rafmacalaba/datause-encoder-data huggingface dataset:
**_Figure 14: Tariffs of Maghreb and Mashreq for different trade partners_**

<!-- Start of picture text --> Maghreb Mashreq<br>40.00 40.00<br>35.00 35.00<br>30.00 30.00<br>25.00 MFN 2000 25.00 MFN 2000<br>20.00 MFN 2007 20.00 MFN 2007<br>15.00 Effectively 15.00 Effectively<br>10.00 Applied 2007 10.00 Applied 2007<br>5.00 Preferential 2007 5.00 Preferential 2007<br>0.00<br>0.00<br>EU25 United China World Maghreb Mashreq GCC EU25 United States China World Maghreb Mashreq GCC<br>States<br><!-- End of picture text --> _Source: TRAINS, Simple average tariff of all applied lines (MFN, preferential, and effectively applied rates)._

**A key policy priority to promote intra-regional trade is to reduce the incidence of NTMs in the Arab world.** PAFTA-based initiatives to lower NTM-related real trade costs offer an opportunity to lower overall levels of trade restrictiveness in a way that does not generate trade diversion.<sup>19</sup> Regional cooperation to reduce trade costs can help stimulate both intra-regional and trade with rest of world. The policy challenge is to identify what types of policies should be given priority attention and how regional cooperation in the PAFTA context can best be used to complement national efforts to facilitate trade. In recent surveys, costs associated with administrative red tape and weaknesses in transport-related infrastructure services are ranked as the most important constraints to intra-regional trade. This suggests that from a policy perspective, efforts to reduce real trade costs deserve priority, including transportation and logistics services. Periodic monitoring and assessment of trade incentives and performance would help governments to benchmark performance and identify priority areas for action, at both the national and the sub-regional levels. These are all areas where the Secretariat, if given the mandate, can play a leading role.

**Different regions of the world have taken different approaches to the streamlining of NTMs.** Because countries typically have large numbers of regulations on the books, many of which with potential trade effects, streamlining them is a long and difficult undertaking. Regional trade agreements can help in several regards.  Here are some examples of how other regional blocs have helped streamline NTMs. First, as in the E.U., supranational institutions—like the European Commission and the European Court of Justice—can play a leading role in fostering the elimination of NTMs, at least on intra-bloc trade, and a general streamlining and

> 19 Countries with high average MFN tariffs can reduce potential trade diversion costs generated by PAFTA by reducing external levels of protection.

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# **1. Introduction**

The coming together of increased awareness of the new economic geography<sup>1</sup> and economic growth<sup>2</sup> is drawing renewed attention to rural isolation, rural access, and the role of transportation in improving rural economic growth and livelihoods.

The rationale for increasing rural access to markets follows the fundamental and compelling economic logic of scale economies and the gains from trade dating back to Adam Smith: rural producers who specialize in production and trade their surpluses can prosper faster.

In low-income countries (LIC) where rural infrastructure and structural transformation is less advanced, the chain of economic logic justifying rural road building is compelling but more complicated than it might at first seem. Most rural dwellers are farmers, many of them self-sufficient in food, with some selling their surplus food and cash crops to buy the necessities they cannot produce (including food out of season), and to pay health and school fees. Their incomes therefore depend largely upon their agricultural production. In turn their agricultural productivity and income can increase if they use more modern inputs (seed, fertilizer, pesticides). This requires that they have: (i) access to markets in which to sell their produce (to raise the money for inputs and other necessities) and to buy these inputs, and (ii) affordable transport through which to attain this market access. Affordability, i.e. monetary access depends on the relative prices they receive and face, which in turn depends on supply and demand and the costs of moving crops and inputs between secondary and primary markets and farm gates; plus market information asymmetries; and the availability of market competition among buyers, sellers, and transporters. For instance crop prices can collapse at times of surplus if markets are localized and oversupplied. But this is less likely to happen if markets are well integrated within a country and with export markets and if price smoothing is possible through storage. The affordability of transport to rural agents in turn depends on its price. This depends upon the fixed and variable costs of moving a unit of inputs and/or product, the value per unit eventually bought or sold, the volumes carried (in turn affected by the number and size of market suppliers and the transactions costs of

> 1 Among others, we can quote Venables and Kanbur (2005) and World Bank (2009.1). 2 Commission on Growth and Development (2008).

