rafmacalaba/datause-encoder-data huggingface dataset:
significance level in most cases, but insignificant when estimating an OLS model. The important influence of grandparents in the impact of childcare policies is in line with recent evidence by Karademir, Lalibert ́e, and Staubli (2023) showing important intergenerational effects of childcare policies.

In addition, we investigate if the presence of the mother’s partner in the same household influences the impact of the program.<sup>6</sup> This analysis is interesting given that partners could absorb some of the care responsibilities faced by women. Incorporation back into the labor force under the reform might then be more feasible. However, at the same time, the need for women with income-generating partners to work might be lower. The evidence in Column 4 of Table 3 points to an important role of partners’ presence in the household. The coefficient is positive and significant. Consequently, mothers of six-year-old children who live with their partners experience a larger effect. These results could mean that partners of women with six-year-old children assume an important role in household care responsibilities, but also in women’s decision-making regarding labor force participation and employment.

Moreover, we analyze if the presence of additional employed household members impacts the effect of the reform on the maternal labor supply. If other household members are employed, there might be additional resources available for household and care responsibilities. Consequently, mothers in these households might be more able to outsource these activities. It might then be easier for them to participate in the labor market. In contrast, one could also argue that they face a lower necessity to work and generate income due to a greater safety net of additional income-generating individuals in the household. The coefficient in Column 5 is positive, showing that maternal employment in households with additional employed members indeed rises more than the one for mothers in households without additional employed members. These findings, together with our findings on the presence of spouses in households, could mean that the employment of other household members indeed plays an important role in the decision-making of mothers about participating in the labor market.

> 6The EU-LFS indicates if the partner of a person is present in the same household by an indicator variable.

16
Where Yit is the outcome of interest in sector _i_ and month _t_ , αi is a dummy for each sector _i_ (i.e. sector fixed effect), β1 is the coefficient on a dummy that is equal to 0 for all months prior to February 2016, and equal to 1 for February 2016 itself and all months after. Similarly, β2 is a dummy equal to 0 for all months prior to October 2016, and equal to 1 for October 2016 itself and all months after. εit is the error term. Robust standard errors are computed and reported in all regression tables.

The interpretation of the coefficients is as follows: β1 represents the average change in outcome _Y_ across all sectors in the interim period between February 2016 and October 2016. β2 represents the additional change in outcome _Y_ after October 2016, and (β1 + β2) represents the total change in outcome _Y_ in the post-October 2016 period. All coefficients report changes relative to the pre-February 2016 average value of outcome _Y_ . αi is simply a sector specific fixed effect and controls for all time-invariant changes across sectors. Moreover, αi ensures that the analysis measures changes _within_ sectors over time, thus controlling for any preexisting differences across sectors.

Since we are interested in the significance levels of β1 , β2, as well (β1 + β2), all regression tables report the p-value of the F-test of joint significance of (β1 + β2).

The bank-level analysis uses an identical specification where the data are analyzed at the bank level instead of the sector level. Hence, all the properties of the regression equation above are maintained, with only the underlying aggregation of the panel data changing from the sectormonth to the bank-month level.

# **_Aggregate Level Analysis_**

As a first step, we use sector-level data from the Central Bank to generate economy-wide aggregate trends in lending. Figure 1(a) presents the trend in aggregate loan amounts across all lending sectors in the financial system. The figure shows trends by the nature of loans: normal portfolio refers to loans in good standing, while the other categories represent various stages of late repayment. Figure 1(b) aggregates the late portfolios together to present a simple comparison of normal vs. abnormal loans.<sup>6</sup> The vertical pink dotted line represents the post-crisis period after February 2016 and the vertical orange dotted line represents the post-caps period after October 2016.

Several interesting findings emerge from these aggregate figures. First, the entire period after February 2016 was characterized by a drop in the normal portfolio of lending. Loan values dropped after February 2016 and remained low after October 2016. Second and converse to normal lending, abnormal loans were on an upward trend indicating an increase in non-

> 6 Note that corresponding data on loan volume are available but subject to numerous errors and missing observations. As such, we are not confident in conducting analysis with them and focus instead on loan value where the data quality is much better.

