rafmacalaba/datause-encoder-data huggingface dataset:
As noted in these examples, fiscal policy directly and indirectly impacts gender inequality and social reproduction. It directly supports social reproduction through provision of goods and services, creation of incentives for behaviors, and setting of prices for goods and services that support social reproduction. However, standard microdata-based fiscal incidence analysis (FIA) distributes cumulative burden and benefit totals uniformly to all household members regardless of the roles that members play in an economic activity (including social reproduction) of the household. In what follows, we propose a series of steps to bring a gendered perspective within the CEQ assessment fiscal incidence analysis framework in a transparent manner and without additional data requirements.

# 4 Methodology

We begin this section with a brief summary of the CEQ assessment method and how the CEQ “income concepts” framework lends itself to a gender-differentiated fiscal incidence analysis. Following this introduction, we discuss in which dimensions of gender inequality we might expect to observe or deduce evidence of fiscal policy impact. We then propose a methodology for examining the impact of fiscal policy on gender by expanding the CEQ assessment method using standard datasets and techniques.

## CEQ Assessments

A CEQ assessment accounts for and quantifies the nature, character, and magnitude of benefits received as well as the burdens experienced by individuals and households from the execution of a fiscal policy. A CEQ assessment generates counterfactual income distributions and computes poverty and inequality indices based on the distribution of each of these income concepts. These pre-fiscal and post-fiscal incomes contain more or fewer of the benefits created from public expenditure or tax contributions to public revenues. The analysis starts with pre-fiscal income (referred to as “market income” or “market income + pensions”) which is earned or unearned from wages and salaries or profits from enterprises, self-employment activities, or as returns on capital and other private income sources (Figure 1). Net market income is what would be left behind after all relevant income taxes are paid and households and the necessary deductions and withholdings have been taken out. If the household is a recipient of any direct cash transfer from the government, this would be added to net market income to arrive at disposable income. At this stage, depending on the consumption basket of the household, various indirect taxes (VAT and excise) as well as indirect subsidies (energy subsidies) would be paid or accrued. Adding or subtracting these as appropriate would yield consumable income or post-fiscal income. Finally, accounting for any in-kind benefits received from the government on things like education and health and discounting associated copayments and user fees would yield final income.

9
# **Table 23c.  Piecemeal Sensitivity Analysis – Sulphur Dioxide Emissions by Region from WTO Accession (percentage change from base year)**

||**Overall**||**Far**||||||**St.**|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Parameter being changed**|**average**|**Central**|**East**|**North**|**Siberia**|**South**|**Urals**|**Moscow**|**Peters.**|**Tumen**|**Northwest**|
|**Central Results for reference**|6.2|11.2|4.7|7.7|4.1|10.6|9.2|4.5|28.8|3.5|10.1|
|**esubc = 0.5**|6.5|11.0|5.1|8.0|4.1|10.5|9.2|5.2|35.5|3.5|10.0|
|**esubc = 1.5**|6.2|11.2|4.6|7.6|4.1|10.6|9.2|4.3|28.7|3.5|10.1|
|**esub = 2**|5.2|8.8|4.2|6.4|3.6|7.9|7.2|2.9|20.9|3.1|9.7|
|**esub = 4**|6.7|11.9|5.1|8.5|4.4|11.7|10.2|5.3|31.9|3.5|10.0|
|**esubs = 0.5**|5.7|9.7|4.5|6.9|3.8|9.5|8.9|3.8|25.5|2.3|8.0|
|**esubs = 2.0**|7.0|13.7|5.3|9.0|4.5|12.5|9.7|5.8|31.3|5.4|13.8|
|**esubva = 0.5**|5.8|10.9|4.1|5.4|3.7|10.6|8.9|4.2|26.4|3.5|9.7|
|**esubva = 1.5**|6.4|11.3|5.0|7.9|4.2|10.6|9.4|4.7|29.9|3.5|10.2|
|**etad = 10**|6.7|11.8|4.8|8.1|4.4|11.3|10.0|5.0|33.7|3.3|9.5|
|**etad = 5**|5.7|10.6|4.8|7.3|3.7|9.8|8.4|3.9|23.0|3.7|11.2|
|**etadx = 3**|5.8|9.8|4.3|5.7|4.0|9.4|8.6|4.6|24.9|3.6|8.5|
|**etadx = 7**|6.6|12.4|5.2|8.0|4.1|11.7|9.8|4.6|33.6|3.4|11.6|
|**etaf = 12.5**|6.1|9.9|5.4|7.7|4.3|9.6|8.7|4.1|25.4|2.9|8.9|
|**etaf = 17.5**|6.3|12.3|4.2|7.6|3.8|11.4|9.8|4.9|32.0|4.0|11.0|
|**sigmadm = 4**|6.7|11.8|5.1|8.1|4.4|11.4|10.0|5.1|32.1|3.5|10.5|
|**sigmadm = 2**|5.4|9.4|4.3|6.9|3.8|8.5|7.8|3.4|22.2|3.1|9.4|
|**Source: Authors' calculations.**||||||||||||
|**Notes:**<br>a. Thepiecemeal sensitivityanalysis e|mploys centr|al values for|allparamete|rs(see belo|w)other than|the testedp|arameter.|||||

