rafmacalaba/datause-encoder-data huggingface dataset:
# **6.1. Potential impact on financing conditions for EMDEs**

**_Cross-border impact of NIRP._** Unconventional monetary policies, including NIRP, might affect capital flows to EMDEs through various channels. In particular, low or negative bond yields in NIRP countries could encourage further portfolio balance adjustments. Faced with negative shortterm rates, international investors are encouraged to search for higher yields, both domestically (higher risk and longer maturity assets) and internationally (similar duration but higher returns). This should help induce capital flows to EMDEs, maintain favorable financing conditions, and mitigate adverse effects of rising interest rates in the United States (Arteta et al. 2015).

In the post-crisis period, the use of highly accommodative monetary policies in some advanced economies coincided with an acceleration in capital inflows to EMDEs. Although there is no consensus on the size of the spillover effects, a number of studies show that quantitative easing (QE) measures were mostly associated with stronger capital inflows (Lim, Mohapatra, and Stocker 2014; Fratzscher, Duca, and Straub 2013; Ahmed and Zlate 2014). Some studies suggest that QE policies also supported EMDE equity prices while compressing government, corporate bond, and CDS spreads (Chen et al. 2011). Over time, large capital inflows could be associated with rapid credit growth and rising private sector leverage in some EMDEs.<sup>31</sup>

**_Role of NIRP economies in EMDE funding._** Negative rates in Europe could be partly contributing to a gradual shift in the sources of funding for some EMDEs, from U.S. dollar- to eurodenominated debt instruments. Since the start of the ECB’s QE and implementation of NIRP, EMDE borrowers have increased the volume of bond issuance in European markets, taking advantage of exceptionally low borrowing costs (Figure 16). In an effort to compensate for negative or extremely low interest rates at home, European insurance companies and other institutional investors have also shown increased appetite for higher-yielding investment-grade EMDE debt. In contrast, cross-border lending by European banks to EMDEs has stalled, equity markets underperformed and flows to emerging market bond and equity flows continued to stagnate.

EMDE regions that could benefit the most from low interest rates in the euro area include Europe and Central Asia (which has the largest share of capital inflows supplied by the euro area), followed by Latin America and the Caribbean (through FDI), Sub-Saharan Africa (primarily bank flows), and the Middle East and North Africa (portfolio and bank flows). The share of capital inflows originating in the euro area is significantly lower in East Asia and the Pacific, and South Asia.

> 31 Using cross-country panel data, Arslan and Taskin (2014) and Lane and McQuade (2013) provide evidence of a positive correlation between capital inflows and domestic credit growth.

36
fiscal policy stance, which describes the time path of primary surpluses, and the following debt accumulation equations for domestic (B) and foreign debt (B*), respectively, can be written as follows: Substituting for SURP using (28) and substituting forward, it is straightforward to determine the amount of domestic and foreign debt that will be outstanding at any future date if the government adheres to its current fiscal policy and financing is forthcoming.<sup>20</sup> We then ask, do these time paths for debt satisfy the NPG conditions for the domestic and foreign lenders, respectively. The only tricky issue is what discount rate to use for each category of lender in order to reflect their respective marginal rates of substitution in consumption.

Regarding the appropriate transversality conditions, O'Connell and Zeldes (1988) emphasize that: the feasibility of a rational Ponzi game depends on some key characteristics of the economy whose agents are going to hold the debt. For the case of external debt, this means that the characteristics of the borrower's economy are irrelevant to the feasibility of perpetual debt rollover. With regard to the Third World debt situation, it follows that the feasibility of perpetual rollover of debt depends on the performance of the economy of the lenders -- in particular, on the relationships between real interest rates, population growth rates, and growth in per capita income -- and not on that of the borrowing countries. (pp.<sup>4 3</sup> 1-2).

Reiterating: 20The spreadsheet developed in section 5 above can presumably be extended to incorporate this distinction between the two types of debt.

