rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 3 Average Total Patient Stay In Ecuador, by Hospital and Type of Procedure<br>O Pre-Evacuation El Post-Evacuation<br>15<br>o 10 923<br>S 3.65 10.58<br>F.58 C____v_L_,___ 1.71<br>ECU1 (n=13) D&C ECU2(rl=11)MVA ECUS(n=.3) MVA<br><!-- End of picture text --> Figure 3 shows the average total patie.,t stay in each of the three Ecuadorian hospitals. At Hospital ECUI, the average total duration of stay for D&C patients was 9.23 hours. For the MVA patients at Hospital ECU2, the average total stay was 12.97 hours, and for the MVA patients at Hospital ECU5, only 1.71 hours.

At Hospital ECU1, pre-evacuation time for D&C patients was 5.58 hours, compared to 2.39 hours for MVA patients at Hospital ECU2, and 1.2 hours at Hospital ECU5, where MVA was done on an outpatient basis. Conversely, at ECU 1, women were discharged from the hospital an average of 3.65 hours after completion of the D&C procedure and 10.58 hours after the procedure at the MVA site (ECU2). Post-evacuation time at Hospital ECU5 was only 0.51 hours.

## **Cost of Treating Patients for Incomplete Abortion**

# _Kon_

Figure 4 and Table 2 show the average patient cost for D&C and MVA at four study sites in Kenya. At these sites, total cost per patient was lower for MVA than for D&C. At Hospital KENI, the average cost per MVA patient was $3.09 -- 23% less than the average patient cost of $3.99 for D&C. At Hospital KEN2, the average MVA patient cost was $5.24 -- 66% less than the average D&C patient cost of $15.25.

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# Appendix 4: Drop‐Out and Attrition

Table A4.1 shows that the firms that completed the interventions are similar on baseline characteristics to those which dropped out.

|**Table A4.1: Comparison of Basel**|**ine Characteris**<br>Indivi|**tics of Firms t**<br>dual Treatmen<br>|**hat Comp**<br>t<br>|**leted Interven**<br>Grou|**tions to Drop**<br>p Treatment<br>|**‐Outs**<br> <br>|
|---|---|---|---|---|---|---|
||Completed|Dropped<br>Out|p‐<br>value|Completed|Dropped<br>Out|p‐<br>value|
|Number of Employees|62.2|54.4|0.746|52.9|53.1|0.981|
|Small Firm (<=50 employees)|0.59|0.57|0.940|0.58|0.59|0.974|
|Medium Firm (>50 employees)|0.41|0.43|0.940|0.42|0.41|0.974|
|Cundinamarca|0.54|0.14|0.049|0.42|0.35|0.665|
|Valle|0.09|0.14|0.645|0.25|0.18|0.559|
|Labor Productivity|32|30|0.780|32|39|0.278|
|Financing Practices|48|50|0.730|53|52|0.855|
|Human Resources Practices|42|40|0.738|44|43|0.784|
|Logistics Practices|43|43|0.989|49|43|0.175|
|Marketing Practices|43|44|0.934|46|46|0.948|
|Production Practices|46|54|0.229|47|44|0.371|
|Level 2 Supplier|0.93|1.00|0.496|0.92|0.94|0.758|
|Metal Products|0.50|0.57|0.731|0.47|0.65|0.242|
|Plastic Products|0.15|0.29|0.390|0.19|0.24|0.738|
|Firm Age (Years)|23.3|21.8|0.829|20.9|24.6|0.375|
|Anexo K score|44.4|46.5|0.679|47.8|45.7|0.487|
|USD Sales in 2013|3158858|7547448|0.189|2767765|2469362|0.799|
|Export at all in 2013|0.43|0.29|0.465|0.47|0.41|0.687|
|Sample Size|46|7||36|17||

Table A4.2 compares the characteristics of those firms for which we have December 2017 sales and employment data to the attritors, and then shows the sample of non-attritors is reasonably well balanced on baseline characteristics.

54
enables us to build a time-varying market access specific for each state-sector combination.

