rafmacalaba/datause-encoder-data huggingface dataset:
migration data collection efforts in low- and middle-income origin countries. Despite some recent progress, there is a specific scarcity of longitudinal and long-term data from migration surveys in developing contexts. Identification issues due to the lack of panel data have long hampered empirical progress, so data collection efforts should be primarily focused on tracking individuals over time, either using self-reported data or information from proxy respondents, also to address non-response issues.

From the discussion on the tight nexus between migration and adaptation emphasized in the previous section, there is a strong case for integration, in multi-topic household surveys, between questions on migration and those on risk management, mitigation and coping strategies adopted in response to shock, such as an explicit distinction between voluntary vs involuntary migration, survival vs risk management migration, immobility due to _in-situ_ adaptation vs immobility due to liquidity constraints and inability to move.<sup>10</sup> These nuances and amendments to the existing surveys and modules would greatly enhance the scope for empirical applications in this active area of research, while being relatively low-cost and easy to implement and collect.

As far as short-term, temporary and seasonal migratory flows are concerned, not only should data be longitudinal, but they should also be high-frequency, i.e., they should be collected annually. As emerged from the meta-analysis of Beine and Jeusette (2021), the frequency of the data employed plays a significant role in determining the findings of econometric analyses: data sampled at higher frequencies tend to support the case of an effect on mobility more, since migration measures spread over several years or longer periods are less able to capture short-term migratory flows driven by climatic hazards.

The use of direct measures of mobility, rather than indirect proxies, has often also been stressed as important but remains the exception rather than the rule. Following Beine and Jeusette (2021), an example of direct measure of mobility can be found in survey data where people are directly asked about their migration history, whereas an indirect measurement means that migration is inferred rather than observed, as in the case of differences in migration stocks reconstructed from censuses. Importantly, econometric analyses using measures of mobility that are computed or derived from proxies tend to find

> 10 A caveat is in order here to remind readers of the potential differences and inconsistencies between ‘stated’ and ‘revealed’ migration preferences, which compound over the already complex and multi-faceted nature of the migration phenomenon, that can take many different forms both across space and over time. The issue is also related to the heterogeneity of the climate risk perceptions of the individuals interviewed.

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# **2. The growth of EM local currency bond markets and foreign investor participation**

Local currency bond markets have grown rapidly in recent years across a wide array of developing countries. Nevertheless, reliable, comprehensive and up to date data in this area remain hard to come by. Here, we provide some views of the broad trend by drawing on data for two different subsets of EMs, prepared by JP Morgan and the Bank for International Settlements (BIS), respectively.

Figure 1 draws on data compiled by JP Morgan for 26 EMs (together accounting for over 50% of GDP of the 150 countries classified as “developing and emerging” by the IMF)<sup>i</sup> . It illustrates the rapid growth of EM domestic sovereign debt since 2000, both relative to external sovereign debt and in absolute terms. Until the economic slowdown in 2009 induced by the global financial crisis, GDP grew even faster than net debt issuance in these economies in aggregate, so the total government debt to GDP ratio dropped (also shown in Figure 1). This trend was interrupted by the crisis, but has since resumed, and the debt ratio remains moderate—and very low compared with that of advanced economies—at under 35%.

This view includes loans from the official and commercial sectors (not just bonds). The bulk of these loans will tend to be foreign currency-denominated, so the relative decline of external debt incorporating such financing supports the idea that a strong de-dollarization trend in EM public borrowing is underway. However, in some cases strong recorded rises in domestic financing may also reflect local commercial banking sector growth, rather than the domestic bond issuance which is our focus here.

We therefore also consider an alternative, securities-specific perspective: the trend in international and government debt securities outstanding since 1994 for a similar, but smaller, subset of 22 countries tracked by BIS (Figure 2).<sup>ii</sup> Again, the rapid relative and absolute increase in domestic sovereign borrowing (this time specifically in the securities markets) is clear.

**Figure 1: Domestic and external debt of 26 EMs**

**Figure 2: Total government securities outstanding of 22 EMs**

<!-- Start of picture text --> USD trn Domestic sovereign debt % GDP USD trn<br>6<br>External sovereign debt<br>9 50%<br>Total govt. debt (RHS)<br>8 5<br>Domestic<br>7 40% securities<br>4<br>6<br>30%<br>5 3<br>4<br>20%<br>3 2<br>2 10% International<br>1 securities<br>1<br>0 0% 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012(f)<br><!-- End of picture text -->

**Source: Calculations based on JP Morgan EM Debt and Source: Calculations based on BIS securities data Fiscal Indicators (2012) and the IMF World Economic Outlook (April 2012)**