35
<!-- Start of picture text --> Ra<br>mlseeneeS ey 7 ES/se<br>—+ TransmissionAfrobarometerLinesTowns (fo Mie_.- Na ageAS Me,<br>_ ~- aie qty F<br>aoe<br><!-- End of picture text --> <!-- Start of picture text --> . Tynes eee wl rap, | Ae<br>SRE: ge ae, i<br>MME 0 .000.016- 0.016 ee RS ee<br>- 0.031 fas DS es é<br>0.062 - 0.078 cod “ee<br>= 0.047 - 0.062 . eS Ce .<br>ial<br><!-- End of picture text -->
The results are very similar for each gender. There is only one statistically significant result in any of the regressions, and there is no result for which the magnitude of the coefficient is major.

One potential concern with the results reported in Tables 3 and 4 is that it only uses data from two or three points in time (2000, 2005 and 2010). In Table 4, we estimate the same regressions but instead use the ENOE which provides quarterly data. We focus on the 2006-2010 period.

The results provide further confirmation to the finding described above: namely, that there was no effect on enrollment rates. The coefficients are all small, and most are not statistically significant. The exceptions are the outcomes for the 15-17 year old groups, which correspond to the typical age range of upper secondary school, where we found the small but significant impact on the number of students (Tables 1 and 2). However, the effect goes on the opposite sign as expected. This gives further strength to the interpretation that the small effect of violence on the number of enrolled students, even if it exists, is likely not a result of individual schooling decisions. In the following sections, we present some evidence that there has been some migration resulting from the increases in homicide rates. Such migration could explain the coexistence of a small effect on the number of students enrolled in the municipality and the lack of an effect on enrollment rates.

24
Cato, J., S. Otwell and A. Coze (2003) Nicaragua’s Shrimp Subsector: Developing a Production Capacity and Export Market During Rapidly Changing Worldwide Safety and Quality Regulations. Case study prepared as part of World Bank program on The Challenges and Opportunities Associated with International Agro-Food Standards. Washington, D.C.

Chan, M. and B. King (2000) Review of the Implications of Changes in EU Pesticides Legislation on the Production and Export of Fruits and Vegetables from Developing Country Suppliers. Natural Resources and Ethical Trade Programme. London.

Crain, W. and J. Johnson (2001) Compliance Costs of Federal Workplace Regulations: Survey Results from U.S. Manufacturers. Regulatory Studies Program. George Mason University, Arlington, Va..

Delgado, C.L., Wada, N., Rosegrant, M.W., Meijer, S. And Ahmed, M. (2003). _Fish to 2020: Supply and Demand in Changing Global Markets._ International Food Policy Research Institute, Washington DC.

Dimanche, P. and A. Kane (2002) Senegal’s Confectionery Peanut Supply Chain: The Challenge of Controlling Aflatoxin Levels, in Food Safety Management in Developing Countries. Proceedings of the International Workshop, CIRAD-FAO, December 11-13, 2000.

Dohlman, E. (2003) Mycotoxin Hazards and Regulations: Impacts on Food and Animal Feed Crop Trade, in Buzby (editor).

Dolan, C. and J. Humphrey (2000) “Governance and Trade in Fresh Vegetables: The Impact of UK Supermarkets on the African Horticulture Industry”. Journal of Development Studies 37 (2).

Egmond, Hans (1999) Worldwide Regulations for Mycotoxins. Third Joint FAO/WHO/UNEP International Conference on Mycotoxins. Tunis, Tunisia, March 3-6, 1999.

El-Tawil, A. (2002). An in-depth study of the problems by the standardizers and other stakeholders from developing countries - ISO/WTO regional workshops: Part 1. International Organization for Standardization, Geneva.

Henson, S.J. (2001). Appropriate Level of Protection: A European Perspective. In: Anderson, K., McRae, C. and Wilson, D. (eds.). _The Economics of Quarantine and the SPS Agreement._ Centre for International Trade Studies, University of Adelaide.

