rafmacalaba/datause-encoder-data huggingface dataset:
our risk index is -0.264 in our preferred speci...cation. Therefore, if Tanzania’s level of ...nancial development reached Senegal’s, then the hazard rate of shrimps exports would decrease by � � Risk � �FinDev = �0:264 � 2:97 � (0:145 � 0:051) � 7%.

# 6 Robustness Checks

## 6.1 Alternative Measures of Financial Development

In Table 2 we report results using alternative measures of risk (column 2) and ...nancial development (column 3 to 5). Column (1) reports our preferred baseline speci...cation for the sake of comparison. In column (2) we use an alternative measure of risk (alt_sanitary_riskk)–constructed by Jaud and al (2009a) using a Poisson model instead of a Negative Binomial. Results remain qualitatively the same<sup>21</sup> . Column (3) and (4) report results using alternative measures of access to ...nancing. Local ...nancial markets are not the only source of ...nance for exporters.

Firms operating in countries with poorly developed ...nancial markets may rely on trade ...nancing provided by institutions in the destination country. The interaction term between our risk index and the measure of short term credit from the BIS banks (TCc) is negative and statistically signi...cant at the 10% level. The coe¢ cient is negative but not signi...cant when using the ratio of trade insurance to GDP (ICc) interacted with risk. Column (5) reports results when using the Ease of Getting Credit index (EGCc): The coe¢ cient on the interaction term with the risk index comes out negative and signi...cant at the 5% level, suggesting that the quality of domestic ...nancial institution increases survival of "risky" products in foreign markets.

> 21 Alternatively, we use the count of noti...cations per product at the HS 6-digit level, as a measure of ...nancing needs. The data is taken from Didiers and al. (2008). The correlation between our measure of risk and this alternative measure is 0.05. Coe¢ cients on both the count of noti...cations and its interaction with the level of ...nancial development are of expected sign but not signi...cant.

19
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result of external and/or policy shocks. In addition, this approach can be used to show the distribution of the measure of required deficit reduction.

In general this approach is implemented by first estimating a VAR model for the foreign real interest rate r<sup>f</sup> , the domestic real interest rate r<sup>d</sup> , the real GDP growth rate g, and depreciation of the real exchange rate e, i.e., _Vt_ = ( _rt f_ , _rtd_ , _g t_ , _et_ ) .

As an illustration, the following regression results for Turkey are obtained from an unrestricted 4-variable VAR model on quarterly data for the period 1990.Q1 – 2004.Q4. The lag order for the Turkey VAR estimation is chosen to be 1.<sup>18</sup> Given this order selection, the augmented Dickey-Fuller tests on the underlying variables reject the hypotheses that there are unit roots at 5% significance level. A regular VAR specification is therefore implemented.

_Table 5. Turkey: VAR coefficients_

||Depreciation of<br>RER|Domestic interest<br>rate|Foreign<br>interestrate|Growth rate|
|---|---|---|---|---|
|Depreciation of RER (-1)|0.6164132***|-0.0764374|-<br>0.0071392***|-0.0163632|
||[0.1214663]|[0.0745745]|[0.0032883]|[0.0578173]|
|Domestic interest rate(-1)|0.5183742**|0.7703966***|-0.0034098|0.2292575***|
||[0.1300432]|[0.0798403]|[0.0035205]|[0.0618999]|
|Foreign interest rate (-1)|2.613194**|0.1130593|0.9929505***|0.8727535|
||[1.310771]|[0.8047509]|[0.0354851]|[0.6239205]|
|Growth rate (-1)|.0078362|-0.0719824|-0.0050722|0.6440931***|
||[0.2706376]|[0.1661585]|[0.0073267]|[0.1288221]|
|Constant|-17.6484**|3.862517|0.0658478|-5.609101*|
||[6.057141]|[3.718795]|[0.1639787]|[2.883169]|

Note: Standard errors in brackets. * indicates significance at 10%, ** at 5%, and *** at 1%.

