rafmacalaba/datause-encoder-data huggingface dataset:
that in principle could be conducted onsite, make a diagnosis, and prescribe a course of treatment and follow-up management plan.

For this study, we identified the lowest common denominators across the countryspecific protocols to define comparable criteria across countries for diagnostic and treatment accuracy for each of the conditions. This methodological choice implies that knowledge is assessed at a lower standard than more stringent World Health Organization (WHO) protocols. We discuss cross-country variation in country-specific protocols in Appendix S6. Based on the responses to each vignette, we generated two binary variables: diagnostic accuracy and treatment accuracy, which are the percentage of respondents who provided an accurate diagnosis and – separately – an accurate treatment for a given condition. Both outcomes should be viewed as measuring the minimum required knowledge to prescribe correct diagnostic and treatment services. (See Table S7 for information on the coding of the two knowledge scores.) These outcomes do not represent the ideal level of knowledge; for example, they do not account for co-morbidities.

To quantify whether providers are available to provide medical service when patients seek care, we followed a standard procedure in which enumerators visited each facility twice.<sup>25</sup> During the first visit, information on all staff working at the facility, by professional cadre, was collected. Provider availability does not, of course, fully capture provider effort, but it is a necessary condition for treating patients. (See Section S8 in the appendix for details on the selection of staff, drugs, and equipment for surveys.) Vignettes were administered to a random sample of doctors, medical assistants, nurses, and nurse-midwives (i.e., any staff that provide outpatient or maternity services) who were present in the facility on the day of the first, announced visit. The first visit also included a visual inspection of drugs and equipment. A few days after the first visit, enumerators made a second, unannounced visit to the facility and assessed the presence of a randomly pre-selected list of providers (or all 7
Finally, in Appendix Table B4, we perform an additional robustness check concerning the validity of our instruments. We use specifications similar to our IV regressions in the previous tables, but also include control variables for the log values of the regional unemployment rate and the average monthly income per capita in every administrative region.<sup>18</sup> These robustness checks largely refute that the impact of our instrumental variables is due to regional differences in living standards. All financial literacy effects remain large and statistically significant, with the only exception being the effects in the spending regressions, where the coefficients become smaller in magnitude and statistically insignificant. This is indeed the variable that is likely to be affected the most by regional living standard differences. Hence, the results confirm the robustness of our instruments, and the magnitude of the majority of the effects remains high and statistically significant.

In unreported regressions, we use self-assessed financial literacy (on a scale from 1 to 5) in place of the financial literacy measures used so far. Our results prove robust to the use of this measure and are available upon request.

# **6. Conclusion**

Our study contributes to the literature on financial literacy by examining its effects on both financial and real behavior in a relatively understudied context, that of an emerging market experiencing a financial crisis. We find that financial literacy is significantly related to greater participation in formal financial markets and negatively related to the use of informal sources of borrowing. Moreover, individuals with higher

> 18 The data is available from the Russian Federation Federal State Statistics service at: http://www.gks.ru/bgd/regl/b10_06/IssWWW.exe/Stg/1/17-01.htm

28
services such as sewers and drains that amplify the negative impacts of small scale hazards. Section 5 summarizes the main conclusions.

# **2. Why disaster risk management in cities is different**

Why treat urban disaster risk separately from disaster risk in general? Many of the concepts and lessons relevant to disaster risk reduction apply generally—in rural, periurban and urban areas. But some issues are specific to cities. Most importantly, as they increase in size, more and more people and assets will be exposed to natural hazards in dense urban areas. This density, of people and economic activity, not only changes the risk equation, it can also change the economics of disaster risk reduction strategies. This section summarizes what is special about disaster risk in cities. It then discusses the lessons learned from the ‘new economic geography’ literature—also reflected in the World Bank’s 2009 World Development Report—in the context of natural hazards.