(2004) National Laws, Regulations, and Institutional Capabilities for Standards Development. Prepared for World Bank training seminar on Standards and Trade, January 27-28, 2004. Washington, D.C.

Henson, S. and W. Mitullah (2004) Kenyan Exports of Nile Perch: Impact of Food Safety Standards on an Export-Oriented Supply Chain. Case study for World Bank program on the Challenges and Opportunities Associated with International Agro-Food Standards, Washington, D.C.

40
- 14 -

|**Ta**|**ble 2. OLS estimation r**|**esults with backward-lo**|**oking expectations**||
|---|---|---|---|---|
||Cost overrun||Project delays||
||Coef.<br>Std. Err.|Coef.<br>Std. Err.|Coef. Std. Err.|Coef.<br>Std. Err.|
|ln_rain_<sup>_t_</sup>|-0.316 (0.265)||0.326 (0.150) **||
|ln_rain_<sup>_e_</sup>||0.069 (0.578)||0.493 (0.294) *|
|ln_rain_<sup>_a_</sup>_/ rain_<sup>_e_</sup>||0.392 (1.043)||0.051 (0.333)|
|ln_rain_<sup>_t_</sup>_/ rain_<sup>_a_</sup>||0.347 (0.693)||1.633 (0.365) ***|
|ln_cost_|0.945 (0.755)|0.723 (0.815)|1.449 (0.297) ***|<br>1.235 (0.285) ***|
|ln_length_|-1.899 (0.687) ***|<br>-1.793 (0.771) **|-0.851 (0.312) ***|<br>-0.849 (0.287) ***|
|ln_lane_|2.028 (3.540)|2.608 (3.723)|1.500 (2.157)|0.202 (1.719)|
|ln_thickness_|-0.372 (0.508)|-0.240 (0.610)|-0.393 (0.202) *|-0.495 (0.181) ***|
|ln_gravel_|-0.038 (0.203)|-0.144 (0.278)|0.022 (0.116)|-0.094 (0.139)|
|ln_bitumen_|0.966 (0.271) ***|<br>0.860 (0.327) ***|<br>-0.123 (0.094)|-0.024 (0.083)|
|ln_earth_|0.505 (0.250) **|0.567 (0.279) **|-0.262 (0.105) **|-0.248 (0.099) **|
|ln_brick_|0.103 (0.399)|0.206 (0.496)|0.042 (0.125)|-0.020 (0.113)|
|ln_gabion_|-0.097 (0.231)|-0.065 (0.238)|-0.135 (0.090)|-0.170 (0.073) **|
|ln_exavate_|-0.168 (0.135)|-0.165 (0.144)|0.061 (0.058)|0.070 (0.050)|
|ln_cement_|0.049 (0.240)|0.033 (0.257)|-0.280 (0.096) ***|<br>-0.160 (0.075) **|
|D(_postqualify_)|4.056 (2.070) **|6.001 (2.488) **|0.474 (0.846)|0.717 (0.611)|
|Constant|-13.976(8.553)*|-16.143(8.866)*|-17.007(3.405)***|<br>-14.836(3.090)***|
|Obs.|155|145|155|145|
|R-squared|0.540|0.546|0.562|0.726|
|_F_-statistics|9.29|13.63|12.91|25.44|
|No. of dummy variables:|||||
|Project districts|18|18|18|18|

Note: Robust standard errors are shown in parentheses. *, **, *** indicate the statistical significance at the 10%, 5% and 1%, respectively.

One might think that there may be potential correlation between cost overruns and project delays. In infrastructure projects, project delays often cause cost overruns. For instance, Flyvbjerg _et al_ . (2004) shows that each year of delay would add on average $4.6 million to a project cost of $100 million in transport projects. To incorporate this possibility, the seemingly unrelated regression (SUR) model is performed, in which error terms are still assumed to be independent across auctions but have cross-equation correlation.

