rafmacalaba/datause-encoder-data huggingface dataset:
or by environmental changes. Some internal migration is actively encouraged by governments, such as is the case with Indonesia’s transmigration program.

3. The majority of people uprooted by conflict, ethnic strife and human rights violations are Internally Displaced People (IDPs) (UNHCR 2006). The UNHCR (2006) suggests that the routes and intermediaries used by migrants fleeing conflicts are increasingly the same as those used by economic migrants.

4. Less than 10% of the world’s international migrants are refugees (people forced to move for fear of persecution) (DESA 2005).

5. The majority (61%) of international migrants are located in developed countries; growth in numbers of new arrivals is higher in the developed than the developing countries; and approximately half of all international migrants are women (DESA 2005). Of these international migrants, 34% live in Europe, 23% in the United States, and 28% in Asia.

6. Half of all the world’s international migrants originate from 20 countries, with the largest source of migrants (21%) coming from European countries (including the Russian Federation and Turkey), followed by 11% from South Asia, 6% from Mexico, 5% from East Asia, 5% from Central Asia, and slightly less than 4% from Africa (Migration DRC 2007).

These patterns are largely explained by barriers to movement, and the requirements to overcome them. There are financial barriers, including the costs of transport, housing on arrival, and living expenses while developing new income streams. There appears therefore to be a ‘migration hump’, where the rate of migration from a community increases as incomes increase beyond a level necessary to meet subsistence needs, and then net migration decreases again as the gap between incomes at the place of origin and the main destination closes (De Haas 2005, De Haas 2007, Lucas 2006, Sorensen at al. 2003a).

The existence of a migration hump implies that the poorest of the poor do not migrate (Amin 1995). However, there is evidence that the poor do move; it is just that they do not seem to move very far. This evidence also suggests that while the volume of resources sent home by poor migrants may be small, and the other benefits of migration less pronounced, the relative contribution to household incomes and capital are large and so significantly increase adaptive capacity (albeit from a low base) (De Haan 1999, Lucas 2006).

There are also information barriers to migration, including knowledge of where to go, how to get there, and ways to make a life upon arrival. Once migrants have established themselves in their new destinations they help others within their social networks to overcome 16
capital inflow shocks using an annual data from 1970 to 2007 for developed and emerging market economies and show that in the event of a capital inflow shock, the ratio of credit to GDP tends to increase during the first two years following the shock but the effect is reversed in the medium-term. They also find that the macroeconomic policy stance of the country may help mitigate the short-term effect of these shocks.

Calderon and Kubota (2012) use quarterly data for a wide array of countries to examine the dynamic relationship between gross inflows and credit booms. They find that an increase in gross capital inflows is more likely to predict subsequent credit booms. They also show that not all types of gross inflows have the same predictive ability: while surges in equity-type flows have no relationship or, at best, reduce the probability of build-ups in credit, a massive inflow of debt-type securities would raise the likelihood of credit booms.

On the other hand, the literature shows that these surges in capital inflows lead to a rapid build-up of leverage which, in turn, may lead to financial fragility (Borio and Disyatat, 2011; Gourinchas and Obstfeld, 2012). A massive rising inflows of foreign capital may lead to excessive monetary and credit expansions (Sidaoui et al., 2011), increase the vulnerability associated to currency and maturity mismatches (Akyuz, 2009), create distortions in asset prices (Agnello and Sousa, 2012a; Agnello et al., 2012a) and, consequently, lead to stock and housing price bubbles as well as an overvaluation of the real exchange rate (Magud et al., 2012). Recent empirical efforts show that the flexibility of the exchange rate arrangement may act as counteract the impact of gross inflows on credit expansions. Magud et al. (2012) show that surges of capital inflows in countries with less flexible monetary arrangements lead to a more rapid credit expansion and to a shift towards foreign currency. As shown in Calvo et. al (2004, 2008), the vulnerability to capital inflow reversals is greater in countries with more inflexible exchange rate regimes. These reversals, in turn, could potentially trigger credit busts and asset price deflation with deleterious effects on real economic activity.