Market access for state _i_ in sector _s_ in year _t_ is built by weighting each predicted market capacity, _M_<sup></sup> _jst_ , by the estimates of the corresponding bilateral trade costs, φ<sup></sup> _ijst_ . These weighted market capacities are then summed up to one single variable per state-sector pair: We sum over _R_ countries, where _R_ includes only foreign countries and not the Brazilian states. This way, market access exclusively captures the _foreign_ demand addressed to each Brazilian state.<sup>12</sup> Market access thus differs from predicted exports as it excludes the local supply capacity.

Our measure can be considered exogenous to bilateral migration rates since all effects of internal migration on the states’ exports (imports) are captured by the estimates of the export (import) capacities of the Brazilian states. These are however not included in our measure. By excluding the exporter fixed effects, we ensure that our measure is exogenous to all domestic factors that affect the state’s export supply capacity, such as its comparative advantage in sector _s_ , the local infrastructure or changes in the labor force.<sup>13</sup> By focusing on foreign market access we eliminate the possible reverse causality that can arise when immigrants raise local consumption and hence the local market capacity: a local shock inducing the arrival of additional migrants may increase consumption in the host region and thus domestic market access but does not affect the access to foreign markets.

> 12 To be consistent across sectors and years, each _MAis_ is constructed using the estimated market capacities and trade costs of always the exact same one hundred countries. These are the countries that import goods from all sectors in all years and thus provides us for all sector-year combinations with the necessary estimates for trade costs and importer fixed effects.

> 13 We present also robustness checks including the difference in the states’ exporter fixed effects as control variable

> ( ∆ _supply_ <sup>), to verify that our market access coefficient is not correlated to supply factors.</sup> _ijst_

11
engaging in greater levels of transactional sex during economic shocks. Wilson (forthcoming) finds that positive economic shocks in the form of the copper mining boom in Zambia result in lower levels of transactional sex. Oster (2012) links export activity on a country-level to higher rates of HIV, providing evidence that economic activity may generate increased movement of high-risk types like truck drivers.

Ultimately understanding the circumstances when transactional sex occurs and the scope of it has important policy implications. If transactional sex is being used as a riskcoping mechanism, than providing women with access to formal insurance or savings may have important public health implications.

The rest of the paper is structured as follows. We first describe the study and the data collected. We then proceed with the analysis and a discussion of the implications of our findings.

# **II. Study and Data**

# _RESPECT Study and STI Testing_

The data used to create the panel of women in our analysis come from the RESPECT study, which involved providing individuals in rural Tanzania cash transfers that were conditioned on testing negative for curable STIs.<sup>6</sup> In-depth details of the study design can be found in de Walque _et al._ (2012). The study took place in Ifakara, which is a rural town in the Morogoro region of Tanzania. HIV-prevalence in the region was estimated at 5.1% at the time of the study (DHS 2008). While we limit the analysis in this paper to women enrolled in the study, men also participated in the RESPECT study as well.<sup>7</sup> Ifakara is a Health and Demographic Surveillance Site, and the RESPECT study was able to recruit a random sample of all residents in the area that generated a representative sample of the general population in the area. The baseline survey was conducted in

> 6 Study participants were randomly assigned to either a control arm or one of two treatment arms which offered $10 USD and $20 USD respectively, conditioned on testing negative for curable STIs in each study round.

> 7 The relationship between negative shocks and STIs is negative for men; in other words shocks lead to lower rates of STIs for men.  However, virtually all of the estimates are imprecisely estimated, and we are unable to reject the null of no effect (Appendix Table A3).

5
**Figure 20: Datt-Ravallion decomposition: Benchmark vs. crisis scenario (% change)**

<!-- Start of picture text --> 4.0 3.70<br>3.45<br>3.5<br>3.07<br>3.0 2.79<br>2.5<br>2.0<br>1.5<br>1.0 0.66 0.63 0.74 0.54<br>0.5 0.20<br>0.0<br>National Urban Rural<br>Total change Growth Redistribution<br><!-- End of picture text --> Source: WB staff calculations based on data from ECH 2011 The results of the Datt-Ravallion decomposition are consistent with the Growth Incidence Curves – GIC – (Figures 21 and 22a and 22b). These figures show, for each percentile, the net income change of the year 2 crisis scenario relative to the benchmark. In all three areas, the mean growth rate (that is, the average growth rates across percentiles) is negative, between -2 percent and -14 percent. In rural areas, the effect of a crisis would be more evenly distributed among the different income percentiles.

