rafmacalaba/data-use-mentions-extended huggingface dataset:
>8</sup> The proxy for industry information complexity is constructed using data from Rauch (1999). First, we convert Rauch’s original classification of products from SITC Rev.2 to ISIC Rev.3. Second, we aggregate the data to the 2-digit ISIC level by calculating the share of SITC products that are neither sold on an organized exchange nor reference priced (i.e., heterogeneous products). An industry with a higher share of heterogeneous products is more likely to be subject to informational frictions.

In the analysis, we further test whether the impact of financial structures on export differs between normal and difficult times, such as recessions and financial crises. Data

> 8We concord the original data to ISIC Rev. 3 by converting BEA’s 1997 I-O industry classification to NAICS 1997, then the NAICS 1997 to NAICS 2002, and finally NAICS 2002 to the 2-digit ISIC Rev.2 industry level. For a more detailed description of the original data, please refer to Nunn (2007).

9
out less than 10 percent of our sample. As with the case of the Sisben score, no clear cutoff area can be defined. Rather, the probability of program receipt declines continuously and stays positive well into fairly high incomes. The actual poverty lines differ by region and municipality, reflecting differences in the cost of living. However, in 2013, they ranged roughly between Col$140,000 and Col$240,000. The corresponding line for indigence was between Col$77,000 and Col$100,000. As is apparent in the graph, there is a high probability of program receipt up to about twice the highest poverty line. And this probability seems to stay positive throughout.

**Figure 3: Kernel Regression of Program Receipt on Per Capita Household Income, Pooled ENCV 2010–13**

# **5. Evaluating the Program**

Colombia Mayor presents a number of formidable obstacles in a rigorous ex post impact evaluation, all of which may be reduced to the lack of a clearly identifiable control group. In this section, we discuss three possible approaches that, based on the program design, seem feasible, but turn out not to be so.

In theory, the Sisben eligibility criterion should be amenable to evaluation through an identification strategy involving a comparison across households within some range to the left and right of the cutoff in a design inspired by regression discontinuity (RD). Given the discussion in the previous section, it 13
241|0.428|0.000|1.000|
|Probability from poor to nonpoor between 2008 and 2010|119,692|0.168|0.076|0.053|0.399|
|Log of population size in 2010|119,692|12.745|0.923|7.988|14.546|
|High flood risk (1=yes, 0=no)|119,692|0.138|0.345|0.000|1.000|

_Sources_ : Based on CASEN 2011 and 2015 and GEIH 2008 and 2010.

_Note_ : Poverty measure is based on per capita household income, with a threshold of US$5.50 per day. The probability of changing poverty status from poor to nonpoor is estimated based on the synthetic panel approach described in Annex B). We classify the areas with top 25 percent flood depth in each country as high flood risk areas (see Section 3.1.3). SD = Standard deviation.

Table 2 presents the summary statistics of key variables for Indonesia. Households’ poverty (1 = nonpoor; 0 = poor) and vulnerability (1 = neither poor nor vulnerable; 0 = poor or vulnerable) status are dummy variables used as the outcome variables for our regression analysis. Around 88 percent of household observations in our five-wave panel data are nonpoor, while 69 percent are neither poor nor vulnerable. The urban location typology—metro core, periphery urban, other urban, and the rural area—are also defined as dummy variables. Around 45 percent of household observations are from periphery urban, followed by other urban (19.2 percent), rural (18.6 percent), and metro core areas (17.4 percent). Households’ movements across locations between each of the five IFLS waves are summarized in Table A5. About 5.9 percent of households are in SPEI-dry areas, 90.7 percent in areas with SPEI-normal
employment or to unemployment? The results obtained by the CASE survey indicated that the latter is closer to the truth, although one has to remember that any conclusions have been obtained based on cross-sectional data only.

Here we will have the opportunity to run the analysis exploiting the quasi-panel nature of the Polish LFS. It will enable us to observe the changes of the relative labor market position of those with higher probability of shadow employment in the booming labor market of Poland during most of the analyzed period. We will also be able to say something about the countercyclicality and pro-cyclicality of shadow employment in Poland.

# **2. Data and Definitions**

The data used for the analysis are as follows: the dedicated CASE survey performed in 2007 (later called CASE_2007) and the micro-files from the Polish LFS data from years 20032008.

The results of the CASE 2007 survey enable one to analyze shadow employment according to several definitions. According to the narrowest one, the shadow employees are only those working without any written contract, i.e. those with verbal agreements only. It is obvious that these workers neither pay any taxes and/or social contributions, nor are they covered by any rules regulating the standard working contract. Those who are self-employed running their business activities without being registered are a very similar group as they do not pay any taxes nor are protected by any legal regulations. The other groups are those having some kind of written contract or registration but either not paying any taxes and/or social contributions, or employees obtaining part of their salaries ―in envelope‖ or the self-employed hiding part of their revenue in order to avoid taxes.

