 2_SW Wave 3_SW Wave 4_SW<br><!-- End of picture text --> c) d) <!-- Start of picture text --> Urban Water Deprivation - by Geopolitical Zone - Rural Water Deprivation - by Geopolitical Zone - MICS<br>MICS<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Children 0-17 Children 0-17<br>MICS 2016/2017_NC MICS 2016/2017_NE MICS 2016/2017_NW<br>MICS 2016/2017_NC MICS 2016/2017_NE MICS 2016/2017_NW<br>MICS 2016/2017_SE MICS 2016/2017_SS MICS 2016/2017_SW MICS 2016/2017_SE MICS 2016/2017_SS MICS 2016/2017_SW<br><!-- End of picture text --> Source: Authors’ estimates based on GHS Wave 1-4 and MICS 2016/17 Note: Individuals with missing age were dropped from the analysis. Estimates are based on children aged 0-17.

Contrary to the national level Figure 30 shows that water deprivation did not decrease across all zones. In the North East and North West urban and rural areas water deprivation of children has been slightly increasing instead comparing estimates from wave 1 to wave 3 and then slightly decreasing to its initial level in wave 4. It is also worth noting that children in the rural North West, that are one of the or the most deprived in many other dimensions, are least deprived in term of severe water deprivation. The reverse holds for the South West where just under half of the rural children are severely water deprived, while in other dimensions children in this region are one of the least deprived. This however does not yet reflect the access to quality water. Just conditioning the water access to be from a somehow protected source, meaning in addition to surface water excluding untreated piped water and unprotected wells from water access options, changes the order of the region in which children are most or least water deprived, as estimates in Appendix Figures A. 3 and A.4 show. Then rural children in the North East and North West 41
2008, even as the share of such workers employed in manufacturing fell from 42.0% in 2008 to 39.1% in 2009 (NBS, 2010; Sheng, 2009). According to the 2010 CULS data, the unemployment rate in the five surveyed cities was only 2.4% for local residents and 0.7% for migrants, both lower than 2005 unemployment rates of 5.3% and 1.4% calculated from the second wave of the China Urban Labor Survey. Job vacancy rates measured by the Ministry of Labor also recovered very quickly to pre-crisis levels by the end of 2009 (Giles et al, 2012). And this robust growth in employment was not facilitated by downward adjustments in wages. Several national surveys convincingly document that real wages of rural migrant workers continued to increase steadily through 2008 and 2009, with even faster wage increases in 2010 and 2011 (Giles et al, 2012).

Thus, despite evidence that the Labor Contract Law was enforced and affected the employment decisions of manufacturing firms, there is no evidence that the higher costs of labor associated with implementation of the new Law has slowed the robust growth in labor demand that has led to rising wages and lower unemployment rates, both of which are symptomatic of increasing labor scarcity.

**_Social insurance_** . One important potential benefit of establishing a formal employment relationship is that it is generally a necessary condition for gaining access to government-run social insurance programs, such as pensions, health insurance, and unemployment insurance. Such coverage provides workers with valuable protection against major life risks, such as poverty in old age, unexpected health care expenditures, and the financial shock of unemployment. In fact, the ILO treats coverage by social insurance programs as the key criteria for defining formal versus informal employment. Strictly enforcing the new Labor Contract Law thus could help further the government’s announced goal of achieving full social insurance coverage of the population by 2020.