Table 5 reports the coefficients from the VAR estimation. The lag order for the Turkey VAR estimation is chosen to be 1 both because more lags would require a longer time series and because SBIC statistics indicate that 1 lag is the most appropriate choice for the current sample. Given this order selection, the augmented Dickey-Fuller tests on the underlying variables reject the hypotheses that there are unit roots at 5% significance level. A regular VAR specification is therefore implemented. Table 5 reports the coefficients from the VAR estimation.

> 18 See Annex 1 for further details on the VAR estimation for Turkey.

28
trade shocks as an instrument for GDP growth, Pritchett and Summers (1996) find that 40% of the differences in mortality rates across countries can be attributed to differences in the growth rates in the previous decades. However, 60% of the variation is still unexplained and significant improvements in health are rarely seen after rapid expansion of an economy (Bloom and Canning 2003).<sup>33</sup> Based on the data from the Panel Study of Income Dynamics (PSID) and using inheritance as an instrument, Meer, Miller, and Rosen (2003) find that in the short-run, changes in wealth are unrelated to the health of individuals. Other studies such as one by Levine and Rothman (2006) seek to draw a link running from trade to health, again using the instrumental variable approach to take account of endogeneity.

The fall of the Berlin Wall and the collapse of the Soviet Union offer two `natural experiments’ to assess the effect of changes in income on the health. Frijters, HaiskenDeNew, and Shields (2005) use the German unification as a “natural experiment” because the unification was mostly unanticipated and there was a large jump in incomes of East Germans at the time of unification. They find a small but statistically significant positive effect of income on self-reported measures of health satisfaction. Using household panel data spanning the Russian Pension Crisis and triple difference-indifference method, Jensen and Richter (2004) find that among affected pensioners, poverty rates doubled, and the intake of calories and protein and the use of health services and medications declined significantly. Pensioners were also 5 percent more likely to die in the two years following the crisis. Adams and others (2003) take advantage of the fact that in elderly populations persistent hidden factors will often be manifest in observed covariates and therefore, can be controlled for. They find that there is no direct causal subsequent mortality rates. This differs significantly between men and women. Men are affected by the GDP at the age of 20-49, while women are affected the most by the GDP when they are 50-64. Another point of view is that “The direct influence of wealth on health may be strongest during childhood and early adulthood when levels and trajectories of health stocks become established ... The dominant causal direction may then reverse as health largely affects wealth among those age 50 and older” (Smith 1999). 33 For instance, Chile and Costa Rica were better able to reduce infant mortality and increase life expectancy compared to Korea and Taiwan (China) during 1960-1995, despite slower growth in Chile and Costa Rica. The success in improving the health conditions in Chile and Costa Rica can be attributable to policies aimed at disease control, health care, nutrition intervention, provision of safe water, and sanitary works in these two countries (McGuire 2001). In addition, at least for the under-5 mortality, simply increasing the public outlay on health may not be as effective as focused spending on improving access and quality of maternal and infant health care (McGuire 2006).

15
the Northeast region, the simple income picture is as follows. In 1999, Rio Grande do Norte, for example, has an average monthly household per-capita income of R$173, slightly above the northeastern average of R$155 but far below the national average of R$320 or the respective figure for São Paulo (R$437)<sup>3</sup> . In 1999, the headcount poverty ratio and the indigence poverty line, which is a low and “food only” poverty line, indicate that 39.7 percent of Rio Grande do Norte’s population is poor. This is below the average for the Northeast (44.3 percent), and the lowest in the region but nearly double the headcount ratio for Brazil (22.4 percent) and about 5 times the headcount ratio of São Paulo (8.6 percent). Maranhão is the poorest state and the poverty headcount counts 52 percent of the population.

