rafmacalaba/datause-encoder-data huggingface dataset:
AFTA. Third, as in any free trade agreement (but unlike in customs unions), bloc members are fully independent to define their external (MFN) tariffs.<sup>5</sup> In parallel to the broader tariff negotiations under AFTA, other specific, sectoral liberalization programs (e.g. in information technology products and in automobiles) were also implemented by ASEAN members in the early 2000s, accelerating the liberalization process within the bloc. To the extent that these sectoral agreements affected the tariffs of the ASEAN members, their impact is incorporated in our analysis. Other institutional elements that are likely to affect our relationships include non-tariff barriers (NTBs) and schemes like the members’ duty drawback system (which provides duty-free treatment of imported intermediate goods for producing exported final goods).6 We do not incorporate those issues explicitly mainly because of unavailability of appropriate data. While we do not expect NTBs or the bloc’s duty drawback system to be the driving forces in our analysis, their effects are nevertheless subsumed in our estimations, which provide the net effect of preferences on external tariffs.<sup>7</sup>

# **III.1 – Data**

We work with a comprehensive data set that includes information on trade, MFN tariffs and preferential tariffs, implemented and scheduled, for all ten members of the ASEAN Free Trade Area. The information on preferential tariffs was provided by the ASEAN Secretariat.8 The implemented rates are those actually employed by the member countries. The main novelty of the dataset is the scheduled rates, which are the ones the members planned to apply when the AFTA negotiations were concluded, in 1992. The MFN tariffs and the trade data at the HS 6-digit level come

> 5 On certain occasions, MFN rates have actually dropped _below_ the preferential rates for AFTA members (see e.g. Ando 2007). Naturally, preferences become redundant in those cases, and AFTA exporters simply use the prevailing MFN rate. Accordingly, in our empirical analysis we set the preferential tariffs to the level of their MFN counterparts when we observe such discrepancies.

> 6 We thank Fukunari Kimura for pointing this out.

> 7 NTBs are likely to weaken the relationship between preferential and MFN tariffs. For example, a large preferential margin may be innocuous in the presence of NTBs on intra-ASEAN trade, therefore having no effect on the governments’ incentives to alter external tariffs. Similarly, if the NTBs are on non-ASEAN imports, MFN tariffs become less important, and governments may not bother to change them despite sizable preferential margins. Duty drawbacks may work in the opposite direction. As Cadot et al. (2003) show theoretically and confirm for Mercosur, duty drawbacks provide an additional reason for external tariffs to fall after the formation of a regional trading bloc.

> 8 We thank the Asian Development Bank for very helpful assistance in obtaining the data.

11
missionary backgrounds and in one specific direction, which seems unlikely. Furthermore, Dreher et al., 2019 tested and rejected the hypothesis that African presidents channel a disproportionate amount of World Bank aid to their birth regions by using a dataset with approximately the same temporal and geographical coverage as the present study.

# **4.2 The Role of Christian Religion and of Human Capital**

Human capital and religion are potential mediators in the relationship between aid and historical missions, although investigation of this is challenging, as is any mediation analysis. Simply including proxies for the candidate mechanism in the baseline equation (1) is problematic for causal inference; if these variables really are mediators, they are by definition not predetermined with respect to Mission _ik_ , which leads to a bad control problem (Angrist & Pischke, 2008). In the absence of a quasi-experimental strategy like the one in Section 4.1, the analysis has to proceed with caution, as interpretation of results relies on several assumptions. First, an assumption is made on the direction of the selection bias that is introduced by controlling for a “posttreatment” variable _Mik_ (education or religion). Then the equation is estimated with or without _Mik_ . When _Mik_ is included, and assuming that Mission _ik_ positively (respectively, negatively) affects _Mik_ and that _Mik_ positively affects aid allocation, it is possible to interpret the coefficient _β_ as a lower (respectively, an upper) bound on the true effect of Mission _ik_ on EverAid _ik_ .<sup>33</sup> If the estimates of _β_ from the regressions with and without _Mik_ are not significantly different from each other, one can conclude that _Mik_ is not the main mechanism of interest.

