rafmacalaba/datause-encoder-data huggingface dataset:
vaccination card. As a result we rely on mothers’ vaccination reports. Our outcomes of interest are the total number of vaccinations, ever having received a vaccination, as well as the incidence of the Polio 1 and measles vaccines. Polio 1 is delivered shortly after birth, and measles is usually delivered close to 1 year of age. For the latter sample, we consider only children aged 1 or more.

For each of these health outcomes, we estimate Equation (2), controlling for household level background characteristics such as urban location, religious affiliation, BPL and SC/OBC status across waves. We include relevant mother’s characteristics and total living siblings at birth. The height-for-age and weight-for age scores are age-adjusted, but for the vaccination outcomes we include an additional 35 individual dummies for age in months. This flexible specification is desirable because prescribed vaccination schedule for children is non-linear, rising sharply to the age of 1 year and then flattening out.

Among female infants and young children, the estimated program effects consistently suggest better health status, although the results are generally not statistically significant (Table 3). Overall, the estimates are consistent with no change in neonatal mortality, (Table 3, Column 1) a lower mortality rate over the next 2 years (Table 5, Columns 2-4), and higher height-for-age and weight-for age scores (Table 5, Columns1 and 2) across the short and medium- term. The estimated effects for stunting are mixed (Table 5, Column 3).

We see strong results in the program’s effect on post-natal health investment in the form of vaccinations (Table 6). Among eligible girls, the number of vaccinations and the probability of ever receiving any vaccine increases significantly. Notably, this effect is present even for measles, which is given relatively late and is relatively expensive (Table 6, Column 2). The effects on vaccinations generally are most significant in the shortterm, while the effect on measles persists in the medium-term.

# **4.3 Education**

A key set of outcomes for this study pertains to girls’ education since a major goal of the program is to increase girls’ school enrollment through age 18. The household roster of NFHS provides data on age and education levels, which enables us to observe educational outcomes for younger school-going children in NFHS3. In particular, we are interested in comparing those in NFHS-3 who would have been exposed to ABAD to their school peer-group in NFHS-2.

For education, our analysis can be performed using only the household eligibility criteria, as data limitations prevent us from determining individual eligibility. We are unable to recover the full sibling composition using the household roster as (1) sibling relationships are not noted (only relationships to the household head) and (2) the roster covers only household members who are physically present. Matching to the mother’s birth history is not possible in all waves: while in NFHS-3, the mother’s line number is recorded for any children below 17 years, this is not the case for NFHS-2. While it is possible to match 13
Table 2

# Average Wages Across Countries

|Explanatory variables|Average wa|Dependent<br>ge in 1985-93, in|variable:<br>percent of per|capita GDP|
|---|---|---|---|---|
||(A)|(B)|(C)|(D)|
|CFA countries|606.7*|578.7**|586.8**|565.9**|
|(dummy variable)|(8.026)|(7.375)|(7.247)|(6.757)|
|Per capita GDP in 1993,|-54.53 **|-50.34 **|-49.70**|-43.48*|
|in thousand PPP dollars|(-4.477)|(-3.078)|(-3.692)|(-2.546)|
|Per capita GDP squared,|2.096 **|2.086 **|1.985 **|1.917 *|
|in million PPP dollars|(3.295)|(2.859)|(2.924)|(2.551)|
|Urban population,||0.403||0.145|
|in percent of the total||(0.348)||(0.119)|
|Mean years of schooling||-12.72||-13.06|
|for ages 25 and above||_(-1.365)_||(-1.334)|
|Countries in Latin America and|||56.82|62.96|
|the Caribbean (dummy variable)|||(1.260)|(1.295)|
|Sub-Saharan African|||63.41|55.79|
|countries (dummy variable)|||(1.141)|(0.934)|
|Independent term|435.0**|458.2**|385.3 **|416.7 **|
||(11.262)|(9.413)|(7.180)|(6.267)|
|Number of observations|108|106|108|106|
|Adjusted R<sup>2</sup>|0.566|0.568|0.566|0.568|
|F test|47.46|28.58|28.91|20.70|

Note: Values in parentheses are "t" statistics. Significant coefficients at the 5 and 1 percent level are indicated by one and two asterisks respectively.

12
dummy attempts to offset the artificial efficiency advantage generated from the absence of this variable as an input in the regression. The criteria we used to classify ports as likely to use ships’ cranes are:

- 1) As a rule-of-thumb, we considered that the productivity of a quay or mobile crane does not exceed 130,000 TEUs per year,<sup>6</sup> and that the productivity of a STS gantry crane does not exceed 390,000 TEUs per year<sup>7</sup> . Therefore, ports in 2009 with throughput in excess of that predicted by the use of all own cranes combined, under the above criteria, are considered to be using ships’ cranes intensively for handling cargo.