These studies explore deeply the links between credit booms, capital flows, crises and the respective outcomes, but neglect the issue of their duration, which is another important dimension to understand the credit booms behavior. Moreover, as the historical analysis 9
fuelled bubbles in all asset classes, particularly the stock market and real estate. Inflated asset prices and non-transparent cross financing and related party lending between banks and holding companies helped mask poor asset quality and facilitated the financing of credit expansion by borrowing abroad, increasing vulnerability. Moreover, like in other European countries, domestic demand grew rapidly, fed by capital inflows and reflecting growing imbalances. Real GDP expanded by 28 percent during 2003-07, driven by private consumption and large investments in power-intensive industries. The spending boom was underpinned by strong growth of disposable income, easy credit, rapid increase in housing and equity wealth, and low unemployment. At the same time, external balances ballooned and net external indebtedness deteriorated sharply, exceeding 110 percent of GDP while the expansion of Icelandic companies and banks abroad bloated gross international liabilities to 625 percent of GDP in 2007.

<!-- Start of picture text --> Figure 15: Foreign bank claims in Iceland<br>60<br>50<br>US banks<br>40<br>British banks<br>German banks<br>30<br>French banks<br>20 Belgian banks<br>Other Europe<br>10 Rest of World<br>0<br>Dec.1999 Dec.2001 Dec.2003 Dec.2005 Dec.2007 Dec.2009 Dec.2011<br>Source: Bank for International Settlements.<br>US$ (billions)<br><!-- End of picture text --> 38. In the wake of the global financial crisis in late-2008, Iceland experienced a deep financial and economic crisis of its own.<sup>15</sup> Given their high leverage, Icelandic banks, which were relying on wholesale funding—primarily from German banks—were cut off from financing and a brief attempt to nationalize one of them revealed the severe problems facing the banking system which contributed to a downgrade of the sovereign. Within a short period of time, the three largest banks (Landsbanki, Glitnir and Kaupthing) collapsed. The banking crisis reflected

> 15 IMF (2012).

21
15 The patterns and trends in FDI in these eleven countries inevitably reflect the same dynamics at the global and regional levels, but the differences in the patterns and trends in FDI among the countries are at least as striking as the similarities. Indeed, the relative ltt', pendence of FDI flows among these eleven countries is apparent in weak correlations in FDI flows for each pair of countries; even the intra-regional country-pair correlations are generally weak. Neither at the global level nor within regions do FDI flows to developing countries fluctuate in synchrony; rather, each host country's experience is relatively autonomous.

# Individual Country Trends

Time series data in constant U.S. dollars for FDI flows to the eleven countries are displayed in charts 3-1 to 3_3.14 Among the eleven countries, Brazil is unique in the pattern of substantial increases into the early 1970s, relatively high levels into the early-1980s, and precipitous decreases since the early 1980s. In the other Latin American countries the large fluctuations in the early and mid-1980s are the most obvious similarity across the selected countries. In Colombia the surge of FDI in the early to mid-1980s is conspicuous among the eleven countries in the -agree of its deviation from an otherwise stable and relatively low-level pattern over the two decades from the late 1960s to the late 1980s. Average annual flows were greater at the end of the period than at the beginning for Argentina, Colombia, and Mexico; nevertheless, the year-to-year fluctuations around the relatively high levels in the 1980s were so great compared with the earlier years that one cannot be confident about any generalized description of a long-term trend.
the household level is found to significantly improve the likelihood of mobile internet adoption. Socioeconomic factors such as gender, age, income, and education level all have a significant impact on the usage of mobile internet. Females, the elderly, those who live in rural areas, and those who have lower levels of income or education are less likely to adopt mobile internet. Social network effects have a significant positive impact on the usage of mobile internet.<sup>13</sup> Individuals whose five closest friends are using an online social network (e.g., Facebook, Twitter) are 63.1 percent more likely to use the mobile internet than those who do not have any close friends using such online social network sites/apps.