Individual-level georeferenced data from the individual recode of the DHS (the same data source as used in Section 3) is used to construct measures of education and Christian religion. The DHS asks respondents (women between the ages of 15 and 49) about their highest level of education achieved: no education, primary, secondary, or tertiary. Three measures of education at the cell level are constructed: the average level of education, the fraction of respondents with at least primary education, and the fraction of respondents

> 33This is the same logic used when controlling for present-day population.

37
Table 3: PMG Test Results – The Cluster Scores of the Individual Countries in Southeast Europe

|**y**|**B&H**||**Serbia**||**North**<br>**Macedonia**||**Croatia**||**Slovenia**||
|---|---|---|---|---|---|---|---|---|---|---|
|**untr**|**Coef.**|**St.Er**|**Coef.**|**St.Er**|**Coef.**|**St.Er**|**Coef.**|**St.Er**|**Coef.**|**St.Er**|
|**Co**|********|********|********|********|********|********|********|********|********|********|
|**_Long run:_**<br>|||||||||||
|_GDPPC_|_-2.2986_|_1.972_|_-2.2986_|_1.972_|_-2.2986_|_1.972_|_-2.2986_|_1.972_|_-2.2986_|_1.972_|
|_FX_|_129.4836_|_1483.247_|_129.4836_|_1483.247_|_129.4836_|_1483.247_|_129.4836_|_1483.247_|_129.4836_|_1483.247_|
|_MMIR_|_-77039.64**_|_30387.31_|_-77039.64**_|_30387.31_|_-77039.64**_|_30387.31_|_-77039.64**_|_30387.31_|_-77039.64**_|_30387.31_|
|_HICP_|_-418.4206*_|_113.3164_|_-418.4206*_|_113.3164_|_-418.4206*_|_113.3164_|_-418.4206*_|_113.3164_|_-418.4206*_|_113.3164_|
|_IPI_|_12.235_|_41.4485_|_12.235_|_41.4485_|_12.235_|_41.4485_|_12.235_|_41.4485_|_12.235_|_41.4485_|
|_BOPNFA_|_3.0562*_|_1.0272_|_3.0562*_|_1.0272_|_3.0562*_|_1.0272_|_3.0562*_|_1.0272_|_3.0562*_|_1.0272_|
|_ECT****_|_-0.0032_|_0.0022_|_-0.0047_|_0.0061_|_-0.2708 [0]*_|_0.0729_|_-0.0787_|_0.0286_|_-0.089_|_0.0326_|
||_[0.152]_||_[0.444]_||||_[0.006]* _||_[0.006]* _||
|**_Short run:_**|||||||||||
|_GDPPC_|_-0.1805_|_0.124_|_00.6208***_|_0.3532_|_-2.7007_|_2.2755_|_-0.3946_|_0.4326_|_0.2095_|_0.1399_|
|_FX_|_249.5239_|_335.5214_|_43076.53_|_41716.68_|_123859.9_|_123730_|_16846.59*_|_6053.735_|_-5.8329_|_269.2273_|
|_MMIR_|_2078.5239_|_2669.836_|_-3219.989***_|_1857.111_|_12153.33_|_15678.54_|_681.8826_|_5148.88_|_4531.887_|_9802.912_|
|_HICP_|_-1.7392*_|_0.6595_|_-46.9989***_|_27.4257_|_16.6579_|_98.4876_|_140.2717**_|_63.2239_|_28.8468_|_36.9282_|
|_IPI_|_-0.3925_|_0.623_|_-1.3669_|_7.2196_|_15.4785_|_16.857_|_2.5539_|_9.5822_|_-0.075_|_3.9573_|
|_BOPNFA_|_0.0589_|_0.05_|_-0.1089_|_0.1757_|_0.3791_|_0.6451_|_-0.0404_|_0.0732_|_0.2473*_|_0.0798_|
|_Constant_|_-146.2289_|_93.1767_|_323.854_|_341.8748_|_14038.3*_|_3757.012_|_4308.254*_|_963.9494_|_4746.2*_|_1001.001_|
|_Log likelihood_|_-1480.709_||||||||||
|_Hausman test_|_3.13 [0.4213]_||||||||||

