rafmacalaba/datause-encoder-data huggingface dataset:
in each round (of the follow-up surveys), which is net of any preprogram differences between treatment and control households and any time trends in the values of the outcome indicator.

Skoufias and Parker (in Figures 4a and 4b of their paper) show that the mean school attendance rate of both boys and girls were nearly identical for control and treatment villages at the outset of the program. For treatment villages, the enrollment rate among boys was higher than that of girls in the baseline year, where the difference is entirely a result of the higher dropout rate among girls of age 12-17 years (Table 1). For the age group 8-11 years, enrollment was high at around 94 percent for both boys and girls.

**Table 1: Impact of Transfers on Children’s Occupational Choice (1997-1999)** Actual and counterfactual school enrollment rates for _PROGRESA_ target population

|||_Baseline_<br>_(pre-program)_|_Ex post A_|_IT_<sup>_1_</sup>|_Ex ante A_<br>_Conditio_|_IT_<sup>_2_</sup><br>_nal_|_Ex ante AIT_<sup>_2_</sup><br>_Unconditional_|
|---|---|---|---|---|---|---|---|
||8-17 years-old|74.5%|-||4.0%|******|-0.1%|
||||||(0.4%)||(0.7%)|
|Bo|8-11 years-old|93.8%|1.8%|******|0.0%||-0.1%|
|ys|||(0.7%)||(0.4%)||(0.5%)|
||12-17 years-old|57.5%|5.8%|******|5.9%|******|0.0%|
||||(2.1%)||(0.8%)||(0.8%)|
||8-17 years-old|69.4%|-||4.3%|******|-0.1%|
||||||(0.7%)||(0.4%)|
|Gir|8-11 years-old|93.9%|-0.3%||-0.2%||-0.1%|
|ls|||(1.0%)||(0.4%)||(0.4%)|
||12-17 years-old|47.9%|9.5%|******|6.6%|******|-0.1%|
||||(2.2%)||(0.8%)||(0.8%)|

_Source_ : Baseline Survey 1997 and Rounds 1-4;authors' calculation. _Note_ : 1: Results from Skoufias and Parker (2001) - Table 6 (only the results for November 1999 are reported). The coefficients reported are the marginal effects of the _PROGRESA_ program on the probability of attending school; 2: Results from simulation on baseline data using BFL model

** Significant at 5% level; * Significant at 10% level.

Standard errors and standard deviations in parentheses. For _ex post_ AIT, robust standard errors are shown, which account for clustering of individuals within villages. For _ex ante_ AIT, standard deviations computed by bootstrap method.

Table 1 shows the AIT estimates of impact of _PROGRESA_ on school enrollment rates by age and gender (Skoufias and Parker, henceforth SP) as well as the _ex ante_ AIT estimates using the BFL simulation model. _Ex ante_ estimates are also shown for a hypothetical case where the transfer is 21
We present exponentiated coefficients (odds ratios) from these models, along with standard errors clustered on the enumerator level.

# **_3.3 Simulating the targeting performance of the QAS_**

To understand what we can learn from QAS, we simulate the performance of the QAS in terms of targeting different potential quality enhancement actions that could follow from the monitoring tool data of the QAS. We refer to these analyses simulating the targeting performance of the QAS for quality enhancement as “simulations” for short. Specifically, we compare who would be the targets of different policy actions using the QAS tool and diagnostic research tool data. We examine:

- Differences in the top 10% and top 25% of districts, schools, and classes/teachers identified with the QAS overall, which might be used for recognition, pay, or promotion

- Differences in the bottom 10% and top 25% of districts, schools, and classes/teachers per the QAS overall, which might be used for shutdown, termination, or other sanctioning decisions

- Differences in targeting repairs (whether the bottom 10% and bottom 25% of districts, schools, or classes with the greatest number of safety hazards is consistent)

- Differences in targeting materials (whether the same districts, schools, or classes would be targeted for supplemental grants and training for materials, based on not having or using any materials)

- Differences in targeting pedagogy training or coaching (based on the poorest performing level in terms of pedagogy) for districts, schools, and classes

- Differences in supplemental reading tutors, based on the poorest-performing 10% and poorest-performing 25% on children’s letter recognition for districts, schools, and classes

Differences are parametrized as the percentage of the simulation targets in the QAS tool data that are identified the same way in the diagnostic research tool data. These different simulations using the QAS levels, the level of a particular QAS dimension, and specific items, illustrate how these different approaches to using QAS data to inform action might perform.

