rafmacalaba/data-use-mentions-extended huggingface dataset:
- S ̈arndal, C.-E., B. Swensson, and J. Wretman (2003) _Model Assisted Survey Sampling_ (Springer)

- Schreiner, M. (2014a) ‘How do the poverty scorecard and the PAT differ?’ _mimeo_

- (2014b) ‘The process of poverty-scoring analysis.’ _mimeo_

- Sitter, R. (1997) ‘Variance estimation for the regression estimator in two-phase sampling.’ _Journal of the American Statistical Association_ 92, 780–787

- Stifel, D., and L. Christiaensen (2007) ‘Tracking poverty over time in the absence of comparable consumption data.’ _World Bank Economic Review_ 21(2), 317–341

- Tamhane, A.C. (1978) ‘Inference based on regression estimator in double sampling.’ _Biometrika_ 65(2), 419–427

- Tarozzi, A. (2007) ‘Calculating comparable statistics from incomparable surveys, with an application to poverty in India.’ _Journal of Business & Economic Statistics_ 25(3), 314–336

- (2011) ‘Can census data alone signal heterogeneity in the estimation of poverty maps?’ _Journal of Development Economics_ 95(2), 170–185

- Tarozzi, A., and A. Deaton (2009) ‘Using census and survey data to estimate poverty and inequality for small areas.’ _Review of Economics and Statistics_ 91(4), 773–792

- van Veelen, Matthijs, and Roy van der Weide (2008) ‘A note on different approaches to index number theory.’ _American Economic Review_ 98(4), 1722–1730

- Yoshida, N., R. Munoz, A. Skinner, C. Kyung-eun Lee, M. Brataj, W. Durbin, and D. Sharma (2015) ‘Swift data collection guidelines version 2.’ mimeo, The World Bank

# **A Proofs**

**Proof of Theorem 6** Letting _k_ = _K_ (or _r_ = 1
# **3.1.3  ALMP Programs in China**

Bidani, Goh and O‟Leary (2002) analyze the effects of a retraining program in China called the “Reemployment Project,” which was designed to promote labor market entry of so-called _xiagang_ , people who were laid off from state-owned enterprises but remained attached to their former employer for unemployment stipends, health insurance, pensions and sometimes also housing. The Reemployment project was administered by labor bureaus in local areas and included a range of active labor market policies such as job search assistance, counseling, training, wage subsidies, tax incentives for firms and assistance for self-employment. Individuals were allowed to be registered with the Reemployment service for up to three years.

The impact evaluation study was carried out in a city with very high unemployment, Shenyang, in northeastern China, and in another city with moderate unemployment, Wuhan, in central China. The laid-off workers were 47% female, tended to be less educated and were concentrated in the <35, and 35-46 year-old range. The training intervention was relatively shortterm (one month, 132 hours of classroom training) and included courses in computer training, beauty and massage, hair cutting, sewing, toy making, cooking, repair training and driver education. Class sizes during the training sessions were often large with 200 to 300 workers in a small classroom.

Analysis samples were drawn from a census which required each state-owned enterprise to provide a list of workers laid off at different times. The sample of trainees was selected from the training registers from the training institutes (in the case of Shenyang) and from a master list supplied by the Wuhan Labor Bureau.<sup>11</sup> Three different treatment/comparison group samples are analyzed using multiple methodologies that include prop score matching, matching on odds ratios, and OLS. The outcomes of interest are employment and earnings, and, with a few exceptions, most of the impact estimates are robust to the use of different methods. Training is
than 180 countries. For each round of the survey in the country, we gather government policy stringency data a day before the day that the survey was started. The final sample used in this study includes 16,038 firm-observations from 41 countries.

# 3.2. Variables

## 3.2.1. Firm Performance and Channel Variables

Firm performance during the COVID-19 pandemic is captured by two variables: firm closure and growth rate. Firm closure, _Survival,_ is a dummy variable that equals one for firms that had not temporarily or permanently closed and zero otherwise. Firm growth is measured by the changes in monthly sales between the latest month (survey month) and the same month a year ago, denoted as _Sales growth_ .

We rely on four variables to gauge strategic responses for explaining firm performance during COVID-19. They are four channels through which firms implement their strategies: stable demand, stable input supply, stable liquidity, and innovation. We use them as dependent variables to see how firm organizational resources and national characteristics affect strategic adjustments by firms, and how such adjustments explain firm performance. Specifically, _Stable demand_ is a dummy variable that equals one for firms that had not experienced decreased demand for production or service compared to a year ago and zero otherwise. _Stable input_ is a dummy variable that equals one for firms that had not experienced decreased supply of inputs compared to a year ago and zero otherwise. _Stable liquidity_ is a dummy variable that equals one for firms that had not experienced decreased liquidity or cash flow availability since the COVID-19 began and zero otherwise. _New products_ is a dummy variable that equals one for firms that have introduced new production or service in response to the COVID-19 and zero otherwise. The original question corresponding to _New Products_ has been added into the questionnaire since November 19, 2020, thus it is available mostly in second-round surveys. Since the pandemic tends to reduce product demand, disrupt input supply, and reduce liquidity access, firms’ stability in demand, input supply, and liquidity represents
the poor get a less than proportionate share of transfers). A negative concentration index is also said to indicate that the transfer is “pro-poor”.

