rafmacalaba/datause-encoder-data huggingface dataset:
the November 2011 round, there was no statistically significant difference between the two groups. We also test for whether the baseline characteristics among _non-attriters_ —namely, those respondents present during the third survey round—differ by treatment modality (table 1, columns 5–8). There are no statistically significant differences between _cash_ and _voucher_ households who remained in the sample during the third round, with the exception of the number of months of adequate household provisioning.<sup>18</sup>

# Table 1 about here

While these results suggest that there is not differential attrition, we might simply be underpowered to detect an effect. Appendix S3 thus shows the determinants of drop-out. Overall, baseline characteristics do not individually or jointly predict attrition in the third survey round. The sole exception is marital status: Married respondents were more likely to drop out, suggesting that perhaps single parents were unable to leave.<sup>19</sup> Overall, however, these results suggest that attrition was primarily driven by the random violence and attacks prevalent in the region, rather than individual household characteristics or the transfer modality. Nevertheless, we construct Lee bounds for the primary outcomes as a robustness check.

# **Price Data**

The second dataset comprises price data and product information from both the voucher fairs and the primary local market in the area (where the voucher fair was also held). This dataset includes prices for over twenty-five food and non-food products between September 2011 and March 2012. These data are used to calculate the value of household asset ownership, as well as to determine whether households faced different prices for the same goods at local markets versus the voucher fairs.<sup>20</sup>

# **Qualitative Data**

The household surveys and administrative data are complemented by qualitative data from focus groups with men, women, market resource persons, school principals and the camp administration in March

> 18 While the difference in means is not statistically significant for household asset ownership, there is a difference in the equality of distributions.

> 19 These results are also robust to interacting each characteristic in appendix S3 with the cash transfer variable, although there is some differential attrition between the _cash_ and _voucher_ groups according to livestock ownership. However, this affects less than 11 percent of households in the sample.

> 20 The voucher fair data also include information on what voucher households purchased, the quantity purchased and the price paid, although we do not have corresponding data for the cash households.

12
people may also gain from conflict in terms of income and wealth and this may explain why some people do not move. The defining attributes of the alternative choices are very different from any other model and the task of economics is to understand what these defining attributes should be.

In terms of independent variables, “push” factors become more important than “pull” factors in forced displacement models. The intensity of a conflict may be more important than the income opportunities in potential destination areas. In addition to the classic socioeconomic variables, risk aversion, stress, anxiety, other traits of personality and behavioral factors in general have to be well understood and measured. Hence, one could think of four essential blocks of independent variables including individual or household socioeconomic characteristics, “push” factors, “pull” factors and behavioral factors. Also, access to and dissemination of information related to the conflict in the place of origin but also in the potential places of destination may be crucial for people to make choices. This is where social psychology, behavioral economics and neuroeconomics may offer insights into such choices.

Forced displacement data are also unusual in their form. Deciding on whether to flee or not to flee a conflict (the migration choice) can be an individual or household choice and risk coping strategies may include temporary migration, shuttling between places, migration of only selected members of the households or migration of the whole household. This implies that individuals may stay put throughout the period observed, join or leave the household during the period, or have several episodes of out and immigration. Households may decide to leave and come back several times. In econometric terms, this means that longitudinal data may be left and right censored and have spells within. They are therefore the most complex set of panel data possible and require particular treatment of data and modeling. Survival or duration models can usually accommodate many of these complexities but it is very rare to find similar data sets used in published articles. Collecting such type of data is also not obvious, particularly if conflict is intense and survey areas cannot be reached. This is an issue where empirical economics could provide a real contribution by defining the optimal data format and adapting panel models to this format.

# Macro models

Macroeconomics has attempted to model forced migration using models borrowed from the trade and economic migration literature such as the gravitational model (Echevarria and Gardeazabal, 2016) or used other macro models to test the impact of refugees on trade (White and Tadess, 2010). A more recent body of work is adapting trade models to take into account stochastic shocks in a dynamic framework (Cameron et al., 2007; Artuc et al., 2008). These are rational expectations models that are able to model the unpredictability of shocks, and recent work has tried to adapt these models to the context of violent 14
requirement. The opportunity cost is estimated at 6.51 percent nominal and –2.49 percent real in 2004, according to data from Banrural and Banco de Guatemala.