The result is shown in Table 3. The coefficients are the same as the OLS results, but the standard errors are more efficient. The SUR result confirms our main results: One of the
8 In the face of this challenge, the literature discussed above suggests that targeted interventions, such as social recognition, can be effective motivators of behavioral change for public officials as well as for consumers and citizens. Based on these insights, the clear need in this setting, and a growing body of evidence showing that “nudges” to influence behavioral choices can be used to address problems in public service delivery (World Bank 2015; Thaler and Sunstein 2008; Ashraf 2013; see also Ashraf et al. 2016), we designed a randomized control trial (RCT) in which we tested in the field the effectiveness of a social recognition intervention to improve performance of record-keeping at medical facilities in Nigeria. In particular, we extended the PETS/RT exercise in the two states Ekiti and Niger, focusing on the quality with which clinic staff filled out the cash book form. The feasibility of the possible implementation of the intervention beyond the study period was an important consideration in its choice and design.

# _DATA AND EXPERIMENTAL DESIGN_

In 2015, the World Bank and the Government of Nigeria embarked on a collaboration to verify that resources – funds, equipment, medication, and so forth – are received at public health facilities in the same quantities that they are sent by state and central government ministries. This exercise involved regularly visiting a set of 140 facilities across two of Nigeria’s 36 states, Ekiti and Niger to measure stocks and recent flows of resources. Basic summary statistics about these facilities are shown in Table 1: most facilities are rural, and are open five days a week, eight or more hours a day. About one-third of facilities have access to electricity, most have running water, but virtually none have a landline or mobile phone specific to the facility.

There are some differences across states in these characteristics, also shown in Table 1. Facilities in Ekiti are larger, open more days per week, and open more hours per day than those in Niger. A comparable fraction of facilities in each state are categorized as rural, however, and while neither state’s facilities are very likely to have access to electricity, both states’ facilities are likely to have access to water. The two states differ in many other ways; Ekiti is more densely populated than Niger, for example: the former has a population density comparable to that of Rwanda; the latter, lower than that of Kenya.
actual expenditure, the mean expenditure on tutoring classes by the wealthiest 20 percent of households is 15 times higher than expenditure by the poorest 20 percent of households. And more expenditure on tutoring is found to increase student grade point average (GPA) ranking in Vietnam, with a larger influence for lower secondary students (Dang, 2007, 2008).

Our calculation (not shown) using the 2006 VHLSS shows that the majority of children age 6-18 have at most three siblings, with 10 percent having no sibling, 48 percent having one sibling, 27 percent having two siblings, and 10 percent having three siblings; only five percent of these children have four siblings or more. Table 3 provides a first look at children age 6-18 that are currently enrolled in school that comprise our estimation sample, of whom 42 percent attended private tutoring in the past 12 months. They spent on average D 104,150 (equivalent to $US 6)<sup>23</sup> and 89 hours on these tutoring classes also in the past 12 months, and had attended tutoring for 1.9 years; for those that attended tutoring in the past 12 months, the corresponding expenditure and hours spent on tutoring are D 246,590 and 215 hours. These children have 1.6 siblings on average, are mostly in secondary school (58 percent), and live an average of 8.6 kilometers away from the nearest family planning center.

# **4. Empirical Framework**

## **4.1. Estimation equations**

Our empirical approach first tests for multivariate correlations between family size and the education-related dependent variables and then instruments for family size in these regressions. The basic estimation equations are for child j, j= 1,..,J in household I, i= 1,..,N

> 23 The exchange rate was D 15,994 for $US 1 in 2006 (World Bank, 2012).

14
# **LIST OF REFERENCES**

Afghanistan Government (2006). _National Drug Control Strategy: An Updated FiveYear Strategy for Tackling the Illicit Drug Problem._ Kabul, Afghanistan: Ministry of Counter-Narcotics (January).

Anderson, Jon Lee (2007). “The Taliban’s Opium War: The Difficulties and Dangers of the Eradication Program”. _The New Yorker_ (July 9).

