rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 4. NAIVS program awareness and implementation**

||Awareness of<br>NAIVS program|Asked about ability<br>to pay in 2010/11|Household received<br>vouchers in 2010/11|Satisfaction with<br>selection of<br>beneficiaries in 2010/11|<br>Household received<br>vouchers in 2011/12|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Beneficiary|0.097***|0.079*|0.339***|0.156***|0.171***|
||_0.007_|_0.093_|_0.000_|_0.009_|_0.000_|
|Lottery|0.036|0.042|0.072|0.110**|0.049|
||_0.320_|_0.267_|_0.171_|_0.035_|_0.382_|
|Lottery x Beneficiary|-0.020|-0.008|-0.062|-0.116|0.016|
||_0.619_|_0.883_|_0.343_|_0.112_|_0.788_|
|Number of households|920|920|920|920|920|
|R-squared|0.029|0.011|0.100|0.019|0.039|
|Mean of dep. var. for NB in NL vil.|0.859|0.146|0.216|0.615|0.235|
|P-val of B = NB in L villages|_0.005_|_0.033_|_0.000_|_0.396_|_0.000_|

Note: This table reports the estimation of the following specification: _y ij = α ij + β 1 Beneficiary ij + β 2 Lottery j + β 3 Lottery j *Beneficiary ij + ε ij_ , where _i_ indexes the individual (household) and _j_ indexes the village. _Beneficiary ij_ is a dummy that takes value of 1 if the individual _i_ in village _j_ is a beneficiary of the NAIVS voucher program in 2010/11. _Lottery j_ is a dummy that takes value of 1 if beneficiaries in village _j_ were chosen using a lottery. The models presented here represent an intention to treat analysis and are not adjusted for any baseline covariates. Dependent variables are constructed based on questions asked on follow-up household survey. The dependent variable in Column 1 is a dummy that indicates whether respondent is aware of the presence of the NAIVS program in his or her village. Column 2 reports the results using a dummy that codes whether the respondent was asked about his or her ability to pay for the voucher top-up in 2010/11. In Columns 3 and 5, the dependent variables are dummies that indicate if respondent reports receiving vouchers in 2010/11 and 2011/12, respectively. For those regressions, respondents self-report receiving or not voucher and the answers may differ from administrative data used to assign beneficiary status. In Column 4, the dependent variable is a dummy variable that indicates whether the respondent reports being very satisfied with the selection of voucher beneficiaries in 2010/11. P-values are computed using the t-asymptotic wild cluster bootstrap procedure described in Cameron et al. (2008) and reported below the coefficients. Clustering was done at the village level. ***,**,* denote statistical significance at the 1, 5 and 10 percent level, respectively. See Appendix Table 1 for definition of variables.
|synergies and opportunities that<br>|
|---|
|exist at the primary care level in<br>order to better coordinate<br>treatment of HIV/AIDS, STD and|
|opportunistic diseases. Strengthen|
|the primary care level.|
|Complement the existing general<br>|
|health system response to HIV/AIDS<br>with programs and interventions for<br>targeting the most at risk groups to|
|be identified by studies at the local<br>level.|
|Improve the regulatory framework|
|for medicines for human use—<br>particularly in regard to the<br>processes of authorization, price-<br>setting and reimbursement,<br>distribution and drug dispensation.<br>Develop the pharmaco-vigilance<br>system.|

