rafmacalaba/datause-encoder-data huggingface dataset:
|annels|All regressions<br> also allow for<br>l is defned as<br> proxy for the<br>Stock markets<br> the country’s<br> hazard ratios.<br>e at the 10%,<br>(6)<br>Lib<br>0.060**<br>(0.030)<br>-1.538***<br>(0.062)<br>0.040*<br>(0.021)<br>0.040<br>(0.057)<br>0.128<br>(0.114)<br>Yes<br>Yes<br>Yes<br>Yes<br>32,892|
|---|---|
|nancial Ch|at time _t_0. <br>. Estimations <br> industry leve<br>ry. Banks is a <br>untry’s GDP. <br>alization over <br>ents and not <br>ical signifcanc<br>(5)<br>All<br>0.062**<br>(0.027)<br>-1.499***<br>(0.058)<br>0.050**<br>(0.020)<br>0.020<br>(0.043)<br>0.179*<br>(0.102)<br>Yes<br>Yes<br>Yes<br>Yes<br>37,056|
|ernative Fi|ket that started<br>me fxed efects<br>endence at the <br> a given indust<br>ctor over the co<br>ck market capit<br>e report coefci<br> denote statist<br>(4)<br>Lib<br>0.133***<br>(0.028)<br>-1.635***<br>(0.058)<br>-0.060***<br>(0.023)<br>0.180***<br>(0.054)<br>-0.101<br>(0.144)<br>Yes<br>Yes<br>Yes<br>Yes<br>41,271|
|vival - Alt|he world mar<br>country and ti<br>al fnance dep<br>median frm in<br>t to private se<br>atio of the sto<br>f Table 3. W<br> *, **, and ***<br>(3)<br>All<br>0.130***<br>(0.025)<br>-1.167***<br>(0.049)<br>0.039***<br>(0.015)<br>0.121***<br>(0.037)<br>0.195*<br>(0.111)<br>Yes<br>Yes<br>Yes<br>Yes<br>77,986|
|xport Sur|country _c_ to t<br>for exporting-<br>riable. Extern<br>ures, for the <br>he bank credi<br>ured as the r<br> column (4) o<br> parentheses. <br>(2)<br>Lib<br>0.149***<br>(0.026)<br>-1.643***<br>(0.054)<br>0.005<br>(0.016)<br>-0.187***<br>(0.042)<br>Yes<br>Yes<br>Yes<br>Yes<br>45,699|
|ng-Term E|oduct _k_ from <br>) and control <br>_k_ as strata va<br>pital expendit<br>s the ratio of t<br> country, meas<br>orresponds to<br>0) level are in <br>(1)<br>All<br>0.158***<br>(0.024)<br>-1.118***<br>(0.048)<br>0.079***<br>(0.011)<br>-0.229***<br>(0.036)<br>Yes<br>Yes<br>Yes<br>Yes<br>85,675|
|le 4: Stock Market Liberalization and Lo|dent variable is the hazard rate for an export spell of pr<br>ed using the Cox Proportional Hazard Model (Cox, 1972<br>aseline hazard rate across products by defning product <br>enditures minus cash fow from operations, divided by ca<br>anking development in the exporting country, measured a<br> for the depth of domestic stock market in the exporting <br>er variables are defned in Table 3. Full set of controls c<br>ndard errors clustered at (exporting country)*time (_c ∗t_<br>% levels, respectively.<br>alization x Distance to comparative advantage<br>alization<br> to comparative advantage<br>alization x External Finance Dependence<br>Distance to comparative advantage<br>rkets x Distance to comparative advantage<br>rkets<br>f controls<br>g country FE<br>stratifcation<br>ions|
|Tab|The depen<br>are estimat<br>diferent b<br>capital exp<br>domestic b<br>is a proxy <br>GDP. Oth<br>Robust sta<br>5%, and 1<br>StM liber<br>StM liber<br>Distance <br>StM liber<br>Banks x <br>Banks<br>Stock Ma<br>Stock Ma<br>Full set o<br>Exportin<br>Time FE<br>Product <br>Observat|

40
included in the simulations, using a sub‐grid channel network set‐up (Neal, Schumann, and Bates 2012). In channel flow was estimated to be 0.5*bankfull Q; rivers were estimated to be at 50% channel capacity.