**3**
In the wake of the 1996 learning assessment results, Uruguay launched an education reform in order to improve equity in the system, both in terms of access to services and the quality of services received. One of the main policy instruments was the introduction of Full-Time Schools, or _Escuelas de Tiempo Completo_ (ETC), targeted primarily to urban areas classified as socioeconomically “disadvantaged” or “very disadvantaged”. In the beginning, ETC consisted simply of adding 3.5 hours to the school day (from 4 to 7.5) and providing school lunches and a snack, without a corresponding change in curriculum or infrastructure. With support from a World Bank project (World Bank Group, 1998), a comprehensive ETC model was introduced in 1998 that featured a new pedagogical approach (“ _propuesta pedagógica_ ”) whereby bilingual education was introduced, teachers received supplemental training on how to more effectively reach disadvantaged students, and teachers were paid 2.5 hours per week to discuss, plan, and evaluate their work with other teachers. This model also had a substantial infrastructure component, and ETC schools received special educational materials.

By 2010, 168 full-time schools had been built (or converted) after 15 years of implementation of the policy. A new Government adopted extending school hours as the central plank to its five-point platform, effectively making ETC a national policy. However, recognizing that the fiscally-cautious approach had resulted in slow scale-up, the administration also introduced an alternative model to add pedagogical hours to the school day. This new extended-day model, known as Extended Time Schools ( _Escuelas de Tiempo Extendido,_ or ETE), consists of 3 additional hours per day, spent largely in activities such as physical education, music, arts and crafts, and the introduction of a second language. The model is rolled out such that selected schools do not require additional infrastructure. Lunch is provided.

# **5.2. Cost Implications of Extending the School Day**

The fiscal implications of extending the school day are far-reaching. Two types of expenditures are considered: investment costs, and operational costs. Investment costs are onetime expenditures, limited to infrastructure and equipment such as furniture. Operational costs are recurrent costs such as wages and school meals. Tables 2 and 3 summarize the investment and operating costs respectively across three types of models in Uruguay: regular schools, full-time schools (ETC), and extended-day schools (ETE).

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incomes with possible further reductions in prices. The longer run effects on poverty are also via non-farm rural output and employment through expenditures on non-food products and services. Irrigation also has socio-economic impacts as negative health effects associated with increase in the incidence of water related diseases such as schistomiasis and malaria, displacement of large numbers of people and negative environmental effects of dam construction. Positive effects could be in the form of improved nutritional outcomes through the availability of increased and more stable food supplies and sometimes-cleaner water.

Three pathways have also been identified in the literature as the mechanisms through which the poverty reducing impacts of irrigation follow. Firstly it is the micro-pathway which manifests itself through increasing returns to physical, human and social capital of the poor households (productivity pathway). Secondly there is the meso-pathway which operates through integrating the poor into factor-product and knowledge/information markets (market participation pathway). Thirdly, there is the macro-pathway which works through improving national growth rates and creating second-generation positive externalities (economic growth pathway) (Hussain and Hanjra, 2003)). These three pathways are interlinked in their functions.

Using evidence from the Uda Walawe Left Bank Irrigation Scheme (WLB) in Sri Lanka, Hussain and Hanjra (2003) concluded that irrigation water does matter for poverty alleviation, unequal land distribution is associated with inequitable distribution of agricultural –water benefits , benefits of irrigation and its antipoverty impact are greatest in settings with low land inequality, rural poverty alleviation requires that agricultural/water irrigation development be targeted at poor communities/areas/localities and that effective poverty alleviation in irrigated agriculture also necessitates broad and targeted interventions in both hardware and software dimensions that entails building partnerships between actors and stakeholders.

Vaughan (1997) studied the potential of irrigation development in South Africa in having positive impacts on rural poverty. The paper concluded that although irrigation development had been a mechanism that was historically used by the apartheid state to realize its political and economic agendas, it offered a range of possibilities for poverty alleviation. The paper recommended that clear policy principles were essential along with relevant information to guide the provincial departments of agriculture and the local authorities.

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Table A.17: Robustness checks, excluding individuals with more than two migration episodes. Estimating the effect of migrants’ legal status on hourly wages after return, unconditional on return migration

||(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|(4)<br>Log of<br>hourly wage|
|---|---|---|---|---|
|Age|0.012***|-0.067**|-0.014**|-0.000|
||(0.001)|(0.034)|(0.006)|(0.005)|
|Age squared|-0.000***|0.001*|0.000***|0.000**|
||(0.000)|(0.000)|(0.000)|(0.000)|
|No education|0.019***|0.507***|-0.030|-0.445***|
||(0.004)|(0.119)|(0.021)|(0.026)|
|Primary or preparatory|0.032***|0.477***|0.049**|-0.419***|
||(0.004)|(0.128)|(0.022)|(0.025)|
|Secondary|0.017***|0.370***|0.034*|-0.286***|
||(0.003)|(0.103)|(0.017)|(0.020)|
|Rural|0.009***|0.245***|0.009|-0.029*|
||(0.003)|(0.084)|(0.015)|(0.016)|
|Oil prices|0.013***<br>(0.000)||||
|Diplomatic exchange||-0.231**<br>(0.106)|||
|Confict|||0.047***<br>(0.013)||
|Illegal status||||-0.232***<br>(0.053)|
|Legal status||||0.206***<br>(0.044)|
|Observations|14,879|1,897|2,071|8,037|
|Parental controls|Yes|Yes|Yes|Yes|
|Decades of migration dummies|Yes|Yes|Yes|No|
|Labor market controls in Egypt|No|No|No|Yes|
|Predicted log wage: Non-mig.=1|.515; Illegal mig.|=1.319; Legal|mig.=1.772||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. We exclude individuals who had more than two migration episodes. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

