rafmacalaba/datause-encoder-data huggingface dataset:
countries are: Tonga, population 112,000, for which Docquier and Marfouk (2005) estimate 75 percent of those with tertiary education live abroad; Papua New Guinea (PNG), population 6.3 million, with an estimated 29 percent brain drain rate; and New Zealand, population 4.1 million, which along with Ireland, has the highest rate of skilled emigration in the OECD, at 24.2%.<sup>2</sup> In each country we have collected the names of individuals who were the highest achieving students in their country at the end of high school, for students graduating high school between 1976 and 2004. Depending on the country, these are either the top students in nationwide competitive examinations, or the students placed top in their class at the most academically prestigious schools in the country. We then tracked down these former top students, wherever in the world they currently live, and surveyed them. The surveys ask detailed questions on incomes and occupations available at home and abroad, risk aversion, discount rates, parental background, and other socioeconomic characteristics which are likely to predict migration and return. As well as economic factors, we include psychological and social factors emphasized by psychologists and sociologists. Finally, the surveys also collect more qualitative evidence on a range of different social and cultural push and pull factors.

Using these data we measure the emigration rates and return migration rates of the most academically talented individuals in each country, and examine which characteristics predict emigration and return. We find the incidence of ever migrating is very high, with 83 percent of Tongan top students, 67 percent of New Zealand top students and 37 percent of PNG top students having ever worked or studied abroad. The incidence of return migration is also high, with between one-quarter and one-third of top students in each country being return migrants.

We find that most of the highly skilled say that salaries would be higher for them overseas. However, among these individuals, the decision to migrate is found to be most strongly associated with preference variables, such as risk aversion, patience, and subjects studied in secondary school, and not strongly linked with economic variables such as liquidity constraints, the extent of the gain in income to be had from migration, or

> 2 See OECD (2005). In comparison, Australia has less than 5% of its tertiary educated natives abroad, and the United States has less than 1%.

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in mental health experienced by non-4Ps households, despite the fact that both groups eventually received the emergency transfers. This underscores the potential for a well-functioning social protection program to yield mental health benefits during crises, even when embedded within a broader emergency response effort.

# **2 Background**

This section provides background information on the 4Ps and the Philippine government’s response to the COVID-19 pandemic. More details can be found in Cho et al. (2021a), Cho et al. (2021b), and Cho et al. (2020), upon which much of this discussion is based. Because the data used in this paper were collected from November 2019 to October 2020, we focus our discussion of the Philippines’ pandemic response on events during this time period.

## **2.1 4Ps program**

The 4Ps program was piloted in 2008 and has since become the flagship social protection program of the Philippines. Households are eligible for the 4Ps if they have at least one child (or a pregnant woman) and if their poverty score falls below a pre-determined cutoff. By the time of the COVID-19 pandemic, the most recent update to these poverty scores had taken place in 2010.

The 4Ps is a CCT program that provides cash to eligible households who meet a set of education and health requirements. During the 10-month school year, households receive PhP 300, PhP 500, or PhP 700 per month per child (depending on age) who is enrolled in and consistently attending school, for a maximum of three children. The year-round health component of the program involves PhP 750 monthly transfers for households who satisfy all health requirements, including vaccinating their children and participating in Family Development Sessions. The majority of households (95% in our sample) receive their transfers electronically through a Land Bank ATM card. Transfers are paid every two months.

4Ps households also benefit from other social programs. They are eligible for free national health insurance (PhilHealth), rice subsidies, and unconditional cash transfers (UCTs) under the Tax Reform for Acceleration and Inclusion (TRAIN) law. A household with three children (one child in each of the three different age categories) who complies with all conditions will receive PhP 2,250 per month through the 4Ps program (PhP 1,500 education payment and PhP 750 health payment) and an additional PhP 900 through the TRAIN UCT and rice subsidy programs.