Coastal simulations under future climate conditions were undertaken using the latest projections of global mean sea‐level rise, outlined in the Fourth Assessment Report (AR5) and Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) (IPCC 2014; IPCC 2007). Estimates of sea‐level changes were taken and used to directly perturb the boundary conditions used in the simulations under current conditions. In order to incorporate uncertainty, simulations were undertaken for a range of projected changes, represented here as Low, Medium and High sea‐level rise (SLR) projections ( Table 1). The simulations are all conducted assuming that no flood defenses are in place; clearly flood defenses are not represented in the available terrain data. Therefore, these simulations should be considered as an upper bound of flood risk in the country.

The possibility of including a storm surge intensity component to the future projections was also discussed, but there are significant uncertainties around quantifying storm intensity changes that would preclude any reasonable modeling being undertaken (Seneviratne et al. 2012). It also seems that changes in surge extremes are going to be largely driven by sea‐level rise (Lowe et al. 2010). That being said, there are studies attempting to quantify changes in storm surge intensity; Lin et al. (2012) reported in a study focused on the North Atlantic, that in some cases changing storm surge intensity was comparable to changing sea‐level rise. Such changes would effectively double change in hazard intensities presented here. As of yet, we have not included this in the simulations due to the uncertainty.

# **9.2. Appendix 2. City‐case: HCMC**

# **9.2.1. Results for flood extent across districts**

Figure 2 presents the differences in relative flooding extent (%) for a flooding event with a 10 year or 50 year return period representing the current and future conditions. Overall, we find that both under current conditions and given a 30 cm sea level rise, relative flooding extent is higher in slum and urban expansion areas than in the non‐slum areas, both when looking at the HCMC‐city level and at the level of districts. This can serve as a first‐order indicator that these slum and urban expansion areas are relatively often located in flood prone areas.

25
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Senegal > Road investments > Macro results**

||||Reduction in<br>|Roads (|% variation<br>|s)<br>||
|---|---|---|---|---|---|---|---|
|||||||||
|||||||||
|Variable|Definition|Reference|<br>expenditure|VAT|<br>duties|<br>aid|<br>tax|
|Ym|Aggregate household income|1,500,000.00|0.51|1.00|0.99|1.01|1.10|
|EV|Equivalent variation||0.50|0.91|0.92|1.00|–0.89|
|s|Wage|1.00|–0.28|1.92|1.90|2.00|2.40|
|yg|Government income|539,000.00|0.84|5.43|5.43|5.43|5.43|
|g|Total government expenditure|295,000.00|–3.85|4.52|4.52|4.52|4.52|
|ye|Firm income|935,000.00|1.50|0.71|0.72|0.69|0.55|
|It|Total private investment|860,000.00|1.43|0.69|0.53|0.04|0.46|
|GDP|GDP|2,100,000.00|1.02|1.02|1.02|1.01|1.02|
|e|Nominal exchange rate|1.00|–0.46|–0.08|–1.19|–4.24|–0.75|

# **Senegal > Road investments > Sectoral results**

|Variable|||Reduction in||Roads|||
|---|---|---|---|---|---|---|---|
||Sector|Base|other public<br>expenditure|VAT|Import<br>duties|Foreign aid|Income<br>tax|
||Crop agriculture|252,000.00|0.95|0.88|0.89|0.91|0.82|
||Export agriculture|221,000.00|1.91|0.59|0.60|0.51|0.78|
|Va<br>|Industries|361,000.00|1.75|0.55|0.58|0.45|0.88|
|Value added or<br>output|Construction|91200.00|3.32|2.62|2.63|2.91|2.78|
||Private services|995,000.00|1.19|0.79|0.76|0.68|0.59|
||Public services|180,000.00|–3.46|3.10|3.15|3.75|3.29|
||Crop agriculture|1.02|–0.24|0.05|–0.11|–0.85|–1.50|
||Export agriculture|1.02|–0.64|0.10|0.02|–0.77|–0.39|
|pq|Industries|1.14|–0.73|0.87|0.89|–2.15|–0.84|
|Market prices|Construction|1.01|5.71|6.01|5.95|5.32|5.71|
||Private services|1.02|–0.51|0.30|0.16|–0.67|–0.62|
||Public services|1.00|–0.41|1.38|1.33|0.74|1.19|
||Crop agriculture|1.00|0.96|0.40|0.84|1.84|–1.50|
|r|Export agriculture|1.00|1.06|–0.09|–0.10|–0.22|0.87|
|Rental rate of<br>|Industries|1.00|0.47|–2.19|–2.09|–2.52|–0.41|
|capital|Construction|1.00|16.23|14.18|14.23|16.20|15.75|
||Private services|1.00|0.72|0.67|0.51|0.14|0.02|