10
teachers, government workers, or doctors in their home countries may face wage structures determined strongly by seniority, and earn more abroad as cleaners, shop assistants, and agricultural workers abroad, but with more stagnant wages. There is a large literature that examines assimilation by comparing the income and career trajectories of migrants to natives (e.g. Chiswick et al, 2005; Akresh, 2008). However, the appropriate counterfactual for our question of interest requires examining the longer-term income and occupation paths of migrants to what these would have been had they stayed in their home countries.

This paper uses a long-term follow-up survey of Tongans applying to the migration lottery conducted under New Zealand’s Pacific Access Category (PAC) in order to measure the long-term impacts of migration, almost ten years after migrants first moved. Such longterm tracking of international migrants is costly and complicated, and unlikely to be possible in many other settings. We deal with the issues that arise in this longer-term including individuals who on-migrate to other countries, individuals who had not moved at the time of the short-term follow-ups but have now moved (and who are hence “slow compliers” rather than non-compliers”) and ballot losers who migrated via substitute pathways. We also consider locational changes within the destination country that were less prevalent when the migrants were first studied, along with (associated) occupational changes.

The largest gains from migration typically occur for the migrants themselves (and the immediate family members who accompany them when migrating), and the focus of our surveys and work is on measuring these gains using unsuccessful applicants to the lotteries as a counterfactual. The gains to migrants are not always counted when the benefits and costs of migration for sending countries are calculated, due to statistical convenience rather than on welfare-theoretic grounds (Clemens and Pritchett, 2008). We consider that these gains are important components of development possibilities, especially for small, island countries where economic opportunities will always be limited by lack of scale.

4
26

# **Table 3: Salary vs Bonus Treatments**

|Dependent variable: Po|or cases as a proportion|of total cases<br>Salary vs:||
|---|---|---|---|
||Non-poor bonus|Equal bonus|Poor bonus|
||I|II|III|
|Treatment|1.566***|1.218**|0.788|
||(0.52)|(0.46)|(0.46)|
|Pro-poor preference|0.659***|0.581***|0.640***|
|Donation to poor school / total donation|(0.21)|(0.19)|(0.16)|
|Interaction: Treatment X Pro-poor preferences|-0.827**|-0.410*|-0.287|
||(0.29)|(0.22)|(0.29)|
|Effort cost ratio (ability round)|0.689**|0.643**|0.683**|
|Score with poor cases / Total score|(0.26)|(0.24)|(0.27)|
|Interaction: Treatment X Effort cost ratio|-0.628*|-1.028***|-0.098|
||(0.36)|(0.34)|(0.55)|
|Time cost ratio (ability round)<br>Time spent with poor cases / Total time|1.029**|0.939*|1.040**|
|<br>spent|(0.44)|(0.46)|(0.44)|
|Interaction: Treatment X Time cost ratio|-1.848**|-0.889|-0.976|
||(0.80)|(0.76)|(0.70)|
|Occupation: Midwife|0.100|-0.048|0.134**|
||(0.08)|(0.12)|(0.06)|
|Occupation: Doctor|0.337**|0.345*|-0.178*|
||(0.13)|(0.16)|(0.09)|
|Occupation: Other|0.069|0.065|0.101|
||(0.06)|(0.08)|(0.07)|
|Female|-0.089|-0.033|-0.059|
|1 = Female|(0.07)|(0.08)|(0.04)|
|Age (years)|0.000|-0.001|-0.002|
||(0.00)|(0.00)|(0.01)|
|Income (in CFA)|0.000|0.000|0.000|
||(0.00)|(0.00)|(0.00)|
|Constant|-0.789**|-1.147***|-0.896**|
||(0.29)|(0.35)|(0.36)|
|R2|0.208|0.141|0.141|
|Log Likelihood|-64.92|-71.42|-84.82|
|Observations|195|175|201|

Notes: OLS regressions. Dependent variable is the proportion of output that are poor cases (number of poor divided by total cases). Each column reports results comparing subject behaviour in the Salary treatment with behaviour in the treatment indicated in the column heading. Specifications are the same as those in Model 5 of Table 2, except for the interactions of ability measures with treatments. * 10%, ** 5%, *** 1% significance level. Clustered standard errors (by day) in parentheses. The specification in columns I-III also includes controls for current state of personal finances, confidence in payment to schools, and interest in task. None of these are significant.
- 19 -