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17 where e* denotes the orthogonalized disturbances.

Bivariate autoregressions such as in equation (1) were estimated for the rate of inflation and another variable selected those appearing in Table 1. Each such run was implemented on monthly data twice: for the 80:6-85:6 period, i.e. before the disinflation program, and for the 85:12-88:12 period. Before we discuss changes in the impacts of shocks, it is well to turn to Table 2 which reports evidence on the size of the shocks in both time periods. The shocks in the system are measured by the estimated disturbances in the econometric equations; thus, wage shocks are the disturbances to the bivariate regression of the rate of change of wages on four own lagged values, four lagged values of the rate of inflation, and a constant. For inflation shocks, we used the disturbances from the autoregression including lagged inflation and wage growth as explanatory variables. The size of the shocks is measured by the standard error of the estimated disturbances.

The evidence reported in Table 2 is quite consistent with that reported in Table 1. That is, the size of most nominal shocks has generally decreased by a large extent. Standard deviations of nominal shocks in the post 1985 period are at about 25-35 percent of their levels before the program. The exceptions are shocks to nominal wage growth and to Ml growth, which had in the later period standard deviations of the same order of magnitude as in the first period. For real variables, the picture is somewhat different in that for most shock-type quantity variables there have been only relatively minor changes in standard deviations. Thus, this evidence tends to support the notion that real shocks have become more important relative to nominal shocks in the period after the 1985 stabilization program.
coefficients to generate predicted levels of financial development and financial inclusion for Sub-Saharan African countries. We then compare those predicted levels with the actual levels of financial development in the African countries. We find that the majority of the African countries have lower levels of financial development and financial inclusion than the one that would be predicted based on their fundamentals. Those benchmarking regressions also indicate that population density is more strongly associated with financial development and financial inclusion in Africa than in other developing countries. These results seem plausible considering that many African countries have relatively scattered populations and that frequent interactions among firms, households, and investors are a necessary condition for business transactions, and hence financial development and financial inclusion.

In our analysis we focus on financial development and financial inclusion measures from 2007 to 2011. We obtain several results. First, in line with our previous findings based on a narrower dimension of African financial development for the period 1990-2006 (Allen et al., forthcoming), we find evidence of the existence of development gaps and of the importance of population density in explaining African financial underdevelopment. Second, as we broaden the dimension of African finance beyond the traditional banking sector development indicators, we provide new evidence indicating that gaps exist in financial inclusion as well relative to other peer non-African low income countries. Finally, based on surveys of users of financial services, we expand the indicators of financial development to include new measures of financial inclusion. The goal is to explore whether innovations in financial services, such as mobile banking, have helped to overcome sparse populations and infrastructural problems and improve financial access.

To evaluate the use of mobile telephones in financial transactions, we run regressions on the determinants of the share of adults that use mobile phones to send money, receive money, and make payments. We find that, controlling for a large set of country-level variables, the use of mobile phones to send and receive money is significantly more prevalent in SubSaharan Africa than in the rest of the developing world. These results indicate that technological advances, such as mobile banking, have been an avenue to facilitate broader financial inclusion.

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Table A8. Regional emissions intensity index of non-CO2 emissions, Central Federal District, SUST-RUS sectoral classification (thousands tons of emission per trillion nominal RUB of output of 2006)

|**Central FD**|**Hydrocarbons**|**Carbon**<br>**monoxide**|**Nitrogen**<br>**oxide**|**Particulate**<br>**matter**|**Sulphur**<br>**dioxide**|**Volatile**<br>**organic**<br>**components**|
|---|---|---|---|---|---|---|
||**CnHm**|**CO**|**NOx**|**PM**|**SO2**|**VOC**|
|CA_col|829,19||||||
|CB||457,13|15,03||||
|DB||6,72|||||
|DD||258,62|||||
|DE||33,35|20,66||||
|DF||6,12||||131,39|
|DG||186,49|12,09|||71,17|
|DH||||||10,80|
|DI||220,37|102,78|170,32|||
|DJ||572,40|||194,46||
|E_distr||305,73|85,05|4,84|||
|E_ely||146,34|859,92|821,11|584,03||
|I_trn|33,58|28,91|||||

_Source: Authors’ calculations based on SUST-RUS data_ 103
nonetheless considerable variation in the size of each type of institutional investor across countries, reflecting, in large part, differences in their institutional and regulatory environments. On average, insurance companies in East Asian countries are usually the largest institutional investors (26 percent of GDP), whereas mutual funds average 17 percent of GDP and pension funds 15 percent.