_Note: The optimal selection order criteria is determined by the Schwarz Bayesian information criterion (SBIC). ARDL (4,4) and data interval 2006Q1-2016Q1 is chosen; [] is p-value._ _* Null hypothesis is rejected at 1 percent level._ _** Null hypothesis is rejected at 5 percent level._ _*** Null hypothesis is rejected at 10 percent level._ _**** Error correction term, coefficient, and standard error._ To closely analyze the results of individual countries, this research utilizes panel PMG quality to allow heterogeneity and cross-countries differing in short-run coefficients in the model regressions, thus enabling dynamism to analyze the relationships of the individual countries in the short run. This study’s test results show statistical significance in the model relationships for each 20 | P a g e
# **8. Robustness Checks**

I assess the robustness of the estimates in columns 4 and 5 of each of the preceding tables (Table 4 to Table 7) by limiting the sample of schools included in the analysis. I first exclude any mosque schools and any schools that are designated as primary schools (column 4). Mosque schools likely do not follow the typical grade progression associated with the program and primary schools (officially at least) are only able to offer instruction up to grade 5. Thus any students enrolled in higher grades in these schools may not be eligible to receive the stipend from the government. In column 5 I take advantage of access to program documentation from the PMIU which explains that two districts though initially assigned to treatment and control status respectively, subsequently had their treatment status interchanged. Specifically, when the program went underway in 2004, Multan was assigned to treatment while Khanewal was assigned to control status. This was done despite the fact that the district literacy figures from the 1998 Census would suggest that Multan was a candidate for control status while Khanewal should have been treated.<sup>12</sup> These restrictions do not make a substantive difference to the results detailed above.

# **9. Discussion**

This paper finds that a gender-targeted CCT has substantial effects on enrollment. I find that not only does this program appear to increase the enrollment levels of the girls in classes 6 to 8 – the targeted group – but that the enrollment levels of otherwise ineligible boys in similar grades also appear to go up. It is possible that when girls are sent to schools their siblings are sent as well in order to accompany them to school or because the receipt of the stipend frees up enough resources to allow the household to increase the number of boys sent to school. It could 12 This determination was made on the basis of a Multiple Indicator Cluster Survey (MICS) carried out at baseline. 19
31

**Wage Gaps.** Overall, it does not appear that consumption cities have significantly less human capital than production cities. In other words, for a given level of human capital, the workers of consumption cities are more likely to work in urban non-tradables (and informal employment). Now, inspired by Gollin et al. (2014), we use I2D2 to investigate the productivity gaps between urban tradables (UT) and urban non-tradables (UNT). For individual _i_ and country-year-sample _t_ , we use OLS to estimate the following model for _each_ country one by one, restricting the sample to 18-67 year-old urban workers belonging either to the UT sector or the UNT sector:

where the dependent variable is the log of monthly earnings ( _lnWit_ ) and _Tit_ is a dummy equal to one if the worker belongs to the UT sector. _δ_ measures the wage gap between the UT and UNT sectors. We first examine the gaps estimated without including controls for human capital ( _X_ ); then we re-estimate the gaps after adding human capital as a control and controlling for sorting. If we find large (conditional) wage gaps, that would suggest that the UT sector may provide “better” jobs than the UNT sector.

Focusing on the sample countries (N = data available for 54 countries in I2D2), we find an unconditional wage gap of about 22%.<sup>46</sup> If we control for gender, years of education, and the experience dummies, the gap reduces to 13%.<sup>47</sup> If we additionally control for (spatial) sorting by including first-level administrative area or primary sampling units FEs, we get 15-17% (N = 28-41). In other words, we find significant gaps even when we compare workers with similar human capital levels and workers residing in the same urban regional or local labor market. Of course, these results should be taken with caution since there may still be unobservable factors that we cannot control for.<sup>48</sup>

**Growth.** The higher wages for jobs in UT relative to UNT imply that workers in UT are more productive than workers in UNT. If most urban areas in a country are consumption cities, this might have negative implications for aggregate labor productivity in the

> 46Indeed, only 54 countries have large enough surveys with earnings data and detailed information on the respondent’s type of residence and subsector, allowing us to distinguish UT and UNT workers.

