rafmacalaba/datause-encoder-data huggingface dataset:
Most officials believe that more capacity is needed, rather than simply more money to improve student learning. This is likely true for several countries in the survey, since above a certain level of minimum spending, additional financing is unlikely to improve learning in an education system that is not aligned for learning (World Bank, 2018). Findings from Education Public Expenditure Reviews in the region show that education budgets are often not allocated based on specific needs and tend to be inequitably distributed across provinces/districts in the country (World Bank, 2020; Afkar et al., 2020; World Bank, 2021). However, if current spending is _below_ a certain level, additional spending could go a long way toward improving learning. At a cross-country level, there is a correlation between education quality and per student spending expressed in per capita GDP (World Bank, 2023), so the data lead us to expect that if Lao PDR or Mongolia spent more on education, learning would improve. The officials surveyed from these countries tend not to agree with this proposition. Over the whole sample, there is surprisingly no association between the extent to which officials believe that more money will not matter, and whether they flagged money as the main barrier to learning (Figure 18). In addition to capacity, there are additional challenges in many EAP education systems that may explicitly or implicitly drive officials’ perceptions about why additional resources will not improve learning (e.g., patronage, corruption, and politics). In addition, officials’ perceptions may be driven by demand-side factors linked to learning unrelated to public spending (children’s ability, household engagement in children’s education, private tutoring availability, etc.).

_Figure 18: Correlation between the officials selecting money as the main barrier to improving learning and officials agreeing with the statement that more money would not make an impact._ <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Did not select money as the main Selected money as the main barrier to<br>barrier improving learning<br>Strongly agrees Agrees Disagrees Strongly disagrees Does not know<br>impact learning<br>Share of officials agreeing with "More money will not<br><!-- End of picture text --> Source: CGD–World Bank survey 2022.

22
d’Ivoire and Cameroon. Japan and Canada have very limited involvement in Africa. On the contrary, the US has investment in all 16 countries used in this study with the exception of Chad. This implies that relative to other G7 countries, the United States is an equal opportunity investor. Nonetheless, it has its favorite host countries, some of which are resource rich.

# **6. Estimation results**

# **6.1. Labor productivity growth effects of BRICS’ versus G7 counties’ direct investment**

To determine the labor productivity growth effects of BRICS’ versus G7 countries’ direct investment, we estimate equation 1 where both measures of FDI are included in the model. Results reported in Table 5 show that while BRICS’s direct investment has productivity enhancing effects in Africa; that of G7 countries retards productivity growth. For example, with every 10 percent increase in FDI stock from BRICS, the growth in labor productivity of African countries increases by 2.4-3.0 percent annually (columns 1-4). In contrast, a 10 percent increase in FDI stock from G7 countries, decreases labor productivity by 1.5-1.6 percent (columns 1-2). Moreover, the level of human capital appears to complement the effectiveness of both BRICS’s and G7 countries’ FDI. In other words, when human capital is included, the level of significance of BRICS’ FDI coefficient increases by between 1 and 10 percent. However, the magnitude of the impact decreases from 0.3 to 0.24. In the case of G7 countries’ investment, controlling for human capital boosts the impact from negative to positive, but the effects are insignificant. The observed increase in the level of significance of the coefficient of BRICS’ FDI when human capital is controlled for, suggests that human capital has played a crucial role in making FDI a significant driver of labor productivity growth in these countries.

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(5.54%) and is only slightly smaller in the case of SMEs (4.85%). By contrast, the increase in employment growth is just under 1 percentage point for larger firms. Thus, introduction of CBs results in an increase in employment growth that is over 6 times larger among MSMEs than large firms. Statistically, we confirm using t-tests that the estimates for MSMEs and SMEs are higher than those for large firm at the 99% confidence level.