**<u>Table 3. Logit regression results on mobile internet adoption</u>**

|Model|Lo|git|
|---|---|---|
||Mobile i<br>adop|nternet<br>tion<br>|
||Coefficient|Marginal<br>effects|
|Infrastructure factors|||
|Household 3G network coverage|0.188**|0.039**|
||(0.085)|(0.017)|
|Household having access to<br>electricity|0.759***|0.143***|
||(0.110)|(0.017)|
|Social-economic factors|||
|Female|-0.142***|-0.031***|
||(0.044)|(0.010)|
|Age group (middle age, 30-60)|-0.695***|-0.150***|
||(0.048)|(0.010)|
|Age group (old people, >60)|-1.985***|-0.282***|
||(0.109)|(0.009)|
|Log value of monthly income|0.195***|0.042***|
||(0.018)|(0.004)|
|Married|-0.138***|-0.030***|
||(0.049)|(0.011)|
|Urban|0.378***|0.081***|
||(0.046)|(0.010)|
|Years of formal education (less<br>than 6)|0.676***|0.155***|
||(0.124)|(0.029)|
|Years of formal education (6-12)|1.603***|0.347***|
||(0.118)|(0.024)|
|Years of formal education (12-16)|2.388***|0.535***|
||(0.125)|(0.022)|
|Years of formal education (>16)|2.551***|0.552***|
||(0.139)|(0.020)|
|Network effects|||
|1 close friend uses an online social<br>network|1.461***|0.349***|

13 It is also worth noting that the adjusted R2 decreases from 0.448 to 0.346 if the variable “how many close friends are using an online social network” is removed from the model.

12
# Table B.1: **Effects of Inpres Schools on Intergenerational Educational Mobility: Mother’s Years of Schooling as Family Background**

**Panel A: Dependent Variable: Primary Completion**

||**Linea**|**r Model**|**Quadra**|**tic Model**|
|---|---|---|---|---|
||**Sons**|**Daughters**|**Sons**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Born 1968-72_×_Inpres|0.184***<br>(0.044)|0.226***<br>(0.043)|0.180***<br>(0.044)|0.212***<br>(0.044)|
|Mother’s Edu_×_Born 1968-72_×_Inpres|-0.021**<br>(0.008)|-0.020***<br>(0.007)|-0.041***<br>(0.014)|-0.023*<br>(0.012)|
|Mother’s Edu Sq_×_Born 1968-72_×_Inpres|||0.003***<br>(0.001)|0.001<br>(0.001)|
|R2|0.099|0.157|0.107|0.169|
|Observations|1083281|738202|1083281|738202|
|**Panel B: Dependent**|**Variable: Ye**<br>**Linea**|**ars of School**<br>**r Model**|**ing**<br>**Quadra**|**tic Model**|
||**Sons**|**Daughters**|**Sons**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Born 1968-72_×_Inpres|1.603***|0.755|1.592***|0.720|
||(0.401)|(0.462)|(0.396)|(0.487)|
|Mother’s Edu_×_Born 1968-72_×_Inpres|-0.129*|0.010|-0.320***|-0.033|
||(0.074)|(0.075)|(0.119)|(0.139)|
|Mother’s Edu Sq_×_Born 1968-72_×_Inpres|||0.023**|0.006|
||||(0.010)|(0.014)|
|R2|0.290|0.378|0.290|0.378|
|Observations|1083281|738202|1083281|738202|

Notes: Robust standard errors in parentheses clustered at the district of birth. (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Quadratic model is adopted following Becker et al. (2015). Sample corresponds to children born between 1957 and 1962, or 1968 to 1972. Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Primary completion takes the value of 1 if the child has completed primary and 0, otherwise. Mother’s Edu represents mother’s years of schooling, which was calculated based on the education level completed. Family background is measured by mother’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. For the sake of parsimony, only intercept, linear, and quadratic terms are reported in this table. The tables with all coefficients are available upon request. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