- 2) Ports that had no mobile, quay or STS gantry cranes for loading or unloading containers in 2009<sup>8</sup> .

The ports meeting these two criteria are shown in _Table 5_ . It is important to highlight that this list does not include all ports that make use of ships’ cranes, but only those that use are likely to use ships’ cranes more often according to the criteria.

_Table 5: Proxy for Ports Using Ships’ Cranes Often_

|**Ports That Meet the First Criteria**|**Ports That Meet the Second Criteria**|
|---|---|
|Puerto Cortés-HON|Arica-CHL|
|Buenaventura-COL|Callao-PER|
|San Vicente-CHL|Paita-PER|
|Puerto Quetzal-GUA|Puerto Barrios-GUA|
|Puerto Limón-CRI|Puerto Caldera-CRI|
|Manaus-BRA|Puerto Castilla-HON|
|Santo Tomás de Castilla-GUA|Rosario-ARG|
|Acajutla-SLV|Santa Marta-COL|

Source: own calculations Consequently, the modified equation becomes: where _Ships’Cranesi_ is a dummy for ports that utilize ships’ cranes more intensively for container handling.

# **b. Container Transshipment**

Another form of productivity boost that is not captured directly by the model is transshipment traffic. Transshipment ports use their available infrastructure differently because most of the containers are in transit. In transshipment ports, containers have to be offloaded and loaded at higher speeds to optimize transit times without much use of port resources such as storage, yard infrastructure and customs. To capture this port characteristic, we introduce a binary variable that takes the value of 1

> 6 A crane operating 24 hours a day and 365 days per year and moving 15 TEUs per hour would move 130,000 TEUs annually. We consider this is a sufficiently high upper-bound for the annual productivity of an average crane. Kent (2011) finds that in its sample of ports, the average productivity of a mobile crane is under 15 moves per hour.

> 7 Considering that modern STS gantry cranes can perform up to 45 moves per hour.

> 8 We applied these criteria for the year 2009.

13
**Table 4: Correlations of expanding permanent employment with other indicators of firm productivity and growth**

||expand_p lo|g_lprod<br>|tfp<br>|rd<br>|newPP|sales_MNCe|du_topmaed|u_prodWft|rain|pct_prod_worker|s_train|
|---|---|---|---|---|---|---|---|---|---|---|---|
|expand_p|.|||Pos|Pos|Pos|Pos|Pos|Pos|Pos|Expanding permanent employment|
|N||7,824|6,662|9,117|9,633|2,352|11,007|9,587|11,468|6,885||
|log_lprod||.|Pos|Pos|Pos|Pos|Pos|Pos|Pos|Pos|Log VA/L|
|N|7,824||6,463|6,031|6,405|2,112|7,282|6,503|7,415|4,351||
|tfp||Pos|.|Pos|Pos|Pos|Pos|Pos|Pos|Pos|TFP|
|N|6,662|6,463||5,577|5,547|1,860|6,444|5,730|6,357|4,110||
|rd|Pos|Pos|Pos|.|Pos|Pos|Pos|Pos|Pos|Pos|Firm does R&D|
|N|9,117|6,031|5,577||7,959|2,279|8,835|8,228|8,973|6,085||
|newPP|Pos|Pos|Pos|Pos|.|Pos|Pos|Pos|Pos||Introduced new product/process|
|N|9,633|6,405|5,547|7,959||2,278|9,164|8,366|9,155|5,926||
|sales_MNC|Pos|Pos|Pos|Pos|Pos|.|Pos||Pos|Pos|Percent of sales to MNCs|
|N|2,352|2,112|1,860|2,279|2,278||2,332|2,177|2,320|367||
|edu_topma|Pos|Pos|Pos|Pos|Pos|Pos|.|Pos|Pos|Pos|Education level of top manager in years|
|N|11,007|7,282|6,444|8,835|9,164|2,332||9,070|10,515|6,548||
|edu_prodW|Pos|Pos|Pos|Pos|Pos||Pos|.|Pos|Pos|Average education level of the production workforce in years|
|N|9,587|6,503|5,730|8,228|8,366|2,177|9,070||9,554|6,135||
|ftrain|Pos|Pos|Pos|Pos|Pos|Pos|Pos|Pos|.|Pos|Training offered|
|N|11,468|7,415|6,357|8,973|9,155|2,320|10,515|9,554||6,565||
|pct_prod_w|Pos|Pos|Pos|Pos|||Pos|Pos|Pos|.|% of production workers that were trained|
|N|6,885|4,351|4,110|6,085|5,926|367|6,548|6,135|6,565|||

Notes: Partial correlations; country fixed effects are partialled out. “Pos” and green indicates a positive correlation significant at the 5% level.