The methodology of creating the proxy variables for shadow employment in the LFS data was based on the idea of predicting the probability of shadow employment for individual workers in LFS, using the previous estimated coefficients of the probabilistic models in the CASE_2007 database. This methodology is based on the idea of Propensity Score Matching; however in this case, different data sets are used for model estimation
used in this paper is the Mexican Family Life Survey (MxFLS), a longitudinal survey that collects an extensive set of information on individuals, households and communities. We use baseline information collected in 2002, and the first follow-up carried out in 2005-2006. Important for our project, these data are, at baseline, representative at the national, rural-urban and regional level. We complement these household data with Oportunidades administrative records to identify in which year each of our MxFLS localities was incorporated into the program.

In order to isolate impact effects using non-experimental data we propose an identification strategy that combines insights from the biology of child growth, the timing in the roll-out of the program, and the panel dimension of our household survey. The nutritional literature has established that height is responsive to nutritional interventions early in life, but is largely determined once children reach the age of 4. Drawing from this evidence, we define exposure to the program as a function of the age of the child at the time the program arrives to the locality of residence, and exploit the variation in exposure generated by age differences. Next, we divide our localities into different groups based on their date of entry into the program. This allows to control for program placement effects and time trends, and to link our results to the existing evidence. Finally, we use the fact that we measure children at two points in time to control for the nonlinearity of height over the life course. This identification strategy is innovative within the Oportunidades literature, but the basic intuition behind it has been successfully implemented in the nutrition and economics literatures (Martorell and Habicht, 1986; Duflo, 2001; Frankenberg et al., 2005). Importantly, it allows to compare impacts as the program expanded.

The results presented in this paper suggest that Oportunidades did have a positive impact on young children that lived in the poorest communities in rural Mexico. These results match existing evidence that exploits the experimental design of the program during the first couple of years of 4
164|
|RAND|<br>Michael D. Hurd; Kathleen<br>McGarry;|Journal Article|2002|Economic Journal|<br>The Predictive Validity of Subjective Probabilities of Survival|164|
|RAND|<br>Willard G. Manning; M. Susan<br>Marquis;|Journal Article|1996|Journal of Health Economics|Health Insurance: The Tradeoff between Risk Pooling and Moral<br>Hazard|146|
|RAND|<br>Melinda Beeuwkes Buntin; Alan<br>M. Zaslavsky;|Journal Article|2004|Journal of Health Economics|<br>Too Much Ado About Two-Part Models and Transformation?<br>Comparing Methods of Modeling Medicare Expenditures|142|
|RAND|<br>Duncan Thomas; Lawrence Haddad;<br>John Hoddinott; Harold Alderman;|Book Section|1997|Intrahousehold resource allocation<br>in developing countries: Models,<br>methods,andpolicy|<br>Incomes, Expenditures, and Health Outcomes: Evidence on<br>Intrahousehold Resource Allocation|134|
|U MI|Robert B. Barsky; et al.;|Journal Article|1997|Quarterly Journal of Economics|Preference Parameters and Behavioral Heterogeneity: An<br>Experimental Approach in the Health and Retirement Study|1056|
|U MI|Frank J. Chaloupka; Kenneth E.<br>Warner;|Book Section|2000|Handbook of health economics.<br>Volume 1B|<br>The Economics of Smoking|639|
|U MI|<br>John Bound; Richard V. Burkhauser;<br>Orley Ashenfelter; David Card;|Book Section|1999|Handbook of labor economics.<br>Volume 3C|Economic Analysis of Transfer Programs Targeted on People with<br>Disabilities|283|
|U MI|<br>John Bound;|Journal Article|1999|Labour Economics|The Dynamic Effects of Health on the Labor Force
the gap in employment provision (or the treatment dosage) between the treatment and control districts and underestimates the effect on various relevant socio-economic outcomes studied in the literature.<sup>1</sup> Specifically, I analyze the general equilibrium impact of NREGA on i) agricultural wages, ii) casual earnings, iii) casual employment in the public and private labor market (for men and women), and iv) human capital outcomes in terms of children’s school enrollments and child labor. For all outcomes, the impact of NREGA is more pronounced when we control for SGRY. The extent of underestimation of program Girish Bahal is Assistant Professor at the University of Western Australia, Perth, Australia; his email address is girish.bahal@gmail.com. The author is very grateful to the editor in charge of this article, Eric Edmonds, and three anonymous referees for thoughtful and constructive comments. The author thanks Toke Aidt, Tiago Cavalcanti, Giancarlo Corsetti, Douglas Gollin, Rema Hanna, Cl ́ement Imbert, Sriya Iyer, Michael Jetter, Kaivan Munshi, Anand Shrivastava, Laura Zimmermann, and seminar participants at Cambridge University, Delhi School of Economics, Indian Statistical Institute Delhi and International Association for Applied Econometrics for helpful comments and suggestions. The author also thanks Thiemo Fetzer for sharing TRMM rainfall data and Manisha Shah and Bryce Millett Steinberg for sharing data and codes for their study on NREGA’s impact on human capital outcomes. Finally, the author gratefully acknowledges the Ministry of Rural Development, Government of India, for help in data collection. A supplementary online appendix is available with this article at The World Bank Economic Review website. 1 See Sukhtankar (2017) for a thorough review of the literature on NREGA.