64
Table 5: Firm Restructuring Indicators

||||||**Operatin**|**g Profitabil**|**ity**|||**L**|**abor Prod**|**uctivity I**|**ndex**||**Annual**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Case**|**Typ**|**e**<br>**Sector**|**1991**|**1992**|**1993**|**1994**|**1995**|**1996**|**1991**|**1992**|**1993**|**1994**|**1995**|**1996**|**TF`P****|
||||||||||||||||**Growth**|
|**I**|NL|car components|-7.13|-12.50|-7.23|-16.18|-20.24|-3.81|100|73|96|78|97|168|1.8%|
|2|<br>VP|<br>paints|7.78|10.26|11.84|7.84|4.57|8.43|100|118|122|155|286|321|6.3%|
|3|<br>NL|<br>electrical engines|-15.20|-14.55|-21.58|-29.38|-23.00|-13.75|100|92|93|99|81|96|0.8%|
|4|<br>VL|<br>steel tire cords|2.99|4.44|4.89|5.82|5.32|6.12|100|101|78|109|133|166|4.7%|
|5|NL|military trucks|7.87|2.88|-57.33|-43.11|-15.38|-2.87|100|34|62|_55_|88|124|1.2%|
|6|<br>NL|<br>skid steer loaders|8.70|-0.83|-3.60|-39.88|-36.62|0.32|100|88|73|55|70|147|4.4%|
|7|VL|rubber and fertilizers|3.89|3.37|-1.02|0.32|1.43|-13.26|100|96|82|135|179|194|3.2%|
|8|VL|steel; cement|-0.03|-0.02|0.04|0.17|0.13|0.20|100|54|63|145|176|204|1.1%|
|9|NL|army uniforms|8.33|6.62|-3.65|-4.96|-16.67|-20.83|100|129|100|96|77|98|-0.4%|
|10|<br>VL|<br>rubber floors|-6.82|-4.47|-8.06|-1.54|3.00|6.16|100|101|115|149|230|310|6.4%|
|11|VL|overheadprojectors|9.86|7.69|2.84|1.85|-15.52|3.28|100|91|86|104|113|133|2.3%|
|12|VL|breadandpastries|_5.06_|1.52|0.99|-7.14|0.81|2.16|100|82|68|74|101|126|1.6%|
|13|VL|rolling bearings|1.48|-1.24|-0.41|0.61|0.49|0.72|100|75|93|144|219|259|1.7%|
|14|VL|industrial chemicals|1.73|1.52|1.49|1.51|0.86|1.41|100|45|43|51|84|104|1.2%|
|15|VP|glassfiber felts and fabrics|1.05|0.94|1.40|1.57|1.65|2.01|100|106|138|167|213|271|6.8%|
|16|VL|technical glass|3.50|2.21|0.85|5.12|4.81|3.53|100|120|144|208|261|278|3.5%|
|17|VP|petrochemicals|3.96|7.26|10.00|9.52|9.94|11.14|100|87|122|191|181|210|4.3%|
|18|NL|military trucks|-0.91|-9.06|-14.93|-13.79|-2.90|10.84|100|74|38|64|216|326|4.4%|
|19|<br>VL|<br>freight wagons|0.84|1.95|5.03|0.90|1.77|2.50|100|74|58|107|146|154|3.6%|
|20|<br>VL|<br>paper and cellulose|0.35|-1.71|-1.21|3.10|6.62|7.29|100|89|89|133|191|227|2.3%|
|21|NL|military trucks|3.25|1.96|-20.75|-42.73|-39.00|-21.33|100|95|24|39|34|42|-0.4%|
|Average||manufacturing|1.93|0.39|-4.78|-7.64|-6.08|-0.46|100|90|88|115|155|193**|3.3%|

* This corresponds to an average annual growth of 14%. ** See Appendix for a description of the estimation. We use firm-specific price indices as reported by managers.

23
coefficients are not statistically significant. Location within Pakistan appears to matter as well. In our IV specification, children in KPK score systematically lower than children in Punjab.

**Consanguinity and Stature** . The children of related-parents are likely to have smaller height for age z- scores compared to other children, with negative coefficients on regressors indicating that parents are related in both OLS and 2SLS specifications (Table 4). The magnitude of the estimated effect increases significantly in IV-LIML models with province fixed effects, and it is statistically significant at the 10% level. The magnitude of the effect remains with district fixed effects, but the estimates are not statistically significant. After identifying consanguinity, the IV estimates indicate that children’s height-for-age z scores are reduced by 1.35 standard deviations.

Next we examine the impacts of parent consanguinity on the likelihood that children are moderately and severely stunted. Following WHO classifications, moderate stunting is defined as having a height for age z-score below -2, and severe stunting below -3. The incidence of stunting and severe stunting is high in rural Pakistan: in our sample 47% are classified as stunted, and 32% suffer severe stunting. The IV estimates reported in Table 5 and 6 indicate that consanguinity increases the likelihood of stunting substantially, with a larger and statistically significant impact on severe stunting (Table 6, columns 5 and 6). Having blood-related parents raises the probability of being extremely stunted by more than 30 percentage points. The CLR test confidence intervals suggest that the point estimate of the effects of stunting are above zero with 95% confidence. While results examining the effect of consanguinity on stature are significant only for severe stunting at the 10<sup>th</sup> percentile, they are consistent with the cognitive outcome results, and recommend further study examining the causal impact of consanguinity on stunting and anthropometrics in study designs promising more statistical power.

