rafmacalaba/datause-encoder-data huggingface dataset:
For the ...rst-stage regression for tari¤s, we use the average tari¤ of the three closest countries, t<sup>�</sup> nij; as an instrument for the tari¤, tnij; to get the ...tted tari¤, t<sup>^</sup> nij : ^ ^ where � and �<sup>t</sup> are the least squares estimates of the coe¢ cients of the second-stage control variables, Zij; and the average tari¤, t<sup>�</sup> nij: For the ...rst-stage regression for the NTMs, because NTM is a dummy variable that equals one when at least one NTM is present, the ...rst-stage regression is a probit regression with a density function, f (:) ; in which we use the average presence of NTMs in the three closest countries, NTM nij; as an instrument for NTMnij : where we retrieve the inverse Mills ratio, invMillnij; constructed based on<sup>^</sup> � and<sup>^</sup> �NTM ; the coe¢ cients of the second-stage control variables, Zij; and the average NTM presence, NTM nij: Finally, we also run the ...rst-stage regressions for the four interaction terms that involved tari¤s and NTMs, using the interaction terms with t<sup>�</sup> nij and NTM nij as the respective in20
education system (Carter et al. 2012). More risk-averse individuals discount the future more because of their aversion to uncertainty. To the extent that moving up the income ladder can be seen as indicative of success in life, beliefs about what underpins such success—hard work, skills, and political connections—is important (Alesina, Stancheva, and Teso 2018). Support for policies promoting meritocratic advancement (advancements achieved through hard work and intelligence/skills) depends on whether human capital investments are seen to be instrumental for socioeconomic mobility. The model also includes country fixed effects to account for heterogeneity across countries that may be correlated with individual beliefs about future mobility and willingness to support tax-financed reforms.

Unobserved factors may also affect our results. For instance, an individual with a pessimistic disposition may have more negative expectations of future socioeconomic mobility and hold pessimistic views about the reforms, and thus be less likely to support such reforms, biasing the coefficients on the mobility expectations variable. To address such endogeneity, we instrument individual expectations of future mobility with cohort-level estimates of past intergenerational mobility for the 10-year country-birth cohort corresponding to a given survey respondent, obtained from the estimates by Torre, Lokshin, and Foster (forthcoming).

Our choice of past intergenerational mobility as an instrument for future mobility expectations draws on the literature showing that past mobility experiences inform people’s perceptions and expectations of the future (Piketty 1995; Kelley and Kelley 2009; Cojocaru 2023). Our exclusion restriction is based on the argument that there is no theoretical reason why measures of past intergenerational mobility at the birth-cohort level should affect individual support for education reforms directly beyond their effect on mobility expectations. We should note, however, that we cannot exclude the possibility of cohort shocks, namely that cohort measures of past mobility may still be correlated with other factors that may affect support for education spending. As such, the IV estimates should similarly be viewed with these caveats in mind.

# **4. Data**

We use data from the 2010, 2016, and 2023 rounds of the Life in Transition Survey (LiTS) conducted by the European Bank of Reconstruction and Development (EBRD) and the World Bank 7
variable for the availability of unspent income has an average value of 2.36, with the average being significantly higher in 2009 (2.57, compared to 2.14 in 2008).

# **4.4** **_Financial Literacy_**

Our survey includes four financial literacy questions, covering the concepts of interest (two questions), inflation (one question), and sales discounts (one question). The exact wording of the questions is reported below: _1) Let’s assume that you deposited 100,000 rubles in a bank account for 5 years at 10% interest rate. The interest will be earned at the end of each year and will be added to the principal. How much money will you have in your account in 5 years if you do not withdraw either the principal or the interest?_

- _More than 150,000 rubles_

- _Exactly 150,000 rubles_

- _Less than 150,000 rubles_

- _I cannot estimate the amount even roughly_

_2) Let’s assume that you took a bank credit of 10,000 rubles to be paid back during a year in equal monthly payments. The credit charge is 600 rubles. Give a rough estimate of the annual interest rate on your credit. The interest rate is about:_