Relative progressivity can be summarized in the Kakwani index. The Kakwani index (Kakwani, 1977) is a measure of the relative progressivity of a social policy intervention (tax or transfer). In the case of a tax, it is defined as the difference between the concentration coefficient of the tax and the Gini coefficient of the pre-tax-and-transfer incomes (or the chosen welfare indicator). In the case of transfers, it is the negative of the difference; namely, the difference between the Gini coefficient of the pre-tax-and-transfer incomes and the concentration coefficient of the transfer. Progressive interventions have positive Kakwani coefficients, and regressive ones have negative coefficients.

All direct and in-kind transfers in Mongolia are “pro-poor” and progressive. Pensions, child money transfers, and other social welfare transfers, as well as expenditures in public pre-school, basic education and health have negative concentration coefficients. Consequently, their Kakwani indexes are all positive, with respect to any income definition (Table 4). This means that all these transfers represent a larger share of income among the poor than among the rich and the larger share of their total budget is allocated to poorer population groups. The only exception is in-kind transfers in tertiary education. In this case the concentration coefficient is positive, which means that poorer groups of the population get a smaller share of the total budget allocated to public expenditures in tertiary education. However, the Kakwani coefficient for tertiary education remains positive, which means that it is still progressive in real terms. This is because the distribution of in-kind transfers in tertiary education is less unequally distributed than pre-tax-andtransfer incomes, so tertiary education still helps to reduce inequality.

Direct taxes are mostly paid by richer population groups and have an equalizing effect. Concentration coefficients of the personal income tax and social security contribution are positive, and their Kakwani index is always positive indicating these are progressive interventions both in relative and
.0696|
|Kyrgyz Republic|||||
|Total Merchandise exports|554.8|1282.6|1,917|1,636|
|Manufactured exports|155|277|441|414|
|Services exports||490|881|838|
|Total Merchandise exports/world exports(%)|0.0077|0.0096|0.0107|0.0094|
|Manufactured exports/world manufactured exports(%)|0.0025|0.0031|0.0038|0.0035|
|Services exports/world services exports(%)||0.0148|0.0192|0.0158|
|Tajikistan|||||
|Total Merchandise exports|777|1239|1,190|1,049|
|Services exports||152|401|245|
|Total Merchandise exports/world exports(%)|0.0109|0.0095|0.0067|0.0060|
|Services exports/world services exports(%)||0.0048|0.0089|0.0046|

*2015-17 for manufactured exports

Services export data are only available from 2005 Source: WTO For services exports, a consistent data series using BOP Manual 6 definitions is only available from 2005 onwards. Armenia and Georgia have developed substantial services export sectors, with the value of the services exports of Georgia exceeding its merchandise exports during 2015-18 while the value of Armenian services exports was almost as great as that of its 15
We estimate this model only with women.<sup>10</sup> The data source is the Arab Barometer, and **X** i includes dummies for 7 age brackets, a dummy variable that equals “1” if a woman is married, and dummies for five categories of education and years fixed effects.<sup>11</sup> Our variable of interest is a dummy that equals “1” if women agree with the claim “A married woman can work outside home if she wishes,” which we will interpret as signaling a positive attitude toward female work.

# **VII. Results: The determinants of female labor supply**

Since the descriptive statistics show that urban and rural areas function as different markets for labor, we estimate the model in equation (1) separately for women in urban and in rural areas in 2001 and 2018 and show the resulting marginal effects in figure 10. During this time period, there are no important qualitative changes in the influence of the covariates and only a few important quantitative changes. We highlight the effect of each covariate in our results:

## **_First, higher educational attainment means more participation, although this relationship_**

**_has decreased over time._** The effect of women’s education differs greatly across areas and educational levels, although, especially in rural areas, more education does not necessarily translate into greater FLFP. In 2001, having fundamental education in rural areas increased the probability of remaining OLF compared to women with no education, contrary to the results obtained for urban areas where fundamental education slightly reduces the probability. In 2001, in rural and urban areas, secondary education decreased the probability

> 10 Further research can estimate this model, including only men’s answers to assess their attitude toward female work.