1
banks by describing their trends. The upper panel reports the evolution of branch installation; it is clear that up until 2012 the trends are indistinguishable, but they start to diverge after the introduction of the policy. Analogously, the lower panel shows a similar story for new branches opened in unbanked locations. Appendix F shows a test of the parallel trend hypothesis, showing that the differences between big and small banks were generally statistically different before the introduction of the policy, while after the introduction of the policy they became in line with the theoretical model.

# **2.2.3 Evidence from Annual Reports**

In this section, we present some supporting evidence concerning the non-negative effect of a shock to _s_ on profits and its positive effect on the number of employees, as a proxy for size. Because these aggregates are collected yearly by banks, we lack the rich within-year variation, which has been exploited in the previous sections. Also, the number of observations becomes a big limitation, because now we only face a panel with five observations at most for each bank. However, we present two tests. The first is a simple difference-in-difference regression, where we verify whether profits 25
seems that high levels of<br>expenditure on infrastructural<br>needs and large equipment<br>purchases (neither of which link<br>specifically to SOML goals) have<br>squeezed<br>out<br>other<br>interventions.<br>The<br>team<br>consistently reflected on a lack<br>of funds and a reliance on<br>implementing<br>partners.<br>And<br>other solutions, such as training<br>of TBAs and procurement of nets<br>do not appear to be being done<br>with deep thought regarding the<br>foundational problem and its<br>drivers.|The one example that the<br>PMU<br>could<br>give<br>of<br>an<br>intervention having impact<br>was the ‘husband schools’.<br>Apparently<br>DHIS<br>data<br>demonstrate improvements in<br>CPR and in SBA in the areas<br>where the schools have been<br>piloted.<br>As with other states, they<br>appear generally unaware of<br>the impact of other inputs.<br>Data are incomplete and not<br>considered reliable.|Akwa Ibom had a sum<br>total of 24% reduction<br>from<br>its<br>baseline<br>according to the 2016<br>MICS survey. We are not<br>privy to the breakdown.|

33
internet access. However, the voluntary nature of participation in an online job-search portal creates an inevitable degree of self-selection bias.

There have been various attempts to correct representation issues, including through use of statistical weights and techniques for predicting missing data (Kureková, Beblavý, and Thum-Thysen 2015). In the case of Babajob, external data can be used as a statistical benchmark to normalize biases, but the lack of sufficiently frequent external data to describe the contemporary Indian labor market remains a constraint. Unstandardized data quality is also a potential source of statistical error, as the data are usually not collected under a specific survey framework and may therefore suffer from an unusually large amount of missing information or internal inconsistencies. Job seeker information is also self-reported and may be inaccurate. However, measurement errors and sampling biases are a risk in most forms of statistical research, and if properly addressed they should not invalidate its results.

# **7. Conclusions**

Research using online job-portal data has enormous potential to inform public policy and advance the academic discourse on labor economics and workforce skills development. These data are not only more frequent and larger in volume than those obtained from traditional employer and workforce surveys, but they also present a greater variety of information and a more intense degree of granularity. Online job portals collect information on employers’ actual demand for skills as expressed through their real business activities, as well as data on job seeker characteristics reported in a context with real economic consequences. Big data analytics has opened new avenues for objectively monitoring workforce skills demand with a wide array of applications for business practices and public policy. The analysis presented above yields five key conclusions.

First, governments should leverage online job-portal data to enhance their labor market policies. Many governments continue to rely on periodic sample-based employer and workforce surveys. However, traditional surveys are conducted too infrequently and their analytical lag time is too long to capture emerging trends in labor market dynamics and demand for workforce skills. While surveys will remain useful for analytical and policy purposes,
11 THE INTENSIVE MARGIN IN TRADE EKS find that average exports per firm are very similar across four origin countries (Brazil, Denmark, France and Uruguay) for which they have customs data. They regress average exports per firm on origin and destination fixed effects and find that the origin fixed effects differ little across their four origins. Running the same regression in our dataset (but pooling across years and including year fixed effects), we find that origin fixed effects do vary significantly across countries (the coefficient of variation in the estimated origin fixed effects ranges from 0.81 to 2.56, depending on the sample used) and are higher for countries with higher GDP per capita and higher total exports.<sup>17</sup> Moreover, origin-year and destination-year fixed effects are not enough to capture the variation in ln _xij_ : a regression of ln _xij_ on origin-year and destinationyear fixed effects yields an R-squared of 0.65 when only country pairs with _Nij >_ 100 are considered and only 0.37 when all country pairs are considered.