41
# **Measuring Social Norms About Female Labor Force Participation in Jordan**

**Varun Gauri, Tasmia Rahman, and Iman Sen**

# **Keywords: Survey methods, Social norms, Female labor force participation**

# **JEL Classification Codes: C83, D91, J29**

Mind, Behavior, and Development (eMBeD) team, World Bank. E-mails: vgauri@worldbank.org, <u>trahman4@worldbank.org, isen@worldbank.org. We thank Mariana Felicio, Samantha Constant, and Anoud Allouzi</u> for inviting us to collaborate on the project. We are grateful to D3 Systems (https://www.d3systems.com/) and their local partner in Jordan for advice on and implementation of the survey that underlies this work. Caren Grown, Elena Ianchovichina, Julian Jamison, Nina Mazar, Ryan Muldoon, Maria Beatriz Orlando, Betsy Paluck, and Tara Vishwanath provided valuable advice. We appreciate the feedback received from seminar participants at the University of Toronto, Oxford University, the Woodrow Wilson Center for Scholars, and the World Bank. All errors remain our own. The views and findings expressed in this paper are those of the authors alone and do not necessarily represent those of the World Bank or its Executive Directors.
# Lending Concentration, Bank Performance and Systemic Risk:

# Exploring Cross-Country Variation<sup>�</sup>

Thorsten Beck<sup>y</sup> Olivier De Jonghe<sup>z</sup> September 16, 2013 Keywords: sectoral specialization, lending concentration, bank risk, systemic stability, herding G01, G21, G28, L5 �The authors would like to thank Martin Melecky and seminar participants at the University of Glasgow for helpful comments and Maria Dimitraki, Janneke Driessen, Christian Hassert, Joni Koch, Pim Meulendijks, Steffie Vandenbosch, Tim van Rijn and Daan van Zonsbeek for excellent research assistance. This paper was prepared as background paper for the World Development Report 2014 entitled Risk and Opportunity: Managing Risk for Development. This paper and its findings do not necessarily reflect

> the opinions of the World Bank, their Executive Directors or the countries they represent.

> yCass Business School, Tilburg University, CEPR and European Banking Center. t.beck@uvt.nl

> zCentER, European Banking Center, Tilburg University. o.dejonghe@tilburguniversity.edu

1
controls.<sup>11</sup> According to our preferred specification (column 3), attending a school that was assigned to the program significantly reduces dropout by 1.3 percentage points (about 4 percent of the baseline dropout rate) and this effect is significant at the 1 percent level. In the second panel, we report the estimated ITT estimates for each arm. The results for our preferred specification (column 3) indicate that the effect of the treatment is similar across the different arms, ranging from 1.1 to 1.5 percentage points, with all the coefficients statically significant at the 5 or 10 percent level (depending on the arm considered). In fact, we cannot reject the hypothesis that the impact is the same across the three treatment arms, suggesting that the basic intervention on _how_ dropout can be prevented is mostly driving the results.

Table 3. Impact of the program on the probability of student dropout

|||ITT||
|---|---|---|---|
||(1)|(2)|(3)|
|**_Panel (i): Pooled Model_**||||
|_ENTRE j_|-0.010|-0.013**|-0.013***|
||(0.018)|(0.006)|(0.004)|
|Observations|152,673|145,628|145,628|
|R-squared|0.000|0.301|0.331|
|**_Panel (ii): Treatment arms_**||||
|_ENTRE 1 j_|-0.013|-0.016**|-0.015***|
||(0.022)|(0.008)|(0.005)|
|_ENTRE 2 j_|-0.011|-0.010|-0.011**|
||(0.022)|(0.007)|(0.005)|
|_ENTRE 3 j_|-0.007|-0.013*|-0.013**|
||(0.023)|(0.007)|(0.005)|
|Observations|152,673|145,628|145,628|
|R-squared|0.000|0.301|0.331|
|Controls at student level|NO|YES|YES|
|School fixed effects|NO|NO|YES|