**Figure 21: Growth incidence curve: Benchmark vs. crisis scenario in year 2**

<!-- Start of picture text --> 0<br>-2<br>-4<br>-6<br>-8<br>-10<br>-12<br>-14<br>-16<br>0 10 20 30 40 50 60 70 80 90 100<br>Annual growth rate, %<br><!-- End of picture text --> 19
# **4.3    Robustness Checks**

## _Coefficient Stability, E. Coli Contamination_

Annex Table 8 examines the robustness of the E. coli contamination coefficients found in specifications (1.2d), (I.2e), (2.2d) and (2.2e) following suggestions from Oster (2014) building on Altonji et al (2005) who previously developed a method to investigate the coefficients’ sensitivity to potential omitted variable bias. The estimates produced in Annex 6 are calculated using the Stata module _psacalc_ introduced in Oster (2014). Since the bounding method can only be implemented on linear models, linear probability model equivalents for each of the probit specifications found in (1.2d), (I.2e), (2.2d) & (2.2e) are estimated. The marginal effect, standard error and model R-squared (in square brackets) for each of the specification are reported in column (5).

First, as suggested by Oster (2014) we hypothetically increase each model’s R-squared in column (5) by a factor of 2.2 and examine the changes on each of the model’s coefficient. In other words, we are asking what would become of our coefficients of interest – E. coli contamination – if we include unobserved variables that increase the current R-squared by a factor of 2.2. If the coefficients estimated in column (5) (and by extension, column (2)) do not change signs or become zero, they are then considered to be robust to omitted variable bias.

The results are shown in columns (7) to (10). For all the specifications, the changes in the coefficients are not modest and not of concern. For example, if E. coli contamination is specified to be high at the household point, it reduces marginally from 13 percentage points to 12 percentage points for the age group 24-60 months and from 6 percentage points to 5 percentage points for the age group 0-60 months when we increase the model R-squared by a factor of 2.2. Furthermore, if E. coli contamination is specified to be high at the source point, the coefficients actually move away from zero for both age groups. This further indicates that the unobserved variables for these two specifications do not confound the effects of E. coli contamination at the source level. These coefficients were computed under the assumption that the unobservables were as important as the controlled variables already included in the model.

Column (11) presents the bias adjusted coefficients for each specification under the assumption that the unoservables are 1.5 times as important as the observables - a situation that is unlikely given that we have extensively included variables that have been found to be important predictors of both 17
telecommunications, which were normalized by the total labor force. We do this to adjust for the wide disparities in size across the countries in our sample.<sup>6</sup> Table 1 reveals a big gap between industrial and developing regions in terms of road density. Latin America and the non-LAC MICs have fallen further behind rich countries and East Asia since 1990. Moreover, while Latin America was roughly on par with non-LAC MICs in 1981-5, by 2001-5 its road density has barely grown and, as a consequence, it is now well below that of middle-income countries, and even further below East Asia’s. Across sub-regions, road density is below the LAC-wide norm in the Andean countries and in the Southern Cone. In contrast, road density in the Caribbean is similar to that of East Asia.

# **2.2 Infrastructure quality**

Broadly speaking, the quality of infrastructure services can be proxied by two types of information: (a) officially-recorded data on quantitative measures of the quality of infrastructure services, and (b) surveys of experts or final users regarding the performance of infrastructure services —typically qualitative in nature.

The availability of officially-recorded statistics on infrastructure quality is scarce relative to that on its quantity. This is particularly problematic in the case of telecommunications. Cross-country data on the telecommunications quality indicators that on conceptual grounds should be most informative —such as the frequency of telephone faults and unsuccessful calls— are extremely sparse. Thus, we complement this information showing data on the waiting time for installation of main lines, which in theory is a measure of excess demand, but in practice shows a significant correlation (see Calderón and Servén 2004b) with the theoretically-preferable measures just cited, over the reduced sample for which the latter are available. Information on waiting times can be collected for a fairly large sample of country-years.<sup>7</sup>

> 6 Using arable land area rather than total area leads to very similar rankings. For this exercise, we exclude Singapore and Hong-Kong from the set of East Asian comparator countries, in view of the particular physical characteristics (small area and very high population density) of both city-states. 7 Empirically, over the samples for which information is available, the waiting time for installation of main lines is associated with the frequency of telephone faults (the simple correlation is 0.19) and the percentage of phone faults cleared the next day (-0.18). Moreover, these correlations are statistically significant.