3
|Birth Year b-2 to b-1|(0.481)|(0.514)<br>0.041<br>(0.473)|(0.500)<br>-0.053<br>(0.509)||||
|---|---|---|---|---|---|---|
|Birth Year b-3 to b-2|||0.0293<br>(0.500)||||
|Birth Year b-3 to b||||0.326<br>(0.212)||-0.035<br>(0.065)|
|Downstream Forest Loss|||||||
|Birth Year b-3 to b|||||0.194<br>(0.226)||
|N|33,683|33,683|33,683|33,683|33,683|75,930|
|Location of cluster|Rural|Rural|Rural|Rural|Rural|Urban|
|Fixed Effects|Y|Y|Y|Y|Y|Y|
|Climate Controls|Y|Y|Y|Y|Y|Y|
|Demographic Controls|Y|Y|Y|Y|Y|Y|
|R-sq|0.096|0.096|0.096|0.096|0.096|0.030|
|Mean stunting incidence|6.95%||||||
|Impact of a 1 percent loss in<br>upstream forest cover 1 year<br>ago as a percent of mean<br>stuntingincidence|12.47%||||||

_Notes_ : Table presents estimates of equation (2) where the dependent variable is the stunting incidence for non-migrant children under the age of 5 between years 2004-2020. See notes to Table 5 for details on the variable construction. Fixed Effects include child’s-birth-year and month, and district fixed effects. Robust standard errors clustered at the district level and are reported in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

# **5. Conclusion**

In this paper, we analyzed the complementarities between natural and human capital using a global analysis of 0.7 million rural non-migrant children across 46 countries. By linking high-resolution forest loss data with individual level health outcomes, our analysis shows that deforestation upstream has significant implications for DD incidence and stunting among children under 5 years old in downstream communities. Moreover, we provide novel evidence that these effects can have long lasting and significant impacts on human capital through stunting. This effect is particularly large if children are exposed to poor water quality in the first thousand days of life. Results for Peru confirm the sign and statistical significance of the result for DD, which is twice as large as the global results. However, the impact on stunting is less pronounced likely due to targeted government and donor health and sanitation campaigns.

Our analysis has both analytical and practical implications. From an analytical point of view, our analysis complements a growing literature on the complementarities between human and natural capital and provides further evidence of the importance of the water channel for short- and long-term health outcomes. On the practical side, our results suggest that maintaining natural capital has the potential to generate meaningful improvements in long-run human capital and that sustainable management of forests should be part of the menu of interventions targeting stunting in rural communities.

The study of the complementarities or substitutability between different forms of capital is a critical yet data demanding task to understand the sustainability of economic development (Cohen et al., 2019). Any future research that analyzes the substitutability and/or complementarity between different types of capital or across subcategories of capital in production or consumption process will help improve our understanding significantly.

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13 substantially over time, and in practice, making these risk weights properly reflect the underlying risks has been extremely challenging. A well-known example is the risk weights for sovereign debt in many euro area countries, which have been too low, as evinced during the euro area turmoil. The static, reactive nature of standard regulation leads to regulatory arbitrage and frequent re-regulation becomes an inevitable part of regulatory reform that is not incentive-robust, i.e. that does not take into account incentive issues in a dynamic, forward looking fashion.

Indeed, recommendations for regulatory reform developed by independent fora (e.g., Geneva Report 2009; the LSE Report on the Future of Finance 2010; the Squam Lake Working Group 2010; and the CEPR Future of Banking report 2010) consistently point out that incentive issues need to be more fully reflected in the design of regulatory systems. The importance of designing regulations that are “incentive-robust” is being increasingly recognized (e.g., Calomiris 2011).