**Source:** Técnicas de Salud, 2011.

10
Table 5: Estimation of Labor Supply across sectors

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Outcome:|OLS<br>∆_t,s_ln_Ls_|OLS<br>∆_t,s_ln_Ls_|IV1<br>∆_t,s_ln_Ls_|IV1<br>∆_t,s_ln_Ls_|IV2<br>∆_t,s_ln_Ls_|IV2<br>∆_t,s_ln_Ls_|
|_κ_|0.179**|-0.383***|1.142***|1.516***|1.490***|2.387***|
||(0.078)|(0.102)|(0.142)|(0.222)|(0.351)|(0.643)|
|Observations|3,192|3,192|3,192|3,192|3,192|3,192|
|Adjusted R-squared|0.237|0.296|0.247|0.302|0.238|0.295|
||||FS1|FS1|FS2|FS2|
|Outcome:|||∆_t,s_ CMA|∆_t,s_ CMA|∆_t,s_ CMA|∆_t,s_ CMA|
|∆_t,s_CMA|||1.981***<br>(0.093)|1.407***<br>(0.070)|||
|_Ti_|||||0.047***<br>(0.004)|0.028***<br>(0.003)|
|Observations|||3,192|3,192|3,192|3,192|
|Adjusted R-squared|||0.588|0.852|0.523|0.822|
|F-stat|||588.91|708.68|77.06|59.95|
|Controls|X|X|X|X|X|X|
|State FE|X||X||X||
|Municipality FE||X||X||X|

_Notes:_ This table reports the results of the estimation of the labor supply elasticity to recover the parameter _κ_ , which governs the reallocation from the informal to the formal sector. The dependent variable is the change in the log ratio between formal and informal workers. The independent variable is the change in CMA across sectors. The first two columns show the results for the OLS. The third and fourth column displays the results of a two-stage least square estimation using as an instrument the change in CMA across sectors and holding constant the number of workers and residents. The fifth and sixth column display the results of a two-stage least square estimation using as an instrument a dummy variable indicator of whether the centroid of the census tract is within a 25 minute walking range. The odd columns include state fixed effects, and the even columns include municipality fixed effects. Standard errors are clustered at the census tract level and reported in parentheses. * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01.

52
means that our data cover both the pre- and the post-entry period. The third advantage is that Romania is a large country. With a population of 22 million and an area about 238,000 km<sup>2</sup> , it encompasses 42 county-level administrative units and eight broader NUTS regions.<sup>5</sup> Thus, in our econometric analysis, we are able to rely not only on inter-temporal but also on cross-regional variation in the presence of foreign chains.

The main data source for this study is the commercial data base Amadeus published by Bureau van Dijk. It contains information on about 9 million public and private companies in 38 European countries over the 1996-2005 period. Amadeus includes data on location, contact information, industry classification, standard financial statements and detailed shareholder information including the country of origin.

To identify global retail chains, we use information on company name, industry classification and ownership from Amadeus which we cross check against the information on major international retail chains in “World Retail Data and Statistics 2006/2007” and “European Marketing Data and Statistics” published by Euromonitor International, “Economist Intelligence Unit (EIU) Industry Briefing, Romania: Consumer goods and retail background”, GAIN report by USDA Foreign Agriculture Service and Dun & Bradstreet Business Report. We identify 9 global retail chains operating in Romania. Their names and characteristics are listed in Table 1.

Amadeus data base provides aggregate figures on company operations in Romania. More detailed data on the presence of global retail chains in different Romanian regions were obtained by contacting each retail chain directly. We were successful at collecting information on the opening date of all stores, their location and selling space for 7 of the 9 chains operating in Romania. We did not manage to obtain the data for Kaufland which entered Romania during the last year of the sample and Mega Image which is one of the smaller entrants. For more details, see Table 1.

In addition to ownership information, we use information on output, production inputs and profit from balance sheets and income statements. We drop observations with negative values of turnover, materials and tangible fixed assets and unusually large fluctuations in values of variables. In manufacturing industries, we end up with 49,552 companies in the sample. When we incorporate industry-level import and export figures in the analysis we further restrict the sample to 49,390 manufacturing companies. In the retail sector, we restrict our attention to firms

> 5 NUTS stands for the EU nomenclature of territorial units for statistics defined by Eurostat.

7
significantly less from truncation bias compared to alternative measures based on years of schooling. This paper thus strengthens the case for rank-based measures for analyzing intergenerational mobility especially in developing countries where most of the available data sets (e.g. LSMS and DHS) suffer from sample truncation due to coresidency restrictions used to define household membership.

# **References**

Asher, S, and P. Novosad, and C. Rafkin (2017), Estimating Intergenerational Mobility with Coarse Data: A Nonparametric Approach, Working Paper, World Bank.

Behrman, J., A. Gaviria and M. Szekely (2001), “Intergenerational Mobility in Latin America,” Economia, Vol. 2 (1): 1 44.