> 47Like Gollin et al. (2014), if we use national accounts data (source: United Nations (2020c); ISIC Rev. 3) and sectoral employment data (ILO (2022); ISIC Rev. 3.1) to estimate GDP per worker, we find for the sample countries much higher gaps around 120%-205%, depending on which sectors are included in UNT.

> 48If we use I2D2 data for the world instead, in order to increase sample size (thus adding developed economies), we find an unconditional gap of 24% (N = 91) and 16%-18% for the conditional gaps (N = 34-73).
## **Box 1. Definitions of Tax Expenditures, Country Examples**

**Austria: “** Government income forgone due to exceptions from the general tax norm to the advantage of other agents with a view to their private activities performed in the interest of the general public.”

**Canada,** using a broad approach: “only the most fundamental structural elements of each tax system are considered part of the benchmark.” So that the deviations from tax benchmarks are tax expenditures

**France: “** Any legislative or administrative measure may be called a tax expenditures if its application entails a loss of revenue from the State, and hence a lessening of taxpayers’ burden in comparison to that which would have resulted under the “norm”, that is the general principles of French tax law.”

**Germany** : tax expenditures are those tax incentives that are special deviations from the central concept of a tax norm, which involve a shortfall of receipts.

**Spain:** “departures from the normal tax structure which represent tax incentives or tax subsidies”.

**United States Federal Government** : Tax expenditures are revenue losses resulting from federal tax provisions that grant special tax relief designed to encourage certain kind of behavior by taxpayers or to aid taxpayer in special circumstances.  These provisions may, in effect, be viewed as spending programs channeled through the tax system (2000).

Sources: H. P. Brixi, et. al. 2003; K.C. Messere, 1993; and the US Congressional Research Service, 2002.

# **C. Tax Expenditures and Related Transactions**

In the tax expenditure concept, two transactions are involved in tax revenue and in direct expenditures: the normative tax liability paid by taxpayers (calculated based on the normative tax structure -- tax expenditures excluded) and a government appropriation of funds to taxpayers who benefit from tax expenditure programs. However, governments use tax expenditures to short-circuit the direct spending process by having only one net payment (netting out the tax expenditures from normative tax liabilities) by the taxpayers (Surrey and McDaniel, 1985).

# **D. Factors in the Revenue Process**

The tax expenditure concept recognizes that there are three key factors involved in the revenue process: the imputed normative tax revenue from a normative tax structure; tax expenditures; and “net tax revenue” or “tax revenue received.” The relationship among those three factors can be stated as follows: “net tax revenue/tax revenue received” is the difference between the “normative tax revenue” and “tax expenditures.” This relationship can be formulated as follows: Normative Tax Revenue – Tax expenditures = Net Tax Revenue (Tax Revenue received)

# **E. Tax expenditures vs. Entitlement Programs**

Tax expenditures are comparable to entitlement programs, which are direct expenditures. A tax expenditure allows any qualified taxpayer to reduce his or her tax 5
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|Dependent Variable:|**Table A**|**4: Exploring M**|**echanisms fo**<br>Dum|**r Conflict in Irrig **<br>my=1 if anyBAT|**ated Lands**<br>TLE in a cell-ye|ar|||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|||G5 S|ahel||Re|st of West Af|rica(excluding|G5)|
|Ln(Share Irrigated) X Post|-0.0773**|-0.015|0.0103|-0.158***|0.0669|0.00452|-0.0533***|0.0176|
||(0.0361)|(0.0123)|(0.0198)|(0.0429)|(0.0481)|(0.0163)|(0.0176)|-0.0471)|
|...X Post X Ln(pop)|0.0118***|||0.0103***|(0.00594|||-0.00675|
||(0.00373)|||(0.00349)|(0.00421|||(0.00445)|
|... X Post X Wetland||0.0718***||0.0709***||-0.00748||0.00687|
|||(0.0166)||(0.0168)||(0.0213)||(0.0233)|
|... X historically Agricultural Ethnic<br>Homeland|||0.0521*|0.0503*|||0.0857***|0.0876***|
||||(0.027)|(0.0264)|||(0.028)|-0.029)|
|Observations|34160|34160|34160|34160|22440|22440|22440|22440|
|R-sq|0.236|0.236|0.235|0.239|0.347|0.347|0.347|0.348|
|Country-Year FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Cell FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Controls X Time|Y|Y|Y|Y|Y|Y|Y|Y|
|Climate Controls<br>Each observation corresponds to a 50km x 50km grid-c|Y<br>ell observed in a give|Y<br>n year between 19|Y<br>98 and 2017. The|Y<br>outcome variable is a du|Y<br>mmy variable indica|Y<br>ting that the grid-c|Y<br>ell experienced a ba|Y<br>ttle event in a given|