Due to data availability, we can get only a glimpse of the private equity and venture capital funds. These funds, through which investors acquire a percentage of an operating firm, are particularly important for the financing of SMEs. Private equity and venture capital funds are relatively well developed in Asian countries. Private equity funds raised on average US$46 billion per year in Asia, a strong contrast to the almost US$4.9 billion raised in Latin America between 2003 and 2009.<sup>1</sup> Moreover, over the same period Asia represented almost10 percent of total worldwide private equity fund raisings, compared with only 1.1 percent for Latin American countries, with the rest taking place in the United States and in Europe. Venture capital funds are less represented in emerging markets in general, with a total of US$12 billion per year raised on average outside the United States and Europe during this period. Albeit smaller in absolute size, these funds have a relatively larger presence in emerging markets: fund raising outside the United States and Europe represented 25 percent over the same period.

# **4.2. The Nature of the Asset Side**

Pension funds, mutual funds, and insurance companies provide a stable demand for domestic financial assets, given regulatory limits on their foreign investments, and thus have a potential role in deepening local capital markets across Asian countries. Surprisingly, institutional investors in Asia, and in emerging markets more broadly, concentrate a significant fraction of

> 1 These statistics are from Preqin, the industry’s leading source of information where country-level information is not available. Therefore, regional statistics cited include all countries geographically located within each region, making them different from the rest of this paper.

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Table 6. PMT OLS Regression of the Log of Per Capita Household Consumption by Area

||**Ru**|**ral**|**Ur**|**ban**|
|---|---|---|---|---|
|**Variables**|**Estimate**|**Std. Error**|**Estimate**|**Std. Error**|
|Household size: 2 members|-0.442***|(0.041)|-0.491***|(0.082)|
|Household size: 3 members|-0.721***|(0.041)|-0.727***|(0.081)|
|Household size: 4 members|-0.93***|(0.041)|-0.908***|(0.081)|
|Household size: 5 members|-1.08***|(0.042)|-1.086***|(0.081)|
|Household size: 6 members|-1.203***|(0.042)|-1.198***|(0.081)|
|Household size: 7+ members|-1.395***|(0.042)|-1.397***|(0.081)|
|Dependency ratio|-0.092***|(0.019)|-0.107***|(0.025)|
|Max. school level: less than Secondary 4|-0.028**|(0.012)|-0.022*|(0.014)|
|Max. school level: less than Primary|-0.041***|(0.012)|-0.046***|(0.016)|
|Livelihood source: farming, aid, other|-0.03***|(0.009)|-0.027*|(0.015)|
|Head: female|-0.093***|(0.012)|||
|Head: age|||-0.001***|(0.000)|
|Head: not married|||-0.067***|(0.016)|
|Rooms per household member|0.033***|(0.009)|0.121***|(0.012)|
|House material: mud, wood, straw, tent, or incomplete|-0.079***|(0.013)|-0.099***|(0.012)|
|Floor material: earth|||-0.06***|(0.015)|
|Lighting source: grass, firewood, or none|-0.075***|(0.01)|-0.088***|(0.017)|
|Drinking water source: not common network|-0.032**|(0.013)|-0.027**|(0.013)|
|Cooking fuel: cow dung, grass, or does not cook|-0.155***|(0.012)|-0.098***|(0.016)|
|Waste disposal: heap, burning, other|-0.067***|(0.017)|||
|No motorcycle|-0.064***|(0.023)|||
|No motor vehicle|-0.255***|(0.023)|-0.184***|(0.022)|
|No radio|-0.07***|(0.01)|-0.027**|(0.012)|
|No computer|-0.152***|(0.049)|-0.178***|(0.023)|
|No refrigerator|-0.054***|(0.018)|-0.096***|(0.015)|
|No television|-0.039***|(0.015)|||
|No AC|||-0.074***|(0.018)|
|No mobile phone|-0.145***|(0.01)|-0.139***|(0.019)|
|No satellite dish|||-0.052***|(0.015)|
|Intercept|10.697***|(0.07)|10.779***|(0.09)|
|Obs.|8,357||3,590||
|Adj. R-Squared|0.523||0.652||

_Notes:_ Variable definitions and reference categories for categorical variables are presented in Table A1.1. in Annex 1. A small percentage of missing categorical and continuous observations imputed by modes and means, respectively. *** p<0.01, ** p<0.05, * p<0.10.

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