rafmacalaba/datause-encoder-data huggingface dataset:
# **Migration and Unemployment**

Unemployment is lower among migrants (6.2 percent) than non-migrants (9.5 percent) and lower still among those who migrated for work (2.6 percent). What is striking is that unemployment rates among migrants and migrants for work are consistently lower in destination governorates with the highest migration inflows (Tables 19 and 20), and even those experiencing high unemployment, such as Port Said with 25% unemployment rate. This might suggest that migrants, especially those migrated to work, have skills that enable them to find jobs.

Furthermore, migrants earn higher wages (after controlling for education level) compared to non-migrants. This may be due to migrant’s matching their skills to the demand for jobs. It also reflects the significance of labor mobility and internal migration as means of achieving higher living standards.

Table 19 – Unemployment Rates of Non-migrants, Migrants and Migrants for Work

||**Overall**|**Non-migrants**|**Migrants**|**Migrants for work**|
|---|---|---|---|---|
|2008|9.9|10.2|3.6|0.2|
|2009|9.4|9.7|5.1|1.1|
|2010|9.3|9.5|6.2|2.6|

Source: Authors’ calculations using Labor Force Survey Table 20 - Unemployment Rates in Governorates with Highest Net Migration Inflows

|Unemployment rate|Overall|Non-migrants|Migrants|Migrated for Work|
|---|---|---|---|---|
|Port Said|25|41.82|8.28|0|
|Ismailia|10.4|11.95|7.8|1.59|
|6 October|10.2|9.91|11.04|8.51|
|Red Sea|6.1|14.29|0|0|
|Suez|11.6|22.88|3.13|0|

Source: Authors’ calculations using Labor Force Survey Table 21 - Unemployment Rate for Governorates with Highest Net migration for Work Inflows

|Unemployment rate|Overall|Non-migrants|Migrants|Migrated to work|
|---|---|---|---|---|
|Cairo|13.4|14.9|5.79|3.6|
|6<sup>th</sup>of October|10.2|9.9|11|8.5|
|Alexandria|11.6|12.5|4.6|0|
|Ismalia|10.4|11.9|7.8|1.5|
|Qalubia|7.8|8.8|3.3|0|

Source: Authors’ calculations using Labor Force Survey To further explore migrants’ employability we ran a probit model for employment (‘1’ if the person is employed and ‘0’ otherwise). The explanatory variables are educational attainment 22
Figure 3 Education Spending Per Program and Level of Government, FY 2005 (in Trillion Rp.) <!-- Start of picture text --> Centr al Gover nm ent Sub-national<br>Government<br>A PB N Total National<br>APBD<br>Ministry of Ministry of<br>Province District<br>Education Religious Affairs<br>2.6 0.9 3.2 26.2 33.0<br>Development<br>2.6 Development Development Development Development<br>0.7 2.6 4.5 10.4<br>Routine Routine Routine Routine Routine<br>0.01 0.3 0.6 21.7 22.6<br>8.3 1.3 0.4 8.8 18.8<br>Development Development Development Development Development<br>8.3 0.9 0.23 0.4 9.8<br>Routine Routine Routine Routine Routine<br>0.004 0.4 0.17 8.4 9.0<br>2.7 0.7 0.2 6.8 10.4<br>Development Development<br>2.5 Development0.5 Development0.09 Development0.16 3.2<br>Routine0.02 Routine0.3 Routine0.13 Routine6.67 Routine7.2<br>5.8 2.4 8.2<br>Development Development Development<br>1.6 0.6 2.2<br>Routine Routine Routine<br>4.3 1.8 6.0<br>0.3 0.02 0.35<br>Development<br>0.3 Development<br>Development 0.32<br>0.02<br>Routine Routine<br>0.0 0.03<br>19.7 5.4 3.8 41.8 70.7<br>Development Development Development Development Development<br>15.3 2.7 2.9 5.1 26.0<br>Routine Routine Routine Routine Routine<br>4.5 2.7 0.9 36.8 44.8<br>Development Routine Development Routine<br>Development18.0 Routine7.1 8.0 37.7 26.0 44.8<br>25.1 45.6 70.7<br>Level of<br>Government<br>Spending Unit<br>Pre-School and Primary<br>Junior<br>Secondary<br>Senior<br>Secondary<br>Tertiary<br>Education<br>Education<br>Non Formal<br>Sub-total<br>Total<br><!-- End of picture text --> _Source_ : Computed by World Bank staff based on MoF and SIKD data. Reported figures do not necessarily add up to totals due to rounding. _Note_ : The functional classification includes sub sectors Education (11.1) and Non-formal Education (11.2) while two other sub sectors (11.3 and 11.4) are aggregated under Tourism and Culture function (08). The Education function also includes the Religious Education sub sector (15.2).