|**Source: Authors' calculations.**<br>**Notes:**<br>a. Thepiecemeal sensitivityanalysis em|ploys cen|tral values for allparameters(see below)other than the testedparameter.|
|---|---|---|
|**Parameter**|**Central**|**Definitions of theparameter(see figures, especially figure 5)**|
||**value**|**for moreprecise elasticity structure).**|
|esubc|1.25|Elasticityof substitution in consumer demand|
|esubs|1.25|Elasticityof substitution between value-added and business services|
|sigmadm|3|"Armington" elasticityof substitution between imports and domesticgoods|
|etaf|15|Elasticityof multinational service firm supplywith respect toprice of output|
|etad|7.5|Elasticityof Russian service firm supplywith respect toprice of output|
|esub|3|Elasticityof substitution between firm varieties in imperfectlycompetitive sectors|
|esubva|1|Elasticityof substitution betweenprimaryfactors ofproduction in value added|
|etadx|5|Elasticityof transformation(domestic output versus exports)|

69
14 Performance of the banking sector in terms of profitability following the reforms have improved despite declining concentration ratios and new entry. As shown in table 3, and pointed out by Atiyas and Ersel (1994) profits in the banking sector increased substantially, particularly after the mid 1980s, and reached to levels about 5 times the OECD average by the end of 1990. At the same time, the declining trend in operating costs following the initial reform years were reversed in 1988 and since then these rose sharply and reached to more than double the OECD average (see table 3). This would imply that deregulation has not yet led to rationalization of the use of capital and labor and hence to improvements in productive efficiency as expected. The ability of banks to increase and maintain high profit rates under these circumstances would seem to suggest that the source of profits was market power or some other market imperfection rather than productive efficiency. Furthermore, as pointed out by Rhoades (1993) it would also mean that additional resources are not entering the market, implying the existence of non-regulatory entry barriers.

# IV. METHODOLOGICAL APPROACH

The review of developments in the banking system suggests that market structure continues to have a significant impact on the conduct and performance of banks, and implicitly on competition. However, observations by themselves are not sufficien **t** o establish a causal link between these parameters, which requires an empirical investigation. In order to do this, this study presents and tests a number of hypotheses by drawing upon the methods of industrial organization. The analysis is in two parts. The first part attempts to determine if there is a relationship between market structure and performance of banks using the structureconduct-performance (SCP) paradigm as a framework of analysis. The study examines the two main hypotheses, namely the "traditional" and "efficient structure" hypotheses found in the genera **l** iterature for the explanation of market structure-performance relationship. The second part focuses more directly on
start of adolescence and then a second growth spurt during adolescence ending in gaining full adult height (Tanner et al., 1966a,b; Beard and Blaser, 2002; Bozzola and Meazza, 2012). Figure A.5 demonstrates height velocity curves for a typical boy and a typical girl. Under adequate nutritional and environmental conditions, the height growth rate is highest during infancy, 26 centimeters per year, and progressively declines until around age three, then stabilizes around 6 centimeters per year until the start of puberty (Beard and Blaser, 2002; Bozzola and Meazza, 2012). Pubertal height spurt among girls starts after age nine, peaks at about 12 years, and stops around the age of 15 (Figure A.5).