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been favorable to female schooling. First, I use INSEC data on the total number of school destructions per district to test whether such destructions had an effect on primary schooling completion (Columns 1 and 2). For both genders I find a statistically insignificant effect, which is likely due to the fact that a district-level analysis lacks power in identifying the effect of school destructions. School destructions were a rare and isolated aspect of the conflict,<sup>7</sup> which one would expect to have had a large effect on schooling at a very disaggregated level. But this effect is unlikely to be captured by an analysis carried out at the district level, unless school destructions are correlated with characteristics of the conflict relevant at the district level, such as the use of schools as army barracks (Hart, 2001; Caddell, 2007). Second, I use INSEC data on the total number of abductions by Maoists per district to test whether a larger number of abductions, often targeted at school children, had an adverse effect on schooling. Results in Columns (3) and (4) indicate that abductions had a negative effect on female primary schooling. An increase in the number of abductions (per 1000 inhabitants) by one standard deviation (16.82) decreases female primary schooling attainment by 3.7 percentage points. Put differently, the effect of a dramatic move from the 5<sup>th</sup> to the 75<sup>th</sup> percentiles of the district-level distribution of total abductions is a decrease of 1.6 percentage points in female primary completion. Third, I test whether primary schooling completion improved more in districts controlled by Maoists, and thus where the insurgents were better able to affect schooling provision according to their ideology (Columns 5 to 8). There is no clear-cut definition of control by the insurgents, with discrepancies between the classifications used by the People’s Army and the government (Hatlebakk, 2007), and so I use two alternative classifications. In Columns (5) and (6), I consider controlled by the Maoists only those districts recognized as such by both the Maoists and the government. In Columns (7) and (8), I use the government’s classification, which includes more districts and is somewhat more convincing according to Hatlebakk (2007). The choice of definition affects the magnitude and significance of estimates, but altogether the message is that primary schooling became _more_ prevalent over time in areas controlled by the Maoists, for both genders.

Caution should however prevail when interpreting the findings on Maoist abductions. Since these abductions specifically targeted school children and teachers, abductions are more likely to be correlated with unobserved variables that are also correlated with schooling outcomes. There may even be reverse causality of an ambiguous direction running from educational attainment to abductions. On the one hand, abductions may be more likely where educational attainment is higher (e.g., if Maoists target schools in areas where attendance is high to abduct large numbers

> 7 76 schools were destroyed according to the data provided by INSEC.

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improvement, it is unlikely that the domestic road network is currently a binding constraint to growth. However, as discussed later in the chapter on spatial dimensions to growth, the road investments likely to have the highest impact on agricultural growth are feeder roads in areas with high agronomic potential and only moderate connectivity to major cities.

# **Import-Export Infrastructure**

The areas immediately adjacent to Malawi in neighboring countries have relatively low populations - Eastern Zambia and North-Western Mozambique – resulting in limited potential for regional trade with direct neighbors. As a result most imports and exports require international shipping giving Malawi a dependence on transit countries for infrastructure, procedures and the logistics performance of foreign ports.

In the absence of a comprehensive transport study, it is difficult to obtain reliable data on the cost of trade transport and hence establish whether transport infrastructure currently constrains growth – either for the export sector or for import intensive industries for the domestic market. Faye et al (2004) estimated that total transport costs as a proportion of exports were as high as 55% making Malawi an exceptional outlier even among landlocked countries. Taken at face value, this data would imply that weak trade infrastructure was a binding constraint on growth, and that investment was likely to respond strongly to improvements in regional infrastructure.

Yet other sources do not corroborate these findings. Firstly using 2006 IFS data, the cost of transport and insurance as a proportion of imports<sup>17</sup> puts Malawi among comparators at about 15%. Secondly the Doing Business Indicators also rank Malawi as comparable to peers for the cost of importing/ exporting a twenty foot container. The DBI data also shows a significant premium on importing caused by the trade deficit, since the number of import journeys outnumbers export journeys. Thirdly anecdotal evidence from the main commodity exporters put transport costs at between five and ten percent of export value (see Trade Facilitation and Transport chapter).

> 17 That is (imports (CIF) – imports (FOB)) / imports (FOB)

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# **Works Cited**

Abdu-Raheem, B.O. (2012). “Effects of Problem-Solving Method on Secondary School Students’ Achievement and Retention in Social Studies in Ekiti State, Nigeria.” _Journal of International Education Research_ , 8(1), First Quarter.

Ajaja, O. Patrick & Ochuko Urhievwejire Eravwoke. (2010). “Effects of Cooperative Learning Strategy on Junior Secondary School Students Achievement in Integrated Science.” _Electronic Journal of Science Education_ , 14(1), 1-18.

Andrabi, T., Das, J. & Khwaja, A. (2009). “Report Cards: The Impact of Providing School and Child Testscores on Educational Markets.” BREAD Working Paper No. 226, Bureau for Research and Economic Analysis of Development.

Andrabi, T., Das, J., Khwaja, A., & Zajonc, T. (2011). “Do Value-Added Estimates Add Value? Accounting for Learning Dynamics.” American Economic Journal: Applied Economics, 3(3), 29-54.

Angrist, J., & Lavy, V. (2001). “Does Teacher Training Affect Pupil Learning? Evidence from Matched Comparisons in Jerusalem Public Schools.” Journal of Labor Economics 19(2).