<mark>Each observation corresponds to a 50km x 50km grid-cell observed in a given year between 1998 and 2017. The outcome variable is a dummy variable indicating that the grid-cell experienced a battle event in a given</mark> year, as reported in the ACLED dataset. Irrigated area is measured as the percentage share of an average plot of land that is equipped for irrigation, as documented by Siebert et al (2015). Controls include log of population at the start of the period as well as dummies to indicate the presence of wetlands, croplands, pasture lands, rivers and the homelands of ethnic groups that historically relied on agricultural livelihood strategies. Standard errors in parentheses clustered at the grid-cell level. Climate controls include mean temperature and log of total rainfall in the wet and dry seasons. * p<0.1, ** p<0.05, *** p<0.01" 28
**-** **<u>Table 2. Determinants of Education Aspiration among Adolescent Girls in Jharkhand (15 24 year olds)</u>**

||(1)|(2)|(3)<br>Educatio|(4)<br>n Asiration|(5)|(6)|
|---|---|---|---|---|---|---|
|Age (years)|0.17***|0.16***|<br>0.11***|p <br>0.05*|0.07*|0.02|
||(0.03)|(0.03)|(0.03)|(0.03)|(0.04)|(0.03)|
|NEET (0/1)|<br>-1.95***|<br>-1.93***|<br>-1.76***|<br>-1.26***|<br>-1.37***|<br>-1.15***|
||(0.14)<br>|(0.15)<br>|(0.15)<br>|(0.16)<br>|(0.21)<br>|(0.15)<br>|
|Married (0/1)|-1.40***<br>|-1.36***<br>|-1.24***<br>|-1.15***<br>|-0.96***<br>|-0.89***<br>|
|Hope (z-score)|(0.17)|(0.19)<br>0.25***|(0.18)<br>0.04|(0.20)<br>-0.01|(0.24)<br>0.01|(0.23)<br>0.02|
|||(0.07)|(0.07)|(0.07)|(0.09)|(0.07)|
|Self-efficacy (z-score)|||0.73***<br>|0.53***<br>|0.48***<br>|0.49***<br>|
||||(0.07)|(0.08)|(0.09)|(0.08)|
|Father's ae (ears)||||<br>-000|<br>000|<br>-000|
|g y||||.<br>(0.01)|.<br>(0.01)|.<br>(0.01)|
|Mother's age (years)||||<br>0.02***|<br>0.02**|<br>0.03***|
|||||(0.01)|(0.01)|(0.01)|
|Father's education (years)||||<br>0.05***|<br>0.04**|<br>0.04**|
|||||(0.02)|(0.02)|(0.02)|
|Mother's education (years)||||<br>0.13***|<br>0.10***|<br>0.13***|
|||||(0.02)<br>|(0.02)<br>|(0.02)<br>|
|Household size||||-0.02|-0.04|-0.01|
|||||(0.03)|(0.04)|(0.03)|
|Log annual HH income||||0.24***|0.21**|0.10|
|||||(0.07)|(0.09)|(0.08)|
|Caste=SC||||-0.19|0.03|-0.47*|
|||||(0.24)|(0.28)|(0.28)|
|Caste=ST||||-0.47*<br>|-0.27<br>|-0.69**<br>|
|||||(0.25)|(0.30)|(0.30)|
|Caste=OBC||||<br>0.11<br>|<br>0.31<br>|<br>-0.11<br>|
|Rlii=Mli||||(0.21)<br>018|(0.23)<br>019|(0.23)<br>026|
|egonusm||||-.<br>|.<br>|-.<br>|
|||||(0.19)|(0.23)|(0.27)|
|Religion=Christian||||<br>-0.17|<br>-0.94***|<br>-0.21|
|||||(0.30)<br>|(0.25)<br>|(0.38)<br>|
|Religion=Sarna||||-0.23|-0.18|-0.34|
|||||(024)|(036)|(028)|
|Has BPL card||||.<br>-0.03|.<br>0.05|.<br>-0.03|
|||||(0.13)|(0.15)|(0.13)|
|No social support (0-9)||||0.10**|0.06|0.03|
|||||(0.04)|(0.05)|(0.04)|
|Knows women in business (0/1)||||0.68***|0.34**|0.47***|
|||||(0.13)|(0.17)|(0.13)|
|Adult connectedness (0-4)||||002|011|007|
|||||.<br>(0.06)|.<br>(0.09)|.<br>(0.07)|
|EA th t|||||<br>029||
|poorer an res|||||-.<br>(0.23)||
|EA h i GP|||||<br>007||
|as women n|||||.<br>(0.20)||
|EA thinks married women should work|||||<br>0.41**<br>(020)||
|EA thinks son's education more important|||||.<br>-0.18||
||||||(028)||
|EA has primary school|||||.<br>0.12<br>046||
|EA has high school|||||(.)<br>-0.04<br>022||
|EA has crime|||||(.)<br>0.34<br>||
|Urban|||||(0.38)<br>040*||
||||||.<br>(0.21)||
|EA fixed effects|No|No|No|No|<br>No|Yes|
|Adj. R-sq|0.17|0.17|0.23|0.32|0.30|0.37|
|<br>N|2106|1843|1843|1668|976|1668|