## **Disaster risk in cities is large and increasing**

Global disaster damage statistics are not classified by urban versus rural location. We therefore have no concrete empirical evidence whether natural disasters have more severe impacts in urban compared to rural areas. This will depend on factors such as a country’s overall urbanization rate, the type of natural disaster studied and the measure of damage considered. A drought or flood in a rural area may cause less direct damage than an urban earthquake or hurricane, but may have equally severe consequences on people’s livelihoods. In the following paragraphs we discuss specific characteristics of hazard risk in urban areas. A core message is that in a rapidly urbanizing developing world, the growth of population and economic assets in cities will lead to a rapidly increasing concentration of hazard risk in urban areas.

## _Geography_

The interplay of economic and physical geography is one major reason for high hazard risk in urban areas. Many cities have historically emerged at a location with good 3
sector, and city. In contrast, more recently initiated programs for residents (the self-employed and those who do not work, including the dependents of workers) are voluntary. Employer-based pension and unemployment insurance reforms were initiated earlier, and enjoyed significant participation from the early 1990s. As national policy focused on implementing social insurance programs after 2000, growth in participation for all types of insurance accelerated. Among the five social insurance programs (pension, health, unemployment, disability, and maternity), pension programs have had the largest number of urban participants, 194.0 million in 2010, which accounted for 55.9 percent of the employed urban population (Appendix Table A.1).<sup>11</sup> Participants in urban medical insurance programs have grown fastest over the last decade, from 12.4 percent of the urban population not covered through an employer in 2000 to 51.3 percent in 2010. Although the number of participants in unemployment insurance has grown, the coverage rate among urban employees has fallen from 43.4 percent in 1993 to 38.6 percent in 2010. Nearly half of urban employees were covered by work injury insurance by 2010, and maternity insurance, which only benefits women, reports a coverage rate of 35.6 percent of urban employees in 2010.<sup>12</sup> As coverage has expanded, benefit levels provided by the main social insurance programs have also increased. Pension and unemployment insurance benefits have been adjusted upward as the cost of living and household incomes have risen. Figure 1 shows a 7.9 percent real annual growth rate in the average pension benefit over time, with average pensions rising from 3,179 to 10,684 yuan from 1994 to 2010. Average unemployment insurance benefits increased more sharply, from 258 yuan to 2076 yuan, or a real annual growth rate of 13.9 percent. Benefit levels for health insurance, work injury and maternity also increased over time, with the reimbursement rate for urban resident health insurance increasing to 60 percent in 2010, and the reimbursement cap increased to 6 times the average annual wage of local urban workers or average annual disposable income for urban residents, whichever is lower. While the rapid expansion in health insurance coverage is quite positive, a reimbursement rate of only 60 percent and caps on coverage still leave urban households exposed to considerable financial risk associated with unexpected illness.<sup>13</sup>

> 11In the body of the paper we cite administrative data from the National Bureau of Statistics (NBS) and Ministry of Human Resources and Social Security (MOHRSS), and include more detailed tables and figures compiled from source data in Appendices.

> 12As employment rates of women under 40 are almost the same as men, this implies that a high share of women with formal sector employment receive maternity coverage.

> 13It should be noted that average reimbursement rates are only a partial indicator of generosity. The mix of exclusions, deductibles and ceilings vary considerably across jurisdiction, and contribute to a far more complex picture of the urban health insurance system.

6
# **Appendix C: Self-reported technical and comprehension difficulties**

Table C1: Debriefing Survey on Technical Issues with ACASI Module

||(1)|(2)|
|---|---|---|
||Mean (=|1 if yes)|
||Malawi|Liberia|
|Was the audio loud enough to hear?|0.99|0.98|
|Was the audio speaking speed okay?|0.99|0.98|
|Observations|866|616|

Note: Questions were asked only to those in the ACASI treatment group (i.e., the FTFI group did not get these questions).