- _3 %_

- _6 %_

- _9 %_

- _12 %_

- _I cannot estimate it even roughly_

_3) Let’s assume that in 2010 your income is twice what it is now and that consumer prices also grow twofold. Do you think that in 2010 you will be able to buy more, less, or the same amount of goods and services as today?_

- _More than today_

- _Exactly the same_

- _Less than today_

- _I cannot estimate it even roughly_

_4) Let’s assume that you saw a TV set of the same model on sale in two different shops. The initial retail price of it was 10,000 rubles. One shop offered a discount of 1,500 rubles, while the other one offered a 10% discount. Which one is a better bargain—a discount of 1,500 rubles or 10%?_

- _A discount of 1,500 rubles_

- _A 10 % discount_

- _I cannot estimate it even roughly_

15
**Table 5. Estimated global health workforce supply versus “need” as defined by WHO, by WHO and World Bank regions and income group for 2013 and 2013.**

||**20**|**13(165 countri**|**es)**|**203**|**0(165 countrie**|**s)**|**# Countries**|
|---|---|---|---|---|---|---|---|
||**Supply (S)**|**Need(N)**|**Diff(S‐N)**|**Supply (S)**|**Need(N)**|**Diff(S‐N)**|**in Category**|
|**WHO Region**||||||||
|Africa|1,874,830|5,891,071|‐4,016,241|3,066,666|8,910,473|‐5,843,806|43|
|Americas|8,385,480|5,439,623|2,945,857|12,742,856|6,246,463|6,496,393|28|
|Eastern Mediterranean|2,690,443|3,797,769|‐1,107,326|4,611,408|5,055,625|‐444,217|15|
|Europe|12,692,401|5,628,533|7,063,868|16,803,264|5,786,268|11,016,996|50|
|South‐East Asia|5,772,250|12,433,083|‐6,660,833|10,168,591|14,712,987|‐4,544,397|8|
|Western Pacific|10,294,627|11,538,553|‐1,243,926|17,261,342|12,270,476|4,990,867|21|
|**WB Region**||||||||
|East Asia and Pacific|11,141,638|13,692,899|‐2,551,261|18,250,702|14,734,499|3,516,203|23|
|Europe and Central Asia|12,594,176|5,578,223|7,015,953|16,640,618|5,722,567|10,918,050|48|
|Latin America|4,140,233|3,287,004|853,229|5,784,767|3,825,876|1,958,892|26|
|Middle East & North Africa|2,570,885|2,354,695|216,190|3,846,948|3,032,910|814,038|15|
|North America|4,245,248|2,152,619|2,092,629|6,958,089|2,420,587|4,537,501|2|
|South Asia|5,357,579|11,745,586|‐6,388,007|10,293,688|14,248,390|‐3,954,702|8|
|Sub‐Saharan Africa|1,660,273|5,917,606|‐4,257,333|2,879,315|8,997,462|‐6,118,146|43|
|**WB Income Groups**||||||||
|Low|692,757|4,861,904|‐4,169,147|1,384,576|7,049,048|‐5,664,472|29|
|Lower‐middle|9,867,919|17,605,293|‐7,737,374|17,958,943|21,940,256|‐3,981,313|44|
|Upper‐middle|13,764,139|14,617,189|‐853,050|21,362,033|15,934,777|5,427,256|46|
|High|17,385,217|7,644,247|9,740,970|23,948,576|8,058,211|15,890,364|46|
|**World**|**41,710,032**|**44,728,633**|**‐3,018,601**|**64,654,127**|**52,982,292**|**11,671,836**|**165**|

22
- 42 -

a general equlilbrium model for Indla, so It will be possible to contrast the seml-inputoutput results with thelrs.