Baird, S., Ferreira, F., Özler, B., & Woolcock, M. (2014). “Conditional, Unconditional and Everything in Between: A Systematic Review of the Effects of Cash Transfer Programmes on Schooling Outcomes.” _Journal of Development Effectiveness_ 6(1), 1-43.

Banerjee, A., Glewwe, P., Powers, S., & Wasserman, M. (2013). “Expanding Access and Increasing Student Learning in Post-Primary Education in Developing Countries: A Review of the Evidence.” Post-Primary Education Initiative Review Paper, Abdul Latif Jameel Poverty Action Lab, Cambridge, MA.

Banerjee, A., Cole, S., Duflo, E., & Linden, L. (2007). “Remedying Education: Evidence from Two Randomized Experiments in India.” _The Quarterly Journal of Economics_ 122(3), 1235-1264.

Barrera-Osorio, F., & Linden, L. (2009). “The Use and Misuse of Computers in Education: Evidence from a Randomized Experiment in Colombia.” Policy Research Working Paper No. 4836, World Bank, Washington DC.

Beuermann, D.W., Cristia, J.P., Cruz-Agayo, Y., Cueto, S., & Malamud, O. (2013). “Home Computers and Child Outcomes: Short-Term Impacts from a Randomized Experiment in Peru.” NBER Working Paper 18818. National Bureau of Economic Research, Cambridge, MA.

Bold, T., Kimenyi, M., Mwabu, G., Ng’ang’a, A., & Sandefur, J. (2013). “Scaling Up What Works: Experimental Evidence on External Validity in Kenyan Education.” Working Paper No. 321, Center for Global Development, Washington, DC.

Bimbola, O., & Daniel, O. I. (2010). “Effect of constructivist-based teaching strategy on academic performance of students in integrated science at the junior secondary school level.” _Educational Research and Reviews_ , 5(7), 347-353.

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in inequality (measures by the log difference in the Gini index) this is significant (at the 5% level), with a negative coefficient, and the share of the variance explained rises to one half.

# _Concentration around the median_

The mean Foster-Wolfson index rose only slightly over the period, at 63.9% and 64.9% for 1990 and 2005 respectively; the median increased from 64.6% and 65.6%.<sup>19</sup> The density of the annualized growth rates in the FW index is centered at a small negative value; the mean change is -0.13% per year. The index has fallen slightly more often than it has risen; 48 out of 91 countries show a decrease. The majority of developing countries have not experienced higher density in a neighborhood of the (country-specific) median, although there has been a clear tendency for bunching up above the $2 line. Consider, for example, the two most populous countries, China and India. In marked contrast to the findings in Table 3, China’s FW index has tended to fall over time, while India’s has remained largely unchanged.<sup>20</sup> As an aside, it can be noted that, despite the conceptual differences noted by Wolfson (1994, 1997), the FW index is highly correlated with a standard inequality index.<sup>21</sup> The correlation coefficients between the FW index of polarization and the Gini index of inequality in the most recent surveys is 0.94 (and 0.92 for the earliest survey). The correlation coefficient between the changes over time in the two measures is 0.83. However, the Gini index is the better predictor of changes in the poverty rate; if one includes both the change in the log Gini index and the change in the log FW polarization index as extra controls in the regression of _gi_ (<sup>_R_</sup> _it_ ) on<sup>_g_</sup> _i_ (<sup>μ</sup> _it_ ) (Figure 7) only the Gini index is significant at the 5% level.

There is little sign that changes in the FW index of concentration around the median have been correlated with rates of economic growth over the same time period. The correlation coefficients between the annualized growth rate in the index and the real annualized growth rate in the survey mean is 0.09 while the correlation with the corresponding growth rates in real consumption per capita from the national accounts is 0.00. It is not the case that more rapidly

> 19  Given that these mean FW indices across countries do no include the between-country component of the distributional change, they are higher than the overall FW indices reported earlier in this paper. Recall also that this is 100 minus the usual FW “polarization” index.

> 20  For the longest time periods available, China’s FW index fell from 74.5% to 61.1% over 1981-2005, while India’s remained fairly similar, at 75.0% and 74.5% for 1977/78 and 2004/05 respectively.

> 21  A similar point is made by Zhang and Kanbur (2001), using data for China.