I borrow these stylized facts from the clinical and bio-medical literature to establish causality. NDHS collects individuals' month and year of birth, which I use to create the age of women at the start of the conict in February 1996. Figure 4 presents cohorts by age at the start of the conict and potential exposure to conict at dierent stages of their physical growing periods. As demonstrated in Figure A.5, girls past their pubertal age i.e. cohorts aged 16 to 21 and 21 to 29 in 1996 (control 1 and 2 respectively) would have gained full adult stature by the time of the start of the conict, and the eect of the conict should be minimal or zero on their nal height. I use cohort 16 to 21 as the main comparison group and use cohort 22 to 29 as a control placebo experiment in a dierence-in-dierence specication. Based on their growing phases at the beginning of the conict, I create three conict-exposed cohorts. Although in the important phase of adolescence height spurt, girls aged 9 to 15 (treatment 3) would have faced conict only in their third phase of height growth. While girls aged 4 to 8 (treatment 2) would have been exposed to conict in the second and third stages of growth, cohort 0 to 3 (treatment 1) would have been exposed to conict through the entire growing period (all three stages).<sup>7</sup> In the baseline specication, I dene conict intensity at the village level, hence, all ve cohorts from any given village would have been exposed to the same total amount of conict during their lifetimes. However, the exposure would have started at dierent times of their lives.

> 7These ve cohorts aged 0 to 3, 4 to 8, 9 to 15, 16 to 21, and 22 to 29 at the start of the conict in 1996 would become 20 to 23, 24 to 28, 29 to 35, 36 to 41, and 42 to 49 at the time of the NDHS 2016 survey.

13
**Table 7: Factors affecting demand for and willingness to pay for formal title**

|||**Want to a**|**pply**|**Amo**|**unt willing to**|**pay in logs**|
|---|---|---|---|---|---|---|
||**All**|**State land**|**Customary land**|**All**|**State land**|**Customary land**|
|**Panel A. Baseline regression**|||||||
|Tenure insecurity|0.095***|-0.021|0.109***|-0.166**|0.002|-0.214***|
||(0.019)|(0.037)|(0.020)|(0.074)|(0.135)|(0.081)|
|Residential parcel|-0.015|0.117***|-0.030***|-0.177***|-0.594***|-0.130***|
||(0.012)|(0.040)|(0.011)|(0.044)|(0.169)|(0.044)|
|Transferability|0.287***|0.269***|0.277***|0.449***|0.228|0.495***|
||(0.022)|(0.041)|(0.023)|(0.082)|(0.164)|(0.089)|
|Title in process|0.040|0.033|0.044|0.271**|0.504***|-0.014|
||(0.036)|(0.044)|(0.064)|(0.137)|(0.162)|(0.189)|
|Has informal certificate|0.162***|0.076|0.174***|0.348***|0.160|0.280**|
||(0.038)|(0.126)|(0.037)|(0.123)|(0.366)|(0.131)|
|Customary tenure|0.013|||0.202|||
||(0.030)|||(0.125)|||
|R-squared|0.179|0.312|0.168|0.152|0.182|0.158|
|**Panel B. Control function appr**|**oach**||||||
|Tenure insecurity|0.095***|-0.022|0.109***|-0.166**|-0.007|-0.214***|
||(0.019)|(0.037)|(0.020)|(0.074)|(0.134)|(0.081)|
|Residential parcel|-0.015|0.118***|-0.031***|-0.176***|-0.593***|-0.128***|
||(0.011)|(0.040)|(0.011)|(0.044)|(0.169)|(0.044)|
|Transferability|0.287***|0.268***|0.277***|0.448***|0.223|0.493***|
||(0.021)|(0.041)|(0.023)|(0.082)|(0.166)|(0.089)|
|Title in process|0.041|0.034|0.041|0.272**|0.506***|-0.008|
||(0.037)|(0.044)|(0.065)|(0.137)|(0.162)|(0.189)|
|Has informal certificate|0.164***|0.077|0.177***|0.345***|0.155|0.274**|
||(0.038)|(0.126)|(0.037)|(0.122)|(0.367)|(0.130)|
|Customary tenure|0.008|||0.207|||
||(0.031)|||(0.126)|||
|Est. Probability of non-response|-0.345|-0.126|-0.499|0.585|-0.746|1.261|
||(0.261)|(0.350)|(0.315)|(0.912)|(1.374)|(1.163)|
|R-squared|0.180|0.312|0.170|0.153|0.183|0.159|
|Observations|6,793|818|5,975|3,304|484|2,820|
|Dep. var. mean|0.486|0.592|0.472|5.702|6.030|5.646|
|Test (p-value):|||||||
|Title in process=Inf. cert.|0.014|0.754|0.060|0.647|0.372|0.158|