We examine these questions at the district, school, and class levels for a variety of reasons. First, policies might operate on these different levels, depending on the design of the quality enhancement system and administration. Second, measurement error may be reduced when looking at aggregates, particularly if outcomes in a school, class, or district are highly correlated. We examine, for the outcomes that target a certain ranked (top or bottom) percentage, both 10% and 25% targeting. The narrowness or coarseness of targeting may affect consistency. These simulations do not incorporate covariates, or use the multivariate models, but rather describe the average, national consistency of the QAS system as a data source for quality enhancement action.

7
# **1 Introduction**

Youth unemployment is a prominent concern among policy makers across the developing world, especially in countries where the young comprise a large and growing share of the population (Carranza and McKenzie, 2024). The substantial gender gap in youth labor force participation is an additional policy challenge in countries like India, where the labor force participation rate for young women (aged 15-29) is 39 percentage points (p.p.) below the labor force participation rate for young men (63.5 percent).<sup>1</sup> Besides demand-side factors, skill gaps, and matching frictions, the youth may struggle to find jobs and to stay employed because they have biased beliefs and unrealistic aspirations about the labor market. A growing body of literature has shown that young job-seekers often overestimate the current market wage and have too high a reservation wage (e.g., Kelley, Ksoll and Magruder (2024)). Although high aspirations can act as a motivator and spur effort, if the aspirations are unrealistically high, they can lead to frustration and poor labor market outcomes (Bandiera et al., 2020; Ray, 2006).

Rapid changes in the nature of work due to technological change, and most recently, the COVID-19 pandemic, are likely to exacerbate these challenges. COVID-19 dramatically slowed economic activity worldwide as governments implemented containment measures affecting firms’ demand for labor and workers’ ability and willingness to work. The negative effect of the pandemic on employment was disproportionately borne by women (“she-cession”) due to greater disruptions in female-dominated industries and occupations, and a larger increase in women’s care burden (e.g., Abraham, Basole and Kesar (2022); Chakravorty et al. (2023); Deshpande (2022)).

We study whether exposure to the COVID-19 pandemic influenced the labor market aspirations and expectations of young women in India, and specifically in the north Indian state of Haryana. The gender gaps in youth labor force participation and unemployment in Haryana are even larger than those at the national level. According to the 2022-23 Periodic Labor Force Survey, the labor force participation and unemployment rates for young women in Haryana are 12.5 percent and 21 percent, respectively, whereas the corresponding numbers for young men are 57.7 percent and 16.8 percent. We focus on aspirations for financial remuneration, which is one of the most important dimensions of what individuals value in a job.<sup>2</sup> We also examine whether the pandemic’s impact on willingness to migrate is an underlying channel for our main results. This line of inquiry is driven by the significant detrimental impact of the pandemic on rural-to-urban migration in India (Arora et al., 2023).

> 1Source: Periodic Labor Force Survey of India, July 2022-June 2023.

> 2In our sample, 93 percent of the respondents consider salary to be a very important aspect of a job.

