rafmacalaba/datause-encoder-data huggingface dataset:
searchers have much more fine-tuned data on dates, and many observations near the threshold. This may occur when applications are processed on a first-come, first-served basis with many applications arriving close in time to one another. A striking example comes from Pinotti (2017), who examine an Italian amnesty system where employers must submit applications on certain “click days”, and are then processed on a first-come, first-served basis, until a quota is reached. He uses a large sample of over 110,000 applicants that had applications processed within one hour around the cutoff, and considers bandwidths ranging from 1 minute to 30 minutes. This fine and continuous data on application times enables RDD to be used to estimate the impact of legalization on crime. A second example is found in Hainmueller et al. (2017), who examine the protective effect of the Deferred Action for Childhood Arrivals (DACA) program in the U.S. on mental health of their young children. Eligibility for the program required a mother to under age 31 as of 15 June, 2012. Using the exact date of birth, they can compare outcomes in children whose mothers were born just before June 15, 1981 (and were hence ineligible) to those born just after (who were eligible).

While language test scores and migration policy eligibility date thresholds seem the most likely avenues for the use of RDD in the study of migration, there are also other opportunities where comparing people who just marginally qualify for some policy to those who just marginally miss out can allow for the use of this method. For example, Hainmueller et al. (2017) use an unusual feature of Swiss immigration policy, which subjected naturalization applications to municipal referendums, where local voters voted on whether or not to accept individual candidates as citizens. Comparing those applicants who received just above the threshold of 50% to those who received just below this threshold in votes enabled them to estimate the causal impacts of naturalization on immigrants’ social integration. Additional applications will be possible when considering the impacts on migration of social welfare programs that use various eligibility score thresholds.

A couple of final notes for researchers planning on using this method. The first is that it can be very data intensive: typically considerably larger sample sizes are needed for RDD than for randomized experiments to have the same statistical power for identifying a given effect size. Secondly, the method is usually much more credible when the analysis occurs in a narrow bandwidth of the threshold, and researchers should be particularly cautious about using global polynomials or relying on observations far from the threshold in identifying the causal

# **5 Conclusion**

Both experimental and non-experimental causal inference methods have increasingly been used to answer a wide variety of research questions surrounding migration. Careful and creative use of these methods have enabled researchers to overcome many of the self-selection 33
Table 6: OLS estimates of the effect of ICT use on the share of white-collar/blue-collar workers and wages: Manufacturing

|(1)|(2)|(3)|
|---|---|---|
|**Dependent variable:** ∆**white/blue-collar workers**|||
|∆Share of labor that uses computers<br>0.0003***|0.0003***|0.0003***|
|(0.0001)|(0.0001)|(0.0001)|
|∆Share of labor that uses Internet<br>0.0003***|0.0003***|0.0003***|
|(0.0001)|(0.0001)|(0.0001)|
|∆ln(stock of computer equipment/worker)<br>0.0192***|0.0153***|0.0151***|
|(0.0016)|(0.0017)|(0.0017)|
|**Dependent variable:** ∆**white/blue-collar wages**|||
|∆Share of labor that uses computers<br>0.0010**|0.0009**|0.0009**|
|(0.0004)|(0.0005)|(0.0005)|
|∆Share of labor that uses Internet<br>0.0010**|0.0010**|0.0012**|
|(0.0005)|(0.0005)|(0.0005)|
|∆ln(stock of computer equipment/worker)<br>-0.0400***|-0.0334***|-0.0322***|
|(0.0110)|(0.0120)|(0.0119)|
|**Controls**|||
|∆ln(capital/worker)<br>No|Yes|Yes|
|Age dummies<br>No|No|Yes|
|Size dummies<br>No|No|Yes|
|Observations<br>11,438|11,438|11,438|

Standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

30
Similarly, export transactions are not conducted with strangers. Several firms direct their trade toward affiliated companies or through buyers/brokers with whom they have had past trading experience. Few of the firms deal directly with Indian processors. Instead, they rely upon offshore brokerage firms (based in places such as Singapore and the Virgin Islands) which provide reliable financial and shipping services and which guarantee product quality to the eventual buyer.