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Figure 3: **Benchmarking** <!-- Start of picture text --> p-value=0.004 p-value=0.125<br>14% 22% 17%<br>N=271 N=564 N=271<br>Benefits upgrade Platinum pooled Benefits upgrade discount<br>Group<br>.25<br>.2<br>.15<br>Take-up Rates .1<br>.05<br>0<br><!-- End of picture text --> Notes: This figure presents the mean (and 95% confidence interval) of take-up rates for the _benefits upgrade_ and _platinum pooled_ groups. We also present the take-up rate for the benefits upgrade with a 25% discount in the annual fee. This takeup rate is based on the _benefits upgrade_ sample. As explained in Section 3.3, we called again customers that declined the benefits upgrade offer in the first call after hearing the price details of the offer, and offered them the benefits upgrade at a 25% price discount. For this case, we calculate the standard error using bootstrap. For the _benefits upgrade_ versus _platinum pooled_ comparison, the p-value is based on a permutation test.

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Under the assumption that the influence of US exporters’ interests- and especially US importers’ interests- strictly prevail over interests of their Mexican counterparts, the following coefficient signs are expected when estimating equation (3):  ˆ1  0 ,  ˆ2  0 ,  ˆ3  0 ,  ˆ4  0 , and  ˆ5  0 .

In implementing (3), the trade-related explanatory variables are either averaged out over a three year period preceding NAFTA, as in the case of the RCA indices and the export figures, or are figures corresponding to 1993, the year previous to NAFTA implementation, as in the case of tariff data. The rationale for this choice is that industrial groups willing to influence RoO during negotiations assessed their gains or losses, only by taking into consideration information available until this date. Moreover, a potential source of endogeneity bias is circumvented by taking explanatory variables corresponding to a period prior to the entry into force of the trade agreement. Section dummies included in the model are assumed to control for unobserved timeinvariant sector characteristics.

The paper focuses solely on RoO, despite the fact that preferential tariff phase-out periods were other key instruments denying market access and subject to negotiations that could simultaneously have been determined with RoO and have analogous effects. However, those instruments have only a temporary character as opposed to RoO, which are, presumably, permanent<sup>15</sup> . Indeed, most import tariffs disappeared before the first ten years of operation. Besides, phase-out periods are highly and significantly correlated with the R-index<sup>16</sup> and it is safe to assume that both policies are complementary in the short run.

> 15 Although, they can in principle be subject to renegotiation in some PTAs, RoO under NAFTA have not been renegotiated to this day.

> 16 Estevadeordal (2000) estimates a two-equation model with a two-stage procedure where the number of years to liberalization is the dependent variable in one of then and RoO is the dependent variable in the other one, while being also a regressor in the former equation.  In this model, tariff phase-out periods are determined by RoO, but the converse is not true.  The appendix of that article explains that the main econometric problem of estimating a system where the number of years to liberalization is also added as a regressor in the RoO equation would be the extreme complexity and non-linearity of the resulting likelihood function. Thus, he recognizes that this likelihood function would be difficult to maximize using standard methods.

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# **5.   Getting to OECD markets**

For most if not all export products, OECD markets are the largest. Even if, on aggregate, they are not necessarily the fastest growing, at the level of the firm they typically offer the largest expansion potential. However they are also often difficult in the sense of requiring product and service quality standards that not all firms and countries can offer. Whether export promotion strategies should target those markets directly, or whether they should proceed in steps, using regional LDC markets as stepping stones for exporters, is a recurrent debate among policy makers in developing countries. We explore in this section what the aggregate evidence has to say, i.e. whether the norm among non-OECD exporters is to try OECD markets first or whether those are reached only after exporting experience has been accumulated on non-OECD markets.

## **5.1   Prima-facie evidence**

We use again bilateral trade data at the SITC4 level. Our unit of observation is _ij_ the first year of a bilateral export spell of an SITC4 product, i.e. a vector **x** _kt_ = [exporting country _i_ , importing country _j_ , product _k_ , initial year _t_ ]. For each non-OECD exporter and product, using mirrored data<sup>10</sup> we isolate (i) the first year of export to any destination in the world, and (ii) the first year of export to any OECD country. A “new export” is a product that is exported for the first time in the sample period ( _not_ an export spell starting after a temporary trade interruption). Thus, a spell starting in 1964 after zero values in 1962-63 is a new export; but a spell starting in 1966 after a 1962-63 spell interrupted in 1964-65 is not. We treat left-censoring at the sample period’s initial year (1962) by

> 10 “Mirroring” consists of using the importing country is the reporter and the exporting one as the partner. This improves the reliability of trade data as national customs administrations (the ultimate source of COMTRADE data) usually monitor imports more closely than exports. Mirroring can however lead to distortions in the presence of smuggling or under-reporting. In order to circumscribe the problem, CEPII reconciles import and export data on a case-by-case basis in the BACI database. We stick to COMTRADE data here because BACI does not cover enough years; in addition, for many exercises in this paper our reporter countries are OECD countries whose data is usually reliable.

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