37
Arbitrarily setting K=1, we use equation (4) to compute pre-upgrade trade flows as index numbers. We add all index numbers for cities in different countries, and divide the result into IMFDOT’s total inter-country trade value. This produces a K-value for equation (4) that translates trade index numbers for city pairs into internally-consistent trade values.

We recognize that this K-value provides only an approximation, because neither our econometric results nor our inter-city trade simulations explicitly allow for modal alternatives (e.g., rail, ocean freight). Railroad connections are probably not important for the analysis, since they are sparse outside the Southern African Customs Union countries (whose inter-country trade is not captured by the IMFDOT data).<sup>25</sup> Within Sub-Saharan Africa, intra-regional sea freight is potentially more significant, although its importance is considered relatively minor by African transport experts whom we have consulted.<sup>26</sup> Exclusion of alternate modes has probably reduced the explanatory power of our regressions (our R<sup>2</sup> values are in the range .36 - .40). However, our estimated road quality and distance parameters do incorporate potential diversion to alternate modes, because our regressions are fitted to total trade data for partner countries. In summary, we believe that index-number translation via our K-value can provide useful insights about the potential value of road quality improvements between cities, countries and regions.

> 25  Most rail lines in Sub-Saharan Africa have been constructed to bring raw materials from the interior to the coast for export.  This paper analyses interior trade among African states, so we believe that exclusion of rail lines is not a source of significant error.  In the future, inclusion of rail traffic will become important if more inter-country rail lines develop outside of southern Africa.

> 26  We have consulted with the following experts, who are unanimous in attributing little importance to ocean transport among Sub-Saharan countries unless they are widely separated:  Jan Hoffmann, Trade Facilitation Section, UNCTAD; Marc Juhel, Acting Sector Manager, Transport Unit, and Brad Julian, Port and Maritime Transport Specialist, Transport and Urban Development Department, World Bank; Simon Pitout, Manager, APM Terminals Africa regional office, Cape Town, South Africa.

18
11 recipient of substantial debt relief from bilateral creditors as well. The debt reduction deal should also materially enhance the country's growth and development prospects.7 To date, the commercial debts of most IDA-only countries have not been actively traded on secondary markets. Those inter-bank swaps that have occured have typically registered deep discounts varying from 50 up to 95 percent. Thus, even assuming a favorable average discount of 80 percent, the new facility's $100 million could retire only $500 million in debts. If the Bank succeeds in mobilizing other donors to provide equal contributions, up to $1 billion in private debts might conceivably be erased. Yet the public and publicly guaranteed medium and long-term commercial debt owed by IDA-only countries exceeds $11 billion.<sup>8</sup> Even subtracting suppliers' credits, debt to financial markets still approaches $9 billion, although a substantial portion of this may be L entually absorbed by official insurance agencies. Even so, the remaining commercial exposure of IDA-only countries will substantially exceed $1 billion.

Tberefore, if the Bank demonstrates its ability to utilize this initial $100 million, the IBRD should consider supplementary financing for this highly efficient use of Bank resources. A deeper pool of resources would also permit some relaxation of the $10 million per country ceiling. Future contributions will, of course, depend on the continued profitability of the IBRD; if there is any trade-off between reducing IBRD lending margins and the availability of funds to replenish this debt reduction facility, the pressing filwncial needs 7. As of August, 1990, no such deals had been concluded, but three countries -- Bolivia, Mozambique, and Niger -- were negotiating World Bankfinanced buybacks.