The interviewed exporters were asked to rate various potential problems on a scale of 1 to 5, with **5** signifying a 'major problem or bottleneck' and 1 signifying that the factor is not a problem faced by them in their procurement or sales. Table 16 aggregates the results of this exercise.

Table 16: Private Exporter Assessment of Problems (1 to **5** Scale; 1 = No Problem, 3 Moderate Problem, **_5=_** Severe Problem)

|Factor/Problem|Rank|Average Rating|
|---|---|---|
|Poor Communications to<br>Producing Areas|1|4.2|
|Poor Roads|2|3.7|
|Taxation/Cesses|3|3.1|
|Availability of Cashew Nuts|4|2.8|
|Access to Short-Term Finance|**_5_**|2.7|
|Lack of Time/Conflicting<br>Activities|6|2.7|
|Inflation/Price Instability|7|2.4|
|Lack of Market Information|8|2.3|
|Availability/Cost of Packaging<br>Materials|9|2.3|
|Official Restrictions on<br>Cashew Procurement|10|2.1|
|Inadequate Transport/Storage<br>Facilities|11|2.0|
|Lack of Market Outlets|12|2.0|
|High Cost of Cashew Nuts|13|1.9|
|Unreliability of Buyers|14|1.8|

Source: Author's Field Survey (March 1993) According to the exporters, the most significant problem is social infrastructure (telephones, roads) linking them or their buying agents with the main production areas. This raises overall transaction costs in crop procurement. Taxation is the next concern of significance, especially the (increasing) local 34
22 Malaysia’s TFPR dispersion (0.78), as measured by the standard deviation (SD), is higher than Turkey’s (0.76 in 2014), China’s (0.63 in 2005), and India’s (0.67 in 1994) (figure 14). The difference between TFPR of 25<sup>th</sup> percentile and 75<sup>th</sup> percentile firms in Malaysia is estimated to be 2.7 times, compared to 2.6 times for Turkey, 2.3 times for China, and 2.2 times for India.

The size of TFPR dispersions not only suggest an inefficient allocation of resources across firms but are also indicative of the magnitude of productivity gains. Countries that show higher dispersions should reap higher hypothetical productivity gains when distortionary elements are reduced or removed from their markets. Our estimates show Malaysia’s TFPR dispersions to be higher than China’s and India’s, yet the hypothetical productivity gains are estimated to be lower than India’s but close to China’s. One possible explanation for such inconsistencies is the differences in the economic structure of these countries. The TFP gains are a weighted sum of each industry’s specific gains. Misallocation of resources in an industry that is insignificant and accounts for a smaller weight in the overall manufacturing sector will result in smaller hypothetical productivity gains. In contrast, the hypothetical productivity gain will be larger if misallocation occurs in an important industry that carries a larger weight in the sector.

**Figure 14:** Dispersions of TFPR and TFPQ in Selected Countries

<!-- Start of picture text --> SD 75-25 90-10<br>3.1<br>3.0<br>2.9<br>2.4<br>2.0 1.9<br>1.5 1.5 1.6 1.6 1.6<br>1.1 1.1 1.3 1.2<br>1.0 1.0 1.0<br>0.8 0.8 0.60.8 0.70.8<br>T F P Q T F P R T F P Q T F P R T F P Q T F P R T F P Q T F P R<br>M A LA Y S I A T U R K E Y ( 2 0 1 4 ) C H I N A ( 2 0 0 5 ) I N D I A ( 1 9 9 4 )<br>( 2 0 1 0 )<br>N = 7 4 1 2 N = 2 2 1 4 8 N = 2 1 1 3 0 4 N = 4 1 0 0 6<br><!-- End of picture text --> Source: Authors’ calculations; Nguyen, Taskin, and Yilmaz (2016); Hsieh and Klenow (2009). Note: Number of firms: Malaysia (7,412); Turkey (22,148); China (211,304); India (41,006).