Using firm-level export data for the U.S., Bernard et al. (2009) present a similar decomposition to the one we present above for multi-product firms, except that they cannot allow for destination fixed effects because their data is for a single origin. They find that IME<sup>_p_</sup> is around 0.23, which is not far from our finding of around 0.29. On the other hand, contrary to our results, Bernard et al. (2007) find that average exports per product per firm increase with distance. We believe that the difference arises from the fact that, by having data for multiple origins, we are able to control for destination fixed effects. In fact, Table 6 shows that regressing ln _x_<sup>_p_</sup> _ij_<sup>on ln</sup><sup>_distij_</sup> with only origin and year fixed effects but without
Policy Research Working Paper 5716

# **Abstract**

The authors combine data from 84 Demographic and Health Surveys from 46 countries to analyze trends and socioeconomic differences in adult mortality, calculating mortality based on the sibling mortality reports collected from female respondents aged 15–49. The analysis yields four main findings. First, adult mortality is different from child mortality: while under-5 mortality shows a definite improving trend over time, adult mortality does not, especially in Sub-Saharan Africa. The second main finding is the increase in adult mortality in Sub-Saharan African countries. The increase is dramatic among those most affected by the HIV/AIDS pandemic. Mortality rates in the highest HIV-prevalence countries of southern Africa exceed those in countries that experienced episodes of civil war. Third, even in Sub-Saharan countries where HIV-prevalence is not as high, mortality rates appear to be at best stagnating, and even increasing in several cases. Finally, the main socioeconomic dimension along which mortality appears to differ in the aggregate is gender. Adult mortality rates in Sub-Saharan Africa have risen substantially higher for men than for women—especially so in the high HIV-prevalence countries. On the whole, the data do not show large gaps by urban/rural residence or by school attainment.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ddewalque@worldbank.org and dfilmer@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations,
% of firms in Africa cite access to finance as a major constraint to growth. However, a higher percentage of small firms report access to finance as a major constraint relative to medium and large enterprises.

> 6 See, for example, Beck, Demirguc-Kunt & Maksimovic (2005); Beck & Demirguc-Kunt (2006); Beck, DemirgucKunt, Laeven, Maksimovic (2006), and Beck, Demirguc-Kunt and Maksimovic (2008).

> 7 The Enterprise Surveys indicators are available at: www.enterprisesurveys.org . 8 All samples are drawn following a stratified random selection. The standard strata for every economy are sector of activity, firm size, and geographical location. Under geographical location the stratification aims at having representativeness in the main economic centers of each country. Firm size is stratified into small (5-19 employees), medium (20 to 99), and large (100 and more).

> 9 Africa includes Sub-Saharan African countries only as there were no North African countries covered in Enterprise Surveys using the global methodology as of May 2012.

13
If a recent migrant was residing in a country other than their birth country, we consider them to be transit migrants. No other data source, to our knowledge, has this level of detailed and extensive information about immigrants and their migration paths. Despite this unique information, we do not actually know when transit migrants moved to their country of last residence. This information is not needed for our modeling purposes but would be quite useful for future analysis.

We construct our database in the following manner. We only include people between the ages of 18 and 65 at the time of the survey. Dropping the elderly (65+) reduces the sample by around 4% while removing the children and young adults shrinks it by around 24%. The main reason for removing young migrants is that they are likely to be moving with their parents and are unlikely to be making the individual mobility decisions that form the basis of our model. We include everybody in our sample who states that they arrived during the year of the survey or during the previous year and were abroad in the year before they arrived.<sup>9</sup> This leads to slightly over 9 million people in the sample.

The ACS surveys have, on average, 175 different countries and territories as possible answers to the place of birth question. On the other hand, ‘country of residence 1 year ago’ question has only 67 categories listed. Many smaller countries in a given region are aggregated into regional categories, such as ‘Other South America’ or ‘Other West Asia’. To complicate the

> 9There are two questions we use for this purpose. The first question asks when the migrant moved to the United States and the second asks the following: ‘Where did this person live 1 year ago?’ We should note these two questions seem to create a certain level of confusion which possibly arises from when the survey is conducted within that year. Suppose the survey is conducted in September 2006 and the migrant arrived in the