The poverty line, which includes more than food, is a little more charitable but the figures reveal that 69.1 percent of the population in Rio Grande do Norte is below this poverty line. These figures are the basic motivation behind this paper, where we analyze poverty and determine factors that may contribute to it.

The aim of this paper is to enhance for the Northeast region the understanding of: (1) the evolution of poverty over the 1970s, 1980s, and 1990s; (2) the incidence, severity and profile of poverty; (3) the main contributing factors to poverty and the volatility of these factors in the 1980s and 1990s; and (4) the public policies and instruments that can be designed to assist the poor population.

Over the past decades, three major factors affected poverty in the Northeast and Brazil as a whole, namely: (1) changes in economic activity and macroeconomic stability; (2) reduction in the fertility rate; and (3) increased urbanization rate.

Macroeconomic instability has played a key role. Since 1985, six stabilization plans have been introduced. When the fifth stabilization plan, the Collor Plan of March 1990, failed, the Brazilian economy entered one of its worst recessions in history. Only after the most recent plan was introduced, the Real Plan in July 1994, did macroeconomic stability return and inflation dropped to a record one-digit low. A year after that, in May 1995, the minimum wage was raised about 40 percent, which had a marked effect on poverty. On the one hand, the progress in reducing poverty in the Northeast has been

> 3 Author’s calculations all through this section are based on PNAD 1999 data set. All income figures are in 1997 prices.

2
|Energy|SACU|Botswana|Lesotho|Namibia|Sout!<br>sien|Swaziland|Upper<br>Middle<br>Income|Lower<br>Middle<br>Income|Low<br>Ieee|SSA|
|---|---|---|---|---|---|---|---|---|---|---|
|auaaato ElectricityNetwork<br>(%o ofPopulation)|4|10|6|0|66|a0|37|32|3]|re|
|ar ReportingAccess to Electricity<br>(%o ofhouseholds)|65|.|.|.|65|.|74|84|33|4|
|Energy Use PerPPP GDP<br>*<br>(kg of oil equivalent/1000 PPP dollars, Constant 2000)|178|“|“|98|257|os|249|242|374|.<br>364|
|ayAverageahsPrices (UScents/k<br>Wh) -<br>(Residential/Non-Residential)|5/3|4/3|8/3|4/2|3/2|4/3|0/7|8/7|6/6|6/5|
|CommercialPerception ofElectricity service<br>(l=worst 7=best)|54|48|.|56|58|.|52|42|18|43|
|fcommercialPerceptionofPublicAgency ElectricityProvider<br>(l=worst 7=best)|6.0|57|.|63|6.0|.|53|50|40|43|
|oePowerTransmissionandDistribution Losses<br>(%of totaloutput)|80|7|7|7|8.0|.|140|15|0|190|

Note: Data used isfrom the latest observation available between 1997 and 2003, Source: Esiache, Antonio and Ana Goicoechea (2005). “A ‘Research’ Database on Infrastructure Economic Performance,” Policy Research Paper No. 3643, Infrastructure Network, Office of the Vice President, June 2005,
Next, we turn to two characteristics of the destination cells which are displayed in light blue in Figure 4. We examine the cell-level share of urban land in 1985 to capture whether urban centers that were already above the developing world mean in 1985 are those most affected We that cities located in more by drought.<sup>14</sup> find historically urbanized cells are particularly likely to grow due to drought. This is consistent with Glaeser’s (2014) observation that much of urban growth in the developing world has been concentrated in precipitously expanding mega-cities. On the other hand, cells that are characterized by relatively brighter night lights (again above the developing world mean) are more prone to see a lesser effect of drought.<sup>15</sup> The divergence between these two metrics is surprising because they should both capture the locations of pre-existing urban centers. One possible explanation is that urban growth in much of the developing world is taking place in informal settlements which are part and parcel of quickly growing mega-cities but which do not necessarily enjoy the necessary infrastructure that would reflect the population density via remotely sensed night lights. While we lack the required data to assess how drought impacts urban _form_ on a global scale, we provide some city-specific statistics on this question in the next section.