Restuccia and Rogerson (2008) argue that productivity losses due to misallocation would be even more significant if distortions are correlated positively with firms’ productivity. Figure 15 shows a positive relationship between log TFPR and log TFPQ, suggesting that productive firms appear to face larger idiosyncratic distortions. This shows that productive firms are “taxed” at a higher rate, and therefore could have expanded their production had they acquired more resources for their production. The constraints face by productive firms will ultimately worsen the overall productivity of the economy.
Widowed women who have the option also frequently go to live with a son. Those who have sufficient means to support themselves, usually because they have independent access to housing, often choose not to remarry, an option aided by having grown sons (Lambert and Rossi 2016).

# **3. Data**

The main data source is the first wave of the survey "Poverty and Family Structure" (PSF, by its French acronym) conducted in Senegal in 2006-2007 and 2011 (De Vreyer et al. 2008).<sup>11</sup> The first wave (PSF1) provides a nationally representative sample of 1,800 households spread over 150 primary sampling units drawn randomly among census districts. About 1,750 records can be exploited. Households have unusually complex structures in Senegal (Bongaarts 2001; van de Walle and Gaye 2006). What we will refer to as a household is often a series of families related in some way and living together in a compound organized under one head and taking their meals together.

In addition to the usual information on individual characteristics, the PSF survey collected detailed information on marital trajectories. Age at first marriage and the number of previous unions are recorded for each individual. The circumstances (divorce or widowhood) of the last dissolution are known, as well as some characteristics of the previous spouse.

A further aspect of the survey that is particularly important for our purposes is that it collected detail on the structure and budgetary arrangements of each household. To best reflect intra-household structure and resource allocation, households were divided into groups or “cells” according to the following rules: the head of household and unaccompanied dependent members, such as his widowed parent or his children whose mothers do not live in the same household, are grouped together. This is in the same spirit as the procedure used for the Senegalese census of 1988 (van de Walle and Gaye 2006). Any unmarried brothers of the head would also be considered part of his cell. Each wife of the head and her children and any other dependents then form separate cells. Other women with children or other dependents, whose husbands are not present, are also considered cell heads. The same goes for any other family nucleus such as a married child of the household head with his/her spouse and children, or a sister of the head residing in the household with her children (typically post-divorce or while

> 11 Momar Sylla and Matar Gueye of the Agence Nationale de la Statistique et de la Démographie of Senegal (ANSD), and Philippe De Vreyer (University of Paris-Dauphine and IRD-DIAL), Sylvie Lambert (Paris School of Economics-INRA) and Abla Safir (now with the World Bank) designed the survey. The data collection was conducted by the ANSD.

7
Schady, 2006). A most directly related to this analysis, the short-term impact evaluation of the FSSP in Pakistan –the program analyzed in this paper –shows an increase in the enrollment of girls of about 9 percentage points between 2003 and 2005 (Chaudhury and Parajuli, 2008). Although the impacts of these studies differ by magnitude, and the empirircal approaches vary, results appear to be consistent across programs and implementation contexts.

Nonetheless, the evidence of the connection between the provision of incentives for school participation and the well-being of adolescent girls in the longer-term remains very thin. Therefore, this paper aims to begin filling this evidence gap by assessing whether girls who benefited from a school stipend program show improved outcomes in the longer-term, or up to four years after their first exposure to the program.

# **3. Schooling Context and the Project**

## **_Schooling in Punjab_**

The literacy rate in Punjab in 2003 was approximately 54 percent (45 percent in Pakistan as a whole) compared to 56 and 63 percent for South Asia and all low income countries, respectively (Lloyd, 2004).<sup>3</sup> Divided into 36 districts (34 districts at the time the program began), Punjab is home to over 55 percent of the total Pakistani population.