> 11 Strictly speaking, these fixed effects correspond to three waves: 2006-2009, 2012-2014, and 2016-2017.

25
**16** **<u>│</u>**

- 3.2 Competitiveness proxies: Productivity, trade performance, skills, and innovation

Competitive investment, often associated with foreign multinationals, drives economic growth. This lies with the type of foreign multinational and its ability to compete and grow in global markets. Whether by boosting labor productivity, enhancing innovation, or utilizing skills, developing countries need a bigger pie to be able to advance the development agenda, and need to compete with established producers abroad in an increasingly globalized context.

In the following, we examine some of the fundamental characteristics of competitive firms – productivity, export orientation, investment in R&D, skills utilization and transfer – and compare their incidence across foreign multinational and domestic firms in the 30 most populous developing countries in the sample of the World Bank Enterprise Surveys on which we have information.

# Productivity

Foreign-owned firms tend to be more productive than domestically-owned firms. Numerous studies have highlighted this pattern in the past, as well as its effects on the productivity of domestic firms. The reason for the productivity premia of foreign firms lies in their possession of more advanced technology and managerial know-how than local firms. To be competitive in markets outside of their home economy, foreign multinationals need to have some competitive advantage, thereby overcoming their ‘liability of foreignness’ (Zaheer, 1995). The cost of entering a foreign market, their lack of local networks and customer knowledge mean that foreign firms require other firm-specific assets to overcome these liabilities. Such assets can take the form of knowledge-related capacities, which can ultimately enhance productivity in the host economy.

The picture that emerges in two simple indicators of productivity is broadly consistent with these premia (see Fig. 2). Total Factor Productivity (TFP) differences in manufacturing across firms vary considerably across countries, reflecting the level of sophistication, the scale as well as the structure of manufacturing production, that more intensively attracts foreign investors. Markets that stand out by attracting productive foreign multinationals
lamps, respectively.<sup>22</sup> We also use an indicator of whether households currently own a lamp of superior quality. Here, quality is approximated by the price of the most recently acquired solar lighting product which, according to formative qualitative field research, is a good indicator for the quality of the adopted technology.<sup>23</sup> Solar energy knowledge is considered a secondary outcome of interest. Specifically, we use dummy variables for awareness and understanding of pico-PV technology. A household is deemed aware if it reports to use solar lighting products as their primary or secondary lighting source (in response to an unprompted question). A household is also considered aware if it correctly selects ‘sun’ among four possible choices (`dry cell battery’, `wind’, `sun’, `mud’) when asked which sources of energy are able to power a lantern or torch.<sup>24</sup> For solar technology understanding, a household is assigned the value one if it reports that a sunny weather is the best condition for the generation of solar power and zero otherwise.<sup>25</sup> Additional secondary outcome variables consist of measures for quality expectations and locus of control. For the former, participants were asked whether they have heard of the Lighting Global Quality Standards (developed by Lighting Africa) and their expectations regarding the lifetime of a typical solar lamp, which was used to construct a binary indicator for whether expected durability exceeds six months (quality expectation). For comparison, a standard battery-driven torch has a maximum warranty period of two months in rural Senegal. Also, people may not be familiar with other quality features such as brightness measured in lumens. For the locus of control measure, participants were asked whether they feel that what happens to them is their own doing (binary indicator takes the value 1) or that they mostly lack control over the direction their life is taking (it 22 We constructed these outcome
the potential to generate multidimensional, granular, real-time information, which can be used to provide new insights into longstanding economic questions, enhance economic research and aid public policy development.

# **2. Objectives**

This paper discusses potential applications of online job-portal data for academic research and policy formulation through an empirical study of data produced by Babajob, an online job-matching platform in India. The paper is designed to both contribute to the academic literature and to support the development

> 2 For example, LinkedIn, a social networking service for businesses and professionals launched in 2003, had data on 433 million individuals as of Q1 2016. The site is available in over 200 countries worldwide and in 20 different languages. The data obtained through LinkedIn have been primarily used for business purposes, rather than for policy formulation, though various industry- and skills-focused analyses have been published on LinkedIn’s official blog. Tambe (2014) used skills data from LinkedIn to measure employers’ investment in big-data-related human resources management.

3
Figure 2: Partial scatter plots <!-- Start of picture text --> (a) Education SP aid (b) Education TC<br>−.2 0 .2 .4 .6 −2 −1 0 1 2 3<br>Education SP aid Education TC<br>(c) Health SP aid (d) Health TC<br>−.4 −.2 0 .2 .4 −.5 0 .5 1<br>Health SP aid Health TC<br>4 4<br>2 2<br>0 0<br>Public education expenditure −2 Public education expenditure −2<br>−4 −4<br>3 3<br>2 2<br>1 1<br>Public health expenditure 0 Public health expenditure 0<br>−1 −1<br><!-- End of picture text --> Note: Partial scatter plots in the full (solid line) and reduced (dotted line) samples correspond to the FE results in Tables 4 and 6, respectively. + denotes observations that are excluded from the reduced sample. Source: Author’s analysis based on data described in the text.