8. World Bank, World Debt Tables 1988-89. Washington, DC: World Bank, 1988.
- 14 -

autocorrelation coefficient in the errors, _ρ_ , is also significantly positive. This can be interpreted to mean that an exogenous shock—for example, drought and flood—in a given location has a substantial spillover effect on its neighboring places. The magnitude of the coefficient is much larger than the autoregressive term _λ_ , suggesting the practical significance of external shocks affecting agricultural production in a wide range of areas.

Regarding the impacts of transport accessibility, port accessibility has a significant impact on crop production: The elasticity is estimated at -0.143, meaning that a 10 percent reduction in transport costs to the port would increase agricultural production by 1.43 percent, which looks like a relatively modest effect. This looks broadly consistent with our companion paper (Iimi et al. 2017), which estimates the same impact of port accessibility with micro household data in Ethiopia: The elasticity is estimated at 0.276.

On the other hand, the impact of domestic market access remains inconclusive. Local market connectivity ( _ΔTC1_ ) still has a positive coefficient, meaning that agriculture production is higher where transport costs are high or connectivity is low. But the coefficient is not statistically significant in the autocorrelation model. This may not be surprising because local transport connectivity is less relevant to ceased rail operations, which are used as our main identification strategy in the estimation: The changes in transport costs to a near large city were too small to affect agricultural production.

Regarding other production inputs, our estimation results suggest that labor and land are two important inputs in Ethiopian agricultural production. In the spatial regression, the elasticities are estimated at 0.35 and 0.40 for labor and land, respectively. These relatively large coefficients may reflect the fact that agricultural production in Ethiopia remains labor- and land-intensive. Irrigation and fertilizer are surely productive, as the literature suggests: The coefficients are both positive and significant. But the magnitude of the elasticities remains small because the use of fertilizer and irrigation is limited in our data.
nimus, Schagen and Schuermann (2002). We address this effect practically by using shortterm multipliers from the estimated NPL regression to approximate the mapping to NPLs in the PIT scenario (i.e., during normal times) and long-term multipliers in the Stress scenarios. This effectively introduces a non-linearity into the response of NPLs to the macroeconomic variables.

The third segment constructs bank-specific PDs and LGDs for each asset class. This is done by taking the aggregate PDs and weighting them by a bank and asset-class specific penalty function that is designed to approximate the relative underwriting standards of banks. The penalty function compares each individual bank’s credit growth to the average credit of the entire at the of the most recent credit or growth banking system peak cycle the latest positive credit growth. The idea here is to approximate the “bad vintage effect” that arises due to weaker underwriting standards during credit booms by the relative aggressiveness of a bank’s lending in each asset class.<sup>5</sup> The bank and asset class specific PDs are then linked to LGDs by means of a correlation parameter. We follow the approach outlined in Moody’s (2010) to calibrate this correlation parameter to be larger during stress times than during normal times.

The fourth segment constructs the exposures at default (EADs) for each bank using the asset class categories of Basel II. In principle, the EAD is composed of two types of exposures: ( _i_ ) an on-balance sheet and ( _ii_ ) an off-balance sheet exposure. Off-balance sheet exposures include items such as pre-approved limits for credit cards, overdrafts and credit lines. In some countries, these exposures are published as part of the Pillar III regulatory information disclosure for each bank and asset class and could therefore be

> 5For instance, during the last credit boom, most Eastern European countries experienced their fastest credit growth in 2007. Therefore, we use the growth in annual credit from 2006 _−_ 2007 in each asset class to construct the bank specific penalty later on in the empirical application in Section 7.

11
liabilities. In this framework, the equilibrium REER is that which allows the economy to achieve a sustainable long-run net foreign asset position. 15 Our assessment of Argentina's equilibrium real exchange is based on an analytical model encompassing these two ingredients. Thus, we take into account simultaneously the internal (productivity) and external (asset position) equilibrium of the economy to draw inferences about the overall equilibrium or disequilibrium position of the real exchange rate.<sup>16</sup> Empirical application of this analytical framework to Argentina using data for 1960-2001 yields the estimated equilibrium REER shown in Figure 3.1. The figure suggests that the trajectory of the equilibrium REER consists of two stages. First, an initial real appreciation in 1991-93 - particularly sharp in the first two years. Second, a steady depreciation from 1994 on, which by 2001 has brought the equilibrium REER below its 1990 value.