Buddenberg, Doris and William A. Byrd, editors (2006). _Afghanistan’s Drug Industry: Structure, Functioning, Dynamics, and Implications for Counter-Narcotics Policy._ Vienna, Austria and Washington, DC: UNODC and the World Bank.

Byrd, William A. and Doris Buddenberg (2006). “Introduction and Overview”. In Buddenberg and Byrd (eds.), _Afghanistan’s Drug Industry,_ Chapter 1, pp. 1-23.

Byrd, William and Olivier Jonglez (2006). “Prices and Market Interactions in the Opium Economy”. In Buddenberg and Byrd (eds.), _Afghanistan’s Drug Industry,_ Chapter 5, pp. 117-154.

Byrd, William and Christopher Ward (2004). “Drugs and Development in Afghanistan”. _World Bank Social Development Papers, Conflict Prevention and Reconstruction, No. 18_ (December).

Macdonald, David (2007). _Drugs in Afghanistan: Opium, Outlaws and Scorpion Tales._ London: Pluto Press.

Maimbo, Samuel Munzele (2003). “The Money Exchange Dealers of Kabul”. _World Bank Working Paper No. 13_ .

Mansfield, David (2001). “An Analysis of Licit Opium Poppy Cultivation in India and Turkey”. Report prepared for the UK Foreign and Commonwealth Office (April).

Mansfield, David (2002). “The Failure of Quid Pro Quo: Alternative Development in Afghanistan”. Paper prepared for the International Conference on Alternative Development in Drug Control and Cooperation, Feldafing (January 7-12).

Mansfield, David (2006). “Opium Poppy Cultivation in Nangarhar and Ghor”. _AREU Case Study Series on Water Management, Livestock and the Opium Economy_ (AREU, December).

Mansfield, David (2007a). _Beyond the Metrics: Understanding the Nature of Change in the Rural Livelihoods of Opium Poppy Growing Households in the 2006/07 Growing Season._ Report for the Afghan Drugs Inter Departmental Unit of the UK Government (May).

28
Figure 3 _Environmental risks across consumption percentiles in rural and urban zones, 2014_ <!-- Start of picture text --> a. Air pollution c. Land degradation e. Rainfall variability g. Flood hazards<br>Rural<br>6 7 8 9 6 7 8 9 6 7 8 9 6 7 8 9<br>ln_pcexp ln_pcexp ln_pcexp ln_pcexp<br>Urban<br>6 7 8 9 10 6 7 8 9 10 6 7 8 9 10 6 7 8 9 10<br>ln_pcexp ln_pcexp ln_pcexp ln_pcexp<br>b. Tree cover loss d. Slope f. Temperature variability h. Drought hazards<br>Rural<br>6 7 8 9 6 7 8 9 6 7 8 9 6 7 8 9<br>ln_pcexp ln_pcexp ln_pcexp ln_pcexp<br>Urban<br>6 7 8 9 10 6 7 8 9 10 6 7 8 9 10 6 7 8 9 10<br>ln_pcexp ln_pcexp ln_pcexp ln_pcexp<br>22 30 65 50<br>40<br>20 25<br>60<br>30<br>pm25 18 ndviloss 20 sd_rain30_yr flood 20<br>55<br>16 15 10<br>14 10 50 0<br>22 40 65 40<br>20 30 60<br>30<br>18<br>pm25 ndviloss 20 55 flood<br>16 sd_rain30_yr<br>20<br>10 50<br>14<br>12 0 45 10<br>4 7 .85 1.2<br>6<br>3 .8 1.1<br>5<br>treeloss 2 slope .75 drought<br>4 sd_temp30_yr 1<br>1 3 .7<br>0 2 .65 .9<br>2 4 .8 1.2<br>1.5<br>3.5 .75 1.1<br>treeloss 1 slope drought 1<br>3 sd_temp30_yr .7<br>.5 .9<br>2.5<br>0 .65 .8<br><!-- End of picture text --> Notes: Binned scatterplots show the non‐parametric relationship between consumption and environmental risks. X‐axis shows percentiles of natural logarithm of per‐capita expenditure in 2014. Y‐axis indicates level of environmental risks.