24
share in the number of enterprises, but not in terms of their contribution to GDP. The unit of analysis is the “establishment” in the manufacture and service sectors.<sup>12</sup> The ES data was developed to provide information on aspects of the investment climate faced by enterprises as well as information on enterprises’ performance. One question asks managers the extent by which various aspects of the investment climate are perceived as obstacles to enterprises’ operations and growth on a scale of 0 (no constraint) to 4 (very severe). These perceptions are useful as they combine an assessment of the condition in question (i.e. access to finance, corruption etc.) with its perceived impact on the enterprise. Access to finance, corruption, regulatory burdens and infrastructure are among the issues with the biggest relative differences across size of enterprises and so particular focus is given to these dimensions in their impact on enterprise growth. However, these variables also need to be interpreted with some caution, particularly due to concerns that they may reflect more on how the business is faring than on the investment climate itself.13 One of the strengths of the dataset is that it also contains a set of objective or quantitative measurements (e.g. time or monetary costs _)_ of those same aspects of the investment climate as they are actually being experienced by the enterprise. As such they are measures of de facto investment climate conditions.14 For example, rather than just reporting whether unreliable infrastructure is a constraint on a scale of 0 to 4, respondents are asked for the frequency of outages, whether they use a generator, what losses they incur due to outages. Summary statistics of these measures are available on Table 1d. With many potential variables available, we selected ones according to the major dimensions of the investment

> 12 In Europe and Central Asia, the unit is the ‘firm’; in all other regions it is the plant or establishment.  As over 90% are single plant firms, the distinction is not likely to affect the results.

> 13 It is not always obvious which way performance may influence responses.  If firms are doing well, they may not see conditions as constraining and report lower levels of constraints.  On the other hand, it could be that expanding firms are precisely the ones bumping up against constraints and that they complain more.  These issues are explored in greater detail in Hallward-Driemeier and Aterido (2009).

> 14  This distinguishes measures from alternatives such as “Doing Business” that provides de jure measures of the regulatory environment.  It should be noted that these more quantitative measures still are based on firm responses and are potentially still influenced by respondent’s level of optimism or performance.  Taking the location-sector-size average addresses this latter concern.

13
nutrition and maternal and child health through the Nutrition cum-Day Care Centers was well justified.<sup>20</sup>

# **_5.d Empowerment_**

Next, we look at the impact of membership in groups on women's empowerment. Empowerment has been defined as "the expansion of people's ability to make strategic life choices in a context where this ability was previously denied to them" (Kabeer, 1999). The survey allows us to look at women's autonomy, mobility, and participation in social activities. Information was collected for 2003 and 2006-08 and, by recall, for 2000.<sup>21</sup> First, we consider women's ability to set money aside for their own use and find that slightly more women were able to do so always or frequently in 2008 than earlier, both IKP participants and non-participants, with no significant differences (Table 18), apart from not-so-poor early joiners, who were able to set aside money more frequently, probably as a reflection of greater availability of money (not reported).

**Table 18: Percentage** **<u>of women able to set some money aside for personal use</u>**

||_Able to set as_<br>_IKP_<br>_participants_|_ide some money_<br>_Non-_<br>_participants_|_for own use_<br>_Difference_<br>_partic. vs._<br>_non-partic._|
|---|---|---|---|
|2000|33|33|0|
|2003|32|32|0|
|2008|36|36|0|

Note: Results for all poverty categories and lengths of exposure combined.

Second, we look at whether women were afraid to disagree with their husbands. Here, we find a significant impact of IKP participation. Over time, all women became less afraid to disagree (especially the poor and not-so-poor), with IKP participants significantly less afraid.

**Table 19: Percentage of women not afraid to disagree with their husbands**

||_Women not afr_<br>_husbands_<br>_IKP_<br>_participants_|_aid to disagree_<br>_Non-_<br>_participants_|_with their_<br>_Difference_<br>_partic. vs._<br>_non-partic._|
|---|---|---|---|
|2000|79|71|8***|
|2003|81|73|8***|
|2008|84|80|4**|

20 By 2008, less than 1% of households surveyed had benefited from a Nutrition Center, so it was too early to detect any impacts.

21 While the survey also asked whether decisions on major events—children's education and marriage, purchase of property and durable goods, savings, health care and family planning—if faced, were taken by the husband, the wife, or jointly, these major decisions are faced only infrequently and sample sizes are too small for meaningful analysis. The reference time periods were up to May 2000, Jan-June 2003, and July 2006-June 2008. Because of the ordinal nature of the data (with ratings such as "always", "sometimes", "rarely", etc.), we generally do not construct differences-in-differences, but look at levels over time using propensity score matching and regression adjustments, unless otherwise specified.