Bishara, A. J., & Hittner, J. B. (2012). “Testing the significance of a correlation with non-normal data: Comparison of Pearson, Spearman, transformation, and resampling approaches.” Psychological Methods, 17, 399-417.

Bjorklund A and K. Salvanes. (2011). Education and Family Background: Mechanisms and Policies, Handbook in the Economics of Education vol 3, E A Hanushek, S Machin and L Woessmann (es.), The Netherlands: North Holland, 2011, pp. 201-247.

Bj ̈orklund, A. and M. J ̈antti (2009) “Intergenerational mobility and the role of family background” in W. Salverda, B. Nolan and T. Smeeding (eds) Oxford Handbook of Economic Inequality, Oxford University Press.

Black, S. E. and P. Devereux (2011). Recent Developments in Intergenerational Mobility, Handbook of Labor Economics, Amsterdam, North-Holland.

Blanden, Jo (2013). “Cross-Country Rankings In Intergenerational Mobility: A Comparison Of Approaches From Economics And Sociology,” Journal of Economic Surveys, Wiley Blackwell, vol. 27(1), pages 38-73 Bloom, David E., and Killingsworth, Mark R. (1985), “Correcting for Truncation Bias caused by a Latent Truncation Variable,” Journal of Econometrics, 1985, pp. 131-135. Chetty, R, and N. Hendren, P. Kline, and E. Saez, (2014). “Where is the land of 21
systems are in place for all major hazards, with outreach to communities” with a score between 0 and 5. We attribute a score equal to zero to countries that did not contribute to the HFA monitoring system.

We assume that individuals with access to early warning can reduce capital losses by ��20% , and that the fraction of the population with access depends linearly on the HFA indicator: 0% for a score of 0, 20% for a score of 1, up to 100% for a score equal to 5. So actual vulnerability is equal to: Where HFA_P2C3 is the HFA indicator related to “Early warning systems are in place for all major hazards, with outreach to communities.”

# 3.3. Exposure

## 3.3.1. Computing population and asset exposure by hazard and return period

The exposure of the population and assets to natural disasters is derived from UN estimates of expected asset losses and our estimates of asset vulnerability. The Global Assessment Report (UN‐ISDR 2015), referred to as the GAR report, provides for each country and hazard (riverine floods, windstorms, storm surge, earthquakes and tsunamis) asset losses per return periods (from 20 to 1,500 years) as well as average annual losses (AAL). We first estimate frequent asset losses per country and hazard (for the one year return period event) such that averaging events over return period matches average annual losses provided by the GAR.

Then, we derive the exposure per country, hazard and return period that is consistent with these asset losses and the vulnerability derived before, that is: Where �� is asset vulnerability as computed in the previous section, Δ� is the value reported in the GAR, and �� is the fraction of assets affected (that is the exposure) we solve for.

## 3.3.2. Summing over return periods

To aggregate asset and welfare losses, taking into account that exposure depends on the return period, we proceed as follows. First, we assume that no other parameter than total exposure depends on the return period (the model structure allows to make arbitrary parameters depend on the return period). Second, we compute asset losses and welfare losses in each country for each return period independently. And third, we aggregate the resulting asset and welfare losses, weighting them with the probability of each event occurring.

## 3.3.3. Exposure bias

For riverine and coastal floods, we specify a different exposure for poor and nonpoor people. To calibrate the exposure bias, we use the estimates published in (Hallegatte et al. 2017). This World Bank study 13
# **1. Introduction**

The importance of international migration for development has received increasing attention from the research and policy communities (e.g. GCIM, 2005; World Bank 2005), leading to a High-Level Dialogue on International Migration and Development at the United Nations General Assembly in September 2006.<sup>1</sup> The focus of much of this research and discussion has been on examining the impacts of international migration on development in the sending countries, and in identifying policies which can maximize the development benefits of migration. However, very few detailed and representative surveys of households of migrants exist, limiting our ability to study the effects of international migration on sending families.