**_Calculating the subsidies and the delivery cost of Banrural’s products_ .** On the basis of the 2004 audited financial statements, the SDI for Banrural was –11 percent. This figure indicates not only that Banrural has fully achieved self-sustainability, but also that its annual profit, minus its opportunity cost of own capital and subsidies received, exceeded the total annual value of subsidies.<sup>_26_</sup> The negative subsidy can be interpreted as a return above “normal” profitability that benefited the bank’s shareholders (society at large in proportion to the state ownership of Banrural).

Banrural could have reduced its average lending rate from 17 percent to 15.2 percent, and still could have achieved adequate profitability as reflected by an SDI of zero (See table 6).

**Table 6. SDI Calculation, 2004**

(Figures in Millions Quetzals, Unless Otherwise Noted)

|||**_Fondos propios_**|
|---|---|---|
|LP|Average OLP|2,517,950|
|LP*I|Revenue from loans|431,482|
|I|Yield on lending|17%|
|P|Profits (losses)|113,934|
|E|Average equity|101,905|
|M|Social opportunity cost|6.5%|
||Average debt to<br>government (IDB-|1,986,967|
|A|linked)||
|A*C|Interest expenses on<br>debt to government|71,535|
|C|Rate paid for debt to<br>government|3.6%|
|A*(M-C)|Discount on debt to||
||government|57,806|
|E*M|Subsidy on equity|6,633|
|S|= {A*(M-C) + [(E*M)<br>–P] + K}|–49,495|
|SDI|=S/(LP*I)|–1.47%|
||I*(1+SDI)=|15.17%|

> **_Examining the Composition of Banrural’s Cost of Lending._** Banrural’s total cost of lending can be broken down into the portion financed by society (subsidy component), by borrowers (yield on OLP), and by Banrural itself (profits and net interest from other earning assets such as investments). As shown in table 7, the average cost of maintaining Banrural’s OLP is 24 percent of the portfolio. That is, it costs 24 cents to maintain 1 Quetzal of OLP annually. Of these 24 cents, 72 percent is borne by the borrower, 36 percent is borne by Banrural’s return

> 26 In an environment of low interest rates, as was the case during the period analyzed, the opportunity cost is expected to be lower. Thus, it may also contribute to the negative SDI.

22
122

||1975|1980|1985|
|---|---|---|---|
|5 N Dominican<br>Republic|6.78|9.30|-|
|5nEcuador|7.29|7.84|7.31|
|5MEl Salvador|8.17|8.97|5.87|
|5xGuateuala||.||
|5 L Guyana||||
|5 L Haiti||||
|53Honduras|12.79|-|-|
|5M Jamaica|9.26|-|-|
|5 N Mexico|4.32|2.40|1.38|
|5 M Nicaragua|8.46|14.38|-|
|S N Panama|14.49|12.71|15.77|
|5 M Paraguay|2.77|3.59|-|
|5 M Peru|4.67|5.60|6.01|
|5xSurineme|8.65|-|-|
|5nTrinidad<br>and Tobago||5.78|-|
|5 M Uruguay|3.94|4.89|4.05|
|5NVenezuela|9.06|8.82|9.00|
|6 Eastern Europe|12.50|0.32|1.44|
|(U)|12.50|0.32|1.44|
|6M Bulgaria||-|-|
|6M Czechoslovakia|.|.||
|6 3GermanDemocratic<br>Republic||-|-|
|<br>6XHungary|-|-|3.56|
|6 N Poland||||
|6KRomania|0.35|0.63|0.76|
|6 M Yugoslavia|24.65|0.00*|0.00*|

Note: The Regional averages are only for the group of countries that have data for that year; L - low income; 3 - middle income.

*Indicates a number less than 0.005.