18
, then the OLS estimate of β (namely, RIF-OLS estimator) provides a consistent estimator of the marginal effect on the unconditional quantile of a small location shift in the distribution of X, holding else constant. So, one can decompose any two distributions at quantiles in a similar spirit as Oaxaca-Blinder approach. It is worth noting, however, that the assumption of linearity used in implementing this detailed decomposition implies that the outcome distribution depends on the marginal distribution of covariates only through their mean (Rothe 2010b).

The decomposition of test score change over-time can be rewritten as<sup>3</sup> : where and are approximation errors that appear because of the first order approximations and the assumption of linear specification. They are calculated as differences between the aggregate explained effect and the total composition effect: , and that between unexplained component and the total coefficients structure effect: .

An identification problem exists in this sort of detailed decompositions as pointed out by Oaxaca and Ransom (1999), as the coefficients effect is not invariant to the choice of reference group; this problem has no easy solution. One approach is to derive results using more than one alternative reference group. The other is to use one of the solutions suggested to deal with this problem. We used the averaging approach proposed by Yun (2005).

# **3. Data and Summary Statistics**

# _3.1. Data_

We used the Trends in International Mathematics and Science Study (TIMSS) data for 8<sup>th</sup> grade Ghanaian students. TIMSS is an international assessment conducted every four years in order to generate information about trends in mathematics and science achievement in each country and over time. The age of tested student population is 9 to10 years (4<sup>th</sup> grade) and 13 to 14 years (8<sup>th</sup> grade) in more than 60 countries; the outcome is an extensive pool of data about the quantity, quality, and content of teaching, which can be used by researchers to assess individual country progress as well as to make comparisons among participating countries. Ghana participated in the TIMSS assessment in 2003 and 2007, but only with 8<sup>th</sup> grade (JHS2) students.

Table A1 in the appendix gives the summary statistics for the student, household, school and teacher characteristics used in the analysis. Notable over-time changes include, from student and household characteristics: the substantial decline in the proportion of children with paid employment (from 55% to 41%), an improvement of parents’ education qualifications (55% with secondary or tertiary qualifications in 2007 compared to 43% in 2003), the decline in the proportion of students who spent no time doing homework; from school characteristics: the increase in average 8<sup>th</sup> grade student enrolment numbers, the large increase in the proportion of students in small communities of less than 3,000 people (from 20% to 35%) and the substantial decline in the proportion of schools with shortages in buildings and teachers; and from the

> 3 Since and , taking expectations on both sides yields . So, .