# **7 Beyond Grid Cells: Climate Change and Growing Cities in the Sahel**

To get a sense of how urban areas have been expanding in a region typically associated with an unfavorable climate risk profile, we combined information from World Settlement Footprint with data we collected for a related project to assess urbanization in the Sahel. A team of research assistants focused on four Sahelian capital cities (Ouagadougou in Burkina Faso, Bamako in Mali, N’Djamena in Chad, and Niamey in Niger) in an attempt to classify the relative proportions of informal and formal buildings. In each city, the team hand-coded several neighborhoods in order to train an algorithm that

> 14We again use mean rather than median because the majority of grid cells in 1985 did not contain any built-up pixels.

> 15The remotely sensed night light metric we use measures average nighttime light emission from the DMSP-OLS Nighttime Lights Time Series Version 4 (Average Visible, Stable Lights, & Cloud Free Coverages). For details, see: https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html.

21
|Business<br>**Table A6.**<br> **Individual and Household Outcomes wit**|Income an<br>**h Self-Emp**|d<br>CO<br>**loyment inte**|Outlook on<br>**ractions**|<br>Decision-|
|---|---|---|---|---|
|Knowledge|Assets|Cohesion|Life|Making|
|(1)|(2)|(3)|(4)|<br>(5)|
|Self-Employment (1=Yes)<br>-0.031<br>(0.042)|-0.015<br>(0.038)|-0.052<br>(0.032)|-0.048<br>(0.042)|-0.045<br>(0.149)|
|Business Training (1=Yes)<br>0.047|0.043|0.067**|0.081**|0.077|
|(0.037)|(0.027)|(0.030)|(0.034)|(0.110)|
|BT x Self-Employment<br>0.035|0.130***|0.078|0.068|0.074|
|<br>(0.058)|(0.049)|(0.050)|(0.056)|(0.209)|
|Lottery Winner (1=Yes)<br>0.023|0.048|0.071*|0.076*|0.088|
|(0.043)|(0.034)|(0.038)|(0.042)|(0.151)|
|LW x Self-Employment<br>-0.102|-0.014|-0.066|0.062|-0.351|
|(0.078)|(0.060)|(0.055)|(0.077)|(0.258)|
|BT and LW<br>0.048|0.016|0.052|0.125***|0.024|
|(0.045)|(0.036)|(0.042)|(0.048)|(0.158)|
|BT and LW x Self-Employment<br>0.048|0.147**|0.101|0.052|-0.182|
|<br>(0.072)|(0.060)|(0.064)|(0.072)|(0.270)|
|R-Squared<br>0.08|0.33|0.02|0.08|0.03|
|P-value of t - test of ...|||||
|BT = BT and LW<br>0.98|0.44|0.68|0.32|0.73|
|LW = BT and LW<br>0.62|0.43|0.70|0.34|0.72|
|BT = LW<br>0.60|0.88|0.94|0.91|0.94|
|BT + BT x Self-Employment = 0<br>0.09|0.00|0.00|0.00|0.40|
|<br>LW + LW x Self Employment = 0<br>0.24|0.51|0.92|0.04|0.25|
|<br>BT and LW+BT and LW x Self Employment=0<br>0.12|0.00|0.01|0.00|0.49|
|<br>Mean of dependent variable among controls<br>-0.02|0.05|-0.06|0.02|-0.10|