It is therefore important to put identification of incentive problems at the center of the regulatory approach rather than leaving it as an afterthought. Some of the incentive issues highlighted during the crisis (e.g., lack of incentives of supervisors from different jurisdictions to share relevant information in situations of stress) have not really been fully addressed. Regulators have often failed to implement the regulations and powers that they already had. As pointed out by Barth, Caprio, and Levine (2012a) finance is subject to factors such as psychological bias in favor of the industry (similar to that prevailing in sports, where referees regularly call games in favor of home teams). This suggest that the key issue to be addressed is not necessarily more regulations (although some additional regulations may be appropriate), but it is how to make sure regulations are designed so that the market participants’ incentives to circumvent them are minimized and the regulators incentives to enforce these rules are maximized.

# **4. Introducing the Incentive Audit**

The discussion so far has highlighted two points. First, incentives play an absolutely crucial role in the financial sector. Second, incentives misalignments need to be addressed head-on if we are to do a better job at preventing future financial crises. That is the motivation for the “incentive audits”, introduced in this section.

The core point of our paper is that the identification of incentive problems in a financial sector would benefit from a specific analysis of incentives -- an incentive audit.<sup>21</sup> Introducing such audits could help to strengthen the policy framework for financial sector as we discuss next.

> 21 The term “incentive audit” was first used by Johnston, Chai and Schumacher (2000) and Chai and Johnston (2000), who discussed incentive analysis in the context of international surveillance.<sup>22</sup> See IMF/FSB/BIS (2011).
11 We now move closer to the core of our empirical analysis and relate workers’ remittances to current account reversals. The source of the data on current account is again the IMF Balance of Payments Statistics database; we divide the current account balance, measured in dollars, by the GDP, again in current dollars, as provided by World Bank’s World Development Indicator database.

Our preferred definition of a current account reversal occurring at time _t_ is based on the following three requirements: i) the current account balance at time _t-1_ must be a deficit; ii) the current account balance must improve by at least 5 percentage points of GDP; iii) the size of the improvements must exceed one half of the current account balance at time _t-1_ . For every country-year observation, we thus create a dummy variable, denoted by BP5<sup>15</sup> , which takes on a value equal to 1 if a current account reversal occurs and 0 otherwise.

Few comments on BP5 are needed. Firstly, BP5 stands in a close relationship with some of the measures used by MFR and Edwards: criteria i) and ii) are common to all measures, while criterion iii) is a stricter version of MFR’s last requirement. This strict analogy with MFR and Edwards is crucial: our purpose is not to innovate on the criteria for identifying current account reversals, but to show that remittances can play a role while using “well-established” measures of current account reversals. Along these lines, we will show that our results hold also when using only criteria i) and ii) so as to resemble more closely Edwards’ first measure; in particular, we provide two versions of it: ED4 denotes an adjustment of the initial current account deficit equal to at least 4 percentage points of GDP, exactly as in Edwards (2004b), while ED5 raises the required adjustment to 5 percentage point<sup>16</sup> . Secondly, we purposely choose not to allow for the adjustment to take more than one year, as in Edwards (2004a)’ second measure and in MFR’s criterion i). We believe that our single-year criterion better capture a crisis episode which wants to be the focus of our analysis<sup>17</sup> .

> 15 A bit presumptuously, BP stands for Bugamelli and Paternò, while 5 refers to the 5 percent minimal adjustment.

> 16 Here the purpose is to create a bridge between Edwards’ measure and BP5 in terms of adjustment size.

> 17 An important feature of BP5 is that it does not require any persistence in the current account adjustment, i.e., any current account adjustment might be reversed in the very next years. Furthermore, it might lead us to break (segue)
<!-- Start of picture text --> (billion won) (percent)<br>300,000 Gam Trend of mortgage loans 18<br>280,000 ——Year on year, by month 16<br>260,000 14<br>240,0004 12<br>220,000 10<br>200,000<br>&<br>180,000<br>160,000 6<br>140,000 4<br>120,000 I | 2<br>100,000 ia)<br>4 ~ a ~ a TM~ a ~ 4 m 4 m a ~<br>st uy wo > oo a o<br>S oS i] > S So a4<br>oS So o =o oS oS o<br>om ol od mTM om om os<br><!-- End of picture text -->
# **Monitoring and evaluation**