7
**Table 2: Baseline characteristics by treatment status**

||Control|Group|T1-C|T2-C|F Test|Obs|
|---|---|---|---|---|---|---|
||Mean|SD|||||
|Male|0.618|0.486|0.008|0.010|0.808|5274|
|Share correct answers COMI -general|50.213|11.806|0.235|-0.063|0.413|5174|
|Share correct answers COMI verbal|54.154|18.987|-0.196|-0.764|0.394|5174|
|Share correct answers COMI Spanish|53.485|18.764|0.392|0.133|0.750|5174|
|Share correct answers COMI Math|39.786|17.537|0.244|-0.470|0.412|5174|
|Share correct answers COMI Geo|52.676|18.138|0.664|0.233|0.422|5174|
|Share correct answers COMI History<br>Share correct answers COMI|47.267|17.535|-0.228|-0.271|0.856|5174|
|Chemistry|47.300|18.222|0.187|0.300|0.866|5174|
|PNBIO|51.407|18.790|0.913*|0.045|0.150|5174|
|Sec. School Average|7.166|0.764|-0.021|-0.013|0.677|5174|
|Took COMIPEMS twice or more|0.278|0.448|0.008|-0.009|0.550|5274|
|Math Average 2nd year Lower Sec|2.872|1.629|0.013|-0.054|0.475|5174|
|Spanish Average 2nd year Lower Sec<br>Income based Scholarship in Lower|3.412|1.783|-0.020|-0.106*|0.209|5174|
|Sec|0.080|0.272|-0.002|-0.004|0.928|5174|
|Mother with Primary|0.176|0.381|-0.001|-0.012|0.653|5108|
|Father with Primary|0.143|0.351|0.007|0.019|0.378|4799|
|Mother with Lower sec|0.378|0.485|0.002|0.027|0.247|5108|
|Father with Lower sec|0.404|0.491|-0.015|0.002|0.580|4799|
|Mother with Upper Sec|0.201|0.401|-0.004|-0.007|0.889|5108|
|Father with Upper Sec|0.240|0.427|-0.015|-0.016|0.507|4799|
|Mother with Higher Ed|0.231|0.422|0.002|-0.009|0.766|5108|
|<br>Father with Higher Ed|0.199|0.399|0.020|-0.011|0.104|4799|
|Number of Preferences|10.478|4.298|0.194|0.324**|0.104|5174|
|Assigned first option|0.189|0.392|-0.024**|-0.003|0.102|5274|
|Assigned second option|0.102|0.303|-0.004|-0.006|0.827|5274|
|Assigned third option or higher|0.703|0.457|0.027*|0.010|0.159|5274|
|Phone at home|0.719|0.450|0.016|0.004|0.543|5174|
|Wash. Machine|0.852|0.355|0.008|0.011|0.655|5174|
|Fridge|0.946|0.226|0.005|0.007|0.654|5174|
|Microwave|0.665|0.472|0.035**|0.017|0.072|5174|
|Cable|0.465|0.499|0.008|-0.035*|0.047|5174|
|Tablet|0.501|0.500|-0.006|-0.021|0.503|5174|
|Number of books|3.478|1.529|0.050|0.039|0.572|5174|

# **4.3 Outcome Measures**

15
# **Social spending**

Overall expenditures in Croatia amounted to 48 percent of GDP in 2014, up from 45 percent in 2008, but have since declined (Eurostat). A large part of public spending is dedicated to social protection (14.2 percent of GDP), while education (4.3 percent of GDP) and health (5.6 percent of GDP) are also relatively important (Table 2). Total social spending in Croatia amounted to 24 percent of GDP or 50 percent of total spending. The analysis presented below covers 45 percent of all government spending and 91 percent of social spending. In what follows, we describe the main highlights of existing social spending. Direct (non-contributory) transfers include the following programs.

- _Family benefit programs,_ including a one-time grant for newborn children received by all parents of newborn children and a child benefit, which is a means-tested benefit received by a parent or other person taking care of one or more children with substantial top-ups for households with three and four or more children. Supplements are also given for children without one or both parents and for children with health challenges.

- _Social assistance programs_ include a subsistence benefit which became a guaranteed minimum benefit in 2014. It is a means-tested benefit intended for households whose income is below a basic needs threshold and depends on the characteristics and composition of the household. There is also a housing benefit, funded and distributed by local self-governments, and is a means-tested benefit that covers the costs of rent, electricity, gas, heating, water, and other housing bills for subsistence-benefit recipients. Local governments determine the income test and benefit amounts, with a maximum of 50 percent of the amount of Subsistence benefit. The recipients of the subsistence benefit also have the right to claim the benefit for covering wood-heating costs, which is also administered and financed by local governments. Finally, there is a lump-sum assistance, which is a purpose-defined benefit received by a household for covering the costs related to transportation, education, clothing, child birth, funeral expenses (Urban and Bezeredi, 2016).

- _Programs for Croatian Defenders of the Homeland War_ include old-age pension supplement, disability pensions, orthopedic allowances, constant care supplement, survivor benefits and child benefits for surviving children. Beneficiaries are persons who participated in the organized armed defense in the period from August 1990 to June 1996. Disabled veterans (HRVIs) are categorized into 10 groups according to the level of bodily impairment sustained during the war, ranging from 20 to 100 percent impairment. Unfortunately, it is impossible to identify beneficiaries using the existing household surveys, so this is not included in the analysis.

7