71. The following estimates use a five sector version of the semi-input-output model. The five sectors Include three In agriculture, Irrigated (1), ralni v..' (A) and nontradable (N) agriculture, In addition to manufacturlng (M) and tertlary activity (T). Nontradable agriculture comprlses high-value livestock and horticultural products. We assume the output of Irrigated and rainfed agriculture Is constrained by technology and resource constraints, but that output In the remaining sectors Is highly elastic and constrained only by domestic demand. This is clearly the case for most tertiary, horticultural and livestock products, since thelr perishability or location specificity severely limits internatlonal trading possibilities. It Is less obvious with manufactured goods, and our assumptlon Is designed to capture the effect of trade restrictions (both domestic and foreign) that essentially pro-determine the amount that can be exported.

72. As new technology Increases Irrigated and rainfed agricultural output, the nontradable agriculture, N, and nonfarm sectors, M and T, respond to the consumption and production linkages that ensue. Hence the key parameters affecting the magnitude of the llnkages are the Input-output coefficients, the marginal propensity to consume nontradable foods and nonfarm goods, savings and tax rates (leakages In that they represent Income not spent on nonfarm goods), and the value-added share In gross output (which allows translation from gross output to Income multipliers). Appendix B develops the model formally,20 while Appendix Table A.19 displays the parameters used to obtain the following results.

2e S Bol I **and HazolI** (1980), **Ha2*l1** (1964) **and Haggblade and** Hazell (1989) for additional applications.
We summarize the behavior of capital flows around country-specific stops and flights by estimating the panel regression, where _i_ refers to specific capital flows, c to the country and t to quarter-year: We regress capital flows of type _i_ , denoted ����, on a dummy variable for the country-specific sudden stop (or flight), ����, country-fixed effects, ��, and country-specific time trends, ���. For ease of comparison, we normalize ���� by subtracting from each observation its country specific mean and dividing it by the country-specific standard deviation.

We see in Table 5 that portfolio equity, portfolio debt and other inflows all turn negative during sudden stops. The decline in inflows is sharpest for other flows and smallest for FDI. In addition, portfolio equity and debt outflows and especially other outflows drop significantly below their average in sudden stops (Table 6). This suggests that resident flows are stabilizing. However, looking at the scale of outflows in Figure 2 and 3, it is evident that the decline in outflows during sudden stops is smaller than the decline in inflows. So even if the decline in outflows by residents partially offsets the decline in inflows by non-residents, this stabilizing impact is only partial, and net inflows still decline.

Finally, during periods of capital flight all categories of capital outflow increase (Table 7). The increase is again largest for other flows, followed by debt outflows. It is smallest for FDI.

**Table 5: Capital Inflows in Sudden Stops**

||FDI|Portfolio Equity|Portfolio Debt|Other flows|
|---|---|---|---|---|
|������|-0.346**|-0.735***|-1.064***|-1.540***|
||[2.37]|[3.33]|[7.22]|[9.40]|
|Country fixed-effects|Yes|Yes|Yes|Yes|
|Country specific trends|Yes|Yes|Yes|Yes|
|Observations|2,401|2,373|2,401|2,401|
|# of countries|34|33|34|34|
|R-squared|0.098|0.053|0.077|0.128|

_Notes:_ The dependent variables are capital flows of the respective type as a percentage of trend GDP. They are standardized by subtracting the country-specific mean and dividing by the country-specific standard deviation. The sample spans from 1990:Q1 to 2015:Q4. Robust t-statistics are reported in brackets. ***, **, * indicate significance at the 1%, 5% and 10% level.