Table C2: Relationship between Reporting Technical Difficulties and Passing Screening

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||=1|if passed|SI screeni|ng<sup>a</sup>|
||Ma|lawi|Lib|eria|
|=1 if said:|||||
|audio loud enough to hear|0.203||-0.043||
||(0.143)||(0.127)||
|audio speaking speed okay||0.016||-0.044|
|||(0.220)||(0.127)|
|R-square|0.002|0.000|0.000|0.000|
|Outcome mean when said no|0.42|0.60|0.75|0.75|
|Observations|866|867|616|615|

Note: Standard errors clustered at individual level are in parentheses. ***, **, and * represent significance at 1%, 5%, and 10%, respectively. a Passing threshold is choosing “yes” for the first five questions in Table 2.

33
In sum, the steady state of the model is unique and qualitatively different from the equilibrium of a frictionless model. It exhibits persistent wage differentials in favor of larger industries, produces more evenly-sized industries than a frictionless model, and does not maximize GDP. Now, we turn attention to the whole time-path of adjustment.

# **4 Dynamic Adjustment**

## **4.1 Preliminaries**

It is in analyzing dynamics and welfare effects along the transition path that the tools of duality are useful for this model. Here we will see how. First, we will show the optimization problem that equilibrium solves (as noted above, it is not maximization of GDP). In the next subsection, we will see how that establishes basic dynamic properties, and in the next section we will use it to derive results on distributional effects along the transition path.

A general rule for labor allocation in this model could be characterized by two functions. The function _d_<sup>_XY_</sup> ( _L_<sup>_X_</sup> _, μ_<sup>_X_</sup> ) gives the probability that a worker in _X_ will move to _Y_ in the current period, given the current stock _L_<sup>_X_</sup> of workers in _X_ and the worker’s idiosyncratic cost _μ_<sup>_X_</sup> of moving from _X_ to _Y_ . The function _d_<sup>_Y X_</sup> ( _L_<sup>_X_</sup> _, μ_<sup>_Y_</sup> ) gives the probability that a worker in _Y_ will move to _X_ in the current period, given _L_<sup>_X_</sup> and the worker’s idiosyncratic cost _μ_<sup>_Y_</sup> of moving from _Y_ to _X_ . These functions define a feasible allocation rule if and only if _d_<sup>_ij_</sup> _∈_ [0 _,_ 1] over the whole domain. For any given _L_<sup>_X_</sup> 0<sup>,thesefunctionsinduceasequence</sup> _L_<sup>_X_</sup> _t_<sup>for</sup><sup>_t_= 1</sup><sup>_, ∞_.The following proposition is proven (in a more general form) as Proposition</sup> 1 in Cameron, Chaudhuri and McLaren (2007):

**Proposition 6.** _Any equilibrium maximizes:_

_within the class of feasible allocation rules d_<sup>_XY_</sup> _and d_<sup>_Y X_</sup> _, subject to L_<sup>_X_</sup> 0<sup>_andLY_</sup> 0<sup>_given,and_</sup> _with L_<sup>_i_</sup> _t_<sup>_determinedbythechoiceofthedijfunctionsfori_=</sup><sup>_X, Y ,t >_0</sup><sup>_._</sup> 19
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 29. Sudan’s current infrastructure spending is average by African standards**

<!-- Start of picture text --> SSA<br>LIC-NoFragile<br>COMESA<br>MIC<br>LIC-Fragile<br>Resource-Rich Capex O&M<br>Ethiopia<br>Kenya<br>Uganda<br>Botswana<br>South Sudan<br>South Africa<br>North Sudan<br>Congo, Dem. Rep.<br>% of GDP 0 2 4 6 8 10 12 14 16<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009. _Note:_ Values excludes irrigation. LIC = low-income country; MIC = middle-income country; COMESA = Common Market for Eastern and Southern Africa; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M = operations and maintenance; CAPEX = capital expenditure.