_Notes_ : Each column reports results from a parcel-level regression that includes all parcels without a formal title (col 1-3) and for parcels for which a title is desired (col 4-6). “Title in process” means that a parcel has invitation to treat or survey plan – i.e., title is incomplete. Household characteristics, region and survey quarter fixed effects are included. Constant is not reported, and standard errors are clustered at village level. Significance is denoted by *** for p<0.01, ** for p<0.05, and * for p<0.1.

27
reported professional development in mathematics (SC35Q02). A very interesting variable from a policy point of view regards the incentives for teachers. School principals were asked to what extent performance appraisal or other forms of feedback are related to incentives for teachers in seven different forms, from salary and bonus to public recognition and greater job responsibilities. The answers were to be given on a 4 point scale: ‘No change’, ‘A small change’, ’A moderate change’ and ’A large change’. We converted the rating into a Rasch index, scaled to an OECD mean of 0 and standard deviation of 1. The mean for Dev7 for this index, TCH INCENTV was -0.03 for Dev7 and 0.27 for Vietnam, indicating greater presence of teacher incentives in Vietnam. The final set of variables in Table 6 deal with the way that quality assurance regarding teacher performance is carried out, with help of a mentor, peer, supervisor or external inspector. These variables indicate a higher prevalence of oversight for teachers in Vietnam, with the difference being greatest for external inspections (86.64% in Vietnam compared to 58.82% in Dev7 countries).

# **2.7 Pedagogical Practices**

Pedagogical practices are an outcome of a complex interaction between curriculum and related educational policies, economic possibilities and the cultural and historical context. It is difficult to trace differences in these practices in a quantitative survey.<sup>7</sup> Table 7 presents a few variables that seek to capture variation in pedagogical practices. They indicate the higher prevalence of national policies in Vietnam regarding the use of computers in the classroom and the use of a standardized curriculum that specifies what has to be taught each month. There is no difference with regard to the use of a single textbook. There is some difference in the use of formative student assessment, with slightly higher percentage of use of assessments to monitor teachers and schools in Vietnam. COGACT represents an OECD-PISA index variable based on response to student reports regarding classroom practices such as teachers requiring students to reflect on a problem or develop new procedures rather than rely on common practices. This variable shows a much lower level of cognitive activation in Vietnam (-0.33) compared to 0.30 for Dev7. In the final set of classroom management variables, an interesting variation can be seen in DISCLIMA, an index variable that measures disciplinary climate in class, and is higher for Vietnam (0.38) than Dev7 (-0.02).

> 7For an interesting recent qualitative study that seeks to emulate the TIMSS video study for Vietnam, see [Vu Dinh Phuong, 2014].

14
receive the transfer (complying with its condition of sending children to school) are left out of the program. Neither does it take into account other types of implementation flaws, including “inclusion errors” of non-eligible households receiving the transfers, lack of compliance with the conditions of the transfer, or schools being unable to accommodate increase in enrollments. Secondly, due to the nature of the BFL model, the _ex ante_ AIT estimator does not take into account any time trend effects on outcomes on which impacts are measured. Given this, it is best to compare the _ex ante_ AIT estimate with _ex post_ AIT estimates obtained using doubledifference or difference in difference (DID) method whenever possible – recognizing that this _ex post_ method removes the time trend effect from the estimated impact.

# _The impact of PROGRESA_

We compare the simulated _ex ante_ impact of _PROGRESA_ , applying the BFL model on the baseline survey (from 1997 – see section IV for details), with _ex post_ results from Skoufias and Parker (2001). To make this comparison possible, all income variables from the baseline survey are converted into November 1999 pesos, and transfer amounts corresponding to the second half of 1999 (see Appendix, Table A-1 for the amounts) are applied in the simulations.