1
# **Appendix C: Additional regression results**

# **_- Appendix Table C1. Baseline impacts of AGOA and GSP allowing for sub_** **_<u>groups in non-apparel</u>_**

|<br>Data at country-HS<br>Dependent v|<br>6-digit-year level<br>ariable is:|
|---|---|
|Log (imports + 1)|Dummy for|
|<br>(1)|<br>positive US<br>imports<br>(2)|
|GSP LDC * Africa * Agriculture<br>0.060*|0.003|
|(2.14)|(1.13)|
|GSP LDC * Africa * Manufacturing<br>0.146***|0.010***|
|(6.86)|(6.15)|
|GSP LDC * Africa * Mining<br>-0.112|-0.009|
|(-0.69)|(-0.77)|
|GSP LDC * Non-Africa * Agriculture<br>-0.075**|-0.007**|
|(-2.77)|(-3.01)|
|GSP LDC * Non-Africa * Manufacturing<br>-0.029|-0.003|
|(-1.28)|(-1.80)|
|GSP LDC * Non-Africa * Mining<br>-0.132|-0.010|
|(-1.69)|(-1.88)|
|AGOA Non-LDC * Agriculture<br>0.007|-0.002|
|(0.28)|(-0.91)|
|AGOA Non-LDC * Manufacturing<br>0.047**|0.002|
|(2.58)|(0.98)|
|AGOA Non-LDC * Mining<br>-0.287|-0.023|
|(-1.52)|(-1.64)|
|AGOA Non-apparel * Agriculture<br>0.147*|0.011*|
|(2.34)|(2.02)|
|AGOA Non-apparel * Manufacturing<br>0.040|-0.002|
|(0.93)|(-0.52)|
|AGOA apparel<br>0.200***|0.012***|
|(4.78)|(3.63)|
|Treatment group-specific time trends<br>Yes|Yes|
|Country-product fixed effects<br>Yes|Yes|
|Country-year fixed effects<br>Yes|Yes|
|Product-year fixed effects<br>Yes|Yes|
|ntheses, clustered by HS 6-digit product. ***, **, and * in<br>Observations<br>27,420,560|dicate significance at 1%, 5%, and<br>27,420,560|

Notes: Robust t-statistics in parentheses, clustered by HS 6-digit product. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. Estimates of a variant of Eq. (1) allowing for separate coefficients for agriculture, manufacturing, and mining sub-sectors shown.

As an alternative to Eq. (1) we use detailed data on the tariffs that the US applied to all HS 6-digit products and all countries over the period 1997-2017, to estimate the trade effects of tariff preferences granted by the US under different schemes, including AGOA, using the following specification: MFN Pref MFN Pref − τ − τ ln(Impcpt) = γ(τcpt cpt ) × 1[c∈AFR] + γ(τcpt cpt ) × 1[c∈NonAFR] + δcp + δct + δpt + εcpt MFN Pref where τcpt − τcpt is the difference between the MFN tariff rate and the best available US preferential tariff rate. As in Equation (2) we control for a stringent set of fixed effects: country-product, country-year, and product-year. Since Eq. (D1) relies on a continuous tariff preference variable (which captures the magnitude of the trade preference) rather than discrete country-product treatment groups, we do not control for treated group-specific time trends. The coefficient of interest γ captures the trade effects of the tariff preferences given to a country-product pair relative to the average export of that country-product pair. To capture the trade effect of the tariff cuts provided to African countries, we include the tariff preference 8
this policy for aggregate TFP and firm behavior.

Second, our exercise is related to the literature on tax enforcement and third-party information (Kopczuk and Slemrod 2006, Gordon and Li 2009, de Paula and Scheinkman 2010, Pomeranz 2015, Naritomi 2016). Almunia and Lopez-Rodriguez [2017] show that Spanish firm bunch at the size-threshold of the large taxpayer unit, while Zareh and Peichl [2016] find that Armenian firms bunch at the full account reporting threshold. Our results provide empirical support to theories where firm size is correlated with tax compliance (Kleven et al. 2016, Bigio and Zilberman 2011). In Kleven et al. [2016], tax avoidance strategies such as double book-keeping and collusion with employees to hide operations are impossible to sustain for large firms, since a single whistle blower can reveal the entire operation. Therefore, large firms disclose third-party information and comply with their tax obligations. In this model the government enforcement strategy is fixed, and increased tax compliance with development is driven by firms’ size growth. However, our results are also consistent with models where increased tax inspection with size is an optimal government policy, given fiscal capacity constraints (Bigio and Zilberman 2011, Ito and Sallee 2016).

The paper is structured as follows. Section 2 discusses the data. Section 3 presents the identification strategy and empirical specifications. Section 4 shows the empirical results and their robustness. Section 5 presents the general equilibrium model, which is calibrated in Section 6 to quantify distortions from size-dependent tax enforcement. Section 7 concludes.