The equilibrium and actual REER are compared in Figure 3.2, which presents the percentage deviation of the actual REER from its equilibrium value, along with the 95 percent confidence bands derived from econometric estimation of the equilibrium real exchange rate model. In the figure, a positive value indicates overvaluation, and a negative one means undervaluation. The graph reveals two stages of real misalignment. Between 1990 and 1996, the REER was undervalued, although after 1991 the degree of undervaluation was fairly small. From 1997 on, however, the REER exceeded its equilibrium counterpart by a widening margin, resulting in an increasing overvaluation. By 2001, the REER exceeded its equilibrium value by 53 percent.<sup>17</sup> Notice the contrast between the degree of misalignment derived from the equilibrium model and that arising from the simple-minded PPP calculations mentioned earlier. While by both yardsticks the peso was substantially overvalued by 2001, the PPP calculations imply that the overvaluation of the peso developed basically between 1991 and 1993, with little change afterwards, while the latter suggests that the overvaluation arose only in the last few years of Convertibility. What lies behind these contrasting assessments ? As discussed earlier, the analytical model used here encompasses two key determinants of the time path of the equilibrium REER: (i) the relative prodictivity differential between Argentina and her trading partners, and (ii) Argentina's net foreign asset position. It is useful to examine them in turn.

> 15 While this stock-based asset view of real exchange rate determination has become mainstream, an alternative flow-based view assigns to exogenous capital flow fluctuations a dominant role in the determination of the equilibrium REER. According to this approach, the equilibrium level of the real exchange rate is that which makes the current account balance equal the (exogenously given) supply of net foreign financing.

> 16 The details are spelled out in Alberola et al. (1999) and Alberola, L6pez and Serven (2003).

> 17 To be specific, this figure is the difference between the logarithms of the actual and equilibrium REER.

20
10 income, but the majority of the significant coefficients are positive. By contrast, higher per capita incomes are associated with a significantly lower probability of ‘catastrophic’ health expenses. It is known (Van Doorslaer, O'Donnell et al. 2005) that China is unusual by Asian standards in that ‘catastrophic’ payments in most other countries are concentrated among the better off. These results show that _even after controlling for the other covariates in our models_ , a higher income reduces the probability of experiencing ‘catastrophic’ health expenses. Like O’Donnell et al. (2005), we find some evidence that higher levels of education, holding constant per capita income, reduces the probability of ‘catastrophic’ expenses. In the H8BS, the effects of ethnicity vary somewhat across the provinces, as do the effects of having small children in the house: for the most part, however, being from China’s Han majority seems to lower one’s chances of experiencing ‘high’ and ‘catastrophic’ expenses, while having young children in the household increases them.

Turning to the insurance effects, we find evidence across all surveys in support of the hypothesis that health insurance can increase financial risk. There is, however, some heterogeneity in effects by type of scheme, and by locality.

In our basic results for the CHNS data (Table 4) we find a significant effect of health insurance on the probabilities of incurring ‘high’ and ‘catastrophic’ health expenses in the sample as a whole and in the urban subsample, but not in rural areas. The marginal effect in the urban subsample indicates that health insurance raises the probabilities of ‘high’ and ‘catastrophic’ medical expenses by 11.3 and 14.5 percent respectively. In Table 5, we find that in urban areas, it is worker insurance (presumably LIS) and ‘other’ insurance (quite likely private insurance) that are associated with an increased risk of ‘high’ and ‘catastrophic’ health expenses, with ‘other’ health insurance raising the probability of ‘high’ out-of-pocket payments in urban
Table A2. Labor Market Adjustments by Gender