School enrollment in Punjab, particularly in secondary education, is largely constrained by the scarcity of schools. Primary education is supplied by both public and private schools. On the contrary, the secondary level is dominated by the public sector, and not every village has access to middle schools (grades 6 to 8) and high schools (grades 9 and 10).<sup>4</sup> For instance, whereas 84 percent of the households in Punjab report that their children are within walking distance (15 minutes) of a primary school, only 55 percent of the households have access to a middle or high school within the same travel time (Pakistan Social & Living Standards Measurement Survey - PSLM, 2004). Differences in access are even larger in rural areas, making it even far less likely that children in rural villages enroll in schools at these levels (Andrabi et al., 2006; Sathar et al., 2003). In 2003, before the program started, while enrollment in primary

> 3 Estimates for Pakistan and region were obtained from published documents ―Pakistan at a Glance‖ 2004 available online; Punjab estimates were derived from the MICS 2003-04 Report, table titled Major Summary Results.

> 4 Around 90 percent of private schools offer only primary classes (Andrabi et al, 2006).

5
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- **Borb ́ely, Szilvia, and L ́aszl ́o Neumann.** 2019. “Neglected by the State: The Hungarian Experience of Collective Bargaining.” In _Collective Bargaining in Europe: Towards an Endgame_ . , ed. Torsten Muller, Kurt Vandaele and Jeremy Waddington, 295–314. European Trade Union Institute.

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- **Carbonnier, Cl ́ement, Cl ́ement Malgouyres, Loriane Py, and Camille Urvoy.** 2022. “Who Benefits from Tax Incentives? The Heterogeneous Wage Incidence of a Tax Credit.” _Journal of Public Economics_ , 206(1): 104577.

- **Card, David, Ana Rute Cardoso, Joerg Heining, and Patrick Kline.** 2018. “Firms and Labor Market Inequality: Evidence and Some Theory.” _Journal of Labor Economics_ , 36(S1): S13–S70.

- **Card, David, J ̈org Heining, and Patrick Kline.** 2013. “Workplace Heterogeneity and the Rise of West German Wage Inequality.” _Quarterly Journal of Economics_ , 128(3): 967– 1015.

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- **Cseres-Gergely, Zsombor, and Gy ̈orgy Moln ́ar.** 2015. “Public Works Programmes in the Public Employment System, 2011-2013–Basic Facts.” In _The Hungarian Labour Market 2015_ . 148–159. Centre for Economic and Regional Studies Institute of Economics.

39
and Portrait (2005) find that poor macroeconomic conditions in infancy lead to higher adult mortality. Almond (2006) finds that individuals who were in utero during the 1918 influenza pandemic experienced as adults lower education attainment, increased rates of physical disability, and lower socioeconomic status compared with other birth cohorts. Alderman, Hoddinott, and Kinsey (2006) use data from Zimbabwe and show that preschool malnutrition has a negative impact on subsequent human capital formation, measured by the number of grades completed. Yamauchi (2006) shows that healthier children in South Africa start school earlier, attain more grades, and repeat fewer classes. Finally, Maccini and Yang (2006) examine the effect of early-life environmental conditions for Indonesians on health and socioeconomic outcomes later in life. They find that higher early-life rainfall leads to improved health, schooling, spousal quality, and socioeconomic status, but the impacts are only for women.

The literature also provides evidence of gender bias in health and socioeconomic outcomes. Rose (1999) shows that when districts experience higher rainfall, the gender bias in infant mortality in India (favoring boys over girls) narrows significantly. Alderman and Gertler (1997) find that demand for girls’ medical care is more income and price-elastic than demand for boys’ medical care in rural Pakistani households. Behrman (1988) finds bias in favor of boys in the intra-household allocation of nutrition during the lean season in India. Recent work questions the perceived view that gender bias is more of problem in Asia than Africa. Verwimp and Van Bavel (2005) find evidence that the usual sex differential in child survival (more girls than boys survive infancy) observed in Africa as well as elsewhere changes under severe living conditions. Dercon and Krishnan (2000) find that adults in poor Ethiopian households are unable to smooth their consumption and women bear the brunt of adverse shocks.