69. Financing is likely to come from multiple sources. Timely and consistent tracking of budgets/commitments and execution is a critical ingredient for assessing reconstruction progress. Financial information is especially sensitive, particularly if donors and private contributions are high. An on-going assessment of commitments and disbursements across all sources (public/private, domestic/international) would highlight the extent of any reconstruction delays and potential financing gaps. Good PFM systems would then measure whether the financial resources are being translated into outcomes. When execution is decentralized across various types of domestic or international agencies or local governments, data systems need to pay particular attention to proper accounting. For instance, commitments need to be separated from disbursements, emergency projects from reconstruction projects, and financing institutions from implementing partners (Agustina, 2007).

70. In recent years, several “tracking systems” have been developed to monitor financial information and improve aid management of the recovery process. The existence of credible and integrated financial tracking systems has become more critical in recent years because of the unprecedented levels of funding after the tsunami, together with other large-scale post-conflict reconstrucion efforts (e.g. Afghanistan and Sudan) with large funding.

71. The most prominent aid management and reconstruction tracking instrument is the Development Assistance Database (DAD). The DAD has been applied in a number of countries including Afghanistan, Sri Lanka, the Maldives and Indonesia. However, coverage has been limited, often focusing on UN and other core donor activities. Although the DAD includes sophisticated technical specifications, including visual breakdowns to the village level, it has faced many implementation challenges, particularly in Aceh and Nias.

72. While information technology has sometimes been an impediment to the effective implementation of information systems, it has typically not been the main obstacle for effective monitoring. Instead, the main challenges are related to data collection and analysis:

- In Aceh and Nias, a labor intensive data collection effort by a joint team of the World Bank (mainly national staff) and the reconstruction agency has proven superior to DAD’s more high-tech and self-entry-based information system in tracking funds. The Indonesia DAD―a more than US$2 million investment―has yet to deliver any significant results.

- In Sri Lanka, DAD has been providing regular financial and project updates. Similar to Aceh and Nias, this system also builds on self-entry-based information. In order to increase compliance, the DAD team also discloses the institutions not providing data.

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**Table 6:** FGI vs. other financial integration measures

||(1)<br>_σR_|(2)<br>_σR_|(3)<br>_σR_|(4)<br>_σR_|(5)<br>_σR_|(6)<br>_σR_|(7)<br>_σR_|(8)<br>_σR_|(9)<br>_σR_|
|---|---|---|---|---|---|---|---|---|---|
|_σRt _-1|0.468***|0.471***|0.472***|0.469***|0.472***|0.472***|0.472***|0.472***|0.472***|
||(0.016)|(0.016)|(0.016)|(0.016)|(0.017)|(0.017)|(0.017)|(0.017)|(0.017)|
|_IM_-1|-0.064**|-0.076***|-0.077***|-0.073***|0.020*|0.004|0.002|0.000|0.007*|
||(0.025)|(0.024)|(0.029)|(0.026)|(0.010)|(0.007)|(0.006)|(0.007)|(0.004)|
|_σGF_|0.297***|0.305***|0.302***|0.298***|0.295***|0.296***|0.296***|0.296***|0.296***|
||(0.018)|(0.019)|(0.018)|(0.018)|(0.018)|(0.017)|(0.017)|(0.018)|(0.018)|
|_FED_|0.118***|0.099***|0.106***|0.114***|0.122***|0.120***|0.120***|0.119***|0.120***|
||(0.013)|(0.012)|(0.013)|(0.013)|(0.013)|(0.013)|(0.013)|(0.013)|(0.013)|
|Constant|-0.004***|-0.005***|-0.004***|-0.007***|-0.007***|-0.006***|-0.006***|-0.006***|-0.006***|
||(0.001)|(0.001)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
||0.468***|0.471***|0.472***|0.469***|0.472***|0.472***|0.472***|0.472***|0.472***|
|Obs.|16,792|16,792|16,792|16,792|16,792|16,792|16,792|16,792|16,792|
|R-squared|0.456|0.456|0.456|0.456|0.455|0.455|0.455|0.455|0.455|
|Countries|81|81|81|81|81|81|81|81|81|
|_IM_|FGI|UFGI|UFGI10|CI|FPEL|FL|LMF|FA|FPEA|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Clustered sta|ndard errors a|t countrylevel|inparentheses|. ***p<0.01,*|*p<0.05,*p<|0.1.||||