The largest single financier of infrastructure investment is China, followed by Indian and Arab investors. Together they account for 40 percent of total annual investments, almost entirely allocated to the power sector. China’s partnership with Sudan in infrastructure development dates back to 2001, with cumulative total financing commitments on the order of $2 billion in 2010. Sudan benefits from a $3 billion framework agreement signed in December 2007, one of the largest to have been accorded to any African country to date. Chinese projects are providing some 2,200 MW of new thermal power generation, and the country is responsible for the now-completed 1,250 MW Merowe Dam and associated 1,800 km transmission line. China has also been involved in the reconstruction of some 1,500 km of roads (table 20).

Sudanese federal and state governments and state-owned enterprises (SOEs) account for an additional third of Sudan infrastructure investments, and have focused on transport and to some extent power. The remaining capital funding comes from the private sector, which, as in most of the rest of Africa, supports the ICT boom. ODA flows have played a very small role in capital infrastructure financing.

Capital investments in Sudan are heavily skewed toward the power sector, much of this funded by China. Investment levels in ICT and transport are comparable

**Table 20. Identified infrastructure projects financed by non-OECD financiers**

|US$million|Project<br>costs|
|---|---|
|China|949|
|Construction of 300 MW gas-fired power plant in Al-Fulah|518|
|Construction of the El-Gaili Combined Cycle Power Plant, Phase 1|150|
|NEC transition line|81|
|White Nile electricity network|200|
|China and Arab countries|1,200|
|Construction of the Merowe hydroelectric dam (1,250 MW)|1,200|
|India|475|
|Setting up 4x125 MW Kosti Combined Cycle Power Plant|475|
|**Total**|**2,624**|
|_Source:_Foster, etc (2009). Calculations based on World Bank-PPIAF<br>project database. (http://ppi.worldbank.org), in current $ millions .|Chinese|

> <sup>41</sup>
With fixed effects for the country and the year (Table 4), the results of regression on temperature and precipitation as the independent variables showed temperature to be significant at the 95% confidence level. Somewhat strangely, in this case the regression coefficient was negative, which is opposite in direction to the effect on per capita GDP growth. When the WASP(-1) variable is included with temperature, the WASP variable is significant at the 95% level while temperature is no longer significant. The sign of the regression coefficient for the WASP(-1) variable is again negative, which is consistent through all model specifications. When the WASP(+1) is included with WASP(-1) and temperature, none of the variables retain their significance. This is the result reported in summary Table 1.

**Table 4: Agricultural Value Added with Country and Year FE’s**

Dependent variable: Agricultural Value Added

||(Precip)|(Temp)|(P,T)|(W+1)|(T,W+1)|(W-1)|(T,W-1)<br>|(T,W-1,W+1)|(W-1,W+1)|
|---|---|---|---|---|---|---|---|---|---|
|Precip|0.01<br>(0.00)*||0.01<br>(0.00)|||||||
|Temp||-0.45|-0.44||-0.15||-0.13||0.13|
|||(0.19)*|* (0.19)**||(0.26)||(0.26)||(0.33)|
|WASP_minus1||||||-1.19<br>(0.47)*|-1.17<br>* (0.48)**|-0.83<br>(1.04)|-0.85<br>(1.04)|
|WASP_plus1||||0.55<br>(0.54)|0.54<br>(0.54)|||0.60<br>(1.17)|0.61<br>(1.17)|
|Observations|4286|4286|4286|2360|2360|2523|2523|1418|1418|
|Countries|171|171|171|166|166|167|167|120|120|
|R-squared|0.152|0.153|0.153|0.155|0.155|0.188|0.188|0.177|0.177|

Notes: Columns represent model specifications. Standard errors are clustered at the country level. Standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. P = Precipitation; T = Temperature; W+1 = WASP(+1); W-1 = WASP(-1).