Notes: 1) Standard errors clustered at school-year level in parenthesis. 2) *** p<0.01, ** p<0.05, * p<0.1. 3) The dependent variable is a dummy variable that is equal to 1 if student _s_ in school _j_ dropped out school between year _t_ and year _t+1_ ; 4) _ENTREj_ represents the interaction _Entrejt_ x _Postt_ where _Entrejt_ : (1= Student _s_ attends a school _j_ in year _t_ that is in the treatment group) and _Postt_ : (1 = year of the intervention; 0 = year before the intervention); _ENTRE ij_ with _i_ =1,2,3 captures the same interaction as _ENTREj_ but using assignment to the treatment arm _i_ instead of assignment to the general treatment group. 5) All specifications in panel (i) include the corresponding _Postt_ and _Entrejt_ dummies, while those in panel (ii) include the _Postt_ and _Entre ijt_ dummies; 6) Student characteristics used as control: age dummies, gender and risk (probability of dropout estimated in the Early Warning System).

11 This is indeed a general pattern that we observe throughout the analysis conducted in this paper. For simplification purposes, in the rest of the paper we mostly report the equivalent to column (iii) –our preferred specification —but more detailed tables are available upon request. We also conduct additional checks in the robustness and placebo sections.

21
14 Using equation (17), we can successively define the short=inm total effect of a change in real interest rates on the demand for domeotic assets and private investment s dOh 3e(la **+** (18) - a _L8+_ or 6 0 dc M98 _+ Ihis)_ dip _P/S(W_ + (2a) (19) -c . _or < O dr (0 + PS-) Equations (18) and (19) suggest that the effect of financial liberalization cannot be a priori determined on domestic assets demand and private investment.

In summary, the complete model consist of three behavioral relationships - equation (4a), (4b) and (13) - and four technical - relationships equations (1), (6), (15) and (16). Together these seven equations can be used to explain AM, OH, 07, _OB,_ OLp and OL.. The complete model is presented in Table 2. Within this framework, we address the critical issue of whether the positive effect suggested by McKinnon and Shaw is strong enough to offset the crowding out of domestic credits and the shift from real savings into monetary assets that could occur in the portfolio of the private sector.

# 4. An EMgirical Test s the Case of Argentina (1961-82)

The model developed in the last section could be used in principle to examine the short-run effect of a variety of shocks on private investment and other endogenous variables of the model, By estimating the model for Argentina and by stimlating it, the response of key variables to changes in real rates of interest will be derived for this country.

The data on S., Y, _ON,_ F, I*. and AR have been obtained from the -International Monetary fund's International Financial Statistics. Series from the World Bank (1985) and the Central Bank of Argentina have also been used for ir, r, iJ, AB, ODp, D0D9 OD, 0LP, OLG# defg, Ie, and IPrL/. All variables have been deflated by the wholesale price index (1974 - 100). The data on private wealth stock (W,,.) have been constructed by cumulatively adding the time series on private savings. Since linear functions are used, the error in estimating the initial wealth stock can be readily absorbed into the intercept term. The net profit of the banking sector ( nw) has been calculated as the residual of the financial system budget constraint2O/. While we attempted to 19/The variable r is defined as s **r** t ((I + - 1 where _It)I(l+I't))_ it is the nominal interest rate.

**20/ Onw** (0LP/P) - Am **+** (bR/P) + _(OLOIP)_
3

* ;$ -The Slovenian results are in sharp contrast to those in East Germany (Krueger and Pischke, 1992; Bird, Schwartzc and Wagner, 1992). East German workers have had decreasing returns to education and experience. However, it i3 not clear if the East German labor market has lessons for other transitional economies because of West Germany's efforts to lessen the adverse effects of transition.<sup>3</sup> Using grouped data, Flanagan (1993) found that returns to education rose in the Czech republic during transition, but that retuns to potential experience fell.

Setting East Germany aside as a unique case, the Slovenian data base offers the first chance to examine the effects of transition on labor market returns based on individual data- Although there are respects in which Slovenia has been atypical (being among the richer and more western-oriented of transitional economies), lessons learned from the Slovenian experience should be applicable elsewhere. The pretransition Slovenian labor market shared key features with those in other formerly-socialist economies, most notably, social ownership, full employment coupled with substatial hidden unemployment, and an egalitarian wage structure. Second, Slovenia has introduced labor market reforms and has experienoed dislocations that are similar to those of other European transitional economies.

The study first reviews the labor market institutions which were in place before and during transition. The data is descrbed in section 2. Empirical outcomes for wages and employment are reported in sections 3 and 4. The study concludes with policy iimplications derived from the analysis.