In addition to the year of introduction of CB’s, the World Bank Doing Business reports<sup>24</sup> also gather information on the coverage of the CBs as a percentage of total population. The estimation of equation (2) using CB reform above switches from 0 to 1 with their introduction, thus treating all CBs in the same manner. It is reasonable to assume that if CBs are introduced with varying degrees of coverage, the subsequent shock to credit supply will be proportionally varying. As a robustness check, we introduce this information into equation 2 by replacing CB reform variable with the interaction of CB reform and CB coverage.<sup>25</sup> Results provided in the Appendix Table A4 show that increases in coverage do appear to have varying degrees of impact on job creation due to a differential effect on credit supply. Countries that introduce CB reform with a 1 percentage point higher coverage, on average, grow employment at a 0.05 percentage point higher rate in the Enterprise Survey sample and a 0.16 percentage point higher rate in the Orbis sample. To put these estimates in context, consider a change in coverage from the 25th percentile to the 75th percentile of countries, which corresponds to 37.4 percent increase in coverage. Such an increase in coverage, according to the estimates in Table A4, would lead to an additional 2 percentage point increase in the growth of employment in the ES sample and 6 percentage point increase in job growth in the Orbis sample. The gulf in employment growth between MSMEs and large firms is still evident in these regressions and t-tests confirm that

> 24 http://www.doingbusiness.org/data/exploretopics/getting-credit

> 25 We do not introduce CB reform and CB coverage individually due to issues of multi-collinearity.

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According to this Federal Reserve study, shocks to earnings and interest rates would be the most harmful. However, if only one of these shocks occurred, the debt-at-risk would still be below those before the AFC. A shock to earnings or interest rates alone could increase debt-at-risk in emerging economies from 24 to 31 percent with no individual economy having debt-at-risk higher than 40 percent. In turn, the impact of a currency shock could be almost negligible, slightly increasing debt-at-risk to 26 percent. This minimal impact of currency shocks might be associated with the overall lower reliance of firms on foreign currency debt, as discussed in the analysis of currency risk in Section 3.

# **5. Margin for Policy Action**

Amid greater risk of corporate distress, a central question is whether there is a motivation and room for policy makers to step in and try to mitigate risks in the nonfinancial corporate sector, even if the COVID-19 crisis gets resolved successfully. Another question is which instruments policy makers could use to reduce debt burdens as well as to mitigate the impact of nonfinancial corporate distress, and what the trade-offs are in the case that intervention is warranted. The fact that emerging market firms have an increased reliance on bond markets (rather than banks) is an important factor to consider when examining these questions.

There are two main reasons why policy makers might want to curb indebtedness in the nonfinancial corporate sector and intervene in the event of nonfinancial corporate debt distress: investor protection and crisis prevention.

First, because debt default implies that individual investors would not get their money back, policy makers might be inclined to take steps to mitigate the risks of defaults and reduce losses whenever defaults occur. When firms issue debt overwhelmingly through banks (as it was the case before the GFC), there are strong reasons for government intervention to protect investors. Banks 21
by our model: where the change in industry j<sup>0</sup> s DVAR equals the weighted average of the changes in the DVAR of all ...rms in industry j (i 2 �j), derived from (6) ; and j subscript is added to the relative price for clarity. In addition, with the estimate of �ID; we can also construct the elasticity of substitution between imported and domestic materials, �jt, for each industry j and year according to (20). Such estimates allow us to assess the time-series variation in �jt and examine whether the rise in ...rm DVAR is driven by an increasing �jt or not.