12
Table 5: Dependent Variable: Arms imports - SIPRI

||(1)<br>All|(2)<br>USA|(3)<br>RUS|(4)<br>CHN|(5)<br>DEU|(6)<br>FRA|(7)<br>RoW|
|---|---|---|---|---|---|---|---|
|Discovery in past 5 years|0.284<sup>_∗∗_</sup>|-0.014|0.114|0.001|0.033|0.012|-0.065|
||(0.135)|(0.101)|(0.129)|(0.122)|(0.085)|(0.101)|(0.161)|
|N|3408|3640|3640|3640|3640|3640|3628|
|R-sq|0.71|0.71|0.56|0.60|0.57|0.61|0.58|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||All|USA|RUS|CHN|DEU|FRA|RoW|
|Discovery in past 5 years|0.608<sup>_∗∗_</sup>|0.058|0.524<sup>_∗∗_</sup>|0.037|0.146|0.086|0.334|
||(0.223)|(0.137)|(0.225)|(0.114)|(0.115)|(0.110)|(0.218)|
|_×_ Oil price|0.646<sup>_∗_</sup>|0.140|0.800<sup>_∗∗_</sup>|0.070|0.219|0.144|0.782<sup>_∗_</sup>|
||(0.376)|(0.223)|(0.321)|(0.158)|(0.135)|(0.128)|(0.394)|
|N|3408|3640|3640|3640|3640|3640|3628|
|R-sq|0.72|0.71|0.57|0.60|0.58|0.61|0.59|

The dependent variable is in inverse hyperbolic sine. The regressions include country and year fixed effects. Robust standard errors in parenthesis, and * stands for statistical significance at the 10% level, ** at the 5% level and *** at the 1% percent level.

on the right-hand side. While the results using SIPRI data suggest that both oil and gas discoveries lead to higher arms imports, the results using COMTRADE data suggest only gas discoveries are associated with larger arms imports. These are not statistically significant results however. This may be because, while both oil and gas discoveries probably can trigger arms imports, identifying their average effects separately requires a larger time period.

The second panel suggests that it might only be offshore discoveries that trigger arms imports. While this result is in line with the Angola and Mozambique anecdotes, it does not appear to be in line with the evidence from Nordvik (2018) who suggested that while onshore oil motivates military build-ups, offshore oil does not. It also goes against the idea that arms imports are to be used to protect onshore fields.

The lower panel looks at how the discoveries’ distance to the border affects their impact on trade. The idea here is based on Caselli et al. (2015) who suggested that bordering countries are more likely to engage in conflict when one of the countries has resources near 21
Table D.6: Non-response by Baseline Covariates Group in Each Post-Treatment Survey Round

||(1)<br>|(2)<br>|
|---|---|---|
||Text Message Survey|Endline Phone Survey|
|Completed at most high school|-0.008|-0.003|
||(0.012)|(0.005)|
|Numeracy score|-0.029|0.003|
||(0.006)|(0.003)|
|Communication score|0.008|0.003|
||(0.006)|(0.003)|
|Concept formation score|-0.019|0.002|
||(0.006)|(0.003)|
|Grit score|-0.001|-0.007|
||(0.005)|(0.003)|
|Other scores|0.001|-0.002|
||(0.004)|(0.003)|
|Perceived numeracy score|-0.000|-0.000|
||(0.000)|(0.000)|
|Perceived literacy score|0.014|-0.003|
||(0.010)|(0.005)|
|Perceived concept formation score|0.010|-0.003|
||(0.009)|(0.004)|
|Self-esteem index|0.006|0.002|
||(0.004)|(0.002)|
|Age|-0.002|0.001|
||(0.001)|(0.001)|
|Male|0.049|0.014|
||(0.010)|(0.005)|
|Employed|-0.005|-0.001|
||(0.008)|(0.005)|
|Above median discount factor|0.012|0.007|
||(0.009)|(0.005)|
|Respondent is present-biased|0.016|0.007|
||(0.011)|(0.006)|
|Above median risk aversion|-0.007|0.001|
||(0.008)|(0.005)|
|p: All coefcients jointly zero|0.000|0.041|
|Mean outcome|0.170|0.040|
|# observations|6891|6891|
|# clusters|84|84|

Coefficients are from regressions of round-specific attrition on the list of baseline covariates displayed here. All assessment scores are standardized to have mean zero and standard deviation one in the control group. Heteroskedasticity-robust standard errors clustered by treatment date are shown in parentheses.

53
information presented in the valuation advice. Alternatively, an increase in the share of declarations subject to valuation advice could be correlated with a change in importer behavior. These concerns are to a large extent mitigated by the fact that our instrument is specific to declarations with a particular risk score. Nonetheless, we will show in robustness tests that our results are maintained when we control explicitly for the inspector’s workload. In addition, we will control for changes in importer behavior by conditioning on initial undervaluation.