Indonesia is spending more per student the higher the level of education and this is consistent with international experience. In Indonesia, expenditure per pupil in junior secondary education is approximately 2.5 times that of primary education, whereas senior secondary education is 3.4 times more than primary. This is a typical phenomenon, as higher levels of education require more expensive inputs, but it is more pronounced in Indonesia than in other countries (UNESCO, 2005). The gap is particularly significant at the primary level; it then narrows at the secondary and tertiary levels of education.

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9 upon either the total withdrawal of remittances or a marginal increase in remittance income. The fourth paper compares the observed level of welfare and poverty with a counterfactual scenario without remittances, that includes an imputation for the home earnings of international migrants. The fifth, and final, paper in this section focuses more on international migration than remittances and adopts a different methodological approach to examine how international migration affects child health outcomes. All of these papers show that international migration and remittances have important effects on welfare and poverty.

The first paper, and probably the broadest in scope, uses results from household surveys in 71 developing countries to analyze the impact of international migration and remittances on poverty in the developing world (Adams and Page, 2005). Since international migration and remittances may be endogenous to poverty outcomes, the paper uses various instruments – including distance between remittance-sending and receiving countries – to correct for this problem. Using these instruments, and controlling for the level of income, income inequality and geographic region, the paper finds that a 10 percent increase in per capita official international remittances in a developing country will lead to a 3.5 percent decline in the share of people living on less than $1.00/person/day in that country. Other estimates suggest that a similar 10 percent increase in per capita remittances will reduce the depth of poverty in a country by 3.9 percent. Since both of these results are similar to those produced by non-instrumented estimates, the paper concludes that international remittances reduce both the level and depth of poverty in the developing world.

The second paper uses data from a 1986/87 survey of 7680 households in rural and urban Lesotho to examine how the cessation of international remittances would affect welfare and poverty in that country (Gustafsson and Makonnen, 1993). Lesotho sends large numbers of migrant workers to work in the mines in South Africa. As a result, international remittances represent the main source of income for 35 percent of Lesotho households. Since it is possible that South African employers might stop hiring Lesotho workers, the paper simulates the effects on household welfare and poverty if international remittances would stop altogether. If remittances were set to zero, the paper finds that average per capita household consumption would fall by 32 percent. Moreover, if
prevent further entry, thereby stemming economic growth (Murphy et al. 1993). On the other extreme, countries like Tajikistan and Turkmenistan have emerged as the least reformed (World Bank 2002). GDP per capita in 2002 in both Tajikistan and Turkmenistan was lower than at the start of the transition period, and their backward-looking approach can hardly be seen as a viable strategy for sustainable growth.<sup>4</sup> As the Greek philosopher Heraclitus would have said: “One cannot step into the same river twice”.

This paper offers a political economy explanation of why institution building has varied so much across transition countries, building on North’s hypothesis that “institutions are not usually created to be socially efficient, [but] are created to serve the interests of those with bargaining power to create new rules” (North, 1990, p. 16). The socialist elite remained a powerful political interest group during the initial phase of the transition process in most transition countries, but its authority varied across countries depending on their entrenchment in power. We conjecture that the incumbent socialist elite or nomenclatura had fewer incentives to create institutions that fostered competition, as this would reduce their economic power. Further, economies that rely more on natural resources offer larger opportunities for the elite (being the old nomenclatura or a newly emerged group of business owners that benefited from privatization and is well-connected to the established elite) to extract rents and as such the politically powerful in these countries have less incentive to establish strong property rights. If the rents are large enough, which is usually the case in natural resource rich countries, the elite may capture the state and block further reforms to encourage entry and to establish sound institutions. Political entrenchment and reliance on natural resources critically determined whether the behavior of the ruling elite and thus the transition process was “catalytic” or “extractive”. We use the number of years a country has been socialist as proxy for the entrenchment of the socialist elite and thus

> 4 Please note that our sample period ends in 2002 and we can therefore not take into account recent political events.