In the case of fixed effects for country only (Table 5), here temperature is the one significant climate variable through all specifications. Again, the regression coefficient sign is negative, which is not consistent with the per capita GDP growth results. This result may be caused by an increasing trend in temperature and a generally decreasing percent of GDP that agriculture plays in most economies. For this 16
Figure 4: Effect of Extended Training on Perceived Malaria Risk, by Home RDT Result <!-- Start of picture text --> Basic Training Extended Training<br>4<br>3<br>2<br>1<br>0<br>RDT Negative RDT Positive<br>Coefficient and p-value on Provider Training x RDT result difference-in-difference is 0.516 (p=0.097).<br>Perceived Malaria Risk (Out of 5)<br><!-- End of picture text --> _Notes:_ Data from ex-ante patient predictions of their RDT test results at home survey. Patients were asked to score risk on a scale of 1 to 5, with 1 being definitely malaria negative, 2 being malaria negative more likely, 3 being positive/negative equally likely, 4 being malaria positive more likely, and 5 being definitely malaria positive. The graph shows average patient scores by provider training arm and actual home RDT result.

36
<!-- Start of picture text --> 3000)<br>2509} Period 2<br>2000}<br>1500 1<br>i\<br>1000 \<br>\<br>500<br>N<br>b va =<br>-4 -3 -2 -1 i 1 2 3 4<br>Domestic Interest Rates (%)<br><!-- End of picture text --> <!-- Start of picture text --> 4500 = ores:<br>4000) ———= Period 2<br>3500]<br>3009]<br>2500)<br>2000<br>1509]<br>1000]<br>500]<br>.<br>20-15 0-1 -05 t) os 1 15 2<br>Foreign Interest Rates (%)<br><!-- End of picture text --> <!-- Start of picture text --> 5000<br>. [ae<br>4500 Period 2<br>4000<br>3500]<br>3009]<br>2800<br>2000<br>1500<br>1000}<br>500<br>0 -20D ~10 0 10 20 Ki)4<br>Depreciation (%)<br><!-- End of picture text --> <!-- Start of picture text --> 4500)<br>HEB Perio 1<br>4000 Period 2<br>3500<br>3000<br>2500}<br>2000}<br>1509]<br>1009] \ i) |<br>500<br>0 -30 -20 10 0 10 20 30<br>GDP Growth (%)<br><!-- End of picture text -->

<!-- Start of picture text --> os Period 1<br>045<br>o4 .<br>«Sox° &~ ><br>93<br>2<br>20<br>02<br>ow<br>on4 ao ae MD A A OO ae<br><!-- End of picture text --> <!-- Start of picture text --> Period 1<br>o42- —— + t +<br>i : +<br>oa —— | £ ;<br>» 0 i! ! 2 ;, +; ¢<br>Ps& 036 —), L !1 a+<br>B03 = 1<br>3 } i<br>5 022 1 =<br>fa} 03 1' 1<br>os aq. 111<br>ae<br>02 ~<br>am as a0B aur au as<br><!-- End of picture text -->