# **2 Data**

To provide global evidence on the relation between industry average firm size, tax inspection and tax compliance, we use the comprehensive firm-level data from the World Bank Enterprise Surveys (WBES). The surveys cover 125,000 firms in 140 countries between 7
|Log of refugees(50km)#To Non-Agric self-employment|-0.0048<br>(0.0153)|-0.0046<br>(0.0153)|0.0129<br>(0.0209)|
|---|---|---|---|
|Observations|3,734|3,734|2,763|
|Root MSE|0.4057|0.4059|0.4213|
|**_Included controls_**||||
|Exogenous controls (age, agesq, sex “male==1”)|_No_|_Yes_|_Yes_|
|Other controls (HH size, marital status, education,<br>occupation, other income sources, land ownership)|_No_|_No_|_Yes_|
|Year fixed effects|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|
|Household fixed effects|_No_|_No_|_No_|

In order to further understand this coping strategy, we further investigate the agriculture channel. We define a variable capturing the level of agricultural production, that is, total agricultural production across both agricultural seasons.<sup>23</sup> These variables are all log transformed before inclusion in the model specification. We redefine the model specification (1) above by including the following controls: whether or not the household experienced shocks in the last 12 months, the distance to the nearest major road, the distance to nearest population center, the distance to the nearest market, the distance to the nearest land border crossing, the distance to the headquarters of district of residence, the annual mean temperature, the annual cumulative precipitation, the average 12 months total rainfall, the percent agriculture within approximately a 1km buffer, and the majority of land cover class within approximately a 1km buffer.

Table 10<sup>24</sup> shows that the few households which switch to commercial farming potentially have higher total agricultural production. These results firstly support the argument that agriculture is an important channel through which the welfare of households living in refugee hosting areas is affected.

Table 10: OLS: Effects of refugee presence on household welfare disaggregated by change in main source of income, - <u>with analysis at cluster level and the distance weighted refugee index at 50km from the clusters (Panel 2009 2012)</u>

||Log of Total Agricu<br>|ltural Productio<br>|n (UGX)<br>|
|---|---|---|---|
||(1)|(2)|(3)|
|Log of refugees (50km)|-0.0012|0.0054|0.0089|
||(0.0258)|(0.0257)|(0.0284)|
|Log of refugees(50km)#To Commercial farming|0.2430**|0.2360*|0.2015**|
||(0.1172)|(0.1266)|(0.0824)|
|Log of refugees(50km)#To Wage employment|-0.0097|-0.0184|0.0128|
||(0.0367)|(0.0362)|(0.0314)|
|Log of refugees(50km)#To Non-Agric self-employment|0.0468|0.0397|0.0620*|
||(0.0365)|(0.0359)|(0.0344)|

> 23 We disaggregate the level of agricultural production by calculating the total harvest of fruits, vegetables and cereals across both agricultural seasons. The total harvests of fruits, vegetables and cereals are converted in monetary terms, that is Uganda shillings (UGX), before the values are aggregated to determine the total household agricultural production. The detailed classification of the food categories is given on the last page in the annexed Appendix.

24 Details in Appendix 4; Table A31.

30
# **5. Conclusions**

Do high interest rates help to defend exchange rates that come under speculative attack? The evidence considered in this paper suggests that the answer is no. Although proponents of the view that high interest rates can support a currency under attack can point to episodes such as Sweden in the summer of 1992, while proponents of the contrarian view that high interest rates weaken currencies can point to Korea in the fall of 1997 as supportive anecdotes, a systematic examination of interest rates around a large number of historical speculative attack episodes indicates a striking lack of evidence that the stance of monetary policy is correlated with the outcome of speculative attacks. In particular, I find no evidence that interest rates systematically increase or decrease during failed speculative attacks, nor that raising interest rates lowers or raises the probability that a speculative attack fails. This basic finding is robust to alternative measures of the stance of monetary policy, to interactions which control for differences in fundamentals across speculative attack episodes, and to controlling for the endogeneity of the policy response to a speculative attack.

Nevertheless, several shortcomings of this paper suggest that it may be premature to conclude that monetary policy is entirely ineffective in during speculative attacks. In the interests of covering a sample of spieculative attacks large enough to include interesting variation in the outcome of speculative attacks, the policy response to the speculative attack, and the fundamentals that are likely to determine both the outcome of the attack and the efficacy of the policy response, I have made several compromises with regards to data and methodology. Three such compromises, and possible strategies to avoid them in future research, deserve mention.