# **6.** **<u>Robustness check</u>**

A first possible concern with our regressions in table 3-4 is that, in dynamic panels (Nickell, 1981), the introduction of fixed effects creates a downward bias in the coefficients of the lagged dependent variable. Despite the fact that such a bias should be limited in panels that are longer than wider, as it is the case in our data set (T=300, N=84), we wanted to verify that our main results hold true if we address the issue. Hence, as standard in the literature, we use the system GMM estimators introduced by Arellano and Bond (1991).<sup>18</sup> When we run GMM estimates, see table A1 in the Appendix, we find that the coefficients for the lagged dependent variable decrease for all market types, as compared to the OLS estimates. In absence of interaction terms, the FGI coefficient remains negative, but loses significance. When we introduce the interacted term (columns 2-5) the coefficient of the FGI remains negative and significant for emerging and frontier and turns non-significant for developed markets; the coefficients of FGI interacted with global financial volatility remain positive and significant for all market types.

While the GMM estimations confirm that the relation between financial globalization and financial volatility depends on the state of the global economy, the point estimates for the different thresholds change significantly. Now, the threshold for which financial globalization becomes a destabilizing

> 18 We implement it using command xtabond2 on Stata for system GMM with robust standard errors.

13
cost effect graphically. Among those who paid in full for the course, those who were assigned a higher price attend more sessions. This continues to hold even after we control flexibly for willingness to pay.<sup>22</sup>

# **TIOLI Sample**

As Table 7 shows, training attendance was much higher in the TIOLI sample than in the BDM sample, consistent with the fact that entrepreneurs in the TIOLI sample have higher WTP for the course. 90% of those offered to take the course for free attended at least 1 class, and 78% attended at least 5 of the 10 classes of the course (Column 1). The average number of classes attended in this group was 7.0 classes.

In Table 9, we study the effects of the randomly allocated offer price on attendance. Here again, when studying the full sample, we see that price monotonically decreases the probability of attending at least one class (Columns (1)-(2)). When we restrict the sample to those who paid a positive price for the course, we find small effects of price paid that are not statistically different from 0. However, there is not much margin for variation since 98 percent of those paying our lowest price of J$5,000 attended at least one class. The share of those who attended the course increases to 100 percent for those who paid J$10,000 or J$15,000 (Column 4). The total number of classes attended averages 8 classes for those who paid J$5,000, and we do not see significant effects of paying a higher price. The lack of effects when comparing those who paid J$5,000 vs. those who paid J$10,000 or J$15,000 is similar to the results for the BDM sample in Table 8.

It is important to note that compared to those who were offered the course for free (dropped from the regressions in Columns (3)-(6) of Table 9), the selected sample of entrepreneurs who were offered a positive price and paid for it have significantly higher attendance rates.<sup>23</sup> This means that while the evidence for sunk-cost effects is more nuanced for the TIOLI sample, these effects seem to be present also in this sample. However, they are not large enough to counteract the extensive margin effects on attendance generated by an increase in prices that prevent many entrepreneurs from purchasing the course.

> 22 We regress attendance on a flexible set of willingness to pay dummies, and then use the residuals from this regression, adding back the sample mean to enable comparability with the raw pattern.

> 23As we can see in Column (1) of Table 9, 90 percent of those offered the course for free attend at least one class, this share increases to 97 percent for those paying J$5,000 (marginally statistically significant difference) and to 99.5 percent for those paying J$10,000 or J$15,000 (statistically significant). In terms of total attendance, the effect compared to those offered a price of 0 is statistically significant for those who paid J$5,000 or J$10,000, but not for those who paid J$20,000.

23