Source: IMY Government Financial Statistics: Consolidated Central Government Expenditure.
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

# **Table 21: Estimated IPD for 35 Low and Middle Income Countries - detailed results**

|**Country**|**Coverage in**<br>**population aged 20+**|**Dependency ratio**<br>**in base year (a)**|**Dependency ratio**<br>**in year 2050 (a)**|**IPD**<br>**wage/2%**|**IPD**<br>**price/2%**|**IPD**<br>**wage/4%**|**IPD**<br>**price/4%**|**IPD**<br>**wage/5%**|**IPD**<br>**price/5%**|
|---|---|---|---|---|---|---|---|---|---|
|Argentina|28|47|41|106|91|85|75|78|70|
|Bolivia|18|16|24|111|92|73|65|62|55|
|Brazil|34|28|62|500|362|330|248|275|211|
|Cape Verde|17|5|26|78|64|47|39|38|32|
|Chile|35|13|42|77|64|60|50|53|45|
|Colombia|22|5|56|88|73|56|48|46|39|
|Costa Rica|33|4|52|203|163|121|100|97|80|
|Croatia|59|35|72|274|205|201|156|175|139|
|Dominican Rep.|20|7|44|80|64|49|40|40|32|
|Ecuador|17|9|39|103|78|63|49|51|40|
|El Salvador|17|10|47|60|46|43|34|37|29|
|Estonia|89|44|81|268|213|189|155|163|135|
|Hungary|78|43|70|300|212|203|150|171|128|
|Iran|26|11|56|146|110|89|70|72|57|
|Korea|22|1|54|57|35|33|21|26|17|
|Kyrgyz Rep.|70|30|60|282|230|185|154|154|129|
|<br>Lithuania|78|50|84|221|164|155|120|134|104|
|Macedonia|49|23|77|441|356|291|241|244|204|
|Malta|57|18|58|356|284|234|190|194|159|
|Mauritius|100|11|60|63|46|47|37|42|33|
|México|25|7|40|101|84|65|54|54|45|
|Moldova|73|63|68|229|178|159|127|136|111|
|Morocco|6|10|53|50|40|32|26|26|22|
|Nicaragua|21|6|33|220|173|131|105|104|84|
|Peru|51|6|26|57|51|40|35|34|30|
|Philippines|41|2|41|185|146|107|85|81|66|
|<br>Poland|95|24|47|379|304|261|212|220|181|
|Portugal|78|36|72|358|271|233|181|193|151|
|Romania|53|45|121|386|292|256|199|214|169|
|Senegal|6|39|78|73|51|51|37|44|32|
|Slovakia|81|37|91|304|241|210|171|179|148|
|Slovenia|76|33|96|429|314|298|226|255|197|
|Turkey|35|28|50|217|154|146|109|123|93|
|Ukraine|91|42|82|365|292|257|211|220|183|
|Uruguay|52|40|61|295|246|214|182|187|160|

**Notes** : (a) Ratio of old age pensioners to contributors; (b) Indexation method applies both to the wage record valorization and to the indexation of pension benefit **Source** : World Bank Pension Database, and own calculations with PROST Model.

- 96 -
- 26 -

> For computational simplicity, _I_ is treated as a continuous variable, instead of binary. Note

> that _I_ is observable in our case. This can be estimated by the Nelson-Olsen (1978) two-stage

> estimation technique. First, the reduced form for _q_ is estimated by the tobit method over _X_   _X_ 1<sup>,</sup><sup>_X_</sup> 2  , and the reduced form for _I_ is estimated by OLS over _X_ . Then, the equation of _q_ is estimated by the tobit method replacing _I_ with _I_<sup>ˆ</sup> _._ Letting the covariance matrix of this estimator be _V_ 0, the asymptotic covariance for  ˆ1, <sup>ˆ</sup> 1  can be written by:

where _G_   2<sup>,</sup><sup>_J_</sup> 1  . Π2 is a vector of the coefficients from the first stage OLS regression for _I_ . _J_ 1 is the matrix consisting of zeros and ones so that _XJ_ 1  _X_ 1 . _d_ is calculated by _d_   12  22  2  1  12 . While  12 is a variance of the error term of the first stage tobit estimation,  12 is a covariance between the two reduced form equations. See Amemiya (1979) and Maddala (1983) for further detail.