# **4.2. Results**

The provision of valuation advice strongly and significantly increases inspectors’ scrutiny, the likelihood of fraud being detected, and it improves tax yield, but at the expense of protracted clearance times, as is shown in Table 2 which presents the main results. OLS estimates are presented in panel A and IV estimates in panel B.<sup>27</sup> According to our preferred IV estimates, declarations subject to valuation advice are 12.0 percentage points more likely to be upgraded but the effect is not statistically significant. They are also 13.2 percentage points more likely to be scanned and take more than twice as long to clear customs. To interpret these numbers, it is useful to recall that the unconditional probability of a declaration for which no valuation advice is issued being upgraded is 10 percent and being scanned is 12.7 percent. IV estimates are much lower than OLS estimates, which seem upward biased, presumably because valuation advice is issued for declarations which are on average riskier. Inspectors seem to spend more effort trying to uncover fraud when valuation advice is issued.<sup>28</sup> The estimates demonstrate that valuation advice increases the likelihood of fraud being recorded by 21.7 percentage points according to our preferred IV estimates. This is a very large effect, especially when one considers that on average only 3.2 percent of all declarations not subject to valuation advice are deemed fraudulent. These large effects attest to the quality of the valuation advice but could also be due to the customs performance management unit’s monitoring of how inspectors use valuation advice, and their use of the share of declarations deemed fraudulent as a

> 27 First-stage estimates corresponding to the IV results in panel B show that the partial correlation between the selection for valuation advice and the exogenous regressor is positive and significant (see Appendix Table 3). In addition, the high F-statistics suggest our instruments are strong. The reason for having several first-stage regressions is the different number of observations included in the estimating samples of the different outcome variables in panel B of Table 2. 28 Interestingly in unreported results, we show that longer clearance times are not solely driven by the fact that more fraud is being recorded. Clearance times also increase significantly for non-fraudulent declarations that receive valuation advice.

19
WPS3945

# 2.2 Schooling Decisions and Child Labor Market Activity: Preliminary Results

Most studies that have looked at the impact of migration on child schooling have focused on accumulated schooling as measured in completed grades. In the context we study, accumulated schooling is likely to re fl ect the combined effects of several distinct schooling decisions since enrollment rates are low, the withdrawal of children from school at the transition point from elementary to middle school is high, and there is signi fi cant participation by children in labor markets. This makes it important to disaggregate the impact of migration on accumulated schooling by examining schooling outcomes at both the intensive and extensive margin. We also examine accumulated schooling conditional on enrollment since this is a better measure of progress through school We have data on schooling outcomes for 7181 children age 5 to 17 who belong to 2126 households. Of these 29% belong to migrant households. There is wide variation, in practice, in the age at which children start school. However, very few begin school after age 10. In examining the school enrollment decision and accumulated schooling, therefore, we con fi ne attention to children age 11-17 in order to ensure that our estimates of enrollment or completed grades are robust to potential late entry. In looking at retention rates, however, we focus on children 10-15, since this is the age group which is most at risk for dropping out of school during the transition point from primary to middle school. In looking at completed grades, conditional on current attendance, however, we include all school age children (5-17).

Overall, 58% of children age 11-17 report having enrolled in school at some point. Of these, 38% had dropped out of school by the survey year. The bulk of dropouts, over 85%, had dropped out either before or at the end of elementary school.

While these overall rates of enrollment and retention and quite poor, they conceal very large gender differences in enrollment and retention rates. In the sample, 58% of girls age 11-17 had never been to school as compared to only 26% for boys in this age range. The picture only worsens when we look at school retention rates. While only 25% of enrolled boys in the sample had dropped out by the survey year without completing high school, 44% of enrolled girls were no longer in school. Not surprisingly, boys also have signi fi cantly more years of schooling, completing an additional half grade more than girls on average (p-val .000).