# 1. INSTITUTIONAL BACKGROUND

To put in perspective changes in wages and other labor market outcomes, it is necessary first to review the featres of the previous self-management system. Subsequent labor market reforms can

> 3 Pehaps effi best indicator of the uniquerss of the East German tansition is tbat real wages rose dramatically for East German workers while they have fallen in every other formerly sociaist economy.
# **Table 7: Two Stage Least Squares Regressions**

Financial Vulnerability Regressions (Marginal Effects)

|||**finburd**|**en**<br>|||**finvul**|||**arrear**|**s1**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**p(Mortgage)**|**p(Rent)**|**Pseudo r**<sup>**2**</sup>|**t-test**|**p(Mortgage)**|**p(Rent)**|**Pseudo r**<sup>**2**</sup>|**t-test**|**p(Mortgage)**|**Pseudo r**<sup>**2**</sup>|
|_New EU Countries_|||||||||||
|Czech Republic|0.0259|-0.0567|0.08|0.65|0.0026|0.0330|0.14|-0.66|0.0872|0.15|
|Estonia|-0.2403 ***|0.3102 **|0.11|-2.92 ***|0.0031|0.0491|0.09|-0.55|-0.0905 **|0.10|
|Hungary|0.1746|-0.1711 *|0.06|1.81 *|-0.1388|0.0734|0.12|-0.93|0.0952|0.10|
|Latvia|-0.4368 ***|-0.0052|0.08|-2.17 **|0.0781|0.0037|0.06|0.79|0.0679|0.07|
|Lithuania|-0.2804|0.0394|0.07|-0.72|0.1015|-0.0985|0.08|0.87|-0.0832|0.26|
|Poland|-0.5140 ***|0.0870|0.08|-5.03 ***|-0.1950 **|0.0458 ***|0.09|-2.48 **|0.0713|0.10|
|Slovak Republic|0.2575 **|0.0930|0.06|0.58|-0.0659|0.1788 **|0.08|-1.59|0.0333 **|0.11|
|_EU-15 and Cyprus_|||||||||||
|Austria|0.0450|-0.0057|0.06|1.03|0.0285|-0.0555 *|0.13|2.49 **|0.0047|0.12|
|Belgium|0.0875 ***|-0.0077|0.04|1.78 *|0.0287|0.0577 ***|0.16|-1.10|-0.0144|0.13|
|Cyprus|0.1998 **|-0.0912|0.08|2.40 **|0.0340|-0.0522|0.09|0.55|0.1916 **|0.06|
|Denmark|-0.0098||0.08||-0.0452 ***||0.18||-0.0172 ***|0.18|
|Finland|0.0201|0.1025 **|0.04|-1.25|-0.1244 **|0.0754 *|0.15|-2.88 ***|-0.0573 *|0.13|
|France|0.0362|0.0079|0.03|0.39|0.0020|0.1077 ***|0.12|-3.15 ***|0.0101|0.09|
|Greece|0.2276 *|-0.0035|0.05|1.58|0.3357 **|0.0826 *|0.13|1.49|0.0955|0.18|
|Ireland|-0.0199|0.0353|0.07|-0.66|-0.0083|0.1187 ***|0.21|-2.78 ***|0.0410|0.16|
|Italy|0.0783|0.0584|0.04|0.16|-0.0186|0.1579 ***|0.10|-3.29 ***|-0.0382|0.09|
|Luxembourg|0.0941|-0.0664|0.08|1.17|0.0155|0.0034|0.17|0.64|0.0116|0.16|
|Netherlands|-0.1724 ***||0.04||-0.0667 ***||0.10||-0.0428 ***|0.09|
|Portugal|0.0079|-0.0294|0.07|0.45|0.0112|0.1083 ***|0.08|-1.46|0.0104|0.13|
|Spain|0.2978 ***|-0.2811 *|0.04|2.76 ***|0.0095|0.0801 *|0.07|-1.30|0.0014|0.08|
|Sweden|0.0679|0.0353 ***|0.06|0.58|0.1294 *|0.0897 *|0.11|0.39|0.0139|0.08|
|United Kingdom|0.1507 ***|0.0563|0.05|2.16 **|0.0928 ***|0.0872 ***|0.18|0.15|0.0136|0.12|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

14
<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>20<br>10<br>0<br>2008 2009 2010 2011 2012 2013 2014<br>@ public wage § private wage m selfemployment income<br>@ social assistance @ transfers B@ persions<br>@ property @rent mother<br><!-- End of picture text -->
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