6
- 2 -

relationship between households income and their nutritional and health status. This paper presents what in our opinion is striking new evidence on these interactions and suggests a framework for the analysis of the interactions between income transfers, the publicly provided inputs, and the household's response, as reflected in the effect on nutrition and health. Such framework can be applied in the evaluation of public policies designed to reduce malnutrition (and improve the health status) among the poor in urban and rural areas.1

# On the Relationship of Nutrition and Income: The Statistical Evidence

In the last fifteen years, a growing body of literature has examined the interaction between nutritional status in developing countries and nutritional intake and household income, and has made recommendations on how to improve nutrition based on parameters from empirical studies of food expenditure systems at the household level. Recent surveys of such studies include Behrman and Deodalikar (1988), Alderman (1989), and Schiff and Valdes (1990b). It is found, however, that the policy recommendations derived from the analysis depend critically on the definition of nutrition and, more broadly, on the conceptual framework used. In particular, critical elements of the pathway from changes in income to its effect on nutritional status are still questioned. Showing that nutrient intakes are unresponsive to changes in income even at very low income levels such as rural South India, recent studies have questioned whether developing country nutrition will improve with income (for example in Behrman and Deodalikar 1987).

While not attempting to survey this vast field, which is already well documented in Behrman and Deodalikar (1988), it is useful to highlight some fundamental findings on which there is a fair degree of agreement. For a small number of food aggregates, estimates from food expenditure systems obtain income elasticities of nutrients (e ) somewhat smaller than but not significantly different from 1.0. Thee are derived indirectly by estimating the income elasticities of food expenditures (also close to 1.0) and by assuming constant nutrient-tofood conversion factors.

More recently, in a re-estimation of these demand systems, such as for example for rural South India, Behrman and Deodalikar (1988) find that the expenditure income elasticity of food for the six food aggregates (120 foods grouped into 6 aggregates) is close to one. However, the elasticity is significantly lower for the more disaggregated food groups. For the major nine nutrients, the income elasticities En are close to and not significantly different from zero. Cross-country eistimates confirm the microeconomic results regarding the 1 Malnutr;tion is a multifactorial issue and there are general deficiencies oc nutrients (calories, proteins) and specific deficiencies (iron, iodine, etc.). Although emphasis is given to the former type, this paper also addresses some measures to deal with the latter.
- fear that a creditor might seize the savings of a delinquent debtor;

- fear that a former spouse might seize the savings of an individual behind on his childsupport payments;

- fear that welfare eligibility would be threatened by a substantial account balance or by a history of deposits from under-the-table earnings;

- a desire to hide earnings from the tax authorities; or

- fear that a bank might report suspected illegal immigrants to the Immigration and Naturalization Service or that the INS could use bank records to discover their presence.

- Interestingly, the least important reasons people give for being unbanked are the hours and locations of bank branches.

The OCC survey was typical of other surveys in the way that it investigated why people did not to have bank accounts. In the OCC survey, if a person did not have an account, the surveyor read a list of possible reasons to explain the lack of an account and asked the respondent to choose the main reasons that applied to him or her. For our purposes, however, there are two problems with the data. First, the most common reasons found in other surveys – the person did not want an account because he or she had no savings – was not included as a possible choice. Second, the public data set excluded the responses of people who selected such reasons as: the bank would not let them open an account, their bank account could be frozen by a creditor, or they thought that they would need a Social Security card to open an account.