<!-- Start of picture text --> Period 2<br>0s<br>045<br>o4 = =<br>8° ox —= =<br>« =><br>ra SS<br>88Vacs03 SS<br>02<br>ow<br>om ao ae 2 Ae OO a<br><!-- End of picture text --> <!-- Start of picture text --> Period 2<br>= +<br>o4 » +<br>H = +<br>c ' * H<br>' H + +<br>8os as 1i i1 psT<br>« r i i<br>o os ! ; ot<br>rc]3; ib. -1| | | 1<br>* 1 7<br>+ i1<br>02 + 1<br>i<br>am 76 aaB aur ae as<br><!-- End of picture text -->
<!-- Start of picture text --> WORLD: HIC* - | 24 % 43 % ( 260.49 M jobs)<br>Costa Rica - | (ss 19% 38 % ( 0.84 M jobs )<br>Uruguay - | se 21% 37 % (0.6 M jobs )<br>Brazil - Me (18% 22% 37 % ( 37.02 M jobs )<br>Ciie- (Ps 20 % 37 % ( 3.29 M jobs )<br>Dominican Republic- (iiZN 18% 36 % ( 1.8 M jobs )<br>Montserrat- [TZ 19% 35 % ( 0 M jobs )<br>Mexico - Me 4% © 18% 35 % ( 20.48 M jobs )<br>Colombia - He 204% 18% 35 % ( 8.24 Mjobs)<br>Suriname - Hs 4 13% 33 % ( 0.08 M jobs)<br>Guatemala - | eo 21% 31 % ( 2.27 M jobs)<br>Belize- (ZN 15% 31 % ( 0.05 M jobs )<br>El Salvador - He 18% 15% 31 % ( 0.87 Mjobs)<br>Peru- HE 832 18% 14% 31 % ( 5.54 M jobs)<br>Panama- ENi 15% 31 % ( 0.6 M jobs)<br>Grenada - | a 14% 31 % ( 0.01 M jobs)<br>Honduras - me 8% 19% 29 % ( 1.28 Mjobs)<br>Guyana - He 212% 14% 29 % ( 0.08 M jobs )<br>Nicaragua - BE 0%» 17% 28 % ( 0.87 M jobs )<br>Bolivia - Be 32 12% 13% 27 % ( 1.59 M jobs )<br>Ecuador - BE WKH 14% 27 % ( 2.28 Mjobs )<br>Barbados - iz 3h 14% 26 % ( 0.03 M jobs )<br>0 20 40 60<br>Share of total employment (%)<br>| Automation Potential Hl Augmentation Potential The Big Unknown<br><!-- End of picture text -->
administrative expenses to total assets (proxy for input price of equipment/fixed capital). Z is a matrix of controls including the ratio of equity to total assets, the ratio of net loans to total assets, and the logarithm of assets (to control for bank size effects). D is a vector of year dummies. Finally, αi denote bank-level fixed effects.<sup>1</sup> The H-statistic equals β1 + β2 + β3.

To verify the condition of long-run equilibrium, the following regression is estimated: Ln(ROAit)= αi + β1 ln(W1,it) + β2 ln(W2,it) + β3 ln(W3,it) + γ ln(Z,it) + δD + εit (2) where ROA is the pre-tax return on assets. Because ROA can take on negative values, we compute the dependent variable as ln(1+ROA). We define the equilibrium E-statistic as β1 + β2 + β3 from equation (2). The test of long-run equilibrium involves testing whether E=0.

We calculate the H-statistic for Jordan over three periods. Using Bankscope data, we estimate the H-statistic for the period 1994-2006 and for the sub-periods, 1994-2001 and 20022006. For every period, we report the p-value for the test of long-run equilibrium. If p-values are larger than 0.05, then we can accept the null of long-run equilibrium.

Results on Table 6 indicate that the Jordanian banking sector operates under monopolistic competition. The H-statistic for Jordan over the period 1994-2006 is 0.19 and we are able to reject the null that the H-statistic is 0 or 1. Comparing the H-statistic for the period 1994-2001 with the period 2002-2006, we see that the degree of competition in Jordan appears to have declined over time, since the value of the H-statistic dropped from 0.34 to 0.19. Note that we are able to accept the null of long-run equilibrium, indicating that the H-statistics are valid, irrespective of the sample considered.

The Jordanian banking sector appears to be less competitive than that of most comparator countries.<sup>6</sup> In particular, the H-statistics for Israel, Lebanon, and Morocco exceeds those obtained for Jordan both during the most recent period and over the entire 1994-2006 period (see Table 6). Furthermore, a cross-country analysis, detailed below, including 91 countries (67 of

> 6 Additional tests, not shown but available upon request, indicate that the confidence intervals for the H statistics for Jordan include lower values of H relative to the other countries. Furthermore, at 5% significance we can reject the null that the H-statistics for Jordan are the same as those for Lebanon and Israel, the countries with the most competitive systems according to their H-statistics.

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