We did not examine factor intensity and product specialization in the US market as the meager volume of imports from EE considerably limits<sup>the</sup> statistical value of such analysis.i

# **Capital Intensity of Exports**

We proxied the capital intensity of the EC's MFA product categories using value added per production worker in the US. This computation is justified by Lary's (1968) findIng that relative factor intensities of products do not change significantly across countries. Then, for each supplier, we calculated the average capital intensity of their exports to the EC under<sup>the</sup> MFA.2 These are tabulated in Figure 2 along the horizontal axis, while the vertical axis gives the per capita GNP of the exporters as a proxy of their relative capital abundance.

2 However, in the US market, where the MFA categories distinguish products by type of fiber, we observed another distinct features wool products were extremely important for the EE suppliers compared to other exporters (see Appendix Table Al). The fiber type subsets, "wool products," "cotton<sup>products,"</sup> "man-made fiber products," and "remaining products" apply to the US<sup>market only.</sup> Fiber type can be discerned from the first digit of the MFA category<sup>number. A</sup> leading "4" in the category number indicates the quota applies<sup>only to wool</sup> products, while a "3" indicates cotton products, and so on. "Remaining<sup>products"</sup> consists of those quotas which could not be placed in one of the other three groups. Such quotas include: (i) joint quotas covering more than one fiber types (ii) the 200 series, which indicates products made from both cotton and man-made fibers; and (iii) the 800 series, which indicates silk blends<sup>and non-cotton</sup> vegetable fibers. The four fiber type subsets therefore sum to 100 percent<sup>of MFA</sup> shipments.

3 For value added per production worker, following the tradition of Lary (1968), we used the US data; 1977 Census of Manufactures. In establishing a concordance between the 115 EC MFA categories (under MFA III and IV) and the US Standard Industrial Classification, 31 MFA groups could not be matched. Nine of these categories were not put under quotas, and the remaining 22 accounted for less than 2 percent of EC's imports under the MFA during the 1985-89<sup>period.</sup> 24 To aggregate over product groups, we used the actual trade values of each exporter.

s Of the 22 suppliers we excluded three, Argentina, Indonesia and Pakistan, as their exports to the EC under the MFA covered considerably less<sup>than half of</sup> their total textile and clothing exports to this market between 1985 and 1989. The GNP per capita data are from the World Bank Atlas, 1990, and pertain<sup>to 1989</sup> or the latest year available. The figures for Czechoslovakia, Romania<sup>and Taiwan</sup> (China) are from unpublished Bank sources. For Macau's GNP per capita, we used Hong Kong's figures.
|Share of total land cultivated in crops (crop<br>mix)|||||
|---|---|---|---|---|
|Grains<br>64.31|26.17|65.90|26.52|0.370|
|Legumes<br>22.81|22.73|24.16|23.63|0.173|
|Tubers<br>1.55|7.65|1.55|8.33|0.035|
|Oils crops<br>0.48|4.09|0.13|1.90|0.107|
|Horticulture crops<br>4.14|11.28|3.38|9.93|0.271|
|Cotton<br>1.29|7.29|1.34|7.31|0.900|
|Tobacco<br>4.48|12.20|2.41|8.64|0.000|

Source: Generated by authors using LSMS data Notes: PSLC=Primary School Leaving Certificate. JCE=Junior Certificate Examination. MSCE=Malawi School Certificate of Education Examination.

40
use a sweeping education reform which results in a binary instrument which is strong enough to allow estimation of IV coefficients with precision.

What is the prior expectation of the magnitude of estimates and in general the nature of the results? Instrumental variables estimates of the return to schooling are expected to differ from OLS estimates. While the standard ability bias would suggest that the OLS estimates are biased upwards, empirical evidence from using a variety of, mainly binary, instruments, several of which are based on education policy reforms, suggest that IV estimates are generally higher that OLS estimates. The dominant explanation for this (as given by Card 2001) is that because of heterogeneity, there is a distribution of returns, and OLS and IV estimates correspond to different weighted averages of this distribution. Therefore, IV estimates can exceed OLS estimates. The fact that the IV estimates are higher than the OLS estimates is interpreted as an indication that the return to the marginal person (the “switchers”) is higher than that of the average person. Furthermore, Carneiro, Heckman and Vytlacil (2005) show that the marginal person can have a return that is substantially lower than the return to the average person, and still the return estimate from IV is greater than the corresponding return from OLS.