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Teacher Incentives in the Kyrgyz Republic competitive incentives, since competition necessarily produces “winners” and “losers”. Teachers typically view other teachers in their own schools as colleagues rather than as competitors, and research on “meritbased” teacher incentives has repeatedly found that teachers prefer to distribute awards equitably rather than allow for winners (Murnane & Cohen 1986). Standards-based performance assessments--despite recognizing different levels of teacher performance or “proficiency” and allowing teachers who are assessed as more “proficient” to receive larger awards than those who are assessed as less “proficient”-provide the possibility that, in principle, all teachers could be assessed as “highly proficient.” Teachers may feel that a standards-based assessment of performance is fairer than a competition.

In addition, research from developed countries suggests that teacher attitudes toward incentive programs will be unfavorable for programs that utilize “extrinsic” rewards, such as salary bonuses, and favorable for those that provide performance feedback (Deci & Ryan 2000).

# 1.2 Prior Research on the Impact of Standards-based Incentives

Individual, standards-based teacher incentives appear to be more effective than competitive teacher incentives. The only evaluated standards-based teacher incentive program designed to reward specific teaching behaviors in a developing country is a well-known randomized control trial (RCT) study of incentives designed to lower teacher absenteeism—“attendance pay” (Duflo, Hanna & Ryan 2008). Contract teachers in schools operated by an NGO in India were paid a bonus contingent upon the teacher showing up more than ten days per month; evidence of the teacher’s attendance was documented by a camera. The incentive was successful, in that the promise of a bonus (which was paid monthly) boosted teacher attendance.

Another rigorously evaluated standards-based teacher incentive program—a “merit pay” program designed to reward teachers for raising student achievement-- was carried out in India (Muralidharan & Sundararaman 2011). In this case, schools were assigned to three incentive conditions (control, individual teacher incentive and whole school (“group”) incentive) conditioned on growth in student learning, with a merit-pay bonus given to teachers for each additional percent of mean test score 4
Table 4 reports the sample size and mean household size of cach panel set.3/ The key observation is that the decline in household size is almost entirely absent within the panels, with the exception of '4 second panel covering the years 1986-87 (a decline from 7.85 in 1986 to 7.36 in 1987). The order of magnitude of the decline in mean household size recorded through non-panel comparisons across the years cannot be considered as simply reflecting a real trend in household formation and composition in CMte d'Ivoire. For example, compare mean household size in set 4 (1986) which is 7.96 and set 11 (1987), which is only 6.54. A decline of almost 20 percent in just one year is extremely unlikely.

There are three alternative explanations of these observed declines in mean household size:

- it is due to non-sampling errors (i.e., measurement errors), arising from either over-zealous enumeration in the early years of the survey, or less rigorous enumeration as the survey was repeated each year (for example, due to a deterioration in field supervision);

- it arises from biases in sampling, with over-sampling of either large households in the early years, or smaller households in the later years of the survey;

# it reflects an underlying reality of demographic change.

There is little evidence that non-sampling errors are responsible. Various tests applied to the data, suggest that data quality was maintained over the four years of the

- 3/ The differmnce in the sample sizes of sets 2 and 5 (the unmatched households in the first panel, and the 1986 replacement households) needs to be explained. Whilst 79 households failed to qualify for the 1st panel, 86 households were selected as replacements. This is because the sample in 1985 was only 1593 (that is 7 short of a full sample of 1600 households). Thus, 7 households were selected in addition to the 79 replacement households to restore the sample to 1600 households in 1986.

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Table 8: Cross-country Comparison of Education Levels.

|Country|GNP<br>$ per<br>capita, 1990|Literacy<br>(%/0)|Net<br>Primary<br>enrollment (%)|Children reaching 4th<br>year|
|---|---|---|---|---|
|Mexico|2680|81|88|81|
|Egypt|600|48|91|99|
|Indonesia|560|77|97|89|
|Pakistan|380|35|29|59|
|China|370|73|99|86|
|India|350|52|66|61|

Source: UNESCO and UNICEF.

Such low basic literacy and numeracy seriously inhibit agricultural productivity, and complicate the task of agricultural support services. Indeed, this could well be the binding constraint on rapid increases in agricultural productivity (and agricultural production) over the next 10 to 15 years.

A study of total factor productivity in Pakistan<sup>6</sup> over 1955-85 found an important role for educational variables. Exploiting data from 35 districts over 30 years, it was found that a 10 percent increase in rural male adult literacy increased total factor productivity by 2.7 percent. This can be compared with the response to a 10 percent increase in the area under irrigation (2.4 percent), and a 10 percent increase in the share under high-yielding varieties (1.3 percent). Thus investments in education represent a potentially major source of productivity growth. The study also finds that falling investmnent in education can explain a significant part of the stagnation of productivity in agriculture after 1975.