|<br>N. Observations<br>3494<br>Note: Self-employment is defined as a dummy that takes value 1 if all o<br>activities. The reported mean of the dependent variable is computed usi<br>assigned to be lottery winners.The dependent variables are aggregates o<br>definition of the aggregates. All regressions are estimated using OLS m<br>variables (eligibility for loan lottery, business ownership at baseline and<br>Standard errors reported in parentheses are clustered at the CO level. Th<br>significance at the 10 , 5, and 1 percent level, respectively.|3494<br>f household<br>ng CO mem<br>f standardiz<br>ethods and i<br>gender) and<br>e following|3494<br>income come<br>bers not offer<br>ed z-scores. S<br>nclude as cov<br>the Self-em<br>symbols *, *|3494<br>from self-em<br>ed business t<br>ee Appendix<br>ariates the st<br>ployment du<br>* and ** * d|3494<br>ployment<br>raining nor<br>B for a<br>ratification<br>mmy.<br>enote|
|**Table 3: Interaction ter**|**m results for C**|**ompetition an**|**d Dependenc**|**e of Sectors o**|**n Female Wor**|**kers**|
|---|---|---|---|---|---|---|
|Dependent variable:_Female_<br>_Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Exports (%, cell<br>average)*Herfindahl-Hirschman|0.772**<br>(0.390)|0.951**<br>(0.380)|||||
|Index|||||||
|Exports (%, cell|||-0.003**|-0.005***|||
|average)*Starting a Business|||(0.002)|(0.002)|||
|<br>(DTF)|||||||
|Exports (%, cell|||||0.418*|0.403*|
|average)*Female Dependence<br>of Sectors|||||(0.219)|(0.215)|
|Exports (%, cell average)|0.099*|-1.000**|0.398***|-0.573|0.009|-0.718|
||(0.053)|(0.504)|(0.126)|(0.509)|(0.092)|(0.449)|
|Herfindahl-Hirschman Index|-10.670***|1.930|-5.828|-5.922|-0.835|-1.032|
||(3.836)|(7.299)|(3.648)|(3.654)|(4.327)|(4.324)|
|Starting a Business (DTF)|0.076*|0.075*|0.120***|0.147**|0.070*|0.074*|
||(0.039)|(0.039)|(0.045)|(0.061)|(0.040)|(0.039)|
|Female Dependence of Sectors|||||48.382***|41.236***|
||||||(6.366)|(9.237)|
|Exports (%, cell average)*GDP||0.120**||0.116**||0.081|
|per capita (logs)|||||||
|||(0.055)||(0.055)||(0.053)|
|No. of workers||-4.336**|||||
|(logs)*Herfindahl-Hirschman|||||||
|<br>Index|||||||
|||(1.726)|||||
|No. of Workers (logs)*Starting<br>a Business (DTF)||||-0.004|||
|||||(0.014)|||
|No. of Workers (logs)*Female||||||2.199|
|Dependence of Sectors|||||||
|||||||(1.897)|
|No. of Workers (logs)|0.917***|1.318***|0.898***|1.166|0.977***|0.242|
||(0.291)|(0.332)|(0.292)|(1.038)|(0.308)|(0.638)|
|GDP per capita (logs)|<br>8.307|<br>5.893|<br>5.817|<br>3.360|<br>1.297|<br>-0.696|
||(5.356)|(5.480)|(5.669)|(5.718)|(6.179)|(6.428)|
|Age of Firm (logs)|-0.806**|-0.800**|-0.832**|-0.846**|-0.736*|-0.705*|
||(0.355)|(0.357)|(0.359)|(0.362)|(0.383)|(0.383)|