# **V. DATA**

The paper uses household surveys in Albania for four consecutive years: 2002-2005. It pools the four surveys. Note that the number of observations for 2002 is relatively small, because of lack of information required for the analysis (see Table 3). The total number of observations is 4,762. The summary statistics is presented in Table 5. The sample average of monthly power consumption is about 350 kWh with a wide range from less than 1 kWh to over 4,000 kWh per month. The metering rate is about 88 percent. The average marginal rate faced by residential customers is about 8 lek per kWh. Note that all the monetary variables are normalized to the 2005 constant term. In theory, the Nordin’s difference variable should be positive under the increasing block tariff regime. However, it can be negative in the real data, because of possible false data reports on bill payments and censored data on electricity
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective"

# 7. **Conclusions**

Coverage statistics are widely used to paint a picture of infrastructure conditions in developing countries, and they are often the only global, cross-country data available for infrastructure services. It is thus important to utilize coverage statistics to their fullest advantage while at the same time being careful not to read more into the data than they can in fact reveal. In this paper we have utilized a new data source, the World Bank's LSMS surveys, to construct infrastructure coverage statistics for a pooled sample of households from fifteen countries.

Several of the results from our analyses using of these LSMS data sets are worth recapping. First, electricity coverage was higher than coverage of other infrastructure services at all income levels; 65 percent of the households in the sample had electricity in their homes. By contrast, only 38 percent of households had in-house water taps (the infrastructure service with the next highest level of coverage). The relative ranking of coverage rates among the four infrastructure services (electricity

* water 0 sewer P telephone) held across all income levels.

Second, infrastructure coverage for electricity, water connections, and sewer connections all rise but at different rates as household income (as measured by a consumption aggregate) increases from about US$100 to US$250 per month. We want to emphasize again that the 55,500 households in this pooled data set are not representative of the global population in developing countries. We believe however, that our findings regarding these relationships between infrastructure coverage and household income art relatively robust with respect to the countries in the pooled sample and the sampling procedures used within countries.

Third, electricity was the only infrastructure service with significant penetration among the poorest 5 percent of the sample households (32 percent had service). Only 6 percent of the poorest households had an in-house water connection; only 3 percent had a sewer connection. Almost 80 percent of the poorest households in urban areas had electricity service. Even in rural areas, 27 percent of the poorest households in our sample had electricity service. When a household had the 20
# **EXECUTIVE SUMMARY**

# **Research Strategy**

This study tests empirically the key hypothesis that households in the Europe and Central Asia (ECA) region lack access to efficient rental housing. This hits especially the young and mobile, as well as the poor and the elderly. Domestic policy makers, who are increasingly talking about the need for rental housing choice, should consider realigning their housing policies and start enabling and promoting a competitive and efficient formal rental market.

To test the key hypothesis, given the considerable resource constraints for this study, a sixcountry sample was used to reflect different housing sectors and housing reform environments— Armenia, Poland, Lithuania, Romania, the Russian Federation, and Serbia (without Montenegro). The study used disaggregated household datasets, other available statistical and survey evidence, and interviews to arrive at an empirical picture of the rental sector in those countries and test the null hypothesis. The study also considered investor types and legal, regulatory and taxation issues that prevent swifter rental sector development.

# **Main Empirical Findings**

Rental supply in housing sectors in the ECA region is smaller than expected in mature market economies with similar patterns of demographics, incomes, mobility needs, and building inventory.

This is especially so in Armenia, Romania, and Serbia where the formal rental sector is a barely traceable residual. In Russia, the rental sector is significant, but only because it consists primarily of social (municipal) rental housing slated for eventual privatization. In Lithuania, social (municipal) rental housing has been decimated, but there is a lively, mostly informal, private rental market in individual apartments. In Poland, the significant rental sector includes formally restituted prewar rental apartment buildings, and cooperative rental apartment stock, as well as the “regular” social (municipal) rental apartment stock.

There is also evidence of a large shadow rental sector that survived the privatization wave. With the notable exception of Poland, in the broadly defined apartment sector, ownership titles to common property in multifamily (M-F) buildings and surrounding grounds are lacking. Such facilities continue to be serviced and maintained mostly by public sector companies typically owned by municipalities. It points to an important side effect of the mass privatization: significant unintended inefficiencies. The privatizations mostly failed to produce either genuine homeowners or professional rental investors willing and able to undertake maintenance and capital repairs on common property in the multifamily buildings dominating ECA urban areas.