Despite these shortcomings, the OCC data are still broadly consistent with the findings of other surveys. As shown in Table 8, the two most common reasons that people cited from this list of possible reasons are the lack of money necessary to open an account and bank fees. But 58 percent of the unbanked indicated that none of the listed reasons were important to them. This is consistent with the finding from other surveys where the most common reason people give for not having an account is that they do not have any savings, a response that was not possible in the OCC survey. Interestingly, in the OCC survey 77 percent of the unbanked said that they were aware that some banks had “basic” checking accounts with low minimum balance requirements, low fees, and a small number of monthly free checks. And 63 percent of the unbanked indicated that they had never had a bank account. This is much higher than is commonly found in other surveys, and may reflect the much higher percentage of Hispanic individuals in the sample areas compared to the sample areas of other surveys.

22
**Figure 8. Income and Urban Apartment Tenure in Romania**

<!-- Start of picture text --> Romania<br>35<br>30<br>25<br>Ow ners<br>20<br>Public Rental<br>15<br>Private Rental<br>10<br>5<br>0<br>1 2 3 4 5<br><!-- End of picture text --> _Source :_ Household surveys (Table 1)

# **Demand Changes—Spatial and Social Mobility**

# **_Spatial Mobility and Labor Markets_**

# _Spatial Mobility / Migration_

Does the emerging rental sector in transition economies play the role in migration and labor market adjustment that it conceptually should play? Do the small rental sector sizes observed for some countries bar labor market adjustments and hamper economic growth? Mainstream migration research for transition countries continues to suffer from the weak linkage between the traditional labor market and the hypotheses formulated by the new spatial economics. Migration analysts invariably test for relative wages and unemployment factors but give little consideration to the effects of factors in the non-tradable goods sector. However, some of the analyses for transition countries hold indirect clues to the relevance of the housing sector.

Fidrmuc (2002), for example, finds for Poland, the Czech Republic, Slovakia, and Hungary that net migration between regions has been far less responsive (elastic) to inter-regional wage and unemployment differentials than expected under traditional labor market models. This suggests the presence of some threshold costs of migration that could include relative housing costs.

Kallai (2003) analyzes interregional migration patterns for Romania with explicit consideration of the costs of migration. She observes an unexpected positive correlation between migration and the regional income level for low-income groups. As in the Fidrmuc study, this suggests a cost threshold for migration that distorts labor market adjustment. The differences in income distribution in Romania are stark (Figure 9): the typical monthly rent of a Bucharest apartment, 33
centers in Peru and cocaine processing laboratories in Colombia. On the other hand, in Colombia, after the demise of the Medellin and Cali cartels by the middle of the 1990s, the Fuerzas Armadas Revolucionarias de Colombia (FARC) and the Autodefensas Unidas de Colombia (AUC), their historical origins as leftist guerrillas and right-wing paramilitaries notwithstanding, started to get increasingly involved in the production and commercialization of cocaine to finance their insurgent activities against each other and against the Colombian state.<sup>14</sup> As a result, coca cultivation reached its highest levels ever recorded in Colombia (about 163,000 hectares) by 2001. The response of the Colombian government to the large increase in coca cultivation was the implementation of Plan Colombia in 2001, the official name of a multi-year, comprehensive strategy designed and implemented to bring about lasting peace by reducing the production of illegal drugs. As a result, from 2000 to 2003 coca cultivation in Colombia decreased by more than 30%, whereas in Bolivia and Peru it remained relatively stable. According to the latest UNODC and ONDCP reports, total coca cultivation in the three producer countries has remained relatively stable in the last three years reported (Figure 2 (a) and (b)). Nevertheless, ONDCP ́s figures should be handled with care as they expanded by 81% the size of the landmass that was imaged and sampled for coca cultivation and, when the new areas covered are taken into account, there is an increase of 39,000 has. cultivated with coca.<sup>15</sup> As ONDCP claims in a recent press release, “Because these areas were not previously surveyed, it is impossible to determine for how long they have been under coca cultivation...The higher cultivation figure in this year’s estimate does not necessarily mean that coca cultivation increased in the last year; but rather reflects an

> 14 See Rangel (2000) and Grossman and Mejía (2008).

> 15 Figures 1 and 2(b) present ONDCP ́s estimates of total coca cultivation preserving the sample fixed, that is, they do not take into account for 2005 the 81% increase in the fields surveyed.

9