Public use microdata from national censuses provide representative information, but only for a very limited set of variables. Nationally representative household surveys, such as the World Bank’s Living Standards Measurement Surveys provide more information about living standards, education, and other outcomes of interest, but usually relatively little information on the migration process. As a result, answering many questions of interest in the study of migration requires specialized surveys. However, most of these specialized surveys are non-probability samples of unknown representativeness, making it hard to generalize any conclusions reached from them. As Fawcett and Arnold (1987) note, common approaches used by many studies are to choose their samples from individuals who belong to church groups, social organizations, or other defined groups; use snowball samples of individuals referred by friends or acquaintances; and/or to focus exclusively on areas of high out-migration.

This paper reports on the results of an experiment designed to compare the performance of three methodologies for sampling households with migrants: i) a stratified sample using the census to randomly sample census tracts, in which each household is then listed, and screened to determine whether or not it has a migrant, with the full length questionnaire then being applied in a second phase only to the households of interest; ii) a

> 1 <u>http://www.un.org/esa/population/hldmigration/</u> [accessed February 10, 2007].

2

-  -
27 Whichever one of these approaches one prefers, or indeed whether one has an alternative proposal, these recent contributions to the debate have the great merit of pointing out that, even as it represented a success in terms of reaching the poor, the rise of social assistance in Latin America in the last two decades has been incoherent and, therefore, inefficient. One challenge that faces the system as it moves forward is the need to integrate the contributory and non‐contributory “halves” in a way that is fair, that preserves social gains to the poor, but also that is less distortionary and restores the right economic incentives for firms and workers – both in terms of static resource allocation, and in terms of long‐term savings. If achieved, this will be no small feat, and the path to success is likely to involve more than tinkering at the margins.

The second challenge for Latin America’s social protection system is that, despite having finally reached the poor, rather than merely the urban middle‐classes, it remains remarkably timid in the kinds of support it offers them in their quest to leave poverty behind permanently. Non‐contributory social insurance programs, including social pensions, are now a mainstay of modern social assistance in Latin America, but they are effectively social‐insurance substitutes, which prevent people from falling deeper into poverty when they become unemployed or too old to work. Programs that move, to paraphrase a recent title, from protection to promotion, are rarer.<sup>27</sup> CCTs obviously claim a spot, since they are designed specifically to break the intergenerational transmission of poverty, by investing in the human capital of poor children. Some of the investments in social and economic infrastructure made under the aegis of social investment funds at the local level also count as efforts to promote greater opportunity for a sustainable exit from poverty. Similarly, recent programs that promote “competency certification” (in Argentina) or lifelong learning skills (in Chile) are steps in the right direction.

Perhaps the most promising recent initiatives that qualify as “opportunity rope” policies, rather than merely “safety nets”, are programs to promote early childhood development. It has long been known that performance in certain cognitive tests at early ages (e.g. 3‐6) are powerful predictors of future achievements in education, labor market attachment, and earnings (see, e.g. Currie, 2001). It has also long been understood that those early childhood skills (both cognitive and non‐cognitive) that appear to have such persistence are responsive to relatively simple interventions. Child weight, height and cognitive skills respond to nutritional supplementation. Psychosocial stimulation also enhances cognitive and non‐cognitive skills. This was documented in LAC as early as 1991, in the influential Lancet study by Grantham‐McGregor et al (1991). Longer‐term longitudinal studies in the United States have shown that

> 27 The title is _For Protection and Promotion_ , by Grosh et al. (2008).
holds and profit earners depends only on the external risk premium:<sup>27</sup> The external risk premium consists of two components: an exogenous element, denoted CONTAG, which captures idiosyncratic changes in “sentiment” on world capital markets (including contagion effects), and an endogenous component, which captures the perceived degree of country risk and depends on the ratio of the economy’s total foreign debt to exports: where κERP > 0 and the quadratic form is used to capture the idea that the external risk premium is a convex function of the debt-to-export ratio.<sup>28</sup> The impact of expectations of exchange rate depreciation on the interest rate on foreign-currency deposits in the domestic banking system can be gauged from Figure 6, which shows the behavior of three-month deposit rates on Turkish liras and U.S. dollars. The lower panel of the figure shows a dramatic fall in the expected depreciation rate in late 1999, which tends to indicate that the stabilization program introduced at that time gained rapid credibility. However, the data also show that the credibility gain disappeared equally rapidly in the ensuing months. Figure 7 displays a measure of the external risk premium for Turkey, J. P. Morgan’s stripped spread for that country. The figure shows as well the behavior of the spread from J. P. Morgan’s emerging markets bond index (EMBI). Co-movements in the two series tend to capture “contagion” effects, which are apparent in the period leading up to Argentina’s peso crisis for instance.