The correlation between building type and tenure form is a crude measure - but the closest available - of housing choice in mature market economies. The preponderance of M-F buildings in the housing stock of ECA countries suggests a much higher incidence of rental tenure than is the case, given that rental form is the easiest for managing such buildings. The incidence should iii
limitations do prevent the Dembombynes et al (2006) study from fully addressing all doubts that have been raised.<sup>2</sup> The population census of Brazil offers a second, richer, setting in which to “test” the ELL method. This dataset provides an opportunity to more completely study the applicability of the basic ELL method and its underlying assumptions. In 2000, the Brazilian Statistical Organization, henceforth IBGE, fielded two questionnaires as part of its Census data collection. The first, traditional, questionnaire was fielded to all households and includes a single-question about the income of the household head. A second questionnaire, more detailed than the traditional “short form” and with a fairly good measure of total household income, was fielded to a 12.5% sample of households within each enumeration area in the country. To date, IBGE has not published any sort of small area poverty statistics based on this household income variable because although it is fairly detailed, it is still judged to be insufficiently comprehensive.<sup>3</sup> While neither of the two available income measures is appealing for the purpose of producing a proper poverty map for Brazil, the databases do provide an attractive context within which to _check_ the ELL methodology.

The analysis in this paper compares the predicted welfare measures obtained following application of the ELL method to the actual, observed, values. For reasons of computational ease and tractability, we focus on the single state of Minas Gerais (see further below). In addition, we focus our attention on the 12.5% census sample. From this data source, we draw synthetic household surveys mimicking the sample design of two existing surveys in Brazil: the PNAD earnings survey and the 2002/2003 POF expenditure survey. We implement the ELL methodology based on these pseudo-surveys, combining the parameter estimates from the survey-based models with unit record data from the census sample to predict poverty at the municipality level. We

> 2 Notably, the Demombynes et al (2006) study is unable to confront the concerns raised regarding the impact of intercluster correlation on standard errors due to the fact that the target populations in this study comprised randomly assembled, non geographically-contiguous villages aggregated together into target populations of roughly 1000 households.

> 3 Indeed, in the 1996 South African Census, which also includes a similar crude measure of household income, the national _income_ estimate in the census amounted to only 85% of the household survey-based national _consumption_ estimate, and moreover deviations between the two were not random (see Alderman et al.,2002).

4
We notice that the portion of variance of country 1 asset due to shock in country 2 is relatively modest (between 5% and 8%), which suggests a more modest contagion, when markets are segmented by barriers to investment, as opposed to the case of perfect market integration described in Kodres and Pritsker (2002).

Overall, our results show that information and liquidity shocks to assets in country 1 will affect both the international and the domestic stock in country 2, with a relative lesser impact on the domestic stock than on the international stock. More importantly, shocks to the international asset are transmitted across countries with greater impact than shocks from the pure domestic asset. Moreover, if investors in the developed market transmits shocks across the two emerging countries through portfolio hedging and cross-market rebalancing. Uninformed investors herding in all countries exacerbates contagion.

The findings in this section support those of Calvo (1998) and Calvo and Mendoza (1999), that international capital markets act as carrier of emerging market crises. Moreover, when information is asymmetric, herding is a profitable strategy for uninformed investors, associated with a higher risk of contagion. Indeed, our results show that most of the price response to shocks comes from the expectation component, which reflects price changes due to the actions of uninformed investors trying to interpret whether the actions of informed investors are motivated by expected low returns or portfolio rebalancing.

Contributions in the literature that focus on the role of international firms in spreading contagion across emerging markets also support our findings. Indeed Frankel and Schmukler (1996) show empirical evidence of contagion from Mexican country funds. They highlight a pattern of contagion where domestic investors react first to shocks, then transmit them to other emerging countries through New York. Boyer, Kumagai, and Yuan (2002) use data on investable stocks, whose shares are open to foreigners, versus non-investable stocks to investigate contagion. They find that there is more transmission of crises in investable stocks that are open to foreigners as compared to non-investable stocks.

# 3.9 Robustness

In order to better understand our results, we investigate how they are affected by changes in the degree of information asymmetry in all three countries, the exposure to common risk factors through the βs , and the exposure to country specific risk through V ar (η).

20