FE estimates inconsistent.

Therefore, I also examine the results that are obtained when the strict exogeneity assumption is relaxed by employing a system GMM estimator (Arellano and Bover, 1995; Blundell and Bond, 1998). This estimator further enables the consistent estimation of a more general model that includes a lagged dependent variable (which removes the serial correlation in _εit_ ):<sup>31</sup> Equation (12) is estimated using a two-step system GMM estimator applying Windmeijer’s (2005) correction for the downward bias in the two-step standard errors. All education (health) aid prefix code variables, support to NGOs, and trade are treated as endogenous, whereas all other variables are treated as predetermined. Time dummies are treated as strictly exogenous and
of all three sets of variables has virtually no effect on the sign, magnitude, or significance of _γ_ , _δ_ , or _θ_ in any of equations 11 through 14.

# **5.5 Data**

I used several data sources, matched geographically at the municipality level. An observation is a municipality-year. Data are generally available for 1995-2008, and are summarized in Table 5.

School-level data on students, teachers, infrastructure, and amenities are available from INEP’s Census of Schools. The data are of very high quality and detail. From these data, I computed school enrollment by level (pre-primary, primary, secondary) and administrative 34This Herfindahl-Hirschman Index (HHI) takes a municipality to be a market and political parties to be firms.

21
Policy Research Working Paper 9329

# **Abstract**

The worsening of Ecuador’s socioeconomic conditions and the rapid inflow of Venezuelan migrants demand a rapid government response. Representative information on the migration and host communities is vital for evidence-based policy design. This study presents an innovative methodology based on the use of big data for sampling design of a representative survey of migrants and host communities’ populations. This approach tackles the difficulties posed by the lack of information on the total number of Venezuelan migrants—regular and irregular—and their geographical location in the country. The total estimated population represents about 3 percent of the total Ecuadoran population. Venezuelans settled across urban areas, mainly in Quito, Guayaquil, and Manta (Portoviejo). The strategy implemented may be useful in designing similar exercises in countries with limited information (that is, lack of a recent census or migratory registry) and scarce resources for rapidly gathering socioeconomic data on migrants and host communities for policy design.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at solivieri@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, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support
- Education: based on answer to questions ar16 (“Highest level of schooling attended by HHM”) and ar17 (“Highest grade ever completed by HHM”) from Book K. Years of education computed combining information from Wikipedia and .

Follow-ups (wave 4 – 2007/08, wave 5 – 2014/15):

- Internal migration: we use information on province, kabupaten, and kecamatan of each household detailed in the htrack file of each survey round.

- International migration: from section AR of Control Book. A household member is classified as international migrant if she lives in a foreign country (from question AR18i ”Where does [...] live now?”) and moved out of the household after baseline (from question AR18e ”When did [...] move from/into this household?”). Note that we exclude from the group of international migrants in wave 5 respondents that had left Indonesia between baseline and wave 3 but then returned between wave 3 and 5.

- Occupation type: same as baseline.

# **B.5 Mexican Family Life Survey (MxFLS)**

Notes: a large fraction of migration instances comes from individuals who leave the household and are reported in the Control Book. MxFLS does not collect information on their occupation at destination.

Baseline (2002):

- status: a is as not if the answer

- Employment respondent classified working to question tb02 from Section TB (Employment) of Book 3A is different than “Worked or carried out an activity that helped household expenditures”.

- Self-employed: if the answer to question tb32 is either “Boss, employer, or business proprietor” or “Self-employed worker (with or without non-remunerated worker)”. Exclude self-employed who work in agricultural activities according to question tb25.

- Wage worker: if the answer to question tb32 is “Non agricultural worker or employee”. Restrict to non-agricultural workers as above.

- Agricultural worker: if the answer to question tb32 is “Peasant
IMF Balance of Payments Statistics database, includes all available country-data on the three items above from 1976 to 2003<sup>10</sup> . After dropping

> 9 <u>Workers’ remittances</u> cover current transfers by migrants (defined as those who come to an economy and stay, or are expected to stay, for a year or more) who are employed in new economies and considered residents there. Persons who work for and stay in new economies for less than a year are considered non residents; their transactions are appropriate mainly to the component for compensation of employees. <u>Migrants capital transfers</u> are not transactions between two parties but contra-entries to flows of goods and change in financial items that arise from the migration of individuals from one economy to another (IMF, BPM5).

> 10 We have chosen to drop observations before 1976 due to the very limited data availability.