_Notes_ : The sample consists of only adolescent girls 15-24 years inclusive. Standard errors, in parentheses, are clustered at the EA level. * significant at 10 percent, ** significant at 5 percent, *** significant at 1 percent. The dependent variable is number of years of education a girl reports to aspiring to if she faced no constraints. The omitted caste group is General Caste and the 8
of income “initially” will catch up with individuals with high income. In our context, the “initial” period corresponds to the time of entry into the work force after the completion of formal education. Since labor income may vary initially for equivalent individuals, catchingup in this context measures the extent to which individuals with initially low incomes catch up to those with initially high incomes.<sup>12</sup> In the terminology of the growth literature, it measures convergence.<sup>13</sup>

# II.2. Mobility

As noted in the introduction, our empirical measure of income mobility between 0 and t, which we denote by mt, is the Hart index, defined as the complement of the correlation in (log) incomes at 0 and t (see Shorrocks (1993)): where we have used the notation σlnyi0 = ~~�~~ var[lnyi0] and σlnyit = ~~�~~ var[lnyit]. Using our income specification from the previous section, we find the following expression for the co-variance:

> 12In this theoretical section, our discussion relates to initial income differences and subsequent mobility between ex-ante identical individuals. In our discussion of empirical methodology and in our empirical application to Mexican data, we will study mobility between observationally equivalent individuals. That is to say, we examine income differences and mobility in residual income after conditioning for the standard determinants of income such as education and experience.

> 13To see this, suppose ρ < 1. In this case, we have convergence towards the “steady state”: E[lnyit|ωi0] → μ. Let ∆0 = lnyi0 − d<sup> ̄</sup> be the initial distance from the steady state and ∆t = lnyit − d<sup> ̄</sup> be the distance in period t. We can then define the time, T , it takes to get halfway towards the steady state, which is simply the solution to ∆T /∆0 = 1/2. Using the expression for ∆T and ∆0, it is straightforward to see that T is increasing in ρ for ρ < 1, that is, an increase in ρ reduces the speed of convergence.

6