The interest rate on domestic-currency loans, IL, is set as a premium over the marginal cost of funds. Given the arbitrage conditions (89) and

> 27Note the importance of distinguishing between the premium-inclusive world interest rate faced by domestic borrowers, IF<sup>W</sup> , on the basis of which banks set the interest rate on foreign-currency deposits held domestically, and the risk-free rate faced by lenders, IFRF<sup>W, which affects the demand for deposits held abroad by households.In the absence of</sup> a risk premium, both rates would be equal and, given the pricing condition (90), equation (75) would imply that the ratio FDh/FDh<sup>Wisconstantovertime.</sup>

28In line with the results of Fiess (2003) for several middle-income Latin American countries, the country risk premium could also be made a function of the domestic public debt to GDP ratio. This would provide an additional channel through which fiscal consolidation may affect the economy.

39
Castaneda Aguilar, R. Andres; Fujs, Tony; Jolliffe, Dean; Lakner, Christoph; Gerszon Mahler, Daniel; Nguyen, Minh C.; Schoch, Marta; Vargas Mogollon, David L.; Viveros Mendoza, Martha C.; Baah, Samuel Kofi Tetteh; Yonzan, Nishant; Yoshida, Nobuo. 2020. September 2020 “PovcalNet Update : What’s New. Global Poverty Monitoring Technical Note; No. 14.” World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/34451 License: CC BY 3.0 IGO.

Christiaensen, L., P. Lanjouw, J. Luoto, and D. Stifel. 2012. “Small area estimation-based prediction methods to track poverty: Validation and applications.” Journal of Economic Inequality, 10(2), 267–297. <u>https://doi.org/10.1007/s10888-011-9209-9</u> Dang, H. A. H., P. F. Lanjouw, and U. Serajuddin. 2017. “Updating poverty estimates in the absence of regular and comparable consumption data: Methods and illustration with reference to a middle-income country.” Oxford Economic Papers, 69(4), 939–962. https://doi.org/10.1093/oep/gpx020 Deaton, Angus, 2005. “Measuring poverty in a growing world (or measuring growth in a poor world).” The Review of Economics and Statistics, 87(1), pp.1-19.

Douidich, Mohamed, Abdeljaouad Ezzrari, Roy Van der Weide, and Paolo Verme. "Estimating quarterly poverty rates using labor force surveys: a primer." The World Bank Economic Review 30, no. 3 (2016): 475-500.

Elbers, Chris, Jean O. Lanjouw, and Peter Lanjouw. "Micro-level estimation of poverty and inequality." Econometrica 71, no. 1 (2003): 355-364.

Jenq, Christina, Lain, Jonathan, and Vishwanath, Tara. “Good jobs for a new generation: delivering quality jobs for young Nigerians after COVID-19”. 2021, Washington DC: The World Bank.

Jolliffe, Dean, and Espen Beer Prydz. "Estimating international poverty lines from comparable national thresholds." The Journal of Economic Inequality 14, no. 2 (2016): 185-198. <u>https://pubdocs.worldbank.org/en/511161631652256763/pdf/Good-Jobs-for-a-New-GenerationDelivering-Quality-Jobs-for-Young-Nigerians-After-COVID-19.pdf</u> Jolliffe, Dean Mitchell; Mahler, Daniel Gerszon; Lakner, Christoph; Atamanov, Aziz; Tetteh Baah, Samuel Kofi. 2022. “Assessing the Impact of the 2017 PPPs on the International Poverty Line and Global Poverty.” Washington, DC: World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/37061 License: CC BY 3.0 IGO.

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