2
section has shown that different indicators of accessibility provide different insights as to which countries are “high access” countries. Still, one can attempt to make one (subjective) indicator of access. Figure 14 shows such a combined accessibility indicators once the different access indicators have been scored according to the methodology described earlier.

**Figure 14. Overall Accessibility**

<!-- Start of picture text --> Netherlands<br>Finland<br>United Kingdom<br>United States<br>Canada<br>Australia<br>Ireland<br>High Income Average<br>France<br>Sweden<br>Italy<br>Average<br>Germany<br>Belgium<br>Austria<br>Colombia<br>Mexico<br>Peru<br>LAC Average<br>Brazil<br>0 10 20 30 40 50 60 70 80 90<br>Overall Accessibility<br><!-- End of picture text --> Sources: Authors’ calculation for the LAC countries and (Usher and Cervenan, 2005) for high-income countries The overall accessibility indicator suggests that accessibility of tertiary education in the selected four LAC countries is relatively low compared to high-income countries. Some countries gain a higher score owing to a high participation rate, while some countries receive a high overall score because of its educational equity and gender parity score.

Colombia has the highest overall accessibility among the LAC countries. Peru is the second most accessible among the four, followed by Brazil and Mexico. In a case of Colombia, it achieves the highest participation rate, attainment rate and gender equity index among the LAC countries. Comparing to Peru, Mexico has a higher participation rate, higher attainment rate, and more equal gender index. Nonetheless, the lower EEI score shifts the overall score lower than Peru. The last LAC country, Brazil, has less favorable results for all four indicators resulting in the least accessible tertiary education system.

According to this (subjective) weighing of access indicators, the Northern European countries have the highest level of access, followed by Anglo-Saxon countries and Continental European countries, then follows LAC.

26
extent of poverty and its causes in that country.<sup>9</sup> The PA’s are clearly the best available source of information on urban-rural differentials for setting international poverty lines, although they have not previously been used for this purpose.

The essential idea of this paper is to use country-specific urban and rural poverty lines from the PA’s in setting the urban-rural differential in the international poverty lines. The fact that PA’s have now been completed for most developing countries makes this feasible. Besides the change in methodology, our methods closely follow those outlined in Chen and Ravallion (2004), which provides the latest available update of the World Bank’s global poverty measures for $1 and $2 a day. We follow the long-standing tradition in poverty measurement at the World Bank and elsewhere of relying on primary survey data to the maximum extent feasible.

An alternative approach to global poverty measurement is to combine pre-existing inequality measures at country level from survey data with the estimates of mean consumption or income from the national accounts (NAS).<sup>10</sup> This is not a defensible option for doing an urbanrural split of global poverty measures, allowing for COL differences, since neither the inequality measures nor the NAS means would then be valid. This method is also questionable in the limiting case when the COL difference is zero. On the one hand, it is not clear that the NAS data can provide a more accurate measure of mean household welfare than the survey data that were collected precisely for that purpose. On the other hand, even acknowledging the problems of income underreporting and selective survey compliance, there can be no presumption that the discrepancies between survey means and the NAS aggregates (such as private consumption per

> 9 To given an indication of the scale of a PA, the average cost is about $250,000. Most, but not all, PA’s are public documents. 10 Examples are Bourguignon and Morrisson (2002),  Bhalla (2002), Sala-i-Martin (2006) and Ackland, Dowrick and Freyens (2006). Note that the internal consistency of the compilations of existing inequality measures is also questionable; the measures differ in terms of the recipient unit (household versus individual) and the ranking variable (household versus per capita).  Only by re-estimating consistently from the micro data (as we have done) is it possible to address these consistency problems.

8