44
Figure 13. EMDE vulnerabilities now and during previous crises

# A. Government debt

<!-- Start of picture text --> A. Government debt B. Nonfinancial corporate debt<br>Percent of GDP Percent of GDP<br>90 Mean Inter-quartile range Current 60 Mean Inter-quartile range Current<br>80 50<br>70<br>40<br>60<br>30<br>50<br>20<br>40<br>30 10<br>20 0<br>t-2 t-1 t t+1 t+2 t-2 t-1 t t+1 t+2<br>C. Cyclically adjusted fiscal balance D. Current account balance<br>Percent of GDP Percent of GDP<br>1 Mean Inter-quartile range Current 2 Mean Inter-quartile range Current<br>0<br>0<br>-1<br>-2<br>-2<br>-3 -4<br>-4<br>-6<br>-5<br>-8<br>-6<br>-7 -10<br>t-2 t-1 t t+1 t+2 t-2 t-1 t t+1 t+2<br>E. External debt F. Short-term external debt<br>Percent of GDP Percent of reserves<br>100 Mean Inter-quartile range Current Mean Inter-quartile range Current<br>200<br>90<br>80<br>150<br>70<br>60<br>100<br>50<br>40 50<br>30<br>20 0<br>t-2 t-1 t t+1 t+2 t-2 t-1 t t+1 t+2<br><!-- End of picture text --> Source: International Monetary Fund, Laeven and Valencia (2018), World Bank.

Note: Crises are currency, sovereign debt, and banking crises as defined by Laeven and Valencia (2018). Horizontal axis indicates years. “Current” denotes unweighted averages for 2018 for government debt, total external debt, and cyclically adjusted fiscal balance; 2017 for short-term external debt to reserves; 2018Q3 for corporate debt in Argentina, Brazil, Chile, China, Colombia, Hungary, India, Indonesia, Malaysia, Mexico, Poland, Russia, Saudi Arabia, South Africa, Thailand, and Turkey; 2016 for corporate debt in Algeria, Malaysia, Peru, and Sri Lanka; and 2017 for corporate debt in 29 other EMDEs. Orange shaded area indicates the inter-quartile range of current observations. t = year of crisis.

- A. Mean and interquartile range based on 94 previous EMDE crisis events.

- B. Based on 31 previous EMDE crisis events.

- C. Based on 158 previous EMDE crisis events.

- D. Based on 295 previous EMDE crisis events.

- E. Based on 170 previous EMDE crisis events.

- F. Based on 136 previous EMDE crisis events.
highlighted the Russian exposure to the so called Dutch disease<sup>8</sup> - high dependence on commodities, rapid growth in wages and a slowdown in many manufacturing industries. Under such circumstances, portfolio managers that expect to provide better returns should diversify into international investments.

**The lessons learned from the experience of Kazakhstan are important to consider** . While the Kazakh government offers a guarantee on the real value of the contributions at retirement, the pension funds of the second pillar have provided negative rates of return on a systematic basis since inception in 1998. Pension funds are starting to pay pensions and government guarantees are being invoked. The Kazakh government has started making payments to pensioners to meet the difference between the real value of the pensions and the accumulated value of the contributions. Although on a different scale, Kazakhstan’s economy shares with Russia some common elements of Dutch disease symptoms. Kazakh pension funds have focused their investment strategy domestically (see Figure 1), which have not been able to offer positive returns. International portfolio diversification would benefit the contributors.  Although international diversification is not likely to bring short term benefits in terms of rates of return, it reduces the pressure on the exchange rate and provides a more stable source of retirement for future generations.

Figure 1 Asset Allocation of Pension Funds in Kazakhstan, January 2009 (as a percentage of Assets) <!-- Start of picture text --> 1,85%<br>8,96%<br>30,85%<br>36,49%<br>10,35%<br>1,65%<br>9,85%<br>Kazakhstan state securities (30,85%) Corporate securities of foreign issuers (10,35%)<br>Foreign state securities (1,65%) Shares of Kazakh issuers (9,85%)<br>Corporate bonds of Kazakhs issuers (36,49%) Deposits in local banks (8,96%)<br>Others (1,85%)<br><!-- End of picture text --> Source: AFN

**Countries with a dependence on natural resources need to define their investment strategy in a way consistent with the macroeconomic challenges of the country** . In the case of Norway, with the explicit objective of (1) fighting against Dutch disease; (2) enhancing the long term value of the assets; and (3)

> 8 Dutch disease is related to the fact that windfall revenues from natural resources give rise to real exchange rate appreciation, which in turn reduces the competitiveness of the manufacturing sector.

10