Assuming that cognitive skills are associated with quality of schooling, and a group of students switched to better quality schools as a result of the reform, we would expect the affected group (especially those who switched to better quality schools at a younger age) to have higher and less dispersed cognitive skills. For example, from the IALS dataset, in some countries (Scandinavian countries as well as the Czech Republic) where we observe high IALS scores and their dispersion is low, the increase in earnings in response to one standard deviation increase in 12
# **CAN PRODUCTION AND TRAFFICKING OF ILLICIT DRUGS BE REDUCED OR MERELY SHIFTED?**

Peter Reuter<sup>*</sup> School of Public Policy and Department of Criminology University of Maryland Drug Policy Research Center RAND

**Key Words:** Drug production, drug trafficking, cocaine, heroin, drug control policy

> * This paper is based on earlier research conducted with RAND colleagues (Robert Klitgaard, K. Jack Riley, Justin Adams, Susan Everingham and Jack Quinlivan) for the World Bank.  I thank Mara D’Angelo and Erica Schmeckpepper  for research assistance and Norman Loyaza for helpful comments.  The author is solely responsible for opinions expressed here.
- Lehner, B. and P. Döll. 2004. ―Development and validation of a global database of lakes, reservoirs and wetlands,‖ _Journal of Hydrology_ , 296(1-4): 1-22.

- Lehner, B., Verdin, K. and Jarvis, A. 2006. Hydrological data and maps based on Shuttle elevation derivatives at multiple scales (HydroSHEDS) - Technical Documentation. Washington, D.C.: World Wildlife Fund US. Available at http://hydrosheds.cr.usgs.gov.

- McCaffrey, Stephen. 2003. ―The Need for Flexibility in Freshwater Treaty Regimes,‖ _Natural Resources Forum_ , 27: 156-162.

Meridian World Data. 2006. Meridian Global Dam Database. <u>http://www.meridianworlddata.com/</u>

- Miller, K. and D. Yates. 2006. Climate Change and Water Resources: A Primer for Municipal Water Providers. Denver, CO: AwwaRF and UCAR. 83 pp

- Milly, P., Dunne, K., and Vecchia, V. 2005. ―Global pattern of trends in streamflow and water availability in a changing climate,‖ _Nature_ , 438(17 November): 347-350.

- Milly, P., Betancourt, J., Falkenmark, M., Hirsch, R., Kundzewicz, Z., Lettenmaier, D., et al. 2008. ―Stationarity Is Dead: Whither Water Management,‖ _Science_ , 319, 1 February, 573-574.

- Mitchell, Timothy D., and Philip D. Jones. 2005. ―An improved method of constructing a database of monthly climate observations and associated high-resolution grids,‖ _International Journal of Climatology_ , 25(6): 693-712.

- NASA. 2006. Flooding on the Zambezi River: Natural Hazards. NASA. Retrieved from <u>http://earthobservatory.nasa.gov/NaturalHazards/view.php?id=16440, accessed 2009.</u>

- Nel, Philip and Marjolein Righarts. 2008. ―Natural Disasters and the Risk of Civil Violent Conflict,‖ _International Studies Quarterly_ , 52: 159-185.

- Nilsson, C., C. Reidy, M. Dynesius, and C. Revenga. 2005. ―Fragmentation and flow regulation of the world's large river systems,‖ _Science_ , 308(405): 407.

- Owe, M. A., A. van de Griend, and A.T.C. Chang. 1992. ―Surface moisture and satellite microwave observations in semiarid southern Africa,‖ _Water Resources Research_ , 28: 829-839.

28
trajectories they had not considered before or to change their view on their abilities to do successfully the ones they knew.