_Source: Authors’ calculation based on data from Brauer et al, 2015, Hansen et al., 2013, Vu et al., 2014b, Harmonized World Soil Database, Climate Research Unit, Bangalore et al., 2016, Winsemius et al., 2015, and VHLSS 2014._ 12
# **Table 5: Which Damaged Firms Recovered Fastest?**

Dependent Variable: Log Real Profits

||(1)|(2)|(3)|
|---|---|---|---|
|Manufacturing dummy|-0.286*|-0.254*|-0.266*|
||(0.147)|(0.150)|(0.145)|
|Female owner|-0.663***|-0.636***|-0.619***|
||(0.164)|(0.165)|(0.162)|
|Years of education of owner|0.00305|-3.02e-05|-0.00301|
||(0.0291)|(0.0292)|(0.0288)|
|Age of owner|-0.00707|-0.00750|-0.00664|
||(0.00734)|(0.00733)|(0.00723)|
|Owner is married|0.0925|0.102|0.0985|
||(0.183)|(0.183)|(0.180)|
|Business<=3 years old|0.0468|0.0197|0.0294|
||(0.154)|(0.156)|(0.152)|
|Firm had a loan at baseline|0.230|0.200|0.179|
||(0.158)|(0.161)|(0.158)|
|Firm is registered|-0.0546|-0.0351|-0.0917|
||(0.190)|(0.191)|(0.188)|
|Log March 2005 profits|0.245**<br>(0.101)|0.232**<br>(0.101)|0.245**<br>(0.0990)|
|Log November 2004 profits|0.216*|0.243**|0.217*|
||(0.113)|(0.116)|(0.111)|
|Log distance to the coast|0.212**<br>(0.0898)|0.230**<br>(0.0914)|0.239***<br>(0.0893)|
|The firm has a major supplier||-0.146<br>(0.147)||
|The firm has a major customer|||-0.326**<br>(0.152)|
|Observations|702|702|702|
|Number of firms|80|80|80|

Notes: Random effects estimation on untreated firms suffering business damage, estimated over waves 2 to 11.

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Regression also contains wave effects.

36
The survey included only permanent households, which were registered on the residence registration booklet ( _hukoubu_ ). Of each household, information on demographics of each member, household assets, geographical location, household income and consumption, and other necessary information concerning compensation and resettlement, were recorded. Household income, including monetary income and income in kind, refers to actual income earned from different sources, such as agriculture, forestry, livestock and fishery, industry, construction, transportation, services and other incomes.

The surveyed area is poor and remote, with low income and shortage of land – rural per capita income and per capita cultivable land are inferior to the average of provincial level. The problem of agricultural surplus labor is of long duration and peasants have a strong incentive to leave land for seeking non-farm employment. As migration plays an important role in the region, information was also recorded on whether any household member had ever been to the outside of the hometown (or township) in search of work during the past year (2002), on work place and occupation of the migrants, and on contribution of remittances to household income.<sup>14</sup> This allows us to calculate the sum of remittances in 2002 for each household. Most of rural-to-urban migrants are temporary and seasonal. They remain closely linked with their places of departure. Among the 1208 households surveyed, 740 have migrants (called migrant households) while 468 do not (called non-migrant households).

# **6 Equation specification and descriptive statistics**

Impacts of migration and remittances on changes in poverty and inequality are conditioned on whether a household participates in migration, and on how migration changes household income. We model this by estimate participation equation and income equation jointly.

Two major categories of factors determine a household’s decision to migration (FAO, 1998): first, factors that affect the relative returns and risks of local production; second, factors that determine the capacity to participate in migration, such as education, access to credit. These two sets of factors are determined by the household’s endowment in physical and human capital and

> 14 Here, household members refer to who normally live in the household, including those who are temporarily working elsewhere.

14