# **7. Conclusions**

Utilizing a unique household survey from Pakistan, we find strong evidence linking consanguinity to reduced cognitive abilities and higher incidence of severe stunting among children. The magnitudes of the estimated effects are much more pronounced in instrumental-variables specifications where we are able to single out causal effects by treating parental consanguinity as an endogenous variable.

Given suspicion that parental consanguinity may be related to a range of negative health outcomes, it is natural to ask what factors drive the decision to marry a relative. In rural Pakistan, we find that if a child’s grandfather owns (or owned) land, then the likelihood of his/her parents’ being in a consanguineous relationship increases significantly. We also find confirmation of the potential importance of maternal 15
of demand for livestock products is around 0.5, or higher, for low- to middle-income households. The income elasticities used by Baldos and Hertel (2012, p 12) show a similar pattern, with the income elasticities of demand for livestock products generally two or more times as high as for crops in low- and middle-income countries. These elasticities are consistent with increasing income elasticities of demand for total food as the animal-product share of consumption rises over this range.

Considerable care is needed in interpreting estimates of the growth of total food demand. If the focus is on the consumer, as in Gouel and Guimbard (2017), where food is measured in calorie equivalents of food consumed, the weight on livestock products is likely to be much smaller than—as in this paper—when the focus is on the resource cost of food consumed and livestock products receive a much larger weight.

Figure 4 plots the income elasticity of food demand implied by equation (1) with respect to PPP GDP per capita for our 134 sample countries. It presents the arc elasticity defined as the change in the log of CE consumption divided by the change in the log of GDP between 2009 and 2050. Specifically, where ��(����� ���� ) and (����� ����) are the logs of predicted per capita CE consumption computed using equation (1) for 2050 and 2009, while ��(�������) and ��(�������) are the logs of per capita GDP for the same years taken from the SSP database.

A striking feature of this graph is the inverted-U shape of the aggregate income elasticity of demand for total food as income rises. At very low levels of income, where the dominant feature of dietary transformation is shifts from coarse grains and root crops to fine grains such as rice or wheat (Timmer _et al._ 1983, p29), we estimate this income elasticity to be relatively low at around 0.2; it rises as income increases to middle-income levels; and peaks at around 0.42 at a PPP GDP of around $10,000; and then decreases as per capita income continues to grow. Evaluated at projected income levels in 2030, income elasticities in populous countries, such as India and Indonesia, are around their peak levels. While the elasticity is beginning to fall in key middleincome countries such as China and Turkey by 2030, this decline is relatively gradual, and income elasticities remain far above their levels in high-income countries. The elasticities for many SSA 10
more appropriate. Given a growth rate of real wages of over 4%, the real rate of return appears low to secure a high replacement rate, despite the very high contribution rate. For instance, when pensions are indexed to prices, working life is 40 years, retirement life (with an allowance for survivor pensions) is 20 years, a contribution rate of at least _25%_ may be required to achieve a replacement ratio of 43% of final earnings, when the growth rate of real wages is 4% and the real rate of return is 2% (Vittas 1992, Schwarz 1992). In Singapore, the effective contribution rate is 33%, but a substantial part of savings is used for investments in housing and other assets as well as for the purpose of financing education. Some of these assets could provide economic support in retirement, though it is clear that not all CPF savings would be available. Moreover, it should be noted that even with a low replacement rate, the absolute level of the pension would be quite high as a result of the high growth rate of real wages.

Table 1

**Central Provident Fund of Singapore**

**Nominal and Real Rates of Interest**

||**1960s**|**1970s**|**1980s**|**1960-90**|
|---|---|---|---|---|
|Nonminal Rate<br>|4.62|6.20|5.27|5.36|
|Inflation (CPI)<br>|1.09<br>|6.45|2.35|3.27|
|Real Rate|3.50|-0.24|2.86|2.02|

It is not easy to understand why the CPF rate of interest is low although, unlike most other national provident funds, it has on average been positive in real terms. Nevertheless, Malaysia which has also long operated a national provident fund has been able to pay a higher real rate of interest than the Singaporean CPF (Table 2).

One explanation for the low rate of interest is that this is what members prefer because the rate of interest on mortgage loans from the Housing Development Board is also linked to the same index. However, a low mortgage rate favors high income employees and also encourages excessive investment in housing. In Singapore, it is claimed that over 90% of people are owner

**12**