To estimate �ID, we estimate the following econometric counterpart of (17) : where ai is the ...rm ...xed e¤ect that subsumes �0I in (17) and �it is the residual. In other words, �ID is estimated from the within-...rm variation in the relative price between imported and domestic materials. Since the dependent variables are measured with errors, we bootstrap the standard errors (based on 500 randomly drawn samples). Moreover, we use the exchange rate indices, (log) FDI and (log) upstream input tari¤s of the sector as the instrumental variables for ln<sup>P</sup> <u>t</u><sup>I</sup> Pt<sup>D.</sup> Table 9 reports the estimated �bID, ...rm average share of imported materials in total b b material cost, and the implied �jt for 15 industries and both 2000 and 2007. Estimated �ID for all industries and years are positive and the resulting �<sup>0</sup> s are all greater than 1, satisfying 35
database compiling detailed civilian casualties from violence and conflict since 2003, to instrument for the probability of forced displacement. These data mainly come from media sources. The total fatality per governorate determines the intensity of exposure to conflict and as such represents a potentially good instrument to the probability of being forcibly displaced at the time of, or right after the violent outbreak. We assume there is no correlation between household current multidimensional poverty status and the intensity of violence at their governorate of residence at the time they migrated for two reasons: first, the dimensions and indicators of multidimensional poverty are less sensitive to any change caused by violence intensity, which may not be the case for monetary poverty; and second, the time lag between violence and the survey period would significantly mitigate any potential direct effect of violence intensity on multidimensional poverty.

For our IV strategy, we follow the two-stage residual inclusion (2SRI) method (Terza et al., 2008) which has been shown to produce unbiased estimates in a non-linear regression specification. The outcome and auxiliary regressions are formulated as in the following models respectively: where _Y_ denotes our dummy for the household deprivation status; _Xe_ is the dummy for the household displacement status which is our endogenous regressor; _Xo_ is the vector of observable exogenous regressors; _Xu_ is the unobservable variable which is correlated with _Xe_ but not correlated with _Xo_ ; _W_ is our instrumental variable (total fatalities in the household’s governorate of origin two months<sup>6</sup> prior to the migration date); β and α are the vectors of parameters to be estimated; _e_ is the random error term.

> 6 It is important to define a good time window between violence and displacement times, to account for the level of aggregation of the IBC and IDP’s origin places, which are recorded at the governorate level. In general, the closer the violence, the quicker people will be displaced. Since we do not know exactly how close people are to

11
Figure 11. Debt Composition of BRI-recipient EMs (In percent) <!-- Start of picture text --> a. By type of financing b. By debt vulnerability (2016)<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>Bilateral concessional2007 2012Multilateral concessional2016 Bilateral concessionalLow Multilateral concessionalHigh scrutiny<br>IMF Bilateral non concessional IMF Bilateral non concessional<br>Multilateral non concessional Bond Multilateral non concessional Bond<br>Other private debt Other private debt<br><!-- End of picture text --> Source: International Debt Statistics.

Source: International Debt Statistics.

- c. Share of countries above investment grade*

<!-- Start of picture text --> 90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Source: Dealogic.<br>*Moody’s ratings of Baa3 or above.<br><!-- End of picture text -->

- d. Number of upgrades/downgrades

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>-4<br>-5<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Upgrades Downgrades Net change<br><!-- End of picture text --> <!-- Start of picture text --> Source: Moody’s ratings from Dealogic.<br><!-- End of picture text --> BRI investment financing could exacerbate existing debt vulnerabilities in a number of countries (Figure 12). Based on the assumptions defined in Section II, we consider that only BRI investment identified from 2016 to 2018 as under construction and planned would result in additional debt financing; and we apply an average 40 percent debt financing ratio for the cost of investment in the power, electricity and mining sectors and 80 percent in transport and all other sectors. Based on these assumptions estimated BRI debt financing is expected to exceed 5 percent of GDP in 4 LIDCs in high risks of debt distress and in 2 LIDCs in moderate risks of debt distress. Six more LIDCs, currently at low risk are expected to receive sizeable BRI investment. For three LIDCs (Cambodia, Tajikistan, and Timor-Leste), the amount of estimated BRI-debt financing is expected to exceed the PPG debt-to-GDP ratio as of end-2016. Five EMs in high scrutiny for potential debt vulnerabilities are also expected to receive BRI debt financing amounting to more than 5 percent of GDP and two in low scrutiny.

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interaction between economic similarity and EIAs is close to zero (columns 2a – 2c), supporting the main results of our analysis.