In parallel, a set of observational studies based on panel data consistently show positive correlations between aspirations and outcomes later in life, even when controlling for confounding factors. For the case of education, young people aspiring to go to college in Ethiopia, India, Peru, and Vietnam were more likely to attend postsecondary education as young adults (Favara, Chang, and Sánchez 2018). Higher aspirations also correlate with better academic outcomes: students in France with higher educational aspirations at the beginning of ninth grade also have better test results at the end of the grade and a higher probability of assignment to an academic track than a vocational track in grade 10 (Guyon and Huillery 2021). Similarly, in Brazil college aspirations of ninth graders are highly correlated with class attendance in eleventh grade, students’ performance in twelfth grade, and with a lower probability of dropping out during secondary school (Gagete-Miranda 2020). Following the theory, a plausible channel is that children with high educational aspirations exert more efforts in their studies, perform better and complete more years of schooling. It is also likely that, along the way, adolescents’ better schooling outcomes reinforce their aspirations and vice versa.

# **Labor market outcomes**

Higher aspirations correlate with better labor market outcomes. The higher aspirations leading to increased education are likely the channel through which aspirations correlate with better labor market outcomes. For example, in Great Britain, individuals who were 16 years old in 1986 who aspired for better quality jobs had higher earnings 26 years later in their mid-careers (Green et al. 2018).

International child sponsorships have been found to increase aspirations and labor market outcomes. This is a program whereby sponsors from high-income countries provide a monthly cash transfer and pay for mentoring and social activities with church and community volunteers. It has been found to raise children’s hope and self-efficacy, which are thought to be some of the drivers of aspirations (Glewwe, Ross, and Wydick 2018). In Indonesia, Kenya, and Mexico children beneficiaries of this type of program increased their educational aspirations (Ross et al. 2021). Moreover, this type of program increased schooling, employment quality, labor force participation, and monthly income of formerly sponsored adults in Bolivia, Guatemala, Kenya, Uganda, India, and the Philippines (Wydick, Glewwe, and Rutledge 2013, 2017).

Aspirations also correlate with better outcomes for microentrepreneurs, who represent the bulk of employment in low- and middle-income countries. Research on small-scale retailers in Indonesia shows that a one standard deviation increase in sales aspirations is associated with 36-percent higher 14
**<u>Table 2.4. Irrigation Development by Regions, 2007 (1,000 ha)</u>**

||DSI Region|Geo.R|DSI|DSI(GWIC)|GDRS|Farmers|Total|
|---|---|---|---|---|---|---|---|
|1|Bursa|W|58|5|31||95|
|2|Izmir|W|122|15|50|147|334|
|3|Eskisehir|W|77|26|68||171|
|4|Konya|C|190|187|163|95|635|
|5|Ankara|C|53|4|81||138|
|6|Adana|W|323|17|86|34|461|
|7|Samsun|C|88|20|67|51|226|
|8|Erzurum|E|84|16|96|154|350|
|9|Elazig|E|82|5|103|101|291|
|10|Diyarbakir|SE|43|0|20||63|
|11|Edirne|W|61|21|55|40|176|
|12|Kayseri|C|82|20|100|58|260|
|13|Antalya|W|80|6|21||107|
|14|Istanbul|W|0||6||6|
|15|Sanliurfa|SE|189|0|22||212|
|17|Van|E|66|1|67|43|177|
|18|Isparta|W|109|61|83|46|299|
|19|Sivas|C|23|1|35|73|132|
|20|K.Maras|SE|48|6|49||103|
|21|Aydin|W|199|18|59|130|406|
|22|Trabzon|E|13|1|35|23|72|
|23|Kastamonu|C|13|2|28|2|44|
|24|Kars|E|71||20|37|128|
|25|Balikesir|W|62|7|38||106|
|26|Artvin|E|||11||11|
||Total||2,136|438|1,394|1,034|5,001|

_Sources: DSI, 2008b ; GDRS, 2007 ; SPO, 2007._ The Western region is more populated and industrialized compared to the rest of the country. In addition, the seven river basins in this area are estimated to have already exceeded their long-term capacity utilization rates (World Bank, 2007). About 90 percent of irrigation methods depend on gravity systems with low water efficiency. Furthermore, the irrigation ratios of the schemes transferred by State Hydraulic Works (DSI) indicate that about 35 percent of the irrigated area are allocated to rainfed agriculture (Table 2.5). Unavailability of water is the top reason for the shift to rainfed agriculture (DSI, 2008a).

13