The selected sample comprises only children who were of age 8-17 years at the time of the baseline survey in 1997. Given how the BFL model is set up, the data from the follow-up survey on the same children, which is to say the panel data, is not utilized for the simulation. Out of the 33,609 children in the baseline sample the simulation was applied to, around 30 percent were not attending school, while only around 4 percent of children were combining work outside the household with school. Children not attending school were on the average older and with higher education level than those who were in school and not working. Households with higher dropout rates were not necessarily poorer, indicating the importance of child income for the household. Child earnings increased with age and girls were more likely to drop out than boys, but boys were more likely to work and attend school simultaneously (see Appendix, Table A-2 for all descriptive statistics). Average household size was similar (around 7 members) for the 3 groups of children and more educated parents were more likely to have children in school.

Enrollment patterns across age groups are important to note as well, with implications for how program impacts should be estimated. Dropout rates increase sharply around the age when children are expected to complete primary schooling, rising from 7 percent at age 11 to nearly 19
# **Appendix 4: Full results for effects on welfare (IV regression)**

For tables A9 to A16, regressions (1) to (4) show results with the regressor (Log or refugees at 50km) predicted by the Log of distance IV generated using the mean border distance. For regressions (5) to (8), the IV is generated using the square of the mean border distance while for regressions (9) to (12), the square root of the mean border distance is used. These represent the decay categories of the IV. Sampling weights are considered and Standard errors clustered at the cluster level in all regressions. Robust standard errors in parentheses. Significance level are: *** p<0.01, ** p<0.05, * p<0.1.

## **Table A9: Second stage: Log Refugees at 50km instrumented by distance-based IV with decay function represented (i.e. IV generated using 1)mean border distance,** **<u>2)square of mean border distance and 3) square root of mean border distance)</u>**

|||||Lo<br>|g of welfare<br>|(consumption<br>|aggregate per ad<br>|ult equivalen<br>|t)<br>||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Log of refugees (50km) predicted by Log of<br>Distance IV (mean border distance)|0.03226|0.03207|0.07808*|0.07384*|||||||||
||(0.04466)|(0.04459)|(0.04453)|(0.04302)|||||||||
|Log of refugees (50km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.03786|0.03803|0.09245*|0.08947*|||||
||||||(0.04543)|(0.04539)|(0.04785)|(0.04609)|||||
|Log of refugees (50km) predicted by Log of|||||||||||||
|Distance IV (square-root of mean border<br>distance)|||||||||0.04197|0.04149|0.07805|0.07528|
||||||||||(0.04920)|(0.04909)|(0.04800)|(0.04687)|
|HH head age||0.00261|0.01187**|-0.00029||0.00262|0.01185**|-0.00038||0.00263|0.01187**|-0.00029|
|||(0.00510)|(0.00558)|(0.01656)||(0.00510)|(0.00558)|(0.01659)||(0.00510)|(0.00558)|(0.01656)|
|Age squared||-0.00003|-0.00008|-0.00000||-0.00003|-0.00008|-0.00000||-0.00003|-0.00008|-0.00000|
|||(0.00005)|(0.00005)|(0.00014)||(0.00005)|(0.00005)|(0.00014)||(0.00005)|(0.00005)|(0.00014)|
|HH head sex (male)||0.00470|0.02499|-0.07482||0.00476|0.02514|-0.07393||0.00479|0.02499|-0.07474|
|||(0.03461)|(0.03745)|(0.06348)||(0.03461)|(0.03745)|(0.06350)||(0.03461)|(0.03744)|(0.06354)|
|Household size|||-0.02626***||||-0.02627***||||-0.02626***||
||||(0.00539)||||(0.00539)||||(0.00539)||
|Married monogamously|||-0.42111**||||-0.41959**||||-0.42112**||
||||(0.16636)||||(0.16682)||||(0.16627)||
|Married polygamously|||-0.36601**||||-0.36431**||||-0.36601**||
||||(0.17389)||||(0.17435)||||(0.17381)||

9
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