**Table 3. EIAs & bilateral distance: Controlling for economic similarity**

_5-year data, 1965-2010_

|**Variables**|(1a)|(1b)|(1c)|(2a)|(2b)|(2c)|
|---|---|---|---|---|---|---|
||**ln_Xijt**|**ln_Intijt**|**ln_Othijt**|**ln_Xijt**|**ln_Intijt**|**ln_Othijt**|
|**EIAijt**|0.830***|1.195***|-0.108|0.796***|1.161***|-0.117|
||(0.164)|(0.177)|(0.173)|(0.165)|(0.177)|(0.174)|
|**EIAijt*lndistij**|-0.073***|-0.116***|0.036*|-0.060***|-0.103***|0.039*|
||(0.020)|(0.021)|(0.021)|(0.020)|(0.022)|(0.022)|
|**adiff_lnGDPcapijt**|-0.236***|-0.238***|-0.250***|-0.218***|-0.222***|-0.246***|
||(0.022)|(0.023)|(0.023)|(0.023)|(0.024)|(0.024)|
|**EIAijt*adiff_lnGDPcapijt**||||-0.055***<br>(0.017)|-0.053***<br>(0.019)|-0.012<br>(0.019)|
|Fixed Effects<br>_(i, t-(t-5)); (j, t-(t-5))_|Yes|Yes|Yes|Yes|Yes|Yes|
|**No. obs.**|86,618|78,752|75,678|86,618|78,752|75,678|
|**R**<sup>**2**</sup>|0.893|0.883|0.897|0.893|0.883|0.897|

Notes: This table shows the estimation results for equations (9) and (10), respectively. Robust standard errors are in parentheses. *, **, and *** denote statistical significance at the 10, 5, and 1% levels, respectively, in two-tailed t-tests.

# **4. Conclusion**

This paper fills a void in the literature with regards to the link between geographic distance and the impact of economic integration agreements (EIAs) on trade flows. Through exploring the impact of the interaction of these two variables on total bilateral exports, we find evidence that the positive effect of trade agreements decreases in distance. Furthermore, when looking at trade flows disaggregated by broad economic category, we show that the primary channel through which this effect takes place is for intermediate goods. These results imply that EIAs reinforce supply and production networks among proximate countries that rely on timely, two-way trade in intermediate inputs that is more feasible when countries are close by.

In addition to a strong negative effect on our variable of interest for intermediate goods exports, we find no effect on the interaction between EIAs and distance for bilateral trade flows of other goods categories when using our baseline specification. When controlling for trade agreement depth, which is correlated with bilateral distance and has been shown to boost trade more than shallower agreements, the effect on non-intermediate goods becomes positive and significant. This result is the same when controlling for economic similarity (also correlated with trade agreement depth). As such, our findings provide evidence not only that EIAs and distance reinforce one another through the channel of intermediates, but also that EIAs allow for trade in final goods (which are differentiated both in terms of variety and quality) to be allocated to distant partners in function of who is the best producer.

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# **Figure 3:** Age today and age when household arrived in El Fasher.

## **(a)** Age distribution of IDPs.

<!-- Start of picture text --> 15<br>10<br>5<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Age in completed years<br>Percent<br><!-- End of picture text -->

- **(b)** Age when household arrived in El Fasher.

<!-- Start of picture text --> 15<br>10<br>5<br>0<br>-20 -10 0 10 20 30 40 50 60 70 80 90 100<br>Age at arrival in completed years<br>Percent<br><!-- End of picture text --> 20
supported by the World Bank-Norway Trust Fund. The views expressed in this paper are those of the author alone and do not necessarily reflect those of the funding agencies.

1 The estimated number of individuals forcibly displaced in Nepal is approximately 200,000 (USAID, 2007). Studies reviewed in this paper have considered the impact of conflict on education in Tajikistan (between 50,000 and 100,000 deaths and 10 percent of the country’s population internally displaced in the two worst years of conflict, Shemyakina, 2011b), Guatemala (200,000 deaths during the worst conflict period, Chamarbagwala and Morán, 2011), Rwanda (nearly 10 percent of the population killed, Akresh and de Walque, 2008), and Peru (just under 70,000 deaths, León 2012).