First, I have relied on readily-available but relatively low-frequency monthly data to identify speculative attacks and the response of policy. This is unfortunate given that much of the economically interesting variation during speculative attack episodes is likely to occur at much higher daily, or even hourly, frequencies. The use of monthly data also precludes modeling the likely path-dependence in the effects of interest rates on speculative pressures, a point emphasized by Drazen (1999). Moving to highfrequency data for the more limited sample of speculative attacks for which such data is 26
# **_Impact on learning_**

As used here, ―impact on learning‖ refers to improving the delivery of education in Jordan through the effective use of ICT, with the ultimate goal of supporting the development of a knowledge economy and producing sustainable national economic growth. To date, certain external reviews of the program have documented positive indications of satisfaction from teachers and students who are using the e-curriculum and technology of JEI (USAID 2008).

Recent changes in the Jordanian education system have created a suitable environment for piloting innovations, given that the country’s main education objectives of access and transition rates to secondary school are well on their way to being achieved. For example, Jordan has established significant positive momentum in student performance in recent years. Net enrollment in basic education increased from 89 percent in 2000 to 96.5 percent in 2005–2006; transition rates to secondary education increased from 63 to 79 percent over the same period. Jordan has also made significant gains in international assessments of student achievement, with a particularly impressive gain of almost 30 points on the science portion of the 2007 Trends in International Mathematics and Science Study (Mullis, Martin, and Foy 2008).

Jordan’s progress in international assessments actually dates to the early 1990s. In the 1991 International Assessment of Educational Progress, which was conducted in 20 countries, Jordan finished ahead only of Brazil and Mozambique in the mathematics and science tests for 13-year-olds. By the late 1990s, marked change had occurred. In the 1999 Third International Mathematics and Science Study (TIMSS), in which 38 countries participated, Jordan finished ahead of six (Iran, Indonesia, Chile, Philippines, Morocco, and South Africa) in mathematics and ahead of eight (Iran, Indonesia, Turkey, Tunisia, Chile, Philippines, Morocco, and South Africa) in science.

However, the country’s progress did not stop there. In 2003, Jordan improved its TIMSS science score to 475, from 450 in 1999—an increase of 25 points, or 0.25 standard deviations. This was a significant increase, equivalent to about an entire year of learning. In 2007, Jordan continued to improve its TIMSS student scores, surpassing several countries that had had a similar or slightly higher performance in 1999. In fact, between 1999 and 2007, no other country has improved as greatly in assessments of the science knowledge of its students.

Discovery Schools, moreover, appear academically superior to other public schools (see Figure 2). Students attending these schools had, on average, higher scores in all three subject areas of the PISA 2006 assessment than did those in regular public schools.

7
<!-- Start of picture text --> 2017 2018-22 2022<br>gross debt/GDP<br>WEO simulation difference<br>Canada 89.7 78.9 2.1 6.0 76.4 66.3 -10.1<br>France 97.0 89.5 1.4 6.0 91.6 78.2 -13.4<br>Germany 64.1 59.2 2.0 6.0 45.5 39.0 -6.4<br><!-- End of picture text -->

||2017||2018-22||2022||
|---|---|---|---|---|---|---|
|||||gr|ossdebt/GD|P|
|||||WEO|simulation|difference|
|Canada|89.7|78.9|2.1<br>6.0|76.4|66.3|-10.1|
|France|97.0|89.5|1.4<br>6.0|91.6|78.2|-13.4|
|Germany|64.1|59.2|2.0<br>6.0|45.5|39.0|-6.4|
|Italy|131.5|124.7|1.3<br>6.0|119.3|104.2|-15.1|
|Japan|236.4|210.0|0.8<br>6.0|230.7|195.6|-35.1|
|United Kingdom|87.0|82.8|1.7<br>6.0|83.6|71.2|-12.5|
|United States|107.8|96.4|2.0<br>6.0|115.2|101.6|-13.6|
|G7average|116.2|105.9|1.6<br>6.0|108.9|93.7|-15.2|
|19AE'saverage|86.3|79.4|1.7<br>6.0|77.5|67.2|-10.3|

<!-- Start of picture text --> Italy 131.5 124.7 1.3 6.0 119.3 104.2 -15.1<br>Japan 236.4 210.0 0.8 6.0 230.7 195.6 -35.1<br>United Kingdom 87.0 82.8 1.7 6.0 83.6 71.2 -12.5<br>United States 107.8 96.4 2.0 6.0 115.2 101.6 -13.6<br>G7 average 116.2 105.9 1.6 6.0 108.9 93.7 -15.2<br>19 AE's average 86.3 79.4 1.7 6.0 77.5 67.2 -10.3<br><!-- End of picture text -->