|||||Among Pop|ulation|||||Am|ong Active|Labor Forc|e|||||Among|Workers|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Region|country|Employm|enmt|Unemplo|yment|Nonpartic|ipation|Unemplo|yment<br>|Wage emp|loyment|Self Empl|oyment|Agricul|ture|Earni|ngs|Ho|urs|Wage R|ates|
|||Men|Women|Men<br>|Women|Men|Women|Men|Women|Men|Women|Men|Women|Men|Women|Men|Women|Men|Women|Men|Women|
||Argentina|-0.59|0.81|1.31|0.70|-0.72|-1.51|1.45|1.56|-1.29|-2.17|-0.17|0.61|-0.92|-0.06|-9.20|-5.67|-5.72|-7.39|-3.48|1.73|
||Brazil|-1.65|-0.81|1.28|0.59|0.37|0.22|1.72|0.96|-1.14|-0.50|-0.57|-0.46|.|.|-0.99|-0.12|-0.18|-0.18|-0.81|0.06|
|LAC|Chile|-2.51|-1.47|1.57|0.34|0.94|1.13|2.36|1.17|-3.79|-2.76|1.42|1.59|-0.42|-0.49|.|.|-0.96|-1.44|.|.|
||Costa Rica|-3.15|-1.37|2.25|2.17|0.90|-0.80|3.27|5.44|-4.53|-6.28|1.26|0.85|0.55|-1.52|-5.27|-2.20|-6.25|-7.43|0.98|5.23|
||Mexico|-1.95|-1.24|1.09|0.56|0.86|0.68|1.53|1.31|-1.70|-1.43|0.17|0.12|0.42|-0.45|-5.24|-4.54|-0.91|-0.30|-4.33|-4.24|
|EAP|Indonesia|0.71|-1.75|-1.25|-1.05|0.54|2.80|-1.04|-1.48|-0.46|1.30|1.49|0.17|1.81|-0.45|3.33|-4.47|0.14|-0.35|3.19|-4.12|
||Philippines|0.14|0.90|0.33|0.15|-0.48|-1.06|0.39|-0.06|-1.18|-0.88|0.79|0.94|-0.79|-0.03|3.63|2.12|-5.07|-4.95|3.63|2.12|
|MENA|Egypt|-0.74|-0.93|0.02|1.44|0.73|-0.51|0.14|5.11|1.18|1.32|-1.32|-6.42|-2.13|-6.00|-8.64|-13.25|3.11|8.79|-11.75|-22.04|
||Jordan|-0.03|-0.39|0.36|-0.03|-0.33|0.43|0.26|0.30|-0.95|-0.35|0.69|0.06|0.12|0.00|-2.83|-4.12|3.37|2.56|-6.20|-6.68|
||Bulgaria|-4.68|-3.40|2.02|1.48|2.66|1.92|3.19|4.08|-3.54|-4.00|0.36|-0.08|.|.|.|.|.|.|.|.|
||Latvia|-12.24|-6.46|9.14|4.63|3.10|1.83|14.14|8.69|-14.15|-10.52|0.01|1.83|.|.|.|.|.|.|.|.|
||Lithuania|-7.86|-1.63|7.98|3.38|-0.11|-1.75|12.29|5.52|-12.63|-7.65|0.34|2.13|.|.|.|.|.|.|.|.|
|ECA|Macedonia|0.60|-0.88|-0.56|-0.26|-0.03|1.14|-0.90|0.70|-1.07|-0.81|1.97|0.11|.|.|.|.|.|.|.|.|
||Poland|-2.94|-1.85|3.30|2.47|-0.36|-0.63|5.40|4.72|-5.02|-4.19|-0.39|-0.52|.||.||.||.||
||Romania|-0.85|-0.05|1.28|1.15|-0.43|-1.10|1.75|1.73|-2.54|-1.71|0.79|-0.01|.||.||.||.||
||Turkey|-1.79|0.39|1.83|0.78|-0.04|-1.17|2.56|2.41|-2.92|-3.19|0.36|0.78|.||.||.||.||
|AFR|South Africa|-3.83|-2.21|0.56|0.37|3.27|1.83|2.57|2.42|-4.70|-3.87|2.13|1.45|||||-4.41|-2.81|||

Notes: The difference between the annual percentage point change in 2009 and the average annual change between 2006 and 2008 for each outcome by gender is presented.

41