2 The correlation coefficient between total conflict deaths and abductions per 1,000 inhabitants is 0.14.

3 Hutt (2005) also links abductions to the weakness of support for the Maoists: “The Maoists know that much of their support is hollow and based on fear. Maoist cadres have taken to mounting temporary abductions of large numbers of school teachers and students, who are taken to remote locations and subjected to political indoctrination sessions” (Hutt, 2005, p.86).

4 In 2001, six out of 257 sampling units had to be dropped from the sample for security reasons (Ministry of Health et al., 2002, p.6).

5 In the 2001 survey, children listed on the household roster cannot be matched to their mothers.

6 The variation in exposure between surveys also varies substantially between districts. For instance, in Mahottari, there were 0.14 additional deaths per 1,000 inhabitants between the two DHS, whereas in Jumla, there were close to three additional deaths per 1,000 inhabitants during the same period.

7 Note that region dummies are subsumed under the district fixed effects, but the interactions between regions and birth year dummies are not.

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WPS3747 income distribution worsens due to a smaller improvement of per capita income in rural areas relative to urban areas.

# **4.3   Monte Carlo Analysis**

Given the stochastic nature of the occupational choice model in the microsimulation model, it makes sense to perform Monte Carlo experiments to examine the sensitivity of poverty and income distribution outcomes.<sup>11</sup> Is it possible that the poverty outcome discussed above in the case of the unemployment closure is not robust? Therefore, we perform Monte Carlo experiments on the full liberalization scenario under the three alternative labor market closure specifications, namely the fixed employment closure (Full-Lib), the flexible employment closure (FLIB_2) and the sector specific labor closure (FLIB_3).

The Monte Carlo results presented in Table 7 give us a much better idea of the robustness of the findings presented in Tables 4 and 6. Firstly, note that the magnitude of the standard deviation on the inequality indicators suggests that the changes in income distribution are not significantly different from zero in any simulation. Moreover, once we consider the full range of possible outcomes in the occupational choice model, the sign of difference in the poverty outcome between the fixed employment specification and the flexible employment closure is reversed. Under the flexible employment closure, the trade liberalization scenario generates higher poverty reductions. However, the size of the standard deviation on the poverty outcomes suggests that the difference in poverty outcomes under the reference scenario and the flexible

> 11 The term Monte Carlo experiments refers here to the replication of microsimulation results using different draws of the residuals for the occupational choice model as well as for the wage equation model. One hundred draws were performed for each simulation. The draws that did not generate a feasible solution where dropped, which explains why the number of observations is smaller than 100 in Table 7.

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Table C.5: Result of the regression on amenities

||Residual utility (log)|
|---|---|
|Proximity to a district park (< 1 km)|-0.015 (0.02)|
|Proximity to the ocean (< 2 km)|0.11<sup>_∗∗∗_</sup>(0.02)|
|Proximity to the ocean (2< . < 4 km)|0.08<sup>_∗∗∗_</sup>(0.02)|
|Proximity to Urban Heritage Site (< 2 km)|-0.01 (0.02)|
|Airport Noise Cone (within)|-0.04 (0.03)|
|Slope (between 1 and 5%)|0.09<sup>_∗∗∗_</sup>(0.02)|
|Slope (_>_5%)|0.15<sup>_∗∗∗_</sup>(0.02)|
|Proximity to a biosphere reserve (< 2 km)|0.004(0.02)|
|Proximity to a train station (< 2 km)|-0.013 (0.02)|
|Constant|-1.96<sup>_∗∗∗_</sup>(0.02)|
|Observations|307|
|R<sup>2</sup>|0.30|
|Adjusted R<sup>2</sup>|0.28|
|F Statistic|14.2<sup>_∗∗∗_</sup>(df = 297)|

_Note:_<sup>_<u>∗</u>_</sup> p _<_ 0.1;<sup>_<u>∗∗</u>_</sup> p _<_ 0.05;<sup>_<u>∗∗∗</u>_</sup> p _<_ 0.01 35