Another survey in Pakistan showed that many farmers did not display basic computational needs for optimal fertilizer usage. The deficiency was strongly correlated with the availability of extension services. A study in a rice-wheat area in Pakistan found the major constraint in closing a large yield gap (40 percent) was farmers' technical knowledge and skills. There is a strong complementarity between education and the provision of extension services.

Quality of education is also a major concem. Butt (1984) found that productivity of farmers with secondary schooling (used as a proxy for quality) was significantly higher than productivity of fanners with just primary education. Primary education increased productivity 7 percent, while secondary education increased productivity by 11 percent.

6Rosegrant and Evenson (1993).

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# **2.1 Aid Data**

Data on foreign aid are sourced from World Bank projects in the period 1995–2014, geocoded by AidData. Other geocoded datasets on aid exist, but this one covers the whole African continent (and beyond) for the longest period. The World Bank dataset contains projects from both the International Bank for Reconstruction and Development (IBRD) and the International Development Association (IDA), totaling 1,900 projects in Africa split across 16,553 different locations. The IBRD provides low or zero interest rate loans to sufficiently creditworthy countries, whereas the IDA gives loans to poorer and less creditworthy countries; 12 percent of IDA funds are given as grants not to be paid back. Both types of lending are accompanied by technical assistance from the World Bank, and projects are monitored by World Bank<sup>12</sup> The data contain information on all locations in which a given project has been implemented. Locations are classified into categories 1 to 8 according to the level of geographical disaggregation of their coordinates, with the categories called, somewhat misleadingly, “precision categories.” Precision 1 locations correspond to a specific place, that is, a populated place of some kind (such as a village, town, or city), in approximately 80 percent of the cases, or to a third-order administrative division (ADM3, i.e., a neighborhood or suburb) in approximately 15 percent of the cases.<sup>13</sup> Precision 2 locations are similar to precision 1 locations, but their reported coordinates, being within 25 km of the exact location, are not as accurate. Precision 3, 4, and 6 locations correspond to second-order administrative divisions (ADM2), firstorder administrative divisions (ADM1), and countries, respectively. Note that precision 3, 4 and 6 categories refer to projects intended to serve the whole administrative division (e.g., training for all public employees in a 12The information available at the project level includes the original World Bank identifier, project title, date of approval, expected date of completion, share in different sectors (finance, transportation, energy, health, education, agriculture, water, industry and trade, information communication technology, public administration), lending instrument (development policy lending versus investment), local implementing agency, total committed and disbursed amounts, completion and supervision costs, and independent evaluation rating.

> 13First-order administrative divisions (ADM1) are the largest administrative units (provinces, states, or governorates); second-order administrative divisions (ADM2) are units at the next level (districts, municipalities, or communes); third-order administrative divisions (ADM3) are subdivisions of ADM2s (neighborhoods or suburbs), and so on.

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ZAIRE: In Zaire, recommendations to narrow the range of tariffs by introducing a minimum tariff of 10 percent and a maximum tariff of 60 percent were expected to increase revenue by US$11 million. The positive revenue effect of the increased minimum duty was expected to more than offset any decline from the lowering of ,h end tariffs. The measures were not effectively implemented so that it is not possible to assess the validity of these priurs.

PHILIPPINES: The ex-ante revenue analysis of the tariff reform under SAL I estimated the revenue loss due to tariff reductions to be negligible and gave the following reasons for this conclusion: a) there would be little revenue loss from the reduction of the high tariff rates because the quantities imported at those rates were small, b) there may be a revenue gain from the increase in tariff rates on high volume items, and c) import liberalization would enable revenue to be earned on items formerly restrained by licensing requirements. Some positive revenue measures were nevertheless recommended: QRs on non-essential consumer imports were to be replaced by higher domestic commodity taxes to discourage consumption and were expect-d to raise P1 billion. New indirect taxes were planned and expected to raise P300 million in additional revenue.

Following these recommendations the average tariff rate was reduced from 43% in 1980 to 30% in 1982. Import values and tariff re-enue in 1982 remained at the 1980 dollar levels suggesting that the tariff reform per s was indeed revenue neutral.<sup>4</sup> However, the economy deteriorated in 1983-86 and the crisis caused the government to impose an across the board 3 percent import tax and a domestic transaction tax based on tur iover. Import liberalization was stalled by the crisis but the tariff reforms were accomplished by 1985.

4 1 am grateful to **Erika** Jorgensen for comments on an **earlier draft** that helped clarify this point.