A number of household and community characteristics account for these low overall rural Pakistan.

6
**Table 12: Number of projects and expected mitigation impact by 2012**

||ECA r<br>|egion<br>|Rest of<br>|World<br>|
|---|---|---|---|---|
||Number of<br>projects|Expected<br>mitigation<br>(ktCO2e)|Number of<br>projects|Expected<br>mitigation<br>(ktCO2e)|
|Agriculture|17|3,781|1,287|244,653|
|Alternative energy|41|11,398|1,635|347,016|
|Assorted gases|88|215,850|150|346,987|
|Biomass energy|13|5,423|34|7,177|
|Cement|4|2,593|51|34,331|
|Energy Efficiency|116|172,977|854|292,732|
|Forest|25|9,579|141|44,556|
|Fossil fuel switch|20|18,939|131|170,931|
|HFCs|5|14,202|24|478,199|
|Hydro|35|12,289|1,674|440,228|
|Landfill gas|78|18,086|349|203,778|
|Methane avoidance|19|50,163|131|116,434|
|Transport|0|0|39|10,342|
|Total|461|535,280|6,500|2,737,365|

Note: The category for assorted gases includes non-agricultural projects designed to capture fugitive gases other than HFCs. Methane avoidance excludes projects to handle methane originating in landfills or agriculture. Source: Risoe (2011) and authors‘ calculations
Table 7: Effect of PVP on Responsiveness of the State

|||_Depende_|_nt variable:_||
|---|---|---|---|---|
||O<br>|n Topic O<br>|fcial Respo<br>|nse<br>|
||(1)|(2)|(3)|(4)|
|I(PVP)|_−_0.05|_−_0.05|_−_0.06<sup>_∗_</sup>|_−_0.06<sup>_∗_</sup>|
||(0.03)|(0.03)|(0.03)|(0.03)|
|I(Female Speaker)|0.005|0.002|0.005|0.004|
||(0.05)|(0.05)|(0.05)|(0.05)|
|I(PVP)xI(Female Speaker)|0.02|0.02|0.02|0.02|
||(0.07)|(0.07)|(0.08)|(0.07)|
|Matched Pair FE|✓|✓|✓|✓|
|Demographic Controls||✓|✓||
|Infrastructure Controls|||✓||
|_p_-Score Control||||✓|
|Observations|1,141|1,141|1,141|1,141|

_Note:_<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01. Robust Standard Errors, clustered at the block-pair, in parenthesis. Data are taken from the full sample of villages, but include only citizen speakers.

Table 8: Effect of PVP on Politician Responsiveness

|||_Depende_|_nt variable:_||
|---|---|---|---|---|
||On|Topic Pol|itician Resp|onse|
||(1)|(2)|(3)|(4)|
|I(PVP)|_−_0.06|_−_0.06|_−_0.10|_−_0.06|
||(0.05)|(0.05)|(0.07)|(0.05)|
|I(Female Speaker)|_−_0.06|_−_0.05|_−_0.03|_−_0.06|
||(0.05)|(0.06)|(0.06)|(0.06)|
|I(PVP)xI(Female Speaker)|0.001|_−_0.02|_−_0.03|0.001|
||(0.10)|(0.09)|(0.10)|(0.10)|
|Matched Pair FE|✓|✓|✓|✓|
|Demographic Controls||✓|✓||
|Infrastructure Controls|||✓||
|_p_-Score Control||||✓|
|Observations|485|485|485|485|

_Note:_<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01. Robust Standard Errors, clustered at the block-pair, in parenthesis. Data are taken from the full sample of villages, but include only citizen speakers.

household level issues (including public service delivery, infrastructure, local law and order, and family disputes), women in the same treatment villages as those studied here were nearly 25 percent more likely than women in control villages to state they would take some form of action — be it speaking to a village official or raising the issue at a _gram sabha_ . Of course, such hypotheticals do not capture whether their promised action will yield results, but the boost in women’s sense of self-efficacy is a positive step towards their public and political action.

22